Databases Intermediate

What is Time-Series Database?

A database optimized for storing and querying timestamped data points like metrics, sensor readings, and event logs.

Time-series databases are specialized for data that arrives sequentially over time. They optimize for high write throughput, time-range queries, and downsampling/aggregation. Examples include TimescaleDB (PostgreSQL extension), InfluxDB, Prometheus (for metrics), and QuestDB. Key features include automatic partitioning by time, built-in aggregation functions (moving averages, percentiles), data retention policies, and compression. They are essential for IoT, monitoring, financial data, and any application tracking changes over time.

Learn More About This Topic

Database Design & Modeling Fundamentals

Related reading

PostgreSQL Database Setup: The Complete Guide

Related reading

MongoDB Fundamentals: Building Scalable NoSQL Applications with Document Databases

Related reading

Related Terms

A database object that enables row-by-row processing of query results, useful for operations that cannot be done in bulk.

A data structure that improves the speed of data retrieval operations on database tables at the cost of additional storage.

Database Connection Pooling

A technique that maintains a cache of database connections for reuse, reducing the overhead of creating new connections.

Database Index Types

Different index structures (B-tree, Hash, GIN, GiST, BRIN) optimized for various query patterns and data types.

A distributed search and analytics engine built on Apache Lucene, optimized for full-text search and log analysis.

Row-Level Security (RLS)

A database feature that restricts which rows a user can access in a table based on security policies.

View All Databases Terms →