ClickHouse vs TimescaleDB 2026: Time-Series Database Comparison

ClickHouse and TimescaleDB are both excellent choices for time-series data, but with different architectural foundations. TimescaleDB extends PostgreSQL with time-series optimisations, while ClickHouse is purpose-built for analytical workloads. This comparison helps you choose the right database for your time-series requirements.

Platform Overview

ClickHouse

ClickHouse is a columnar analytical database designed for fast aggregations on large datasets, with excellent performance for time-series workloads.

Time-series strengths:

• Blazing fast aggregations
• Exceptional compression
• Real-time ingestion
• Horizontal scaling
• Native time-series functions

TimescaleDB

TimescaleDB is a PostgreSQL extension that adds time-series capabilities while maintaining full PostgreSQL compatibility.

Time-series strengths:

• Full PostgreSQL compatibility
• Automatic partitioning (hypertables)
• Continuous aggregates
• Native compression
• Familiar SQL ecosystem

Architecture Comparison

ClickHouse Time-Series Architecture

-- Optimised time-series table
CREATE TABLE metrics (
    timestamp DateTime64(3),
    metric_name LowCardinality(String),
    tags Map(LowCardinality(String), String),
    value Float64
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (metric_name, timestamp)
TTL timestamp + INTERVAL 90 DAY

Key features:

• Columnar storage for efficient scans
• Partitioning by time periods
• Sorted data for range queries
• Automatic TTL-based deletion

TimescaleDB Time-Series Architecture

-- Hypertable for time-series
CREATE TABLE metrics (
    timestamp TIMESTAMPTZ NOT NULL,
    metric_name TEXT NOT NULL,
    tags JSONB,
    value DOUBLE PRECISION
);

SELECT create_hypertable('metrics', 'timestamp');

-- Add compression
ALTER TABLE metrics SET (
    timescaledb.compress,
    timescaledb.compress_segmentby = 'metric_name'
);

Key features:

• Automatic time-based partitioning
• Transparent chunk management
• Native PostgreSQL compatibility
• Built-in compression policies

Performance Comparison

Query Performance

Query Type	ClickHouse	TimescaleDB
Simple aggregation (1B rows)	0.5s	3-5s
Time-bucket aggregation	0.3s	1-2s
Downsampling query	0.8s	2-4s
Last value per series	0.2s	0.5-1s
High-cardinality GROUP BY	1s	5-10s
Complex JOIN	2s	1-2s

ClickHouse advantages:

• 3-10x faster for pure analytical queries
• Better performance at extreme scale
• More efficient high-cardinality handling

TimescaleDB advantages:

• Better JOIN performance (PostgreSQL optimiser)
• Continuous aggregates reduce query load
• More efficient for mixed OLTP/OLAP

Ingestion Performance

Metric	ClickHouse	TimescaleDB
Ingestion rate	1M+ rows/sec	100K-500K rows/sec
Batch insert	Excellent	Good
Single-row insert	Good (batched)	Excellent
Write latency	<100ms	<10ms

Compression Comparison

Data Type	ClickHouse	TimescaleDB
Metrics data	15-25x	10-15x
IoT sensor data	20-30x	12-18x
Log data	10-15x	8-12x

Feature Comparison

Feature	ClickHouse	TimescaleDB
PostgreSQL compatibility	No	Full
Automatic partitioning	Manual config	Automatic (hypertables)
Continuous aggregates	Materialized views	Native support
Compression	Excellent	Very good
JOINs	Basic	Full PostgreSQL
Transactions	Limited	Full ACID
Extensions	Limited	PostgreSQL ecosystem
Horizontal scaling	Native	TimescaleDB multi-node
Managed services	ClickHouse Cloud	Timescale Cloud

Use Case Recommendations

Choose ClickHouse When:

High-volume metrics and monitoring

-- Fast aggregations on billions of data points
SELECT
    toStartOfMinute(timestamp) AS minute,
    metric_name,
    avg(value) AS avg_value,
    max(value) AS max_value,
    quantile(0.99)(value) AS p99
FROM metrics
WHERE timestamp >= now() - INTERVAL 1 HOUR
GROUP BY minute, metric_name
ORDER BY minute DESC

Log analytics with time-series queries

• Combined log and metrics analysis
• High-cardinality tag dimensions
• Long-term retention with compression

Real-time dashboards at scale

• Sub-second query response
• High concurrent query load
• Grafana integration

Choose TimescaleDB When:

PostgreSQL-based applications

-- Leverage PostgreSQL features
SELECT
    time_bucket('1 hour', timestamp) AS hour,
    device_id,
    avg(temperature) AS avg_temp,
    first(temperature, timestamp) AS first_temp,
    last(temperature, timestamp) AS last_temp
FROM sensor_readings
WHERE timestamp > now() - interval '7 days'
GROUP BY hour, device_id
ORDER BY hour DESC;

Mixed transactional and analytical workloads

• Sensor data with device management
• IoT applications with relational context
• Applications requiring JOINs with reference data

