What Is ClickHouse and Why Analytics Teams Use It

The short answer

ClickHouse is an open-source columnar database management system for OLAP — online analytical processing. Its job is to answer questions like "how many purchases per city per day over the last year" across very large tables, fast enough for dashboards and ad-hoc exploration.

Analytics teams pick it because it combines high query speed, strong compression and plain SQL that analysts already know.

Columnar storage: why it is fast

A classic database like PostgreSQL stores data by rows: all fields of one record sit together. That is ideal for "fetch order #1042 and update its status".

ClickHouse stores data by columns: all values of city together, all values of amount together, and so on. For analytics this changes everything:

• A query that uses 3 columns out of 50 reads only those 3 from disk.
• Values in one column are similar, so they compress very well.
• Processing works on batches of values (vectorized execution), which uses the CPU efficiently.

Compression is configurable per column. LZ4 is the default; ZSTD gives a higher ratio, and specialized codecs such as Delta, DoubleDelta or Gorilla help with timestamps and metrics.

MergeTree: the core table engine

Most ClickHouse tables use the MergeTree family of engines:

• Each insert creates an immutable part on disk; background processes merge parts into larger ones.
• ORDER BY defines how data is sorted and builds a sparse primary index — it points to blocks of rows (granules), not to every row.
• PARTITION BY splits data, usually by month or day, so old partitions are easy to drop.

Specialized variants solve common tasks: ReplacingMergeTree (keeps the latest version of a row), SummingMergeTree and AggregatingMergeTree (pre-aggregate during merges), and Replicated versions for replication.

CREATE TABLE events
(
    event_time DateTime,
    user_id    UInt64,
    event_type LowCardinality(String),
    url        String
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(event_time)
ORDER BY (event_type, event_time);

SELECT event_type, count() AS events, uniq(user_id) AS users
FROM events
WHERE event_time >= now() - INTERVAL 7 DAY
GROUP BY event_type
ORDER BY events DESC;

Typical workloads

• Product and web analytics: clicks, page views, funnels, retention.
• Event logs from applications and services.
• Observability: logs, metrics and traces at large volume.
• Ad tech and marketing: impressions, attribution, campaign reports.
• Business dashboards in BI tools that need to stay fast on large history.

The common pattern: lots of append-only events, rare changes, queries that aggregate many rows.

ClickHouse vs PostgreSQL

In practice, many systems use both: PostgreSQL runs the product, while events and copies of business data flow into ClickHouse through ETL or change data capture for reporting.

Common mistakes

• Inserting one row at a time. Every insert creates a part; thousands of tiny inserts overload merges. Batch them or use asynchronous inserts.
• Choosing ORDER BY carelessly. Put the columns you filter by most often first.
• Treating it like an OLTP database with frequent updates of individual rows.
• Moving to ClickHouse too early. If PostgreSQL with good indexes handles your reports, you may not need a second database yet.