Apache Flink 2.1.0: Ushers in a New Era of Unified Real-Time Data + AI with Comprehensive Upgrades

July 31, 2025 - Ron Liu (@Ron999)

The Apache Flink PMC is proud to announce the release of Apache Flink 2.1.0. This marks a significant milestone in the evolution of the real-time data processing engine into a unified Data + AI platform. This release brings together 116 global contributors, implements 16 FLIPs (Flink Improvement Proposals), and resolves over 220 issues, with a strong focus on deepening the integration of real-time AI and intelligent stream processing:

  1. Breakthroughs in Real-Time AI:

    • Introduces AI Model DDL, enabling flexible management of AI models through Flink SQL and the Table API.

    • Extends the ML_PREDICT Table-Valued Function (TVF), empowering real-time invocation of AI models within Flink SQL, laying the foundation for building end-to-end real-time AI workflows.

  2. Enhanced Real-Time Data Processing:

    • Process Table Functions (PTFs) open up the Flink SQL engine for more event-driven application. Giving access to Flink’s managed state, event-time and timer services, and underlying table changelogs.

    • Adds the VARIANT data type for efficient handling of semi-structured data like JSON. Combined with the PARSE_JSON function and lakehouse formats (e.g., Apache Paimon), it enables dynamic schema data analysis.

    • Significantly optimizes streaming joins with the innovative introduction of DeltaJoin and MultiJoin strategies, eliminating state bottlenecks and improving resource utilization and job stability.

Flink 2.1.0 seamlessly integrates real-time data processing with AI models, empowering enterprises to advance from real-time analytics to real-time intelligent decision-making, meeting the evolving demands of modern data applications. We extend our gratitude to all contributors for their invaluable support!

Let’s dive into the highlights.

Flink SQL Improvements #

Model DDLs #

Since Flink 2.0, we have introduced dedicated syntax for AI models, enabling users to define models as easily as creating catalog objects and invoke them like standard functions or table functions in SQL statements. In Flink 2.1, we have also added Model DDLs Table API support, enabling users to define and manage AI models programmatically via the Table API in both Java and Python. This provides a flexible, code-driven alternative to SQL for model management and integration within Flink applications.

Example:

  • Defining a Model via Flink SQL
CREATE MODEL my_model
INPUT (f0 STRING)
OUTPUT (label STRING)
WITH (
  'task' = 'classification',
  'type' = 'remote',
  'provider' = 'openai',
  'openai.endpoint' = 'remote',
  'openai.api_key' = 'abcdefg',
);
  • Defining a Model via Table API (Java)
tEnv.createModel(
    "MyModel", 
    ModelDescriptor.forProvider("OPENAI")
      .inputSchema(Schema.newBuilder()
        .column("f0", DataTypes.STRING())
        .build())
      .outputSchema(Schema.newBuilder()
        .column("label", DataTypes.STRING())
        .build())
      .option("task", "classification")
      .option("type", "remote")
      .option("provider", "openai")
      .option("openai.endpoint", "remote")
      .option("openai.api_key", "abcdefg")
      .build(),
    true);

More Information

Realtime AI Function #

Based on the AI model DDL, In Flink 2.1, we expanded the ML_PREDICT table-valued function (TVF) to perform realtime model inference in SQL queries, applying machine learning models to data streams seamlessly. The implementation supports both Flink builtin model providers (OpenAI) and interfaces for users to define custom model providers, accelerating Flink’s evolution from a real-time data processing engine to a unified realtime AI platform. Looking ahead, we plan to introduce more AI functions such as ML_EVALUATE, VECTOR_SEARCH to unlock end-to-end experience for real-time data processing, model training, and inference.

Take the following SQL statements as an example:

-- Declare a AI model
CREATE MODEL `my_model`
INPUT (text STRING)
OUTPUT (response STRING)
WITH(
  'provider' = 'openai',
  'endpoint' = 'https://api.openai.com/v1/llm/v1/chat',
  'api-key' = 'abcdefg',
  'system-prompt' = 'translate to Chinese',
  'model' = 'gpt-4o'
);

-- Basic usage
SELECT * FROM ML_PREDICT(
  TABLE input_table,
  MODEL my_model,
  DESCRIPTOR(text)
);

-- With configuration options
SELECT * FROM ML_PREDICT(
  TABLE input_table,
  MODEL my_model,
  DESCRIPTOR(text)
  MAP['async', 'true', 'timeout', '100s']
);

-- Using named parameters
SELECT * FROM ML_PREDICT(
  INPUT => TABLE input_table,
  MODEL => MODEL my_model,
  ARGS => DESCRIPTOR(text),
  CONFIG => MAP['async', 'true']
);

More Information

Process Table Functions (PTFs) #

Apache Flink now includes support for Process Table Functions (PTFs), the most powerful function kind for Flink SQL and Table API.

Conceptually, a PTF is a superset of all other user-defined functions, mapping zero, one, or multiple tables to zero, one, or multiple rows. They enable implementing user-defined operators that can be as feature-rich as built-in operations. PTFs have access to Flink’s managed state, event-time, timer services, and table changelogs.

