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Efficient Time-Series Data Handling: Exploring TimescaleDB in PostgreSQL

Efficient Time-Series Data Handling: Exploring TimescaleDB in PostgreSQL

TimescaleDB is an open-source time-series database extension for PostgreSQL. It is designed to efficiently manage and query time-series data, offering features such as automatic data partitioning, data retention policies, and specialized time-series functions. 

This extension provides scalability, improved performance, and seamless integration with PostgreSQL, making it a powerful choice for applications dealing with large volumes of time-stamped data, including IoT, monitoring, and analytics.

What does time-series data mean?

Time-series data is a sequence of data points recorded or collected at specific time intervals. Each data point is associated with a timestamp, allowing for chronological ordering. This type of data is commonly used in various fields like finance, weather, and IoT, where observations change over time. Analyzing time-series data helps detect trends, patterns, and anomalies, making it valuable for forecasting, monitoring, and decision-making in applications that rely on historical and real-time data trends.

How to set up TimescaleDB with PostgreSQL?

To utilize TimescaleDB, ensure that PostgreSQL is installed and operational on your system. Refer to the official PostgreSQL documentation at for guidance on server setup.

After installing PostgreSQL, proceed to install TimescaleDB. Ensure compatibility by installing the TimescaleDB version that corresponds to your PostgreSQL installation. For instance, if PostgreSQL-15 is installed, choose the appropriate version of TimescaleDB accordingly.

echo "deb $(lsb_release -c -s) main" | sudo tee /etc/apt/sources.list.d/timescaledb.list
wget --quiet -O - | sudo apt-key add -
sudo apt-get update
sudo apt-get install timescaledb-2-postgresql-15

After TimescaleDB installation, proceed to update the postgresql.conf file. Locate the property named shared_preload_libraries and modify it to

shared_preload_libraries = 'timescaledb'. 

Subsequently, restart the PostgreSQL server:


Once the restart is successful, connect to psql and create the TimescaleDB extension

postgres@c8b1e79ddf18:~$ psql
psql (15.4 (Ubuntu 15.42.pgdg22.04+1))
Type "help" for help.
postgres=# create extension timescaledb;

The TimescaleDB extension is now successfully loaded and ready for use within your database.

TimescaleDB configuration and tuning parameters

Similar to PostgreSQL parameters, TimescaleDB also includes specific parameters that can be configured as necessary within the postgresql.conf file. Here are some of these main parameters

timescaledb.max_background_workers (int): determines the maximum number of background workers that TimescaleDB can use. Background workers in TimescaleDB perform various tasks such as compression, data retention policies, continuous aggregates maintenance, and more. Its default value is 10

timescaledb.enable_2pc (bool): The timescaledb.enable_2pc configuration parameter in TimescaleDB is a boolean (true/false) parameter that determines whether to enable or disable the use of Two-Phase Commit (2PC) for distributed transactions. Two-Phase Commit is a protocol used in distributed databases to ensure the atomicity of transactions that involve multiple nodes. When timescaledb.enable_2pc is set to true, TimescaleDB will use 2PC for distributed transactions, providing stronger guarantees of transactional consistency across multiple nodes. It is by default enabled.

timescaledb.max_insert_batch_size (int): It determines the maximum number of rows allowed in a single batch during insert operations. This parameter is specifically relevant for bulk data insertion scenarios. When performing large-scale inserts, it’s common to use batch processing to improve efficiency. The max_insert_batch_size parameter allows you to control the size of these batches. Adjusting this parameter can have an impact on the efficiency of bulk data insertion operations, optimizing them for your specific hardware and use case. Its default value is 1000

timescaledb.passfile (string): It specifies the location of a password file. This file typically contains the password required for connecting to the PostgreSQL/TimescaleDB database. When using the passfile configuration parameter, it allows you to securely store and manage database passwords outside of the database connection settings.

timescaledb.ssl_dir (string): Defines the directory path where user certificates and keys are sought for connecting to data nodes with certificate authentication. The default location is timescaledb/certs within the PostgreSQL data directory.

What are hypertables and how to create them?

Hypertables within PostgreSQL automatically partition data by time in DESC order, streamlining the management of time-series data with advanced features. Interaction with hypertables closely resembles that of standard PostgreSQL tables but includes additional functionalities for a more straightforward approach to handling time-series data. In Timescale, hypertables coexist alongside conventional tables, providing enhanced insert and query performance alongside specialized time-series features. Conventional PostgreSQL tables remain suitable for non-time-series relational data. Timescale facilitates performance optimization by autonomously partitioning time-series data in the background, enabling users to interact with their data seamlessly, as though it were within a unified PostgreSQL table.

Creating a hypertable

CREATE TABLE sensor_data (
sensor_id INT,

Convert heap table to hypertable with create_hypertable function provided by Timescaledb

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




(1 row)

Relation between Hypertables and Heap tables

Hypertables in TimescaleDB are like regular PostgreSQL tables, but they automatically split your data into time-based partitions, enhancing how you manage time-series data. While they offer specialized features for improved data insertion and querying, they seamlessly coexist with standard PostgreSQL tables. This setup optimizes performance and simplifies time-series data handling, with the database taking care of partition maintenance, allowing users to work with data as if it were in a single regular table.

What are chunk tables?

