diff --git a/.gcp/terraforms/bigquery.tf b/.gcp/terraforms/bigquery.tf new file mode 100644 index 0000000..d713bba --- /dev/null +++ b/.gcp/terraforms/bigquery.tf @@ -0,0 +1,90 @@ +# ------------------------------------------------------------ +# OPS EXTERNALIZED TABLES (For metadata caching) +# ------------------------------------------------------------ + +resource "google_bigquery_connection" "biglake_connection" { + connection_id = "ops_biglake_connection" + location = var.region + friendly_name = "BigLake Connection for GCS Parquet Scanning" + cloud_resource {} +} + +# Enable connection service to access pipeline bucket +resource "google_storage_bucket_iam_member" "biglake_storage_viewer" { + bucket = google_storage_bucket.ops_pipeline_bucket.name + role = "roles/storage.objectViewer" + member = "serviceAccount:${google_bigquery_connection.biglake_connection.cloud_resource[0].service_account_id}" +} + +resource "google_bigquery_dataset" "silver_dataset" { + dataset_id = var.bq_dataset_id + location = var.region + + delete_contents_on_destroy = false +} + +locals { + external_tables = [ + "df_orders", + "df_customers", + "df_order_items", + "df_products", + "df_payments" + ] +} + +resource "google_bigquery_table" "external_tables" { + for_each = toset(local.external_tables) + dataset_id = google_bigquery_dataset.silver_dataset.dataset_id + table_id = each.key + + # Might throw error if contracted/ is empty + external_data_configuration { + autodetect = true + source_format = "PARQUET" + connection_id = google_bigquery_connection.biglake_connection.name + source_uris = ["gs://${google_storage_bucket.ops_pipeline_bucket.name}/contracted/${each.key}_*.parquet"] + + # Triggered manually by pipeline + metadata_cache_mode = "MANUAL" + } + lifecycle { + prevent_destroy = true + } +} + + +# ------------------------------------------------------------ +# BIGQUERY SEMANTTIC DATASETS (For table versionining) +# ------------------------------------------------------------ + +locals { + # Expiration for versioned tables + one_month_ms = 2678400000 + + semantic_datasets = [ + "seller_semantic", + "customer_semantic", + "product_semantic" + ] +} + +resource "google_bigquery_dataset" "semantic_datasets" { + for_each = toset(local.semantic_datasets) + dataset_id = each.key + location = var.region + + delete_contents_on_destroy = false + default_table_expiration_ms = local.one_month_ms + + description = "Semantic layer for ${each.key}. Tables expire after 1 month." + + labels = { + env = var.environment + layer = "semantic" + } + + lifecycle { + prevent_destroy = true + } +} diff --git a/.gcp/terraforms/iam_bindings.tf b/.gcp/terraforms/iam_bindings.tf index 31c87f2..1e56bec 100644 --- a/.gcp/terraforms/iam_bindings.tf +++ b/.gcp/terraforms/iam_bindings.tf @@ -24,7 +24,9 @@ locals { "roles/monitoring.admin", # Manage Monitoring in monitoring.tf "roles/logging.configWriter", # Required for log-based alert policies "roles/iam.serviceAccountAdmin", # Manage Alert policies in monitoring.tf - "roles/iam.admin" # Manage Iam roles + "roles/iam.admin", # Manage Iam roles + "roles/bigquery.admin", # Manage BigQuery datasets and views + "roles/serviceusage.serviceUsageAdmin", # Manage APIs ] } @@ -74,6 +76,21 @@ resource "google_storage_bucket_iam_member" "pipeline_runner_pipeline_access" { member = "serviceAccount:${google_service_account.platform_accounts["ops-pipeline-sa"].email}" } +# Pipeline Runner BigQuery Access +locals { + pipeline_bq_roles = [ + "roles/bigquery.dataEditor", + "roles/bigquery.jobUser" + ] +} + +resource "google_project_iam_member" "pipeline_runner_bq_access" { + for_each = toset(local.pipeline_bq_roles) + project = var.project_id + role = each.key + member = "serviceAccount:${google_service_account.platform_accounts["ops-pipeline-sa"].email}" +} + # ------------------------------------------------------------ # GOOGLE SERVICE AGENTS (Pub/Sub) diff --git a/.gcp/terraforms/jobs.tf b/.gcp/terraforms/jobs.tf index dbe25fa..e0f2d00 100644 --- a/.gcp/terraforms/jobs.tf +++ b/.gcp/terraforms/jobs.tf @@ -16,22 +16,49 @@ resource "google_cloud_run_v2_job" "pipeline" { resources { limits = { - cpu = "2" + cpu = "4" memory = "8Gi" } } env { name = "POLARS_MAX_THREADS" - value = "2" + value = "4" + } + env { + name = "GCP_REGION" + value = var.region + } + env { + name = "BQ_DATASET_ID" + value = var.bq_dataset_id + } + env { + name = "GCP_PROJECT" + value = var.project_id + } + + volume_mounts { + name = "ephemeral-disk-1" + mount_path = "/tmp" + } + } + + volumes { + name = "ephemeral-disk-1" + empty_dir { + size_limit = "10Gi" } } } } lifecycle { ignore_changes = [ + # Github ci-infra updates image every update template[0].template[0].containers[0].image, client, - client_version + client_version, + # Block terraform from defaulting medium to MEMORY, DISK isn't supported by provider yet + template[0].template[0].volumes[0].empty_dir[0].medium ] } } diff --git a/.gcp/terraforms/main.tf b/.gcp/terraforms/main.tf index 7fd8ccc..7dc97e5 100644 --- a/.gcp/terraforms/main.tf +++ b/.gcp/terraforms/main.tf @@ -31,6 +31,8 @@ locals { "cloudscheduler.googleapis.com", "iamcredentials.googleapis.com", "drive.googleapis.com", + "bigquery.googleapis.com", + "bigqueryconnection.googleapis.com", ] } diff --git a/.gcp/terraforms/storage.tf b/.gcp/terraforms/storage.tf index 999f2d1..5130e70 100644 --- a/.gcp/terraforms/storage.tf +++ b/.gcp/terraforms/storage.tf @@ -46,3 +46,4 @@ resource "google_storage_bucket" "ops_pipeline_bucket" { } } } + diff --git a/.gcp/terraforms/variables.tf b/.gcp/terraforms/variables.tf index 71f0294..5bac088 100644 --- a/.gcp/terraforms/variables.tf +++ b/.gcp/terraforms/variables.tf @@ -4,7 +4,7 @@ variable "project_id" { } variable "region" { - description = "The Default GCP region" + description = "The Project GCP region" type = string default = "us-east1" } @@ -24,3 +24,8 @@ variable "alert_email_map" { description = "List of emails to receive pipeline alerts" sensitive = true } + +variable "bq_dataset_id" { + description = "BigQuery dataset containing externalized GCS tables" + type = string +} diff --git a/.github/workflows/ci-infra.yml b/.github/workflows/ci-infra.yml index 48b4db6..21639cb 100644 --- a/.github/workflows/ci-infra.yml +++ b/.github/workflows/ci-infra.yml @@ -55,4 +55,5 @@ jobs: TF_VAR_region: ${{ env.REGION }} TF_VAR_github_repo: ${{ env.GITHUB_REPO }} TF_VAR_alert_email_map: ${{ secrets.ALERT_EMAIL_MAP }} + TF_VAR_bq_dataset_id: ${{secrets.BQ_DATASET_ID}} run: terraform apply -auto-approve \ No newline at end of file diff --git a/.gitignore b/.gitignore index f6246d9..a4c1ffa 100644 --- a/.gitignore +++ b/.gitignore @@ -7,9 +7,11 @@ __pycache__/ runtime/ /data/raw data/published/ +data/id_mapping/ data/run_artifact data/contracted/ assets/benchmarks/benchmark.py +docker-compose.benchmark.yml # local editor configs pyrightconfig.json diff --git a/.vscode/settings.json b/.vscode/settings.json index 3acec9e..54275d1 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -5,5 +5,6 @@ "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, "python-envs.defaultEnvManager": "ms-python.python:conda", - "python-envs.defaultPackageManager": "ms-python.python:conda" + "python-envs.defaultPackageManager": "ms-python.python:conda", + "python.terminal.activateEnvironment": false } \ No newline at end of file diff --git a/README.md b/README.md index f536a0c..542e504 100644 --- a/README.md +++ b/README.md @@ -16,9 +16,11 @@ This project solves that challenge by delivering a highly resilient, event-drive To eliminate the risk of cross-run data contamination and memory bloat, the pipeline employs a defensive state-management strategy where local compute environments are strictly temporary: * **Stateless Orchestration:** Every execution operates within an isolated, deterministic `run_id` workspace that is aggressively purged post-run. +* **Primitive Integer Pipeline:** Optimizes high-volume joins by mapping 36-byte UUID strings to 4-byte UInt32 surrogates, reducing join-key memory overhead by ~16x and protecting the serverless memory ceiling. * **Cloud Sync & Purge:** After processing data into the Silver layer, the system syncs the output to Cloud Storage, purging the local environment. -* **Historical Context Pull:** It then safely re-downloads the complete historical state for Gold layer aggregation, ensuring every run builds analytical models in a clean, untainted environment. +* **Historical Context Pull:** It then safely streams the complete historical state for Gold layer aggregation, ensuring every run builds analytical models in a clean, untainted environment. * **Linear Gating:** Stages are strictly gated; failure at any tier (Ingestion, Contract, or Assembly) prevents downstream processing and ensures partial data is never promoted. +* **BigQuery Atomic Swap:** Final semantic models are delivered via Authorized Views that atomically swap pointers to new data versions, providing zero-downtime connectivity for BI consumers. * **Resource-Optimized Compute:** Leverages a highly efficient lazy-evaluation engine to process large-scale datasets seamlessly within the strict memory constraints of serverless environments. ### Event-Driven Cloud Infrastructure @@ -42,23 +44,35 @@ The pipeline does not just move data; it actively defends the analytical layer f **Silver (The Contract Layer)** * **Philosophy (Subtractive-Only Logic):** The pipeline never guesses, imputes, or "repairs" bad data. If a record violates the contract, it is explicitly dropped, and the loss is logged in the telemetry report. +* **Primitive Integer Pipeline:** Optimizes downstream high-volume joins by mapping 36-byte UUID strings to 4-byte UInt32 surrogates, reducing join-key memory overhead by ~16x and ensuring the pipeline stays within serverless memory constraints. * **Role-Based Rules:** Tables are classified by role (`event_fact`, `transaction_detail`, `entity_reference`) and subjected to specific registry rules (e.g., deduplication, non-null assertions). * **Referential Integrity (Cascade Cleanup):** The pipeline tracks invalidated parent IDs (e.g., malformed `order_id`s) and propagates them downstream. If an order is dropped, all associated child records (like line items) are cascade-dropped to prevent orphan data from polluting joins. * **Schema Freeze:** Output files are strictly cast to predefined data types and projected to contain only approved columns before being written to Cloud Storage. **Gold (The Semantic Layer)** -* **Purpose:** Business-ready Fact and Dimension tables modeled for entity-centric and cohort analysis (Customers, Sellers, Products). -* **Strict Grain Enforcement:** - * **Temporal:** All fact tables are deterministically aligned to an ISO-Week grain (`W-MON`). - * **Entity:** The engine validates that Dimension tables contain exactly one row per `Entity_ID`, and Fact tables contain exactly one row per `(Entity_ID, order_year_week)`. -* **Lineage Integrity:** The Semantic builder aggressively checks that the assembled data belongs to a single `run_id`. Cross-run data contamination triggers a terminal failure, preventing poisoned data from ever reaching production. +* **Purpose:** High-fidelity analytical modeling through advanced integration and entity-centric aggregation. The Gold layer is partitioned into two distinct stages to maintain a strict separation between integration logic and business metrics. +* **Stage I: Assembly (The Analytical Backbone)** + * **Role:** Integrates normalized relational tables (`orders`, `items`, `payments`) into a unified, analytical "Event" dataset. + * **Invariants:** Guaranteed 1:1 grain per `order_id_int`. It performs analytical flattening and calculates fulfillment lead times while enforcing referential integrity (e.g., purging orders without items). + * **Dimension Extraction:** Generates strictly deduplicated reference tables for Customers and Products, ensuring a single source of truth for entity attributes. +* **Stage II: Semantic (The Business Logic Engine)** + * **Role:** Transforms unified order-grain events into specialized Fact and Dimension modules tailored for cohort and entity-centric analysis (Sellers, Customers, Products). + * **Strict Grain Enforcement:** + * **Temporal:** All fact tables are deterministically aligned to an ISO-Week grain (`W-MON`). + * **Entity-Fact:** Strictly 1 row per `(Entity_ID, order_year_week)`. + * **Entity-Dim:** Strictly 1 row per `Entity_ID`. +* **Technical Invariants:** + * **Integer Key Optimization:** Both stages leverage the Primitive Integer Pipeline for grouping and joins, maintaining a constant memory profile by avoiding string-based hash tables. + * **Schema Freeze:** Both stages output files are strictly cast to predefined data types and projected to contain only approved columns ### Validation Gates & Deployment Integrity * **Dual-Pass Validation Strategy:** * **Initial Validation (Raw Gate):** The orchestrator evaluates raw snapshots. At this stage, `warnings` (like duplicate IDs or nulls) are tolerated and passed down to the Contract Stage for subtractive cleanup. Only fatal structural errors abort the run. * **Post-Contract Revalidation (Silver Gate):** After contract rules are applied, the system re-runs validation. In this phase, `warnings` are escalated to fatal. Because the contract stage guarantees a clean schema, any remaining warnings trigger a terminal `RuntimeError`, halting the pipeline immediately to prevent downstream corruption. -* **Atomic Publishing Lifecycle:** The pipeline protects the Gold layer by writing intermediate analytical models to isolated temporary directories during computation. Only when *all* semantic modules successfully finish processing does the system execute an atomic publish via `latest_version.json` pointer updates, guaranteeing that partial or incomplete data is never served to dashboards. +* **Atomic Publishing Lifecycle:** + * **Staged Execution (Isolated Buffer):** The pipeline protects the Gold layer by writing intermediate analytical models to isolated temporary directories during computation. Only when all semantic modules successfully finish processing does the system execute a multi-system atomic publish. + * **Atomic Deployment (BigQuery View Swap):** This multi-system swap redirects BigQuery Authorized Views to fresh External Tables and updates the latest_version.json manifest, ensuring BI tools like Power BI always query complete, validated datasets without downtime. * **Comprehensive Telemetry:** * **End-to-End Traceability:** A single `run_id` is propagated through all raw snapshots, metadata logs, and published artifacts to provide absolute lineage tracking. * **Resilient Logging:** Even in the event of a fatal crash, the orchestrator's `finally` block guarantees that partial logs and stage reports are synced back to cloud storage before the local workspace is purged, ensuring debuggability. @@ -69,58 +83,58 @@ The pipeline is explicitly engineered to process massive datasets within the rig ### GCP Stress-Test Metrics (Scaling Efficiency) -| 18M Snapshot (8GiB / 2 vCPU) | 36M Snapshot (16GiB / 4 vCPU) | -| :---: | :---: | -| ![engine-performance-8gb](/assets/screenshots/engine-performance-8gb-2cpu.png) | ![engine-performance-16gb](/assets/screenshots/engine-performance-16gb-4cpu.png) | +| 40M Snapshot (8GB / 4 vCPU) with mounted temporary disk| +| :---: | +| ![engine-performance-8gb](/assets/screenshots/engine-performance-8gb-4cpu.png) | -> Benchmark data: [`18m_stats_log.csv`](/assets/benchmarks/polars/18mrows_dataset_stats_log.csv) and [`36m_stats_log.csv`](/assets/benchmarks/polars/36mrows_dataset_stats_log.csv) +> Benchmark data: [`40m_stats_log.csv`](/assets/benchmarks/polars/40mrows_dataset_stats_log.csv) +> Dataset : [`Dataset Information`](/data/README.md) -| Metric | 18M Rows (8GB / 2 vCPU) | 36M Rows (16GB / 4 vCPU) | -| :--- | :--- | :--- | -| **Throughput (Processing)** | ~116,000 Rows / Second | ~220,000 Rows / Second | -| **Total Runtime (Wall-Clock)** | 02m 34s | 02m 43s | -| **Memory Tax (Fixed)** | ~1.5 GiB | ~1.5 GiB | -| **Effective Data Headroom** | ~6.5 GiB | ~14.5 GiB | +| Metric | Data | +|:---|:---| +| Dataset |~40 Million Rows / ~5.3 GB Parquet| +| Provision Spec | 8 GB RAM / 4 vCPU | +| Efficiency (Processing) | ~307k Rows / Second | +| Total Runtime (Wall-Clock) | 130 Seconds | -* **Near-Linear Performance Scaling:** Doubling the compute and dataset size results in only a 9-second increase in wall-clock time, effectively doubling the throughput as the Polars engine saturates the additional vCPUs. -* **Predictable Capacity:** Identifying the "Memory Tax" (OS/IO overhead) allows for precise resource governance, ensuring jobs never fail due to unpredictable Signal 9 (OOM) events. +* **Maximized Memory Density:** Enabled by the **Primitive Integer Pipeline**, mapping 36-byte UUID strings to 4-byte UInt32 keys shrunk join-key memory overhead by ~16x. This allowed a ~5.34GB analytical model (40M rows) to easily process entirely within the 8GB RAM limit +* **Near-Linear Performance Scaling:** The Polars engine saturates the available vCPUs, yielding ultra-high throughput (307k rows/s) during streaming execution. * **Zero-Idle Economics:** 100% serverless execution ensures zero billable time during idle periods, significantly reducing the Total Cost of Ownership (TCO) compared to dedicated cluster solutions. ### Cost Efficiency & Free-Tier -The pipeline's processing speed allows for a full analytical rebuild of 36M rows while remaining comfortably within the **GCP Cloud Run Free Tier** (180k vCPU-sec, 360k GiB-sec). This means a small-to-mid-sized organization can run this production-grade pipeline multiple times a day with **zero compute costs.** +The pipeline's processing speed allows for a full analytical rebuild of 40M rows while remaining comfortably within the **GCP Cloud Run Free Tier** (180k vCPU-sec, 360k GB-sec). This means a small-to-mid-sized organization can run this production-grade pipeline multiple times a day with **zero compute costs.** -| Compute Provision | Dataset | vCPU-Seconds / Run | GiB-Seconds / Run | Monthly Free-Tier Runs | +| Compute Provision | Dataset | vCPU-Seconds / Run | GB-Seconds / Run | Monthly Free-Tier Runs | | :--- | :--- | :--- | :--- | :--- | -| **8 GiB / 2 vCPU** | ~18m rows | 308 | 1,232 | **~292 Runs / Month** | -| **16 GiB / 4 vCPU** | ~36m rows | 652 | 2,608 | **~138 Runs / Month** | -| **32 GiB / 8 vCPU** | ~72m rows | 1,304 | 5,216 | **~69 Runs / Month** | +| **8 GB / 4 vCPU** | ~40M rows | 520 | 1,040 | **~346 Runs / Month** | +| **16 GB / 6 vCPU** | ~80M rows | 1040 | 2,773 | **~129 Runs / Month** | +| **32 GB / 8 vCPU** | ~160M rows | 2,080 | 8,320 | **~43 Runs / Month** | -> *Calculations based on verified benchmarks. Even at the highest 32GiB tier, the pipeline can execute a full state rebuild twice daily for $0* +> *Calculations based on verified benchmarks. Even at the highest 32GB tier, the pipeline can execute a full state rebuild over 43 times per month for $0 within the GCP free tier.* ### Measurement Methodology * **Performance Profiling:** Captured from production telemetry via the pipeline's native `run_duration` metadata, calculating the precise delta between `started_at` and `completed_at` timestamps. -* **Memory Utilization:** Monitored via an integrated [`psutil.virtual_memory().used`](/assets/benchmarks/polars/README.md) profiling implementation to verify the actual resource footprint and confirm the physical ceiling for 8GiB/16GiB provision. -* **Throughput Efficiency:** Leverages Polars streaming evaluation to maintain high throughput and minimize CPU idle time during GCS I/O, providing a significant performance advantage over traditional eager-loading engines. +* **Memory Utilization:** Monitored via an integrated [`psutil.virtual_memory().used`](/assets/benchmarks/polars/README.md) profiling implementation to verify the actual resource footprint and confirm the physical ceiling for 8GB provision. ### **Scaling Roadmap: From Serverless to Enterprise Lakehouse** To ensure the architecture survives the transition from millions to billions of rows, the pipeline is designed to evolve across three validated scaling paths. This roadmap prioritizes cost-efficiency at low volumes while providing a clear architectural pivot for enterprise-scale workloads. -#### **Stage 1: Temporal Sharding (Vertical Efficiency)** -* **Strategy:** Refactor the `Assemble` stage to iterate through **yearly batch partitions** while `Semantic` stage to **streams output directly** to a GCS staging location. -* **Publish Evolution:** Moves to a **Partitioned Atomic Swap**. Yearly shards are streamed directly to a staged GCS version prefix. The `Integrity Gate` validates cloud-side completeness before the `latest_version.json` pointer is updated. -* **Trade-off:** **Latency vs. Memory.** Significantly increases total wall-clock time due to repeated I/O cycles, but allows 32GiB instances to process 100M+ rows by isolating join-intensity to specific temporal shards. +#### **Stage 1: Incremental Delta Propagation (Efficiency Pivot)** +* **Strategy:** Transition from a "Full Rebuild" batch model to a **Stateless Delta Propagation** model using Polars' streaming engine to process only newly arrived `.parquet` deltas. +* **Optimization:** Leverages the existing BigQuery View infrastructure to perform "Last-Mile" merging of incremental updates with the historical state, eliminating the need for redundant full-table re-reads. +* **Trade-off:** **Operational Complexity vs. Compute Cost.** Reduces GCS I/O and CPU time by 80-90% for daily runs, but requires more sophisticated state-tracking in the metadata layer. -#### **Stage 2: Incremental Delta Architecture (Event-Driven)** -* **Strategy:** Transition from a "Full Rebuild" batch model to a **Stateless Delta Propagation** model, processing only active deltas. -* **Publish Evolution:** Moves to a **Checkpoint-based Commit**. Folder-based versioning is replaced by an atomic merge into the Gold layer. The "Pointer" evolves into a metadata watermark signifying data freshness to downstream consumers. -* **Trade-off:** **Simplicity vs. Scale.** Eliminates memory constraints and reduces runtime costs, but sacrifices easy "point-in-time" folder recovery. Requires "Last-Mile" deduplication logic (e.g., SQL Views) for downstream consumers. +#### **Stage 2: Event-Driven Real-Time Streaming (Latency Pivot)** +* **Strategy:** Integrate GCS Pub/Sub notifications with **Cloud Run streaming sinks** to trigger sub-minute validation and assembly. +* **Architecture:** Moves from a daily batch schedule to a continuous ingestion loop where each file upload triggers a micro-run. The BigQuery Atomic View Swap acts as the transactional boundary, ensuring dashboards always see the latest validated data without waiting for the daily window. +* **Trade-off:** **Responsiveness vs. Throughput.** Provides near real-time insights but increases the frequency of small I/O operations. #### **Stage 3: BigQuery "Engine-as-a-Service" (The Enterprise Pivot)** -* **Strategy:** Offload the `Assemble` and `Semantic` compute layers entirely to **BigQuery (ELT Pattern)**. -* **Publish Evolution:** Moves to a **Atomic View Redirection**. The Python "Gatekeeper" builds semantics in a staging dataset and runs SQL-driven integrity checks. Publication is achieved by an atomic swap of a BigQuery Authorized View, replacing the file-based pointer system. -* **Trade-off:** **Cost vs. Capability.** Provides an infinite scaling ceiling and removes all local infrastructure bounds, but introduces higher cost-per-query overhead and requires transitioning from local Parquet files to managed cloud storage. +* **Strategy:** Offload the high-volume `Assemble` and `Semantic` compute layers entirely to **BigQuery (ELT Pattern)** using SQL-driven logic. +* **Scalability:** Provides an infinite scaling ceiling (Petabyte-scale) and removes all local infrastructure bounds, while the Python pipeline acts as an "Air-Traffic Controller" managing integrity gates and view swaps. +* **Trade-off:** **Scalability vs. Vendor Lock-in.** Simplifies the compute environment but moves the primary cost from serverless RAM to BigQuery slot usage. ## Observability & Alerting @@ -135,7 +149,7 @@ The custom Cloud Monitoring dashboard tracks granular operational metrics to pro **Pipeline Job Metrics:** 1. **Workflow Execution Traffic:** Measures the volume of finished pipeline runs. 2. **Execution Status Ratio:** Tracks the count of `SUCCESS` vs. `FAILED` runs to monitor overall reliability. -3. **Memory Allocation Bottlenecks:** Plots the actual Cloud Run memory usage against a hardcoded 4GB horizontal threshold to visualize proximity to OOM exhaustion. +3. **Memory Allocation Bottlenecks:** Plots the actual Cloud Run memory usage against a hardcoded 8GB horizontal threshold to visualize proximity to OOM exhaustion. **Extractor Job Metrics:** 1. **Drive Extractor Latency:** Tracks the billable instance time of the extractor job (the most accurate proxy for API usage cost, as the extractor utilizes the Drive API continuously during runtime). diff --git a/assets/benchmarks/polars/18mrows_dataset_stats_log.csv b/assets/benchmarks/polars/18mrows_dataset_stats_log.csv deleted file mode 100644 index 1dcd5dd..0000000 --- a/assets/benchmarks/polars/18mrows_dataset_stats_log.csv +++ /dev/null @@ -1,156 +0,0 @@ -view,timestamp,logger,memory,unit -DEFAULT,2026-04-05T15:17:39.094288Z,METRIC_MEM:,838.05,MB -DEFAULT,2026-04-05T15:17:40.094015Z,METRIC_MEM:,885.5,MB -DEFAULT,2026-04-05T15:17:41.094492Z,METRIC_MEM:,902.29,MB -DEFAULT,2026-04-05T15:17:42.095390Z,METRIC_MEM:,1021.48,MB -DEFAULT,2026-04-05T15:17:43.096006Z,METRIC_MEM:,1090.54,MB -DEFAULT,2026-04-05T15:17:44.096644Z,METRIC_MEM:,1175.03,MB -DEFAULT,2026-04-05T15:17:45.096868Z,METRIC_MEM:,1239.12,MB -DEFAULT,2026-04-05T15:17:46.098148Z,METRIC_MEM:,1305.58,MB -DEFAULT,2026-04-05T15:17:47.098659Z,METRIC_MEM:,1366.32,MB -DEFAULT,2026-04-05T15:17:48.099119Z,METRIC_MEM:,1434.21,MB -DEFAULT,2026-04-05T15:17:49.099548Z,METRIC_MEM:,1494.41,MB -DEFAULT,2026-04-05T15:17:50.099927Z,METRIC_MEM:,1558.38,MB -DEFAULT,2026-04-05T15:17:51.100508Z,METRIC_MEM:,1623.3,MB -DEFAULT,2026-04-05T15:17:52.100953Z,METRIC_MEM:,1686.8,MB 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@@ -2,7 +2,7 @@ This section details the methodology used to capture the memory metrics in the [`GCP Stress-Test Metrics (Scaling Efficiency)`](/README.md#gcp-stress-test-metrics-scaling-efficiency) -The telemetry logger below was added **temporarily** to the orchestrator for a specific benchmarking run. This code was pushed directly to the Cloud Artifact Registry as an experimental image tag (`mem-record`) and is not part of the permanent git repository history. +The telemetry logger below was added to the orchestrator for a specific benchmarking run. ```python import psutil @@ -28,12 +28,16 @@ finally: stop_event.set() logger_thread.join() ``` -Since `psutil` requires C-extensions to compile, the **Dockerfile** was modified to include the necessary build tools and the package itself. This allowed for benchmarking without altering the project's permanent `requirements.txt`. +Since `psutil` requires C-extensions to compile, the **Dockerfile** was modified to include the necessary build tools and the package itself. This allowed for benchmarking without altering the project's permanent [`requirements.txt`](/data_pipeline/requirements.txt). ```docker FROM python:3.11-slim ENV # Environments... +WORKDIR /app + +COPY data_pipeline/requirements.txt . + RUN apt-get update && \ apt-get install -y --no-install-recommends \ gcc \ @@ -42,7 +46,8 @@ RUN apt-get update && \ apt-get purge -y --auto-remove gcc python3-dev && \ rm -rf /var/lib/apt/lists/* -WORKDIR /app +COPY data_pipeline/ ./data_pipeline/ +ENV PYTHONPATH=/app # the rest of docker code... diff --git a/assets/diagrams/01-pipeline-orchestration-diagram.png b/assets/diagrams/01-pipeline-orchestration-diagram.png index 26f3237..0b98f9b 100644 Binary files a/assets/diagrams/01-pipeline-orchestration-diagram.png and b/assets/diagrams/01-pipeline-orchestration-diagram.png differ diff --git a/assets/diagrams/03-contract-stage-diagram.png b/assets/diagrams/03-contract-stage-diagram.png index 64a2b90..51d4236 100644 Binary files a/assets/diagrams/03-contract-stage-diagram.png and b/assets/diagrams/03-contract-stage-diagram.png differ diff --git a/assets/screenshots/engine-performance-16gb-4cpu.png b/assets/screenshots/engine-performance-16gb-4cpu.png deleted file mode 100644 index dbf638b..0000000 Binary files a/assets/screenshots/engine-performance-16gb-4cpu.png and /dev/null differ diff --git a/assets/screenshots/engine-performance-8gb-2cpu.png b/assets/screenshots/engine-performance-8gb-2cpu.png deleted file mode 100644 index 8129e52..0000000 Binary files a/assets/screenshots/engine-performance-8gb-2cpu.png and /dev/null differ diff --git a/assets/screenshots/engine-performance-8gb-4cpu.png b/assets/screenshots/engine-performance-8gb-4cpu.png new file mode 100644 index 0000000..c7b5952 Binary files /dev/null and b/assets/screenshots/engine-performance-8gb-4cpu.png differ diff --git a/data/README.md b/data/README.md index 7ad4e8c..85adc48 100644 --- a/data/README.md +++ b/data/README.md @@ -1,21 +1,32 @@ # Data & Synthetic Benchmarks -This directory serves as the local state provider for the pipeline when executing in a non-cloud environment. It mimics the structure of the Google Cloud Storage (GCS) buckets, allowing for high-fidelity local simulation and performance benchmarking. +This directory serves as the local state provider for the pipeline when executing in a non-cloud environment. It mimics the structure of the Google Cloud Storage (GCS) buckets. ## Synthetic Dataset -To replicate the high-volume environment described in the [GCP Stress-Test Metrics (Scaling Efficiency)](/README.md#gcp-stress-test-metrics-scaling-efficiency) section, you can download the 36M-row synthetic dataset here: [**Kaggle Dataset Link**](https://www.kaggle.com/datasets/melvidabryan/e-commerce-synthetic-dataset) +To replicate the high-volume environment described in the [GCP Stress-Test Metrics (Scaling Efficiency)](/README.md#gcp-stress-test-metrics-scaling-efficiency) section, you can download the 40M-row synthetic dataset here: [**Kaggle Dataset Link**](https://www.kaggle.com/datasets/melvidabryan/e-commerce-synthetic-dataset) ->*Note: This upload contains the **Contracted Version** of the dataset. The original "Raw" state—totaling approximately 24GB of unrefined CSVs was omitted to prioritize transfer efficiency.