PTFs enable the following tasks:

  • Apply transformations on each row of a table.
  • Logically partition the table into distinct sets and apply transformations per set.
  • Store seen events for repeated access.
  • Continue the processing at a later point in time enabling waiting, synchronization, or timeouts.
  • Buffer and aggregate events using complex state machines or rule-based conditional logic.

This moves Flink SQL significantly closer to the DataStream API, leveraging the robustness and familiarity of the existing SQL ecosystem. Detailed information on PTF syntax and semantics can be found here: Process Table Functions.

Take the following code as an example:

// Declare a ProcessTableFunction for memorizing your customers
public static class GreetingWithMemory extends ProcessTableFunction<String> {
    public static class CountState { 
        public long counter = 0L;
    }

    public void eval(@StateHint CountState state, @ArgumentHint(SET_SEMANTIC_TABLE) Row input) {
        state.counter++;
        collect("Hello " + input.getFieldAs("name") + ", your " + state.counter + " time?");
    }
}

TableEnvironment env = TableEnvironment.create(...);

// Call the PTF in Table API
env.fromValues("Bob", "Alice", "Bob")
   .as("name")
   .partitionBy($("name"))
   .process(GreetingWithMemory.class)
   .execute()
   .print();

// Call the PTF in SQL
env.executeSql("SELECT * FROM GreetingWithMemory(TABLE Names PARTITION BY name)")
   .print();

More Information

Variant Type #

Variant is a new data type for semi-structured data(e.g. JSON), it supports storing any semi-structured data, including ARRAY, MAP(with STRING keys), and scalar types—while preserving field type information in a JSON-like structure. Unlike ROW and STRUCTURED types, VARIANT provides superior flexibility for handling deeply nested and evolving schemas.

Users can use PARSE_JSON orTRY_PARSE_JSON to convert JSON-formatted VARCHAR data to VARIANT. In addition, table formats like Apache Paimon now support the VARIANT type, this enable users to efficiently process semi-structured data in lakehouse using Flink SQL.

Take the following SQL statements as an example:

CREATE TABLE t1 (
  id INTEGER,
  v STRING -- a json string
) WITH (
  'connector' = 'mysql-cdc',
  ...
);
 
CREATE TABLE t2 (
  id INTEGER,
  v VARIANT
) WITH (
  'connector' = 'paimon'
  ...
);
 
-- write to t2 with VARIANT type
INSERT INTO t2 SELECT id, PARSE_JSON(v) FROM t1;

More Information

Structured Type Enhancements #

In Flink 2.1, we enabled declare user-defined objects via STRUCTURED TYPE directly in CREATE TABLE DDL statements, resolving critical type equivalence issues and significantly improving API usability.

Take the following SQL statements as an example:

CREATE TABLE MyTable (
    uid BIGINT,
    user STRUCTURED<'com.example.User', name STRING, age INT NOT NULL>
);

-- Casts a row type into a structured type
INSERT INTO MyTable SELECT 1, CAST(('Bob', 42) AS STRUCTURED<'com.example.User', name STRING, age INT>);

More Information

Delta Join #

Introduced a new DeltaJoin operator in stream processing jobs, along with optimizations for simple streaming join pipeline. Compared to traditional streaming join, delta join requires significantly less state, effectively mitigating issues related to large state, including resource bottlenecks, slow checkpointing, and lengthy job recovery times. This feature is enabled by default.

More Information

Multiple Regular Joins #

Streaming Flink jobs with multiple cascaded streaming joins often experience operational instability and performance degradation due to large state sizes. This release introduces a multi-join operator (StreamingMultiJoinOperator) that drastically reduces state size by eliminating intermediate results. The operator achieves this by processing joins across all input streams simultaneously within a single operator instance, storing only raw input records instead of propagated join output.

This “zero intermediate state” approach primarily targets state reduction, offering substantial benefits in resource consumption and operational stability. This feature is now available for pipelines with multiple INNER/LEFT joins that share at least one common join key, enable with SET 'table.optimizer.multi-join.enabled' = 'true'.

Benchmark: we conducted a benchmark comparing the benefits of the multi-join operator with default binary joins, more detail can see MultiJoin Benchmark.

More Information

Async Lookup Join Enhancements #

In previous versions of async lookup join, even if users set table.exec.async-lookup.output-mode to ALLOW_UNORDERED, the engine would still forcibly fallback to ordered mode when processing update streams to ensure correctness. Starting from Flink 2.1, the engine allows parallel processing of unrelated update records while still ensuring correctness, thereby achieving higher throughput when handling changelog streams.

More Information

Sink Reuse #

Within a single Flink job, when writing multiple INSERT INTO statements updating identical columns ( different columns will be supported in next release) of a target table, the planner will optimize the execution plan and merge the sink nodes to achieve reuse. This will be a great usability improvement for users using partial-update features with data lake storages like Apache Paimon.

