ClickHouse vs Postgres for analytics workloads

Postgres and ClickHouse® both run SQL. They are not interchangeable. Postgres is a row-oriented OLTP database built for transactions, constraints, and single-row updates. ClickHouse is a columnar OLAP database built for aggregating large event datasets with append-optimized ingestion.

Most production teams that compare ClickHouse vs Postgres are not choosing one database for everything. They are deciding whether analytics stays on Postgres or moves to ClickHouse while Postgres continues handling application transactions.

Storage model

Postgres stores each row as a contiguous block on disk with all columns together. Fetching or updating a user record reads one row from one location. This layout optimizes OLTP access patterns.

ClickHouse stores each column separately. A query that aggregates sum(revenue) and count() over millions of rows reads only the columns it needs, skipping the rest. Per ClickHouse documentation, columnar storage and vectorized execution are the core reasons analytical queries run faster on ClickHouse at large scale.

For when to use a columnar database, the storage model difference is the first decision point.

Workload fit

Postgres handles your application's users, orders, and inventory. ClickHouse handles events, metrics, and logs that accumulate continuously.

For OLTP vs OLAP, the workload type matters more than any single feature comparison.

Performance at scale

Postgres analytical queries work on moderate data volumes. As tables reach hundreds of millions of rows, aggregate queries that scan large portions of the table compete with transactional writes for the same resources.

ClickHouse aggregation queries over the same data volume can run much faster when the sort key aligns with query filters, because the engine reads compressed column blocks and processes them with vectorized execution.

The ClickHouse published benchmark results show ClickHouse leading OLAP benchmark categories against other analytical systems. Postgres does not target those workloads.

For analytics workloads on Postgres, teams typically hit the ceiling when dashboards time out, nightly ETL jobs fall behind, or aggregate queries require dedicated read replicas.

What Postgres extensions change

Postgres extensions like TimescaleDB, Citus, and pg_mooncake add time-series, distribution, or columnar capabilities to Postgres. They reduce the performance gap for specific patterns without leaving the Postgres ecosystem.

Extensions help when:

• Data volume is moderate and team expertise is Postgres-centric
• You need transactional and analytical data in one database for simplicity
• Migration to a separate OLAP engine is not yet justified

ClickHouse still leads when event volume reaches billions of rows, query concurrency is high, and sub-second dashboard latency is a product requirement. For extension-level detail, see ClickHouse vs PostgreSQL with extensions.

The hybrid architecture

The pattern that scales for most teams: Postgres stays the system of record. ClickHouse handles analytics via CDC.

Postgres (OLTP) → logical replication/CDC → Kafka → ClickHouse/Tinybird → SQL APIs

Postgres continues serving application transactions unchanged. Changes stream to ClickHouse where columnar storage handles aggregation queries.

For Postgres CDC, logical replication slots export insert, update, and delete events as a change stream. On the ClickHouse side, ReplacingMergeTree handles upserts from CDC.

The migration trigger is usually dashboard latency and replica load, not a specific row count.

Schema differences

Postgres schemas use normalized tables with foreign keys. ClickHouse analytics schemas use denormalized event tables optimized for append-only writes:

-- Postgres: normalized
CREATE TABLE orders (id SERIAL PRIMARY KEY, user_id INT, amount NUMERIC);
CREATE TABLE users (id SERIAL PRIMARY KEY, email TEXT);

-- ClickHouse: denormalized events
CREATE TABLE order_events
(
    event_time  DateTime64(3),
    user_id     String,
    email       String,
    amount      Float64,
    event_type  LowCardinality(String)
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (user_id, event_time);

Denormalization is intentional in ClickHouse. Joins work but wide event tables with pre-joined dimensions outperform star-schema joins at billion-row scale.

The CDC pipeline maps Postgres row changes into ClickHouse event rows without dual writes from the application.

When to keep analytics on Postgres

Stay on Postgres when:

• Tables are under tens of millions of rows and queries complete in acceptable time
• Analytics needs are simple reports, not sub-second dashboards
• Team has no capacity to operate a second database
• Extensions like TimescaleDB cover your time-series requirements

Optimize Postgres first: indexes, materialized views, read replicas, partitioning. Those optimizations buy time but do not change the underlying row-oriented storage model.

When to add ClickHouse

Add ClickHouse when:

• Aggregate queries timeout or require aggressive caching
• Event volume grows continuously with no plateau
• Product features need real-time analytics APIs, not batch reports
• Read replicas exist primarily to serve analytics, not HA

For best database for OLAP, ClickHouse is the engine most teams pair with Postgres for the analytical layer.

Tinybird for Postgres teams

Tinybird is managed ClickHouse for moving analytical workloads off Postgres. Per Tinybird's Postgres migration guide, you choose the sync path that fits your freshness and table size:

1. PostgreSQL table function for periodic Copy Pipe imports of smaller or dimension tables.
2. CDC through Kafka for real-time sync: a CDC tool (Debezium, Redpanda Connect, Estuary, or similar) captures logical replication changes, writes them to a Kafka-compatible topic, and Tinybird's Kafka connector consumes the stream.
3. Events API when your application writes new analytical events directly to Tinybird.

Postgres continues handling transactions. Tinybird handles the analytical SQL and API layer without you operating ClickHouse clusters. Write SQL Pipes for the queries your dashboards and APIs need, then publish them as sub-second API endpoints. For multi-tenant access, use JWTs with fixed parameters so each tenant's requests are scoped to their data.

Resend processes 100TB per month on Tinybird with 62ms p90 query latency without relying on cache, per Tinybird's Resend customer story. Tinybird is SOC 2 Type II certified.

Branches give you zero-copy environments with prod data for iterating on analytics schema and Pipes alongside application changes.

What the choice comes down to

Postgres wins for transactions, data integrity, and ecosystem compatibility. ClickHouse wins for analytical query speed, event ingestion throughput, and storage efficiency at scale.

The question is not ClickHouse or Postgres. It is whether analytics stays on Postgres long enough to justify the performance cost, or moves to ClickHouse via CDC while Postgres keeps doing what it does best.