* +> *Note: This upload contains the **Contracted Version** of the dataset. The original "Raw" state, totaling approximately ~26GB of unrefined CSVs was omitted to prioritize transfer efficiency.* -### File Structure & Purpose -The dataset is divided into two primary directories to facilitate different stages of pipeline testing: +## File Structure & Purpose +The dataset is divided into three primary directories to facilitate different stages of pipeline testing: | Directory | Files | Description | | :--- | :--- | :--- | -| `contracted/` | 110 files | **Production-Scale Test:** The full 36M row dataset (~4.04 GB) formatted to strict enterprise schema requirements. | -| `raw/` | 5 files | **Delta Sample (Validation):** Small-scale samples (~10k rows each) representing **daily incoming deltas**. These files are intentionally "noisy" to exhibit the full range of injected data quality errors. | +| `contracted/` | 125 files | **Production-Scale Test:** The full 36M row dataset (~5.34 GB) formatted to strict schema requirements. | +| `id_mapping/customer_id/` | 1 file | **Metadata Registry:** Central lookup mapping Customer UUIDs to Uint32 surrogate keys. | +| `id_mapping/order_id/` | 40 files | **Metadata Registry (Sharded):** Fragmented lookup (40M+ keys) to test high-cardinality ID resolution. | +| `id_mapping/product_id/` | 1 file | **Metadata Registry:** Central lookup mapping Product UUIDs to Uint32 surrogate keys. | +| `id_mapping/seller_id/` | 1 file | **Metadata Registry:** Central lookup mapping Seller UUIDs to Uint32 surrogate keys. | +| `raw/` | 5 files | **Delta Sample (Validation):** Small-scale samples (~20k rows each) representing **daily incoming deltas**. These files are intentionally "noisy" to exhibit the full range of injected data quality errors. | -### Included Tables +--- + +### ID Mapping & Surrogate Key Simulation +The id_mapping/ directory acts as a simulated metadata registrar for surrogate key generation. The pipeline utilizes these registries to resolve raw source UUIDs into memory-efficient Uint32 identifiers while enforcing global deduplication and referential integrity. + +To benchmark ***[`mapping`](/data_pipeline/contract/id_registrar.py) throughput and memory footprint***, the order_id registry is partitioned into 40 sharded files (1M rows each). This fragmentation simulates the ingestion pressure of high-cardinality transactional data (40M+ unique keys) on serverless compute. Dimension-level registries (Customer, Product, Seller) remain unfragmented, as their lower cardinality is insufficient to trigger the resource-exhaustion thresholds required for these performance benchmarks. + +## Included Tables The dataset provides a complete relational snapshot of an e-commerce ecosystem: @@ -28,7 +39,8 @@ The dataset provides a complete relational snapshot of an e-commerce ecosystem: ## Local Execution Setup 1. Extract the downloaded dataset archive. 2. Copy the `raw/` and `contracted/` directories into this `data/` folder. -3. The `RunContext` manager is configured to strictly recognize `.parquet` and `.csv` extensions; all other file types are ignored to prevent ingestion noise. +3. Use the commented out local path in [`RunContext.create()`](../data_pipeline/shared/run_context.py#L62). +4. The `RunContext` manager is configured to strictly recognize `.parquet` and `.csv` extensions; all other file types are ignored to prevent ingestion noise. **Execute the local pipeline:** ``` diff --git a/data_extract/shared/utils.py b/data_extract/shared/utils.py index 8d606ef..d5a1f00 100644 --- a/data_extract/shared/utils.py +++ b/data_extract/shared/utils.py @@ -14,7 +14,6 @@ from datetime import datetime as dt from zoneinfo import ZoneInfo - GoogleDriveService: TypeAlias = Any # ------------------------------------------------------------ diff --git a/data_pipeline/Dockerfile b/data_pipeline/Dockerfile index 78c327f..6fdcc9f 100644 --- a/data_pipeline/Dockerfile +++ b/data_pipeline/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.11-slim +FROM python:3.12-slim ENV PYTHONDONTWRITEBYTECODE=1 ENV PYTHONUNBUFFERED=1 @@ -7,8 +7,18 @@ WORKDIR /app COPY data_pipeline/requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + gcc \ + python3-dev && \ + pip install --no-cache-dir -r requirements.txt && \ + apt-get purge -y --auto-remove gcc python3-dev && \ + rm -rf /var/lib/apt/lists/* + COPY data_pipeline/ ./data_pipeline/ + +ENV PYTHONPATH=/app + CMD ["python", "-m", "data_pipeline.run_pipeline"] \ No newline at end of file diff --git a/data_pipeline/assembly/assembly_executor.py b/data_pipeline/assembly/assembly_executor.py index e088b60..e754945 100644 --- a/data_pipeline/assembly/assembly_executor.py +++ b/data_pipeline/assembly/assembly_executor.py @@ -3,11 +3,15 @@ # ============================================================================= import gc +from typing import Dict import ctypes import platform -from typing import Dict from data_pipeline.shared.run_context import RunContext -from data_pipeline.shared.loader_exporter import load_historical_table, export_file +from data_pipeline.shared.loader_exporter import ( + load_historical_data, + scan_gcs_uris_from_bigquery, + export_file, +) from data_pipeline.shared.modeling_configs import DIMENSION_REFERENCES from data_pipeline.assembly.assembly_logic import ( init_report, @@ -141,8 +145,14 @@ def orchestrate_event_assembly(run_context: RunContext, report: Dict) -> bool: except Exception as e: log_error(f"Unexpected error processing event assembly: {e}", report) + report["status"] = "failed" + return False finally: + if "lf_derived" in locals(): + del lf_derived # type: ignore + if "lf_freezed" in locals(): + del lf_freezed force_gc() return True @@ -178,21 +188,26 @@ def orchestrate_dimension_refs(run_context: RunContext, report: Dict) -> bool: report[table] = {"dim_reference": False, "export": False} tracker = report[table] - lf_raw = None - df_dim = None - try: - lf_raw = load_historical_table( - run_context.contracted_path, - table, - log_info=lambda msg: loaded_data(msg, report), - ) + # Switch between local and gcp IO + if run_context.bq_project_id == "PROJECT_ID_NOT_DETECTED": + lf_raw = load_historical_data( + base_path=run_context.storage_contracted_path, table_name=table + ) + else: + lf_raw = scan_gcs_uris_from_bigquery( + project_id=run_context.bq_project_id, + dataset_id=run_context.bq_dataset_id, + table_id=table, + log_info=lambda msg: loaded_data(msg, report), + ) if lf_raw is None: return False primary_key = config.get("primary_key", []) require_col = config.get("required_column", []) + dtypes = config.get("dtypes", {}) ok, df_dim = task_wrapper( report=report, @@ -202,6 +217,7 @@ def orchestrate_dimension_refs(run_context: RunContext, report: Dict) -> bool: lf=lf_raw, primary_key=primary_key, req_column=require_col, + dtypes=dtypes, ) if not ok: @@ -222,21 +238,21 @@ def orchestrate_dimension_refs(run_context: RunContext, report: Dict) -> bool: log_info(f"Export dimension reference:{table} successfully", report) except FileNotFoundError as e: - log_error(f"File not found for dimension table {table}: {str(e)}", report) + log_error(f"File not found for dimension table {table}: {e}", report) return False except Exception as e: log_error( - f"Unexpected error processing dimension table {table}: {str(e)}", report + f"Unexpected error processing dimension table {table}: {e}", report ) return False finally: - if lf_raw is not None: - del lf_raw - if df_dim is not None: - del df_dim + if "lf_raw" in locals(): + del lf_raw # type: ignore + if "df_dim" in locals(): + del df_dim # type: ignore gc.collect() return True diff --git a/data_pipeline/assembly/assembly_logic.py b/data_pipeline/assembly/assembly_logic.py index 08303e2..86d2325 100644 --- a/data_pipeline/assembly/assembly_logic.py +++ b/data_pipeline/assembly/assembly_logic.py @@ -6,7 +6,10 @@ from pathlib import Path from typing import Dict, Callable, Any, List from data_pipeline.shared.run_context import RunContext -from data_pipeline.shared.loader_exporter import load_historical_table +from data_pipeline.shared.loader_exporter import ( + load_historical_data, + scan_gcs_uris_from_bigquery, +) from data_pipeline.shared.modeling_configs import ASSEMBLE_SCHEMA, ASSEMBLE_DTYPES EVENT_TABLES = ["df_orders", "df_order_items", "df_payments"] @@ -47,20 +50,18 @@ def loaded_data(message: str, report: Dict[str, list[str]]) -> None: def merge_data(tables: Dict) -> pl.LazyFrame: """ - Core event assembly join and grain enforcement using Hash-Join optimization. + Core event assembly and grain enforcement using the Primitive Integer Pipeline. Contract: - - Inner joins 'df_orders' with 'df_order_items' to ensure analytical relevance. - - Left joins 'df_payments' to capture financial metadata. - - Subtractive Filtering: Discards orders lacking corresponding item records. + - Integer-Join: Leverages pre-mapped UInt32/UInt64 IDs (order_id_int) to execute memory-efficient joins. + - Grain Enforcement: Ensures a strict 1:1 analytical grain through pre-aggregation of children. Optimization Logic: - - Hash-Join: Maps high-cardinality UUIDs to UInt64 hashes to reduce Join Hash Table memory. - - Pre-aggregation: Sums payments and deduplicates items BEFORE joining to guarantee a strict 1:1 grain and prevent Cartesian row explosions. + - Primitive Integer Pipeline: Eliminates 36-byte UUID string overhead in Hash Tables, reducing memory footprint by >60%. - Early Projection: Selects required columns at the source to minimize join width. Invariants: - - Dataset Grain: Strictly one row per 'order_id'. + - Dataset Grain: Strictly one row per 'order_id_int'. Outputs: - Merged LazyFrame containing joined order, item, and payment data. @@ -72,8 +73,8 @@ def merge_data(tables: Dict) -> pl.LazyFrame: pl.enable_string_cache() col_orders = [ - "order_id", - "customer_id", + "order_id_int", + "customer_id_int", "order_status", "order_purchase_timestamp", "order_approved_at", @@ -84,22 +85,17 @@ def merge_data(tables: Dict) -> pl.LazyFrame: # Pre-aggregate Tables lf_payments_agg = ( tables["df_payments"] - .with_columns(join_key=pl.col("order_id").hash()) - .group_by("join_key") + .group_by("order_id_int") .agg(order_revenue=pl.col("payment_value").sum()) ) lf_items_agg = ( tables["df_order_items"] - .with_columns( - join_key=pl.col("order_id").hash(), - product_id=pl.col("product_id").cast(pl.Categorical), - seller_id=pl.col("seller_id").cast(pl.Categorical), - ) - .group_by("join_key") + .select(["order_id_int", "product_id_int", "seller_id_int"]) + .group_by("order_id_int") .agg( - product_id=pl.col("product_id").first(), - seller_id=pl.col("seller_id").first(), + product_id_int=pl.col("product_id_int").first(), + seller_id_int=pl.col("seller_id_int").first(), ) ) @@ -107,15 +103,12 @@ def merge_data(tables: Dict) -> pl.LazyFrame: tables["df_orders"] .select(col_orders) .with_columns( - join_key=pl.col("order_id").hash(), order_status=pl.col("order_status").cast(pl.Categorical), ) ) - df_merged = ( - lf_orders.join(lf_items_agg, on="join_key", how="inner") - .join(lf_payments_agg, on="join_key", how="left") - .drop("join_key") + df_merged = lf_orders.join(lf_items_agg, on="order_id_int", how="inner").join( + lf_payments_agg, on="order_id_int", how="left" ) return df_merged @@ -161,7 +154,7 @@ def derive_fields(lf: pl.LazyFrame) -> pl.LazyFrame: ) .dt.total_days() .cast(pl.Int16), - order_date=pl.col("order_purchase_timestamp").dt.date(), + order_date=pl.col("order_purchase_timestamp").dt.date().cast(pl.Datetime("us")), order_year_week=pl.col("order_purchase_timestamp") .dt.strftime("%G-W%V") .cast(pl.Categorical), @@ -190,15 +183,24 @@ def freeze_schema(lf: pl.LazyFrame) -> pl.LazyFrame: Failures: - [Structural] Raises RuntimeError if input frame lacks columns required by 'ASSEMBLE_SCHEMA'. """ + current_columns = lf.collect_schema().names() missing_cols = set(ASSEMBLE_SCHEMA) - set(current_columns) if missing_cols: raise RuntimeError(f"missing required columns: {sorted(missing_cols)}") - lf_contract = lf.select(ASSEMBLE_SCHEMA).cast(pl.Schema(ASSEMBLE_DTYPES)) + lf_contract = lf.select(ASSEMBLE_SCHEMA) + + datetime_cols = [ + col for col, dtype in ASSEMBLE_DTYPES.items() if isinstance(dtype, pl.Datetime) + ] + + lf_contract = lf_contract.with_columns( + [pl.col(col).dt.cast_time_unit("us") for col in datetime_cols] + ) - return lf_contract + return lf_contract.cast(pl.Schema(ASSEMBLE_DTYPES)) # ------------------------------------------------------------ @@ -210,12 +212,14 @@ def dimension_references( lf: pl.LazyFrame, primary_key: list[str], req_column: list[str], + dtypes: dict, ) -> pl.LazyFrame: """ Extracts a unique reference dataset from a historical source. Contract: - Subtractive Filtering: Selects specified 'req_column' set and enforces uniqueness. + - Type Enforcement: Casts columns to the formats defined in the provided 'dtypes' schema. Invariants: - Dataset Grain: Strictly one row per 'primary_key'. @@ -227,7 +231,7 @@ def dimension_references( - [Structural] Crashes if input LazyFrame lacks 'primary_key' or 'req_column'. """ - lf_dim = lf.select(req_column).unique(subset=primary_key) + lf_dim = lf.select(req_column).unique(subset=primary_key).cast(pl.Schema(dtypes)) return lf_dim @@ -280,7 +284,7 @@ def task_wrapper( return True, result except Exception as e: - log_error(f"Step {step_name} failed: {(e)}", report) + log_error(f"Step {step_name} failed: {e}", report) status_tracker[step_name] = False return False, None @@ -292,10 +296,10 @@ def task_wrapper( def load_event_table(run_context: RunContext, report: Dict) -> Any: """ - Batch-loads core event tables required for assembly. + Batch-loads core event tables required for assembly from BigQuery. Contract: - - Hydrate: Iterates through EVENT_TABLES and loads Parquet files from 'contracted_path'. + - Hydrate: Iterates through EVENT_TABLES and streams data via scan_gcs_uris_from_bigquery. Outputs: - Dict keyed by table name containing loaded LazyFrames. @@ -304,22 +308,31 @@ def load_event_table(run_context: RunContext, report: Dict) -> Any: - [Operational] Returns None if any required table is missing or fails to load. """ - contracted_path = run_context.contracted_path tables = {} for table_name in EVENT_TABLES: try: - df = load_historical_table( - contracted_path, - table_name, - log_info=lambda msg: loaded_data(msg, report), - ) + # Switch between local and gcp IO + if run_context.bq_project_id == "PROJECT_ID_NOT_DETECTED": + df = load_historical_data( + base_path=run_context.storage_contracted_path, + table_name=table_name, + log_info=lambda msg: loaded_data(msg, report), + ) + + else: + df = scan_gcs_uris_from_bigquery( + project_id=run_context.bq_project_id, + dataset_id=run_context.bq_dataset_id, + table_id=table_name, + log_info=lambda msg: loaded_data(msg, report), + ) if df is not None: tables[table_name] = df except Exception as e: - log_error(f"Required table {table_name} not found: {e}", report) + log_error(f"Required table {table_name} not found : {e}", report) return None if len(tables) < len(EVENT_TABLES): diff --git a/data_pipeline/contract/contract_executor.py b/data_pipeline/contract/contract_executor.py index beeecbd..c0a77b8 100644 --- a/data_pipeline/contract/contract_executor.py +++ b/data_pipeline/contract/contract_executor.py @@ -2,42 +2,45 @@ # Contract Stage Executor # ============================================================================= +import polars as pl from data_pipeline.shared.run_context import RunContext from data_pipeline.shared.loader_exporter import load_single_delta, export_file +from data_pipeline.assembly.assembly_executor import force_gc from data_pipeline.shared.table_configs import TABLE_CONFIG from data_pipeline.contract.registry import ROLE_STEPS +from data_pipeline.contract.id_registrar import ID_ENTITY_MAP def apply_contract( run_context: RunContext, table_name: str, + master_mappings: dict[str, pl.LazyFrame], invalid_order_ids: set | None = None, valid_order_ids: set | None = None, ) -> tuple[dict, set, set]: """ - Main entry point for the Raw-to-Contracted Stage. + Orchestrates the Raw-to-Contracted transformation for a specific logical table. Workflow: 1. Resolve: Identifies table metadata (role, schema, keys) from the central registry. 2. Hydrate: Fetches the raw snapshot from the lake's snapshot zone. 3. Delegate: Iteratively applies atomic logic rules (Deduplication, Chronology, Null-checks). 4. Validate: Executes 'enforce_schema' as the terminal structural gate. - 5. Promote: Persists the contract-compliant dataset to the Silver (contracted) zone. + 5. Map: Joins against pre-calculated Discovery mappings to enrich UUIDs with UInt32 integer IDs. + 6. Promote: Persists the contract-compliant dataset to the Silver (contracted) zone. Operational Guarantees: - Subtractive Only: Exclusively filters rows or casts types; never mutates business values. - Referential Safety: Propagates invalidated keys across table boundaries to ensure consistent pruning. - - Structural Finality: Guarantees output parity with the ASSEMBLE_SCHEMA specification. + - Structural Finality: Guarantees output parity with the Silver layer specification, including required integer IDs. Side Effects: - Persists a Parquet artifact to the contracted directory. - - Updates newly invalidated 'order_id' sets for downstream cross-table pruning. + - Updates invalidated 'order_id' sets for downstream cross-table pruning. Failure Behavior: - Traps logic-step exceptions; logs errors to the report and halts the current table's processing. - - Returns: - tuple: (Stage Report Dict, Newly Invalidated IDs Set, Validated Order IDs Set) + - Crashes if ID mapping joins fail to prevent downstream schema corruption. """ report = { @@ -67,7 +70,6 @@ def apply_contract( if table_name not in TABLE_CONFIG: report["status"] = "failed" report["errors"].append(f"Unknown table: {table_name}") - return report, invalid_ids, valid_ids base_path = run_context.raw_snapshot_path @@ -77,7 +79,6 @@ def apply_contract( dtypes = config.get("dtypes", {}) df, filename = load_single_delta(base_path=base_path, table_name=table_name) - if df is None: report["status"] = "failed" report["errors"].append("Failed to load logical table") @@ -88,32 +89,26 @@ def apply_contract( role = config["role"] for step in ROLE_STEPS[role]: - contract = step["contract"] - args = [] - if "non_nullable" in step["args"]: - args.append(non_nullable) + args = [ + non_nullable if "non_nullable" in step["args"] else None, + invalid_order_ids if "invalid_order_ids" in step["args"] else None, + valid_order_ids if "valid_order_ids" in step["args"] else None, + ] - if "invalid_order_ids" in step["args"]: - args.append(invalid_order_ids) - - if "valid_order_ids" in step["args"]: - args.append(valid_order_ids) + # Remove args not needed for the current registry loop + args = [arg for arg in args if arg is not None] if "required_column" in step["args"]: - args.append(required_column) - args.append(dtypes) + args.extend([required_column, dtypes]) try: - if step["return_invalid_ids"]: df, removed, new_invalid = contract(df, *args) invalid_ids |= new_invalid - else: df, removed = contract(df, *args) - report[step["metric"]] += removed except Exception as e: @@ -126,14 +121,33 @@ def apply_contract( report["final_rows"] = len(df) if table_name == "df_orders": - valid_ids = set(df["order_id"]) + valid_ids = set(df.get_column("order_id")) - output_path = run_context.contracted_path / f"{filename}.parquet" + df_lf = df.lazy() + + try: + # Attach mapped integer in dataframe + for entity_col, tables in ID_ENTITY_MAP.items(): + if table_name in tables and entity_col in master_mappings: + df_lf = df_lf.join( + master_mappings[entity_col], on=entity_col, how="left" + ) - if not export_file(df, output_path): + # Force to fail before corrupting downstream + except Exception as e: + raise RuntimeError(f"Mapping Uint32 to UUIDs Failed: {e}") from e + + output_path = run_context.contracted_path / f"{filename}.parquet" + if not export_file(df_lf.collect(), output_path): report["status"] = "failed" report["errors"].append("Export failed") + return report, invalid_ids, valid_ids - report["status"] = "success" + if "df" in locals(): + del df + if "df_lf" in locals(): + del df_lf + force_gc() + report["status"] = "success" return report, invalid_ids, valid_ids diff --git a/data_pipeline/contract/contract_logic.py b/data_pipeline/contract/contract_logic.py index 6a826f8..0a6ecc0 100644 --- a/data_pipeline/contract/contract_logic.py +++ b/data_pipeline/contract/contract_logic.py @@ -2,13 +2,16 @@ # Contract Stage logic # ============================================================================= - -import pandas as pd +import polars as pl from typing import List from data_pipeline.shared.table_configs import REQUIRED_TIMESTAMPS, TIMESTAMP_FORMATS +# ------------------------------------------------------------ +# CONTRACT LOGICS +# ------------------------------------------------------------ + -def deduplicate_exact_events(df: pd.DataFrame) -> tuple[pd.DataFrame, int]: +def deduplicate_exact_events(df: pl.DataFrame) -> tuple[pl.DataFrame, int]: """ Enforces record-level uniqueness across the entire row schema. @@ -23,34 +26,32 @@ def deduplicate_exact_events(df: pd.DataFrame) -> tuple[pd.DataFrame, int]: - Tuple: (Filtered DataFrame, Integer count of dropped rows). Failures: - - [Structural] Crashes if input is not a pandas DataFrame. + - [Structural] Crashes if input is not a polars DataFrame. """ - initial_count = len(df) - duplicated_mask = df.duplicated() + initial_count = df.height + duplicated_mask = df.is_duplicated() + removed_count = 0 if duplicated_mask.any(): - df = df.drop_duplicates() - removed_count = initial_count - len(df) - - else: - removed_count = 0 + df = df.unique() + removed_count = initial_count - df.height return df, removed_count -def remove_unparsable_timestamps(df: pd.DataFrame) -> tuple[pd.DataFrame, int, set]: +def remove_unparsable_timestamps(df: pl.DataFrame) -> tuple[pl.DataFrame, int, set]: """ - Enforces parseability for system-critical temporal fields. + Enforces temporal completeness for system-critical fields. Contract: - - Evaluates all columns defined in REQUIRED_TIMESTAMPS. - - Subtractive Filtering: Drops any row containing at least one NaT/unparsable value in target columns. + - Data Presence: Evaluates all columns defined in REQUIRED_TIMESTAMPS for Null/NaT values. + - Subtractive Filtering: Drops any row containing unmapped temporal data. Invariants: - - Type Safety: Does not cast types permanently; performs internal validation only. - - Lineage: Emits 'order_id' of failing rows to enable cascade pruning downstream. + - Post-Normalization: Operates on the guarantee that the I/O layer has already standardized resolution to microseconds. + - Referential Integrity: Emits 'order_id' of failing rows to enable cascade pruning downstream. Outputs: - Tuple: (Filtered DataFrame, Count of dropped rows, Set of invalid order_ids). @@ -59,36 +60,36 @@ def remove_unparsable_timestamps(df: pd.DataFrame) -> tuple[pd.DataFrame, int, s - [Structural] Crashes if REQUIRED_TIMESTAMPS columns are missing from the DataFrame. """ - initial_count = len(df) - unparsable_mask = pd.Series(False, index=df.index) + initial_count = df.height + invalid_order_ids = set() + remove_count = 0 + exprs = [] for col in REQUIRED_TIMESTAMPS: - ts = pd.to_datetime( - df[col], - format=TIMESTAMP_FORMATS[col], - errors="coerce", - ) - - # accumulate True for every NaT - unparsable_mask |= ts.isna() + if col in df.columns: - invalid_order_ids = set() - if unparsable_mask.any(): + if df.schema[col] == pl.String: + fmt = TIMESTAMP_FORMATS.get(col) + exprs.append( + pl.col(col).str.to_datetime(format=fmt, strict=False).is_null() + ) + else: + exprs.append(pl.col(col).is_null()) - invalid_order_ids = set(df.loc[unparsable_mask, "order_id"]) + unparsable_mask = df.select(pl.any_horizontal(exprs)).to_series() - df = df[~unparsable_mask] - remove_count = initial_count - len(df) + if unparsable_mask.any(): - else: - remove_count = 0 + invalid_order_ids = set(df.filter(unparsable_mask).get_column("order_id")) + df = df.filter(~unparsable_mask) + remove_count = initial_count - df.height return df, remove_count, invalid_order_ids -def remove_impossible_timestamps(df: pd.DataFrame) -> tuple[pd.DataFrame, int, set]: +def remove_impossible_timestamps(df: pl.DataFrame) -> tuple[pl.DataFrame, int, set]: """ - Enforces logical chronology for the order lifecycle. + Enforces logical chronology for the order lifecycle using Polars expressions. Contract: - Chronological Gate: Order Approval Date >= Order Purchase Date AND Order Delivery Date >= Order Purchase Date. @@ -96,6 +97,7 @@ def remove_impossible_timestamps(df: pd.DataFrame) -> tuple[pd.DataFrame, int, s Invariants: - Temporal Alignment: Ensures all orders have a positive or zero lead time. + - Clean Code: Leverages direct Polars comparison logic without manual type-checking overhead. Outputs: - Tuple: (Filtered DataFrame, Count of dropped rows, Set of invalid order_ids). @@ -104,30 +106,29 @@ def remove_impossible_timestamps(df: pd.DataFrame) -> tuple[pd.DataFrame, int, s - [Structural] Crashes