ClickHouse

ClickHouse is a columnar DBMS for Online Analytical Processing (OLAP). It is famous for allowing real-time generation of analytical reports using SQL queries on petabytes of data.

When to Use

• Real-time Analytics: User-facing dashboards (Google Analytics style).
• Log Management: A cheaper, faster alternative to Elasticsearch/Splunk for logs (Observability).
• Huge Throughput: Ingesting millions of rows per second.

Quick Start

SELECT
    toStartOfHour(EventTime) as Hour,
    count(),
    avg(Duration)
FROM events
GROUP BY Hour
ORDER BY Hour

Core Concepts

MergeTree Engine

The default table engine. Features primary keys (for sorting/skipping), data partitioning, and background data replication.

Columnar Storage

Stores columns separately. If you select 5 columns out of 100, it only reads those 5 files.

Vectorized Execution

Processes data in blocks (Vectors), maximizing CPU cache and SIMD usage.

Best Practices (2025)

Do:

• Insert in Batches: Never insert row-by-row. Batch at least 1,000 rows.
• Use Materialized Views: ClickHouse MVs function like insert triggers. They calculate aggregations on write.
• Use LowCardinality: A data type key for strings with few unique values (Country, OS).

Don’t:

• Don’t use it for OLTP: No real transactions, updates/deletes are “mutations” (heavy async background processes).
• Don’t use standard joins for massive tables: Use dictionaries or JOIN carefully (Right table must fit in RAM or use distributed join).

References

• ClickHouse Documentation