ClickHouse Query Performance 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 Performance Settings in 26.8 LTS: 14 Proven Changes

ChistaDATA Inc.

The ClickHouse performance settings that changed between 26.3 LTS and 26.8 LTS: what each default does to ingest, aggregation, joins and distributed execution, how to measure it, and the rollout method we use.

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Real-Time Analytics on ClickHouse 26.8: 6 Proven Levers

ChistaDATA Inc.

Real-time analytics on ClickHouse 26.8: the ingest-to-dashboard latency budget, async-insert freshness, top-K and cache serving, lightweight UPDATE, and ingest/serving isolation on self-managed clusters versus ClickHouse Cloud.

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ClickHouse Explain

ClickHouse EXPLAIN PIPELINE: A Proven 7-Step Guide to Decoding Query Execution Bottlenecks

ChistaDATA Inc.

Read ClickHouse EXPLAIN PIPELINE like a principal engineer: processor names, x1 choke points, and measured confirmation via system.processors_profile_log.

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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 […]

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 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 […]

7 Powerful ClickHouse Matrices for Troubleshooting Query and IOPS Performance

ChistaDATA Inc.

When a ClickHouse cluster starts feeling sluggish, the hardest part is rarely fixing the problem — it’s finding it. Slow queries, saturated disks, and unpredictable latency all leave fingerprints, but those fingerprints are scattered across […]

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