This topic describes the major feature updates and bug fixes in the Realtime Compute for Apache Flink release on August 8, 2025.
The version upgrade is gradually released to users. For more information, see the latest announcement in the Realtime Compute for Apache Flink console. You can use the new features in this version only after the upgrade is complete for your account. To apply for an expedited upgrade, submit a ticket.
Overview
A new version of Realtime Compute for Apache Flink was released on August 8, 2025. This release includes updates to the platform, engine, and connectors, along with performance optimizations and bug fixes.
Engine
Ververica Runtime (VVR) 11.2 is now available. Built on Apache Flink 1.20.2, VVR 11.2 provides additional optimizations and enterprise enhancements. Highlights:
Flink SQL
This version greatly expands the built-in SQL function library:
Scalar functions:
String processing: PRINTF, TRANSLATE, ELT, BTRIM, STARTSWITH, and ENDSWITH
JSON processing: JSON_QUOTE and JSON_UNQUOTE
Regular expression: REGEXP_SUBSTR, REGEXP_INSTR, REGEXP_COUNT, and REGEXP_EXTRACT_ALL
Arithmetic: UNHEX
Data type support
Adds support for the Variant type, enhancing flexibility in schema handling.
Table API
Supports the Hive dialect, enabling familiar Hive SQL syntax in Table API jobs.
AI function
Introduces configurable strategies for handling messages that exceed the AI model's maximum context window: Discard or Truncate.
Connector
MySQL CDC connector: Optimizes handling of
VARCHARfields, improving synchronization performance and stability.Now supports the Canal-JSON format for data ingestion. Can extract both event timestamps (
ts) and event sequences (es) fields.AnalyticDB for MySQL connector: Adds support for
INSERT IGNORE, improving fault tolerance during data writes.
Security
The Paimon and OSS connectors now support RAM-based authorization, replacing AccessKey pairs for improved security and permission management.
Performance enhancements
The MongoDB CDC connector supports concurrent oplog parsing, enhancing data sync stability and reliability. The Tair (Redis OSS-compatible) connector supports asynchronous lookup joins, improving cache access efficiency and job performance.
Platform
New features
Executing multiple DDL/DML statements in a single batch job: Create tables, perform computations, and delete tables, all in one job.
Refreshing materialized tables on schedule: Periodically refresh historical partitions to backfill late data, ensuring eventual consistency.
Automatically releasing idle session clusters: If new session clusters stay idle for over 30 minutes, they are automatically released to improve resource utilization.
Blackout periods for automatic tuning: Restrict automatic resource scaling during business-critical hours, ensuring business stability. Performance tuning advice is still provided.
Comprehensive Git integration: Support more mainstream Git tools, like Alibaba Cloud DevOps. Pull directory structure and troubleshoot with helpful error messages.
Granular access control for data queries
Experience optimizations
Supports console-based AI model creation, deletion, and modification. This lets you better manage AI models in the Catalogs page.
Displays CU usage across hours for batch jobs. This metric better reflects the performance of batch jobs.
Supports workflow fuzzy search by name in the Workflows page.
Supports console-based Iceberg catalog creation.
API
This release includes two new APIs, two deprecated APIs, and two bug fixes. To use the new APIs, upgrade your cluster and update the pom dependency to version 1.8.0.
Previously, APIs related to Resource and DeploymentTarget could not manage hybrid billing workspaces. This release upgrades these APIs:
Introduces new APIs:
CreateDeploymentTargetV2UpdateDeploymentTargetV2
Deprecates APIs:
CreateDeploymentTargetandUpdateDeploymentTargetare deprecated. Transition to the new APIs as soon as possible.Resource object enhancement: Add new fields to support the configuration of hybrid billing workspaces.
The
createDeploymentDraftandmodifyDeploymentDraftAPIs are optimized to fix the issue that the maximum number of labels was not validated.The
listDeploymentsAPI is optimized to validate the input of thesortNameandsortOrderparameters: Only these strings are allowed: letters (a-z, A-Z) and underscores (_).
Features and enhancements
Feature | Description | References |
Optimizing | Improves performance and stability of data sync for | |
Enhancing Flink CDC | Adds support for Canal-JSON and es/ts timestamp extraction in Kafka connector. | |
Supporting Hive SQL for Table API | Allows use of the Hive dialect in jobs developed with the Table API. | |
Authorizing access to Paimon and OSS using RAM roles | Enables secure access to Paimon and OSS via RAM roles instead of AccessKey pairs, improving security and permission management. | |
Supporting | Enhances fault tolerance during data writes by supporting the INSERT IGNORE syntax. | |
Optimizing asynchronous lookup joins for the Tair (Redis OSS-compatible) connector | Improves cache access efficiency and stability with enhanced asynchronous lookup join functionality. | |
Enhancing PyFlink to support direct use of built-in connectors | Improves Python developer experience by allowing direct use of built-in connectors without manual dependency management. | |
Concurrent oplog parsing for the MongoDB CDC connector | Supports concurrent parsing of MongoDB oplogs to improve synchronization stability and reliability. | |
New Flink SQL built-in functions | Adds multiple built-in functions for enhanced SQL. | |
Automatic schema evolution for Kafka-Paimon data ingestion | Supports automatic schema evolution during data ingestion from Kafka to Paimon, enhancing data model flexibility. | |
SQL Variant type support | Supports the Variant type, enhancing the flexibility of data processing. | |
Configurable AI function behavior | Allows users to define how oversized messages (exceeding the AI model's context window) are handled—options include discard or truncate. |
Notable fixes
This release addresses several key issues to improve the stability, performance, and functionality of Flink and its connectors.
Connector
Kafka: Fixed time zone conversion and data sync issues.
MySQL: Resolved permission errors affecting connectivity.
Paimon: Fixed Avro timestamp precision validation and resolved issues.
DLF: Resolved issues with data access token expiration for improved reliability.
MySQL 8.0: Addressed compatibility issues for smoother integration.
SQL and transformation
Fixed
LIKEsyntax parsing in Paimon.Fixed issues related to date handling and
REGEXP_REPLACEin YAML scripts.Resolved NullPointerException when accessing schema registry.
Stability and performance
Addressed metadata inconsistencies that could arise after job failover, ensuring a more reliable state.
Fixed resource cleanup on unexpected job exits.
Patched a checkpointing crash in the Paimon connector.
Optimized connector retry mechanism for greater job resilience.