Release Notes - Flink 2.2
This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.

Release notes - Flink 2.2 #

These release notes discuss important aspects, such as configuration, behavior or dependencies, that changed between Flink 2.1 and Flink 2.2. Please read these notes carefully if you are planning to upgrade your Flink version to 2.2.

Table SQL / API #

Apache Flink has supported leveraging LLM capabilities through the ML_PREDICT function in Flink SQL since version 2.1, enabling users to perform semantic analysis in a simple and efficient way. This integration has been technically validated in scenarios such as log classification and real-time question-answering systems. However, the current architecture allows Flink to only use embedding models to convert unstructured data (e.g., text, images) into high-dimensional vector features, which are then persisted to downstream storage systems. It lacks real-time online querying and similarity analysis capabilities for vector spaces. The VECTOR_SEARCH function is provided in Flink 2.2 to enable users to perform streaming vector similarity searches and real-time context retrieval directly within Flink.

See more details about the capabilities and usages of Flink’s Vector Search.

Realtime AI Function #

Apache Flink has supported leveraging LLM capabilities through the ML_PREDICT function in Flink SQL since version 2.1. In Flink 2.2, the Table API also supports model inference operations that allow you to integrate machine learning models directly into your data processing pipelines.

See more details about the capabilities and usages of Flink’s Model Inference.

Materialized Table #

By specifying data freshness and query when creating Materialized Table, the engine automatically derives the schema for the materialized table and creates corresponding data refresh pipeline to achieve the specified freshness.

From Flink 2.2, the FRESHNESS clause is not a mandatory part of the CREATE MATERIALIZED TABLE and CREATE OR ALTER MATERIALIZED TABLE DDL statements. Flink 2.2 introduces a new MaterializedTableEnricher interface. This provides a formal extension point for customizable default logic, allowing advanced users and vendors to implement “smart” default behaviors (e.g., inferring freshness from upstream tables).

Besides this, users can use DISTRIBUTED BY orDISTRIBUTED INTO to support bucketing concept for Materialized tables. Users can use SHOW MATERIALIZED TABLES to show all Materialized tables.

SinkUpsertMaterializer V2 #

SinkUpsertMaterializer is an operator in Flink that reconciles out of order changelog events before sending them to an upsert sink. Performance of this operator degrades exponentially in some cases. Flink 2.2 introduces a new implementation that is optimized for such cases.

Delta Join #

In 2.1, Apache Flink has introduced a new delta join operator to mitigate the challenges caused by big state in regular joins. It replaces the large state maintained by regular joins with a bidirectional lookup-based join that directly reuses data from the source tables.

Flink 2.2 enhances support for converting more SQL patterns into delta joins. Delta joins now support consuming CDC sources without DELETE operations, and allow projection and filter operations after the source. Additionally, delta joins include support for caching, which helps reduce requests to external storage.

See more details about the capabilities and usages of Flink’s Delta Joins.

SQL Types #

Before Flink 2.2, row types defined in SQL e.g. SELECT CAST(f AS ROW<i NOT NULL>) did ignore the NOT NULL constraint. This was more aligned with the SQL standard but caused many type inconsistencies and cryptic error message when working on nested data. For example, it prevented using rows in computed columns or join keys. The new behavior takes the nullability into consideration. The config option table.legacy-nested-row-nullability allows to restore the old behavior if required, but it is recommended to update existing queries that ignored constraints before.

Casting to TIME type now considers the correct precision (0-3). Casting incorrect strings to time (e.g. where the hour component is higher than 24) leads to a runtime exception now. Casting between BINARY and VARBINARY should now correctly consider the target length.

Use UniqueKeys instead of Upsertkeys for state management #

This is considerable optimization and an breaking change for the StreamingMultiJoinOperator. As noted in the release notes, the operator was launched in an experimental state for Flink 2.1 since we’re working on relevant optimizations that could be breaking changes.

Runtime #

Balanced Tasks Scheduling #

Introducing a balanced tasks scheduling strategy to achieve task load balancing for TMs and reducing job bottlenecks.

See more details about the capabilities and usages of Flink’s Balanced Tasks Scheduling.

Enhanced Job History Retention Policies for HistoryServer #

Before Flink 2.2, HistoryServer supports only a quantity-based job archive retention policy and is insufficient for scenarios, requiring time-based retention or combined rules. Users can use the new configuration historyserver.archive.retained-ttl combining with historyserver.archive.retained-jobs to fulfill more scenario requirements.

Metrics #

Since 2.2.0 users can now assign custom metric variables for each operator/transformation used in the Job. Those variables are later converted to tags/labels by the metric reporters, allowing users to tab/label specific operator’s metrics. For example, you can use this to name and differentiate sources.

Users can now control the level of details of checkpoint spans via traces.checkpoint.span-detail-level. Highest levels report tree of spans for each task and subtask. Reported custom spans can now contain children spans. See more details in Traces.

Introduce Event Reporting #

Since 2.1.0 users are able to report custom events using the EventReporters. Since 2.2.0 Flink reports some built-in/system events.

Connectors #

Introduce RateLimiter for Source #

Flink jobs frequently exchange data with external systems, which consumes their network bandwidth and CPU. When these resources are scarce, pulling data too aggressively can disrupt other workloads. In Flink 2.2, we introduce a RateLimiter interface to provide request rate limiting for Scan Sources and connector developers can integrate with rate limiting frameworks to implement their own read restriction strategies. This feature is currently only available in the DataStream API.

Balanced splits assignment #

SplitEnumerator is responsible for assigning splits, but it lacks visibility into the actual runtime status or distribution of these splits. This makes it impossible for SplitEnumerator to guarantee that the sharding is evenly distributed, and data skew is very likely to occur. From Flink 2.2, SplitEnumerator has the information of the splits distribution and provides the ability to evenly assign splits at runtime.

Python #

Support async function in Python DataStream API #

In Flink 2.2, we have added support of async function in Python DataStream API. This enables Python users to efficiently query external services in their Flink jobs, e.g. large-sized LLM which is typically deployed in a standalone GPU cluster, etc.

Furthermore, we have provided comprehensive support to ensure the stability of external service access. On one hand, we support limiting the number of concurrent requests sent to the external service to avoid overwhelming it. On the other hand, we have also added retry support to tolerate temporary unavailability which maybe caused by network jitter or other transient issues.

Dependency upgrades #

Upgrade commons-lang3 to version 3.18.0 #

Upgrade org.apache.commons:commons-lang3 from 3.12.0 to 3.18.0 to mitigate CVE-2025-48924.

Upgrade protobuf-java from 3.x to 4.32.1 with compatibility patch for parquet-protobuf #

Flink now uses protobuf-java 4.32.1 (corresponding to Protocol Buffers version 32), upgrading from protobuf-java 3.21.7 (Protocol Buffers version 21). This major upgrade enables:

  • Protobuf Editions Support: Full support for the new edition = "2023" and edition = "2024" syntax introduced in Protocol Buffers v27+. Editions provide a unified approach that combines proto2 and proto3 functionality with fine-grained feature control.
  • Improved Proto3 Field Presence: Better handling of optional fields in proto3 without the limitations of older protobuf versions, eliminating the need to set protobuf.read-default-values to true for field presence checking.
  • Enhanced Performance: Leverages performance improvements and bug fixes from 11 Protocol Buffers releases (versions 22-32).
  • Modern Protobuf Features: Access to newer protobuf capabilities including Edition 2024 features and improved runtime behavior.

Users with existing proto2 and proto3 .proto files will continue to work without changes.