Introduction
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
TimescaleDB
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
💁 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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