Chunk tables in TimescaleDB are the underlying storage units for time-series data. They contain data within specific time intervals and are part of the hypertable architecture. Chunks are automatically created, managed, and compressed to optimize data retrieval and storage efficiency. These smaller, more manageable units improve query performance and simplify the handling of large volumes of time-series data. The default table chunk size in TimescaleDB is 7 days means each chunk table will contain the data for 7 days. We can actually increase or decrease that duration as well with the following query

SELECT create_hypertable('sensor_data', 'time', chunk_time_interval => interval '1 day');

here we are mentioning 1 day as chunk table size so each chunk table will contain data of 1 day only

Chunk tables vs Partitioning in the Heap table

In simpler terms, we can think of Chunk tables in TimescaleDB are similar to Partitioning in heap tables but there are quite of few differences as well
Chunk tables are purpose-built for efficient time-series data management, automating data partitioning, optimizing for time intervals, and improving query performance. They also support data compression and retention policies. In contrast, heap table partitioning in PostgreSQL is a manual and versatile method for organizing data in standard relational tables, requiring explicit criteria for partitioning and lacking dedicated time-series enhancements. In essence, chunk tables simplify time-series data management, while heap table partitioning is more versatile for handling various data types.

Compression in TimescaleDB

Compression in TimescaleDB is a storage optimization technique that efficiently encodes and reduces the space needed for time-series data in chunk tables. Various compression algorithms are employed to minimize redundant information, leading to substantial storage space savings without compromising data accuracy.
Typically, compression is applied to older chunk tables, leaving the most recent chunk uncompressed. This approach optimizes data insertion performance for real-time data without the overhead of compression. This strategy ensures efficient management of time-series data, benefiting applications with large volumes of time-stamped information.

Continuous Aggregates and PostgreSQL materialized views

PostgreSQL materialized views are database objects that store the results of a query as a physical table, improving query performance by reducing computation. TimescaleDB utilizes PostgreSQL materialized views in its Continuous Aggregates feature. Continuous Aggregates leverage materialized views to create and maintain precomputed roll-up summaries of time-series data. As new data arrives, these views are incrementally updated, optimizing query response times for time-series data analytics by avoiding the need for expensive, on-the-fly aggregations. This approach enhances query efficiency and is particularly valuable for managing large and dynamic time-series datasets.

Data Retention Policies

Data Retention Policies in TimescaleDB are rules that automate the management of time-series data. These policies define how long data should be retained in the database before it’s automatically removed. You can specify policies based on time intervals or other criteria, ensuring that older or less relevant data is purged, making room for newer data.


Hyperfunctions are specialized functions in TimescaleDB designed to simplify and optimize time-series data manipulation and analysis. They extend the functionality of standard SQL and PostgreSQL functions, making it easier to work with time-series data. Hyperfunctions include functions for interpolation, filling gaps in data, calculating percentiles, and more.

Distributed Hypertables

Distributed hypertables in TimescaleDB are an advanced feature that extends the scalability of the database. They allow you to distribute time-series data across multiple physical servers or nodes. This horizontal scaling capability is essential for handling very large volumes of time-series data in high-throughput applications, such as IoT sensor networks or large-scale monitoring systems. Distributed hypertables enable TimescaleDB to distribute and manage data across a cluster of servers, improving performance, redundancy, and overall capacity.

A simple example to understand timeseries data via TimescaleDB

In this scenario, we create a standard table, “store_sales,” to store time-series data with columns for time, product_id, and sale_amount. The table is then transformed into a hypertable, enabling automatic partitioning with a 1-day chunk size. Subsequently, data is inserted into the hypertable using the INSERT command, introducing five rows with various timestamps, product IDs, and sale amounts. Lastly, we retrieve sales data from the hypertable for the previous hour, displaying time, product_id, and sale_amount columns, resulting in two rows.

-- Create a regular PostgreSQL heap table
CREATE TABLE store_sales (
    product_id INT,
    sale_amount DECIMAL

-- Convert the table into a hypertable with a chunk size of 1 day
SELECT create_hypertable('store_sales', 'time', chunk_time_interval => interval '1 day');
(1 row)

-- Insert data into the hypertable
INSERT INTO store_sales (time, product_id, sale_amount)
    (NOW() - interval '3 hours', 101, 50.0),
    (NOW() - interval '2 hours', 102, 75.0),
    (NOW() - interval '1 hour', 101, 60.0),
    (NOW() - interval '30 minutes', 102, 90.0),
    (NOW() - interval '15 minutes', 101, 55.0);

-- Query to display sales for the last 1 hour
FROM store_sales
WHERE time >= NOW() - interval '1 hour';
             time              | product_id | sale_amount
2023-11-09 03:40:57.257735+05 |        102 |        90.0
2023-11-09 03:55:57.257735+05 |        101 |        55.0
(2 rows)

In conclusion, Time-series data is crucial in today’s world for trend analysis, anomaly detection, and predictive insights. It enables informed decision-making by capturing and analyzing temporal patterns, crucial in dynamic environments. the integration of TimescaleDB into your PostgreSQL environment marks a strategic move towards efficient time-series data handling. 

By leveraging hypertables, automatic partitioning, and greater ingestion speed with compression you optimize storage and retrieval mechanisms. 

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