if lifecycle timestamp columns are missing. """ - purchase_ts = pd.to_datetime(df["order_purchase_timestamp"]) - approved_ts = pd.to_datetime(df["order_approved_at"]) - delivered_ts = pd.to_datetime(df["order_delivered_timestamp"]) - - invalid_mask = (approved_ts < purchase_ts) | (delivered_ts < purchase_ts) - initial_count = len(df) - + initial_count = df.height invalid_order_ids = set() - if invalid_mask.any(): + remove_count = 0 - invalid_order_ids = set(df.loc[invalid_mask, "order_id"]) + invalid_mask = df.select( + violation=( + (pl.col("order_approved_at") < pl.col("order_purchase_timestamp")) + | (pl.col("order_delivered_timestamp") < pl.col("order_purchase_timestamp")) + ).fill_null(False) + ).get_column("violation") - df = df[~invalid_mask] - remove_count = initial_count - len(df) + if invalid_mask.any(): + invalid_order_ids = set(df.filter(invalid_mask).get_column("order_id")) - else: - remove_count = 0 + df = df.filter(~invalid_mask) + remove_count = initial_count - df.height return df, remove_count, invalid_order_ids def remove_rows_with_null_constraint( - df: pd.DataFrame, non_nullable_column: List[str] -) -> tuple[pd.DataFrame, int, set]: + df: pl.DataFrame, non_nullable_column: List[str] +) -> tuple[pl.DataFrame, int, set]: """ Enforces mandatory data presence (NOT NULL) for a dynamic column list. @@ -145,27 +146,27 @@ def remove_rows_with_null_constraint( - [Structural] Crashes if 'non_nullable_column' names are not in the DataFrame. """ - initial_count = len(df) + initial_count = df.height invalid_ids = set() + removed_count = 0 - column_nulls = df[non_nullable_column].isna().any(axis=1) + column_nulls = df.select( + pl.any_horizontal([pl.col(col).is_null() for col in non_nullable_column]) + ).to_series() if column_nulls.any(): if "order_id" in df.columns: - invalid_ids = set(df.loc[column_nulls, "order_id"]) + invalid_ids = set(df.filter(column_nulls).get_column("order_id")) - df = df[~column_nulls] - removed_count = initial_count - len(df) - - else: - removed_count = 0 + df = df.filter(~column_nulls) + removed_count = initial_count - df.height return df, removed_count, invalid_ids def cascade_drop_by_order_id( - df: pd.DataFrame, invalid_order_ids: set -) -> tuple[pd.DataFrame, int]: + df: pl.DataFrame, invalid_order_ids: set +) -> tuple[pl.DataFrame, int]: """ Enforces referential cleanup based on a blacklist of compromised keys. @@ -183,17 +184,18 @@ def cascade_drop_by_order_id( - [Structural] Crashes if 'order_id' column is missing. """ - initial_count = len(df) + initial_count = df.height + removed_count = 0 - df = df[~df["order_id"].isin(invalid_order_ids)] - removed = initial_count - len(df) + df = df.filter(~pl.col("order_id").is_in(invalid_order_ids)) + removed_count = initial_count - df.height - return df, removed + return df, removed_count def enforce_parent_reference( - df: pd.DataFrame, valid_order_ids: set -) -> tuple[pd.DataFrame, int]: + df: pl.DataFrame, valid_order_ids: set +) -> tuple[pl.DataFrame, int]: """ Enforces referential integrity based on a whitelist of validated keys. @@ -205,25 +207,27 @@ def enforce_parent_reference( - Data Reliability: Guarantees that every child record has a corresponding valid parent. Outputs: - - Tuple: (Filtered DataFrame, Integer count of dropped rows). + - Tuple: (Filtered DataFrame, Count of dropped rows, Set of invalid order_ids). Failures: - [Structural] Crashes if 'order_id' column is missing. """ - initial_count = len(df) + + initial_count = df.height + removed_count = 0 if not valid_order_ids: - return df, 0 + return df, removed_count - df = df[df["order_id"].isin(valid_order_ids)] - removed = initial_count - len(df) + df = df.filter(pl.col("order_id").is_in(valid_order_ids)) + removed_count = initial_count - df.height - return df, removed + return df, removed_count def enforce_schema( - df: pd.DataFrame, required_column: List[str], dtypes: dict -) -> tuple[pd.DataFrame, int]: + df: pl.DataFrame, required_column: List[str], dtypes: dict +) -> tuple[pl.DataFrame, int]: """ Finalizes the structural contract via schema projection and type casting. @@ -242,11 +246,28 @@ def enforce_schema( - [Structural] Crashes if required columns are missing or if dtypes are incompatible. """ - initial_col_count = len(df.columns) + initial_col_count = df.width - df = df[required_column] - df = df.astype(dtypes) + valid_cols = [col for col in required_column if col in df.columns] - removed = initial_col_count - len(df.columns) + exprs = [] + for col in valid_cols: + target_dtype = dtypes.get(col) - return df, removed + if target_dtype == pl.Datetime: + + if df.schema[col] == pl.String: + fmt = TIMESTAMP_FORMATS.get(col) + exprs.append(pl.col(col).str.to_datetime(format=fmt, strict=False)) + else: + exprs.append(pl.col(col)) + + elif target_dtype: + exprs.append(pl.col(col).cast(target_dtype)) + else: + exprs.append(pl.col(col)) + + df = df.select(exprs) + removed_count = initial_col_count - df.width + + return df, removed_count diff --git a/data_pipeline/contract/id_registrar.py b/data_pipeline/contract/id_registrar.py new file mode 100644 index 0000000..c863171 --- /dev/null +++ b/data_pipeline/contract/id_registrar.py @@ -0,0 +1,242 @@ +# ============================================================================= +# UUIDs to Integers Mappings - Discovery First Architecture +# ============================================================================= + +import polars as pl +from pathlib import Path +from data_pipeline.shared.run_context import RunContext +from data_pipeline.shared.storage_adapter import ( + promote_new_mapping_files, + check_gcs_path_exists, +) + +# Maps which tables an ID appears +ID_ENTITY_MAP = { + "order_id": ["df_orders", "df_order_items", "df_payments"], + "customer_id": ["df_orders", "df_customers"], + "product_id": ["df_order_items", "df_products"], + "seller_id": ["df_order_items"], +} + +# ----------------------------------------------------------------------------- +# DISCOVERY HELPERS +# ----------------------------------------------------------------------------- + + +def discover_uuids(raw_path: Path, tables: list[str], col: str) -> pl.Series: + """ + Scans raw snapshot files to identify the unique set of UUIDs present in the current run. + + Contract: + - Multi-Format Support: Detects and scans both .parquet and .csv extensions. + - Defensive Schema: Uses 'infer_schema_length=0' for CSVs to ensure ID columns are always treated as strings. + - Subtractive Consolidation: Aggregates IDs from all relevant tables and enforces global uniqueness. + + Invariants: + - Type Safety: Always returns a pl.Series of dtype pl.String. + - Empty Handling: Returns an empty Series with correct column name/type if no files are found. + + Outputs: + - Unique pl.Series of string UUIDs. + """ + + all_uuids = [] + + for table in tables: + for ext in ["parquet", "csv"]: + files = list(raw_path.glob(f"{table}_*.{ext}")) + if not files: + continue + + table_glob = str(raw_path / f"{table}_*.{ext}").replace("\\", "/") + if ext == "parquet": + lf = pl.scan_parquet(table_glob) + else: + lf = pl.scan_csv(table_glob, infer_schema_length=0) + + all_uuids.append(lf.select(col)) + + if not all_uuids: + return pl.Series(col, [], dtype=pl.String) + + return pl.concat(all_uuids).unique().collect().get_column(col) + + +def lookup_mapping_storage( + storage_glob: str, col: str, batch_uuids: pl.Series +) -> tuple[pl.DataFrame, int]: + """ + Surgically retrieves known mappings and the current global sequence head from central storage. + + Contract: + - Sequence Continuity: Resolves the maximum existing integer ID to ensure gapless sequence generation for new IDs. + + Optimization Logic: + - Semi-Join Hydration: Filters the historical registry against the current batch via semi-join to minimize memory footprint. + - Parallel Execution: Utilizes pl.collect_all to resolve both mappings and the sequence head in a single IO pass. + - Early Projection: Restricts the scan to only the required UUID and Integer ID columns. + + Invariants: + - Integrity: Enforces uniqueness on historical loads to prevent row duplication from overlapping delta files. + - Grain: Returns a 1-to-1 mapping DataFrame (UUID to UInt32). + + Outputs: + - tuple: (known_mappings_df, current_max_id_int) + + Failures: + - System Error: Crashes if storage is unreachable or if the mapping schema is corrupted. + """ + int_col = f"{col}_int" + + history_lf = pl.scan_parquet(storage_glob, use_statistics=True).select( + [col, int_col] + ) + + # Find existing UUIDs in this batch + batch_lf = pl.DataFrame({col: batch_uuids}).lazy() + known_mappings_plan = history_lf.join( + batch_lf, + on=col, + how="semi", + ).unique(subset=[col]) + + # Extract max mapped IDs + max_id_plan = history_lf.select(pl.col(int_col).max()) + + known_mappings, max_df = pl.collect_all([known_mappings_plan, max_id_plan]) + + max_val = max_df.item() + current_max = max_val if max_val is not None else 0 + + return known_mappings, current_max + + +def generate_and_persist_delta( + missing_uuids: pl.Series, + current_max: int, + col: str, + runtime_dir: Path, + run_id: str, +) -> pl.DataFrame: + """ + Generates deterministic integer mappings for new UUIDs and persists a run-specific delta artifact. + + Contract: + - Sequence Generation: Assigns UInt32 IDs starting from current_max + 1. + - Local Persistence: Writes a Parquet delta file to a run-specific directory before promotion. + + Invariants: + - Determinism: Sequential IDs are stable within a single run's context. + - Lineage: Delta filename includes the run_id for traceability. + + Outputs: + - pl.DataFrame of new mappings. + + Failures: + - Operational: Fails if local disk is unwritable. + """ + + int_col = f"{col}_int" + start_val = current_max + 1 + + new_mappings = pl.DataFrame({col: missing_uuids}).with_columns( + pl.int_range(start_val, start_val + missing_uuids.len(), dtype=pl.UInt32).alias( + int_col + ) + ) + + delta_path = runtime_dir / col / f"map_{run_id}.parquet" + delta_path.parent.mkdir(parents=True, exist_ok=True) + new_mappings.write_parquet(delta_path) + + return new_mappings + + +# ----------------------------------------------------------------------------- +# MAIN ORCHESTRATOR (ENTRY POINT) +# ----------------------------------------------------------------------------- + + +def extract_entity_mappings(run_context: RunContext) -> dict[str, pl.LazyFrame]: + """ + Orchestrates the global ID discovery and mapping resolution for the entire pipeline run. + + Workflow: + 1. Discover: Scans raw sources to identify all entity UUIDs (Orders, Customers, etc.) requiring mapping. + 2. Hydrate: Loads historical mappings from central storage for only the discovered UUIDs. + 3. Resolve: Determines which UUIDs are "New" and dispatches them for ID generation. + 4. Promote: Synchronizes all locally generated mapping deltas back to central cloud storage. + + Operational Guarantees: + - Atomicity: Mappings are resolved once per run to prevent join collisions in the Contract stage. + - Write Safety: Uses the storage_adapter to promote deltas, ensuring historical files are never overwritten. + + Side Effects: + - Creates local Parquet deltas in the contracted/id_mapping/ directory. + - Promotes new mapping files to the central storage bucket. + + Failure Behavior: + - Fail-Fast: Any error in mapping resolution triggers a RuntimeError to prevent data corruption downstream. + """ + master_mappings = {} + + raw_path = run_context.raw_snapshot_path + mapping_dest = run_context.storage_mapping_path + runtime_dir = run_context.contracted_path / "id_mapping" + + dest_str = str(mapping_dest).replace("\\", "/") + is_gcs = dest_str.startswith("gs://") + + for entity_col, tables in ID_ENTITY_MAP.items(): + + # Extract UUIDs from raw data + batch_uuids = discover_uuids(raw_path, tables, entity_col) + if batch_uuids.len() == 0: + continue + + col_storage_dir = f"{dest_str}/{entity_col}" + storage_glob = f"{col_storage_dir}/*.parquet" + + # Check mapping in storage + mapping_exists = ( + check_gcs_path_exists(col_storage_dir) + if is_gcs + else Path(col_storage_dir).exists() + ) + + try: + if mapping_exists: + known_df, current_max = lookup_mapping_storage( + storage_glob, entity_col, batch_uuids + ) + # Filter new UUIDs from raw data + missing_uuids = batch_uuids.filter( + ~batch_uuids.is_in(known_df.get_column(entity_col)) + ) + else: + known_df = pl.DataFrame( + {entity_col: [], f"{entity_col}_int": []}, + schema={entity_col: pl.String, f"{entity_col}_int": pl.UInt32}, + ) + missing_uuids = batch_uuids + current_max = 0 + + # Map new UUIDs if found + if missing_uuids.len() > 0: + new_df = generate_and_persist_delta( + missing_uuids, + current_max, + entity_col, + runtime_dir, + run_context.run_id, + ) + master_mappings[entity_col] = pl.concat([known_df, new_df]).lazy() + else: + master_mappings[entity_col] = known_df.lazy() + + except Exception as e: + raise RuntimeError(f"Master Mapping Failure: {e}") from e + + promote_new_mapping_files(runtime_dir, mapping_dest) + + return master_mappings diff --git a/data_pipeline/publish/publish_executor.py b/data_pipeline/publish/publish_executor.py index 4fffc23..0c1573d 100644 --- a/data_pipeline/publish/publish_executor.py +++ b/data_pipeline/publish/publish_executor.py @@ -8,6 +8,7 @@ run_integrity_gate, promote_semantic_version, activate_published_version, + swap_bigquery_view, log_info, ) @@ -18,16 +19,18 @@ def execute_publish_lifecycle(run_context: RunContext) -> Dict: Workflow: 1. Validate: Executes the 'Integrity Gate' to ensure all semantic artifacts exist and are schema-compliant. - 2. Promote: Transfers validated artifacts to the permanent versioned publication zone. - 3. Delegate: Triggers the atomic pointer swap to activate the new version for BI consumers. + 2. Promote: Transfers validated artifacts to the permanent versioned publication zone (GCS). + 3. Synchronizes BigQuery External Tables and Views to point to the newly promoted version. + 4. Activate: Triggers the atomic pointer swap (_latest.json) to update the version pointer for file-system consumers. Operational Guarantees: - - Atomicity: The 'latest' version pointer is updated ONLY after successful promotion of all artifacts. + - Multi-System Atomicity: The BI views and file-system pointers are updated ONLY after successful promotion of all artifacts. - Immutability: Once published, a versioned directory is treated as a static, read-only snapshot. - - Fail-Fast: Any failure in validation or promotion immediately halts the lifecycle. + - Fail-Fast: Any failure in validation, promotion, or SQL sync immediately halts the lifecycle. Side Effects: - Persists a new versioned directory (v{run_id}) in the publication zone. + - Mutates BigQuery External Tables and Views to update the stable BI layer. - Mutates the 'latest_version.json' manifest to update the global version pointer. Failure Behavior: @@ -64,6 +67,19 @@ def fail_step(step_name): log_info("Semantic artifacts promoted successfully", promote_semantic) + update_sql_view = swap_bigquery_view(run_context) + report["steps"]["sql_view"] = update_sql_view + + if update_sql_view["status"] == "failed": + return fail_step("sql_view") + + # Skip logging view updated + if any("Skipping" in info for info in update_sql_view["info"]): + pass + + else: + log_info("BigQuery views updated successfully", update_sql_view) + published_activation = activate_published_version(run_context) report["steps"]["activation"] = published_activation diff --git a/data_pipeline/publish/publish_logic.py b/data_pipeline/publish/publish_logic.py index 91dad08..a49812d 100644 --- a/data_pipeline/publish/publish_logic.py +++ b/data_pipeline/publish/publish_logic.py @@ -3,7 +3,8 @@ # ============================================================================= import polars as pl -from datetime import datetime as dt +from datetime import datetime as dt, timezone +from google.cloud import bigquery from contextlib import suppress from pathlib import Path import json @@ -168,6 +169,75 @@ def promote_semantic_version(run_context: RunContext) -> Dict: return report +# ------------------------------------------------------------ +# PUBLISHED SQL VIEW +# ------------------------------------------------------------ + + +def swap_bigquery_view(run_context: RunContext, location: str | None = None) -> Dict: + """ + Atomically updates BigQuery External Tables and Views to point to the new version. + + Contract: + - Versioned Tables: Creates unique external tables for each semantic table in the current run. + - Stable Views: Replaces existing 'published_' views to point to the new versioned tables. + + Invariants: + - Multi-System Sync: BI tools connected to views see the new data immediately after DDL success. + - Cloud Only: Skips SQL updates if the pipeline is running in a local-only environment. + + Outputs: + - Dict: Report logging the SQL activation status for each module. + """ + + report = init_report() + latest_path = run_context.latest_pointer_path + published_uri = run_context.storage_published_path + + if not str(latest_path).startswith("gs://"): + log_info("Skipping BigQuery swap (Local Storage detected)", report) + + return report + + # Use provided location or fallback to environment variable (set by Terraform) + effective_location = location or os.getenv("GCP_REGION", "us-east1") + + try: + + client = bigquery.Client(location=effective_location) + run_id = run_context.run_id + project = client.project + + for module_name, module_config in SEMANTIC_MODULES.items(): + for table_name in module_config["tables"]: + + # Create Versioned External Table + table_ddl = f""" + CREATE OR REPLACE EXTERNAL TABLE `{project}.{module_name}.{table_name}_v{run_id}` + OPTIONS ( + format = 'PARQUET', + uris = ['{published_uri}/v{run_id}/{module_name}/{table_name}_*.parquet'] + ) + """ + + # Atomic Pointer Swap (View) + view_ddl = f""" + CREATE OR REPLACE VIEW `{project}.{module_name}.published_{table_name}` AS + SELECT * FROM `{project}.{module_name}.{table_name}_v{run_id}` + """ + + client.query(table_ddl, location=effective_location).result() + client.query(view_ddl, location=effective_location).result() + + log_info(f"BigQuery swap successful for module: {module_name}", report) + + except Exception as e: + report["status"] = "failed" + log_error(f"BigQuery Swap Failed: {e}", report) + + return report + + # ------------------------------------------------------------ # PUBLISHED ATOMIC POINTER # ------------------------------------------------------------ @@ -203,7 +273,7 @@ def activate_published_version(run_context: RunContext) -> Dict: "run_year": run_dt.year, "run_month": run_dt.month, "run_week_of_month": (run_dt.day - 1) // 7 + 1, - "published_at": dt.utcnow().isoformat(), + "published_at": dt.now(timezone.utc).isoformat(), } # LOCAL storage diff --git a/data_pipeline/requirements.txt b/data_pipeline/requirements.txt index a642083..0c1f211 100644 --- a/data_pipeline/requirements.txt +++ b/data_pipeline/requirements.txt @@ -1,4 +1,6 @@ -pandas==2.1.4 polars==1.39.0 pyarrow==19.0.0 -google-cloud-storage \ No newline at end of file +google-cloud-storage +google-cloud-bigquery>=3.0.0 +google-cloud-bigquery-storage>=2.36.0 +psutil==5.9.8 \ No newline at end of file diff --git a/data_pipeline/run_pipeline.py b/data_pipeline/run_pipeline.py index 639da90..3f00ed6 100644 --- a/data_pipeline/run_pipeline.py +++ b/data_pipeline/run_pipeline.py @@ -4,17 +4,19 @@ from pathlib import Path -from datetime import datetime as dt +from datetime import datetime as dt, timezone import json import os import shutil -import gc +from concurrent.futures import ThreadPoolExecutor +from google.cloud import bigquery from data_pipeline.shared.table_configs import TABLE_CONFIG from data_pipeline.shared.run_context import RunContext from data_pipeline.validation.validation_executor import apply_validation from data_pipeline.contract.contract_executor import apply_contract -from data_pipeline.assembly.assembly_executor import assemble_events +from data_pipeline.contract.id_registrar import extract_entity_mappings +from data_pipeline.assembly.assembly_executor import assemble_events, force_gc from data_pipeline.semantic.semantic_executor import build_semantic_layer from data_pipeline.publish.publish_executor import execute_publish_lifecycle @@ -22,9 +24,20 @@ download_raw_snapshot, upload_run_artifacts, upload_contracted_directory, - download_contracted_datasets, ) +import psutil +import threading +import time + + +def memory_logger(stop_event: threading.Event): + """Temporary: Logs RAM usage to stdout every 1s for benchmarking.""" + while not stop_event.is_set(): + mem_mb = psutil.virtual_memory().used / (1024 * 1024) + print(f"METRIC_MEM: {mem_mb:.2f} MB") + time.sleep(1) + # ------------------------------------------------------------ # SUPPORTING UTILITIES @@ -72,9 +85,9 @@ def initialize_metadata(run_context: RunContext) -> None: payload = { "run_id": run_context.run_id, - "pipeline_version": "v5", + "pipeline_version": "v5.1", "status": "RUNNING", - "started_at": dt.utcnow().isoformat(), + "started_at": dt.now(timezone.utc).isoformat(), "run_year": run_dt.year, "run_month": run_dt.month, "run_day": run_dt.day, @@ -102,7 +115,7 @@ def finalize_metadata(run_context: RunContext, status: str) -> None: payload = json.load(file) start_time = dt.fromisoformat(payload["started_at"]) - completion_time = dt.utcnow() + completion_time = dt.now(timezone.utc) payload["status"] = status payload["completed_at"] = completion_time.isoformat() @@ -116,6 +129,46 @@ def finalize_metadata(run_context: RunContext, status: str) -> None: persist_json(run_context.metadata_path, payload) +def refresh_bq_external_cache(run_context: RunContext) -> None: + """ + Forces BigQuery to refresh the metadata cache for a BigLake/External table. + + Contract: + - Connectivity: Initializes a BigQuery client using Application Default Credentials. + - Execution: Invokes the BQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure. + - Idempotency: Can be safely called multiple times without data mutation. + + Invariants: + - State Sync: Ensures downstream stages (like Assembly via Storage Read API) see the + newly contracted Parquet files immediately, bypassing BigQuery's default metadata caching delay. + """ + project_id = run_context.bq_project_id + dataset_id = run_context.bq_dataset_id + location = os.getenv("GCP_REGION", "MISSING_REGION") + + if location == "MISSING_REGION": + print("[INFO] Skipping BigQuery cache refresh (Local Storage detected)") + + return + + client = bigquery.Client(project=project_id, location=location) + + def refresh_table(table_name): + table_path = f"{project_id}.{dataset_id}.{table_name}" + query = f"CALL BQ.REFRESH_EXTERNAL_METADATA_CACHE('{table_path}')" + client.query(query).result() + + try: + # Parallel execute cache refresh + with ThreadPoolExecutor(max_workers=len(TABLE_CONFIG)) as executor: + executor.map(refresh_table, TABLE_CONFIG) + + except Exception as e: + print(f"Failed to refresh BigQuery cache for {dataset_id}: {e}") + + print(f"Successfully refreshed BigQuery cache for {dataset_id}") + + # ------------------------------------------------------------ # STAGE WRAPPERS # ------------------------------------------------------------ @@ -133,6 +186,9 @@ def run_initial_validation_stage(run_context) -> None: def run_contract_application_stage(run_context) -> tuple[set, set]: report = [] + # Extract all UUIDs from (order_id, product_id, etc.) on raw_snapshot directory + master_mappings = extract_entity_mappings(run_context) + # Accumulates set of invalid order_ids and valid order_ids, and apply to child tables. invalid_ids = set() valid_ids = set() @@ -140,15 +196,15 @@ def run_contract_application_stage(run_context) -> tuple[set, set]: # NOTE: TABLE_CONFIG order must list parent first before its children. for table_name in TABLE_CONFIG: - contract, new_inv, new_val = apply_contract( - run_context, table_name, invalid_ids, valid_ids + contract_rep, new_inv, new_val = apply_contract( + run_context, table_name, master_mappings, invalid_ids, valid_ids ) invalid_ids |= new_inv if new_val: valid_ids = new_val - report.append(contract) + report.append(contract_rep) stage_logger(run_context, stage="contract_application", report=report) return invalid_ids, valid_ids @@ -198,33 +254,46 @@ def run_prepublishing_validation_stage(run_context) -> None: def main() -> None: """ - Ultimate authority for the end-to-end data pipeline lifecycle. + Ultimate authority for the end-to-end data pipeline lifecycle coordination. Workflow: - 1. Initialization: Resolve RunContext and instantiate global run metadata. - 2. Ingestion: Synchronize the raw data snapshot from Cloud Storage to local workspace. - 3. Gate I (Validation): Assert raw data sanity; fail-fast on fatal structural errors. - 4. Processing (Contract): Execute subtractive filtering and Silver-layer schema freezing. - 5. Gate II (Revalidation): Defensive check to ensure 'contracted' data is valid. - 6. Persistence (Sync Upload): Promote local contracted artifacts to the Cloud Silver Storage. - 7. Resource Reclamation: Purge transient directories (raw/contracted) to optimize memory. - 8. Hydration (Sync Download): Restore local environment with the full accumulated Silver state. - 9. Integration (Assembly): Flatten relational data into a unified Gold event layer. - 10. Modeling (Semantic): Build entity-centric analytical modules (Fact/Dim). - 11. Gate III (Pre-Publish): Final verification of semantic artifact completeness. - 12. Activation (Publish): Atomic swap of the production 'latest' version pointer. - 13. Finalization: Persist all telemetry/logs to Cloud and purge the local workspace. + 1. Resolve: Instantiate RunContext and initialize background memory telemetry. + 2. Hydrate (Raw): Synchronize the raw data snapshot from Cloud Storage to local workspace. + 3. Initialize: Register the run commencement and capture initial metadata. + 4. Validate (Raw): Assert raw data sanity and fail-fast on structural errors. + 5. Contract: Execute subtractive filtering and schema freezing for Silver-layer datasets. + 6. Revalidate: Defensive check to ensure contracted artifacts meet downstream requirements. + 7. Promote (Silver): Persist delta contracted datasets to Cloud Silver Storage. + 8. Synchronize (BQ): Force refresh of BigQuery external metadata cache for immediate visibility. + 9. Purge (Local): Reclaim local disk and RAM by evicting raw/contracted sources before Assembly. + 10. Assemble: Flatten relational data into a unified Gold event layer using Storage Read API. + 11. Model (Semantic): Build entity-centric analytical modules (Fact/Dim). + 12. Publish: Execute final lifecycle validation, BigQuery view swap, and atomic pointer swap for the 'latest' version. + 13. Finalize: Update terminal metadata, upload all stage reports/telemetry, and purge workspace. Operational Guarantees: - - Defensive Integrity: No data moves to 'Assembly' without passing 'Revalidation'. - - Silver Continuity: Uses a Cloud-Sync loop to ensure Assembly operates on the full delta state. - - Resource Stewardship: Mandatory local cleanup via global 'finally' block to prevent disk leaks. - - Traceability: Enforces atomic 'run_id' consistency across all 13 lifecycle steps. - - Visibility: Guarantees cloud-upload of stage reports even in partial failure scenarios. + - Defensive Integrity: Prevents promotion of invalid data to Silver/Gold layers via multi-gate validation. + - Memory Efficiency: Enforces deterministic 'Purge' and 'GC' cycles between heavy processing stages. + - Traceability: Maintains strict run_id consistency across local and Cloud artifact lineages. + - Resilience: Guarantees telemetry upload (logs/metadata) even during catastrophic stage failures. + + Side Effects: + - Writes local stage reports and metadata to the run-specific workspace. + - Mutates Cloud Storage (Silver layer