Influxdb

InfluxDB

Overview

Configure InfluxDB for time-series data storage and analysis. Covers bucket management, Flux querying, retention policies, downsampling tasks, and API usage for metrics ingestion and retrieval.

Instructions

Task A: Initial Setup and Configuration

# Deploy InfluxDB with Docker
docker run -d --name influxdb \
  -p 8086:8086 \
  -v influxdb_data:/var/lib/influxdb2 \
  -v influxdb_config:/etc/influxdb2 \
  -e DOCKER_INFLUXDB_INIT_MODE=setup \
  -e DOCKER_INFLUXDB_INIT_USERNAME=admin \
  -e DOCKER_INFLUXDB_INIT_PASSWORD=changeme123 \
  -e DOCKER_INFLUXDB_INIT_ORG=myorg \
  -e DOCKER_INFLUXDB_INIT_BUCKET=metrics \
  -e DOCKER_INFLUXDB_INIT_RETENTION=30d \
  influxdb:2.7

# /etc/influxdb2/config.toml — InfluxDB configuration
bolt-path = "/var/lib/influxdb2/influxd.bolt"
engine-path = "/var/lib/influxdb2/engine"

[http]
  bind-address = ":8086"
  flux-enabled = true

[storage-cache]
  snapshot-memory-size = 26214400
  max-concurrent-compactions = 2

[logging]
  level = "info"
  format = "auto"

Task B: Bucket and Token Management

# Create buckets via CLI
influx bucket create \
  --name infrastructure \
  --retention 30d \
  --org myorg

influx bucket create \
  --name app-metrics \
  --retention 90d \
  --org myorg

influx bucket create \
  --name downsampled \
  --retention 365d \
  --org myorg

# Create a scoped API token for Telegraf
influx auth create \
  --org myorg \
  --description "Telegraf write token" \
  --write-bucket infrastructure \
  --write-bucket app-metrics

# Create a read-only token for Grafana
influx auth create \
  --org myorg \
  --description "Grafana read token" \
  --read-bucket infrastructure \
  --read-bucket app-metrics \
  --read-bucket downsampled

Task C: Write Data via API

# Write metrics using line protocol
curl -X POST "http://localhost:8086/api/v2/write?org=myorg&bucket=app-metrics&precision=s" \
  -H "Authorization: Token ${INFLUX_TOKEN}" \
  -H "Content-Type: text/plain" \
  --data-binary '
http_requests,service=api-gateway,method=GET,status=200 count=1523,latency_ms=45.2 1708300800
http_requests,service=api-gateway,method=POST,status=201 count=234,latency_ms=120.5 1708300800
http_requests,service=payment,method=POST,status=500 count=3,latency_ms=5020.0 1708300800
queue_depth,service=order-processor queue_size=142,consumers=5 1708300800
'

Task D: Flux Queries

// Query: CPU usage over last hour, grouped by host
from(bucket: "infrastructure")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_percent" and r.cpu == "cpu-total")
  |> aggregateWindow(every: 5m, fn: mean)
  |> yield(name: "cpu_usage")

// Query: Top 5 services by error count in last 24h
from(bucket: "app-metrics")
  |> range(start: -24h)
  |> filter(fn: (r) => r._measurement == "http_requests" and r.status =~ /^5/)
  |> group(columns: ["service"])
  |> sum(column: "_value")
  |> sort(columns: ["_value"], desc: true)
  |> limit(n: 5)

// Query: Calculate error rate percentage per service
errors = from(bucket: "app-metrics")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "http_requests" and r._field == "count" and r.status =~ /^5/)
  |> group(columns: ["service"])
  |> sum()

total = from(bucket: "app-metrics")
  |> range(start: -1h)
  |> filter(fn: (r) => r._measurement == "http_requests" and r._field == "count")
  |> group(columns: ["service"])
  |> sum()

join(tables: {errors: errors, total: total}, on: ["service"])
  |> map(fn: (r) => ({ r with error_rate: (r._value_errors / r._value_total) * 100.0 }))

// Query: P95 latency with moving average
from(bucket: "app-metrics")
  |> range(start: -6h)
  |> filter(fn: (r) => r._measurement == "http_requests" and r._field == "latency_ms")
  |> aggregateWindow(every: 5m, fn: (tables=<-, column) =>
    tables |> quantile(q: 0.95, column: column))
  |> movingAverage(n: 6)

Task E: Downsampling Tasks

// Task: Downsample infrastructure metrics hourly
option task = {name: "downsample-infra", every: 1h, offset: 5m}

from(bucket: "infrastructure")
  |> range(start: -task.every)
  |> filter(fn: (r) => r._measurement == "cpu" or r._measurement == "mem" or r._measurement == "disk")
  |> aggregateWindow(every: 1h, fn: mean)
  |> to(bucket: "downsampled", org: "myorg")

# Create the task via CLI
influx task create --org myorg -f downsample-infra.flux

# List and manage tasks
influx task list --org myorg
influx task run list --task-id <TASK_ID> --limit 10

Task F: Alerting and Checks

// Check: Alert when CPU exceeds 85%
import "influxdata/influxdb/monitor"

option task = {name: "cpu-alert", every: 1m}

data = from(bucket: "infrastructure")
  |> range(start: -5m)
  |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_percent" and r.cpu == "cpu-total")
  |> mean()

data
  |> monitor.check(
    crit: (r) => r._value > 85.0,
    warn: (r) => r._value > 70.0,
    messageFn: (r) => "CPU at ${string(v: r._value)}% on ${r.host}",
    data: { "_check_name": "High CPU", "_type": "threshold" }
  )

Best Practices

• Use separate buckets for raw and downsampled data with different retention periods
• Create scoped tokens with minimal permissions (write-only for collectors, read-only for dashboards)
• Use aggregateWindow() instead of window() + mean() for cleaner downsampled output
• Set precision in write requests to match your data granularity (seconds is usually sufficient)
• Use line protocol batch writes (multiple lines per request) to reduce HTTP overhead
• Monitor InfluxDB's own metrics at /metrics endpoint for storage and query performance