Quick Start with TimescaleDB
Get started with TimescaleDB in under 10 minutes. This guide will help you run TimescaleDB locally, create your first hypertable with columnstore enabled, write data to the columnstore, and see instant analytical query performance.
What You'll Learn
- How to run TimescaleDB with a one-line install or Docker command
- How to create a hypertable with columnstore enabled
- How to insert data directly to the columnstore
- How to execute analytical queries
Prerequisites
- Docker installed on your machine
- 8GB RAM recommended
psql client (included with PostgreSQL) or any PostgreSQL client like pgAdmin
Step 1: Start TimescaleDB
You have two options to start TimescaleDB:
Option 1: One-line install (Recommended)
The easiest way to get started:
Important: This script is intended for local development and testing only. Do not use it for production deployments. For production-ready installation options, see the TimescaleDB installation guide.
Linux/Mac:
curl -sL https://tsdb.co/start-local | sh
This command:
- Downloads and starts TimescaleDB (if not already downloaded)
- Exposes PostgreSQL on port 6543 (a non-standard port to avoid conflicts with other PostgreSQL instances on port 5432)
- Automatically tunes settings for your environment using timescaledb-tune
- Sets up a persistent data volume
Option 2: Manual Docker command also used for Windows
Alternatively, you can run TimescaleDB directly with Docker:
docker run -d --name timescaledb \
-p 6543:5432 \
-e POSTGRES_PASSWORD=password \
timescale/timescaledb-ha:pg18
Note: We use port 6543 (mapped to container port 5432) to avoid conflicts if you have other PostgreSQL instances running on the standard port 5432.
Wait about 1-2 minutes for TimescaleDB to download & initialize.
Step 2: Connect to TimescaleDB
Connect using psql:
psql -h localhost -p 6543 -U postgres
# When prompted, enter password: password
You should see the PostgreSQL prompt. Verify TimescaleDB is installed:
SELECT extname, extversion FROM pg_extension WHERE extname = 'timescaledb';
Expected output:
extname | extversion
-------------+------------
timescaledb | 2.x.x
Prefer a GUI? If you'd rather use a graphical tool instead of the command line, you can download pgAdmin and connect to TimescaleDB using the same connection details (host: localhost, port: 6543, user: postgres, password: password).
Step 3: Create Your First Hypertable
Let's create a hypertable for IoT sensor data with columnstore enabled:
-- Create a hypertable with automatic columnstore
CREATE TABLE sensor_data (
time TIMESTAMPTZ NOT NULL,
sensor_id TEXT NOT NULL,
temperature DOUBLE PRECISION,
humidity DOUBLE PRECISION,
pressure DOUBLE PRECISION
) WITH (
tsdb.hypertable
);
tsdb.hypertable - Converts this into a TimescaleDB hypertable
See more:
Step 4: Insert Sample Data
Let's add some sample sensor readings:
-- Enable timing to see time to execute queries
\timing on
-- Insert sample data for multiple sensors
-- SET timescaledb.enable_direct_compress_insert = on to insert data directly to the columnstore (columnnar format for performance)
SET timescaledb.enable_direct_compress_insert = on;
INSERT INTO sensor_data (time, sensor_id, temperature, humidity, pressure)
SELECT
time,
'sensor_' || ((random() * 9)::int + 1),
20 + (random() * 15),
40 + (random() * 30),
1000 + (random() * 50)
FROM generate_series(
NOW() - INTERVAL '90 days',
NOW(),
INTERVAL '1 seconds'
) AS time;
-- Once data is inserted into the columnstore we optimize the order and structure
-- this compacts and orders the data in the chunks for optimal query performance and compression
DO $$
DECLARE ch TEXT;
BEGIN
FOR ch IN SELECT show_chunks('sensor_data') LOOP
CALL convert_to_columnstore(ch, recompress := true);
END LOOP;
END $$;
This generates ~7,776,001 readings across 10 sensors over the past 90 days.
Verify the data was inserted:
SELECT COUNT(*) FROM sensor_data;
Step 5: Run Your First Analytical Queries
Now let's run some analytical queries that showcase TimescaleDB's performance:
-- Enable query timing to see performance
\timing on
-- Query 1: Average readings per sensor over the last 7 days
SELECT
sensor_id,
COUNT(*) as readings,
ROUND(AVG(temperature)::numeric, 2) as avg_temp,
ROUND(AVG(humidity)::numeric, 2) as avg_humidity,
ROUND(AVG(pressure)::numeric, 2) as avg_pressure
FROM sensor_data
WHERE time > NOW() - INTERVAL '7 days'
GROUP BY sensor_id
ORDER BY sensor_id;
-- Query 2: Hourly averages using time_bucket
-- Time buckets enable you to aggregate data in hypertables by time interval and calculate summary values.
