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ClickHouse 25.9: A Deep Dive into the New Release

Sanjeev Kumar G avatar
ClickHouse 25.9: A Deep Dive into the New Release

ClickHouse has released version 25.9, bringing new features, important fixes, and performance improvements. This post summarizes the most relevant updates in a beginner-friendly way.

1. Backward Incompatible Changes

  • IPv4/IPv6 operations tightened: Adding or subtracting IP addresses with non-integer types (like floats or DateTime) is now disabled. Earlier, it led to confusing results. Now, only integer offsets are allowed.
  • Dynamic metadata for Iceberg removed: The setting allow_dynamic_metadata_for_data_lakes is deprecated. Iceberg tables will now always refresh schema from storage before running a query.
  • Outer JOIN resolution fixed: When using USING in OUTER JOIN, the coalesced column now behaves consistently. Queries will return the correct combined value instead of defaulting to the left side.

2. Major New Features

  • NATS JetStream support: ClickHouse can now consume messages directly using NATS JetStream.
  • ArrowFlight improvements: Authentication and SSL are now supported, making it safer to use in production.
  • S3 storage class option: New parameter storage_class_name allows setting AWS storage tiers (e.g., intelligent-tiering).
  • Iceberg and Delta Lake upgrades:
    • ALTER UPDATE is supported for Iceberg tables.
    • New system table iceberg_metadata_log for inspecting metadata.
    • Support for ORC and Avro writes.
    • Azure disks and Unity Catalog integration added.
  • New functions:
    • arrayExcept() → subtract one array from another.
    • isValidASCII() → check if a string is ASCII only.
  • System tables:
    • database_replicas → view database replica info.
    • system.aggregated_zookeeper_log → stats on ZooKeeper operations.
  • Temporary Views: You can now create temporary views (CREATE TEMPORARY VIEW) similar to temporary tables.

3. Performance Improvements

  • Join optimization:
    • Automatic join reordering for faster execution.
    • Conversions of certain joins (ANY → SEMI/ANTI, FULL → LEFT/RIGHT) for efficiency.
  • Better indexing: Skip indexes can now reduce unnecessary reads.
  • Faster merges and updates: Improvements in vertical merges, lightweight deletes, and patch part application.
  • Radix sort optimization: Improved sorting speed, especially on Intel CPUs.
  • Iceberg writes: Reduced memory usage and support for multiple files in one insert.
  • Short query handling: Faster execution when working with many small parts.

4. Bug Fixes and Stability

This release includes many fixes addressing crashes, logical errors, and edge cases. Highlights:

  • More reliable behavior when altering replicated databases.
  • Better handling of shutdown and recovery for replicas.
  • Fixes in Delta Lake and Iceberg engines (schema resolution, metadata consistency).
  • Improved compatibility for ArrowFlight, Hive, and Parquet.
  • Many memory management and concurrency bug fixes, reducing risks of deadlocks and crashes.

5. Conclusion

ClickHouse 25.9 strengthens stability, especially for data lake integrations (Iceberg, Delta Lake, S3, Azure), while also improving performance of joins, indexing, and query execution. Developers now get new functions (arrayExcept, isValidASCII), better system visibility, and safer operations for distributed environments.

If you rely on Iceberg, Delta Lake, or S3, this update is especially relevant. As always, review the backward incompatible changes before upgrading.

Exploring ClickHouse for Your Analytics?

At Quantrail, we’ve spent time understanding the challenges teams face with real-time analytics. That’s why we provide hands-on support around ClickHouse – from smooth deployments and guided migrations to ongoing service agreements that keep systems reliable.

At Quantrail, we see these updates in action when helping teams adopt ClickHouse for demanding analytics pipelines. For example, we recently shared how a customer achieved bare-metal level performance with ClickHouse in production – you can read the full story here Success Story: Quantrail Baremetal

Whether you’re moving from another database or simply want to get the best out of ClickHouse, our goal is to make the process straightforward and sustainable. If this resonates, we’d be happy to share how others are approaching it and explore what could work for you.

Contact

Quantrail Data

References

https://clickhouse.com/docs/whats-new/changelog#259