A Beginner's Guide to Time Series Databases

Have you ever wondered how companies like Google, Netflix, and Amazon store and process massive amounts of time-stamped data? They use time series databases, which are specially designed to handle time-stamped data efficiently.

A time series database is a type of database that is optimized for handling time-stamped or time-series data. Time series databases are becoming increasingly important in the world of real-time data streaming processing, where real-time data processing is essential. In this article, we'll explore what time series databases are, how they work, and their advantages over traditional relational databases for time series data.

What is a Time Series Database?

A time series database is a database that stores time-stamped data. These databases are designed to handle time-stamped data efficiently, making it easier for you to query, analyze and visualize time-based data. Unlike traditional databases, time series databases are not designed to store arbitrary data. They are built specifically for storing and processing time series data, which is data that is indexed by time.

A time series database is not a replacement for a traditional relational database. It is rather a complementary tool that can be used in conjunction with a traditional database. In fact, most companies use a combination of databases to store and process their data.

How Does a Time Series Database Work?

Time series databases are optimized for handling time-stamp data. To achieve this, they use a different data structure than relational databases called a time-series database engine. This engine is specifically designed to handle time-stamp data more efficiently.

In traditional databases like MySQL or Oracle, data is typically stored in tables, and each table has a set of columns that represent the data that the table contains. Time series databases, on the other hand, store data in a more specialized way. They use "sharding" to store data in smaller, uniform time intervals.

Sharding is the process of dividing a large database into smaller, more manageable pieces called shards. Sharding enables the database to handle larger data volumes and improves performance by distributing data processing workload to separate machines.

Time series databases also use columnar storage, which is a technique that stores data in columns rather than rows. Storing data in columns can be more efficient for certain types of queries, as it enables faster data retrieval for a specific column.

Advantages of Time Series Databases

Time series databases offer several advantages over traditional relational databases for time-series data. The following are some of the advantages of Time series databases:

Faster Query Performance

Time series databases are designed to handle time-stamp data, which typically involves a lot of data points recorded over time. With traditional databases, querying large datasets could take a long time, as the data is stored in a row-based structure. With time series databases, however, data is stored more efficiently using columnar storage, which enables faster query performance.


Time series databases are more scalable than traditional databases. They use sharding to store data in smaller pieces, which makes it easier to add more data to the database as required. This improves scalability, as the database has the ability to grow as your data grows.

Better Data Compression

Time series data tends to have repeating patterns over time, which makes it a good candidate for data compression. Time series databases typically use specialized compression algorithms to compress data. This reduces the amount of storage required to store the data, which improves database performance and reduces the cost of data storage.

Simpler Data Analysis

With time series data, you often need to analyze data over time. Traditional databases store data in a row-based fashion, which makes time-based queries more complicated. Time series databases, on the other hand, store data in a columnar fashion, which makes time-based queries simpler.

When to Use a Time Series Database

Time series databases are particularly useful when dealing with time-stamped data, such as sensor data, stock prices, social media data, or website traffic data. Time series databases can also be used in many other applications, such as IoT devices or high-speed trading.

If your application involves a significant amount of time-stamped data, it's worth considering using a time series database instead of a traditional database. Time series databases offer better query performance, scalability, and simplicity, making them ideal for handling time-stamped data.

Popular Time Series Databases

There are several popular time series databases available today. The following are some of the most widely used time series databases:


InfluxDB is a popular open-source time series database that is widely used in the IoT, finance, and monitoring industries. InfluxDB is designed to handle large volumes of time-stamped data and offers scalability, high availability, and data visualization.


TimescaleDB is an open-source time series database that is built on top of PostgreSQL. TimescaleDB is designed to handle time-series data efficiently and offers a SQL interface for querying data.


OpenTSDB is an open-source time series database that is built on top of HBase. OpenTSDB is designed to handle time-series data and offers a scalable distributed architecture.


Time series databases are an essential tool for handling time-stamped data efficiently. They offer many advantages over traditional relational databases, including faster query performance, scalability, and better data compression. If you are dealing with a significant amount of time-stamped data in your application, it's worth considering using a time series database. With the popularity of time series databases increasing, there are now several open-source options available for developers to choose from.

If you are new to time series databases, we recommend trying out one of the popular open-source options and experimenting with your data to see the benefits time series databases can provide. As businesses continue to adopt real-time data processing as part of their applications, time series databases are becoming increasingly essential for handling time-series data efficiently.

Additional Resources

assetbundle.dev - downloading software, games, and resources at discount in bundles
labeleddata.dev - machine learning pre-labeled data sources and sites, about labeling automation and labeling third party services
dataopsbook.com - database operations management, ci/cd, liquibase, flyway, db deployment
tacticalroleplaying.games - tactical roleplaying games
blockchainjob.app - A jobs board app for blockchain jobs
crates.guide - rust package management, and package development
datacatalog.app - managing ditital assets across the organization using a data catalog which centralizes the metadata about data across the organization
learnpython.page - learning python
techdebt.app - tech debt, software technology debt, software code rot, software maintenance and quality assurance
contentcatalog.dev - managing content, data assets, data asset metadata, digital tags, lineage, permissions
graphml.app - graph machine learning
cloudsimulation.dev - running simulation of the physical world as computer models. Often called digital twin systems, running optimization or evolutionary algorithms which reduce a cost function
ocaml.tips - ocaml tips
learnrust.app - learning the rust programming language and everything related to software engineering around rust, and software development lifecyle in rust
codinginterview.tips - passing technical interview at FANG, tech companies, coding interviews, system design interviews
gslm.dev - Generative Spoken Language Model nlp developments
learnpromptengineering.dev - learning prompt engineering a new field of interactively working with large language models
buildpacks.app - build packs. transform your application source code into images that can run on any cloud. Cloud native
datalineage.dev - data lineage, tracking data as it moves from its source to down stream sources, data quality and data identification
privacyad.dev - privacy respecting advertisements

Written by AI researcher, Haskell Ruska, PhD (haskellr@mit.edu). Scientific Journal of AI 2023, Peer Reviewed