Developer's Catalog

Database

TimescaleDB

Overview

TimescaleDB is a time-series database built as a PostgreSQL extension designed for high-performance real-time analytics. It maintains full PostgreSQL functionality while adding time-series optimizations and specialized features, achieving up to 1,000x faster queries and 90% data compression compared to standard PostgreSQL. In 2024, AI integration and vector search capabilities have been significantly enhanced.

Details

TimescaleDB takes an innovative approach to solving time-series data challenges by leveraging the power of the PostgreSQL ecosystem. The core Hypertables automatically partition data by time while appearing as a single virtual table for operations. Each Hypertable is divided into child tables called "chunks," and during query execution, only relevant chunks are scanned, resulting in dramatic performance improvements.

Continuous Aggregates functionality automatically performs aggregation processing in the background, reducing analytical query response times from minutes to milliseconds. The 2024 update introduced real-time continuous aggregates, enabling immediate computation of results for newly inserted data.

Major 2024 feature enhancements include chunk skipping, performance improvements through SIMD (Single Instruction, Multiple Data) vectorization, and integration of pgai and pgvectorscale extensions for AI applications. These features enable performance comparable to specialized time-series databases while maintaining PostgreSQL compatibility.

Pros and Cons

Pros

1. Full PostgreSQL Compatibility: Use existing PostgreSQL tools, connectors, and ecosystem without modification
2. Dramatic Performance Improvements: Up to 1,000x faster queries and 90% data compression for time-series data
3. Automatic Optimization: Automatic partitioning and chunk management through Hypertables
4. Continuous Aggregates: High-speed real-time analytics through automatic background aggregation
5. Horizontal Scalability: Multi-node configuration supports over 1 million inserts per second
6. Rich Feature Set: Data retention policies, compression, backup and restore functionality
7. AI Integration: Machine learning and vector search integration through 2024's pgai and pgvectorscale extensions
8. Relational Features: Complete support for standard SQL features like JOIN queries, foreign keys, and transactions

Cons

1. Increased Complexity: Query planning becomes more complex due to managing multiple chunks in Hypertables
2. Higher Learning Curve: Need to learn TimescaleDB-specific features in addition to PostgreSQL
3. Increased Memory Usage: Performance degradation when indexed tables don't fit in memory
4. Comparison with Specialized DBs: In cases requiring ultra-high throughput, specialized DBs like InfluxDB may be advantageous
5. Inherited PostgreSQL Constraints: PostgreSQL-derived constraints like schema rigidity remain
6. Cost Considerations: Enterprise features require paid Timescale Cloud subscription

Reference Pages

• TimescaleDB Official Site
• TimescaleDB GitHub
• TimescaleDB Official Documentation
• TimescaleDB vs PostgreSQL Comparison
• Timescale Cloud
• TimescaleDB 2024 Feature Updates

Code Examples

Installation and Setup

# Start TimescaleDB using Docker
docker run -d --name timescaledb -p 5432:5432 \
  -e POSTGRES_PASSWORD=password \
  timescale/timescaledb:latest-pg17

# Connect to container
docker exec -it timescaledb psql \
  -d "postgres://postgres:password@localhost/postgres"

Basic Hypertable Creation

-- Enable TimescaleDB extension
CREATE EXTENSION IF NOT EXISTS timescaledb;

-- Create a regular table
CREATE TABLE conditions (
  time        TIMESTAMPTZ       NOT NULL,
  location    TEXT              NOT NULL,
  temperature DOUBLE PRECISION  NULL,
  humidity    DOUBLE PRECISION  NULL
);

-- Convert to hypertable (partition by time)
SELECT create_hypertable('conditions', by_range('time'));

Data Insertion and Basic Queries

-- Insert data
INSERT INTO conditions VALUES
  (NOW(), 'office',   70.0, 50.0),
  (NOW(), 'basement', 66.5, 60.0),
  (NOW(), 'garage',   77.0, 65.2);

-- Search by time range
SELECT COUNT(*) FROM conditions 
WHERE time > NOW() - INTERVAL '12 hours';

-- Time-series aggregation using time_bucket function
SELECT
  time_bucket('1 day', time) AS bucket,
  AVG(temperature) AS avg_temp
FROM conditions
GROUP BY bucket
ORDER BY bucket ASC;

Compression Settings and Policies

-- Enable columnar compression
ALTER TABLE conditions SET (
  timescaledb.compress,
  timescaledb.compress_segmentby = 'location'
);

-- Add policy for automatic compression after 7 days
SELECT add_compression_policy('conditions', INTERVAL '7 days');

Continuous Aggregates

-- Create daily summary continuous aggregate
CREATE MATERIALIZED VIEW conditions_summary_daily
WITH (timescaledb.continuous) AS
SELECT
  location,
  time_bucket(INTERVAL '1 day', time) AS bucket,
  AVG(temperature),
  MAX(temperature),
  MIN(temperature)
FROM conditions
GROUP BY location, bucket;

-- Set automatic refresh policy for continuous aggregate
SELECT add_continuous_aggregate_policy(
  'conditions_summary_daily',
  start_offset => INTERVAL '1 month',
  end_offset => INTERVAL '1 day',
  schedule_interval => INTERVAL '1 hour'
);

Advanced Time-Series Analytics

-- Gap filling (filling missing data)
SELECT
  time_bucket_gapfill('1 hour', time) AS bucket,
  location,
  locf(avg(temperature)) AS temperature
FROM conditions
WHERE time >= NOW() - INTERVAL '24 hours'
GROUP BY bucket, location
ORDER BY bucket, location;

-- Moving average calculation
SELECT
  time,
  location,
  temperature,
  AVG(temperature) OVER (
    PARTITION BY location 
    ORDER BY time 
    ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
  ) AS moving_avg
FROM conditions
ORDER BY location, time;

Data Retention and Backup

-- Automatic deletion policy for old data (delete after 1 year)
SELECT add_retention_policy('conditions', INTERVAL '1 year');

-- Backup and restore
-- Standard PostgreSQL tools can be used
pg_dump -h localhost -U postgres -d timescaledb > backup.sql
psql -h localhost -U postgres -d new_db < backup.sql