Introduction

TimescaleDB is an open-source time-series database built on PostgreSQL that combines the reliability and SQL interface of PostgreSQL with the performance and scalability needed for time-series data. It’s designed to handle massive volumes of time-series data while maintaining full SQL compatibility and PostgreSQL’s ecosystem.

What is TimescaleDB?

TimescaleDB is a time-series database extension for PostgreSQL that provides:

  • Hypertables: Automatic partitioning by time
  • Continuous Aggregates: Pre-computed materialized views
  • Data Compression: Automatic compression for old data
  • Data Retention: Automatic data retention policies
  • Full SQL: Complete PostgreSQL SQL compatibility
  • PostgreSQL Ecosystem: Works with all PostgreSQL tools

Why TimescaleDB?

Key Advantages:

  • PostgreSQL Compatible: Full SQL, ACID compliance, all PostgreSQL features
  • Automatic Partitioning: Hypertables automatically partition by time
  • High Performance: Optimized for time-series queries
  • Compression: Up to 90% storage reduction
  • Scalability: Handles billions of rows efficiently
  • Open Source: Free, open-source, active community
  • Easy Migration: Works with existing PostgreSQL applications

Common Use Cases:

  • IoT and sensor data
  • Monitoring and observability
  • Financial data (tick data, OHLCV)
  • DevOps metrics
  • Real-time analytics
  • Industrial telemetry
  • Application metrics

Architecture

High-Level Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Client    │────▶│   Client    │────▶│   Client    │
│ Application │     │ Application │     │ Application │
└──────┬──────┘     └──────┬──────┘     └──────┬──────┘
       │                    │                    │
       └────────────────────┴────────────────────┘
                            │
                            │ PostgreSQL Protocol
                            │
                            ▼
              ┌─────────────────────────┐
              │   TimescaleDB           │
              │   (PostgreSQL Extension) │
              │                         │
              │  ┌──────────┐           │
              │  │ Hypertable│           │
              │  │ Interface│           │
              │  └────┬─────┘           │
              │       │                 │
              │  ┌────┴─────┐           │
              │  │  Chunks  │           │
              │  │(Partitions│           │
              │  └──────────┘           │
              │                         │
              │  ┌───────────────────┐  │
              │  │  PostgreSQL       │  │
              │  │  Storage          │  │
              │  └───────────────────┘  │
              └─────────────────────────┘

Explanation:

  • Client Applications: Applications that connect to TimescaleDB using standard PostgreSQL clients and drivers (e.g., time-series applications, IoT platforms, monitoring systems).
  • TimescaleDB: PostgreSQL extension that adds time-series capabilities to PostgreSQL, making it optimized for time-series data.
  • Hypertable Interface: Logical table interface that looks like a regular PostgreSQL table but is automatically partitioned by time.
  • Chunks (Partitions): Physical partitions of data organized by time ranges. Chunks are automatically created and managed.
  • PostgreSQL Storage: Underlying PostgreSQL storage engine that stores the actual data.

Core Architecture

TimescaleDB Architecture:

┌─────────────────────────────────────────┐
│      Application Layer                  │
│  (Standard PostgreSQL Clients)          │
└──────────────┬──────────────────────────┘
               │
┌──────────────▼──────────────────────────┐
│      PostgreSQL + TimescaleDB           │
│  ┌──────────────────────────────────┐   │
│  │     Hypertable Interface        │   │
│  └──────────────┬───────────────────┘   │
│                 │                        │
│  ┌──────────────▼───────────────────┐   │
│  │     Chunk Management             │   │
│  │  ┌──────┐  ┌──────┐  ┌──────┐   │   │
│  │  │Chunk1│  │Chunk2│  │Chunk3│   │   │
│  │  └──────┘  └──────┘  └──────┘   │   │
│  └──────────────────────────────────┘   │
│                 │                        │
│  ┌──────────────▼───────────────────┐   │
│  │     PostgreSQL Storage           │   │
│  └──────────────────────────────────┘   │
└─────────────────────────────────────────┘

Key Components:

  • Hypertable: Logical table that spans multiple chunks
  • Chunks: Physical partitions (time-based)
  • Chunk Scheduler: Manages chunk creation and retention
  • Compression: Automatic compression for old chunks
  • Continuous Aggregates: Materialized views for fast queries

Hypertables

What is a Hypertable?

