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influxdata/influxdb

📈 Trending★ 31,498RustAudience · ops devopsComplexity · 4/5ActiveLicenseSetup · moderate

TLDR

Open-source time-series database optimized for storing and querying timestamped measurements like server metrics, sensor data, and stock prices at high speed.

Mindmap

mindmap
  root((InfluxDB))
    What it does
      Stores timestamped data
      Queries time ranges fast
      Handles high-speed ingestion
    Data & Storage
      Apache Parquet format
      Local disk or S3
      Line protocol input
    Query Languages
      SQL support
      InfluxQL dialect
      HTTP and FlightSQL APIs
    Use Cases
      Infrastructure monitoring
      IoT sensor collection
      Application performance
      Financial market data
    Tech Stack
      Rust engine
      Apache Arrow
      DataFusion

Things people build with this

USE CASE 1

Monitor server CPU, memory, and disk usage over time and alert on anomalies.

USE CASE 2

Collect temperature and humidity readings from thousands of IoT sensors and visualize trends.

USE CASE 3

Track application response times and error rates to identify performance regressions.

USE CASE 4

Store and analyze historical stock prices and trading volumes for backtesting strategies.

Tech stack

RustApache ParquetApache ArrowDataFusionSQLInfluxQL

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Rust toolchain installation and compilation from source; pre-built binaries may not be available for all platforms.

Use freely for any purpose, including commercial use, under MIT or Apache 2.0 licenses.

In plain English

InfluxDB is an open-source database built specifically for time series data, which is data where each record has a timestamp and represents a measurement at a point in time. Examples include server CPU usage sampled every second, temperature sensor readings from IoT devices, stock prices, application error rates, or network traffic counters. Unlike a general-purpose database, InfluxDB is optimized to ingest this kind of data at high speed and answer time-range queries very quickly. The version documented in this repository's main branch is InfluxDB 3 Core. It is built using a modern storage architecture: data is stored as Apache Parquet files, which is a columnar format that compresses well and supports fast analytical queries. Queries can be written in SQL or InfluxQL, the database's own time-series query language. The query engine is powered by Apache Arrow and DataFusion, which are open-source frameworks for fast in-process analytics. Data can be stored on local disk or in object storage like Amazon S3, Azure Blob, or Google Cloud Storage, giving a diskless deployment option. InfluxDB accepts data in line protocol format, which is a simple text format where each line describes one measurement with tags, fields, and a timestamp. It also provides HTTP and FlightSQL API endpoints for querying. You would use InfluxDB when your application generates time-stamped measurements that you need to store, query, and visualize, such as infrastructure monitoring, IoT sensor data collection, application performance tracking, or financial market data analysis. The tech stack is Rust for the core database engine, with Apache Arrow and DataFusion for query processing. The database can be run locally or in Docker, and the project is licensed under MIT and Apache 2.0.

Copy-paste prompts

Prompt 1
Show me how to set up InfluxDB 3 Core locally and ingest server metrics using line protocol.
Prompt 2
Write a SQL query to find the average CPU usage per hour for the last 7 days in InfluxDB.
Prompt 3
How do I configure InfluxDB to store data in Amazon S3 instead of local disk?
Prompt 4
Create a Docker Compose setup that runs InfluxDB with a Grafana dashboard for monitoring.
Prompt 5
Explain how to migrate time-series data from Prometheus to InfluxDB.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.