What is a log database?
A log database is a specialized data store used to collect and analyze large volumes of ordered log events generated by applications, infrastructure, and devices. Log data typically arrives as time ordered events, often in semi structured formats like JSON, at very high velocity.
A modern log database must:
- Ingest millions of events per second without losing data
- Normalize and index semi structured and unstructured payloads
- Provide fast search and aggregations over recent and historical logs
- Retain months or years of data at manageable cost
- Integrate with observability stacks, dashboards, and alerting systems
Why traditional log management tools struggle
Many teams start with file based logs or legacy log management stacks, then hit limits when data and query complexity grow:
- High storage costs for full text search indexes that often outweigh raw log data
- Slow queries when searching across long retention windows or large clusters
- Limited analytics focused on keyword search, not deep aggregations or correlations
- Data silos between logs, metrics, business events, and relational data
- Operational overhead for scaling, sharding, and index lifecycle management
CrateDB as a log database
CrateDB is a distributed SQL database that stores structured, semi structured, and unstructured logs in a columnar format. It combines the strengths of log engines and analytical databases:
High throughput ingestion: Ingest millions of log events per second from agents like Fluent Bit, Beats, Vector, OpenTelemetry collector, Kafka, or MQTT. Data becomes queryable within milliseconds.
Flexible schema for semi structured logs: Store JSON payloads and nested structures without rigid schemas. Add fields over time as applications evolve while still benefiting from automatic indexing.
Fast search and aggregations with SQL: Use standard SQL to filter logs, run aggregations, group by dimensions like service, customer, or region, and correlate logs with metrics and business data.
Columnar storage and compression: Store large log volumes efficiently with columnar storage and compression that significantly reduce disk footprint while keeping query performance high.
Real time and historical analytics in one system: Query fresh logs from the last seconds thanks to instant indexing and petabytes of historical data without moving data to separate systems.
Runs anywhere: Deploy CrateDB as your log database in your preferred environment (cloud, on premises, or edge) while keeping the same SQL interface and behavior.
Key capabilities for log data
Centralized log collection
- Centralize logs from microservices, containers, VMs, network equipment, IoT devices, and security tools
- Use standard shipping tools (Fluent Bit, Filebeat, Vector, OpenTelemetry) to send logs directly into CrateDB
- Partition and route data by tenant, region, or environment for multi tenant setups
Real time log analytics
- Search logs in real time for incident investigation and root cause analysis
- Run complex aggregations to detect patterns, spikes, and anomalies
- Build time series dashboards that combine log counts, error rates, and latency metrics
Observability and SRE
- Use CrateDB as the log database behind your observability platform
- Correlate logs with metrics and traces to understand system behavior end to end
- Link logs to deployment events, feature flags, and configuration changes
Security and compliance
- Store security logs from firewalls, IDS, SIEM feeds, and identity providers
- Build audit trails for user access, configuration changes, and data access
- Retain logs for compliance and forensics with efficient compression and tiering
AI ready log database
- Use logs as training data to build anomaly detection and predictive models
- Feed LLM and AI copilots with rich operational and user behavior logs
- Run vector search over embedded log messages for semantic similarity and smarter incident diagnosis
How CrateDB fits into your logging and observability stack
A typical CrateDB based log architecture looks like this:
Log producers: Applications, containers, databases, OS, network devices, industrial equipment
Collectors and shippers: Fluent Bit, Filebeat, Vector, OpenTelemetry collector, custom agents
Ingestion layer: Data flows directly into CrateDB over HTTP, JDBC, PostgreSQL wire protocol, or via Kafka and other streaming platforms
CrateDB log database cluster:
- Distributed cluster with automatic sharding and replication
- Columnar storage for efficient analytics
- Automatic indexing for fast search
- Role based access control per schema and table
Consumption layer:
- Dashboards in Grafana or BI tools
- Alerting systems and incident management tools
- AI models and downstream data pipelines
Comparing CrateDB to specialized log engines
CrateDB is not a drop in UI replacement for full observability platforms. Instead it focuses on being the log database underneath.
Compared to search centric stacks:
- Stronger analytical performance on aggregations over large data volumes
- Lower storage requirements thanks to columnar layout and compression
- Standard SQL instead of proprietary query languages
Compared to time series only databases:
- Better handling of semi structured and text heavy logs
- Full text search combined with analytics in one system
- Support for joins with relational and dimensional data
