A large scale distributed system is a system consisting of multiple interconnected computers that communicate and coordinate their actions to achieve a common goal. These systems are used to solve complex problems that require a high degree of processing power, storage, and bandwidth. In this note, we will discuss some important concepts and techniques used in large scale distributed systems.
Scalability is the ability of a system to handle increasing amounts of work without negatively impacting performance. In a large scale distributed system, scalability is achieved by adding more resources (e.g., servers, storage, bandwidth) to the system as the workload increases. Horizontal scaling, also known as scaling out, involves adding more servers to the system to handle increased demand. Vertical scaling, also known as scaling up, involves adding more resources to a single server to handle increased demand.
Fault tolerance is the ability of a system to continue functioning in the event of a failure or fault. In a large scale distributed system, fault tolerance is achieved by replicating data and services across multiple servers. This way, if one server fails, another server can take over the failed server's workload. Replication can be done at various levels, such as data replication, service replication, and server replication.
Consistency is the property of a system in which all nodes in the system see the same data at the same time. In a large scale distributed system, maintaining consistency is challenging due to network delays, node failures, and other factors. To achieve consistency, distributed systems use techniques such as two-phase commit, consensus algorithms like Paxos and Raft, and distributed locking mechanisms.
Load balancing is the process of distributing workloads across multiple servers to ensure that no server is overwhelmed while others are idle. In a large scale distributed system, load balancing is essential to ensure that the system can handle increased traffic and workloads. Load balancing can be achieved through various techniques, such as round-robin, weighted round-robin, and IP hash.
Message passing is the process of exchanging information between nodes in a distributed system. In a large scale distributed system, message passing is essential for coordination and communication between nodes. Message passing can be done through various protocols, such as HTTP, TCP, and UDP.
MapReduce is a programming model used for processing large datasets in a distributed system. In MapReduce, the input data is divided into smaller chunks, which are processed in parallel across multiple servers. The results are then combined to produce the final output. MapReduce is used in various big data processing systems such as Hadoop and Spark.
Large scale distributed systems are essential for solving complex problems that require high processing power, storage, and bandwidth. Scalability, fault tolerance, consistency, load balancing, message passing, and MapReduce are some important concepts and techniques used in large scale distributed systems. Understanding these concepts is essential for designing and building robust and scalable distributed systems.