ClickHouse vs PostgreSQL 2026: Complete Comparison for Analytics and OLTP

ClickHouse and PostgreSQL represent fundamentally different approaches to data management. PostgreSQL excels as a general-purpose transactional database with analytical extensions, while ClickHouse is purpose-built for high-performance analytical queries. Understanding when to use each—or both together—is essential for building effective data architectures in 2026.

Architecture Fundamentals

PostgreSQL Architecture

PostgreSQL uses a row-oriented storage engine optimised for transactional workloads (OLTP). Each row is stored contiguously on disk, making it efficient to read or write complete records.

Core characteristics:

• ACID-compliant transactions with MVCC
• Row-based storage with B-tree indexes
• Rich SQL support including CTEs, window functions, and JSON
• Extensive extension ecosystem (PostGIS, TimescaleDB, pgvector)
• Strong consistency guarantees

ClickHouse Architecture

ClickHouse uses a column-oriented storage engine designed for analytical queries (OLAP). Data is stored by column, enabling efficient compression and vectorised processing.

Core characteristics:

• Column-oriented storage with aggressive compression
• Vectorised query execution using SIMD instructions
• MergeTree engine family for sorted, partitioned data
• Eventual consistency with async replication
• SQL dialect optimised for analytics

Performance Comparison

Analytical Query Performance

ClickHouse dramatically outperforms PostgreSQL for analytical workloads:

Query Type	PostgreSQL	ClickHouse	Speedup
Full table scan (1B rows)	45 minutes	8 seconds	337x
Aggregation with GROUP BY	12 minutes	1.2 seconds	600x
Time-series rollup	8 minutes	0.5 seconds	960x
Count distinct (high cardinality)	25 minutes	3 seconds	500x

Why ClickHouse is faster for analytics:

• Reads only required columns (vs entire rows)
• Compression reduces I/O by 10-20x
• Vectorised execution processes thousands of values per CPU cycle
• Parallel query execution across cores and nodes

Transactional Performance

PostgreSQL excels at transactional workloads where ClickHouse struggles:

Operation	PostgreSQL	ClickHouse
Single row INSERT	<1ms	50-100ms (batched)
UPDATE by primary key	<1ms	Not supported natively
DELETE by primary key	<1ms	Async (mutations)
Transaction with rollback	Supported	Not supported

Why PostgreSQL is better for OLTP:

• Row-level locking enables concurrent updates
• ACID transactions with rollback capability
• Immediate consistency after writes
• Efficient point lookups by primary key

Data Modelling Differences

PostgreSQL Data Model

PostgreSQL supports normalised schemas with foreign keys and referential integrity:

-- Normalised transactional schema
CREATE TABLE customers (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    created_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE orders (
    id SERIAL PRIMARY KEY,
    customer_id INTEGER REFERENCES customers(id),
    total DECIMAL(10,2) NOT NULL,
    status VARCHAR(50) DEFAULT 'pending',
    created_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX idx_orders_customer ON orders(customer_id);
CREATE INDEX idx_orders_status ON orders(status);

ClickHouse Data Model

ClickHouse favours denormalised, wide tables optimised for query patterns:

-- Denormalised analytical schema
CREATE TABLE order_analytics (
    order_id UInt64,
    order_date Date,
    customer_id UInt64,
    customer_email String,
    customer_segment LowCardinality(String),
    product_id UInt64,
    product_name String,
    product_category LowCardinality(String),
    quantity UInt32,
    unit_price Decimal(10,2),
    total_amount Decimal(10,2),
    country LowCardinality(String)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(order_date)
ORDER BY (customer_segment, order_date, customer_id)

Feature Comparison

Feature	PostgreSQL	ClickHouse
ACID Transactions	Full support	Limited
JOINs	Efficient for normalised data	Best avoided; use denormalisation
UPDATE/DELETE	Native support	Async mutations
Real-time inserts	Yes	Batch recommended
Compression	Limited (TOAST)	10-20x compression
Replication	Sync/async streaming	Async only
Extensions	Extensive ecosystem	Limited
JSON support	Native JSONB	Yes
Full-text search	Yes (tsvector)	Basic
Geospatial	PostGIS	Basic functions

Use Case Recommendations

Choose PostgreSQL When:

