What is InfluxDB?
InfluxDB is an open-source time series database (TSDB) developed by InfluxData. It's purpose-built for handling high write and query loads of time-stamped data including metrics, events, and real-time analytics. InfluxDB is optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, IoT sensor data, and real-time analytics.
Unlike traditional relational databases that struggle with time series workloads, InfluxDB provides specialized features like automatic data retention, continuous queries, and built-in time-based functions. It's part of the TICK stack (Telegraf, InfluxDB, Chronograf, Kapacitor) for complete time series data platform solutions.
InfluxDB Capacity Calculator
Daily Points: 864M points
Index Size: 100GB (TSI)
Shard Duration: 7d
InfluxDB Data Model
Measurement
Container for time series data, similar to a table in SQL.
measurement: "temperature"
measurement: "http_requests"
Tags
Indexed metadata for fast filtering and grouping queries.
region=us-west
environment=production
Fields
The actual measured values (numbers, strings, booleans).
count=142
status="healthy"
Timestamp
Nanosecond precision timestamp (automatically added).
(2022-01-01T00:00:00Z)
InfluxDB Query Languages
InfluxQL (SQL-like)
SQL-like query language for familiar syntax and easy adoption.
WHERE "host" = 'server1' AND time >= now() - 1h
GROUP BY time(5m), "region"
ORDER BY time DESC
Flux (Functional Language)
Modern functional language for advanced data processing and transformations.
from(bucket: "metrics")
|> range(start: -1h)
|> filter(fn: (r) => r._measurement == "cpu_usage")
|> aggregateWindow(every: 5m, fn: mean)
|> yield(name: "_results")Common Time Functions
Built-in functions for time series analysis and aggregation.
mean(), median(), sum(), count(), min(), max()
// Time manipulation
now(), time(), duration(), timeShift()
// Mathematical functions
derivative(), difference(), rate(), increase()
Real-World InfluxDB Implementations
Tesla
Uses InfluxDB for vehicle telemetry and energy storage monitoring.
- • Vehicle sensor data collection
- • Battery performance monitoring
- • Charging station analytics
- • Powerwall energy metrics
Cisco
Leverages InfluxDB for network monitoring and infrastructure analytics.
- • Network device monitoring
- • Traffic pattern analysis
- • Performance metrics collection
- • Real-time alerting systems
IBM
Implements InfluxDB for IoT data collection and real-time analytics.
- • Industrial IoT sensor data
- • Predictive maintenance analytics
- • Supply chain monitoring
- • Environmental sensor networks
eBay
Uses InfluxDB for application performance monitoring and business metrics.
- • Application performance metrics
- • User behavior analytics
- • Transaction monitoring
- • A/B testing result tracking
TICK Stack Components
Telegraf
Agent for collecting and reporting metrics and events.
• System metrics collection
• Custom metric gathering
• Data transformation
InfluxDB
Time series database for storage and querying.
• InfluxQL and Flux queries
• Automatic retention
• Horizontal scaling
Chronograf
Visualization and administrative interface.
• Query editor
• Alert management
• Data exploration tools
Kapacitor
Real-time streaming data processing engine.
• Real-time alerting
• Anomaly detection
• Data transformation
InfluxDB Best Practices
✅ Do
- • Keep tag cardinality low (<100K unique values)
- • Use appropriate retention policies
- • Design schema for your query patterns
- • Batch writes for better performance
- • Use continuous queries for downsampling
- • Monitor series cardinality regularly
❌ Don't
- • Use high-cardinality tags (UUIDs, timestamps)
- • Store non-time series data in InfluxDB
- • Ignore memory usage and series limits
- • Use InfluxDB for relational data
- • Skip proper shard group configuration
- • Forget to monitor query performance