What is a Time-series Database (”TSDB”)

A time-series database (TSDB) is a database optimized for storing and analyzing temporal data from sources like IoT sensors, application logs, and financial markets. TSDBs enable high-performance ingestion and queries of time-oriented data.

They utilize specialized storage like data compression and retention policies to efficiently manage massive amounts of time-series data, often together with a message broker for data ingestion.

What does a TSDB do/how does it work?

A TSDB ingests and stores timestamped data in optimized, compressed structures indexed by time. This allows fast writes and lookups of data points across user-defined time ranges.

TSDBs employ data rollover, downsampling, aggregation to implement retention policies as data ages. Purpose-built time-series queries, partitioning, and compression algorithms enable high performance.

Why is it important? Where is it used?

TSDBs are critical for monitoring and analyzing temporal data at scale. They efficiently store and process massive volumes of time-series data from IoT, finance, and other domains.

Use cases include ingesting IoT sensor data, analyzing financial time-series, monitoring server metrics, log analysis, and industrial telemetry. Their time-oriented capabilities make TSDBs standard for time-series data.

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FAQ

What capabilities do TSDBs provide?

Time series databases are specialized databases optimized for storing and analyzing time-stamped data from metrics, sensors, applications. Their unique architecture provides specific capabilities to handle the nature of time series workloads involving high velocity writes, retention needs, and time-based operations.

• High write throughput for time-stamped data
• Data compression algorithms to reduce storage needs
• Time-based data retention policies to manage historical data
• Time-series oriented query functions like aggregation over time
• Optimized time-series indexing for fast reads

When should you use a TSDB?

Time series databases are designed to handle time series data and are well suited for certain use cases needing high performance reads and writes on frequently updated timestamped data from metrics and sensors.

• You need to store data from many time-based sources.
• You want to retain high-fidelity time-series data without downsampling.
• You need high-performance reads and writes on time-series data.
• You want to run time-series specific analytics like identifying trends.

What are key challenges with TSDBs?

However, working with time series data in TSDBs also comes with inherent complexities around scale, functionality, and storage.

• Computational cost of complex data compression techniques
• Possible gaps in the built-in analytic function library
• Scaling reads and writes with increased data volumes
• Managing storage and retention costs for historical data
• Lack of native support in some databases

What are examples of TSDBs?

• InfluxDB
• Prometheus
• TimescaleDB
• Graphite
• OpenTSDB

References

• [Book] Time Series Databases: New Ways to Store and Access Data, O’Reilly
• [Article, PDF] Time Series Management Systems: A Survey
• [Article, PDF] Efficient mining of partial periodic patterns in time series database
• [Article, PDF] Gorilla: a fast, scalable, in-memory time series database
• [List, Ranking] DB-Engines Ranking of Time Series DBMS
• [Post] What Are Time Series Databases?