A time-series database is optimized for timestamp or time-series data. Time series data mean measurements or events that are tracked, monitored, collected, or aggregated over a period of time. These could be data collected from heartbeats of motion tracking sensors, JVM metrics from the java applications, market trade data, network data, API responses, process uptime, etc.
Time-series databases are completely customized with timestamped data, which is indexed and efficiently written in such a way that you can insert time-series data. You can query those time series data much faster than how you will be doing it in a relational or NoSQL database.
Lately, it has gained a lot of popularity. And why not? It does a fantastic job for business and IT operations monitoring. The good news is – there are plenty of options to choose from, and most of them are open-source.
InfluxDB is one of the most popular time series databases among DevOps, which is written in Go. InfluxDB was designed from the ground up to provide a highly scalable data ingestion and storage engine. It is very efficient at collecting, storing, querying, visualizing, and taking action on streams of time series data, events, and metrics in real-time.
It provides downsampling and data retention policies to support keeping high value, high precision data in memory, and lower value data to disk. It is built on a cloud-native fashion for providing scalability across multiple deployment topologies, including cloud on-premises and hybrid environments.
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InfluxDB is an open-source solution and enterprise-ready. It uses InfluxQL, which is very similar to a structure query language, for interacting with data. The latest version offers agents, dashboards, queries, and tasks in a toolkit. It is an all-in-one tool for dashboarding, visualizing, and alerting.
InfluxDB Features:
Since it’s open-source, you can download and get it started on your server. However, they do offer InfluxDB Cloud on AWS, Azure, and GCP.
TimescaleDB is an open-source relational database that makes SQL scalable for time-series data. This database is built on PostgreSQL.
It offers two products – the first option is a community edition, free to use that you can install on your server. The second option is TimescaleDB Cloud, where you get fully hosted and managed infrastructure on the cloud for your deployment needs.
It can be used for DevOps monitoring, understanding application metrics, tracking data from IoT devices, understanding financial data, etc. You can measure logs, Kubernetes events, Prometheus metrics, even custom metrics.
For product owners, you can use it to understand a product’s performance over time, which helps in making strategic decisions for growth.
Prometheus Features:
Prometheus has hundreds of exporters to export the data from Windows, Linux, Java, Database, APIs, Website, Server Hardware, PHP, Messaging, and more. To monitor Linux, check this Prometheus + Grafana setup.
Prometheus is an open-source monitoring solution used to understand insights from metrics data and send necessary alerts. It has a local on-disk time-series database that stores data in a custom format on disk.
Prometheus’s data model is multi-dimensional based on time series; it stores all the data as streams of timestamped values. It is very much useful when working with a fully numeric time series. Collecting microservices data and querying it is one of the strengths of Prometheus.
It can be used for DevOps monitoring, understanding application metrics, tracking data from IoT devices, understanding financial data, etc. You can measure logs, Kubernetes events, Prometheus metrics, even custom metrics.
For product owners, you can use it to understand a product’s performance over time, which helps in making strategic decisions for growth.
TimescaleDBs Features :
Graphite is an all-in-one solution for storing and efficiently visualizing real-time time-series data. Graphite can do two things, store time-series data and render graphs on demand. But it doesn’t collect data for you; for that, you can use tools such as collectd, Ganglia, Sensu, telegraf, etc It has three components – Carbon, Whisper, and Graphite-Web. Carbon receives the time series data, aggregates it, and persists it to the disk. Whisper is time-series database storage that stores the data. Graphite-Web is the front-end for creating dashboards and visualizing the data.
Graphite features:
QuestDB is a relational column-oriented database that can perform real-time analytics on time series data. It works with SQL and some extensions to create a relational model for time series data. QuestDB has been coded from scratch and has no dependencies which enhance its performance. QuestDB supports relational, and time-series joins, which helps in correlating the data. The easiest way to get started with QuestDB is to deploy it inside a Docker container.
QuestDB features:
AWS Timestream is a serverless time series database service that is fast and scalable. It is used majorly for IoT applications to store trillions of events in a day and 1000 times faster with 1/10th cost of relational databases. Using its purpose-built query engine, you can query recent data and historical stored data simultaneously. It provides multiple built-in functions to analyze time-series data to find useful insights.
Amazon Timestream features:
OpenTSDB is a scalable time-series database that has been written on top of HBase. It is capable of storing trillions of data points at millions of writes per second. You can keep the data in OpenTSDB forever with its original timestamp and precise value, so you don’t lose any data. It has a Time-series daemon (TSD) and command-line utilities. Time series daemon is responsible for storing data in HBase or retrieving it from it. You can talk to TSD using HTTP API, telnet, or simple built-in GUI. You need tools like flume, collectd, vacuumetrix, etc., to collect data from various sources into OpenTSDB.
Amazon Timestream features:
Since more and more IoT/Smart devices are getting used these days, huge real-time traffic is getting generated on websites with millions of events in a day, trading on the market is increasing, and the time-series database has arrived! Time-series databases are a must-have in your production stack for monitoring. Most of the above-listed time-series database is available to self-host, so go ahead, get a cloud VM and give it a try to see what works for you.