Flink 2.0: Navigating the Future of Unified Stream and Batch Processing
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"The Apache Flink community is working on a significant milestone with the planned release of Flink 2.0, marking a major evolution since the inception of Flink 1.0 in 2016. In this insightful talk, we will delve into the key enhancements and transformations slated for the 2.0 version, offering a comprehensive overview for users and developers eager to embrace the next frontier of stream and batch processing. The talk will commence by exploring some of the core philosophies of Flink, emphasizing the unification of batch and streaming processing. We'll dissect the roadmap's commitment to a seamless blending of batch and streaming applications. Flink 2.0 isn't just about features; it's also a reimagining of Flink's architecture. We'll delve into the disaggregated state management approach, leveraging distributed file systems. This evolution is geared towards better load balancing and improved efficiency in cloud-native architectures. APIs play a pivotal role in Flink's evolution, and Flink 2.0 is no exception. The talk will outline plans to retire deprecated APIs, overhaul the configuration layer, new abstractions and of course other plans that live in the community. Join us on this exploration of Flink 2.0, where we'll unravel the future of stream and batch processing and showcase how these advancements will shape the landscape of real-time data analytics."
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Flink 2.0: Navigating the Future of Unified Stream and Batch Processing
- 1. Flink 2.0: Navigating the Future of Unified Stream and Batch Processing Martijn Visser Senior Product Manager and Apache Flink PMC member
- 2. 2 Real-time services rely on stream processing Real-time Data A Sale A Shipment A Trade Rich Front-End Customer Experiences A Customer Experience Real-Time Backend Operations Real-time Stream Processing
- 3. 3 Developers choose Flink because of its performance and rich feature set Scalability and Performance Fault Tolerance Flink is a top 5 Apache project and boasts a robust developer community Unified Processing Flink is capable of supporting stream processing workloads at tremendous scale Language Flexibility Flink's fault tolerance mechanisms ensure it can handle failures effectively and provide high availability Flink supports Java, Python, & SQL with 150+ built-in functions, enabling devs to work in their language of choice Flink supports stream processing, batch processing, and ad-hoc analytics through one technology
- 4. 4 The Future of Unified Stream and Batch Processing
- 5. 5 Four Focus Areas Mixed Unification Mixed Unification Use the Unified API and Mix and Switch automagically between Batch and Stream Execution modes, for example when needing to reprocess or backfill data. Unified SQL Platform Add support for other common SQL elements like DELETE, UPDATE, Stored Procedures, Time Travel and unstructured data types. Streaming Warehouses Integrate Streaming and Batch processing with real-time analytics and up-to-date storage, blending traditional data warehouse benefits with instant insights. Engine Evolution Engine Evolution Cloud-native, Disaggregated State Backends, New APIs, SQL Gateway, JDBC Driver and much more
- 6. Mixed Unification 6 • Flink supports Batch Execution and Streaming Execution mode • What if you want to do backfill or reprocessing? MySCL CDC → phase 1 reads from bounded snapshot, phase 2 from unbounded binlog. S3 + Kafka (HybridSource) → read historical data from your lake before switching to real-time A couple of proposals: • FLIP-327: Support switching from batch to stream mode to improve throughput when processing backlog data • FLIP-309 Larger checkpointing interval processing backlog • FLIP-326: Enhance Watermark to Support Processing-Time Temporal Join
- 7. Unified SQL Platform: New DML Syntax 7 DELETE FROM user WHERE id = -1; DELETE FROM user WHERE id > (SELECT count(*) FROM employee); UPDATE user SET name = "u1" WHERE id > 10; UPDATE user SET name = "u1" WHERE id > (SELECT count(*) FROM employee); TRUNCATE TABLE user; CALL `my_cat`.`my_db`.add_user("Martijn","Product Manager");
- 8. Unified SQL Platform: Time Travel 8 now SELECT * FROM t FOR SYSTEM_TIME AS OF TIMESTAMP '2023-03-19 00:00:00'; SELECT * FROM t; SELECT * FROM t AS OF FOR SYSTEM_TIME AS OF CURRENT_TIMESTAMP;
- 9. Streaming Warehouses 9 • Unified changelog & table representation, originated as FLIP-188: Introduce Built-in Dynamic Table Storage Now known as Apache Paimon (Incubating) • Improve OLAP support, like quicker short-lived jobs to support OLAP queries with low latency and concurrent execution. • CBO (cost-based optimizations) with statistics • Make full use of the layout and indexes on streaming lakehouse to reduce data reading and processing for streaming queries.
- 10. Engine Evolution: Cloud-native, Disaggregated State 10 • FLIP-423: Disaggregated State Storage and Management • FLIP-424: Asynchronous State APIs • FLIP-425: Asynchronous Execution Model • FLIP-426: Grouping Remote State Access • FLIP-427: ForSt - Disaggregated state Store • FLIP-428: Fault Tolerance /Rescale Integration for Disaggregated State
- 11. Engine Evolution: DataStream API V2 11 FLIP-408: Introduce DataStream API V2 1. DataStream API exposes internal concepts and implementation details to users. 2. Complex API that provides primitives that corresponds to concepts of many different levels. 3. Started for Streaming, Batch was added later
- 12. Engine Evolution: Dynamic Tables 12 • Flink SQL and Table API always had the concept of Dynamic Tables • FLIP-435: Introduce a New Dynamic Table for Simplifying Data Pipelines proposes new entity CREATE DYNAMIC TABLE dwd_orders ( PRIMARY KEY(ds, id) NOT ENFORCED) DISTRIBUTED BY (ds) FRESHNESS = INTERVAL '3' MINUTE AS SELECT * FROM orders as o LEFT JOIN order_pay AS pay ON o.id = pay.order_id and o.ds = pay.ds
- 13. Engine Evolution: Polymorphic Table Functions 13 CREATE OR REPLACE PACKAGE BODY dynamic_cols_pkg IS FUNCTION get_dynamic_cols(column_list column_list_t) RETURN TABLE PIPELINED IS BEGIN -- Example logic to select different columns based on input -- In practice, use dynamic SQL to build and execute the query IF column_list.EXISTS(1) THEN IF column_list(1) = 'name' THEN PIPE ROW ('John Doe'); ELSIF column_list(1) = 'age' THEN PIPE ROW (30); END IF; END IF; RETURN; END get_dynamic_cols; END dynamic_cols_pkg; SELECT * FROM TABLE(dynamic_cols_pkg.get_dynamic_cols(colu mn_list_t('name')));
- 14. 14 Flink 2.0
- 16. Flink 2.0 is primarily a clean-up 16 Removed in 2.0 Refactored in 2.0 New in 2.0 (or sooner) • DataSet API • Deprecated methods/fields/classes in DataStream API and Table API • Scala APIs • Deprecated Source / Sink / TableSource / Table Sink interfaces • Legacy SQL Function and Operator stack • Old configuration layer • Java 8 and 11 support • Refactor the REST API • Refactor the Metrics System • No default-to-Kryo serialization • Default to Java 17 (or 21) • DataStream API V2 • Dynamic Tables • Disaggregated State Backend/Management APIs
- 17. Flink 2.0 Expected Timeline 17 Flink 1.19 released March 2024 Flink 1.20 Four/five months after Flink 1.19 Jul/Aug 2024 Flink 2.0 Four/five months after Flink 1.20 Dec/Jan
- 18. Thank You 18