Understanding Flink’s key concepts

 

Understanding Flink’s key concepts:

• DataStream API: Flink's main tool for creating stream processing applications, providing operations to transform data streams.
• Windows: Defines a finite set of stream events for computations, based on count, time, or sessions.
• Transformations: Operations applied to data streams to produce new streams, including map, filter, flatMap, keyBy, reduce, aggregate, and window.
• Sinks: The endpoints of Flink applications where processed data ends up, such as a file, database, or message queue.
• Sources: The starting points of Flink applications that ingest data from external systems or generate data internally, such as a file or Kafka topic.
• Event Time vs. Processing Time: Flink supports different notions of time in stream processing. Event time is the time when an event occurred, while processing time is the time when the event is processed by the system. Flink excels at event time processing, which is crucial for correct results in many scenarios.
• CEP (Complex Event Processing): Flink supports CEP, which is the ability to detect patterns and complex conditions across multiple streams of events.
• Table API & SQL: Flink offers a Table API and SQL interface for batch and stream processing. This allows users to write complex data processing applications using a SQL-like expression language.
• Stateful Functions (StateFun): StateFun is a framework by Apache Flink designed to build distributed, stateful applications. It provides a way to define, manage, and interact with a dynamically evolving distributed state of functions.
• Operator Chain and Task: Flink operators (transformations) can be chained together into a task for efficient execution. This reduces the overhead of thread-to-thread handover and buffering.
• Savepoints: Savepoints are similar to checkpoints, but they are triggered manually and provide a way to version and manage the state of Flink applications. They are used for planned maintenance and application upgrades.
• State Management: Flink provides fault-tolerant state management, meaning it can keep track of the state of an application (e.g., the last processed event) and recover it if a failure occurs.
• Watermarks: These are a mechanism to denote progress in event time. Flink uses watermarks to handle late events in stream processing, ensuring the system can handle out-of-order events and provide accurate results.
• Checkpoints: Checkpoints are a snapshot of the state of a Flink application at a particular point in time. They provide fault tolerance by allowing an application to revert to a previous state in case of failures.