State management is an important requirement for real-time stream processing. Learn how Flink supports real-time processing through various state management capabilities.
- [Instructor] Unless your streaming application is basic, … application and element state always plays a big role … in real time processing. … We need state management for various use cases, … we may need to refer to previous events and metrics, … we want to keep track of active browser or user sessions, … we want to track custom counters … and make decisions based on them. … We may also need to store machine learning models … and share them across the application. … Flink supports state management in two ways. … I supports a keyed state feature … where it keeps track of state by specific keys. … For example, if we do a KeyBy … on a data stream by every user, … then Flink keeps track of the state by each user. … Each user gets their own copy of the state variable … that can then be managed based on the user. … Flink supports various data types to store states. … They include the simple value state … to store a single value, … list and map states to store collections, … it also supports advanced states called …
- Streaming with Apache Flink
- Using the DataStream API for basic stream processing
- Working with process functions
- Windowing and joins
- Setting up event-time processing
- State management in Flink
Skill Level Advanced
1. Apache Flink
2. DataStream API
4. Event Time Processing
5. State Management
6. Use Case Project
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