  • Logical table that looks like a regular PostgreSQL table
  • Automatically partitioned into chunks by time
  • Transparent to applications (standard SQL)
  • Optimized for time-series queries

Hypertable Creation:

-- Create regular table
CREATE TABLE sensor_data (
    time TIMESTAMPTZ NOT NULL,
    device_id INTEGER NOT NULL,
    temperature DOUBLE PRECISION,
    humidity DOUBLE PRECISION
);

-- Convert to hypertable
SELECT create_hypertable('sensor_data', 'time');

How Hypertables Work:

Hypertable: sensor_data
    ├── Chunk 1: 2024-01-01 to 2024-01-07
    ├── Chunk 2: 2024-01-08 to 2024-01-14
    ├── Chunk 3: 2024-01-15 to 2024-01-21
    └── Chunk 4: 2024-01-22 to 2024-01-28

Benefits:

  • Automatic partitioning by time
  • Efficient queries (only relevant chunks scanned)
  • Parallel chunk processing
  • Easy data management (drop old chunks)

Chunks

Chunk Characteristics:

  • Physical partitions of hypertable
  • Created automatically based on time interval
  • Default chunk interval: 7 days
  • Can be customized per hypertable

Chunk Interval:

-- Create hypertable with custom chunk interval
SELECT create_hypertable(
    'sensor_data',
    'time',
    chunk_time_interval => INTERVAL '1 day'
);

Chunk Management:

  • Automatic creation for new time ranges
  • Automatic deletion via retention policies
  • Can be compressed individually
  • Can be moved to different storage

Chunk Sizing Guidelines:

  • Small chunks: Better for recent data queries
  • Large chunks: Better for historical queries
  • Default: 7 days (good balance)
  • High-frequency data: Smaller chunks (1 day)
  • Low-frequency data: Larger chunks (30 days)

Data Model

Time-Series Data Structure

Typical Schema:

CREATE TABLE sensor_readings (
    time TIMESTAMPTZ NOT NULL,
    device_id INTEGER NOT NULL,
    sensor_type TEXT NOT NULL,
    value DOUBLE PRECISION,
    metadata JSONB
);

-- Create hypertable
SELECT create_hypertable('sensor_readings', 'time');

Key Design Principles:

  • Time Column: Must be TIMESTAMPTZ or TIMESTAMP
  • Partitioning Key: Time column (required)
  • Space Partitioning: Optional, for multi-tenant data
  • Indexes: Create on time and other query columns

Space Partitioning

Multi-Dimensional Partitioning:

  • Partition by time (required)
  • Optionally partition by space (device_id, location, etc.)
  • Reduces chunk size
  • Improves query performance for multi-tenant data

Space Partitioning Example:

-- Partition by time and device_id
SELECT create_hypertable(
    'sensor_readings',
    'time',
    partitioning_column => 'device_id',
    number_partitions => 4
);

Benefits:

  • Smaller chunks per device
  • Better parallelization
  • Improved query performance
  • Easier data management per tenant

Indexing

Time-Series Indexes:

  • Time Index: Automatically created on time column
  • Composite Indexes: Time + other columns
  • Partial Indexes: Indexes on recent data only

Index Examples:

-- Time index (automatic)
CREATE INDEX ON sensor_readings (time DESC);

-- Composite index
CREATE INDEX ON sensor_readings (device_id, time DESC);

-- Partial index (recent data only)
CREATE INDEX ON sensor_readings (device_id, time DESC)
WHERE time > NOW() - INTERVAL '30 days';

Index Best Practices:

  • Index frequently queried columns
  • Use DESC for time column (newest first)
  • Consider partial indexes for hot data
  • Monitor index usage

Continuous Aggregates

What are Continuous Aggregates?