SELECT
time_bucket('1 hour', time) AS hour,
sensor_id,
ROUND(AVG(temperature)::numeric, 2) as avg_temp,
ROUND(AVG(humidity)::numeric, 2) as avg_humidity
FROM sensor_data
WHERE time > NOW() - INTERVAL '24 hours'
GROUP BY hour, sensor_id
ORDER BY hour DESC, sensor_id
LIMIT 20;
-- Query 3: Daily statistics across all sensors
SELECT
time_bucket('1 day', time) AS day,
COUNT(*) as total_readings,
ROUND(AVG(temperature)::numeric, 2) as avg_temp,
ROUND(MIN(temperature)::numeric, 2) as min_temp,
ROUND(MAX(temperature)::numeric, 2) as max_temp
FROM sensor_data
GROUP BY day
ORDER BY day DESC
LIMIT 10;
-- Query 4: Latest reading for each sensor
-- Highlights the value of Skipscan executing in under 100ms without skipscan it takes over 5sec
SELECT DISTINCT ON (sensor_id)
sensor_id,
time,
ROUND(temperature::numeric, 2) as temperature,
ROUND(humidity::numeric, 2) as humidity,
ROUND(pressure::numeric, 2) as pressure
FROM sensor_data
ORDER BY sensor_id, time DESC;
Notice how fast these analytical queries run, even with aggregations across millions of rows. This is the power of TimescaleDB's columnstore.
What's Happening Behind the Scenes?
TimescaleDB automatically:
- Partitions your data into time-based chunks for efficient querying
- Write directly to columnstore using columnar storage (90%+ compression typical) and faster vectorized queries
- Optimizes queries by only scanning relevant time ranges and columns
- Enables time_bucket() - a powerful function for time-series aggregation
See more:
Next Steps
Now that you've got the basics, explore more:
Create Continuous Aggregates
Continuous aggregates make real-time analytics run faster on very large datasets. They continuously and incrementally refresh a query in the background, so that when you run such query, only the data that has changed needs to be computed, not the entire dataset. This is what makes them different from regular PostgreSQL materialized views, which cannot be incrementally materialized and have to be rebuilt from scratch every time you want to refresh them.
Let's create a continuous aggregate for hourly sensor statistics:
Step 1: Create the Continuous Aggregate
CREATE MATERIALIZED VIEW sensor_data_hourly
WITH (timescaledb.continuous) AS
SELECT
time_bucket('1 hour', time) AS hour,
sensor_id,
AVG(temperature) AS avg_temp,
AVG(humidity) AS avg_humidity,
AVG(pressure) AS avg_pressure,
MIN(temperature) AS min_temp,
MAX(temperature) AS max_temp,
COUNT(*) AS reading_count
FROM sensor_data
GROUP BY hour, sensor_id;
This creates a materialized view that pre-aggregates your sensor data into hourly buckets. The view is automatically populated with existing data.
Step 2: Add a Refresh Policy
To keep the continuous aggregate up-to-date as new data arrives, add a refresh policy:
SELECT add_continuous_aggregate_policy(
'sensor_data_hourly',
start_offset => INTERVAL '3 hours',
end_offset => INTERVAL '1 hour',
schedule_interval => INTERVAL '1 hour'
);
This policy:
- Refreshes the continuous aggregate every hour
- Processes data from 3 hours ago up to 1 hour ago (leaving the most recent hour for real-time queries)
- Only processes new or changed data incrementally
Step 3: Query the Continuous Aggregate
Now you can query the pre-aggregated data for much faster results:
-- Get hourly averages for the last 24 hours
SELECT
hour,
sensor_id,
ROUND(avg_temp::numeric, 2) AS avg_temp,
ROUND(avg_humidity::numeric, 2) AS avg_humidity,
reading_count
FROM sensor_data_hourly
WHERE hour > NOW() - INTERVAL '24 hours'
ORDER BY hour DESC, sensor_id
LIMIT 50;
Benefits of Continuous Aggregates
- Faster queries: Pre-aggregated data means queries run in milliseconds instead of seconds
- Incremental refresh: Only new/changed data is processed, not the entire dataset
- Automatic updates: The refresh policy keeps your aggregates current without manual intervention
- Real-time option: You can enable real-time aggregation to combine materialized and raw data
Try It Yourself
Compare the performance difference:
-- Query the raw hypertable (slower on large datasets)
\timing on
SELECT
time_bucket('1 hour', time) AS hour,
AVG(temperature) AS avg_temp
FROM sensor_data
WHERE time > NOW() - INTERVAL '60 days'
GROUP BY hour
ORDER BY hour DESC
LIMIT 24;
-- Query the continuous aggregate (much faster)
SELECT
hour,
avg_temp
FROM sensor_data_hourly
WHERE hour > NOW() - INTERVAL '60 days'
ORDER BY hour DESC
LIMIT 24;
Notice how the continuous aggregate query is significantly faster, especially as your dataset grows!