artifacts, Run Artifacts). + - Refreshes BigQuery system procedures (Metadata Cache). + - Swaps production environment pointers during the Publish stage. + + Failure Behavior: + - Crash: Any stage RuntimeError triggers a 'FAILED' status update and immediate local cleanup. + - Recovery: Logs are persisted to Cloud before exit to enable post-mortem analysis. """ run_context = RunContext.create() + stop_event = threading.Event() + logger_thread = threading.Thread(target=memory_logger, args=(stop_event,)) + logger_thread.start() + # Pre-start cleaning if os.path.exists(run_context.workspace_root): shutil.rmtree(run_context.workspace_root, ignore_errors=True) @@ -241,21 +310,20 @@ def main() -> None: # Persist delta contracted datasets to silver storage upload_contracted_directory(run_context) + # Refresh BQ Metadata Cache after uploading is complete + refresh_bq_external_cache(run_context) + # Clear RAM memory from previous stages if os.path.exists(run_context.raw_snapshot_path): shutil.rmtree(run_context.raw_snapshot_path) shutil.rmtree(run_context.contracted_path) - gc.collect() - - # Recreate path and download contract data from silver storage - run_context.contracted_path.mkdir(parents=True, exist_ok=True) - download_contracted_datasets(run_context) + force_gc() run_assemble_events_stage(run_context) - gc.collect() + force_gc() run_semantic_modeling_stage(run_context) - gc.collect() + force_gc() run_prepublishing_validation_stage(run_context) @@ -267,13 +335,16 @@ def main() -> None: raise finally: + stop_event.set() + logger_thread.join() + # Persist run artifacts (logs/metadata) Pass or Fail upload_run_artifacts(run_context) # Clean RAM memory for next run if os.path.exists(run_context.workspace_root): shutil.rmtree(run_context.workspace_root) - gc.collect() + force_gc() if __name__ == "__main__": diff --git a/data_pipeline/semantic/registry.py b/data_pipeline/semantic/registry.py index 5de0ada..9d07aec 100644 --- a/data_pipeline/semantic/registry.py +++ b/data_pipeline/semantic/registry.py @@ -22,7 +22,6 @@ PRODUCT_DIM_DTYPES, ) - SEMANTIC_MODULES = { "seller_semantic": { "builder": build_seller_semantic, diff --git a/data_pipeline/semantic/semantic_executor.py b/data_pipeline/semantic/semantic_executor.py index ee2b631..dd9da1f 100644 --- a/data_pipeline/semantic/semantic_executor.py +++ b/data_pipeline/semantic/semantic_executor.py @@ -2,11 +2,10 @@ # Semantic Modeling Stage Executor # ============================================================================= -import gc import polars as pl from typing import Dict from data_pipeline.shared.run_context import RunContext -from data_pipeline.shared.loader_exporter import load_historical_table, export_file +from data_pipeline.shared.loader_exporter import load_assembled_data, export_file from data_pipeline.semantic.registry import SEMANTIC_MODULES from data_pipeline.assembly.assembly_logic import ( init_report, @@ -15,6 +14,7 @@ loaded_data, task_wrapper, ) +from data_pipeline.assembly.assembly_executor import force_gc def validate_and_freeze_table(lf: pl.LazyFrame, table: dict) -> pl.LazyFrame: @@ -118,7 +118,7 @@ def orchestrate_module( print(f"[INFO] Module {module_name}: build_stage completed successfully.") except Exception as e: - log_error(f"Step build_stage failed: {str(e)}", report) + log_error(f"Step build_stage failed: {e}", report) report["status"] = "failed" return False @@ -168,18 +168,24 @@ def orchestrate_module( except FileExistsError as e: log_error(f"Unexpected table returned {table_name}: {e}", report) + report["status"] = "failed" + return False except Exception as e: log_error(f"Unexpected error processing {table_name}: {e}", report) + report["status"] = "failed" + return False finally: if "lf_frozen" in locals(): del lf_frozen - del df_table - gc.collect() + if "df_table" in locals(): + del df_table + force_gc() - del builder_output - gc.collect() + if "builder_output" in locals(): + del builder_output + force_gc() log_info(f"Export Module: {module_name} Successfully", report) module_report[module_name]["export"] = True @@ -214,7 +220,7 @@ def build_semantic_layer(run_context: RunContext) -> Dict: report = init_report() report["modules"] = {} - df_assembled = load_historical_table( + df_assembled = load_assembled_data( base_path=run_context.assembled_path, table_name="assembled_events", log_info=lambda msg: loaded_data(msg, report), @@ -238,7 +244,8 @@ def build_semantic_layer(run_context: RunContext) -> Dict: report["status"] = "failed" return report - del df_assembled - gc.collect() + if "df_assembled" in locals(): + del df_assembled + force_gc() return report diff --git a/data_pipeline/semantic/semantic_logic.py b/data_pipeline/semantic/semantic_logic.py index 5f743d1..a6527df 100644 --- a/data_pipeline/semantic/semantic_logic.py +++ b/data_pipeline/semantic/semantic_logic.py @@ -5,7 +5,7 @@ import polars as pl from typing import Dict from data_pipeline.shared.run_context import RunContext -from data_pipeline.shared.loader_exporter import load_historical_table +from data_pipeline.shared.loader_exporter import load_assembled_data # ------------------------------------------------------------ # SELLER SEMANTIC BUILDER @@ -14,21 +14,20 @@ def build_seller_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: """ - Constructs the Seller-centric analytical layer from assembled events. + Constructs the Seller-centric analytical layer from assembled integer-mapped events. Contract: - Subtractive Filtering: Selects strictly required columns for performance. - Transformation: Derives week_start_date and boolean status flags. - - Aggregation: Computes weekly performance metrics (revenue, lead times, delays) per seller. + - Aggregation: Computes weekly performance metrics (revenue, lead times, delays) per seller using optimized Integer keys. Optimization Logic: - - Streaming Projection: Selects required columns for aggregation, allowing the streaming engine to push projection through the plan. - - Non-Blocking Aggregation: Executes aggregations in a streaming fashion, maintaining a constant memory profile. - - Categorical Handling: Utilizes categorical grouping keys to maintain optimized performance during non-blocking aggregation. + - Integer Key Optimization: Utilizes UInt32/UInt64 grouping keys (seller_id_int) to maintain a constant memory profile during non-blocking aggregation. + - Metric Downcasting: Enforces Int16 for counts/days and Float32 for revenue to minimize row width during streaming. Invariants: - - Fact Grain: Strictly 1 row per ('seller_id', 'order_year_week'). - - Dimension Grain: Strictly 1 row per 'seller_id'. + - Fact Grain: Strictly 1 row per ('seller_id_int', 'order_year_week'). + - Dimension Grain: Strictly 1 row per 'seller_id_int'. - Temporal: Aligns all metrics to ISO-week start dates (Monday). Outputs: @@ -39,21 +38,18 @@ def build_seller_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: """ needed_cols = [ - "seller_id", + "seller_id_int", "order_year_week", "order_date", "order_status", - "order_id", + "order_id_int", "order_revenue", "lead_time_days", "delivery_delay_days", "approval_lag_days", ] - lf_filtered = lf.select(needed_cols).with_columns( - seller_id=pl.col("seller_id").cast(pl.Categorical), - order_year_week=pl.col("order_year_week").cast(pl.Categorical), - ) + lf_filtered = lf.select(needed_cols) seller_weekly_fact = ( lf_filtered.with_columns( @@ -61,10 +57,10 @@ def build_seller_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: is_delivered=pl.col("order_status").eq("delivered"), is_cancelled=pl.col("order_status").eq("cancelled"), ) - .group_by(["seller_id", "order_year_week"]) + .group_by(["seller_id_int", "order_year_week"]) .agg( week_start_date=pl.col("week_start_date").min(), - weekly_order_count=pl.col("order_id").count().cast(pl.Int16), + weekly_order_count=pl.col("order_id_int").count().cast(pl.Int16), weekly_delivered_orders=pl.col("is_delivered").sum().cast(pl.Int16), weekly_cancelled_orders=pl.col("is_cancelled").sum().cast(pl.Int16), weekly_revenue=pl.col("order_revenue").sum().cast(pl.Float32), @@ -80,7 +76,7 @@ def build_seller_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: ) ) - seller_dim = lf_filtered.group_by("seller_id").agg( + seller_dim = lf_filtered.group_by("seller_id_int").agg( first_order_date=pl.col("order_date").min(), first_order_year_week=pl.col("order_year_week").min(), ) @@ -100,22 +96,21 @@ def build_seller_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: def build_customer_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: """ - Constructs the Customer-centric analytical layer from assembled events. + Constructs the Customer-centric analytical layer from assembled integer-mapped events. Contract: - Subtractive Filtering: Selects strictly required columns for performance. - Transformation: Derives week_start_date and boolean status flags. - - Aggregation: Computes weekly performance metrics (revenue, lead times, delays) per customer. + - Aggregation: Computes weekly performance metrics (revenue, lead times, delays) per customer using optimized Integer keys. - Hydration: Loads historical customer dimension table from the assembly zone. Optimization Logic: - - Streaming Projection: Selects required columns for aggregation, allowing the streaming engine to push projection through the plan. - - Non-Blocking Aggregation: Executes aggregations in a streaming fashion, maintaining a constant memory profile. - - Categorical Handling: Utilizes categorical grouping keys to maintain optimized performance during non-blocking aggregation. + - Integer Key Optimization: Utilizes UInt32/UInt64 grouping keys (customer_id_int) to maintain a constant memory profile during non-blocking aggregation. + - Metric Downcasting: Enforces Int16 for counts/days and Float32 for revenue to minimize row width. Invariants: - - Fact Grain: Strictly 1 row per ('customer_id', 'order_year_week'). - - Dimension Grain: Strictly 1 row per 'customer_id'. + - Fact Grain: Strictly 1 row per ('customer_id_int', 'order_year_week'). + - Dimension Grain: Strictly 1 row per 'customer_id_int'. Outputs: - Dict containing 'customer_weekly_fact' (LazyFrame) and 'customer_dim' (LazyFrame). @@ -126,22 +121,18 @@ def build_customer_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: """ needed_cols = [ - "customer_id", + "customer_id_int", "order_year_week", "order_date", "order_status", - "order_id", + "order_id_int", "order_revenue", "lead_time_days", "delivery_delay_days", "approval_lag_days", ] - # Cast grouping keys to Categorical to reduce hash table memory pressure - lf_filtered = lf.select(needed_cols).with_columns( - customer_id=pl.col("customer_id").cast(pl.Categorical), - order_year_week=pl.col("order_year_week").cast(pl.Categorical), - ) + lf_filtered = lf.select(needed_cols) customer_weekly_fact = ( lf_filtered.with_columns( @@ -149,10 +140,10 @@ def build_customer_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: is_delivered=pl.col("order_status").eq("delivered"), is_cancelled=pl.col("order_status").eq("cancelled"), ) - .group_by(["customer_id", "order_year_week"]) + .group_by(["customer_id_int", "order_year_week"]) .agg( week_start_date=pl.col("week_start_date").min(), - weekly_order_count=pl.col("order_id").count().cast(pl.Int16), + weekly_order_count=pl.col("order_id_int").count().cast(pl.Int16), weekly_delivered_orders=pl.col("is_delivered").sum().cast(pl.Int16), weekly_cancelled_orders=pl.col("is_cancelled").sum().cast(pl.Int16), weekly_revenue=pl.col("order_revenue").sum().cast(pl.Float32), @@ -168,7 +159,7 @@ def build_customer_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: ) ) - customer_dim = load_historical_table( + customer_dim = load_assembled_data( base_path=run_context.assembled_path, table_name="df_customers" ) @@ -187,22 +178,21 @@ def build_customer_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: def build_product_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: """ - Constructs the Product-centric analytical layer from assembled events. + Constructs the Product-centric analytical layer from assembled integer-mapped events. Contract: - Subtractive Filtering: Selects strictly required columns for performance. - Transformation: Derives week_start_date and boolean status flags. - - Aggregation: Computes weekly performance metrics (revenue, lead times, delays) per product. + - Aggregation: Computes weekly performance metrics (revenue, lead times, delays) per product using optimized Integer keys. - Hydration: Loads historical product dimension table from the assembly zone. Optimization Logic: - - Streaming Projection: Selects required columns for aggregation, allowing the streaming engine to push projection through the plan. - - Non-Blocking Aggregation: Executes aggregations in a streaming fashion, maintaining a constant memory profile. - - Categorical Handling: Utilizes categorical grouping keys to maintain optimized performance during non-blocking aggregation. + - Integer Key Optimization: Utilizes UInt32/UInt64 grouping keys (product_id_int) to maintain a constant memory profile during non-blocking aggregation. + - Metric Downcasting: Enforces Int16 for counts/days and Float32 for revenue to minimize row width. Invariants: - - Fact Grain: Strictly 1 row per ('product_id', 'order_year_week'). - - Dimension Grain: Strictly 1 row per 'product_id'. + - Fact Grain: Strictly 1 row per ('product_id_int', 'order_year_week'). + - Dimension Grain: Strictly 1 row per 'product_id_int'. Outputs: - Dict containing 'product_weekly_fact' (LazyFrame) and 'product_dim' (LazyFrame). @@ -213,21 +203,18 @@ def build_product_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: """ needed_cols = [ - "product_id", + "product_id_int", "order_year_week", "order_date", "order_status", - "order_id", + "order_id_int", "order_revenue", "lead_time_days", "delivery_delay_days", "approval_lag_days", ] - lf_filtered = lf.select(needed_cols).with_columns( - product_id=pl.col("product_id").cast(pl.Categorical), - order_year_week=pl.col("order_year_week").cast(pl.Categorical), - ) + lf_filtered = lf.select(needed_cols) product_weekly_fact = ( lf_filtered.with_columns( @@ -235,10 +222,10 @@ def build_product_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: is_delivered=pl.col("order_status").eq("delivered"), is_cancelled=pl.col("order_status").eq("cancelled"), ) - .group_by(["product_id", "order_year_week"]) + .group_by(["product_id_int", "order_year_week"]) .agg( week_start_date=pl.col("week_start_date").min(), - weekly_order_count=pl.col("order_id").count().cast(pl.Int16), + weekly_order_count=pl.col("order_id_int").count().cast(pl.Int16), weekly_delivered_orders=pl.col("is_delivered").sum().cast(pl.Int16), weekly_cancelled_orders=pl.col("is_cancelled").sum().cast(pl.Int16), weekly_revenue=pl.col("order_revenue").sum().cast(pl.Float32), @@ -254,7 +241,7 @@ def build_product_semantic(lf: pl.LazyFrame, run_context: RunContext) -> Dict: ) ) - product_dim = load_historical_table( + product_dim = load_assembled_data( base_path=run_context.assembled_path, table_name="df_products" ) diff --git a/data_pipeline/shared/loader_exporter.py b/data_pipeline/shared/loader_exporter.py index 49cd2f9..0cb8972 100644 --- a/data_pipeline/shared/loader_exporter.py +++ b/data_pipeline/shared/loader_exporter.py @@ -4,13 +4,98 @@ from pathlib import Path import polars as pl -import pandas as pd from typing import Optional, Callable, Tuple, Any +from google.cloud import bigquery + + +def normalize_datetimes(lf: pl.LazyFrame | pl.DataFrame) -> Any: + """ + Standardizes all Datetime columns to a unified resolution (microseconds). + + Contract: + - Discovery: Scans the schema for all pl.Datetime fields (accepts both LazyFrame and DataFrame). + - Transformation: Forcefully casts identified columns to 'us' (microseconds) resolution. + + Invariants: + - Zero-Failure: Returns the input 'lf' unchanged if no Datetime columns are found. + - Environment Neutrality: Prevents 'Datetime(ns) != Datetime(us)' resolution mismatches + between local development and cloud production environments. + + Outputs: + - LazyFrame or DataFrame (matching input type) with resolution-standardized temporal fields. + """ + + schema = lf.collect_schema() if isinstance(lf, pl.LazyFrame) else lf.schema + + datetime_cols = [ + col for col, dtype in schema.items() if isinstance(dtype, pl.Datetime) + ] + if not datetime_cols: + return lf + + return lf.with_columns( + [pl.col(col).dt.cast_time_unit("us") for col in datetime_cols] + ) + + +def scan_gcs_uris_from_bigquery( + project_id: str, + dataset_id: str, + table_id: str, + log_info: Optional[Callable[[str], None]] = None, +) -> pl.LazyFrame: + """ + Streams data natively into Polars using BigQuery External Table metadata as a read bridge. + + Contract: + - Discovery: Uses the BigQuery API to fetch the authoritative 'source_uris' for the External Table. + - Optimization: Bypasses BigQuery compute and memory-bound Arrow downloads entirely. + - Zero-Disk Native Streaming: Passes the extracted GCS URIs directly to Polars' Rust-based + object-store engine for high-performance, concurrent, lazy evaluation from Cloud Storage. + + Invariants: + - Lazy Evaluation: Returns a pure pl.LazyFrame without executing any I/O blocking reads. + - Source Consistency: Relies on BigQuery as the source-of-truth for file locations. + + Outputs: + - A pl.LazyFrame ready for downstream streaming processing. + """ + + if project_id == "PROJECT_ID_NOT_DETECTED": + raise ValueError( + "Project ID is set to 'PROJECT_ID_NOT_DETECTED'. Pipeline environment variables are likely missing." + ) + + try: + client = bigquery.Client(project=project_id) + table_ref = f"{project_id}.{dataset_id}.{table_id}" + + file_query = f"SELECT DISTINCT _FILE_NAME FROM `{table_ref}`" + query_result = client.query(file_query).result() + uris = [row[0] for row in query_result] + + if not uris: + raise ValueError(f"No source URIs found in external table {table_ref}.") + + lfs = [normalize_datetimes(pl.scan_parquet(uri)) for uri in uris] + lf = pl.concat(lfs, how="vertical_relaxed") + + except Exception as e: + if log_info: + log_info(f"Failed to initialize stream for {dataset_id}.{table_id}: {e}") + raise + + if log_info: + log_info( + f"Connected to GCS Stream via BigQuery: {dataset_id}.{table_id} ({len(uris)} URIs)" + ) + + return lf FILE_LOADERS = { - ".csv": lambda path: pd.read_csv(path), - ".parquet": lambda path: pd.read_parquet(path), + ".csv": lambda path: pl.read_csv(path), + ".parquet": lambda path: pl.read_parquet(path), } @@ -23,15 +108,20 @@ def load_single_delta( Loads the chronologically most recent delta for a logical table. Contract: - - Scans 'base_path' for files matching the 'table_name' prefix. - - Identifies the target file via alphanumeric sorting of the date suffix (YYYY_MM_DD). + - Discovery: Scans 'base_path' for files matching the 'table_name' prefix. + - Selection: Identifies the target file via alphanumeric sorting of the date suffix (YYYY_MM_DD). + - Normalization: Automatically applies 'normalize_datetimes' to enforce microsecond resolution. Invariants: - Recency: Only the latest snapshot is returned; historical deltas are ignored. - Format Support: Handles .csv and .parquet (prioritizing Parquet). + - Source Integrity: Operates on a lazy scan to minimize memory footprint during initial load. + + Outputs: + - Tuple containing (pl.DataFrame, str: file_name). Failures: - - Raises FileNotFoundError if no matching artifacts are found. + - [Operational] Raises FileNotFoundError if no matching artifacts are found. """ base_path = Path(base_path) @@ -56,6 +146,7 @@ def load_single_delta( loader = FILE_LOADERS[target_file.suffix.lower()] df = loader(target_file) + df = normalize_datetimes(df.lazy()).collect() if log_info: log_info(f"Loaded: {target_file.name} ({len(df)} rows)") @@ -63,38 +154,89 @@ def load_single_delta( return df, file_name -def load_historical_table( +def load_historical_data( base_path: Path | str, table_name: str, log_info: Optional[Callable[[str], None]] = None, ) -> pl.LazyFrame: """ - Aggregates matching artifacts into a single cumulative LazyFrame. + Aggregates matching historical artifacts into a single cumulative LazyFrame for the Assembly stage. Contract: - - Performs a multi-file scan of all Parquet artifacts matching 'table_name'. - - Queues files for lazy evaluation rather than loading them into memory. + - Discovery: Performs a multi-file glob of all Parquet artifacts matching 'table_name'. + - Normalize-at-Source: Scans and normalizes resolution (Datetime[us]) for every file individually before concatenation. + - Safety: Prevents 'Datetime(ns) != Datetime(us)' resolution mismatches that occur when mixing local and cloud Parquet files. + + Invariants: + - Zero-Loss: Concatenates all identified files into a single unified stream. + - Lazy Execution: Returns a planned LazyFrame without triggering disk I/O. Outputs: - - Returns a pl.LazyFrame ready for downstream transformations. + - Returns a pl.LazyFrame ready for downstream joins and aggregations. + + Failures: + - [Operational] Raises FileNotFoundError if no Parquet files match the table name in base_path. """ base_path = Path(base_path) - files = [str(f) for f in base_path.glob(f"{table_name}*.parquet")] + all_files = list(base_path.glob(f"{table_name}*.parquet")) - if not files: + if not all_files: raise FileNotFoundError(f"No Parquet files found for {table_name}") - lf_unified = pl.scan_parquet(files) + lfs = [normalize_datetimes(pl.scan_parquet(file)) for file in all_files] + lf_unified = pl.concat(lfs, how="vertical_relaxed") if log_info: log_info( - f"Scanned: {table_name} ({len(files)} files queued for lazy evaluation)" + f"Hybrid Scan: {table_name} ({len(all_files)} total files queued for lazy evaluation)" ) return lf_unified +def load_assembled_data( + base_path: Path, + table_name: str, + log_info: Optional[Callable[[str], None]] = None, +) -> pl.LazyFrame: + """ + Optimized loader for high-volume assembled datasets targeting the Semantic stage. + + Contract: + - Discovery (Rust): Passes the glob pattern directly to Polars for high-performance file discovery in Rust. + - Efficiency: Minimizes Python-side overhead by avoiding explicit file listing. + - Normalization: Applies resolution standardization to the unified scan result. + + Invariants: + - Consistency: Assumes uniform resolution across assembled files (standardized by the Assembly stage). + - Validation: Performs a quick existence check before initiating the lazy scan. + + Outputs: + - A pl.LazyFrame optimized for streaming through semantic model construction. + + Failures: + - [Operational] Raises FileNotFoundError if no Parquet files matching the pattern are found. + """ + + pattern = str(base_path / f"{table_name}*.parquet") + + if not any(base_path.glob(f"{table_name}*.parquet")): + raise FileNotFoundError(f"No Parquet files found for {table_name}") + + lf = normalize_datetimes( + pl.scan_parquet( + pattern, + cast_options=pl.ScanCastOptions(datetime_cast="nanosecond-downcast"), + ) + ) + + if log_info: + log_info(f"Scanned: {table_name} for lazy evaluation") + + return lf + + def export_file( df: Any, output_path: Path, @@ -106,18 +248,21 @@ def export_file( Persists DataFrames or LazyFrames to disk using standardized formats. Contract: - - Automates directory creation for the target 'output_path'. - - Enforces Parquet with Brotli compression as the internal standard. + - Hydrate: Automatically ensures parent directories for 'output_path' exist. + - Persist: Enforces Parquet with compression as the internal standard. Optimization Logic: - - Streaming Sink: When provided with a pl.LazyFrame, uses sink_parquet() to - stream data in chunks, bypassing full in-memory materialization. + - Streaming Sink: If 'df' is a LazyFrame, uses 'sink_parquet()' to execute + non-blocking writes directly from the query plan to disk. Invariants: - - Compression: Parquet exports always utilize 'brotli' to optimize storage. + - Compression: Utilizes 'brotli' for DataFrames and 'snappy' for LazyFrame streaming sinks. + + Outputs: + - Boolean: True if write succeeded, False on I/O exception. - Returns: - bool: True if write succeeded, False on I/O exception. + Failures: + - [Operational] Returns False and logs to 'log_error' if disk I/O fails or permissions are denied. """ output_path = Path(output_path) @@ -126,18 +271,27 @@ def export_file( output_path.parent.mkdir(parents=True, exist_ok=True) row_count = 0 - if isinstance(df, pd.DataFrame): - df.to_parquet( - output_path, index=index, engine="pyarrow", compression="brotli" - ) - row_count = len(df) - - elif isinstance(df, pl.DataFrame): + if isinstance(df, pl.DataFrame): + df = normalize_datetimes(df) df.write_parquet(output_path, compression="brotli") row_count = len(df) elif isinstance(df, pl.LazyFrame): - df.sink_parquet(output_path, compression="snappy") + df = normalize_datetimes(df) + + try: + pa_schema = df.limit(0).collect().to_arrow().schema + df.sink_parquet( + output_path, compression="snappy", arrow_schema=pa_schema + ) + + except Exception as e: + print( + f"[WARNING] Arrow schema override failed, falling back to native sink:{e}" + ) + + df.sink_parquet(output_path, compression="snappy") + row_count = "streaming" else: diff --git a/data_pipeline/shared/modeling_configs.py b/data_pipeline/shared/modeling_configs.py index 22da458..2f2870d 100644 --- a/data_pipeline/shared/modeling_configs.py +++ b/data_pipeline/shared/modeling_configs.py @@ -12,11 +12,11 @@ # Assemble events enforced schema and dtypes ASSEMBLE_SCHEMA = [ - "order_id", + "order_id_int", "order_revenue", - "seller_id", - "customer_id", - "product_id", + "seller_id_int", + "customer_id_int", + "product_id_int", "order_status", "order_purchase_timestamp", "order_approved_at", @@ -29,20 +29,20 @@ ] ASSEMBLE_DTYPES: Mapping[str, pl.DataType] = { - "order_id": pl.String(), + "order_id_int": pl.UInt32(), "order_revenue": pl.Float32(), - "seller_id": pl.String(), - "customer_id": pl.String(), - "product_id": pl.String(), + "seller_id_int": pl.UInt32(), + "customer_id_int": pl.UInt32(), + "product_id_int": pl.UInt32(), "order_status": pl.Categorical(), - "order_purchase_timestamp": pl.Datetime(), - "order_approved_at": pl.Datetime(), - "order_delivered_timestamp": pl.Datetime(), + "order_purchase_timestamp": pl.Datetime(time_unit="us"), + "order_approved_at": pl.Datetime(time_unit="us"), + "order_delivered_timestamp": pl.Datetime(time_unit="us"), "lead_time_days": pl.Int16(), "approval_lag_days": pl.Int16(), "delivery_delay_days": pl.Int16(), - "order_date": pl.Datetime(), - "order_year_week": pl.String(), + "order_date": pl.Datetime(time_unit="us"), + "order_year_week": pl.Categorical(), } # ------------------------------------------------------------ @@ -52,8 +52,11 @@ dimension_table = ["df_customers", "df_products"] DIMENSION_REFERENCES = { table: { - "primary_key": TABLE_CONFIG[table]["primary_key"], - "required_column": TABLE_CONFIG[table]["required_column"], + "primary_key": [key + "_int" for key in TABLE_CONFIG[table]["primary_key"]], + "required_column": [ + key + "_int" if "_id" in key else key + for key in TABLE_CONFIG[table]["required_column"] + ], } for table in dimension_table } @@ -65,21 +68,21 @@ # Seller dimension enforced schema and dtypes SELLER_DIM_SCHEMA = [ - "seller_id", + "seller_id_int", "first_order_date", "first_order_year_week", ] SELLER_DIM_DTYPES: Mapping[str, pl.DataType] = { - "seller_id": pl.String(), - "first_order_date": pl.Datetime(), - "first_order_year_week": pl.String(), + "seller_id_int": pl.UInt32(), + "first_order_date": pl.Datetime(time_unit="us"), + "first_order_year_week": pl.Categorical(), } # Seller Facts enforced schema and dtypes SELLER_FACT_SCHEMA = [ - "seller_id", + "seller_id_int", "order_year_week", "week_start_date", "weekly_order_count", @@ -94,9 +97,9 @@ ] SELLER_FACT_DTYPES: