Redis Data Integration release notes 1.2.8 (August 2024)

API server with a set of APIs to support Redis Insight with creating, testing, deploying & monitoring RDI pipelines. RDI Installer verifies all components are running at the end of installation.

Redis Data Integration 1.2.8 GA

This maintenance release replaces the 1.2 release.

RDI’s mission is to help Redis customers sync Redis Enterprise with live data from their slow disk-based databases to:

  • Meet the required speed and scale of read queries and provide an excellent and predictable user experience.
  • Save resources and time when building pipelines and coding data transformations.
  • Reduce the total cost of ownership by saving money on expensive database read replicas.

RDI keeps the Redis cache up to date with changes in the primary database, using a Change Data Capture (CDC) mechanism. It also lets you transform the data from relational tables into convenient and fast data structures that match your app's requirements. You specify the transformations using a configuration system, so no coding is required.

Headlines

  • API server with a set of APIs to support Redis Insight with creating, testing, deploying & monitoring RDI pipelines.
  • RDI Installer verifies all components are running at the end of installation.

Fixed Bugs

  • Fixed issue with CoreDNS configuration. Previously, it could only resolve from the host DNS.
  • Fixed incorrect validation schema for jobs in Redis Insight.
  • The pipeline advanced settings were missing when downloading the pipeline from RDI but this is now fixed.

Limitations

  • RDI can write data to a Redis Active-Active database. However, it doesn't support writing data to two or more Active-Active replicas. Writing data from RDI to several Active-Active replicas could easily harm data integrity as RDI is not synchronous with the source database commits.
  • RDI write-behind (which is currently in preview) should not be used on the same data set that RDI ingest is writing to Redis. This would either cause an infinite loop or would harm the data integrity, since both ingest and write-behind are asynchronous, eventually-consistent processes.
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