The Complete Guide to Time Series Databases in 2026: InfluxDB, TimescaleDB, and QuestDB

Time series databases became essential infrastructure for IoT, monitoring, and observability in 2025-2026. With the explosion of sensor data, metrics, and events, choosing the right time series database determines whether your queries run in milliseconds or minutes.

Here's the practical guide.

When to Use Time Series

Use time series when:
- Data has a timestamp as a primary dimension
- You primarily query recent data
- You aggregate over time windows
- Data arrives continuously (streaming)
- You need retention policies (drop old data)

Don't use for:
- Primary entity data (users, accounts)
- Random access by non-time keys
- Complex relational joins

InfluxDB Line Protocol

measurement,tag1=value1,tag2=value2 field1=value1,field2=value2 timestamp

# Example
temperature,location=room1,device=sensor1 value=23.5,sensor_version="v2" 1704067200000000000

InfluxDB Queries

-- Basic range query
SELECT * FROM temperature
WHERE time >= '2026-01-01' AND time <= '2026-01-02'

-- Aggregations
SELECT
  MEAN(value) as avg_temp,
  MIN(value) as min_temp,
  MAX(value) as max_temp,
  PERCENTILE(value, 95) as p95
FROM temperature
WHERE time >= now() - 7d
GROUP BY time(1h), location

TimescaleDB (PostgreSQL Extension)

-- Enable TimescaleDB
CREATE EXTENSION IF NOT EXISTS timescaledb;

-- Convert to hypertable
SELECT create_hypertable('metrics', 'time',
  chunk_time_interval => INTERVAL '1 day'
);

-- Continuous aggregates
CREATE MATERIALIZED VIEW metrics_hourly
WITH (timescaledb.continuous) AS
SELECT
  time_bucket('1 hour', time) AS bucket,
  device_id,
  AVG(value) as avg_value,
  MAX(value) as max_value
FROM metrics
GROUP BY bucket, device_id;

-- Real-time aggregation
SELECT add_continuous_aggregate_policy('metrics_hourly',
  start_offset => INTERVAL '3 hours',
  end_offset => INTERVAL '1 hour',
  schedule_interval => INTERVAL '1 hour'
);

QuestDB (Fastest for High Throughput)

-- QuestDB uses ILP (InfluxDB Line Protocol) for ingestion
-- And standard SQL for queries

-- Time series query
SELECT * FROM readings
WHERE timestamp > NOW() - 1000ms
LATEST ON timestamp PARTITION BY sensor_id;

-- As-of join (latest value per entity)
SELECT * FROM sensors
ASOF JOIN latest_readings
ON sensors.id = latest_readings.sensor_id;

Retention Policies

-- InfluxDB retention
CREATE RETENTION POLICY "one_day" ON "mydb"
  DURATION 1d
  REPLICATION 1
  DEFAULT;

-- Auto-downsample
CREATE RETENTION POLICY "one_week" ON "mydb"
  DURATION 7d
  REPLICATION 1;

SELECT * INTO "one_week".."metrics"
FROM "mydb".."metrics"
WHERE time < now() - 7d
GROUP BY time(5m);

Monitoring Stack Integration

# Prometheus scraping
# prometheus.yml
scrape_configs:
  - job_name: 'app-metrics'
    static_configs:
      - targets: ['app:9090']

  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node-exporter:9100']

# Grafana connects to Prometheus or InfluxDB
# dashboards query time bucketed metrics

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