Mapping[str, pl.DataType] = { - "seller_id": pl.String(), - "order_year_week": pl.String(), - "week_start_date": pl.Datetime(), + "seller_id_int": pl.UInt32(), + "order_year_week": pl.Categorical(), + "week_start_date": pl.Datetime(time_unit="us"), "weekly_order_count": pl.Int16(), "weekly_delivered_orders": pl.Int16(), "weekly_cancelled_orders": pl.Int16(), @@ -115,7 +118,7 @@ # Customer Dimension and dtypes CUSTOMER_DIM_SCHEMA = [ - "customer_id", + "customer_id_int", "customer_state", "customer_city", "customer_segment", @@ -123,16 +126,16 @@ ] CUSTOMER_DIM_DTYPES: Mapping[str, pl.DataType] = { - "customer_id": pl.String(), + "customer_id_int": pl.UInt32(), "customer_state": pl.Categorical(), "customer_city": pl.Categorical(), "customer_segment": pl.Categorical(), - "account_creation_date": pl.Datetime(), + "account_creation_date": pl.Datetime(time_unit="us"), } # Customer Fact and dtypes CUSTOMER_FACT_SCHEMA = [ - "customer_id", + "customer_id_int", "order_year_week", "week_start_date", "weekly_order_count", @@ -147,9 +150,9 @@ ] CUSTOMER_FACT_DTYPES: Mapping[str, pl.DataType] = { - "customer_id": pl.String(), - "order_year_week": pl.String(), - "week_start_date": pl.Datetime(), + "customer_id_int": pl.UInt32(), + "order_year_week": pl.Categorical(), + "week_start_date": pl.Datetime(time_unit="us"), "weekly_order_count": pl.Int16(), "weekly_delivered_orders": pl.Int16(), "weekly_cancelled_orders": pl.Int16(), @@ -168,7 +171,7 @@ # Product Dim and dtypes PRODUCT_DIM_SCHEMA = [ - "product_id", + "product_id_int", "product_category_name", "product_length_cm", "product_height_cm", @@ -179,7 +182,7 @@ ] PRODUCT_DIM_DTYPES: Mapping[str, pl.DataType] = { - "product_id": pl.String(), + "product_id_int": pl.UInt32(), "product_category_name": pl.Categorical(), "product_length_cm": pl.Float32(), "product_height_cm": pl.Float32(), @@ -192,7 +195,7 @@ # Product Fact and dtypes PRODUCT_FACT_SCHEMA = [ - "product_id", + "product_id_int", "order_year_week", "week_start_date", "weekly_order_count", @@ -208,9 +211,9 @@ PRODUCT_FACT_DTYPES: Mapping[str, pl.DataType] = { - "product_id": pl.String(), - "order_year_week": pl.String(), - "week_start_date": pl.Datetime(), + "product_id_int": pl.UInt32(), + "order_year_week": pl.Categorical(), + "week_start_date": pl.Datetime(time_unit="us"), "weekly_order_count": pl.Int16(), "weekly_delivered_orders": pl.Int16(), "weekly_cancelled_orders": pl.Int16(), diff --git a/data_pipeline/shared/run_context.py b/data_pipeline/shared/run_context.py index d027ea6..08a17bc 100644 --- a/data_pipeline/shared/run_context.py +++ b/data_pipeline/shared/run_context.py @@ -5,12 +5,12 @@ from dataclasses import dataclass from pathlib import Path from typing import Callable -from datetime import datetime +from datetime import datetime as dt, timezone import uuid def _generate_run_id() -> str: - timestamp = datetime.utcnow().strftime("%Y%m%dT%H%M%S") + timestamp = dt.now(timezone.utc).strftime("%Y%m%dT%H%M%S") random_suffix = uuid.uuid4().hex[:6] return f"{timestamp}_{random_suffix}" @@ -49,11 +49,16 @@ class RunContext: # Storage paths storage_raw_path: str storage_contracted_path: str + storage_mapping_path: str storage_published_path: str version_path: str latest_pointer_path: str storage_runs_path: str + # BigQuery coordinates + bq_project_id: str + bq_dataset_id: str + # NOTE: base =./runtime and storage= ./data were local test paths @classmethod def create( @@ -62,6 +67,8 @@ def create( storage: str | Path = "gs://ops-pipeline-storage-dev", # "./data", run_id: str | None = None, run_id_factory: Callable[[], str] | None = None, + bq_project_id: str | None = None, + bq_dataset_id: str | None = None, ) -> "RunContext": """ Factory method for instantiating a fresh execution context. @@ -74,6 +81,8 @@ def create( RunContext: An initialized context with all path mappings resolved. """ + import os + base_path = Path(base) if run_id is None: @@ -95,11 +104,19 @@ def create( storage_root = str(storage) storage_raw_path = f"{storage_root}/raw" storage_contracted_path = f"{storage_root}/contracted" + storage_mapping_path = f"{storage_root}/id_mapping" storage_published_path = f"{storage_root}/published" version_path = f"{storage_published_path}/v{run_id}" latest_pointer_path = f"{storage_published_path}/_latest.json" storage_runs_path = f"{storage_root}/run_artifact/{run_id}" + bq_project_id = os.getenv("GCP_PROJECT", "PROJECT_ID_NOT_DETECTED") + + if not bq_project_id: + bq_project_id = "PROJECT_ID_NOT_DETECTED" + + bq_dataset_id = os.getenv("BQ_DATASET_ID", "BQ_DATASET_ID_NOT_DETECTED") + return cls( run_id=run_id, # Workspace paths @@ -115,10 +132,14 @@ def create( # Storage paths storage_raw_path=storage_raw_path, storage_contracted_path=storage_contracted_path, + storage_mapping_path=storage_mapping_path, storage_published_path=storage_published_path, version_path=version_path, latest_pointer_path=latest_pointer_path, storage_runs_path=storage_runs_path, + # BigQuery + bq_project_id=bq_project_id, + bq_dataset_id=bq_dataset_id, ) def initialize_directories(self) -> None: diff --git a/data_pipeline/shared/storage_adapter.py b/data_pipeline/shared/storage_adapter.py index bfdc522..7c1e367 100644 --- a/data_pipeline/shared/storage_adapter.py +++ b/data_pipeline/shared/storage_adapter.py @@ -23,6 +23,18 @@ def _split_gcs_path(path: str): return bucket, prefix +def check_gcs_path_exists(gcs_uri: str) -> bool: + """ + Helper to check if a GCS prefix has any blobs (effectively checking if 'directory' exists). + """ + client = storage.Client() + bucket_name, prefix = _split_gcs_path(gcs_uri) + + bucket = client.bucket(bucket_name) + blobs = list(bucket.list_blobs(prefix=prefix, max_results=1)) + return len(blobs) > 0 + + def download_raw_snapshot(run_context: RunContext) -> None: """ Synchronizes the raw data snapshot from Cloud Storage to the local workspace. @@ -141,6 +153,7 @@ def upload_contracted_directory(run_context: RunContext) -> None: Contract: - Synchronizes the local 'contracted/' directory to 'storage_contracted_path'. + - Excludes the 'id_mapping' directory to prevent cross-contamination. - Purpose: Archives newly cleaned data for delta accumulation and historical lineage. """ @@ -167,40 +180,81 @@ def upload_contracted_directory(run_context: RunContext) -> None: bucket = client.bucket(bucket_name) for file in source.rglob("*"): - if file.is_file(): + if file.is_file() and "id_mapping" not in file.parts: blob = bucket.blob(f"{prefix}/{file.relative_to(source)}") blob.upload_from_filename(file) -def download_contracted_datasets(run_context: RunContext) -> None: +# NOTE: Legacy architecture helper, retain for fallback. +# def download_contracted_datasets(run_context: RunContext) -> None: +# """ +# Populate the reconstructed local contracted/ with full historical delta set from Silver Cloud storage. + +# Contract: +# - Downloads the full accumulated Silver state from 'storage_contracted_path'. +# """ + +# source = run_context.storage_contracted_path +# destination = run_context.contracted_path + +# # Local filesystem case +# if not str(source).startswith("gs://"): +# shutil.copytree(source, destination, dirs_exist_ok=True) +# return + +# # GCS case +# client = storage.Client() + +# bucket_name, prefix = _split_gcs_path(source) + +# bucket = client.bucket(bucket_name) + +# for blob in bucket.list_blobs(prefix=prefix): +# if blob.name.endswith("/"): +# continue + +# target = destination / Path(blob.name).name +# target.parent.mkdir(parents=True, exist_ok=True) + +# blob.download_to_filename(target) + + +def promote_new_mapping_files(runtime_dir: Path, destination: Path | str) -> None: """ - Populate the reconstructed local contracted/ with full historical delta set from Silver Cloud storage. + Synchronizes new UUID mapping files from the local temporary directory to central storage. Contract: - - Downloads the full accumulated Silver state from 'storage_contracted_path'. + - Recursively identifies all '*.parquet' files in the local 'runtime_dir' subdirectories. + - Promotes them to the persistent 'destination' under matching subdirectories. """ - source = run_context.storage_contracted_path - destination = run_context.contracted_path + if not runtime_dir.exists(): + return + + destination_str = str(destination).replace("\\", "/") # Local filesystem case - if not str(source).startswith("gs://"): - shutil.copytree(source, destination, dirs_exist_ok=True) + if not destination_str.startswith("gs://"): + dest_base = Path(destination) + + for file in runtime_dir.rglob("*.parquet"): + if file.is_file(): + # Reconstruct relative path in destination + # (e.g., destination/order_id/run_id.parquet) + relative_path = file.relative_to(runtime_dir) + target_path = dest_base / relative_path + target_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(file, target_path) return # GCS case client = storage.Client() - - bucket_name, prefix = _split_gcs_path(source) - + bucket_name, prefix = _split_gcs_path(destination_str) bucket = client.bucket(bucket_name) - for blob in bucket.list_blobs(prefix=prefix): - if blob.name.endswith("/"): - continue - - target = destination / Path(blob.name).name - target.parent.mkdir(parents=True, exist_ok=True) - - blob.download_to_filename(target) + for file in runtime_dir.rglob("*.parquet"): + if file.is_file(): + relative_path = file.relative_to(runtime_dir).as_posix() + blob = bucket.blob(f"{prefix}/{relative_path}") + blob.upload_from_filename(str(file)) diff --git a/data_pipeline/shared/table_configs.py b/data_pipeline/shared/table_configs.py index e68c7c4..0f939b9 100644 --- a/data_pipeline/shared/table_configs.py +++ b/data_pipeline/shared/table_configs.py @@ -2,6 +2,8 @@ # Table configuration for Validation and Contract stage # ============================================================================= +import polars as pl + TABLE_CONFIG = { "df_orders": { "role": "event_fact", @@ -22,13 +24,13 @@ "order_purchase_timestamp", ], "dtypes": { - "order_id": "string", - "customer_id": "string", - "order_status": "category", - "order_purchase_timestamp": "datetime64[ns]", - "order_approved_at": "datetime64[ns]", - "order_delivered_timestamp": "datetime64[ns]", - "order_estimated_delivery_date": "datetime64[ns]", + "order_id": pl.String, + "customer_id": pl.String, + "order_status": pl.Categorical, + "order_purchase_timestamp": pl.Datetime(time_unit="us"), + "order_approved_at": pl.Datetime(time_unit="us"), + "order_delivered_timestamp": pl.Datetime(time_unit="us"), + "order_estimated_delivery_date": pl.Datetime(time_unit="us"), }, }, "df_order_items": { @@ -47,10 +49,10 @@ "price", ], "dtypes": { - "order_id": "string", - "product_id": "string", - "seller_id": "string", - "price": "float32", + "order_id": pl.String, + "product_id": pl.String, + "seller_id": pl.String, + "price": pl.Float32, }, }, "df_customers": { @@ -71,11 +73,11 @@ "account_creation_date", ], "dtypes": { - "customer_id": "string", - "customer_state": "category", - "customer_city": "category", - "customer_segment": "category", - "account_creation_date": "datetime64[ns]", + "customer_id": pl.String, + "customer_state": pl.Categorical, + "customer_city": pl.Categorical, + "customer_segment": pl.Categorical, + "account_creation_date": pl.Datetime(time_unit="us"), }, }, "df_payments": { @@ -90,8 +92,8 @@ "payment_value", ], "dtypes": { - "order_id": "string", - "payment_value": "float32", + "order_id": pl.String, + "payment_value": pl.Float32, }, }, "df_products": { @@ -118,14 +120,14 @@ "supplier_tier", ], "dtypes": { - "product_id": "string", - "product_category_name": "category", - "product_length_cm": "float32", - "product_height_cm": "float32", - "product_width_cm": "float32", - "product_fragility_index": "category", - "product_weight_g": "float32", - "supplier_tier": "category", + "product_id": pl.String, + "product_category_name": pl.Categorical, + "product_length_cm": pl.Float32, + "product_height_cm": pl.Float32, + "product_width_cm": pl.Float32, + "product_fragility_index": pl.Categorical, + "product_weight_g": pl.Float32, + "supplier_tier": pl.Categorical, }, }, } diff --git a/data_pipeline/validation/validation_executor.py b/data_pipeline/validation/validation_executor.py index 55fc2f6..d3e3729 100644 --- a/data_pipeline/validation/validation_executor.py +++ b/data_pipeline/validation/validation_executor.py @@ -3,7 +3,7 @@ # ============================================================================= from typing import Dict -import pandas as pd +import polars as pl from pathlib import Path from data_pipeline.shared.loader_exporter import load_single_delta from data_pipeline.shared.table_configs import TABLE_CONFIG @@ -46,7 +46,7 @@ def apply_validation(run_context: RunContext, base_path: Path | None = None) -> report = init_report() - tables: Dict[str, pd.DataFrame] = {} + tables: Dict[str, pl.DataFrame] = {} loaded_table_names = set() # Get assigned table configs @@ -66,12 +66,12 @@ def apply_validation(run_context: RunContext, base_path: Path | None = None) -> tables[table_name] = df if not run_base_validations( - df, - table_name, - config["primary_key"], - config["required_column"], - config["non_nullable_column"], - report, + df=df, + table_name=table_name, + primary_key=config["primary_key"], + required_column=config["required_column"], + non_nullable_column=config["non_nullable_column"], + report=report, ): continue diff --git a/data_pipeline/validation/validation_logic.py b/data_pipeline/validation/validation_logic.py index 6122fd3..c55e61f 100644 --- a/data_pipeline/validation/validation_logic.py +++ b/data_pipeline/validation/validation_logic.py @@ -3,7 +3,8 @@ # ============================================================================= from typing import Dict, List -import pandas as pd +import polars as pl +import polars.selectors as cs from data_pipeline.shared.table_configs import ( REQUIRED_TIMESTAMPS, TIMESTAMP_FORMATS, @@ -44,7 +45,7 @@ def log_error(message: str, report: Dict[str, List[str]]) -> None: def run_base_validations( - df: pd.DataFrame, + df: pl.DataFrame, table_name: str, primary_key: List[str], required_column: List[str], @@ -52,15 +53,16 @@ def run_base_validations( report: Dict[str, List[str]], ) -> bool: """ - Enforces foundational structural integrity for a logical table. + Enforces foundational structural integrity using Polars-native expressions. Contract: - Mandatory Schema: All 'required_column' names must exist in the DataFrame. - Uniqueness: Enforces primary key uniqueness and detects conflicting duplicates. - - Non-Nullability: Columns in 'non_nullable_column' must not contain NaN values. + - Non-Nullability: Columns in 'non_nullable_column' must not contain Null values. Invariants: - Diagnostic Safety: Read-only; does not mutate the input DataFrame. + - Performance: Leverages Polars lazy-style evaluations for memory efficiency. Outputs: - Boolean: True if all mandatory structural checks pass. @@ -69,7 +71,7 @@ def run_base_validations( - [Structural] Logs findings to 'report["errors"]' and returns False for missing columns, empty datasets, or PK conflicts. """ - if df.empty: + if df.is_empty(): log_error(f"{table_name}: dataset is empty", report) return False @@ -92,20 +94,15 @@ def run_base_validations( return False - duplicate_mask = df.duplicated(subset=primary_key, keep=False) - if duplicate_mask.any(): + duplicate_mask = df.select(pl.col(primary_key).is_duplicated()).to_series() - duplicate_rows = df[duplicate_mask] + if duplicate_mask.any(): - # Count of rows per PK - pk_group_size = duplicate_rows.groupby(primary_key, dropna=False).size() + duplicate_rows = df.filter(duplicate_mask) # number of unique rows per PK (full row comparison) - pk_unique_rows = ( - duplicate_rows.drop_duplicates().groupby(primary_key, dropna=False).size() - ) - - conflicting = (pk_unique_rows > 1).any() + pk_unique_rows = duplicate_rows.unique().group_by(primary_key).len() + conflicting = (pk_unique_rows.get_column("len") > 1).any() if conflicting: log_error( @@ -114,7 +111,11 @@ def run_base_validations( ) return False - repairable_count = int((pk_group_size - 1).sum()) # Exclude 1st PK occurrence + # Count of rows per PK + pk_group_size = duplicate_rows.group_by(primary_key).len() + repairable_count = int( + (pk_group_size.get_column("len") - 1).sum() + ) # Exclude 1st PK occurrence if repairable_count > 0: log_warning( @@ -122,28 +123,35 @@ def run_base_validations( report, ) - duplicate_columns = df.columns[df.columns.duplicated()].tolist() + columns = df.columns + duplicate_columns = [col for idx, col in enumerate(columns) if col in columns[:idx]] if duplicate_columns: log_warning( f"{table_name}: duplicate column names detected: {duplicate_columns}", report, ) - pk_null_count = df[primary_key].isnull().any(axis=1).sum() + pk_null_count = ( + df.select(pl.any_horizontal(pl.col(primary_key).is_null())).to_series().sum() + ) + if pk_null_count > 0: log_warning( f"{table_name}: {pk_null_count} rows with null primary key values", report ) # Null rows in non nullable columns - column_nulls = df[non_nullable_column].isna().sum() + if non_nullable_column: + column_nulls = df.select(pl.col(non_nullable_column).null_count()).row( + 0, named=True + ) - for col, count in column_nulls.items(): - if count > 0: - log_warning( - f"{table_name}: {count} null values in non-nullable column {col}", - report, - ) + for col, count in column_nulls.items(): + if count > 0: + log_warning( + f"{table_name}: {count} null values in non-nullable column {col}", + report, + ) return True @@ -154,17 +162,18 @@ def run_base_validations( def run_event_fact_validations( - df: pd.DataFrame, table_name: str, report: Dict[str, List[str]] + df: pl.DataFrame, table_name: str, report: Dict[str, List[str]] ) -> bool: """ - Enforces business-logic chronology for Event-Role tables. + Enforces business-logic chronology and resolution standards for Event-Role tables. Contract: - - Chronological Check: Evaluates temporal sequence (Purchase <= Approval <= Delivery). - - Parseability: Validates timestamp string compatibility with system formats. + - Resolution Verification: Asserts that all timestamps are pre-normalized to microseconds (us) by the I/O layer. + - Chronological Check: Evaluates temporal sequence (Purchase <= Approval <= Delivery) using clean Polars syntax. Invariants: - Temporal Consistency: Flags records where delivery precedes purchase as Warnings. + - Zero-Tolerance Resolution: Assumes compliance with the 'Normalize-at-Source' I/O strategy. Outputs: - Boolean: True if all temporal checks are executed. @@ -182,39 +191,54 @@ def run_event_fact_validations( return False - parsed = {} + safe_parse_expr = [] for col in REQUIRED_TIMESTAMPS: - ts = pd.to_datetime( - df[col], - format=TIMESTAMP_FORMATS[col], - errors="coerce", - ) - parsed[col] = ts - invalid_count = ts.isna().sum() + # Parse only string columns + if col in df.columns and df.schema[col] == pl.String: + safe_parse_expr.append( + pl.col(col) + .str.to_datetime(format=TIMESTAMP_FORMATS[col], strict=False) + .alias(col) + ) + + parsed_df = df.with_columns(safe_parse_expr) if safe_parse_expr else df + + unparsable_counts = parsed_df.select( + [ + pl.col(col).is_null().sum().alias(col) + for col in REQUIRED_TIMESTAMPS + if col in df.columns + ] + ).row(0, named=True) + + for col, invalid_count in unparsable_counts.items(): if invalid_count > 0: log_warning( f"{table_name}: {invalid_count} unparsable timestamp values in {col}", report, ) - purchase_ts = parsed["order_purchase_timestamp"] - approved_ts = parsed["order_approved_at"] - delivered_ts = parsed["order_delivered_timestamp"] + invalid_temporal_counts = parsed_df.select( + invalid_approval=( + pl.col("order_approved_at") < pl.col("order_purchase_timestamp") + ).sum(), + invalid_delivery=( + pl.col("order_delivered_timestamp") < pl.col("order_purchase_timestamp") + ).sum(), + ).row(0, named=True) # Check for invalid temporal ordering - invalid_approval = (approved_ts < purchase_ts).sum() - if invalid_approval > 0: + if invalid_temporal_counts["invalid_approval"] > 0: log_warning( - f"{table_name}: {invalid_approval} records where approval precedes purchase", + f"{table_name}: {invalid_temporal_counts['invalid_approval']} records where approval precedes purchase", report, ) - invalid_delivery = (delivered_ts < purchase_ts).sum() - if invalid_delivery > 0: + if invalid_temporal_counts["invalid_delivery"] > 0: log_warning( - f"{table_name}: {invalid_delivery} records where delivery precedes purchase", + f"{table_name}: {invalid_temporal_counts['invalid_delivery'] } records where delivery precedes purchase", report, ) @@ -222,7 +246,7 @@ def run_event_fact_validations( def run_transaction_detail_validations( - df: pd.DataFrame, table_name: str, report: Dict[str, List[str]] + df: pl.DataFrame, table_name: str, report: Dict[str, List[str]] ) -> bool: """ Enforces domain and range constraints for Transaction-Role tables. @@ -237,13 +261,13 @@ def run_transaction_detail_validations( - [Operational] Logs out-of-range values to 'report["errors"]'. """ - numeric_columns = df.select_dtypes(include=["number"]).columns.tolist() + negative_counts = df.select((cs.numeric() < 0).sum()).row(0, named=True) - for col in numeric_columns: - negative_count = (df[col] < 0).sum() - if negative_count > 0: + # 2. Iterate through the resulting dictionary + for col, count in negative_counts.items(): + if count > 0: log_error( - f"{table_name}: {negative_count} negative values in numeric column `{col}`", + f"{table_name}: {count} negative values in numeric column `{col}`", report, ) @@ -251,7 +275,7 @@ def run_transaction_detail_validations( def run_cross_table_validations( - tables: Dict[str, pd.DataFrame], report: Dict[str, List[str]] + tables: Dict[str, pl.DataFrame], report: Dict[str, List[str]] ) -> bool: """ Enforces referential integrity (Foreign Key) across the dataset. @@ -284,19 +308,16 @@ def run_cross_table_validations( order_items_df = tables["df_order_items"] payments_df = tables["df_payments"] - # Orders PK reference - order_id_set = set(orders_df["order_id"].dropna().unique()) + order_id_set = set(orders_df.get_column("order_id").drop_nulls().unique()) - # OrderItems to Orders integrity - orphan_items = ~order_items_df["order_id"].isin(order_id_set) + orphan_items = ~order_items_df.get_column("order_id").is_in(order_id_set) if orphan_items.any(): log_warning( f"df_order_items: {orphan_items.sum()} orphan records referencing non-existent order_id", report, ) - # Payments to Orders integrity - orphan_payments = ~payments_df["order_id"].isin(order_id_set) + orphan_payments = ~payments_df.get_column("order_id").is_in(order_id_set) if orphan_payments.any(): log_warning( f"df_payments: {orphan_payments.sum()} orphan records referencing non-existent order_id", diff --git a/dev-cloud-test.ps1 b/dev-cloud-test.ps1 new file mode 100644 index 0000000..8875b4a --- /dev/null +++ b/dev-cloud-test.ps1 @@ -0,0 +1,42 @@ +param( + [string]$ProjectId = "", + [string]$ArtifactReg = "", + [string]$GcpDocker = "", + [string]$ImageTag = "", + [string]$Region = "", + [string]$BqDatasetId = "", + [string]$Memory = "", + [string]$Cpu = "", + [string]$Threads = "" +) + +$ErrorActionPreference = 'Stop' + +$IMAGE_PATH = "$GcpDocker/$ProjectId/$ArtifactReg/$ImageTag" + +Write-Host "" +Write-Host "BUILDING IMAGE LOCALLY" -ForegroundColor Blue + +docker build --no-cache -t $IMAGE_PATH -f data_pipeline/Dockerfile . + +Write-Host "" +Write-Host "PUSHING IMAGE TO CLOUD REPO" -ForegroundColor Blue + +docker push $IMAGE_PATH + +Write-Host "" +Write-Host "UPDATING JOB" -ForegroundColor Blue + +gcloud run jobs update operations-pipeline-dev ` + --image $IMAGE_PATH ` + --update-env-vars GCP_PROJECT=$ProjectId ` + --update-env-vars BQ_DATASET_ID=$BqDatasetId ` + --update-env-vars POLARS_MAX_THREADS=$Threads ` + --region $Region ` + --memory $Memory ` + --cpu $Cpu + +Write-Host "" +Write-Host "EXECUTING CLOUD JOB" -ForegroundColor Blue + +gcloud run jobs execute operations-pipeline-dev --region $Region \ No newline at end of file diff --git a/dev-local-test.ps1 b/dev-local-test.ps1 new file mode 100644 index 0000000..adf3395 --- /dev/null +++ b/dev-local-test.ps1 @@ -0,0 +1,28 @@ +param( + [string]$Buildtag = "", + [string]$Testname = "", + [string]$Memory = "", + [string]$Memswap = "", + [string]$Cpu = "", + [string]$Threads = "", + [string]$Data = "" +) + +$ErrorActionPreference = 'Stop' + +Write-Host "" +Write-Host "BUILDING IMAGE LOCALLY" -ForegroundColor Blue + +docker build --no-cache -t $Buildtag -f data_pipeline/Dockerfile . + +Write-Host "" +Write-Host "MOUNTING LOCAL DATA DIRECTORY AND RUN TEST" -ForegroundColor Blue + +docker run --rm ` + --name $Testname ` + -v "${Data}:/app/data" ` + --memory=$Memory ` + --memory-swap=$Memswap ` + --cpus=$Cpu ` + -e POLARS_MAX_THREADS=$Threads ` + $Buildtag \ No newline at end of file diff --git a/dev-requirements.txt b/dev-requirements.txt index 1362d4f..9421ec7 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -8,11 +8,16 @@ pyparsing<3.0.0 tzdata # pipeline -pandas==2.1.4 polars==1.39.0 -pytest==9.0.2 pyarrow==19.0.0 -black==24.3.0 -ruff==0.0.264 +# duckdb==1.5.2 google-cloud-storage -pytest-cov \ No newline at end of file +google-cloud-bigquery>=3.0.0 +google-cloud-bigquery-storage>=2.36.0 + +# dev & testing +psutil==7.2.2 +pytest==9.0.2 +pytest-cov +black==24.3.0 +ruff==0.0.264 \ No newline at end of file diff --git a/docker-compose.benchmark.yml b/docker-compose.benchmark.yml deleted file mode 100644 index fb55da0..0000000 --- a/docker-compose.benchmark.yml +++ /dev/null @@ -1,19 +0,0 @@ -services: - stress-test: - container_name: pipeline-memory-stress-test - build: - context: . - dockerfile: data_pipeline/Dockerfile - - # Mount the local data folder - volumes: - - ./data:/app/data - - # Provision Specs: Memory 8 Gb/ 2cpu/2 threads - # Memory tax = Sandbox Tax + Import Tax + I/O Buffer Tax - # Max Data Size = (Total RAM- 8GB) - (Memory Tax - 1.5GB) = 6.5GB - mem_limit: 6.5G - memswap_limit: 6.5G - cpus: '2.0' - environment: - - POLARS_MAX_THREADS=2 diff --git a/docs/data_pipeline/assembly_stage.md b/docs/data_pipeline/assembly_stage.md index bf42e9e..957aaae 100644 --- a/docs/data_pipeline/assembly_stage.md +++ b/docs/data_pipeline/assembly_stage.md @@ -12,12 +12,12 @@ **Purpose** -Integrates multiple normalized relational tables into a unified, analytical "Event" dataset and extracts high-fidelity "Dimension" references. It transforms raw business facts into a ready-to-model state by enforcing cardinality rules and calculating temporal performance metrics. +Integrates multiple normalized relational tables into a unified, analytical "Event" dataset and extracts high-fidelity "Dimension" references. It transforms raw business facts into a ready-to-model state by enforcing cardinality rules, leveraging the Primitive Integer Pipeline for memory efficiency, and calculating temporal performance metrics. **Invariants** -* **Strict Order-ID Grain:** The primary event output is guaranteed to be exactly 1 row per `order_id`. Any operation causing cardinality explosion triggers a terminal failure. +* **Strict Order-ID Grain:** The primary event output is guaranteed to be exactly 1 row per `order_id_int`. Any operation causing cardinality explosion triggers a terminal failure. * **Inner-Join Priority:** To maintain analytical integrity, orders without corresponding items are purged. -* **Temporal Determinism:** All lead times, lags, and delays are calculated as integer-day durations based on validated UTC timestamps. +* **Temporal Determinism:** All lead times, lags, and delays are calculated as integer-day durations based on validated UTC timestamps pre-normalized to microsecond resolution. * **Reference Uniqueness:** Dimension reference tables (Customers, Products) are strictly deduplicated by their primary keys. **Inputs** @@ -36,7 +36,7 @@ The **Executor** coordinates two distinct sub-orchestrations: ### **Workflow I: Event Assembly** 1. **Batch Load:** Fetches the required triplet (`orders`, `items`, `payments`) from the Silver zone. 2. **Merge:** Joins datasets using `merge_data`. It performs an inner join on items and a left join on payments to preserve financial data without losing order context. - * **Optimization:** Employs Hash-Joins on `UInt64` keys derived from `order_id` to drastically reduce memory overhead for high-cardinality UUIDs. Utilizes pre-aggregation on payments and items to ensure a strict 1:1 grain, preventing row explosions. + * **Optimization:** Employs **Integer-Joins** on pre-mapped `UInt32/UInt64` IDs (e.g., `order_id_int`) provided by the Contract Registrar to drastically reduce memory overhead. Utilizes pre-aggregation on payments and items to ensure a strict 1:1 grain, preventing row explosions. 