The Apache Flink SQL Cookbook is a curated collection of examples, patterns, and use cases of Apache Flink SQL. Many of the recipes are completely self-contained and can be run in Ververica Platform as is.
The cookbook is a living document. 🌱
- Creating Tables
- Inserting Into Tables
- Working with Temporary Tables
- Filtering Data
- Aggregating Data
- Sorting Tables
- Encapsulating Logic with (Temporary) Views
- Writing Results into Multiple Tables
- Aggregating Time Series Data
- Watermarks
- Analyzing Sessions in Time Series Data
- Rolling Aggregations on Time Series Data
- Continuous Top-N
- Deduplication
- Chained (Event) Time Windows
- Detecting Patterns with MATCH_RECOGNIZE
- Maintaining Materialized Views with Change Data Capture (CDC) and Debezium
- Hopping Time Windows
- Window Top-N
- Retrieve previous row value without self-join
- Working with Dates and Timestamps
- Building the Union of Multiple Streams
- Filtering out Late Data
- Overriding table options
- Expanding arrays into new rows
- Split strings into maps
- Regular Joins
- Interval Joins
- Temporal Table Join between a non-compacted and compacted Kafka Topic
- Lookup Joins
- Star Schema Denormalization (N-Way Join)
- Lateral Table Join
Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities.
Learn more about Flink at https://flink.apache.org/.
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Distributed under Apache License, Version 2.0.