Definition:

  • Materialized views that automatically refresh
  • Pre-computed aggregations over time windows
  • Significantly faster than querying raw data
  • Automatically maintained as new data arrives

Benefits:

  • Performance: 100-1000x faster queries
  • Automatic Refresh: Updates as new data arrives
  • Storage Efficient: Stores only aggregated data
  • Real-Time: Can include recent data in queries

Creating Continuous Aggregates

Basic Example:

-- Create continuous aggregate
CREATE MATERIALIZED VIEW hourly_stats
WITH (timescaledb.continuous) AS
SELECT
    time_bucket('1 hour', time) AS bucket,
    device_id,
    AVG(temperature) AS avg_temp,
    MAX(temperature) AS max_temp,
    MIN(temperature) AS min_temp,
    COUNT(*) AS readings
FROM sensor_readings
GROUP BY bucket, device_id;

-- Create index
CREATE INDEX ON hourly_stats (bucket DESC, device_id);

Time Bucket Function:

  • Groups data into time intervals
  • Supports: 1 minute, 1 hour, 1 day, etc.
  • Efficient aggregation
  • Standard SQL GROUP BY compatible

Refresh Policy:

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

Real-Time Aggregates

Include Recent Data:

  • Continuous aggregates store historical data
  • Can include recent (unaggregated) data in queries
  • Combines materialized + real-time data
  • Best of both worlds

Real-Time Query:

-- Query with real-time data included
SELECT * FROM hourly_stats
WHERE bucket > NOW() - INTERVAL '1 day';
-- Automatically includes recent data not yet aggregated

Configuration:

-- Enable real-time aggregates
ALTER MATERIALIZED VIEW hourly_stats
SET (timescaledb.materialized_only = false);

Continuous Aggregate Best Practices

1. Choose Appropriate Bucket Size:

  • Match query patterns
  • Balance storage vs. query performance
  • Common: 1 hour, 1 day, 1 week

2. Refresh Policies:

  • Refresh frequently enough for use case
  • Consider data arrival patterns
  • Balance freshness vs. cost

3. Indexes:

  • Index bucket and grouping columns
  • Use DESC for time bucket
  • Consider composite indexes

4. Storage:

  • Continuous aggregates are materialized views
  • Take up storage space
  • Consider compression

Compression

Data Compression

Purpose:

  • Reduce storage costs
  • Improve query performance (less I/O)
  • Automatic compression for old data
  • Up to 90% storage reduction

How Compression Works:

  • Compresses chunks older than threshold
  • Uses columnar compression
  • Transparent to queries
  • Can decompress if needed

Enabling Compression:

-- Add compression policy
SELECT add_compression_policy(
    'sensor_readings',
    INTERVAL '7 days'
);

Compression Settings:

-- Enable compression on hypertable
ALTER TABLE sensor_readings SET (
    timescaledb.compress,
    timescaledb.compress_segmentby = 'device_id',
    timescaledb.compress_orderby = 'time DESC'
);

Compression Policies

Automatic Compression:

  • Compress chunks older than threshold
  • Runs automatically via policy
  • Can be scheduled
  • Configurable per hypertable

Compression Policy:

-- Compress data older than 7 days
SELECT add_compression_policy(
    'sensor_readings',
    compress_after => INTERVAL '7 days'
);

Manual Compression:

-- Manually compress specific chunk
SELECT compress_chunk('_timescaledb_internal._hyper_1_1_chunk');

Compression Best Practices

1. Segment By:

  • Choose columns with low cardinality
  • Common: device_id, location_id
  • Improves compression ratio

2. Order By:

  • Order by time (DESC)
  • Helps compression algorithm
  • Better compression ratio

3. Compression Threshold:

  • Compress old data (e.g., 7+ days)
  • Keep recent data uncompressed
  • Balance query performance vs. storage

4. Monitoring:

  • Monitor compression ratio
  • Check compression status
  • Verify compression policies

Data Retention

Retention Policies

Purpose:

  • Automatically delete old data
  • Manage storage costs
  • Comply with data retention requirements
  • Keep database size manageable

Creating Retention Policy:

-- Delete data older than 90 days
SELECT add_retention_policy(
    'sensor_readings',
    INTERVAL '90 days'
);

How It Works:

  • Drops entire chunks (efficient)
  • Runs automatically via scheduler
  • Configurable retention period
  • Can be per hypertable

Retention Policy Example:

-- Keep 1 year of data
SELECT add_retention_policy(
    'sensor_readings',
    INTERVAL '1 year',
    if_not_exists => true
);

Tiered Storage

Move Old Data:

  • Move old chunks to slower storage
  • Keep recent data on fast storage
  • Reduce costs
  • Maintain access to historical data

Tiered Storage Example:

-- Move chunks older than 1 year to S3
SELECT add_tiering_policy(
    'sensor_readings',
    INTERVAL '1 year',
    's3://bucket/path'
);

Benefits:

