This topic describes the major feature changes and bug fixes in Realtime Compute for Apache Flink released on April 22, 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
This release includes the following updates to the platform:
Cloning a namespace: You can copy entities (such as drafts and catalogs) and configurations from a source namespace to a target one in the same region, improving resource reusability and reducing development costs. Namespace cloning can also be used for backing up entities to ensure data security and recoverability.
Modifying the query of a materialized table: You can modify the query of an existing materialized table to reflect changes in business logic and retrieve the latest data. To update historical data, you can manually refresh historical partitions and configure updates cascading.
Converting an existing SQL draft that uses the CTAS or CDAS statement to a YAML draft: When creating a new YAML draft, you can select an existing SQL draft containing the CREATE TABLE AS (CTAS) or CREATE DATABASE AS (CDAS) statement, which will be automatically converted to a YAML draft.
Accessing a Kerberized Hive cluster with Flink SQL jobs: A job developed using Flink SQL can read from and write to a Kerberized Hive cluster for enhanced security.
Features
Feature | Description | References |
Cloning a namespace | This feature helps you quickly copy entities (such as drafts and catalogs) and configurations between source and target namespaces, effectively improving asset reusability and resource utilization. It also serves as a valuable tool for data backups to safeguard against failures. | |
Converting an SQL draft that uses the CTAS or CDAS statement to a YAML draft | When creating a new YAML draft, you can select an SQL draft containing the CTAS or CDAS statement, which will be automatically transformed into a YAML draft. | |
Scenario-based rule settings for automatic tuning | When using Autopilot's adaptive strategy, you can set scaling conditions based on your business requirements. You have the flexibility to enable or disable specific conditions, with scaling triggered when any active condition is met. | |
Modifying the query of a materialized table |
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Accessing a Kerberized Hive cluster | A job developed using SQL can read from and write to a Kerberized Hive cluster. |