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    Realtime Compute for Apache Flink:January 9, 2026

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    Realtime Compute for Apache Flink:January 9, 2026

    Last Updated:Jan 12, 2026

    This topic describes the major feature changes and key bug fixes for Realtime Compute for Apache Flink released on January 9, 2026.

    Important

    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

    On January 9, 2026, Realtime Compute for Apache Flink officially released the new engine version Ververica Runtime (VVR) 11.5.0. This upgrade focuses on optimizing the flexibility of Flink Change Data Capture (CDC) for handling complex scenarios via YAML configurations, enhancing the performance of Paimon in streaming data lakehouses, and introducing native support for the long-awaited Bitmap type. Additionally, this release provides significant feature enhancements and stability fixes for mainstream connectors such as PolarDB-X, OceanBase, and MongoDB to improve the efficiency of large-scale data integration. This version also includes key bug fixes from Apache Flink 1.20.2 and 1.20.3.

    Engine

    This release strengthens core engine capabilities by providing significant upgrades to data type support, job fault tolerance and recovery, and synchronization with Apache Flink capabilities.

    SQL enhancements

    Bitmap type support: This release adds the native Bitmap type and its initial set of functions, such as bitmap construction and cardinality statistics. This supports efficient, real-time, and precise deduplication scenarios, such as unique visitor (UV) counting.

    Data Ingestion via YAML (Flink CDC)

    • Metadata reference: Directly reference source and sink tables registered in the Catalog in Flink CDC jobs' YAML scripts.

    • Complex routing logic: Configure table names in Route using standard regular expressions. This supports sharded table merging scenarios.

    • Dirty data handling: JSON format parsing now supports a dirty data handling mechanism to improve job robustness.

    • Automatic precision expansion: StarRocks now partially supports automatic precision expansion for the Decimal type.

    • Empty schema fault tolerance: Kafka source now supports creating tables with an empty schema when the source has no data. This prevents job startup failures.

    • Paimon source support: Apache Paimon is now officially supported as a Flink CDC source.

    Connectors

    • MySQL:

      • Throttling: A data throttling feature is added to prevent excessive load on the source database.

      • Metadata extension: Support the file and pos binary logging metadata fields. You can use these metadata fields to determine the modification order of the same data.

      • Performance optimization: The process in snapshot-only mode is optimized by no longer executing any binary logging-related commands.

    • PolarDB-X CDC (public preview):

      • Supports subscribing to binary logging streams with multiple concurrent connections, significantly increasing ingestion throughput.

      • Supports subscribing to binary logging at the table level.

    • OceanBase (public preview): Supports the new bypass import write feature, which significantly improves write performance for large data batches.

    • MongoDB: The sink table now supports Partial Update.

    • Elasticsearch:

      • Supports explicit configuration of the doc_as_upsert parameter.

      • Supports the connection timeout parameter.

    • Apache Kafka: Redundant consumer groups created by the Kafka connector can be automatically deleted.

    Data lakehouse integration (Iceberg)

    Iceberg Connector: You can now configure Hadoop-related parameters to improve the flexibility of connections in complex Hadoop environments.

    Bug fixes

    • Incorporated key fixes from Apache Flink 1.20.2 and 1.20.3.

    • Fixed an issue where data fields were incorrectly read as null when Flink consumes data from a Hologres binary logging source table.

    • Fixed a data loss issue that occurred when Flink reads data from Kafka and writes it to OSS after transactions were enabled in the Kafka connector.

    • Fixed an issue where a disconnected PolarDB-X connection caused a sharp increase in latency and threw an EOFException error.

    • Optimized the CDC execution logic by removing unnecessary instructions in specific modes.