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ClickHouse

by ClickHouse Inc.

Open-source column-oriented OLAP database with sub-second analytical query performance across billions of rows — purpose-built for APAC real-time analytics dashboards, log analytics, and high-ingestion event data workloads.

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AIMenta verdict
Recommended
5/5

"ClickHouse is the open-source OLAP database for APAC analytics teams — sub-second queries across billions of rows for real-time dashboards and log analytics. Best for APAC data engineering teams needing fast aggregation queries over large event and log datasets."

Features
7
Use cases
4
Watch outs
4
What it does

Key features

  • Columnar storage — column-oriented layout with LZ4/ZSTD compression for APAC analytical query performance
  • MergeTree engine — ClickHouse's primary table engine with primary key indexing and automatic data merging
  • Materialized views — pre-computed aggregations maintained automatically on insert for APAC real-time dashboards
  • Distributed tables — horizontal sharding across ClickHouse nodes for APAC petabyte-scale deployments
  • Kafka integration — native Kafka engine for real-time ingestion from APAC event streams
  • S3 integration — direct query of Parquet, CSV, and JSON files in S3 without data loading
  • ClickHouse Cloud — managed ClickHouse in AWS/GCP/Azure APAC regions with auto-scaling
When to reach for it

Best for

  • APAC analytics engineering teams building real-time dashboards over billions of event records requiring sub-second query response
  • Data engineering teams running log analytics (nginx, application logs, security events) at APAC production scale
  • APAC product analytics teams computing funnel analysis, cohort analysis, and retention metrics over large event datasets
  • Engineering teams wanting ClickHouse as the analytical backend for Grafana or Superset APAC operational dashboards
Don't get burned

Limitations to know

  • ! ClickHouse is not a transactional database — it does not support ACID transactions; APAC workloads requiring row-level updates should use a relational database as primary and ClickHouse as analytical replica
  • ! ClickHouse JOIN performance degrades with large right-side tables — APAC queries with large-to-large JOINs require pre-materialised join results or query restructuring
  • ! Self-hosted ClickHouse cluster operations — ZooKeeper/ClickHouse Keeper coordination, shard and replica configuration, and MergeTree tuning require dedicated platform expertise in APAC teams
  • ! ClickHouse SQL has APAC compatibility caveats — some standard SQL features behave differently; APAC teams migrating queries from PostgreSQL or MySQL should test query compatibility
Context

About ClickHouse

ClickHouse is an open-source column-oriented OLAP (Online Analytical Processing) database originally developed at Yandex that provides APAC analytics engineering and data engineering teams with sub-second query performance on analytical workloads across billions of rows — enabling real-time dashboard queries, log analytics, and event data analysis at APAC production data volumes that relational databases and data warehouses cannot serve at interactive query latency.

ClickHouse's column-oriented storage model — where data for each column is stored contiguously on disk rather than row-by-row, enabling analytical queries that access only the columns needed for aggregation to read a fraction of the data a row-oriented database would scan — is the architectural foundation of ClickHouse's query performance. An APAC analytics query computing daily revenue by country from a 10-billion-row events table reads only the `date`, `country`, and `amount` columns rather than all columns for every row — reducing I/O by 90%+ compared to MySQL or PostgreSQL for typical analytical query patterns.

ClickHouse's compression model — which applies LZ4 or ZSTD compression per column, achieving 5–10× compression ratios on analytical datasets where column values have high cardinality repetition (dates, country codes, event types, boolean flags) — further reduces the I/O cost of analytical queries by allowing ClickHouse to decompress only the data actually needed for each query. APAC log analytics deployments storing billions of nginx access log records achieve 5–8× compression with ClickHouse column-oriented storage.

ClickHouse's materialized views — which automatically maintain pre-computed aggregations (daily counts, hourly averages, running totals) as data is ingested, without requiring separate batch ETL jobs — enable APAC analytics engineering teams to serve real-time dashboard queries from pre-computed summaries that return in milliseconds regardless of underlying data volume. APAC e-commerce real-time dashboards showing live revenue, order counts, and conversion rates by APAC market serve sub-100ms query responses through ClickHouse materialized views over billions of raw event rows.

ClickHouse Cloud — the managed ClickHouse service available in AWS, GCP, and Azure APAC regions — provides APAC data engineering teams with the full ClickHouse query engine with automated scaling, backup, and cluster management, eliminating the operational overhead of self-hosted ClickHouse cluster administration.

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