Parallelism

Parallelism in the context of computing, and specifically in stream processing systems like RisingWave, refers to the ability of the system to execute multiple tasks or parts of a single task concurrently. This is a fundamental technique for achieving high performance, scalability, and efficient resource utilization, especially when dealing with large volumes of data or computationally intensive workloads.

Core Concepts

Concurrency vs. Parallelism:
- Concurrency is about dealing with many things at once (managing multiple tasks, potentially by interleaving their execution on a single CPU core).
- Parallelism is about doing many things at once (simultaneously executing multiple tasks or parts of tasks, typically using multiple CPU cores or multiple machines). Parallelism is a way to achieve concurrency.
Task Parallelism: Different independent tasks are executed simultaneously.
Data Parallelism: The same task is executed simultaneously on different subsets of a larger dataset. This is highly relevant in stream processing.

Parallelism in Stream Processing

Stream processing systems leverage parallelism extensively to handle continuous, high-velocity data streams:

Pipeline Parallelism (Inter-Operator Parallelism):
- Different stages (operators) of a streaming pipeline (dataflow graph) run concurrently. For example, a source operator can be ingesting new data while a subsequent filter operator processes previously ingested data, and a further downstream aggregation operator updates its results.
- Data flows between these concurrent operators, often through intermediate buffers or queues.
Data Parallelism (Intra-Operator Parallelism):
- A single operator (e.g., filter, map, aggregate) can be parallelized by creating multiple instances of that operator, each processing a distinct partition of the incoming data stream.
- Partitioning: Input data streams are divided (partitioned) based on a key (e.g., user ID, sensor ID). Each parallel instance of an operator processes one or more partitions. This ensures that related data (e.g., all events for a specific user) are processed by the same operator instance, which is crucial for stateful operations like aggregations or joins.
- The number of parallel instances for an operator is often referred to as its degree of parallelism or parallelism factor.
Distributed Execution:
- Parallelism is often achieved by distributing the processing across multiple machines (nodes) in a cluster. Each node can run one or more parallel operator instances.
- Stream processing engines manage the deployment, communication, and coordination of these distributed parallel tasks.

Benefits of Parallelism in Streaming

Scalability: Allows the system to handle increasing data volumes and processing loads by adding more resources (CPU cores, nodes).
High Throughput: Enables processing a larger number of events per unit of time.
Low Latency: Can reduce processing latency by dividing work and processing it simultaneously, though network communication in distributed systems can also introduce latency.
Efficient Resource Utilization: Makes better use of multi-core processors and distributed cluster environments.

Parallelism in RisingWave

RisingWave is architected to exploit parallelism for high performance and scalability:

Dataflow Graph Execution: SQL queries are compiled into a dataflow graph of streaming operators.
Automatic Parallelization: RisingWave attempts to parallelize these operators automatically. The degree of parallelism for different parts of the dataflow can be influenced by cluster configuration and the nature of the query.
Compute Nodes: RisingWave's Compute Nodes are responsible for executing the parallel tasks of the dataflow graph. A RisingWave cluster can scale out by adding more Compute Nodes.
Actors Model: Internally, RisingWave often uses an actor-based model, where individual operators or parallel instances of operators can be implemented as lightweight, concurrent actors.
Data Shuffling/Exchange: When data needs to be repartitioned between parallel operator instances (e.g., for a GROUP BY operation on a different key), RisingWave manages the data shuffling across the network efficiently.

Considerations

Partitioning Skew: If data is not partitioned evenly, some parallel instances may become bottlenecks, limiting overall performance.
State Management: In stateful stream processing, managing distributed state across parallel operator instances requires careful design (e.g., consistent hashing, state replication, or distributed state stores). RisingWave's Hummock state store is designed for this.
Communication Overhead: In distributed parallelism, the cost of network communication between tasks can become significant.
Complexity: Designing, debugging, and optimizing parallel streaming applications can be more complex than sequential ones.