3. **Derivation:** Executes `derive_fields` to calculate fulfillment lead times and extract ISO-calendar attributes. * **Optimization:** Applies memory-efficient casting (e.g., `Int16` for durations, `Categorical` for repetitive strings) and drops intermediate columns early to minimize row width. 4. **Schema Freeze:** Projects the final `ASSEMBLE_SCHEMA` and casts all columns to `ASSEMBLE_DTYPES`. @@ -51,7 +51,7 @@ The **Executor** coordinates two distinct sub-orchestrations: ## **Optimization & Memory Invariants** * **Primitive Integer Pipeline:** To operate within 4GB RAM, the pipeline converts 36-byte UUID strings into 8-byte `UInt64` hashes for joins, and 4-byte `UInt32` categoricals for payloads. This is the primary driver of memory efficiency for 36M+ row datasets. -* **Streaming-First Join:** By deferring aggregations until after raw joins on `order_id`, we leverage Polars' streaming engine to avoid massive, materialized hash tables. +* **Streaming-First Join:** By deferring aggregations until after raw joins on `order_id`, leveraging Polars' streaming engine to avoid massive, materialized hash tables. * **Low-Level Memory Reclamation:** The executor utilizes `ctypes.CDLL('libc.so.6').malloc_trim(0)` at high-water mark transitions. This forces the Linux allocator to release free memory back to the OS, preventing Cloud Run from terminating the process due to bloated (but unused) heap memory. * **Zero-Copy Streaming:** `sink_parquet()` is used to prevent the pipeline from fully materializing the assembly result set in memory. diff --git a/docs/data_pipeline/contract_stage.md b/docs/data_pipeline/contract_stage.md index 7dd13bd..ab7218c 100644 --- a/docs/data_pipeline/contract_stage.md +++ b/docs/data_pipeline/contract_stage.md @@ -3,27 +3,29 @@ **Files:** * **Executor:** [`contract_executor.py`](../../data_pipeline/contract/contract_executor.py) * **Logic:** [`contract_logic.py`](../../data_pipeline/contract/contract_logic.py) +* **Registrar:** [`id_registrar.py`](../../data_pipeline/contract/id_registrar.py) * **Registry:** [`registry.py`](../../data_pipeline/contract/registry.py) -**Role:** Structural Enforcement and Subtractive Filtering. +**Role:** Structural Enforcement, Subtractive Filtering, and Discovery-First ID Mapping. ![contract-stage-diagram](/assets/diagrams/03-contract-stage-diagram.png) ## **System Contract** **Purpose** - -Enforces role-based structural rules on logical tables to ensure that only "contract-compliant" records reach the Silver layer. It acts as a gate that prunes malformed data, enforces referential integrity via ID propagation, and freezes the technical schema. +Enforces role-based structural rules and referential integrity on raw snapshots to ensure that only "contract-compliant" records reach the Silver layer. It acts as a gate that prunes malformed data, enforces referential integrity via ID propagation, and freezes the technical schema using a discovery-first integer mapping approach. **Invariants** -* **Subtractive-Only Row Logic:** With the exception of type casting, this stage never modifies data values or "repairs" them. If a row is non-compliant, it is dropped. -* **Grain Enforcement:** Guarantees the removal of duplicates and the enforcement of the primary key grain defined in the registry. +* **Subtractive-Only Row Logic:** With the exception of type casting, this stage never modifies business values or "repairs" data. If a row is non-compliant, it is dropped. +* **Structural Parity:** Every file within a logical table's contracted zone MUST share an identical schema width and data types to support high-speed vertical concatenation in the Assembly stage. * **ID Propagation:** If an `order_id` is invalidated (e.g., due to nulls or unparsable dates), that ID is propagated to child tables to ensure a clean cascade drop. -* **Final Schema Freeze:** The terminal step always ensures the output contains only approved columns with strictly defined data types. +* **Discovery-First Mapping:** Guarantees that all UUIDs are resolved and mapped to deterministic `UInt32` integers BEFORE table enforcement begins, preventing join collisions and schema drift. +* **Final Schema Freeze:** The terminal step for every role always executes `enforce_schema` to project only required columns and cast to strictly defined types. **Inputs** * `run_context`: `RunContext` (Path resolution for source raw snapshots and destination contracted zone). * `table_name`: `str` (Logical identifier used to look up role-based rules). +* `master_mappings`: `dict[str, pl.LazyFrame]` (The pre-resolved dictionary of UUID-to-Integer mappings). * `invalid_order_ids`: `set` (Blacklist of IDs from preceding tables to be dropped). * `valid_order_ids`: `set` (Whitelist of IDs used to ensure child-parent referential integrity). @@ -31,38 +33,45 @@ Enforces role-based structural rules on logical tables to ensure that only "cont * **Contract Report:** `dict` (Telemetry including `initial_rows`, `final_rows`, and counts for each rule applied). * **Invalidated IDs:** `set` (New IDs discovered to be non-compliant during this run). * **Valid IDs:** `set` (Emitted specifically by the `orders` table to act as a parent whitelist). -* **Side Effect:** Writes a schema-enforced Parquet file to the `contracted/` directory. +* **Side Effect:** Writes a schema-enforced and integer-mapped Parquet file to the `contracted/` directory. ## **Execution Workflow** -The **Executor** applies the contract through a registry-driven sequence: +The Contract stage is split into a global Discovery phase and a table-specific Enforcement phase: + +### **Phase A: Global Discovery** +1. **Discover:** Scans all raw sources (CSV/Parquet) for the unique set of UUIDs in the current run. +2. **Lookup:** Surgically retrieves existing mappings from Cloud Storage. +3. **Generate:** Maps truly new UUIDs to a continuous integer sequence. +4. **Promote:** Persists new mapping deltas to local disk and synchronizes them to central storage. -1. **Role Resolution:** Identifies if the table is an `event_fact`, `transaction_detail`, or `entity_reference`. -2. **Logic Sequencing:** Fetches the specific list of rules (e.g., `deduplicate`, `remove_nulls`) from the `ROLE_STEPS` registry. -3. **Atomic Filtering:** Iteratively applies each logic function. For `event_fact` roles, it captures any `order_id` that triggers a violation. -4. **Cascade Cleanup:** If `invalid_order_ids` are provided, it drops child records whose parents were previously invalidated. -5. **Referential Gate:** If `valid_order_ids` are provided (post-orders processing), it prunes orphan records. -6. **Schema Freeze:** As the final operation, it executes `enforce_schema` to project only required columns and cast types. -7. **Persistence:** Saves the resulting compliant DataFrame to the Silver layer. +### **Phase B: Table Enforcement** +1. **Hydrate:** Fetches the raw snapshot from the lake's snapshot zone. +2. **Logic Sequencing:** Fetches rules (dedupe, null-checks, cascade drops) from `ROLE_STEPS`. +3. **Atomic Filtering:** Iteratively applies rules. For `event_fact` roles, it captures IDs triggering violations. +4. **Structural Freeze:** Executes `enforce_schema` as the final step in the registry sequence to project the required columns. +5. **ID Mapping:** Joins the filtered and projected DataFrame against the `master_mappings` to attach integer IDs. +6. **Persistence:** Saves the resulting compliant and integer-mapped dataset to the Silver layer. ## **Boundaries** | This component **DOES** | This component **DOES NOT** | | :--- | :--- | -| Remove rows violating structural rules (Nulls, Duplicates). | Calculate business metrics or durations. | -| Drop child records based on parent invalidation (Cascade). | Impute missing values or "fix" bad data. | -| Enforce chronological logic (Purchase < Delivery). | Join multiple tables (delegated to Assembly stage). | -| Project a final schema and enforce strictly defined types. | Rename columns or change business definitions. | -| Track exactly how many rows were lost at each rule. | Handle global orchestration of all tables. | +| Discover UUIDs across all raw sources (CSV/Parquet) before processing. | Calculate business metrics, KPIs, or aggregates. | +| Subtractively filter rows violating structural or temporal rules. | Impute missing values or repair malformed records. | +| Propagate `order_id` invalidations to child tables (Cascade Drop). | Perform cross-table business joins (delegated to Assembly). | +| Guarantee fixed-width schemas via terminal `enforce_schema`. | Alter business definitions or rename columns. | +| Map UUIDs to UInt32 primitives for optimized joins. | Handle cross-run global state (delegated to Storage Adapter). | ## **Failure & Severity Model** -### **Operational Failures (System Level)** -* **Configuration Mismatch:** If a `table_name` is not in `TABLE_CONFIG` or `ROLE_STEPS`, the executor returns a `failed` status immediately. -* **Schema Breach:** If `enforce_schema` is called but a required column is missing from the data, it raises a `KeyError` and halts the export. +### **Operational Failures (Fatal)** +* **Discovery Failure:** If mappings cannot be resolved, the pipeline halts to prevent schema corruption. +* **Schema Breach:** If `enforce_schema` is called but a required column is missing from the source data. +* **Persistence Failure:** If disk I/O or GCS promotion fails during the write phase. -### **Functional Findings (Data Level)** -* **Contract Violations:** Data issues (duplicates, nulls) are not treated as "pipeline crashes." They are treated as expected noise; the rows are removed, the count is logged, and the pipeline continues with the remaining "clean" data. +### **Functional Findings (Warnings)** +* **Contract Violations:** Data issues (duplicates, nulls) result in row removal and are logged in the telemetry report. * **Referential Cleanup:** - * **Cascade:** Compromised IDs from parents (e.g., orders) trigger removal of children (e.g., items) logged under `removed_cascade_rows`. - * **Orphans:** Ghost records without any parent reference are logged under `removed_ghost_orphan_rows`, ensuring downstream joins in the Assembly stage are 100% clean. \ No newline at end of file + * **Cascade:** Dropped child records are logged under `removed_cascade_rows`. + * **Orphans:** Records without parent references are dropped and logged under `removed_ghost_orphan_rows`. diff --git a/docs/data_pipeline/pipeline_orchestrator.md b/docs/data_pipeline/pipeline_orchestrator.md index 18806b0..eb91d28 100644 --- a/docs/data_pipeline/pipeline_orchestrator.md +++ b/docs/data_pipeline/pipeline_orchestrator.md @@ -31,38 +31,37 @@ Serves as the central nervous system of the pipeline. It synchronizes data betwe ## **Execution Workflow** -The orchestrator manages the lifecycle in three high-level phases, featuring a defensive synchronization loop: +The orchestrator manages the lifecycle through a strictly gated 13-step sequence, emphasizing memory efficiency and cloud-local synchronization: ### **Phase I: Environment Initialization** -1. **Context Resolution**: Instantiates the `RunContext`. -2. **Metadata Start**: Persists the initial "RUNNING" state to `run_metadata.json`. -3. **Ingestion**: Downloads the required raw data snapshot from the cloud to the local workspace. - -### **Phase II: The Defensive Cloud-Sync Loop** -1. **Gate I (Raw Validation)**: Asserts the health of the downloaded raw data. -2. **Contract Processing**: Filters rows and freezes schemas into the local `contracted/` path. -3. **Gate II (Revalidation)**: Defensive check to ensure the local Silver data is structurally sound. -4. **Silver Synchronization (Upload)**: Promotes the newly contracted data to the **Cloud Silver Storage** to ensure delta accumulation and persistence. -5. **Environment Purge**: Deletes the local `raw_snapshot/` and `contracted/` directories and triggers `gc.collect()` to free system memory. -6. **Silver Restoration (Download)**: Recreates the local `contracted/` directory and downloads the **accumulated Silver deltas** from the Cloud storage. -7. **Integration (Assembly)**: Merges the restored data into the Gold-layer event grain. -8. **Modeling (Semantic)**: Builds the final analytical modules. -9. **Gate III (Pre-Publish)**: Verifies the completeness of semantic artifacts. - -### **Phase III: Finalization & Cleanup** -1. **Promotion**: Atomically updates the production pointer (`latest_version.json`). -2. **Persistence**: Uploads all logs and metadata back to cloud storage. -3. **Final Purge**: Deletes the entire local `workspace_root`. +1. **Resolve**: Instantiates the `RunContext` and initializes background memory telemetry for real-time benchmarking. +2. **Hydrate (Raw)**: Synchronizes the required raw data snapshot from Cloud Storage to the local workspace. +3. **Initialize**: Registers the run commencement by generating `run_metadata.json` with initial "RUNNING" status. + +### **Phase II: Processing & Memory Reclamation** +4. **Validate (Raw)**: Asserts the health of the raw data snapshot; fail-fast on structural errors. +5. **Contract Processing**: Executes subtractive filtering and freezes schemas into the local `contracted/` path (Silver layer). +6. **Gate II (Revalidation)**: Defensive check to ensure contracted data meets downstream semantic requirements. +7. **Promote (Silver)**: Persists the newly contracted datasets to **Cloud Silver Storage**. +8. **Synchronize (BQ)**: Forces a metadata cache refresh for BigQuery External Tables via system procedures (`BQ.REFRESH_EXTERNAL_METADATA_CACHE`) for immediate visibility. +9. **Purge (Local)**: Deterministically deletes local `raw/` and `contracted/` directories and invokes `force_gc()` to reclaim RAM before the high-compute Assembly stage. +10. **Assemble**: Flattens relational data into a unified Gold-layer event grain using the **BigQuery Storage Read API** (bypassing the need for local Silver restoration). +11. **Modeling (Semantic)**: Builds entity-centric analytical modules (Fact/Dim tables). + +### **Phase III: Activation & Finalization** +12. **Publish**: Executes final integrity gates, performs the **BigQuery View Swap** for the BI layer, and triggers the atomic pointer swap (`_latest.json`) to activate the new version. +13. **Finalize**: Updates terminal metadata (status, duration), uploads all telemetry/stage reports to Cloud Storage, and purges the entire local workspace. ## **Boundaries** | This component **DOES** | This component **DOES NOT** | | :--- | :--- | | Coordinate the sequence of high-level executors. | Modify rows, columns, or data values. | -| Manage local/cloud data synchronization. | Implement business logic or aggregation rules. | -| Manage the Silver "Upload-Purge-Download" cycle. | Define the technical schema for Fact/Dim tables. | +| Manage local/cloud data synchronization and BQ caching. | Implement business logic or aggregation rules. | +| Enforce the "Purge-before-Assembly" memory optimization. | Define the technical schema for Fact/Dim tables. | | Manage the `finally` block for resource safety. | Direct file-level I/O within a stage (Delegated). | | Aggregate stage-level reports into a run summary. | Perform granular row-level validation. | +| Monitor and log real-time memory telemetry. | Execute SQL transformations directly (Delegated). | ## **Failure & Severity Model** diff --git a/docs/data_pipeline/publishing_stage.md b/docs/data_pipeline/publishing_stage.md index 65148f2..57a6380 100644 --- a/docs/data_pipeline/publishing_stage.md +++ b/docs/data_pipeline/publishing_stage.md @@ -10,31 +10,32 @@ **Purpose** -Serves as the final gate and deployment mechanism for the pipeline. It transitions validated semantic artifacts into a permanent, versioned storage layer and updates the system's "latest" pointer to ensure Business Intelligence (BI) tools consume the most recent high-quality data. +Serves as the final gate and deployment mechanism for the pipeline. It transitions validated semantic artifacts into a permanent, versioned storage layer and updates a dual-pointer system: a `latest_version.json` manifest for automated systems and BigQuery Authorized Views for Power BI/Business Intelligence tools. **Invariants** * **Integrity-Gated Promotion:** Promotion to the production zone is strictly prohibited if any table defined in the `SEMANTIC_MODULES` registry is missing or inaccessible. -* **Atomic Activation:** The update to the `latest_version.json` pointer must be atomic (e.g., using `os.replace` locally) to prevent downstream tools from reading a partially written or corrupted manifest. +* **Atomic Multi-System Swap:** The "switch" to the new version must happen across both GCS and BigQuery. The BigQuery View swap ensures Power BI never experiences "partial data" reads during the file promotion phase. * **Version Immutability:** Once a run is archived in a `v{run_id}` directory, the files are treated as read-only snapshots; they are never updated or overwritten by subsequent runs. -* **Decoupled Storage:** Supports transparent publishing across both Local Filesystems and Google Cloud Storage (GCS) via a storage adapter. +* **SQL Decoupling:** Dashboards connect to "Stable" Views (e.g., `published_seller_weekly_fact`) which are dynamically redirected to version-specific External Tables (e.g., `seller_weekly_fact_v20260413`). **Inputs** * `run_context`: `RunContext` (Contains the unique `run_id` and the semantic/published path configurations). * `SEMANTIC_MODULES`: `Registry` (The source of truth for which artifacts must exist to pass the integrity gate). **Outputs** -* **Publish Report:** `dict` (Telemetry for the integrity check, file promotion status, and pointer update). +* **Publish Report:** `dict` (Telemetry for the integrity check, file promotion status, and SQL/JSON pointer updates). * **Versioned Artifacts:** A new directory `/published/v{run_id}/` containing the full suite of semantic Fact and Dimension tables. +* **BigQuery Pointers:** Updated External Tables and Authorized Views reflecting the new version. * **Latest Pointer:** An updated `latest_version.json` file in the root of the published zone. ## **Execution Workflow** -The **Executor** ensures the production release follows a fail-fast, three-phase sequence: +The **Executor** ensures the production release follows a fail-fast, four-phase sequence: 1. **Integrity Gate:** `run_integrity_gate` scans the semantic zone to verify that 100% of the expected tables (defined in the registry) were successfully produced. 2. **Promotion:** `promote_semantic_version` transfers all verified artifacts from the transient run-scoped directory to a permanent versioned path (`/published/v{run_id}`). -3. **Metadata Generation:** Constructs a publication manifest containing the `run_id`, a timestamped `published_at` field, and temporal metadata (Year/Month/Week). -4. **Activation:** `activate_published_version` performs the terminal swap of the `latest_version.json` file, effectively "going live" for downstream consumers. +3. **SQL Sync:** `swap_bigquery_view` executes DDL commands to create versioned External Tables and atomically redirect the "Published" Views used by dashboards. +4. **Activation:** `activate_published_version` performs the terminal swap of the `latest_version.json` file, effectively "going live" for downstream file-system consumers. ## **Boundaries** @@ -42,16 +43,13 @@ The **Executor** ensures the production release follows a fail-fast, three-phase | :--- | :--- | | Verify the physical existence of semantic artifacts. | Re-validate data quality (handled in Validation/Contract). | | Copy or upload files to a versioned production path. | Perform any data transformation or aggregation. | -| Update the atomic production pointer (`latest`). | Manage historical version cleanup (Garbage collection). | -| Provide abstraction for Local vs. Cloud storage. | Handle automated rollbacks (pointer must be reverted manually). | +| Manage BigQuery DDL for External Tables and Views. | Manage historical version cleanup (Garbage collection). | +| Update the atomic production pointers (SQL and JSON). | Handle automated rollbacks (pointers must be reverted manually). | | Capture lifecycle metadata (Publication timestamps). | Modify the contents of the `.parquet` files. | ## **Failure & Severity Model** ### **Operational Failures (System Level)** * **Storage Access Denied:** If the service account lacks write permissions to the published zone (Local or GCS), the lifecycle halts before activation. -* **Network/IO Exception:** Interrupted file transfers during the promotion phase result in an immediate `failed` status, ensuring the `latest` pointer remains on the previous stable version. - -### **Functional Findings (Data Level)** -* **Integrity Breach:** If a builder in the Semantic stage failed to produce even one required table, the `run_integrity_gate` will fail. This prevents "partial data" from being promoted to production. -* **Activation Collision:** If the `latest_version.json` cannot be replaced atomically, the executor traps the error and logs a fatal failure, preserving the existing production state. \ No newline at end of file +* **BigQuery DDL Error:** If the SQL swap fails (e.g., dataset permissions or syntax), the `latest_version.json` is never updated, ensuring systems stay in sync. +* **Network/IO Exception:** Interrupted file transfers during the promotion phase result in an immediate `failed` status, ensuring the pointers remain on the previous stable version. \ No newline at end of file diff --git a/docs/data_pipeline/semantic_stage.md b/docs/data_pipeline/semantic_stage.md index 92ce4a5..718b9bf 100644 --- a/docs/data_pipeline/semantic_stage.md +++ b/docs/data_pipeline/semantic_stage.md @@ -13,7 +13,7 @@ **Purpose** -Transforms the unified Gold-layer "Order-Grain" event table into entity-centric Fact and Dimension modules. It performs temporal aggregations, calculates long-term performance metrics, and organizes data into a schema optimized for time-series and cohort analysis. +Transforms the unified Gold-layer "Order-Grain" event table into entity-centric Fact and Dimension modules. It performs temporal aggregations, calculates long-term performance metrics, and leverages the Primitive Integer Pipeline for efficient, high-fidelity analytical modeling. **Invariants** @@ -49,9 +49,9 @@ The **Executor** coordinates the semantic build through a modular, registry-driv ## **Optimization & Memory Invariants** -* **Local Categorical Aggregation:** To optimize memory during grouping operations, builders cast high-cardinality grouping keys (e.g., `seller_id`) to `pl.Categorical` locally. This creates a temporary, localized dictionary optimized specifically for that module's aggregation plan, bypassing the need for a persistent global string cache. -* **Narrow Aggregation Payloads:** All aggregation results (counts, sums) are immediately cast to `Int16` or `Float32` within the `agg()` block. This prevents the materialized result set from expanding in memory. -* **Schema Hand-off:** While the building process uses `Categorical` for performance, the final output is cast back to `pl.String()` via the registry/freezing process. This ensures downstream compatibility with BI tools and prevents "dictionary leakage" between pipeline runs. +* **Integer Key Optimization:** To optimize memory during grouping operations, builders leverage pre-mapped `UInt32/UInt64` keys (e.g., `seller_id_int`). This maintains a constant memory profile during non-blocking aggregation and eliminates the overhead of string-based hash tables. +* **Narrow Aggregation Payloads:** All aggregation results (counts, sums) are immediately downcast to `Int16` or `Float32` within the `agg()` block. This prevents the materialized result set from expanding in memory. +* **Metric Downcasting:** Durations, counts, and years are forced to `Int16` (2 bytes) to minimize row width during streaming. * **Streaming Export:** `sink_parquet()` is utilized for all fact and dimension table exports, enabling zero-copy streaming of results directly from the query plan to storage. ## **Boundaries** @@ -61,7 +61,7 @@ The **Executor** coordinates the semantic build through a modular, registry-driv | Perform multi-level aggregations (Sum, Mean, Count). | Filter "bad" data (handled in Validation/Contract stages). | | Derive entity-level attributes (e.g., `first_order_date`). | Resolve order-item join cardinality. | | Align all temporal metrics to the ISO Week grain. | Mutate the "Assembled Events" source. | -| Enforce technical schemas and data types lazily. | Manage the physical publish/pointer logic. | +| Utilize Integer-Key grouping for constant memory. | Manage the physical publish/pointer logic. | | Organize data into Fact/Dimension modules via streaming. | Perform cross-module joins. | ## **Failure & Severity Model** diff --git a/docs/data_pipeline/validation_stage.md b/docs/data_pipeline/validation_stage.md index b98463d..b6d5a87 100644 --- a/docs/data_pipeline/validation_stage.md +++ b/docs/data_pipeline/validation_stage.md @@ -12,10 +12,11 @@ **Purpose** -Evaluates raw datasets against declared structural contracts before any mutation or transformation occurs. It prevents "garbage-in" scenarios by detecting schema violations, structural inconsistencies, and referential integrity issues that would compromise downstream aggregation. +Evaluates raw datasets against declared structural contracts before any mutation or transformation occurs. It prevents "garbage-in" scenarios by detecting schema violations, structural inconsistencies, and referential integrity issues. In the modern Polars-native architecture, it also serves as a verification gate for the 'Normalize-at-Source' I/O strategy. **Invariants** * **Non-Mutation Guarantee:** This stage is strictly read-only. It never modifies values, removes rows, or casts types in the source data. +* **Resolution Verification:** Asserts that all timestamps are pre-normalized to microsecond (us) resolution by the I/O layer. * **Severity Hierarchy:** * `errors`: Fatal structural violations (e.g., missing columns, duplicate PKs). * `warnings`: Admissible integrity issues (e.g., orphan records, chronological anomalies). @@ -37,10 +38,10 @@ The **Executor** coordinates the validation lifecycle through the following dete 2. **Data Loading:** Attempts to load each table as a DataFrame. If a table is missing, an `error` is logged to the report. 3. **Base Validation:** Dispatches the DataFrame to `run_base_validations` to check for: * Presence of required columns. - * Uniqueness of Primary Keys and column names. + * Uniqueness of Primary Keys and column names using Polars-native expressions. * Compliance with non-nullable constraints. 4. **Role-Specific Dispatch:** If base validations pass, the executor applies specialized rules: - * `event_fact`: Triggers `run_event_fact_validations` (temporal chronology). + * `event_fact`: Triggers `run_event_fact_validations` (temporal chronology and microsecond resolution verification). * `transaction_detail`: Triggers `run_transaction_detail_validations` (numeric range checks). 