Metric Cardinality

Understanding metric cardinality

Metric cardinality is a fundamental concept in time-series databases that measures the uniqueness of data points based on their identifying characteristics. For example, in a system monitoring CPU usage across multiple servers, the cardinality would be influenced by:

• Number of servers (hosts)
• Number of CPU cores per server
• Different types of CPU metrics (usage, idle time, interrupts)
• Additional labels like datacenter location or environment type

Impact on database performance

High metric cardinality can create several challenges for time-series databases:

Memory usage

Each unique combination requires separate:

• Index entries
• Memory buffers
• Cache space

Storage requirements

High cardinality metrics lead to:

• Increased storage overhead
• More complex compression patterns
• Greater write amplification

Query performance

Querying high-cardinality data can result in:

• Slower query execution
• Increased resource consumption
• Higher query latency

Managing metric cardinality

Label optimization

Carefully design metric labels to avoid unnecessary combinations:

// High cardinality (avoid)
cpu_usage{host="server1", core="0", thread="1234", user="app123"}

// Lower cardinality (better)
cpu_usage{host="server1", core="0"}

Aggregation strategies

Use downsampling and rollup tables to manage high-cardinality data:

Cardinality limits

Implement controls to prevent cardinality explosion:

• Set maximum unique series per metric
• Monitor cardinality growth rates
• Alert on sudden cardinality increases

Best practices for cardinality management

1. Label selection

   • Use only necessary labels
   • Avoid high-variability labels (like session IDs)
   • Standardize label naming conventions

2. Monitoring and alerting

   • Track cardinality growth over time
   • Set alerts for abnormal increases
   • Monitor impact on system resources

3. Data lifecycle management

   • Implement appropriate retention policies
   • Use tiered storage strategies
   • Regular cleanup of stale metrics

By understanding and properly managing metric cardinality, organizations can maintain efficient time-series databases while ensuring comprehensive monitoring coverage of their systems.