What ClickHouse's Latest Release 26.5 Says About the Future of AI Infrastructure

Kanishga Subramani — Tue, 02 Jun 2026 09:45:18 +0000

AI applications are generating more data than ever before. From model telemetry and user interactions to observability events and real-time analytics, modern systems need infrastructure that can ingest, process, and query massive datasets with low latency.

That's exactly the problem ClickHouse is targeting with its latest release.

The update introduces improvements across query performance, memory management, Kafka integration, lakehouse support, and developer tooling. While many of these changes appear incremental on the surface, together they highlight a much larger shift happening across the industry.

One of the most notable additions is improved memory management for large joins. ClickHouse can now automatically spill hash joins to disk when memory usage exceeds configured thresholds. Instead of failing due to memory pressure, queries can continue running using more efficient execution strategies. For teams working with large feature tables, event enrichment, AI telemetry, or observability data, this can significantly improve reliability.

The release also expands ClickHouse's Kafka capabilities with Schema Registry integration, AvroConfluent write support, metadata mapping, and zone-aware communication. These improvements make it easier to integrate ClickHouse into real-time event pipelines while reducing latency and unnecessary cross-zone traffic in cloud environments.

Another major focus is support for modern lakehouse architectures. Improvements for Apache Iceberg and Apache Paimon strengthen ClickHouse's ability to query data stored in open table formats while maintaining high analytical performance. As more organizations separate storage and compute, ClickHouse is increasingly positioning itself as a high-speed query layer on top of cloud-native data lakes.

Performance optimization remains a major theme throughout the release. Improvements include faster JOIN execution, better ORDER BY LIMIT performance, enhanced JSON processing, smarter index pruning, reduced lock contention, and optimizations for object storage workloads. While these changes may seem low-level, they can have a meaningful impact when processing billions of rows or running large distributed analytics workloads.

The developer experience also continues to improve. An experimental web terminal, query editor enhancements, syntax highlighting, prepared statements, and caching improvements make ClickHouse easier to work with and reinforce a broader trend toward more interactive and developer-friendly data platforms.

The most interesting aspect of this release isn't any individual feature. It's what these updates collectively represent. Modern databases are no longer just systems for storing data. They are becoming real-time analytics engines, streaming platforms, observability backends, and AI infrastructure layers capable of supporting increasingly demanding workloads.

As AI adoption continues to accelerate, organizations will need data platforms that can process, analyze, and act on information in real time. ClickHouse's latest release demonstrates how rapidly the data infrastructure landscape is evolving and why performance, scalability, and operational efficiency are becoming critical competitive advantages in the AI era.

How Slow Query Debugging in ClickHouse Becomes a Manual Nightmare

Kanishga Subramani — Tue, 02 Jun 2026 08:02:31 +0000

Modern data platforms are expected to deliver analytics in real time. Teams rely on fast queries to power dashboards, AI pipelines, monitoring systems, and customer-facing applications. But when a query suddenly becomes slow inside ClickHouse, debugging the issue is rarely straightforward.

For many database administrators and engineers, identifying the root cause of a slow query turns into a time-consuming manual investigation spread across multiple system tables and logs.

The process usually starts with system.query_log. This table provides the basic information needed to identify problematic queries. Engineers search using the query_id and inspect metrics like read_rows, memory_usage, query_duration_ms, and elapsed execution time. While these numbers can confirm that a query is slow, they often fail to explain why the slowdown happened.

At this point, the investigation becomes more complicated.

If deeper analysis is required, the DBA has to move into system.trace_log for CPU profiling data. This log contains low-level tracing information that helps identify where CPU time was spent during execution. However, interpreting this data is difficult because it is highly technical and not designed for quick operational debugging. Engineers may need to correlate stack traces, thread activity, or execution samples manually just to isolate a bottleneck.

The complexity increases even further when resource usage changes over time need to be analyzed. For that, teams often turn to system.query_metric_log, which stores per-second snapshots of query metrics such as memory consumption, CPU usage, and I/O activity. While valuable, this information exists separately from the original query logs, forcing engineers to manually combine datasets from multiple sources.

This fragmented workflow creates several major operational challenges.

First, every debugging step requires writing different SQL queries against different system tables. Engineers constantly switch context between logs, timestamps, and metrics while trying to correlate events manually. Even experienced DBAs can spend significant time piecing together the full picture.

Second, the data itself is presented mostly as raw numbers. There is no unified visualization showing how memory usage changed during execution, where CPU spikes occurred, or how resource consumption evolved over time. As a result, teams often export logs into external monitoring tools or spreadsheets just to understand performance behavior visually.

Third, debugging becomes highly dependent on expert knowledge. Junior engineers may know how to check query duration, but understanding trace logs or correlating metric snapshots requires deep familiarity with ClickHouse internals. This creates operational bottlenecks where only a few experienced team members can effectively troubleshoot production performance issues.

The problem becomes even more severe in environments running thousands of queries per minute. Large-scale analytics platforms cannot afford long debugging cycles when slow queries impact dashboards, customers, or downstream AI systems. Without centralized visibility, identifying root causes becomes slower than the incidents themselves.

What teams increasingly need is a single-pane performance investigation workflow – one that automatically correlates query logs, CPU traces, memory usage, and resource metrics into a unified debugging experience. Instead of manually querying multiple system tables, engineers should be able to trace query behavior visually and immediately identify bottlenecks.

As ClickHouse deployments continue scaling across AI, observability, and real-time analytics workloads, simplifying slow-query debugging is becoming less of a convenience and more of a necessity.

DEV Community: Kanishga Subramani

What ClickHouse's Latest Release 26.5 Says About the Future of AI Infrastructure

How Slow Query Debugging in ClickHouse Becomes a Manual Nightmare