ClickHouse Reliability Archives

Real-Time Payments Analytics on ClickHouse: 6 Proven Layers for Southeast Asia

A reference model for real-time payments analytics on ClickHouse with the Apache stack (Kafka, Flink, Iceberg, Spark, Airflow, Superset) for a high-volume Southeast Asian mobile payment platform: schema, ingestion, serving, residency and operations.

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ClickHouse Troubleshooting Techniques: 8 Proven Drills We Teach

ChistaDATA Inc.

The ClickHouse troubleshooting techniques taught in CHT-400 at ChistaDATA University: a scope, prove, fix method and the diagnostic queries for eight production incidents, from too many parts to distributed timeouts.

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ClickHouse 26.8 LTS: 7 Essential Performance and HA Changes

ChistaDATA Inc.

ClickHouse 26.8 LTS read for production: adaptive aggregation, IEJoin, plan-based parallel replicas, Keeper on disk, always_fetch_mutated_part, and the 26.3 to 26.8 breaking-change checklist for self-managed and Cloud.

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Lakehouse ClickHouse With 3 Signed SLOs: Proven DAPE Platform

ChistaDATA Inc.

Lakehouse ClickHouse sold as an engineered platform: ChistaDATA DAPE signs SLOs for freshness across tiers, p95/p99 per tier and query class, and a measured cost curve — commitments that neither software-plus-support nor cloud-consumption contracts carry. With SQL, storage-policy config, and diagrams.

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ClickHouse Upgrade Guide: 6 Proven Steps for Zero-Downtime Rolling LTS Upgrades

ChistaDATA Inc.

A production ClickHouse upgrade done properly is a rolling, replica-by-replica operation with zero query downtime, a measured before/after regression check, and a rollback posture decided before the first package is installed. Done casually, it is […]

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 […]

Data Reliability Engineering for ClickHouse: Principles and Practices

ChistaDATA Inc.

Data Reliability Engineering ClickHouse (DRE) is the discipline that keeps analytical data trustworthy at every stage of its lifecycle — ingestion, transformation, storage, and serving. For teams running ClickHouse as their analytical backbone, DRE is […]

Data Compression in ClickHouse for Performance and Scalability

ChistaDATA Inc.

Implementing Data Compression in ClickHouse: A Complete Guide to Optimal Performance and Scalability Introduction Data compression in ClickHouse is a critical optimization technique that can dramatically improve query performance, reduce storage costs, and enhance overall […]

Overview of ClickHouse Architecture and Query Performance Techniques

Shiv Iyer

Introduction: ClickHouse Architecture Understanding the internals of ClickHouse reveals why it’s renowned for its exceptional performance, especially in the realm of online analytical processing (OLAP). ClickHouse is a column-oriented database management system (DBMS) that employs […]

ClickHouse MergeTree: Overview of ClickHouse Storage Engines

Shiv Iyer

Introduction ClickHouse supports several storage engines, each optimized for different use cases. Understanding the characteristics of each engine can help you choose the right one for your specific needs, thereby improving performance. ClickHouse MergeTree Engine […]

* 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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