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Time Series Database Performance Benchmark Analysis

Time series databases (TSDBs) have become increasingly important in today’s data-driven world, especially for applications like IoT monitoring, financial analytics, and operational intelligence. With numerous options available, choosing the right time series database can be challenging. This article provides a comprehensive performance benchmark analysis of popular TSDBs to help you make an informed decision.

Key Factors in Time Series Database Comparison

When evaluating time series databases, several critical performance metrics should be considered:

• Write throughput (data ingestion rate)
• Query performance (read latency)
• Storage efficiency (compression ratios)
• Scalability (horizontal and vertical)

Keyword: time series database comparison

• Resource utilization (CPU, memory, disk I/O)

Benchmark Methodology

Our benchmark tests were conducted on identical hardware configurations with the following specifications:

• 8-core CPU @ 3.2GHz
• 32GB RAM
• 500GB SSD storage
• Ubuntu 20.04 LTS

The test dataset consisted of 100 million time series points with various cardinalities to simulate real-world scenarios.

Performance Comparison Results

Write Performance

The write performance test measured how many data points each database could ingest per second:

• InfluxDB: 250,000 points/sec
• TimescaleDB: 180,000 points/sec
• Prometheus: 120,000 points/sec
• OpenTSDB: 90,000 points/sec

Query Performance

For read operations, we tested simple and complex queries across different time ranges:

• InfluxDB: 5ms (simple), 120ms (complex)
• TimescaleDB: 8ms (simple), 150ms (complex)
• Prometheus: 15ms (simple), 200ms (complex)
• OpenTSDB: 25ms (simple), 300ms (complex)

Storage Efficiency

Compression ratios varied significantly between solutions:

• InfluxDB: 10:1 compression
• TimescaleDB: 7:1 compression
• Prometheus: 5:1 compression
• OpenTSDB: 3:1 compression

Conclusion

Based on our comprehensive benchmark analysis, InfluxDB demonstrated superior performance across all key metrics, making it an excellent choice for high-throughput time series applications. However, TimescaleDB showed strong performance as well, particularly for users already familiar with PostgreSQL. The right choice ultimately depends on your specific use case, existing infrastructure, and performance requirements.

For organizations with extremely high cardinality data or specialized requirements, it may be worth considering additional options or custom solutions. Regular performance testing is recommended as all these databases continue to evolve and improve their capabilities.