Performance Troubleshooting Archives

ClickHouse system.parts storage diagnosis guide featured image with a merge level pyramid of part levels L0 to L3

How to Read system.parts to Diagnose ClickHouse Storage and Performance Issues

A ClickHouse system.parts query cookbook: compression ratios, part levels, partition sizing, parts_columns drill-downs, index memory, and detached parts.

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Memory Limit Exceeded in ClickHouse: Root Causes and Permanent Fixes

ChistaDATA Inc.

Fix ClickHouse memory limit exceeded (code 241) for good: error taxonomy, external aggregation, join strategy, and layered per-query limits.

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Troubleshooting ClickHouse high CPU usage with the sampling profiler, system.trace_log stacks and flamegraphs

Troubleshooting High CPU Usage in ClickHouse: From system.trace_log to Flamegraphs

ChistaDATA Inc.

Diagnose ClickHouse high CPU usage with the sampling profiler, system.trace_log stacks, and flamegraphs, plus the five hotspots we see most often.

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ClickHouse latency analysis featured image: query duration distribution for one query shape with p50, p95 and p99 markers highlighting the latency tail

ClickHouse p95/p99 Latency Regression Analysis: A Data SRE Playbook

ChistaDATA Inc.

A Data SRE playbook for ClickHouse latency analysis: baseline p95/p99 per query shape, detect regressions in query_log, and fix root causes.

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ClickHouse

Using EXPLAIN PIPELINE to Decode ClickHouse Query Execution Bottlenecks

ChistaDATA Inc.

Learn to read ClickHouse EXPLAIN PIPELINE processor graphs, spot single-threaded stages and pipeline stalls, and fix production bottlenecks.

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ClickHouse slow query troubleshooting: diagnosing query latency with system.query_log, a ChistaDATA production methodology

Diagnosing Slow ClickHouse Queries with system.query_log: A Production Methodology

ChistaDATA Inc.

Master ClickHouse slow query troubleshooting with a production-proven system.query_log methodology to find, classify, and fix query latency fast.

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* Everything changes over time – Our blogs/posts and comments changes over time, That’s how it should be! Whatever we comment from ChistaDATA Inc. Teams (including Shiv Iyer) and other stakeholders or guest bloggers posted here are never permanent, These things worked for us. But, there is no guarantee they will work for you too, When using the recommendations from ChistaDATA or MinervaDB or MinervaSQL or any other online resources / Google, You must test the advice before applying them to your production systems, and always invest for a robust Database DR solution, Thank you for understanding.

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