Stream Processing

Stream Processing (also known as Event Stream Processing or ESP) is a data processing paradigm that deals with continuous, unbounded sequences of data, often called "data streams" or "event streams." Unlike traditional batch processing, which collects and processes data in large, static groups (batches) after a certain period, stream processing analyzes and acts upon data as it arrives, typically with very low latency (milliseconds to seconds).

The core idea is to process data "in motion" rather than "at rest."

Key Concepts

• Data Stream / Event Stream: An ordered, immutable, and potentially infinite sequence of data records or events. Examples include sensor readings, user clickstreams, financial transactions, log messages, social media feeds, and database change events (CDC).
• Continuous Processing: Queries or analytical models run continuously, evaluating new data as it arrives and updating results dynamically.
• Low Latency: A primary goal is to minimize the time between an event occurring, it being processed, and an insight or action being generated.
• Real-time or Near Real-time: Stream processing enables systems to react to events and provide insights almost instantaneously.
• Windowing: Since streams are unbounded, operations like aggregations are often performed over "windows" of data, which can be based on time (e.g., the last 5 minutes) or other criteria (e.g., a session of user activity).
• Stateful vs. Stateless Operations:
  • Stateless: Operations that process each event independently (e.g., filtering, simple transformations).
  • Stateful: Operations that require maintaining context or information from previous events (e.g., aggregations, joins, pattern detection). Managing state reliably and efficiently is a key challenge.

Stream Processing vs. Batch Processing

Common Use Cases

• Real-time Analytics: Dashboards, business intelligence, operational monitoring.
• Event-Driven Applications: Microservices reacting to events, real-time personalization.
• Fraud Detection & Security: Identifying suspicious patterns in financial transactions or network activity.
• IoT Data Processing: Analyzing data from sensors and connected devices for monitoring and control.
• Log Analysis: Processing application and system logs for monitoring and alerting.
• Personalization & Recommendation: Updating user profiles and recommendations in real-time based on activity.
• Complex Event Processing (CEP): Detecting patterns and relationships among multiple events to identify significant occurrences.

Stream Processing Engines

Specialized systems called stream processing engines (SPEs) or streaming databases are designed to handle the unique challenges of stream processing. Examples include:

• RisingWave: A distributed SQL streaming database designed for ease of use, performance, and stateful processing via materialized views.
• Apache Flink
• Apache Kafka Streams
• Apache Spark Streaming (micro-batching)
• Google Cloud Dataflow
• Amazon Kinesis Data Analytics

RisingWave, as a streaming database, focuses on making stream processing accessible via SQL and providing efficient incremental computation for low-latency, fresh results.