Time Series Database (TSDB): A Guide With Examples

A couple of years ago, during my first week in a new software engineering role, I was asked to investigate time series databases (TSDBs) to replace our Postgres solution.

I knew absolutely nothing about the topic and had so many questions. What even is a time series database? How does it work? How is it different from a traditional database? Why should we use one? Do I need specific skills for that?

Since then, I’ve learned a lot about TSDBs and have applied this knowledge in various companies to solve a wide range of problems.

In this article, I’ll sum up what I learned over the past few years to give you a good idea of what TSDBs are, how they work, and what use cases they best work for. I’ll also walk you through some of the TSDBs currently on the market and give you tips so you can choose the one that best suits your needs.

What Are Time Series Databases?

Imagine a smart thermostat sold by company X that records temperature readings every 30 seconds. In a single day, this one device generates thousands of data points. Now, multiply that by hundreds or thousands of devices across a city, and the volume of time-stamped data gathered by company X becomes staggering.

To store this data efficiently and analyze trends (like temperature changes over time or sudden spikes), company X needs a database that can handle massive write speeds and perform time-based queries efficiently.

Traditional databases tend to struggle with this kind of workload because they aren't designed to handle high-frequency data writes or query data efficiently over specific time ranges. That’s where time series databases come in.

Time series databases are specialized databases designed to manage data that is organized and indexed by time. Unlike traditional databases, which are optimized for general-purpose data storage, TSDBs focus on efficiently storing, querying, and analyzing sequences of time-stamped data points.

TSDBs are particularly suited for applications that deal with continuous data streams, such as IoT, DevOps monitoring, and financial analytics.

Characteristics of Time Series Databases

There are a few things that TSDBs do differently than traditional databases.

Optimized for time-stamped data

At their core, TSDBs are built to handle data with timestamps as a fundamental attribute. Every data point in a TSDB includes a timestamp, which serves as its primary index. This allows these databases to efficiently store and retrieve time-ordered sequences and provide quick access to historical trends or recent events.

Most TSDBs use time-based partitioning, meaning the data is stored in partitions based on time intervals (e.g., hourly, daily). This enables efficient pruning, where queries ignore irrelevant partitions altogether. 

They can also implement time buckets, grouping data into predefined time windows (e.g., 1 minute, 1 hour) for faster aggregations.

High ingestion rates

Time series data is often generated at a rapid pace—think of IoT devices sending thousands of data points per second or a server monitoring tool capturing system metrics in real time. TSDBs are optimized for these high write rates and can ingest vast amounts of data without slowing down or losing information.

This is usually achieved using append-only data storage models and in-memory buffers to prevent locks or transactional bottlenecks.

Efficient queries for time ranges

Analyzing time series data often involves querying specific time intervals or windows, such as “last 24 hours” or “this year compared to last year.” TSDBs are built with this in mind, offering specialized query capabilities that allow users to quickly retrieve data over defined time ranges. They also support aggregations like averages, sums, or trends to offer valuable analytics without complex query logic.

The query optimization techniques include:

• Pre-aggregated data: TSDBs often pre-calculate summaries for common time intervals (e.g., hourly or daily averages).
• Sliding window algorithms: These help efficiently compute metrics over moving time windows, such as rolling averages.

Data compression and retention policies

To manage the vast amount of time series data generated over time, TSDBs use advanced data compression techniques. These methods reduce storage requirements while preserving query performance.

TSDBs usually include retention policies so the users can define how long data should be kept. For example, a system might retain detailed data for the past month while downsampling for older data. Downsampling is the process of reducing the granularity of data over time. For example:

• Raw temperature readings might be recorded every 10 seconds for the most recent 7 days.
• For older data, the system might downsample to hourly averages to save space while still retaining historical trends.

Examples of advanced compression techniques include:

• Delta encoding: Storing the difference between consecutive values instead of the full value.
• Gorilla compression: A method used to efficiently compress floating-point time series data by storing changes in binary format.

Use Cases for Time Series Databases

Time series databases are used in many modern data-driven applications and across diverse industries. Let’s explore the main use cases.

1. Internet of Things (IoT)

IoT devices, like smart thermostats, industrial sensors, and environmental monitors, generate continuous streams of time-stamped data. TSDBs are used to store and analyze this data, and power applications like:

• Smart homes: Monitoring and controlling appliances based on time-sensitive data.
• Industrial automation: Tracking machine performance and detecting anomalies in real time to minimize downtime.
• Environmental monitoring: Collecting data from sensors to track air quality, weather patterns, or water levels over time.

2. DevOps and system monitoring

In DevOps, TSDBs are widely used to monitor IT infrastructure and applications by collecting metrics like CPU usage, memory consumption, and network throughput. They enable:

• Performance monitoring: Visualizing system health and performance metrics in real time.
• Anomaly detection: Identifying unusual patterns, such as spikes in server load or network latency.
• Capacity planning: Using historical trends to predict and allocate future resource needs.

Tools like Prometheus and Grafana often integrate with TSDBs to provide visualization and alerting capabilities for DevOps teams.

3. Financial markets

TSDBs are critical for processing and analyzing the vast amounts of high-frequency data generated in financial markets. They are used for:

• Algorithmic trading: Storing and analyzing market data in milliseconds to execute trades based on real-time trends.
• Risk management: Monitoring financial metrics over time to assess and mitigate risks.
• Market analysis: Analyzing historical data to identify patterns, trends, and anomalies in market behavior.

4. Other applications

While the three use cases above are very common, time series databases can also find applications in a variety of other fields:

• Healthcare: Monitoring patient vitals in real time and analyzing medical device data.
• Scientific research: Collecting and analyzing data for climate modeling, astronomical observations, and other time-dependent phenomena.
• Business analytics: Tracking customer behavior, analyzing sales trends, and monitoring key performance indicators over time.

Best Time Series Databases: A Comparative Overview

Time series databases come in various shapes and forms, each tailored for specific use cases.

InfluxDB

InfluxDB is a popular open-source time series database developed by InfluxData. It was designed specifically for high ingestion rates and efficient querying of time-stamped data, making it a common solution for IoT monitoring, DevOps metrics, and real-time analytics.

TimescaleDB

TimescaleDB is an open-source extension for PostgreSQL, designed to combine the power of relational databases with time-series functionality. It allows you to leverage SQL while efficiently handling time-stamped data. This makes it particularly well-suited for use cases that require integrating time-series data with relational data, such as business analytics or IoT telemetry.

Prometheus

Prometheus is a monitoring and alerting system with a built-in TSDB, widely adopted in DevOps for real-time system metrics, performance tracking, and alert management.

Clickhouse

ClickHouse is an open-source columnar database designed for high-performance analytical queries. While it is not a traditional TSDB, its architecture makes it exceptionally suited for time-series data, especially when fast query performance is critical.

Apache Cassandra

Apache Cassandra is a distributed NoSQL database built for horizontal scalability and high availability. While not exclusively a TSDB, it can be used effectively for time-series workloads, particularly when durability and fault tolerance are critical.

Amazon Timestream

Amazon Timestream is a fully managed time series database service offered by AWS. Built for scalability and simplicity, it is ideal for organizations already leveraging AWS infrastructure for IoT and application monitoring.