  • Lower storage costs
  • Maintain data access
  • Automatic data movement
  • Transparent to queries

Performance Characteristics

Maximum Read & Write Throughput

Single Node (Standard Configuration):

  • Max Write Throughput:
    • Simple inserts: 10K-50K inserts/sec
    • Batch inserts: 100K-500K rows/sec
    • With hypertables: 50K-200K rows/sec (optimized for time-series)
  • Max Read Throughput:
    • Simple queries (indexed): 5K-25K queries/sec
    • Complex queries (aggregations): 1K-5K queries/sec
    • With continuous aggregates: 10K-50K queries/sec (pre-computed)

Horizontal Scaling (Read Replicas):

  • Max Read Throughput: 5K-25K queries/sec per replica (linear scaling)
  • Example: 5 read replicas can handle 25K-125K queries/sec total
  • Write Throughput: Limited by primary (single write node)

Factors Affecting Throughput:

  • Chunk size (smaller chunks = better query performance)
  • Compression settings (compressed data = faster queries)
  • Continuous aggregates (pre-computed = much faster queries)
  • Index usage
  • Partitioning strategy (time + space partitioning)
  • Hardware (CPU, RAM, disk I/O, SSD recommended)
  • Connection pooling

Optimized Configuration:

  • Max Write Throughput: 200K-500K rows/sec (with optimized batch inserts and hypertable settings)
  • Max Read Throughput: 50K-100K queries/sec (with continuous aggregates and proper indexing)

Performance Optimization

Query Optimization

1. Time-Based Queries:

  • Always filter by time
  • Use time_bucket for aggregations
  • Leverage chunk exclusion
  • Use appropriate time ranges

2. Chunk Exclusion:

  • TimescaleDB automatically excludes irrelevant chunks
  • Only scans chunks in time range
  • Significant performance improvement
  • Transparent to application

3. Parallel Chunk Processing:

  • Queries parallelize across chunks
  • Better performance for large datasets
  • Automatic parallelization
  • Configurable parallelism

4. Indexes:

  • Index time column (automatic)
  • Index frequently queried columns
  • Use composite indexes
  • Consider partial indexes

Insert Performance

High-Throughput Inserts:

  • Batch inserts (use COPY or batch INSERT)
  • Use prepared statements
  • Disable indexes during bulk load
  • Use compression for old data

Insert Optimization:

-- Batch insert
INSERT INTO sensor_readings (time, device_id, value)
VALUES
    (NOW(), 1, 25.5),
    (NOW(), 2, 26.0),
    (NOW(), 3, 24.8);
-- Much faster than individual inserts

COPY Command:

-- Fast bulk load
COPY sensor_readings (time, device_id, value)
FROM '/path/to/data.csv' CSV;

Query Patterns

1. Recent Data Queries:

  • Query recent chunks (uncompressed)
  • Fast queries
  • Use indexes
  • Consider partial indexes

2. Historical Data Queries:

  • Query compressed chunks
  • Use continuous aggregates
  • Aggregate at appropriate granularity
  • Consider data retention

3. Time-Range Queries:

  • Always specify time range
  • Use time_bucket for aggregations
  • Leverage chunk exclusion
  • Use appropriate indexes

Use Cases

1. IoT and Sensor Data

Sensor Monitoring:

  • Store sensor readings
  • Query recent and historical data
  • Aggregate by time windows
  • Monitor device health

Example Schema:

CREATE TABLE sensor_data (
    time TIMESTAMPTZ NOT NULL,
    device_id INTEGER NOT NULL,
    sensor_type TEXT NOT NULL,
    value DOUBLE PRECISION,
    location TEXT
);

SELECT create_hypertable('sensor_data', 'time');

Use Cases:

  • Temperature monitoring
  • Humidity tracking
  • Motion detection
  • Energy consumption

2. Monitoring and Observability

Application Metrics:

  • Store application metrics
  • Monitor performance
  • Alert on anomalies
  • Historical analysis

Metrics Types:

  • CPU usage
  • Memory usage
  • Request latency
  • Error rates
  • Throughput

Example:

CREATE TABLE metrics (
    time TIMESTAMPTZ NOT NULL,
    service_name TEXT NOT NULL,
    metric_name TEXT NOT NULL,
    value DOUBLE PRECISION,
    tags JSONB
);

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

3. Financial Data

Tick Data:

  • Store high-frequency financial data
  • Query price history
  • Calculate indicators
  • Backtesting

OHLCV Data:

  • Open, High, Low, Close, Volume
  • Time-series aggregation
  • Technical analysis
  • Historical queries

Example:

CREATE TABLE stock_ticks (
    time TIMESTAMPTZ NOT NULL,
    symbol TEXT NOT NULL,
    price DOUBLE PRECISION,
    volume BIGINT
);

SELECT create_hypertable('stock_ticks', 'time');

4. DevOps Metrics

Infrastructure Monitoring:

  • System metrics
  • Application metrics
  • Log aggregation
  • Performance monitoring

Metrics:

  • Server metrics
  • Container metrics
  • Application performance
  • Error tracking

5. Industrial Telemetry

Manufacturing Data:

  • Production metrics
  • Equipment monitoring
  • Quality control
  • Predictive maintenance

Telemetry:

  • Machine sensors
  • Production rates
  • Quality metrics
  • Maintenance logs

Best Practices

1. Hypertable Design

Time Column:

  • Use TIMESTAMPTZ (timezone-aware)
  • Make it NOT NULL
  • Index automatically created
  • Use as partitioning key

Chunk Interval:

  • Default: 7 days (good starting point)
  • High-frequency: 1 day
  • Low-frequency: 30 days
  • Consider query patterns

Space Partitioning:

  • Use for multi-tenant data
  • Partition by tenant ID
  • Reduces chunk size
  • Improves parallelization

2. Indexing Strategy

Time Index:

  • Automatically created
  • Use DESC for newest first
  • Essential for performance

Composite Indexes:

  • Index time + other columns
  • Match query patterns
  • Consider selectivity

Partial Indexes:

  • Index recent data only
  • Reduces index size
  • Faster for hot data queries

3. Continuous Aggregates

Bucket Size:

  • Match query patterns
  • Common: 1 hour, 1 day
  • Balance storage vs. performance

Refresh Policies:

  • Refresh frequently enough
  • Consider data arrival patterns
  • Balance freshness vs. cost

Indexes:

  • Index bucket and grouping columns
  • Use DESC for time bucket
  • Essential for performance

4. Compression

Compression Threshold:

  • Compress old data (7+ days)
  • Keep recent data uncompressed
  • Balance query performance vs. storage

Segment By:

  • Choose low-cardinality columns
  • Common: device_id, location_id
  • Improves compression ratio

Order By:

  • Order by time DESC
  • Helps compression algorithm
  • Better compression ratio

5. Data Retention

Retention Policies:

  • Set appropriate retention period
  • Consider compliance requirements
  • Balance storage costs vs. data needs

Tiered Storage:

  • Move old data to slower storage
  • Reduce costs
  • Maintain data access

6. Query Optimization

Time Filters:

  • Always filter by time
  • Use appropriate time ranges
  • Leverage chunk exclusion

Aggregations:

  • Use time_bucket
  • Use continuous aggregates
  • Aggregate at appropriate granularity

Indexes:

  • Index frequently queried columns
  • Use composite indexes
  • Consider partial indexes

Comparison with Other Databases

TimescaleDB vs InfluxDB

Feature TimescaleDB InfluxDB
SQL Full PostgreSQL SQL InfluxQL/Flux
ACID Full ACID compliance Eventual consistency
Ecosystem PostgreSQL ecosystem InfluxDB ecosystem
Compression Automatic compression Built-in compression
Continuous Aggregates Yes Continuous queries
Open Source Yes Yes (open source version)

TimescaleDB vs PostgreSQL

Feature TimescaleDB PostgreSQL
Time-Series Optimized for time-series General-purpose
Partitioning Automatic (hypertables) Manual partitioning
Compression Automatic compression Manual compression
Continuous Aggregates Yes Materialized views (manual)
Performance Optimized for time-series General-purpose
SQL Full PostgreSQL SQL Full SQL

TimescaleDB vs ClickHouse

Feature TimescaleDB ClickHouse
SQL PostgreSQL SQL SQL (different dialect)
ACID Full ACID Eventual consistency
Ecosystem PostgreSQL ecosystem ClickHouse ecosystem
Compression Automatic compression Built-in compression
Real-Time Yes Yes
Open Source Yes Yes

Limitations and Considerations

Limitations

PostgreSQL Limitations:

