ClickHouse Performance Archives

ClickHouse Partition Pruning vs Primary Key: 3 Proven Gates

ClickHouse partition pruning and primary-key pruning are two different mechanisms that happen to show up on the same summary line of the server log, which is why so many people treat them as one thing. […]

ClickHouse Sharding Troubleshooting and Performance Optimization

ChistaDATA Inc.

A field guide to ClickHouse sharding troubleshooting: Distributed send-queue backlogs, duplicate rows from internal_replication, shard skew, initiator merge bottlenecks, distributed JOIN failures and unavailable shards, each with the system.* evidence, the mechanism, the staged fix, and the alert that catches it early.

[…]

ClickHouse Performance

ClickHouse Performance Troubleshooting: 9 Proven Techniques for 26.7

ChistaDATA Inc.

ClickHouse performance troubleshooting is mostly a reading problem, not a tooling problem. A 26.7 server already writes down almost everything you need — every query it ran, every byte it touched, every microsecond each operator […]

ClickHouse Ingestion Performance: Batch Sizing, Async Inserts, and Kafka Pipelines

ChistaDATA Inc.

ClickHouse ingestion performance is decided long before the query planner ever touches your data. It is decided by how many parts your writers create per second, how much background merge work those parts generate, and […]

ClickHouse Merge Performance: Diagnosing Too Many Parts, Slow Merges, and Stuck Mutations

ChistaDATA Inc.

ClickHouse merge performance is the hinge on which every high-ingest MergeTree deployment turns. When background merges keep pace with inserts, part counts stay flat and SELECT latency stays predictable. When they fall behind, the failure […]

Scaling ClickHouse Horizontally: Sharding, Distributed Tables, and Parallel Replicas

ChistaDATA Inc.

Horizontal scale is where most ClickHouse fleets accumulate their deepest operational debt. ClickHouse sharding gets introduced months before the workload justifies it, and once a sharding key is baked into a production Distributed table, unwinding […]

ClickHouse MergeTree Optimization: Sort Keys, Partitioning, Skip Indexes, and Projections

ChistaDATA Inc.

ClickHouse MergeTree optimization is the difference between a table that answers analytical queries in milliseconds and one that scans hundreds of gigabytes for every request. This guide is written for senior engineers who already understand […]

ClickHouse Query Performance Tuning: A Production Diagnostic Playbook

ChistaDATA Inc.

Effective ClickHouse query performance tuning is less about magic settings and more about a repeatable diagnostic loop: measure, read the plan, isolate the bottleneck, change one variable, and re-measure. This playbook is written for senior […]

ClickHouse IOPS Troubleshooting: A Complete Guide to Diagnosing and Fixing Disk I/O Bottlenecks

ChistaDATA Inc.

NEEDS FEATURED IMAGE. ClickHouse IOPS troubleshooting guide for DBAs and SREs: diagnose disk I/O bottlenecks with ClickHouse system tables, verify at the OS layer with iostat and pidstat, fix merge storms and part explosion, tune caches, tier storage, and monitor IOPS continuously with Prometheus and Grafana.

[…]

ClickHouse Performance Observability and Monitoring: A Complete Whitepaper

ChistaDATA Inc.

ClickHouse performance observability and monitoring is the discipline of measuring, visualizing, and continuously improving how a ClickHouse cluster behaves under real-world workloads. As organizations adopt ClickHouse to power sub-second analytics over petabyte-scale datasets, the gap […]

* 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.

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

PostgreSQL is a registered trademark of the PostgreSQL Community Association. ClickHouse is a registered trademark of ClickHouse, Inc. MongoDB is a registered trademark of MongoDB, Inc. Couchbase is a registered trademark of Couchbase, Inc. Redis is a registered trademark of Redis Ltd. Apache Cassandra is a registered trademark of the Apache Software Foundation. Milvus is a registered trademark of Zilliz. MinIO is a registered trademark of MinIO, Inc. Amazon Redshift and Amazon Aurora are registered trademarks of [Amazon.com](http://amazon.com/), Inc. Google Cloud is a registered trademark of Google LLC. Snowflake is a registered trademark of Snowflake Inc. Databricks is a registered trademark of Databricks, Inc. MySQL and InnoDB are registered trademarks of Oracle Corporation. MariaDB is a trademark of MariaDB Corporation Ab. All other trademarks are the property of their respective owners. Any other product or company names mentioned may be trademarks or trade names of their respective owners. Copyright © 2010–2026. All Rights Reserved by ChistaDATA®.