The Log Analysis Challenge

Every application generates logs. Web servers, databases, microservices - they all leave digital breadcrumbs. But when things go wrong, finding relevant information feels like searching for a needle in a haystack.

Traditional log analysis relies on basic pattern matching. That works for simple cases, but modern applications generate complex, semi-structured logs that need smarter search capabilities.

GreptimeDB's Full-Text Search Evolution

Version 0.14 brought significant improvements to full-text indexing. Now you have two backend options optimized for different use cases:

Bloom Filter Backend

• Efficient filtering for general log search
• Low storage overhead (1GB index for 10GB data)
• Stable performance across query patterns
• Best for: Regular log analysis workflows

Tantivy Backend

• Inverted indexes for high-selectivity queries
• 5x faster for unique identifier searches
• Higher storage costs (index size ~= data size)
• Best for: TraceID and specific value lookups

The New matches_term Function

Exact phrase matching becomes crucial for log analysis. The new @@ operator and matches_term function enable:

SELECT * FROM logs WHERE message @@ 'connection timeout';
-- Or using the full function
SELECT * FROM logs WHERE matches_term(message, 'error');

This case-sensitive matching respects word boundaries, making it perfect for structured log parsing.

Performance Comparison Insights

Our benchmarks reveal interesting patterns:

Query Type	High Selectivity	Low Selectivity
Bloom	1x (baseline)	1x (baseline)
Tantivy	5x faster	5x slower
Like Query	50x slower	1x

Choose your backend based on actual query patterns, not just peak performance numbers.

Storage vs. Performance Trade-offs

Full-text indexing isn't free. Consider these factors:

• Index storage requirements
• Write performance impact
• Query latency expectations
• Cost sensitivity for your deployment

GreptimeDB's flexible approach lets you optimize per table based on specific requirements.

Best Practices for Log Analytics

Effective log search implementation:

• Structure logs when possible for better compression
• Use appropriate index backends for your query patterns
• Monitor index size growth
• Consider retention policies for historical data

Start with GreptimeDB's log analytics features to streamline your troubleshooting workflows.

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About Greptime

GreptimeDB is an open-source, cloud-native database purpose-built for real-time observability. Built in Rust and optimized for cloud-native environments, it provides unified storage and processing for metrics, logs, and traces—delivering sub-second insights from edge to cloud —at any scale.

• GreptimeDB OSS – The open-sourced database for small to medium-scale observability and IoT use cases, ideal for personal projects or dev/test environments.

• GreptimeDB Enterprise – A robust observability database with enhanced security, high availability, and enterprise-grade support.

• GreptimeCloud – A fully managed, serverless DBaaS with elastic scaling and zero operational overhead. Built for teams that need speed, flexibility, and ease of use out of the box.

🚀 We’re open to contributors—get started with issues labeled good first issue and connect with our community.

⭐ GitHub | 🌐 Website | 📚 Docs

💬 Slack | 🐦 Twitter | 💼 LinkedIn

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