See more:
Examples
Learn TimescaleDB with complete, standalone examples using real-world datasets. Each example includes sample data and analytical queries.
Or try some of our workshops
Want TimescaleDB hosted and managed for you? Try Tiger Cloud
Tiger Cloud is the modern PostgreSQL data platform for all your applications. It enhances PostgreSQL to handle time series, events, real-time analytics, and vector search—all in a single database alongside transactional workloads. You get one system that handles live data ingestion, late and out-of-order updates, and low latency queries, with the performance, reliability, and scalability your app needs. Ideal for IoT, crypto, finance, SaaS, and a myriad other domains, Tiger Cloud allows you to build data-heavy, mission-critical apps while retaining the familiarity and reliability of PostgreSQL. See our whitepaper for a deep dive into Tiger Cloud's architecture and how it meets the needs of even the most demanding applications.
A Tiger Cloud service is a single optimized 100% PostgreSQL database instance that you use as is, or extend with capabilities specific to your business needs. The available capabilities are:
- Time-series and analytics: PostgreSQL with TimescaleDB. The PostgreSQL you know and love, supercharged with functionality for storing and querying time-series data at scale for real-time analytics and other use cases. Get faster time-based queries with hypertables, continuous aggregates, and columnar storage. Save on storage with native compression, data retention policies, and bottomless data tiering to Amazon S3.
- AI and vector: PostgreSQL with vector extensions. Use PostgreSQL as a vector database with purpose built extensions for building AI applications from start to scale. Get fast and accurate similarity search with the pgvector and pgvectorscale extensions. Create vector embeddings and perform LLM reasoning on your data with the pgai extension.
- PostgreSQL: the trusted industry-standard RDBMS. Ideal for applications requiring strong data consistency, complex relationships, and advanced querying capabilities. Get ACID compliance, extensive SQL support, JSON handling, and extensibility through custom functions, data types, and extensions.
All services include all the cloud tooling you'd expect for production use: automatic backups, high availability, read replicas, data forking, connection pooling, tiered storage, usage-based storage, and much more.
Check build status
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Linux i386 |
Windows |
Coverity |
Code Coverage |
OpenSSF |
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Get involved
We welcome contributions to TimescaleDB! See Contributing and Code style guide for details.
Learn about Tiger Data
Tiger Data is the fastest PostgreSQL for transactional, analytical and agentic workloads. To learn more about the company and its products, visit tigerdata.com.
Troubleshooting
Docker container won't start
# Check if container is running
docker ps -a
# View container logs (use the appropriate container name)
# For one-line install:
docker logs timescaledb-ha-pg18-quickstart
# For manual Docker command:
docker logs timescaledb
# Stop and remove existing container
# For one-line install:
docker stop timescaledb-ha-pg18-quickstart && docker rm timescaledb-ha-pg18-quickstart
# For manual Docker command:
docker stop timescaledb && docker rm timescaledb
# Start fresh
# Option 1: Use the one-line install
curl -sL https://tsdb.co/start-local | sh
# Option 2: Use manual Docker command
docker run -d --name timescaledb -p 6543:5432 -e POSTGRES_PASSWORD=password timescale/timescaledb-ha:pg18
Can't connect with psql
- Verify Docker container is running:
docker ps
- Check port 6543 isn't already in use:
lsof -i :6543
- Try using explicit host:
psql -h 127.0.0.1 -p 6543 -U postgres
TimescaleDB extension not found
The timescale/timescaledb-ha:pg18 image has TimescaleDB pre-installed and pre-loaded. If you see errors, ensure you're using the correct image.
Clean Up
When you're done experimenting:
If you used the one-line install:
# Stop the container
docker stop timescaledb-ha-pg18-quickstart
# Remove the container
docker rm timescaledb-ha-pg18-quickstart
# Remove the persistent data volume
docker volume rm timescaledb_data
# (Optional) Remove the Docker image
docker rmi timescale/timescaledb-ha:pg18
If you used the manual Docker command:
# Stop the container
docker stop timescaledb
# Remove the container
docker rm timescaledb
# (Optional) Remove the Docker image
docker rmi timescale/timescaledb-ha:pg18
Note: If you created a named volume with the manual Docker command, you can remove it with docker volume rm <volume_name>.
TimescaleDB 是一个 PostgreSQL 扩展，用于对时间序列和事件数据（传感器读数、金融行情、应用事件等）做高性能的实时分析。你用普通的 SQL 就能操作它。