Scripts2026年4月11日·1 分钟阅读

InfluxDB — Scalable Time Series Datastore for Metrics & Events

InfluxDB is a scalable time series database for metrics, events, and real-time analytics. InfluxDB 3.0 is a complete rewrite in Rust with columnar Apache Arrow storage, SQL and InfluxQL queries, and unlimited cardinality.

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快速使用

先拿来用，再决定要不要深挖

这里应该同时让用户和 Agent 知道第一步该复制什么、安装什么、落到哪里。

# Docker
docker run -d --name influxdb -p 8086:8086 influxdb:3

# Or binary
brew install influxdb                      # macOS

# Setup (v3)
influxdb3 create token --permission "*"
influxdb3 create database metrics

Write data via HTTP line protocol:

curl -XPOST "http://localhost:8086/write?db=metrics" \
  -H "Authorization: Token $INFLUXDB_TOKEN" \
  --data-raw "cpu,host=server01,region=us-west usage_user=23.5,usage_system=12.3 $(date +%s)000000000"

Query via SQL (InfluxDB 3):

SELECT host, mean(usage_user) as avg_cpu
FROM cpu
WHERE time >= now() - INTERVAL "1 hour"
GROUP BY host
ORDER BY avg_cpu DESC;

Or InfluxQL (v1/v2 style):

SELECT mean("usage_user") FROM "cpu"
WHERE time > now() - 1h
GROUP BY time(5m), "host"
介绍

InfluxDB is a scalable time series database for metrics, events, and real-time analytics. InfluxDB 3.0 is a complete rewrite in Rust built on Apache Arrow columnar storage (via DataFusion), providing unlimited cardinality, SQL support, and much better analytics performance than v1/v2. Used by Cisco, eBay, IBM, and countless IoT deployments.

What InfluxDB Does

  • Time series storage — optimized for time-ordered data
  • High write throughput — millions of points per second
  • Downsampling — continuous queries / tasks to roll up data
  • Retention policies — auto-delete old data
  • SQL + InfluxQL + Flux — multiple query languages
  • Apache Arrow/Parquet — columnar storage in v3
  • Unlimited cardinality — v3 removes series cardinality limits
  • Telegraf — 200+ plugins for data collection
  • Kapacitor — alerting and stream processing
  • Grafana integration — first-class data source

Architecture

InfluxDB 3.0 architecture:

  • Write — line protocol to WAL, flushed to Parquet files
  • Query — DataFusion execution engine on Parquet + in-memory WAL
  • Storage — object storage (S3, local disk) for Parquet
  • Compactor — merges small Parquet files into larger ones

Self-Hosting

# docker-compose.yml
version: "3"
services:
  influxdb:
    image: influxdb:3-core
    ports: ["8086:8086"]
    volumes: ["influx-data:/var/lib/influxdb3"]
    environment:
      INFLUXDB3_NODE_IDENTIFIER_PREFIX: "node-1"
      INFLUXDB3_OBJECT_STORE: "file"
      INFLUXDB3_DB_DIR: "/var/lib/influxdb3"
volumes:
  influx-data:

Key Features

  • Purpose-built for time series
  • v3 built on Apache Arrow/Parquet
  • SQL and InfluxQL query
  • Unlimited cardinality (v3)
  • High ingest throughput
  • Downsampling and retention
  • Telegraf ecosystem (200+ plugins)
  • Grafana integration
  • Cloud-native managed offering

Comparison

Database Model Storage SQL
InfluxDB v3 Time series Parquet + Arrow Yes + InfluxQL
TimescaleDB Postgres extension Postgres Native
Prometheus Time series Local TSDB PromQL only
VictoriaMetrics Time series Custom MetricsQL
QuestDB Time series Columnar SQL
ClickHouse OLAP Columnar SQL

常见问题 FAQ

Q: v2 vs v3 区别？ A: v3 完全重写（Rust + Apache Arrow/DataFusion），支持 SQL、无 cardinality 限制、storage 放对象存储。v2 是 Go + TSM 引擎，有 cardinality 问题。

Q: 和 Prometheus 怎么选？ A: Prometheus 适合监控（pull 模型、简单部署）；InfluxDB 适合通用时序数据（push 模型、SQL 查询、更长期存储）。

Q: 支持 PromQL 吗？ A: v3 有实验性 PromQL 支持，目标是 Prometheus 替代品。

来源与致谢 Sources

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