• Primary transactional database - User data, orders, inventory requiring ACID
• Complex relationships - Normalised schemas with referential integrity
• Real-time updates - Frequent single-row updates and deletes
• General-purpose needs - Mixed workloads with moderate analytics
• Existing ecosystem - Applications already using PostgreSQL

Choose ClickHouse When:

• Large-scale analytics - Billions of rows with aggregation queries
• Time-series data - Logs, metrics, events, IoT sensor data
• Real-time dashboards - Sub-second query response on large datasets
• Data warehousing - Historical analysis and reporting
• High ingestion rates - Millions of events per second

Use Both Together:

Many architectures combine both databases:

[Application] → [PostgreSQL] → [CDC/Kafka] → [ClickHouse]
     ↓              ↓                              ↓
  OLTP Queries   Transactions              Analytics Queries

Pattern: PostgreSQL for OLTP, ClickHouse for analytics

• PostgreSQL handles transactional workloads
• Change Data Capture streams changes to ClickHouse
• ClickHouse powers dashboards and reports
• Each database optimised for its workload

This pattern is common in cloud native data architectures where different databases serve different purposes.

Operational Considerations

PostgreSQL Operations

Strengths:

• Mature tooling and extensive documentation
• Wide hosting options (RDS, Cloud SQL, managed providers)
• Familiar to most developers and DBAs
• Strong backup and recovery capabilities

Challenges:

• Vacuum overhead for write-heavy workloads
• Connection management at scale
• Analytics queries can impact OLTP performance

ClickHouse Operations

Strengths:

• Minimal tuning required for analytical performance
• Efficient resource utilisation
• Simple horizontal scaling
• Low storage costs due to compression

Challenges:

• Mutations (UPDATE/DELETE) require careful planning
• Less mature ecosystem than PostgreSQL
• Requires different data modelling mindset
• Fewer managed service options

For production deployments, integrating both databases with comprehensive observability ensures visibility into performance and health.

Migration Strategies

PostgreSQL to ClickHouse (Analytics)

When offloading analytics from PostgreSQL to ClickHouse:

1. Identify analytical queries - Find slow aggregation queries
2. Design ClickHouse schema - Denormalise for query patterns
3. Set up data pipeline - CDC with Debezium or direct ETL
4. Migrate historical data - Bulk load existing data
5. Redirect analytics queries - Point dashboards to ClickHouse
6. Monitor and optimise - Tune ClickHouse for specific queries

Data Pipeline Example

-- ClickHouse materialized view for real-time aggregation
CREATE MATERIALIZED VIEW daily_sales_mv
ENGINE = SummingMergeTree()
ORDER BY (product_category, sale_date)
AS SELECT
    product_category,
    toDate(created_at) AS sale_date,
    count() AS order_count,
    sum(total_amount) AS revenue
FROM orders_raw
GROUP BY product_category, sale_date

Cost Comparison

Storage Costs

ClickHouse typically requires 5-10x less storage than PostgreSQL for the same data due to columnar compression:

Data Volume	PostgreSQL Storage	ClickHouse Storage
100M rows	50 GB	5-8 GB
1B rows	500 GB	50-80 GB
10B rows	5 TB	500-800 GB

Compute Costs

• PostgreSQL: Requires more resources for analytical queries
• ClickHouse: Efficient resource utilisation for analytics, but needs adequate memory

For cost-optimised architectures, see our guide on AWS cloud cost optimisation.

Conclusion

PostgreSQL and ClickHouse serve different purposes and often complement each other:

Choose PostgreSQL for transactional workloads, complex relationships, and real-time updates where ACID compliance matters.

Choose ClickHouse for analytical workloads, time-series data, and dashboards where query speed on large datasets is critical.

Use both when you need strong transactional capabilities and high-performance analytics—let each database do what it does best.

The decision isn’t PostgreSQL or ClickHouse but rather understanding where each fits in your data architecture. Many successful organisations use PostgreSQL as their operational database while streaming data to ClickHouse for analytics, achieving the best of both worlds.

For help designing your data architecture, contact our team to discuss your requirements.

Related Resources

• How Tasrie IT Services Uses ClickHouse
• Top 10 NoSQL Databases in 2026
• Cloud Native Database Guide 2026
• Understanding the CAP Theorem

External Resources:

• ClickHouse Documentation
• PostgreSQL Documentation
• ClickHouse vs PostgreSQL Benchmark