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32.0.0 Release Notes Summary

Key Features

1. New Overlord APIs:

  • Migrated segment management from Coordinator to Overlord, deprecating old Coordinator APIs
  • Introduced new APIs for marking segments as used/unused at various levels

2. Realtime Query Processing for Multi-Value Strings:

  • Improved real-time query performance and consistency
  • Fixed topN query inconsistencies by aligning real-time and published segment behaviors

3. Join Hints in MSQ Task Engine Queries:

  • Enabled SQL hints for granular control over JOIN query types
  • Improved query performance with per-join-level optimization

4. Java Support Updates:

  • Removed Java 8 support
  • Deprecated Java 11; Java 17 is now recommended

5. Hadoop-Based Ingestion Deprecation:

  • Deprecated Hadoop ingestion in favor of SQL-based ingestion

6. Web Console Enhancements:

  • Enhanced Explore view and segment timeline
  • Added timezone picker, UNNEST autocomplete, and resizable panels

Improvements

1. Ingestion Enhancements:

  • CSV/TSV Parsing introduced tryParseNumbers for configurable numeric input handling
  • Memory Optimization reduced buffer usage in non-query tasks
  • Streaming Efficiency added maxColumnsToMerge for controlled segment merging

2. Query Optimization:

  • Window Query Updates deprecated legacy fields for window queries
  • Query Prioritization implemented segment-age-based prioritization
  • Error Reporting enhanced feedback for incomplete queries

3. Cluster Management:

  • Metadata IO Reduction optimized segment allocation to reduce overhead
  • Autoscaling Enhancements improved efficiency in task publishing
  • Leadership Recovery accelerated Overlord recovery following ZooKeeper restarts

4. Data Management:

  • Compaction Improvements enabled sorting of non-time columns
  • Schema Handling improved consistency in datasource schemas

5. Metrics & Monitoring:

  • GroupBy Query Metrics added detailed tracking for merge buffer usage and query spills
  • CgroupV2 Monitoring introduced CPU, disk, and memory monitoring
  • Ingestion & Query Metrics expanded statsd metrics for performance tracking

6. Extensions & Compatibility

  • Delta Lake enhanced decimal type support and snapshot filtering
  • gRPC Queries introduced a gRPC API for SQL and native queries
  • Iceberg integrated AWS Glue Iceberg catalog support

Bug Fixes

  • Fixed incorrect handling of null values in real-time segment processing
  • Resolved query failures due to incorrect boolean logic handling
  • Improved handling of JSON and SQL-based ingestion headers
  • Addressed segment publishing delays when re-submitting supervisors
  • Enhanced resilience to ZooKeeper-induced service leadership changes
  • Fixed real-time segment metric reporting inconsistencies
  • Various UI and usability bug fixes in the web console

For detailed information, refer to the full release notes.

Installation

If you are new to Apache Druid

Before beginning, make sure your system requirements for the installation are met:

  • Linux, Mac OS X, or other Unix-like OS. (Windows is not supported)
  • Java 8u92+, 11, or 17
  • Python 3 (preferred) or Python 2

You will need a workstation or virtual server with 6 GiB of RAM.

Proceed with Apache Druid installation option that fits your needs.

If you would like to upgrade to 32.0.0 Apache Druid

Review the upgrade notes and incompatible changes on GitHub before you start your Druid upgrade. 

Community

Connect and engage with the Druid community across various channels to stay informed, seek assistance, and connect with users, committers, and enthusiasts:

Slack: https://druid.apache.org/community/join-slack.

GitHub: use apache/druid to follow Druid development, report issues, or contribute pull requests.

Development mailing list: dev@druid.apache.org for discussion about project development.

User mailing list: druid-user@googlegroups.com for general discussion, questions, and announcements.

LinkedIn: https://www.linkedin.com/groups/13841905/ and https://www.linkedin.com/groups/8791983/ to connect with other Apache Druid professionals.

Deep.BI's Contributions on GitHubFollow Deep.BI on LinkedIn
Slack: https://druid.apache.org/community/join-slack.
GitHub: use apache/druid to follow Druid development, report issues, or contribute pull requests.
Development mailing list: dev@druid.apache.org for discussion about project development.
‍User mailing list: druid-user@googlegroups.com for general discussion, questions, and announcements.
LinkedIn: https://www.linkedin.com/groups/8791983/ to connect with other Apache Druid professionals.
[LINK]: Deep.BI’s contributions on GitHub          [LINK] Deep.BI LinkedIn

Support

The official Apache Druid Slack is the first way to get help. You can also report issues and problems, or suggest new features, on GitHub.
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License

The Apache License Version 2.0 is an open-source license that grants users a perpetual, worldwide, non-exclusive, no-charge, royalty-free, and irrevocable copyright license to reproduce, modify, distribute, and sublicense the work and its derivatives.
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28.0 Release Notes Summary

Key Features:

1. Enhanced SQL Compliance:

  • Improved SQL engine for analytics.
  • Graduated async queries, deep storage querying, and UNNEST to GA.
  • Added UNION ALL support in multi-stage query engine (MSQ).

2. Ingestion System Enhancements:

  • Default nested column ingestion for stability.
  • Multi-topic Kafka ingestion with regex matching.

3. Experimental Features:

  • Introduced window functions in SQL.
  • Experimental support for concurrent append and replace.

Improvements:

1. Async Query and Deep Storage:

  • MSQ engine fetches real-time data for improved query results.
  • Added Azure BLOB storage support for fault-tolerant storage.

2. Array and UNNEST Enhancements:

  • SQL-based ingestion supports true arrays.
  • General availability of SQL UNNEST for flexible reporting.

3. Performance Optimizations:

  • Improved segment file handling for better query performance.

4. ANSI SQL Support Upgrade:

  • Upgrade to Calcite 1.35 for improved query planning.
  • Enabled NULL support and ANSI SQL standard behaviors.

Bug Fixes:

  1. Various bug fixes in query planning and correctness.
  2. Addressed issues with compaction tasks and late arrival data.

For detailed information, refer to the full release notes.

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