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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.

• 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

• Docker installed on your machine
• 8GB RAM recommended
• psql client (included with PostgreSQL) or any PostgreSQL client likepgAdmin

You have two options to start TimescaleDB:

The easiest way to get started:

Important: This script is intended for local development and testing only. Donot use it for production deployments. For production-ready installation options, see theTimescaleDB 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 port6543 (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

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 port6543 (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.

Connect usingpsql:

psql -h localhost -p 6543 -U postgres# When prompted, enter password: password

You should see the PostgreSQL prompt. Verify TimescaleDB is installed:

SELECT extname, extversionFROM pg_extensionWHERE 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 downloadpgAdmin and connect to TimescaleDB using the same connection details (host:localhost, port:6543, user:postgres, password:password).

Let's create a hypertable for IoT sensor data with columnstore enabled:

-- Create a hypertable with automatic columnstoreCREATETABLEsensor_data (timeTIMESTAMPTZNOT NULL,    sensor_idTEXTNOT NULL,    temperatureDOUBLE PRECISION,    humidityDOUBLE PRECISION,    pressureDOUBLE PRECISION) WITH (tsdb.hypertable);-- create indexCREATEINDEXidx_sensor_id_timeON sensor_data(sensor_id,timeDESC);

tsdb.hypertable - Converts this into a TimescaleDB hypertable

See more:

• About hypertables
• API reference
• About columnstore
• Enable columnstore manually
• API reference

Let's add some sample sensor readings:

-- Enable timing to see time to execute queries\timingon-- Insert sample data for multiple sensors-- SET timescaledb.enable_direct_compress_insert = on to insert data directly to the columnstore (columnnar format for performance)SETtimescaledb.enable_direct_compress_insert=on;INSERT INTO sensor_data (time, sensor_id, temperature, humidity, pressure)SELECTtime,'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')AStime;-- 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 compressionDO $$DECLARE chTEXT;BEGIN    FOR chINSELECT show_chunks('sensor_data') LOOP        CALL convert_to_columnstore(ch, recompress := true);    END LOOP;END $$;

This generates ~7,776,001 readings across 10 sensors over the past 90 days.

Verify the data was inserted:

SELECTCOUNT(*)FROM sensor_data;

Now let's run some analytical queries that showcase TimescaleDB's performance:

-- Enable query timing to see performance\timingon-- Query 1: Average readings per sensor over the last 7 daysSELECT    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_pressureFROM sensor_dataWHEREtime> NOW()- INTERVAL'7 days'GROUP BY sensor_idORDER 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_humidityFROM sensor_dataWHEREtime> NOW()- INTERVAL'24 hours'GROUP BY hour, sensor_idORDER BY hourDESC, sensor_idLIMIT20;-- Query 3: Daily statistics across all sensorsSELECT    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_tempFROM sensor_dataGROUP BY dayORDER BY dayDESCLIMIT10;-- Query 4: Latest reading for each sensor-- Highlights the value of Skipscan executing in under 100ms without skipscan it takes over 5secSELECT DISTINCTON (sensor_id)    sensor_id,time,    ROUND(temperature::numeric,2)as temperature,    ROUND(humidity::numeric,2)as humidity,    ROUND(pressure::numeric,2)as pressureFROM sensor_dataORDER BY sensor_id,timeDESC;

Notice how fast these analytical queries run, even with aggregations across millions of rows. This is the power of TimescaleDB's columnstore.

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:

• Query data
• Write data
• About time buckets
• API reference
• All TimescaleDB features

Now that you've got the basics, explore more:

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 PostgreSQLmaterialized 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:

CREATE MATERIALIZED VIEW sensor_data_hourlyWITH (timescaledb.continuous)ASSELECT    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_countFROM sensor_dataGROUP 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.

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

Now you can query the pre-aggregated data for much faster results:

-- Get hourly averages for the last 24 hoursSELECT    hour,    sensor_id,    ROUND(avg_temp::numeric,2)AS avg_temp,    ROUND(avg_humidity::numeric,2)AS avg_humidity,    reading_countFROM sensor_data_hourlyWHERE hour> NOW()- INTERVAL'24 hours'ORDER BY hourDESC, sensor_idLIMIT50;

• 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

Compare the performance difference:

-- Query the raw hypertable (slower on large datasets)\timingonSELECT    time_bucket('1 hour',time)AS hour,AVG(temperature)AS avg_tempFROM sensor_dataWHEREtime> NOW()- INTERVAL'60 days'GROUP BY hourORDER BY hourDESCLIMIT24;-- Query the continuous aggregate (much faster)SELECT    hour,    avg_tempFROM sensor_data_hourlyWHERE hour> NOW()- INTERVAL'60 days'ORDER BY hourDESCLIMIT24;

Notice how the continuous aggregate query is significantly faster, especially as your dataset grows!

See more:

• About continuous aggregates
• API reference
• TimescaleDB Documentation
• Time-series Best Practices
• Continuous Aggregates

Learn TimescaleDB with complete, standalone examples using real-world datasets. Each example includes sample data and analytical queries.

• NYC Taxi Data - Transportation and location-based analytics
• Financial Market Data - Trading and market data analysis
• Application Events - Event logging with UUIDv7

Or try some of our workshops

• AI Workshop: EV Charging Station Analysis - Integrate PostgreSQL with AI capabilities for managing and analyzing EV charging station data
• Time-Series Workshop: Financial Data Analysis - Work with cryptocurrency tick data, create candlestick charts

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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.
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Linux/macOS	Linux i386	Windows	Coverity	Code Coverage	OpenSSF

We welcome contributions to TimescaleDB! SeeContributing andCode style guide for details.

Tiger Data is the fastest PostgreSQL for transactional, analytical and agentic workloads. To learn more about the company and its products, visittigerdata.com.

# Check if container is runningdocker 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 installcurl -sL https://tsdb.co/start-local| sh# Option 2: Use manual Docker commanddocker run -d --name timescaledb -p 6543:5432 -e POSTGRES_PASSWORD=password timescale/timescaledb-ha:pg18

• 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

Thetimescale/timescaledb-ha:pg18 image has TimescaleDB pre-installed and pre-loaded. If you see errors, ensure you're using the correct image.

When you're done experimenting:

# Stop the containerdocker stop timescaledb-ha-pg18-quickstart# Remove the containerdocker rm timescaledb-ha-pg18-quickstart# Remove the persistent data volumedocker volume rm timescaledb_data# (Optional) Remove the Docker imagedocker rmi timescale/timescaledb-ha:pg18

# Stop the containerdocker stop timescaledb# Remove the containerdocker rm timescaledb# (Optional) Remove the Docker imagedocker rmi timescale/timescaledb-ha:pg18

Note: If you created a named volume with the manual Docker command, you can remove it withdocker volume rm <volume_name>.