Streaming Latency

Streaming Latency refers to the end-to-end delay experienced by a data event as it travels through a stream processing system. It measures the time taken from the moment an event is generated by a source system (or ingested into the streaming platform) until the moment its processed output (e.g., an updated metric, a triggered alert, a record in a sink system) is available and visible to a downstream consumer or application.

Minimizing latency is a critical goal for most real-time stream processing applications, as it directly impacts the freshness of insights and the responsiveness of systems relying on the processed data.

Components of Latency

Streaming latency is an aggregate of several individual delays encountered at different stages of the pipeline:

Ingestion Latency: The time taken for data to be ingested from the source system (e.g., database, message queue, IoT device) into the stream processing engine. This can be affected by network delays, source system performance, and the ingestion mechanism itself.
Processing Latency: The time the stream processing engine takes to execute the defined logic (transformations, aggregations, joins, windowing operations) on the incoming data. This depends on the complexity of the computation, the efficiency of the processing engine, and the available resources.
Network Latency: Delays incurred as data moves between different components of the distributed streaming system (e.g., between brokers, workers, state stores, and sinks).
State Access Latency: If the processing is stateful (e.g., involving aggregations or joins over time), the time taken to read from and write to the state store can contribute to latency.
Sink Latency / Commit Latency: The time taken to write the processed results to the downstream sink system (e.g., database, data warehouse, alerting system, another message queue) and for those results to become queryable or actionable.
Buffering/Batching Delays: Many systems introduce micro-batching or buffering at various stages (e.g., at sources, within the engine, at sinks) to improve throughput or efficiency, which can add to latency.
Watermark Propagation Delay (for Event Time processing): In systems using event time, the delay in watermark progression can impact when results for certain time windows are finalized and emitted, contributing to perceived latency for event-time based results.

Types of Latency

End-to-End Latency: The total delay, as defined above. This is the most comprehensive measure from a user's perspective.
System Latency (or Processing Latency): Often refers more narrowly to the time spent within the stream processing engine itself, excluding source ingestion and sink commit times.

Factors Influencing Streaming Latency

Complexity of Processing Logic: More complex calculations, joins, or stateful operations naturally take longer.
Data Volume and Velocity: Higher throughput can strain system resources and increase queueing delays if not scaled appropriately.
System Architecture & Resources: The design of the streaming platform, its scalability, and the provisioned compute, memory, and network resources are crucial.
Network Conditions: Unreliable or high-latency networks can significantly impact performance.
Choice of Technologies: Different streaming engines, message queues, and state stores have varying performance characteristics.
Configuration Parameters: Tuning options like batch sizes, parallelism, and checkpointing intervals can affect latency.
Fault Tolerance Mechanisms: Checkpointing and replication, while essential for reliability, can introduce some latency overhead.

Measuring and Monitoring Latency

End-to-End Tracking: Injecting timestamps at the source and comparing them with timestamps when results are available at the sink.
System Metrics: Most stream processing platforms (like RisingWave) expose internal metrics on processing delays, operator execution times, and buffer occupancies.
Percentiles (e.g., p95, p99): Measuring average latency can be misleading; percentiles provide a better understanding of the user experience for the majority of events.

Latency vs. Throughput

Latency and throughput are often competing goals. Optimizing solely for minimal latency might involve processing each event individually, which can reduce throughput. Conversely, optimizing for maximum throughput often involves batching or buffering, which can increase latency. A common challenge is to find the right balance for the specific application requirements.

RisingWave is designed to achieve low latency by processing data incrementally and efficiently managing state for continuous computations.