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- Added InputConfig and FileInputConfig classes for handling file inputs. - Introduced ZMQInputConfig for ZMQ address configuration. - Refactored output configurations to inherit from FileOutput. - Added ZMQOutput class for handling streaming outputs to ZMQ addresses. - Updated the DarshanAnalyzer to use new input handling methods. - Enhanced DFTracerAnalyzer to support reading from ZMQ streams. - Added utility functions for handling pandas DataFrames and streaming. - Introduced new constants for file and host hashes. - Updated meson.build to include new utility files for pandas and streaming.
- Updated CI configuration to include 'streaming' in pip install dependencies. - Refactored DFAnalyzer to remove unused imports and improve code clarity. - Enhanced the epoch_window_via_dict class in streaming.py to better handle epoch events and added logging for error handling. - Added new tests for epoch window functionality, ensuring proper buffering and emission of events based on epoch.start and epoch.end signals. - Created new end-to-end tests for ZMQ analysis pipeline, validating the integration of streaming with the analyzer. - Removed outdated test_streaming.py and consolidated tests into more relevant files. - Added a tar.gz file containing real trace data for testing purposes.
…ailable for testing
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #29 +/- ##
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+ Coverage 57.48% 70.87% +13.39%
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Files 26 27 +1
Lines 2164 2414 +250
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+ Hits 1244 1711 +467
+ Misses 920 703 -217 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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… in set_unique_counts
- Updated `test_analyzer_dftracer_read_zmq` to use `dftracer_ai_logging_posix_events` instead of `epoch_posix_events`. - Modified `_test_e2e` in `test_e2e.py` to conditionally add `analyzer.assign_epochs=True` based on the analyzer and preset. - Changed `test_e2e_zmq` to utilize `dftracer_ai_logging_posix_events` for event data. - Introduced a new test file `test_metrics.py` with comprehensive tests for `set_main_metrics`, `set_view_metrics`, and `set_cross_layer_metrics` functions, ensuring proper handling of edge cases and metrics calculations.
…r time, count, and size
…ring "sum" for consistency
…lly for improved clarity and performance
…actions and ensure proper type casting
- Add _detect_fabric_protocol() to detect CXI fabric on Cray EX systems - Check for /dev/cxi* and /dev/hfi* devices for Slingshot interconnect - Fall back to TCP for local development - Support environment variable overrides for testing
Replace streamz-based streaming with a simpler callback architecture using direct ZMQ sockets. This removes the streamz dependency and provides more control over the streaming lifecycle. - Add zmq_io module with open_consumer/open_producer functions - Refactor analyze_zmq to use output_handler callback instead of Stream - Extract common streaming logic into _analyze_stream method - Update ZMQOutput to send results as multipart messages with parquet - Deprecate read_zmq and postread_zmq methods - Update tests to use new callback-based approach - Remove streamz-based streaming tests
…erformance insights Add a new analysis facts framework that evaluates trace data against configurable rules to produce structured performance findings. The implementation includes: - FactEngine and FactRule classes for rule-based fact evaluation - FactsConfig for enabling/disabling fact generation with configurable options - New dataclasses: AnalysisFact, FactWindow, FactScope, FactSeverity, FactProvenance, FactEnvelope for structured fact representation - DLIO-specific fact rules (dlio.yaml) for detecting common performance patterns (fetch_pressure, compute_dominance, epoch_straggler, rank_imbalance, etc.) - JSON schema for fact envelope validation - Integration with Analyzer to evaluate facts from flat views - Fact envelope output support in ZMQ and Mofka streaming outputs - Conditional debug output for metric_boundaries and flat_views The facts system allows users to define custom rules that evaluate trace metrics and emit structured findings with severity scores, confidence levels, and opportunity tags.
…mprovements - Add --num-ranks parameter to wait for all ranks' epoch.block events before triggering analysis (ensures 1 analysis per epoch, not per rank) - Add SIGTERM-based graceful shutdown with timestamp-bounded trace drain - Add drain summary logging (cat_counts, top event names per epoch) - Fix fact engine NA handling: use numpy fmax/fmin for nullable pd.NA values in derived metric max()/min() expressions - Add facts.evaluate.done info log and analysis_facts count to analysis_complete log for pipeline observability - Publish analysis_facts as JSON envelope to Mofka output topic - Update streaming tests for multi-rank and fact engine scenarios
…ming This change replaces the EpochBuffer implementation with a WindowBuffer that provides more flexible window boundary tracking across multiple ranks. The new implementation: - Uses WindowBoundaryTracker to handle overlapping windows and out-of-order boundary events from different ranks - Maintains backward compatibility by preserving the "epoch" field in events - Adds "window" field for new window-based analysis - Improves test coverage for window boundary tracking logic - Updates fact engine to support layer-scoped facts with fillna0 support The change enables more sophisticated streaming analysis patterns while maintaining compatibility with existing epoch-based workflows.
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