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Table of contents
When to use Timescale DB
Architecture basics
Hyper tables
Relational data support
Installing Timescale Cloud
Frequently Asked Questions
How much data can Timescale DB handle?
What is Timescale DB used for?
Is Timescale DB scalable?
Last Updated: Mar 27, 2024

A guide to TimescaleDB

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Let's ensure we understand the foundational concepts before delving further into the subjects. Here is a brief introduction if you are unfamiliar with Timescale DB.


An open-source database called Timescale DB was developed to make SQL scalable for time-series data. This database system is fairly fresh. Timescale DB was released onto the market two years ago, and in September 2018, it reached version 1.0. It is built on top of an established RDBMS system, though.    

This article explains the details of Timescale DB, in which we talk about when we use timescale DB, its architecture, and installing Timescale cloud.

Without further ado, let's get started.

Must Recommended Topic, Generalization in DBMS,Multiple Granularity in DBMS


As a PostgreSQL extension, Timescale DB is packaged. Only a small portion of the source code for the time-series enterprise features, which is licenced under the Timescale License, is not released under the Apache-2 open-source licence (TSL).

Due to its nature as a time-series database, it offers automated splitting across date and key values. If you want to store time-series data and are already familiar with the SQL language, TimescaleDB is an excellent option because it supports native SQL.

This blog may help you find the correct time-series database if you're seeking one that supports replication, rich SQL, high availability (HA), a reliable backup solution, and other corporate features.

When to use Timescale DB

When we use TimescaleDB

💁 Let's check where Timescale DB features can fit before we get started. With a focus on time series, Timescale DB was created to combine the finest features of relational and NoSQL databases. What, however, are time series data?

The Internet of Things, monitoring systems, and numerous other solutions centered on frequently changing data all depend on time series data. Time-series data, as the name suggests, vary over time. There are many applications for this kind of DBMS. The manufacturing, mining, oil and gas, retail, healthcare, dev ops monitoring, and financial information sectors are just a few of the industries you can use it in. It can also be a very useful component of machine learning pipelines or a source for corporate intelligence and operations.

Without a doubt, there will be an increase in demand for IoT and related technologies. With that said, we may also anticipate the need for various data processing and analysis types. You will rarely be altering old data because time-series data is often simply appended. Normally, you wouldn't delete specific rows, but occasionally, you might wish to aggregate the data over time. In addition to storing how our data evolves, we want to examine and draw conclusions from it.

New kinds of database systems have the drawback that they frequently employ their query language. Users must dedicate some time to language learning. The capability for SQL is what sets Timescale DB apart from other well-known time series Databases. Timescale DB is compatible with all SQL features, including time-based aggregates, windows, sub queries, joins functions, and secondary indexes. Additionally, the client code must not be altered if your application currently uses PostgreSQL.

You can also read about the Multiple Granularity Locking and  Checkpoint in DBMS.

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Architecture basics

A time-scale database functions as part of a larger PostgreSQL instance because Timescale DB is implemented as an extension of the database management system. Through the extension model, the database can benefit from many of PostgreSQL's features, including security, connectivity, and dependability, with a wide range of external tools. Adding hooks deep into the query planner, data model, and execution engine of PostgreSQL allows Timescale DB to use the great level of customization offered to extensions.



Hyper tables

💁 From the user's point of view, Timescale DB data appears as single tables or hyper tables. Hypertables are a concept or an implied view of numerous distinct tables carrying the data known as chunks. Data in a hyper table might have one or two dimensions. A time period and an optional "partition key" value may be used to aggregate it.

Hyper tables are essentially the only way that users interact with Timescale DB. The hyper table should be used for all operations, such as creating tables, indexes, modifying tables, choosing data, and entering data.

Timescale DB applies this thorough partitioning to both single-node and clustered installations (in development). We can scale up to high write rates even on single computers thanks to partitioning, which is often only utilised for scaling across several machines.

Relational data support

Since it is a relational database, SQL is fully supported. Timescale DB supports flexible data models that can be tuned for various use cases. Due to this, Timescale differs slightly from most other time-series databases. The PostgreSQL-based DBMS is designed for quick ingest and complicated queries, and when necessary, we have access to reliable time-series processing.

You can also read about - Specialization and Generalization in DBMS and Recursive Relationship in DBMS


Installing Timescale Cloud

📁 Create an account to install Timescale Cloud. The first thirty days are free. You don't need to install anything on your personal computers because it's a cloud service.

1️⃣ With your name and email address, create an account on Timescale Cloud. To get going, you don't have to supply any financial information. The email address you supply is used to send a confirmation message.

Step image


2️⃣ The link in the email you received can be used to confirm your email. In case the email ends up there, don't forget to check it.


3️⃣ Using the password, you created, log in to the Timescale Cloud portal:


Must Recommended Topic, Schema in DBMS

Frequently Asked Questions

How much data can Timescale DB handle?

With 90–95% storage savings, TimescaleDB's storage is quite effective. Users can scale to petabytes over numerous servers or store 100s of billions of rows and 10s of terabytes of data on a single system.

What is Timescale DB used for?

A relational database for time-series data that is open-source is called Timescale DB. It employs full SQL, is just as simple to use, and scales in ways previously only possible with NoSQL databases.

Is Timescale DB scalable?

An open-source database called Timescale DB was developed to make SQL scalable for time-series data. Performance and efficiency can only be attained by providing their cluster with an automatic means of expansion.


Congratulations on finishing the blog! We have discussed the details of Timescale DB, in which we talk about when we use timescale DB, its architecture, and installing Timescale cloud.

We hope this blog has helped you enhance your knowledge of A guide to Timescale DB. If you'd like to learn more, Check out the following links:

🔥 Introduction to DBMS

🔥 Data Model

🔥 DBMS Architecture

🔥 Introduction to C#

Also See, difference between sql and nosql


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