5. **Cross-Table Integrity:** Once all tables are processed individually, `run_cross_table_validations` evaluates Foreign Key relationships (e.g., ensuring all Items belong to an existing Order). @@ -50,10 +51,10 @@ The **Executor** coordinates the validation lifecycle through the following dete | :--- | :--- | | Load logical tables from the snapshot zone. | Remove rows or filter data. | | Detect schema and primary key violations. | Correct or impute missing values. | -| Evaluate timestamp validity and chronological ordering. | Deduplicate records (delegated to Contract stage). | -| Detect numeric anomalies (negative prices/lags). | Perform data type casting. | -| Evaluate cross-table referential integrity (orphans). | Halt the pipeline (Decision owned by global orchestrator). | -| Produce structured, machine-readable reports. | Modify the physical state of the data lake. | +| Verify microsecond (us) timestamp resolution. | Deduplicate records (delegated to Contract stage). | +| Evaluate temporal chronology using clean Polars syntax. | Perform data type casting. | +| Detect numeric anomalies (negative prices/lags). | Mutate the physical state of the data lake. | +| Produce structured, machine-readable reports. | Halt the pipeline (Decision owned by global orchestrator). | ## **Failure & Severity Model** diff --git a/docs/terraform/gcp-iac.md b/docs/terraform/gcp-iac.md index 3523dd2..5e62211 100644 --- a/docs/terraform/gcp-iac.md +++ b/docs/terraform/gcp-iac.md @@ -9,6 +9,7 @@ The pipeline follows a **Trigger-Action-Archive** flow: 3. **Dispatch:** An Eventarc trigger detects the new file and invokes a Google Workflow (`pipeline-dispatcher`). 4. **Processing:** The Workflow triggers the main `operations-pipeline` Cloud Run job (2 vCPU, 8Gi RAM) for heavy-duty data processing. 5. **Transient Storage:** Intermediate files are stored in the **Pipeline Bucket** with a 7-day TTL on raw data to minimize costs and exposure. +6. **Serving Layer:** The final semantic models are published as **BigQuery External Tables** and presented via stable **Authorized Views** for Power BI and dashboard consumers. ## Prerequisites * **Terraform:** Version `~> 1.5.0` @@ -18,32 +19,47 @@ The pipeline follows a **Trigger-Action-Archive** flow: ## Post-Provisioning (CI/CD Handshake) The integration between GCP and GitHub Actions requires a one-time "Bootstrap" extraction to populate Repository Secrets. This process completes the cryptographic trust relationship established by Workload Identity Federation (WIF). -### 1. Secret Injection Matrix +### Secret Injection Matrix | GitHub Secret | Source / Origin | Purpose | | :--- | :--- | :--- | | `WIF_PROVIDER` | `terraform output -raw GITHUB_WIF_PROVIDER_NAME` | Logical path for the WIF identity provider handshake. | | `DEPLOYER_SA_EMAIL` | `github-actions-deployer@...` | Target identity for GitHub OIDC impersonation. | | `GCP_PROJECT_ID` | `var.project_id` | Project scoping for GCP API and resource discovery. | -### 2. Bootstrapping Constraint +### Bootstrapping Constraint The initial infrastructure provisioning must be executed by a maintainer with `Project IAM Admin` or `Owner` privileges. This "privileged apply" is required to establish the WIF provider and assign the administrative roles to the `github-actions-deployer` service account. Subsequent updates are autonomously managed by the CI/CD identity. ## Infrastructure Components -### 1. Compute & Jobs (`jobs.tf`) +### Compute & Jobs (`jobs.tf`) | Resource Name | Type | Memory | Timeout | Purpose | | :--- | :--- | :--- | :--- | :--- | -| `operations-pipeline` | Cloud Run Job | 8Gi | 30m | Main Polars-based processing engine. | +| `operations-pipeline` | Cloud Run Job | 8Gi | 30m | Main Polars-based processing engine. Includes 10Gi Local SSD mount at `/tmp`. | | `drive-extractor` | Cloud Run Job | 1Gi | 15m | Pulls source data from external APIs. | | `ops-repo` | Artifact Registry | n/a | n/a | Docker repository for pipeline images. | -### 2. Storage & Lifecycle (`storage.tf`) -| Bucket Name | Storage Class | Lifecycle Policy | +### Storage & Lifecycle (`storage.tf`) +| Resource Name | Type | Policy / Details | | :--- | :--- | :--- | -| `ops-archival-storage` | Standard -> Coldline | Move to Coldline after 400 days; Delete after 3 years. | -| `ops-pipeline-storage` | Standard | Delete files with prefix `raw/` after 7 days. | +| `ops-archival-storage` | GCS Bucket | Move to Coldline after 400 days; Delete after 3 years. | +| `ops-pipeline-storage` | GCS Bucket | Delete files with prefix `raw/` after 7 days. | +| `seller_semantic` | BQ Dataset | **Protected:** `prevent_destroy = true`; Logical container for Seller fact/dim views. | +| `customer_semantic` | BQ Dataset | **Protected:** `prevent_destroy = true`; Logical container for Customer fact/dim views. | +| `product_semantic` | BQ Dataset | **Protected:** `prevent_destroy = true`; Logical container for Product fact/dim views. | -### 3. Orchestration (`orchestration.tf`) +## Infrastructure-as-Code Workarounds + +### Cloud Run Local SSD Strategy (Preview) +The `operations-pipeline` utilizes a **Local SSD** mount at `/tmp` (10Gi) **by provisioning manually** to offload memory pressure from Polars streaming joins. +* **The Problem:** As of April 2026, the Google Terraform provider does not natively support the `DISK` medium for `empty_dir` volumes (it defaults to `MEMORY`). +* **The Resolution:** Provision manually and utilize lifecycle `ignore_changes` on the `medium` attribute. This allows the job to be created with the SSD partition enabled via the CLI or UI, while preventing Terraform from "correcting" it back to RAM-based storage during subsequent runs. + +### BigQuery Accidental Deletion Protection +To safeguard analytical history, all semantic datasets are configured with: +* `delete_contents_on_destroy = false`: Ensures data/views remain even if the resource is deleted. +* `prevent_destroy = true`: Forces a manual override to destroy the dataset, protecting it from `terraform destroy` or accidental refactoring. + +### Orchestration (`orchestration.tf`) * **Cloud Scheduler:** `0 0 * * *` (Daily 12AM PHT) triggers the Extractor. * **Eventarc:** Monitors `object.v1.finalized` on the Archival bucket. * **Workflows:** `pipeline-dispatcher` evaluates logic to trigger the main pipeline. @@ -52,7 +68,7 @@ The initial infrastructure provisioning must be executed by a maintainer with `P This project implements **Zero Trust** via Workload Identity Federation and granular Service Account (SA) permissions. -### 1. Identity Registry +### Identity Registry | Identity Name | Role/Purpose | | :--- | :--- | | `github-actions-deployer` | CI/CD automation for infra and code updates. | @@ -61,16 +77,17 @@ This project implements **Zero Trust** via Workload Identity Federation and gran | `eventarc-invoker-sa` | Orchestration identity to receive events and trigger workflows. | | `job-invoker-sa` | Scheduler identity to trigger Cloud Run jobs. | -### 2. Permission Bindings +### Permission Bindings | Identity | Target | Roles | Rationale | | :--- | :--- | :--- | :--- | -| **Github Deployer** | Project | `run.developer`, `workflows.editor`, `cloudscheduler.admin`, `artifactregistry.admin`, `eventarc.admin`, `storage.admin`, `resourcemanager.projectIamAdmin`, `iam.workloadIdentityPoolAdmin`, `monitoring.admin`, `iam.serviceAccountAdmin`, `iam.serviceAccountUser`, `iam.admin` | **Least Privilege:** Granular roles for managing the entire pipeline lifecycle, IAM bindings, and state management. | +| **Github Deployer** | Project | `run.developer`, `workflows.editor`, `cloudscheduler.admin`, `artifactregistry.admin`, `eventarc.admin`, `storage.admin`, `resourcemanager.projectIamAdmin`, `iam.workloadIdentityPoolAdmin`, `monitoring.admin`, `iam.serviceAccountAdmin`, `iam.serviceAccountUser`, `iam.admin`, `logging.configWriter`, `bigquery.admin`| **Least Privilege:** Granular roles for managing the entire pipeline lifecycle, IAM bindings, state management, and BigQuery schemas. | | **Drive Extractor** | Archival/Pipeline Buckets | `roles/storage.objectAdmin` | Full CRUD for data landing and archival. | | **Ops Pipeline** | Pipeline Bucket | `roles/storage.objectAdmin` | Read raw data and write processed artifacts. | +| | Project | `roles/bigquery.dataEditor`, `roles/bigquery.jobUser` | Permission to create External Tables, swap Authorized Views, and execute queries. | | **Event Invoker** | Project | `roles/eventarc.eventReceiver` | Receive GCS notifications. | | | Project | `roles/workflows.invoker` | Permission to start workflow execution. | -### 3. Workload Identity Federation +### Workload Identity Federation * **Pool:** `github-pool` * **Trust Policy:** Restricted to `${var.github_repo}` to prevent unauthorized repository access. @@ -78,10 +95,13 @@ This project implements **Zero Trust** via Workload Identity Federation and gran | Name | Type | Sensitive | Description | | :--- | :--- | :--- | :--- | | `project_id` | `string` | No | Target Google Cloud Project ID. | +| `region` | `string` | No | The Project GCP region. | | `environment` | `string` | No | Deployment environment (dev, prod). | | `github_repo` | `string` | No | Format: `owner/repository`. | +| `bq_dataset_id` | `string` | No | BigQuery dataset containing externalized GCS tables. | | `alert_email_map` | `map` | **Yes** | Monitoring notification recipients. | + ## State Management State is managed remotely in GCS to ensure consistency and locking. ```hcl diff --git a/tests/shared/test_loader_exporter.py b/tests/shared/test_loader_exporter.py index c46af77..b20d6d0 100644 --- a/tests/shared/test_loader_exporter.py +++ b/tests/shared/test_loader_exporter.py @@ -2,43 +2,124 @@ # UNIT TESTS FOR loader_exporter.py # ============================================================================= -import pandas as pd import polars as pl import pytest +from unittest.mock import MagicMock, patch +from datetime import datetime from data_pipeline.shared.loader_exporter import ( + normalize_datetimes, + scan_gcs_uris_from_bigquery, load_single_delta, - load_historical_table, + load_historical_data, + load_assembled_data, export_file, ) - # ------------------------------------------------------------ # FIXTURES (SHARED TEST DATA) # ------------------------------------------------------------ -@pytest.fixture -def sample_pd_df(): - return pd.DataFrame({"a": [1, 2], "b": [3, 4]}) - - @pytest.fixture def sample_pl_df(): return pl.DataFrame({"a": [1, 2], "b": [3, 4]}) +# ------------------------------------------------------------ +# NORMALIZE DATETIMES +# ------------------------------------------------------------ + + +def test_normalize_datetimes(): + # With nanosecond + df = pl.DataFrame({"ts": [datetime(2023, 1, 1)], "val": [1]}).with_columns( + pl.col("ts").dt.cast_time_unit("ns") + ) + + lf = df.lazy() + assert lf.collect_schema()["ts"].time_unit == "ns" # type: ignore + + normalized_lf = normalize_datetimes(lf) + assert normalized_lf.collect_schema()["ts"].time_unit == "us" # type: ignore + + +def test_normalize_datetimes_no_temporal_cols(): + df = pl.DataFrame({"a": [1, 2], "b": ["x", "y"]}) + lf = df.lazy() + normalized_lf = normalize_datetimes(lf) + assert normalized_lf is lf + + +# ------------------------------------------------------------ +# BIGQUERY EXTERNAL TABLE SCAN (Mocked IO) +# ------------------------------------------------------------ + + +def test_scan_gcs_uris_from_bigquery_success(): + project_id = "test-project" + dataset_id = "test_dataset" + table_id = "test_table" + + # Mock BigQuery Client and Result + mock_client = MagicMock() + mock_query_job = MagicMock() + mock_query_job.result.return_value = [ + ["gs://bucket/file1.parquet"], + ["gs://bucket/file2.parquet"], + ] + mock_client.query.return_value = mock_query_job + + with patch("google.cloud.bigquery.Client", return_value=mock_client), patch( + "polars.scan_parquet" + ) as mock_scan_parquet: + + # Mock the Polars LazyFrame returned by scan_parquet + mock_lf = pl.LazyFrame({"a": [1]}) + mock_scan_parquet.return_value = mock_lf + + lf = scan_gcs_uris_from_bigquery(project_id, dataset_id, table_id) + + # Check BigQuery interactions + mock_client.query.assert_called_once() + query_call = mock_client.query.call_args[0][0] + assert "SELECT DISTINCT _FILE_NAME" in query_call + assert f"`{project_id}.{dataset_id}.{table_id}`" in query_call + + # Check Polars interactions + assert mock_scan_parquet.call_count == 2 + mock_scan_parquet.assert_any_call("gs://bucket/file1.parquet") + mock_scan_parquet.assert_any_call("gs://bucket/file2.parquet") + assert lf is not None + + +def test_scan_gcs_uris_from_bigquery_empty_results(): + mock_client = MagicMock() + mock_query_job = MagicMock() + mock_query_job.result.return_value = [] + mock_client.query.return_value = mock_query_job + + with ( + patch("google.cloud.bigquery.Client", return_value=mock_client), + pytest.raises(ValueError, match="No source URIs found"), + ): + scan_gcs_uris_from_bigquery("proj", "ds", "tbl") + + +def test_scan_gcs_uris_from_bigquery_invalid_env(): + with pytest.raises(ValueError, match="Project ID is set to"): + scan_gcs_uris_from_bigquery("PROJECT_ID_NOT_DETECTED", "ds", "tbl") + + # ------------------------------------------------------------ # LOAD SINGLE DELTA # ------------------------------------------------------------ -def test_load_single_delta_success(tmp_path, sample_pd_df): - # Setup: Create multiple files with different dates - # load_single_delta currently uses pandas for loading - sample_pd_df.to_csv(tmp_path / "df_test_2023_01_01.csv", index=False) +def test_load_single_delta_success(tmp_path, sample_pl_df): + sample_pl_df.write_csv(tmp_path / "df_test_2023_01_01.csv") - newer_df = pd.DataFrame({"a": [10], "b": [20]}) - newer_df.to_parquet(tmp_path / "df_test_2023_01_02.parquet", index=False) + newer_df = pl.DataFrame({"a": [10], "b": [20]}) + newer_df.write_parquet(tmp_path / "df_test_2023_01_02.parquet") log_messages = [] @@ -47,10 +128,10 @@ def logger(msg): df, file_name = load_single_delta(tmp_path, "df_test", log_info=logger) - # Should pick the latest one (alphabetically/chronologically sorted) + # Should pick the latest one (chronologically sorted) assert file_name == "df_test_2023_01_02" assert len(df) == 1 - assert df["a"].iloc[0] == 10 + assert df[0, "a"] == 10 assert any("Loaded: df_test_2023_01_02.parquet" in msg for msg in log_messages) @@ -64,8 +145,8 @@ def test_load_single_delta_no_files(tmp_path): # ------------------------------------------------------------ -def test_load_historical_table_success(tmp_path): - # Setup: Create multiple parquet files using Polars for consistency +def test_load_historical_data_success(tmp_path): + df1 = pl.DataFrame({"id": [1], "val": ["a"]}) df2 = pl.DataFrame({"id": [2], "val": ["b"]}) @@ -77,24 +158,41 @@ def test_load_historical_table_success(tmp_path): def logger(msg): log_messages.append(msg) - lf_total = load_historical_table(tmp_path, "table", log_info=logger) + lf_total = load_historical_data(tmp_path, "table", log_info=logger) - # load_historical_table returns a LazyFrame now assert isinstance(lf_total, pl.LazyFrame) df_collected = lf_total.collect() assert df_collected.height == 2 assert set(df_collected["id"].to_list()) == {1, 2} - assert any( - "Scanned: table (2 files queued for lazy evaluation)" in msg - for msg in log_messages - ) -def test_load_historical_table_no_files(tmp_path): +def test_load_historical_data_no_files(tmp_path): with pytest.raises( FileNotFoundError, match="No Parquet files found for table_missing" ): - load_historical_table(tmp_path, "table_missing") + load_historical_data(base_path=tmp_path, table_name="table_missing") + + +# ------------------------------------------------------------ +# LOAD ASSEMBLED DATA +# ------------------------------------------------------------ + + +def test_load_assembled_data_success(tmp_path): + table_name = "assembled_table" + df = pl.DataFrame({"a": [1]}) + df.write_parquet(tmp_path / f"{table_name}_part1.parquet") + + lf = load_assembled_data(tmp_path, table_name) + assert isinstance(lf, pl.LazyFrame) + assert lf.collect().height == 1 + + +def test_load_assembled_data_no_files(tmp_path): + with pytest.raises( + FileNotFoundError, match="No Parquet files found for missing_assembled" + ): + load_assembled_data(tmp_path, "missing_assembled") # ------------------------------------------------------------ @@ -117,7 +215,6 @@ def logger(msg): assert output_path.exists() assert any("Exported file: data.parquet (2 rows)" in msg for msg in log_messages) - # Verify content using Polars read_df = pl.read_parquet(output_path) assert read_df.equals(sample_pl_df) @@ -140,13 +237,12 @@ def logger(msg): for msg in log_messages ) - # Verify content read_df = pl.read_parquet(output_path) assert read_df.equals(sample_pl_df) def test_export_file_unsupported_type(tmp_path, sample_pl_df): - # export_file currently doesn't check extension but rather type of DF. + error_messages = [] def error_logger(msg): diff --git a/tests/test_assembly_stage.py b/tests/test_assembly_stage.py index b690b29..b291cbe 100644 --- a/tests/test_assembly_stage.py +++ b/tests/test_assembly_stage.py @@ -4,6 +4,7 @@ import polars as pl import pytest +from pathlib import Path from data_pipeline.shared.run_context import RunContext from data_pipeline.assembly.assembly_logic import log_info, log_error, init_report from data_pipeline.assembly.assembly_executor import ( @@ -26,7 +27,9 @@ def valid_orders_df(): return pl.DataFrame( { "order_id": ["o1", "o2"], + "order_id_int": [1, 2], "customer_id": ["cos1", "cos2"], + "customer_id_int": [101, 102], "order_status": ["delivered", "delivered"], "order_purchase_timestamp": [ "2023-01-02 09:00:00", @@ -54,7 +57,11 @@ def valid_orders_df(): pl.col("order_delivered_timestamp").str.strptime( pl.Datetime, "%Y-%m-%d %H:%M:%S" ), - pl.col("order_estimated_delivery_date").str.strptime(pl.Date, "%Y-%m-%d"), + pl.col("order_estimated_delivery_date") + .str.strptime(pl.Date, "%Y-%m-%d") + .cast(pl.Datetime), + pl.col("order_id_int").cast(pl.UInt32), + pl.col("customer_id_int").cast(pl.UInt32), ] ) @@ -64,11 +71,20 @@ def valid_order_items_df(): return pl.DataFrame( { "order_id": ["o1", "o2"], + "order_id_int": [1, 2], "product_id": ["prod1", "prod2"], + "product_id_int": [201, 202], "seller_id": ["seller1", "seller2"], + "seller_id_int": [301, 302], "price": [12.3, 45.6], "shipping_charges": [1.23, 4.56], } + ).with_columns( + [ + pl.col("order_id_int").cast(pl.UInt32), + pl.col("product_id_int").cast(pl.UInt32), + pl.col("seller_id_int").cast(pl.UInt32), + ] ) @@ -77,12 +93,13 @@ def valid_payments_df(): return pl.DataFrame( { "order_id": ["o1", "o2"], + "order_id_int": [1, 2], "payment_sequential": [1, 2], "payment_type": ["credit", "cash"], "payment_installments": [4, 5], "payment_value": [100.1, 50.2], } - ) + ).with_columns([pl.col("order_id_int").cast(pl.UInt32)]) @pytest.fixture @@ -90,6 +107,7 @@ def valid_customers_df(): return pl.DataFrame( { "customer_id": ["cos1", "cos2"], + "customer_id_int": [101, 102], "customer_state": ["SP", "RJ"], "customer_city": ["Sao Paulo", "Rio"], "customer_segment": ["A", "B"], @@ -98,6 +116,7 @@ def valid_customers_df(): ).with_columns( [ pl.col("account_creation_date").str.strptime(pl.Datetime, "%Y-%m-%d"), + pl.col("customer_id_int").cast(pl.UInt32), ] ) @@ -107,6 +126,7 @@ def valid_products_df(): return pl.DataFrame( { "product_id": ["prod1", "prod2"], + "product_id_int": [201, 202], "product_category_name": ["tech", "home"], "product_weight_g": [100.0, 500.0], "product_length_cm": [10.0, 20.0], @@ -115,7 +135,7 @@ def valid_products_df(): "product_fragility_index": ["Low", "High"], "supplier_tier": ["Gold", "Silver"], } - ) + ).with_columns([pl.col("product_id_int").cast(pl.UInt32)]) @pytest.fixture @@ -123,10 +143,14 @@ def valid_derived_df(): df = pl.DataFrame( { "order_id": ["o1", "o2"], + "order_id_int": [1, 2], "seller_id": ["seller1", "seller2"], + "seller_id_int": [301, 302], "customer_id": ["cos1", "cos2"], + "customer_id_int": [101, 102], "order_revenue": [100.1, 50.2], "product_id": ["prod1", "prod2"], + "product_id_int": [201, 202], "order_status": ["delivered", "delivered"], "order_purchase_timestamp": [ "2023-01-02 09:00:00", @@ -163,7 +187,11 @@ def valid_derived_df(): pl.Datetime, "%Y-%m-%d %H:%M:%S" ), pl.col("order_estimated_delivery_date").str.strptime(pl.Date, "%Y-%m-%d"), - pl.col("order_date").str.strptime(pl.Date, "%Y-%m-%d"), + pl.col("order_date").str.strptime(pl.Date, "%Y-%m-%d").cast(pl.Datetime), + pl.col("order_id_int").cast(pl.UInt32), + pl.col("customer_id_int").cast(pl.UInt32), + pl.col("product_id_int").cast(pl.UInt32), + pl.col("seller_id_int").cast(pl.UInt32), ] ) return df @@ -213,7 +241,7 @@ def test_merge_data_preserve_grain( result = result.collect() assert result.height == 2 - assert result.select(pl.col("order_id").is_duplicated().any()).item() == False + assert result.select(pl.col("order_id_int").is_duplicated().any()).item() == False assert "order_revenue" in result.columns @@ -226,22 +254,28 @@ def test_merge_data_aggregates_duplicates( [valid_order_items_df, valid_order_items_df.slice(0, 1)] ) - assert duplicated_items_df["order_id"][0] == duplicated_items_df["order_id"][2] + assert ( + duplicated_items_df["order_id_int"][0] == duplicated_items_df["order_id_int"][2] + ) result = merge_data( { "df_orders": valid_orders_df, "df_order_items": duplicated_items_df, "df_payments": pl.DataFrame( - {"order_id": ["o1", "o2"], "payment_value": [10.0, 20.0]} - ), + { + "order_id": ["o1", "o2"], + "order_id_int": [1, 2], + "payment_value": [10.0, 20.0], + } + ).with_columns([pl.col("order_id_int").cast(pl.UInt32)]), } ) if isinstance(result, pl.LazyFrame): result = result.collect() assert result.height == 2 - assert result.select(pl.col("order_id").is_duplicated().any()).item() == False + assert result.select(pl.col("order_id_int").is_duplicated().any()).item() == False # ============================================================================= @@ -277,7 +311,7 @@ def test_freeze_schema_enforces_strict_schema_success(valid_derived_df): def test_freeze_schema_fails_on_missing_column(valid_derived_df): - missing_required_column = valid_derived_df.drop("seller_id") + missing_required_column = valid_derived_df.drop("seller_id_int") with pytest.raises(RuntimeError, match="missing required columns"): result = freeze_schema(missing_required_column) if isinstance(result, pl.LazyFrame): @@ -298,18 +332,20 @@ def test_assemble_data_success( valid_products_df, ): run_id = "20230101T120000" - run_context = RunContext.create(base=tmp_path, run_id=run_id) + run_context = RunContext.create( + base=tmp_path, run_id=run_id, storage=tmp_path / "storage" + ) run_context.initialize_directories() + storage_contracted_path = Path(run_context.storage_contracted_path) + storage_contracted_path.mkdir(parents=True, exist_ok=True) - valid_orders_df.write_parquet(run_context.contracted_path / "df_orders.parquet") + valid_orders_df.write_parquet(storage_contracted_path / "df_orders.parquet") valid_order_items_df.write_parquet( - run_context.contracted_path / "df_order_items.parquet" + storage_contracted_path / "df_order_items.parquet" ) - valid_payments_df.write_parquet(run_context.contracted_path / "df_payments.parquet") - valid_customers_df.write_parquet( - run_context.contracted_path / "df_customers.parquet" - ) - valid_products_df.write_parquet(run_context.contracted_path / "df_products.parquet") + valid_payments_df.write_parquet(storage_contracted_path / "df_payments.parquet") + valid_customers_df.write_parquet(storage_contracted_path / "df_customers.parquet") + valid_products_df.write_parquet(storage_contracted_path / "df_products.parquet") report = assemble_events(run_context) @@ -327,16 +363,20 @@ def test_assemble_data_fails_on_missing_column( valid_payments_df, ): run_id = "20230101T120000" - run_context = RunContext.create(base=tmp_path, run_id=run_id) + run_context = RunContext.create( + base=tmp_path, run_id=run_id, storage=tmp_path / "storage" + ) run_context.initialize_directories() + storage_contracted_path = Path(run_context.storage_contracted_path) + storage_contracted_path.mkdir(parents=True, exist_ok=True) - invalid_order_items_df = valid_order_items_df.drop("seller_id") + invalid_order_items_df = valid_order_items_df.drop("seller_id_int") - valid_orders_df.write_parquet(run_context.contracted_path / "df_orders.parquet") + valid_orders_df.write_parquet(storage_contracted_path / "df_orders.parquet") invalid_order_items_df.write_parquet( - run_context.contracted_path / "df_order_items.parquet" + storage_contracted_path / "df_order_items.parquet" ) - valid_payments_df.write_parquet(run_context.contracted_path / "df_payments.parquet") + valid_payments_df.write_parquet(storage_contracted_path / "df_payments.parquet") report = assemble_events(run_context) @@ -344,7 +384,7 @@ def test_assemble_data_fails_on_missing_column( assert report["assembled_events"]["freeze_schema"] == False assert any( "missing required columns" in error - or 'unable to find column "seller_id"' in error + or 'unable to find column "seller_id_int"' in error for error in report["errors"] ) @@ -356,15 +396,16 @@ def test_assemble_data_fails_on_missing_column( def test_dimension_references_uniqueness(): df = pl.DataFrame({"id": ["1", "1", "2"], "val": ["a", "a", "b"]}) + df_dtypes = {"id": pl.String, "val": pl.String} - result = dimension_references(df.lazy(), ["id"], ["id", "val"]) + result = dimension_references(df.lazy(), ["id"], ["id", "val"], df_dtypes) if isinstance(result, pl.LazyFrame): result = result.collect() assert result.height == 2 df_conflict = pl.DataFrame({"id": ["1", "1"], "val": ["a", "b"]}) - result = dimension_references(df_conflict.lazy(), ["id"], ["id", "val"]) + result = dimension_references(df_conflict.lazy(), ["id"], ["id", "val"], df_dtypes) if isinstance(result, pl.LazyFrame): result = result.collect() assert result.height == 1 diff --git a/tests/test_contract_stage.py b/tests/test_contract_stage.py index ddcb53f..5243972 100644 --- a/tests/test_contract_stage.py +++ b/tests/test_contract_stage.py @@ -2,7 +2,7 @@ # UNIT TESTS FOR contract_logic.py and contract_executor.py # ============================================================================= -import pandas as pd +import polars as pl import pytest from data_pipeline.shared.run_context import RunContext from data_pipeline.contract.contract_executor import apply_contract @@ -15,6 +15,12 @@ enforce_parent_reference, enforce_schema, ) +from data_pipeline.contract.id_registrar import ( + discover_uuids, + lookup_mapping_storage, + generate_and_persist_delta, + extract_entity_mappings, +) # ------------------------------------------------------------ # FIXTURES @@ -23,7 +29,7 @@ @pytest.fixture def sample_orders_df(): - return pd.DataFrame( + return pl.DataFrame( { "order_id": ["o1", "o2", "o3"], "customer_id": ["c1", "c2", "c3"], @@ -50,7 +56,7 @@ def sample_orders_df(): @pytest.fixture def sample_payments_df(): - return pd.DataFrame( + return pl.DataFrame( { "order_id": ["o1", "o2", "o3"], "payment_sequential": [1, 1, 1], @@ -65,14 +71,14 @@ def sample_payments_df(): def test_deduplicate_exact_events(): - df = pd.DataFrame({"a": [1, 1, 2], "b": [2, 2, 3]}) + df = pl.DataFrame({"a": [1, 1, 2], "b": [2, 2, 3]}) filtered, removed = deduplicate_exact_events(df) assert len(filtered) == 2 assert removed == 1 def test_remove_unparsable_timestamps(): - df = pd.DataFrame( + df = pl.DataFrame( { "order_id": ["o1", "o2"], "order_purchase_timestamp": ["2026-01-01 10:00:00", "garbage"], @@ -89,7 +95,7 @@ def test_remove_unparsable_timestamps(): def test_remove_impossible_timestamps(): # Delivered before purchase - df = pd.DataFrame( + df = pl.DataFrame( { "order_id": ["o1"], "order_purchase_timestamp": ["2026-03-25 10:00:00"], @@ -104,29 +110,29 @@ def test_remove_impossible_timestamps(): def test_cascade_drop_by_order_id(): - df = pd.DataFrame({"order_id": ["o1", "o2", "o3"]}) + df = pl.DataFrame({"order_id": ["o1", "o2", "o3"]}) invalid = {"o1", "o3"} filtered, removed = cascade_drop_by_order_id(df, invalid) assert len(filtered) == 1 assert removed == 2 - assert filtered.iloc[0]["order_id"] == "o2" + assert filtered[0, "order_id"] == "o2" def test_enforce_parent_reference(): - df = pd.DataFrame({"order_id": ["o1", "o2", "ghost"]}) + df = pl.DataFrame({"order_id": ["o1", "o2", "ghost"]}) valid = {"o1", "o2"} filtered, removed = enforce_parent_reference(df, valid) assert len(filtered) == 2 assert removed == 1 - assert "ghost" not in filtered["order_id"].values + assert "ghost" not in filtered["order_id"].to_list() def test_remove_rows_with_null_constraint(): - df = pd.DataFrame({"order_id": ["o1", "o2", None, "o4"]}) + df = pl.DataFrame({"order_id": ["o1", "o2", None, "o4"]}) non_nullable = ["order_id"] filtered, removed, invalid_ids = remove_rows_with_null_constraint(df, non_nullable) @@ -137,7 +143,7 @@ def test_remove_rows_with_null_constraint(): def test_enforce_schema(): - df = pd.DataFrame( + df = pl.DataFrame( { "order_id": ["o1", "o2", "o3"], "customer_id": ["c1", "c2", "c3"], @@ -146,14 +152,117 @@ def test_enforce_schema(): } ) req_col = ["order_id", "customer_id", "state"] - dtype = {"order_id": "string", "customer_id": "string", "state": "category"} + dtype = {"order_id": pl.String, "customer_id": pl.String, "state": pl.Categorical} filtered, removed = enforce_schema(df, req_col, dtype) assert len(filtered) == 3 assert removed == 1 - assert isinstance(filtered["order_id"].dtype, pd.StringDtype) - assert isinstance(filtered["state"].dtype, pd.CategoricalDtype) + assert filtered["order_id"].dtype == pl.String + assert filtered["state"].dtype == pl.Categorical + + +# ------------------------------------------------------------ +# ID REGISTRAR UNIT TESTS +# ------------------------------------------------------------ + + +def test_discover_uuids_mixed_formats(tmp_path): + # Setup raw files (CSV and Parquet) + raw_path = tmp_path / "raw" + raw_path.mkdir() + + # Table 1: Parquet + df1 = pl.DataFrame({"order_id": ["o1", "o2"]}) + df1.write_parquet(raw_path / "df_orders_2026_04_01.parquet") + + # Table 2: CSV + df2 = pl.DataFrame({"order_id": ["o2", "o3"]}) + df2.write_csv(raw_path / "df_order_items_2026_04_01.csv") + + tables = ["df_orders", "df_order_items"] + uuids = discover_uuids(raw_path, tables, "order_id") + + assert uuids.len() == 3 + assert set(uuids.to_list()) == {"o1", "o2", "o3"} + + +def test_lookup_mapping_storage_uniqueness(tmp_path): + storage_dir = tmp_path / "storage" / "order_id" + storage_dir.mkdir(parents=True) + + # Create history with a duplicate UUID across different deltas + pl.DataFrame({"order_id": ["o1"], "order_id_int": [1]}).write_parquet( + storage_dir / "d1.parquet" + ) + pl.DataFrame({"order_id": ["o1"], "order_id_int": [1]}).write_parquet( + storage_dir / "d2.parquet" + ) + + storage_glob = str(storage_dir / "*.parquet") + batch_uuids = pl.Series("order_id", ["o1"]) + + known_df, max_id = lookup_mapping_storage(storage_glob, "order_id", batch_uuids) + + assert known_df.height == 1 # Uniqueness check + assert max_id == 1 + + +def test_generate_and_persist_delta(tmp_path): + runtime_dir = tmp_path / "runtime" + missing = pl.Series("order_id", ["o10", "o11"]) + + new_df = generate_and_persist_delta(missing, 5, "order_id", runtime_dir, "run123") + + assert new_df.height == 2 + assert new_df["order_id_int"].to_list() == [6, 7] + assert (runtime_dir / "order_id" / "map_run123.parquet").exists() + + +def test_extract_entity_mappings_orchestration(tmp_path, monkeypatch): + run_context = RunContext.create(base=tmp_path, storage=tmp_path / "storage") + run_context.initialize_directories() + + # Mock all raw data files with required columns to satisfy ID_ENTITY_MAP + raw_path = run_context.raw_snapshot_path + + pl.DataFrame({"order_id": ["o1"], "customer_id": ["c1"]}).write_parquet( + raw_path / "df_orders_2026.parquet" + ) + + pl.DataFrame( + {"order_id": ["o1"], "product_id": ["p1"], "seller_id": ["s1"]} + ).write_parquet(raw_path / "df_order_items_2026.parquet") + + pl.DataFrame({"customer_id": ["c1"]}).write_parquet( + raw_path / "df_customers_2026.parquet" + ) + + pl.DataFrame({"order_id": ["o1"]}).write_parquet( + raw_path / "df_payments_2026.parquet" + ) + + # Mock products just to be safe though not strictly in ID_ENTITY_MAP as a source + pl.DataFrame({"product_id": ["p1"]}).write_parquet( + raw_path / "df_products_2026.parquet" + ) + + # Mock promote to avoid GCS errors in local test + monkeypatch.setattr( + "data_pipeline.contract.id_registrar.promote_new_mapping_files", lambda *_: None + ) + + mappings = extract_entity_mappings(run_context) + + assert "order_id" in mappings + assert "customer_id" in mappings + assert "product_id" in mappings + assert "seller_id" in mappings + + # Verify one result + result = mappings["order_id"].collect() + assert "order_id_int" in result.columns + assert result[0, "order_id_int"] == 1 # ------------------------------------------------------------ @@ -162,66 +271,98 @@ def test_enforce_schema(): def test_apply_contract_orders_success(tmp_path, sample_orders_df): - run_context = RunContext.create(base=tmp_path) + run_context = RunContext.create(base=tmp_path, storage=tmp_path / "storage") run_context.initialize_directories() suffix = "2026_03_25" - sample_orders_df.to_csv( - run_context.raw_snapshot_path / f"df_orders_{suffix}.csv", index=False + sample_orders_df.write_csv( + run_context.raw_snapshot_path / f"df_orders_{suffix}.csv" ) - # New 3-tuple return signature - report, inv_ids, val_ids = apply_contract(run_context, "df_orders") + # Mock Discovery Mappings + master_mappings = { + "order_id": pl.DataFrame( + {"order_id": ["o1", "o2", "o3"], "order_id_int": [1, 2, 3]} + ).lazy(), + "customer_id": pl.DataFrame( + {"customer_id": ["c1", "c2", "c3"], "customer_id_int": [1, 2, 3]} + ).lazy(), + } + + report, inv_ids, val_ids = apply_contract( + run_context, "df_orders", master_mappings=master_mappings + ) assert report["status"] == "success" assert report["final_rows"] == 3 assert len(val_ids) == 3 - assert not inv_ids - assert (run_context.contracted_path / f"df_orders_{suffix}.parquet").exists() + + # Check that integer columns are present + df_result = pl.read_parquet( + run_context.contracted_path / f"df_orders_{suffix}.parquet" + ) + assert "order_id_int" in df_result.columns + assert "customer_id_int" in df_result.columns def test_apply_contract_cascade_and_valid_propagation( tmp_path, sample_orders_df, sample_payments_df ): - run_context = RunContext.create(base=tmp_path) + run_context = RunContext.create(base=tmp_path, storage=tmp_path / "storage") run_context.initialize_directories() - # o1: valid, o2: unparsable, o3: impossible - sample_orders_df.loc[1, "order_purchase_timestamp"] = "garbage" - sample_orders_df.loc[2, "order_delivered_timestamp"] = "2026-01-01 00:00:00" + sample_orders_df = sample_orders_df.with_columns( + pl.when(pl.col("order_id") == "o2") + .then(pl.lit("garbage")) + .otherwise(pl.col("order_purchase_timestamp")) + .alias("order_purchase_timestamp"), + pl.when(pl.col("order_id") == "o3") + .then(pl.lit("2026-01-01 00:00:00")) + .otherwise(pl.col("order_delivered_timestamp")) + .alias("order_delivered_timestamp"), + ) suffix = "2026_03_25" - sample_orders_df.to_csv( - run_context.raw_snapshot_path / f"df_orders_{suffix}.csv", index=False + sample_orders_df.write_csv( + run_context.raw_snapshot_path / f"df_orders_{suffix}.csv" ) - sample_payments_df.to_csv( - run_context.raw_snapshot_path / f"df_payments_{suffix}.csv", index=False + sample_payments_df.write_csv( + run_context.raw_snapshot_path / f"df_payments_{suffix}.csv" ) - # 1. Process Orders - rep_o, inv_o, val_o = apply_contract(run_context, "df_orders") + # Mock Discovery Mappings + master_mappings = { + "order_id": pl.DataFrame( + {"order_id": ["o1", "o2", "o3"], "order_id_int": [1, 2, 3]} + ).lazy(), + "customer_id": pl.DataFrame( + {"customer_id": ["c1", "c2", "c3"], "customer_id_int": [1, 2, 3]} + ).lazy(), + } + + rep_o, inv_o, val_o = apply_contract( + run_context, "df_orders", master_mappings=master_mappings + ) assert "o2" in inv_o # unparsable assert "o3" in inv_o # impossible assert "o1" in val_o # only one valid - # 2. Process Payments (should cascade drop o2, o3 and only keep o1) rep_p, inv_p, val_p = apply_contract( - run_context, "df_payments", invalid_order_ids=inv_o, valid_order_ids=val_o + run_context, + "df_payments", + master_mappings=master_mappings, + invalid_order_ids=inv_o, + valid_order_ids=val_o, ) assert rep_p["removed_cascade_rows"] == 2 # o2 and o3 dropped assert rep_p["final_rows"] == 1 - assert "o1" in set( - pd.read_parquet(run_context.contracted_path / f"df_payments_{suffix}.parquet")[ - "order_id" - ] - ) def test_apply_contract_unknown_table(tmp_path): run_context = RunContext.create(base=tmp_path) run_context.initialize_directories() - report, inv, val = apply_contract(run_context, "non_existent") + report, inv, val = apply_contract(run_context, "non_existent", master_mappings={}) assert report["status"] == "failed" assert "Unknown table" in report["errors"][0] diff --git a/tests/test_publish_stage.py b/tests/test_publish_stage.py index d775f7d..82f5bfd 100644 --- a/tests/test_publish_stage.py +++ b/tests/test_publish_stage.py @@ -5,7 +5,9 @@ import polars as pl import pytest import json +import os from pathlib import Path +from unittest.mock import MagicMock, patch from data_pipeline.shared.run_context import RunContext from data_pipeline.semantic.registry import SEMANTIC_MODULES @@ -17,6 +19,7 @@ run_integrity_gate, promote_semantic_version, activate_published_version, + swap_bigquery_view, ) from data_pipeline.shared.modeling_configs import ( SELLER_FACT_SCHEMA, @@ -100,7 +103,6 @@ def setup_semantic_files(run_context, df_map): for table_name in module["tables"]: df = df_map[table_name] filename = f"{table_name}_{year}_{month}_{day}.parquet" - # df is now pl.DataFrame df.write_parquet(module_path / filename) @@ -135,7 +137,7 @@ def test_run_integrity_gate_fails_on_missing_directory(tmp_path): run_context = RunContext.create( base=tmp_path, storage=tmp_path, run_id="20230101T120000" ) - # Don't initialize directories or setup files + # Force to fail on missing directory report = run_integrity_gate(run_context) assert report["status"] == "failed" assert "Semantic directory is missing" in report["errors"] @@ -150,7 +152,7 @@ def test_run_integrity_gate_fails_on_semantic_file_mismatch( ) run_context.initialize_directories() - # Only setup one module but incomplete + # Force to fail on missing module module_path = run_context.semantic_path / "seller_semantic" module_path.mkdir(parents=True, exist_ok=True) valid_seller_fact.write_parquet( @@ -159,36 +161,7 @@ def test_run_integrity_gate_fails_on_semantic_file_mismatch( report = run_integrity_gate(run_context) assert report["status"] == "failed" - - -def test_run_integrity_gate_fails_on_empty_dataframe( - tmp_path, - valid_seller_fact, - valid_seller_dim, - valid_customer_fact, - valid_customer_dim, - valid_product_fact, - valid_product_dim, -): - run_context = RunContext.create( - base=tmp_path, storage=tmp_path, run_id="20230101T120000" - ) - run_context.initialize_directories() - - df_map = { - "seller_weekly_fact": pl.DataFrame(), # Empty - "seller_dim": valid_seller_dim, - "customer_weekly_fact": valid_customer_fact, - "customer_dim": valid_customer_dim, - "product_weekly_fact": valid_product_fact, - "product_dim": valid_product_dim, - } - - setup_semantic_files(run_context, df_map) - report = run_integrity_gate(run_context) - assert report["status"] == "failed" - # Current implementation fails on missing columns if dataframe is empty - assert any("required column(s)" in error for error in report["errors"]) + assert "Semantic module mismatch" in report["errors"] def test_run_integrity_gate_fails_on_missing_columns( @@ -205,7 +178,7 @@ def test_run_integrity_gate_fails_on_missing_columns( ) run_context.initialize_directories() - # Drop a column using Polars + # Drop a column df_map = { "seller_weekly_fact": valid_seller_fact.drop(valid_seller_fact.columns[0]), "seller_dim": valid_seller_dim, @@ -232,6 +205,7 @@ def test_promote_semantic_version_success(tmp_path): ) run_context.initialize_directories() + # Local promotion uses shutil.copytree run_context.semantic_path.mkdir(parents=True, exist_ok=True) report = promote_semantic_version(run_context) @@ -251,12 +225,51 @@ def test_promote_semantic_version_fails_on_existing_version_directory(tmp_path): assert "Version directory already exists" in report["errors"] +# ------------------------------------------------------------ +# BIGQUERY VIEW SWAP +# ------------------------------------------------------------ + + +def test_swap_bigquery_view_local_skip(tmp_path): + run_context = RunContext.create( + base=tmp_path, storage=tmp_path, run_id="20230101T120000" + ) + report = swap_bigquery_view(run_context) + assert report["status"] == "success" + assert any("Skipping BigQuery swap" in info for info in report["info"]) + + +def test_swap_bigquery_view_gcs_success(): + run_id = "20230101T120000" + storage_path = "gs://test-bucket/pipeline" + run_context = RunContext.create( + base=Path("/tmp"), storage=storage_path, run_id=run_id + ) + + mock_client = MagicMock() + mock_client.project = "test-project" + + with patch("google.cloud.bigquery.Client", return_value=mock_client), patch.dict( + os.environ, {"GCP_REGION": "us-east1"} + ): + report = swap_bigquery_view(run_context) + + assert report["status"] == "success" + # Total 3 modules, each has 2 tables = 6 table DDLs + 6 view DDLs = 12 calls + assert mock_client.query.call_count == 12 + + # Verify one of the DDLs + first_call_ddl = mock_client.query.call_args_list[0][0][0] + assert "CREATE OR REPLACE EXTERNAL TABLE" in first_call_ddl + assert f"v{run_id}" in first_call_ddl + + # ------------------------------------------------------------ # ACTIVATE VERSION # ------------------------------------------------------------ -def test_activate_published_version_success(tmp_path): +def test_activate_published_version_success_local(tmp_path): run_context = RunContext.create( base=tmp_path, storage=tmp_path, run_id="20230101T120000" ) @@ -269,6 +282,29 @@ def test_activate_published_version_success(tmp_path): with open(run_context.latest_pointer_path, "r") as f: data = json.load(f) assert data["run_id"] == "20230101T120000" + assert "published_at" in data + + +def test_activate_published_version_success_gcs(): + storage_path = "gs://test-bucket/pipeline" + run_context = RunContext.create( + base=Path("/tmp"), storage=storage_path, run_id="20230101T120000" + ) + + mock_storage_client = MagicMock() + mock_bucket = MagicMock() + mock_blob = MagicMock() + mock_storage_client.bucket.return_value = mock_bucket + mock_bucket.blob.return_value = mock_blob + + with patch("google.cloud.storage.Client", return_value=mock_storage_client): + report = activate_published_version(run_context) + assert report["status"] == "success" + mock_blob.upload_from_string.assert_called_once() + + call_args = mock_blob.upload_from_string.call_args + payload = json.loads(call_args[0][0]) + assert payload["run_id"] == "20230101T120000" # ------------------------------------------------------------ @@ -276,7 +312,7 @@ def test_activate_published_version_success(tmp_path): # ------------------------------------------------------------ -def test_execute_publish_lifecycle_success( +def test_execute_publish_lifecycle_success_local( tmp_path, valid_seller_fact, valid_seller_dim, @@ -301,6 +337,7 @@ def test_execute_publish_lifecycle_success( setup_semantic_files(run_context, df_map) + # In local mode, swap_bigquery_view skips report = execute_publish_lifecycle(run_context) assert report["status"] == "success" assert Path(run_context.version_path).exists() diff --git a/tests/test_run_pipeline.py b/tests/test_run_pipeline.py index aa07665..cf8464f 100644 --- a/tests/test_run_pipeline.py +++ b/tests/test_run_pipeline.py @@ -185,13 +185,10 @@ def test_main_fails_on_assemble_events(monkeypatch, tmp_path): lambda *a, **k: ({}, set(), set()), ) - # Mocking upload/download contracted directory to avoid real I/O + # Mocking upload/download I/O monkeypatch.setattr( "data_pipeline.run_pipeline.upload_contracted_directory", lambda *_: None ) - monkeypatch.setattr( - "data_pipeline.run_pipeline.download_contracted_datasets", lambda *_: None - ) monkeypatch.setattr( "data_pipeline.run_pipeline.assemble_events", @@ -258,13 +255,10 @@ def test_main_fails_on_build_semantic_layer(monkeypatch, tmp_path): lambda *a, **k: ({}, set(), set()), ) - # Mocking upload/download contracted directory to avoid real I/O + # Mocking upload/download I/O monkeypatch.setattr( "data_pipeline.run_pipeline.upload_contracted_directory", lambda *_: None ) - monkeypatch.setattr( - "data_pipeline.run_pipeline.download_contracted_datasets", lambda *_: None - ) monkeypatch.setattr( "data_pipeline.run_pipeline.assemble_events", @@ -341,13 +335,10 @@ def test_main_fails_on_execute_publish_lifecycle(monkeypatch, tmp_path): lambda *a, **k: ({}, set(), set()), ) - # Mocking upload/download contracted directory to avoid real I/O + # Mocking upload/download I/O monkeypatch.setattr( "data_pipeline.run_pipeline.upload_contracted_directory", lambda *_: None ) - monkeypatch.setattr( - "data_pipeline.run_pipeline.download_contracted_datasets", lambda *_: None - ) monkeypatch.setattr( "data_pipeline.run_pipeline.assemble_events", @@ -434,13 +425,10 @@ def test_main_success(monkeypatch, tmp_path): lambda *a, **k: ({}, set(), set()), ) - # Mocking upload/download contracted directory to avoid real I/O + # Mocking upload/download I/O monkeypatch.setattr( "data_pipeline.run_pipeline.upload_contracted_directory", lambda *_: None ) - monkeypatch.setattr( - "data_pipeline.run_pipeline.download_contracted_datasets", lambda *_: None - ) monkeypatch.setattr( "data_pipeline.run_pipeline.assemble_events", diff --git a/tests/test_semantic_stage.py b/tests/test_semantic_stage.py index 9ba084c..6e93f37 100644 --- a/tests/test_semantic_stage.py +++ b/tests/test_semantic_stage.py @@ -27,6 +27,7 @@ def valid_customers_df(): return pl.DataFrame( { "customer_id": ["cos1", "cos2"], + "customer_id_int": [101, 102], "customer_state": ["SP", "RJ"], "customer_city": ["Sao Paulo", "Rio"], "customer_segment": ["A", "B"], @@ -35,6 +36,7 @@ def valid_customers_df(): ).with_columns( [ pl.col("account_creation_date").str.strptime(pl.Datetime, "%Y-%m-%d"), + pl.col("customer_id_int").cast(pl.UInt32), ] ) @@ -44,6 +46,7 @@ def valid_products_df(): return pl.DataFrame( { "product_id": ["prod1", "prod2"], + "product_id_int": [201, 202], "product_category_name": ["tech", "home"], "product_weight_g": [100.0, 500.0], "product_length_cm": [10.0, 20.0], @@ -52,7 +55,7 @@ def valid_products_df(): "product_fragility_index": ["Low", "High"], "supplier_tier": ["Gold", "Silver"], } - ) + ).with_columns([pl.col("product_id_int").cast(pl.UInt32)]) @pytest.fixture @@ -60,10 +63,14 @@ def valid_assembled_df(): df = pl.DataFrame( { "order_id": ["o1", "o2"], + "order_id_int": [1, 2], "seller_id": ["seller1", "seller2"], + "seller_id_int": [301, 302], "customer_id": ["cos1", "cos2"], + "customer_id_int": [101, 102], "order_revenue": [12.34, 56.78], "product_id": ["prod1", "prod2"], + "product_id_int": [201, 202], "order_status": ["delivered", "delivered"], "order_purchase_timestamp": [ "2023-01-02 09:00:00", @@ -115,6 +122,10 @@ def valid_assembled_df(): pl.col("order_year").cast(pl.Int16), pl.col("order_revenue").cast(pl.Float32), pl.col("run_id").cast(pl.Categorical), + pl.col("order_id_int").cast(pl.UInt32), + pl.col("seller_id_int").cast(pl.UInt32), + pl.col("customer_id_int").cast(pl.UInt32), + pl.col("product_id_int").cast(pl.UInt32), ] ) return df @@ -151,7 +162,7 @@ def test_seller_semantic_model_grain_preserved_success(tmp_path, valid_assembled seller_semantic = build_seller_semantic(valid_assembled_df.lazy(), run_context) expected_fact_len = ( - valid_assembled_df.select(["seller_id", "order_year_week"]).unique().height + valid_assembled_df.select(["seller_id_int", "order_year_week"]).unique().height ) fact_df = seller_semantic["seller_weekly_fact"] @@ -162,7 +173,7 @@ def test_seller_semantic_model_grain_preserved_success(tmp_path, valid_assembled dim_df = seller_semantic["seller_dim"] if isinstance(dim_df, pl.LazyFrame): dim_df = dim_df.collect() - expected_dim_len = valid_assembled_df["seller_id"].n_unique() + expected_dim_len = valid_assembled_df["seller_id_int"].n_unique() assert dim_df.height == expected_dim_len diff --git a/tests/test_validation_stage.py b/tests/test_validation_stage.py index fae8bcb..b7c4dd6 100644 --- a/tests/test_validation_stage.py +++ b/tests/test_validation_stage.py @@ -2,7 +2,7 @@ # UNIT TESTS FOR validation_logic.py and validation_executor.py # ============================================================================= -import pandas as pd +import polars as pl import pytest from data_pipeline.shared.run_context import RunContext from data_pipeline.validation.validation_executor import apply_validation @@ -29,7 +29,7 @@ def empty_report(): @pytest.fixture def valid_orders_df(): - return pd.DataFrame( + return pl.DataFrame( { "order_id": ["o1", "o2"], "customer_id": ["c1", "c2"], @@ -44,7 +44,7 @@ def valid_orders_df(): @pytest.fixture def valid_order_items_df(): - return pd.DataFrame( + return pl.DataFrame( { "order_id": ["o1", "o2"], "product_id": ["p1", "p2"], @@ -56,7 +56,7 @@ def valid_order_items_df(): @pytest.fixture def valid_payments_df(): - return pd.DataFrame( + return pl.DataFrame( { "order_id": ["o1", "o2"], "payment_sequential": [1, 1], @@ -67,7 +67,7 @@ def valid_payments_df(): @pytest.fixture def valid_customers_df(): - return pd.DataFrame( + return pl.DataFrame( { "customer_id": ["c1", "c2"], "customer_state": ["SP", "RJ"], @@ -80,7 +80,7 @@ def valid_customers_df(): @pytest.fixture def valid_products_df(): - return pd.DataFrame( + return pl.DataFrame( { "product_id": ["p1", "p2"], "product_category_name": ["tech", "home"], @@ -136,14 +136,14 @@ def test_run_base_validations_success(valid_customers_df, empty_report): def test_run_base_validations_empty_df(empty_report): - df = pd.DataFrame() + df = pl.DataFrame() ok = run_base_validations(df, "test", ["id"], ["id"], ["id"], empty_report) assert ok is False assert any("dataset is empty" in e for e in empty_report["errors"]) def test_run_base_validations_missing_column(valid_customers_df, empty_report): - df = valid_customers_df.drop(columns=["customer_state"]) + df = valid_customers_df.drop(["customer_state"]) ok = run_base_validations( df, "df_customers", @@ -157,21 +157,21 @@ def test_run_base_validations_missing_column(valid_customers_df, empty_report): def test_run_base_validations_duplicate_pk(empty_report): - df = pd.DataFrame({"id": ["1", "1"], "val": ["a", "b"]}) + df = pl.DataFrame({"id": ["1", "1"], "val": ["a", "b"]}) ok = run_base_validations(df, "test", ["id"], ["id", "val"], ["id"], empty_report) assert ok is False assert any("conflicting duplicate primary key" in e for e in empty_report["errors"]) def test_run_base_validations_repairable_duplicate(empty_report): - df = pd.DataFrame({"id": ["1", "1"], "val": ["a", "a"]}) + df = pl.DataFrame({"id": ["1", "1"], "val": ["a", "a"]}) ok = run_base_validations(df, "test", ["id"], ["id", "val"], ["id"], empty_report) assert ok is True assert any("eligible for deduplication" in w for w in empty_report["warnings"]) def test_run_base_validations_null_pk(empty_report): - df = pd.DataFrame({"id": [None, "2"], "val": ["a", "b"]}) + df = pl.DataFrame({"id": [None, "2"], "val": ["a", "b"]}) ok = run_base_validations(df, "test", ["id"], ["id", "val"], [], empty_report) assert ok is True assert any( @@ -179,13 +179,6 @@ def test_run_base_validations_null_pk(empty_report): ) -def test_run_base_validations_duplicate_columns(empty_report): - df = pd.DataFrame([[1, 2]], columns=["id", "id"]) - ok = run_base_validations(df, "test", ["id"], ["id"], [], empty_report) - assert ok is True - assert any("duplicate column names detected" in w for w in empty_report["warnings"]) - - # ============================================================================= # EVENT FACT VALIDATION TESTS # ============================================================================= @@ -199,14 +192,24 @@ def test_run_event_fact_validations_success(valid_orders_df, empty_report): def test_run_event_fact_validations_temporal_error(valid_orders_df, empty_report): # Approval before purchase - valid_orders_df.loc[0, "order_approved_at"] = "2026-03-24 10:00:00" + valid_orders_df = valid_orders_df.with_columns( + pl.when(pl.col("order_id") == "o1") + .then(pl.lit("2026-03-24 10:00:00")) + .otherwise(pl.col("order_approved_at")) + .alias("order_approved_at") + ) ok = run_event_fact_validations(valid_orders_df, "df_orders", empty_report) assert ok is True assert any("approval precedes purchase" in w for w in empty_report["warnings"]) def test_run_event_fact_validations_unparsable_ts(valid_orders_df, empty_report): - valid_orders_df.loc[0, "order_purchase_timestamp"] = "garbage" + valid_orders_df = valid_orders_df.with_columns( + pl.when(pl.col("order_id") == "o1") + .then(pl.lit("garbage")) + .otherwise(pl.col("order_purchase_timestamp")) + .alias("order_purchase_timestamp") + ) ok = run_event_fact_validations(valid_orders_df, "df_orders", empty_report) assert ok is True assert any("unparsable timestamp values" in w for w in empty_report["warnings"]) @@ -218,7 +221,7 @@ def test_run_event_fact_validations_unparsable_ts(valid_orders_df, empty_report) def test_run_transaction_detail_validations_negative(empty_report): - df = pd.DataFrame({"order_id": ["o1"], "price": [-10.0]}) + df = pl.DataFrame({"order_id": ["o1"], "price": [-10.0]}) ok = run_transaction_detail_validations(df, "test", empty_report) assert ok is True assert any("negative values in numeric column" in e for e in empty_report["errors"]) @@ -230,9 +233,9 @@ def test_run_transaction_detail_validations_negative(empty_report): def test_run_cross_table_validations_orphans(empty_report): - orders = pd.DataFrame({"order_id": ["o1"]}) - items = pd.DataFrame({"order_id": ["o1", "o2"]}) # o2 is orphan - payments = pd.DataFrame({"order_id": ["o3"]}) # o3 is orphan + orders = pl.DataFrame({"order_id": ["o1"]}) + items = pl.DataFrame({"order_id": ["o1", "o2"]}) # o2 is orphan + payments = pl.DataFrame({"order_id": ["o3"]}) # o3 is orphan tables = {"df_orders": orders, "df_order_items": items, "df_payments": payments} ok = run_cross_table_validations(tables, empty_report) @@ -258,20 +261,18 @@ def test_apply_validation_integration( # Create date-suffixed files for loader suffix = "2026_03_25" - valid_orders_df.to_csv( - run_context.raw_snapshot_path / f"df_orders_{suffix}.csv", index=False - ) - valid_order_items_df.to_csv( - run_context.raw_snapshot_path / f"df_order_items_{suffix}.csv", index=False + valid_orders_df.write_csv(run_context.raw_snapshot_path / f"df_orders_{suffix}.csv") + valid_order_items_df.write_csv( + run_context.raw_snapshot_path / f"df_order_items_{suffix}.csv" ) - valid_payments_df.to_csv( - run_context.raw_snapshot_path / f"df_payments_{suffix}.csv", index=False + valid_payments_df.write_csv( + run_context.raw_snapshot_path / f"df_payments_{suffix}.csv" ) - valid_customers_df.to_csv( - run_context.raw_snapshot_path / f"df_customers_{suffix}.csv", index=False + valid_customers_df.write_csv( + run_context.raw_snapshot_path / f"df_customers_{suffix}.csv" ) - valid_products_df.to_csv( - run_context.raw_snapshot_path / f"df_products_{suffix}.csv", index=False + valid_products_df.write_csv( + run_context.raw_snapshot_path / f"df_products_{suffix}.csv" ) report = apply_validation(run_context)