Log Management and Analytics
Transform your log data into actionable insights with real-time analysis and industry-leading storage efficiency.
Why Use OpenObserve for Logs?
Modernize your log management with powerful processing that scales from gigabytes to petabytes while dramatically reducing costs through intelligent compression and efficient querying.
Real-Time Analytics
Instant Processing
Query logs the moment they arrive using familiar SQL syntax, with sub-second response times even at petabyte scale.
Drag n Drop dashboards
Create and easily share interactive visualizations that update in real-time with an intuitive drag-and-drop interface.
Powerful Search
Full-Text Search
Find specific log entries across massive datasets in seconds using powerful search capabilities and pattern matching.
Structured Queries
Leverage SQL for precise log analysis and complex aggregations across multiple log sources.
Log Processing
Automated Parsing
Convert raw logs into structured data automatically with intelligent built-in parsing capabilities for common formats, including JSON, CSV, Syslog, CEF, nginx, and more.
Custom Parsing
Transform and enrich logs during ingestion using Vector Remap Language (VRL). Parse, enrich, redact, reduce, and aggregate any log format while maintaining high performance through flexible pipeline configurations.
Optimized Storage
Very High compression
Reduce storage costs by up to 140x compared to Elasticsearch through advanced columnar storage and Apache Parquet format, while maintaining lightning-fast query performance.
Flexible Retention
Set custom retention policies by data source and leverage long-term data storage without breaking the bank.
Get Started with Log Management
Begin managing your logs with OpenObserve. Start with our free tier or schedule a demo.
Openobserve Cloud Free Tier
Monthly Limits:
Ingestion - 50 GB logs, 50 GB metrics , 50 GB traces- Query volume - 200 GB
- Pipelines - 50 GB of Data Processing
- 1K RUM & Session Replay
- 1K Action Script Runs
- 3 Users
- 7-Days Retention
Get started in minutes—no credit card required.
Log Management FAQs
How does OpenObserve handle log ingestion?
OpenObserve provides multiple ingestion methods for different use cases:
- Direct log shipping via HTTP/HTTPS endpoints with API authentication.
- Integration with industry-standard log forwarders like Vector, Fluentd, and Fluent Bit.
- Kubernetes environments supported through the OpenObserve operator or DaemonSet.
- Cloud provider logs ingested through native integrations such as AWS Kinesis Firehose or GCP Pub/Sub.
What log formats are supported?
OpenObserve supports a wide range of log formats:
- Structured JSON logs are handled natively, with automatic field detection and indexing.
- Unstructured logs, such as nginx, Apache, and system logs, are supported via built-in parsers.
- Cloud provider logs from AWS, GCP, and Azure are parsed using predefined templates.
- Custom log formats can be parsed using Vector Remap Language (VRL).
How does log parsing work in OpenObserve?
Log parsing uses a multi-stage approach:
- Format detection identifies structured or unstructured data.
- Structured JSON logs have fields automatically extracted and indexed.
- Unstructured logs are parsed using configured rules in VRL, enabling complex transformations like field extraction, timestamp parsing, and data enrichment.
- Parsed logs are indexed in columnar format for efficient querying and storage.
What search and query capabilities does OpenObserve provide?
OpenObserve offers a SQL-compatible query engine:
- Full-text searches across all log fields.
- Support for complex conditions, regular expressions, time-based filtering, field-specific searches, and advanced aggregations.
- Nested queries, mathematical functions, joins across different log streams, and aggregations (e.g., count, sum, average).
How does OpenObserve optimize log storage?
Optimization techniques include:
- Storing logs in columnar format using Apache Parquet for excellent compression ratios.
- Implementing dictionary encoding for repeated values and specific compression algorithms for different data types.
- Organizing storage into hot and warm tiers with configurable retention periods per data source.
- Lifecycle management automates data movement and cleanup based on retention policies.
What real-time analysis capabilities are available?
Real-time features include:
- Live tail functionality for monitoring logs as they're ingested.
- Real-time dashboards that update automatically as new data arrives.
- Pattern detection for identifying anomalies or specific conditions in log streams.
- An alerting system to trigger notifications based on custom query conditions.
How does OpenObserve handle high-volume log ingestion?
The platform uses a distributed architecture to handle high-throughput ingestion:
- Efficient write paths with batch processing and parallel ingestion.
- Write-ahead logging ensures durability while buffering incoming logs during high load.
- Optimized storage engine maintains query performance even under heavy write loads.
What visualization and dashboard features are available?
OpenObserve offers flexible dashboards for visualization:
- Custom dashboards with various visualization types (e.g., time series graphs, tables).
- Dynamic time ranges and auto-refresh capabilities.
- Saved queries for frequent use and template variables for reusable dashboards.
- Data export options in various formats for external analysis.
Want to Learn More? Check out our blog.
Explore log management best practices and OpenObserve's capabilities on our blog.