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Support Smile Format for Compiled Plan Serialization #

In Flink 2.1, we added smile binary format support for compiled plans, providing a memory-efficient alternative to JSON for serialization/deserialization. By default JSON is used, in order to use smile format need to call CompiledPlan#asSmileBytes and PlanReference#fromSmileBytes method.

More Information

Runtime #

Add Pluggable Batching for Async Sink #

In Flink 2.1, we introduced a pluggable batching mechanism for async sink that allows users to define custom batching write strategies tailored to specific requirements.

More Information

Split-level Watermark Metrics #

In Flink 2.1, we added some split level watermark metrics, covering watermark progress and per-split state gauges to enhance the watermark observability:

  • currentWatermark: the last watermark this split has received.
  • activeTimeMsPerSecond: the time this split is active per second.
  • pausedTimeMsPerSecond: the time this split is paused due to watermark alignment per second.
  • idleTimeMsPerSecond: the time this split is marked idle by idleness detection per second.
  • accumulatedActiveTimeMs: accumulated time this split was active since registered.
  • accumulatedPausedTimeMs: accumulated time this split was paused since registered.
  • accumulatedIdleTimeMs: accumulated time this split was idle since registered.

More Information

Connectors #

Introduce SQL Connector for Keyed State #

In Flink 2.1, we introduced a new connector for keyed state. This connector allows users to query keyed state directly from checkpoint or savepoint using Flink SQL, making it easier to inspect, debug, and validate the state of Flink jobs without custom tooling. This feature is especially useful for analyzing long-running jobs and validating state migrations.

With a simple DDL, you can expose ValueState as table and run Flink SQL query the snapshot:

CREATE TABLE keyed_state (
    k INTEGER,
    user_id STRING,
    balance DOUBLE
) WITH (
    'connector' = 'savepoint',
    'path' = 'file:///savepoint/path&',
    'uid' = 'my-operator-id'
);

-- Query the keyed state
SELECT * FROM keyed_state;

More Information

Others Improvements #

In PyFlink 2.1, we added support for Python 3.12 and removed Python 3.8.

More Information

Bump flink-shaded version to 20.0 to support Smile format.

More Information

Upgrade Parquet version to 1.15.3 #

Bump parquet version to 1.15.3 to resolve parquet-avro module vulnerability found in CVE-2025-30065.

More Information

Upgrade Notes #

The Flink community tries to ensure that upgrades are as seamless as possible. However, certain changes may require users to make adjustments to certain parts of the program when upgrading to version 2.1. Please refer to the release notes for a comprehensive list of adjustments to make and issues to check during the upgrading process.

List of Contributors #

The Apache Flink community would like to express gratitude to all the contributors who made this release possible:

Ahmed Hamdy, Alan Sheinberg, Aleksandr Iushmanov, Aleksandr Savonin, AlexYinHan, Ammu Parvathy, Anupam Aggarwal, Ao Li, Arvid Heise, Au-Miner, Benchao Li, Bonnie Varghese, Chris, David Moravek, David Radley, David Wang, Dawid Wysakowicz, Dian Fu, Efrat Levitan, Feng Jin, Ferenc Csaky, Francesco Di Chiara, Gabor Somogyi, Gunnar Morling, Gustavo de Morais, Hangxiang Yu, Hao Li, Hongjia Liang, HuangXingBo, Jiaan Geng, Jiabao Sun, Jiangjie (Becket) Qin, Joery, JunRuiLee, Junrui Lee, Juntao Zhang, Kunni, Kurt Ostfeld, Laffery, Lukas Schwerdtfeger, Luke Chen, Martijn Visser, Mate Czagany, Matthias Pohl, Mika Naylor, Mina Asham, Mingliang Liu, Muhammet Orazov, Márton Balassi, PB, Pan Yuepeng, Peter Huang, Piotr Nowojski, Roc Marshal, Rui Fan, Ryan van Huuksloot, Sasaki Toru, Sergey Nuyanzin, Shengkai, Shuyi Chen, Stepan Stepanishchev, Thomas Cooper, Tianzhu Wen, Timo Walther, Venkata krishnan Sowrirajan, Weijie Guo, Xiangyu Feng, Xu Huang, XuShuai, Xuannan, Xuyang, Yanfei Lei, Yi Zhang, Yuepeng Pan, Yun Tang, Zakelly, Zdenek Tison, Zhanghao Chen, atu-sharm, beliefer, big face cat, chenyuzhi459, fengli, fredia, gengbiao.gb, glorinli, hejufang, huangyanyanyan, jingge, lincoln lee, mayuehappy, moses, mzzx, nacisimsek, nilmadhab mondal, noorall, novakov-alexey, r-sidd, slankka, slfan1989, sunxia, sxnan, wangfeifan, wangqh, wangxinglong, xiangyu0xf, xiaoyu, xingbo, xuyang, yanand0909, yhx, yuhang2.zhang, yunfengzhou-hub, 余良, 皆非, 马越

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