Data Partitioning (Streaming)

Data Partitioning in the context of distributed stream processing refers to the strategy used to divide a data stream into multiple substreams (partitions) and distribute these partitions across the available parallel instances (tasks or sub-tasks) of downstream operators within a Dataflow Graph.

The primary goal of partitioning in streaming is scalability. By processing partitions in parallel across multiple workers (CPU cores or nodes), the system can handle much higher data volumes than a single instance could manage. Correct partitioning is also essential for the correctness of stateful operations.

The Need for Partitioning in Streaming

Processing unbounded data streams often requires handling high throughput and performing stateful computations (like joins or aggregations). A single processing instance quickly becomes a bottleneck. Partitioning addresses this by:

1. Load Balancing: Distributing the processing load evenly across available parallel operator instances.
2. Scalability: Allowing the system to scale horizontally by adding more parallel instances to handle increased data volume or computation complexity.
3. Stateful Correctness: Ensuring that all data records relevant to a particular state entity (e.g., all events for a specific 'user_id' in an aggregation) are processed by the same operator instance. This is crucial for maintaining consistent state.

How Streaming Partitioning Works

1. Partitioning Key Selection: A field (or combination of fields) within the data records is chosen as the partitioning key (e.g., 'user_id', 'device_id', 'order_id'). The choice of key significantly impacts load distribution and state locality.
2. Partitioning Strategy/Function: A function is applied to the partitioning key of each incoming record to determine which downstream parallel instance it should be routed to. Common strategies include:
   • Hash Partitioning (Keyed Streams): A hash function is applied to the key, and the result (modulo the number of downstream instances) determines the target partition/instance. This ensures records with the same key always go to the same instance, essential for keyed state operations (like 'GROUP BY user_id'). This is the most common strategy for stateful operations.
   • Round-Robin: Records are distributed evenly across downstream instances in a circular fashion. Useful for stateless operations where key locality doesn't matter, ensuring even load distribution.
   • Broadcast: Each record is sent to all downstream instances. Useful when every parallel instance needs to see all the data (e.g., joining a stream against a small, broadcasted table).
   • Global: All records are sent to a single downstream instance (parallelism = 1). Necessary for certain final aggregation steps but creates a bottleneck.
   • Custom Partitioning: Allows defining user-specific logic for routing records.
3. Data Shuffling: The stream processing framework physically shuffles (transfers) the data records across the network between the upstream operator instances and the appropriate downstream parallel instances based on the partitioning decision.

Partitioning and Stateful Operations

For stateful operations like 'GROUP BY' aggregations or joins based on a key, hash partitioning (or key-based partitioning) is critical:

• Aggregation: To calculate 'SUM(amount) GROUP BY user_id', all records for a specific 'user_id' must be processed by the same aggregation instance to maintain the correct sum for that user. Hash partitioning on 'user_id' ensures this data locality.
• Join: To join two streams on 'order_id', all records from both streams with the same 'order_id' must arrive at the same join operator instance so they can be matched correctly. Hash partitioning both input streams on 'order_id' achieves this.

Incorrect partitioning for stateful operations leads to incorrect results.

Key Considerations

• Key Skew: If certain key values appear much more frequently than others (data skew), hash partitioning can lead to uneven load distribution, with some instances becoming bottlenecks. Advanced techniques might be needed to handle severe skew.
• Parallelism: The number of partitions often corresponds to the degree of parallelism configured for the downstream operator.
• Network Shuffle: Partitioning (except for local forwarding within the same task) involves network data transfer, which introduces latency and consumes bandwidth.

Data Partitioning in RisingWave

RisingWave automatically handles data partitioning as part of executing distributed Streaming SQL queries:

• Automatic Partitioning: When a stateful operation requiring key-based processing is defined in SQL (e.g., 'GROUP BY', joins on specific keys, 'DISTINCT ON'), RisingWave's query planner inserts necessary data shuffling (exchange) steps into the Dataflow Graph using appropriate hash partitioning on the relevant keys.
• Parallelism: RisingWave executes operators in parallel across its Compute Nodes. Data is automatically shuffled between these parallel instances based on the query plan's partitioning strategy.
• State Locality: The partitioning ensures that state for specific keys resides on the compute instance responsible for processing those keys, enabling efficient stateful computation.
• Configuration: While largely automatic based on SQL semantics, advanced configurations might allow influencing parallelism hints, but the core partitioning strategy is determined by the query logic (especially join keys and grouping keys).

This automatic handling of partitioning and shuffling allows users to write declarative SQL queries without needing to manually manage the complexities of distributed data routing for stateful operations.

Related Glossary Terms

• Dataflow Graph
• Parallelism
• Scalability (Compute & Storage)
• Stateful Stream Processing
• Stateless Stream Processing
• Join (Streaming)
• Streaming Aggregation
• Data Skew (Concept)
• Network Shuffle (Concept)