Continuous aggregates for common queries

-- Pre-computed rollups
CREATE MATERIALIZED VIEW hourly_metrics
WITH (timescaledb.continuous) AS
SELECT
    time_bucket('1 hour', timestamp) AS hour,
    metric_name,
    avg(value) AS avg_value,
    max(value) AS max_value
FROM metrics
GROUP BY hour, metric_name;

-- Automatic refresh policy
SELECT add_continuous_aggregate_policy('hourly_metrics',
    start_offset => INTERVAL '3 hours',
    end_offset => INTERVAL '1 hour',
    schedule_interval => INTERVAL '1 hour');

Familiar PostgreSQL ecosystem

• Existing PostgreSQL tooling
• PostGIS for geospatial
• Full-text search integration

Observability Integration

ClickHouse for Observability

ClickHouse powers several observability platforms:

• SigNoz: Open-source APM using ClickHouse
• Jaeger: Distributed tracing backend
• Grafana: Native data source plugin

-- Trace analysis query
SELECT
    trace_id,
    span_name,
    service_name,
    duration_ms,
    status_code
FROM traces
WHERE timestamp >= now() - INTERVAL 1 HOUR
  AND duration_ms > 1000
ORDER BY duration_ms DESC
LIMIT 100

TimescaleDB for Observability

TimescaleDB integrates with PostgreSQL-based tools:

• Promscale: Prometheus long-term storage
• pg_prometheus: Prometheus metrics storage
• Standard PostgreSQL monitoring tools

For comprehensive observability options, see our observability platforms guide.

Cost Comparison

Storage Costs

Scenario	ClickHouse	TimescaleDB
1TB raw metrics	~50GB stored	~80GB stored
Monthly storage cost	~$2	~$3-4
1 year retention	~$24	~$40-48

Compute Costs

Workload	ClickHouse	TimescaleDB
Light queries	$100-200/month	$150-300/month
Heavy analytics	$500-1000/month	$800-1500/month

ClickHouse typically offers 30-50% cost savings for pure time-series analytics. For cost optimisation, see our cloud cost guide.

Migration Considerations

PostgreSQL to ClickHouse

When analytics performance becomes critical:

-- Export from PostgreSQL/TimescaleDB
COPY (SELECT * FROM metrics WHERE timestamp > '2025-01-01')
TO '/tmp/metrics.csv' WITH CSV HEADER;

-- Import to ClickHouse
INSERT INTO metrics
SELECT * FROM file('/tmp/metrics.csv', CSVWithNames);

ClickHouse to TimescaleDB

When PostgreSQL compatibility is needed:

-- ClickHouse export
SELECT * FROM metrics
INTO OUTFILE '/tmp/metrics.parquet'
FORMAT Parquet;

-- TimescaleDB import (via PostgreSQL)
-- Use foreign data wrapper or ETL tool

Operational Comparison

ClickHouse Operations

Strengths:

• Simple architecture at scale
• Predictable performance
• Low operational overhead

Challenges:

• Schema changes require planning
• Different SQL dialect
• Smaller ecosystem

TimescaleDB Operations

Strengths:

• Familiar PostgreSQL operations
• Mature tooling and ecosystem
• Easy for PostgreSQL teams

Challenges:

• PostgreSQL tuning knowledge required
• Chunk management at scale
• Resource-intensive for large deployments

Hybrid Architecture

Use both databases for different requirements:

┌─────────────────────────────────────────┐
│              Data Sources               │
└─────────────────┬───────────────────────┘
                  │
    ┌─────────────┴─────────────┐
    ▼                           ▼
┌─────────┐               ┌───────────┐
│ Click   │               │TimescaleDB│
│ House   │               │           │
└────┬────┘               └─────┬─────┘
     │                          │
     ▼                          ▼
High-volume               PostgreSQL-integrated
Analytics                 Applications
Real-time Dashboards      Relational Context
Long-term Storage         Transactional Needs

Conclusion

Both ClickHouse and TimescaleDB excel at time-series workloads with different strengths:

Choose ClickHouse for high-volume analytics, real-time dashboards, and scenarios where query performance and compression are paramount. It’s ideal when you don’t need PostgreSQL compatibility.

Choose TimescaleDB when PostgreSQL compatibility matters, you have mixed OLTP/OLAP workloads, or your team has strong PostgreSQL expertise. Continuous aggregates make it excellent for predictable query patterns.

Consider your ecosystem: If you’re already running PostgreSQL, TimescaleDB is a natural extension. If you’re building a dedicated analytics platform, ClickHouse offers superior performance.

For help selecting the right time-series database, contact our team to discuss your requirements.

Related Resources

• How Tasrie IT Services Uses ClickHouse
• ClickHouse vs PostgreSQL 2026
• Top 10 Observability Platforms
• Cloud Native Database Guide 2026

External Resources:

• ClickHouse Documentation
• TimescaleDB Documentation
• TimescaleDB vs ClickHouse Benchmark