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.

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).

Log parsing uses a multi-stage approach:

1. Format detection identifies structured or unstructured data.
2. Structured JSON logs have fields automatically extracted and indexed.
3. Unstructured logs are parsed using configured rules in VRL, enabling complex transformations like field extraction, timestamp parsing, and data enrichment.
4. Parsed logs are indexed in columnar format for efficient querying and storage.

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).

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.

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.

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.

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.