# 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.

## Install

Save as a script file and run:

## Quick Use

```bash
# 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:
```bash
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):
```sql
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"
```

## Intro

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.

- **Repo**: https://github.com/influxdata/influxdb
- **Stars**: 31K+
- **Language**: Rust
- **License**: Apache 2.0

## 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

```yaml
# 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

- Docs: https://docs.influxdata.com
- GitHub: https://github.com/influxdata/influxdb
- License: Apache 2.0

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Source: https://tokrepo.com/en/workflows/90136b66-35f6-11f1-9bc6-00163e2b0d79
Author: Script Depot