  • Inherits PostgreSQL limitations
  • Single-node limitations (without TimescaleDB Cloud)
  • Connection limits
  • Transaction limits

Hypertable Limitations:

  • Must have time column
  • Chunk size considerations
  • Compression overhead
  • Continuous aggregate refresh overhead

Considerations:

1. Chunk Size:

  • Too small: Many chunks, overhead
  • Too large: Slower queries, less parallelization
  • Default 7 days is good starting point

2. Compression:

  • Compression adds overhead
  • Decompression for queries
  • Balance compression vs. query performance

3. Continuous Aggregates:

  • Storage overhead
  • Refresh overhead
  • Balance freshness vs. cost

4. Data Retention:

  • Drops entire chunks
  • Cannot partially delete
  • Plan retention carefully

Additional Resources

Official Documentation

Getting Started

Community Resources

Learning Resources

  • TimescaleDB University: Free courses and tutorials
  • Blog: TimescaleDB blog with use cases and tutorials
  • Webinars: Regular webinars on time-series data

What Interviewers Look For

Time-Series Database Knowledge & Application

  1. Time-Series Understanding
    • Time-based partitioning
    • Compression strategies
    • Retention policies
    • Red Flags: No time-series understanding, wrong storage, inefficient
  2. Hypertable Design
    • Partition key selection
    • Chunk sizing
    • Red Flags: Poor partitioning, wrong chunk size, inefficient
  3. Continuous Aggregates
    • Pre-computed aggregations
    • Refresh strategies
    • Red Flags: No aggregates, slow queries, poor performance

System Design Skills

  1. When to Use TimescaleDB
    • Time-series data
    • Need PostgreSQL compatibility
    • High write volume
    • Red Flags: Wrong use case, non-time-series, can’t justify
  2. Scalability Design
    • Automatic partitioning
    • Compression
    • Red Flags: No partitioning, no compression, high storage
  3. Query Optimization
    • Time-based queries
    • Aggregation optimization
    • Red Flags: Slow queries, no optimization, poor performance

Problem-Solving Approach

  1. Trade-off Analysis
    • Storage vs query speed
    • Compression vs performance
    • Red Flags: No trade-offs, dogmatic choices
  2. Edge Cases
    • Large time ranges
    • Compression issues
    • Retention policies
    • Red Flags: Ignoring edge cases, no handling
  3. Data Retention
    • Retention policies
    • Cost optimization
    • Red Flags: No retention, high costs, inefficient

Communication Skills

  1. TimescaleDB Explanation
    • Can explain time-series features
    • Understands hypertables
    • Red Flags: No understanding, vague explanations
  2. Decision Justification
    • Explains why TimescaleDB
    • Discusses alternatives
    • Red Flags: No justification, no alternatives

Meta-Specific Focus

  1. Time-Series Expertise
    • Deep TimescaleDB knowledge
    • Time-series patterns
    • Key: Show time-series expertise
  2. PostgreSQL Integration
    • PostgreSQL compatibility
    • Feature utilization
    • Key: Demonstrate PostgreSQL knowledge

Conclusion

TimescaleDB is a powerful time-series database that extends PostgreSQL with time-series optimizations. It combines the reliability and SQL interface of PostgreSQL with the performance and scalability needed for time-series data.

Key Takeaways:

  1. PostgreSQL Compatible: Full SQL, ACID compliance, all PostgreSQL features
  2. Automatic Partitioning: Hypertables automatically partition by time
  3. High Performance: Optimized for time-series queries
  4. Compression: Up to 90% storage reduction
  5. Continuous Aggregates: Pre-computed aggregations for fast queries
  6. Easy Migration: Works with existing PostgreSQL applications

When to Use TimescaleDB:

  • Time-series data workloads
  • Need PostgreSQL compatibility
  • Want automatic partitioning
  • Need compression and retention
  • Require full SQL support
  • Existing PostgreSQL infrastructure

When Not to Use TimescaleDB:

  • Non-time-series data
  • Need distributed database (consider TimescaleDB Cloud)
  • Very simple key-value use cases
  • Don’t need PostgreSQL features

TimescaleDB is an excellent choice for applications that need to store and query time-series data while leveraging the power and ecosystem of PostgreSQL. With proper hypertable design, continuous aggregates, and compression policies, TimescaleDB can handle massive volumes of time-series data efficiently.