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About Apache Druid
Apache Druid is a real-time analytics database designed for fast slice-and-dice analytics (OLAP queries) on large datasets. Built with a distributed, microservices architecture, Druid excels at sub-second queries on streaming and batch data with high concurrency support. It features specialized processes including Broker (query routing), Historical (immutable data storage), and Middle Manager (real-time ingestion) nodes. Druid stores data in time-partitioned segments within datasources and supports both SQL and native JSON APIs for querying.
GreptimeDB vs. Apache Druid
Feature/AspectGreptimeDBApache Druid
Data ModelUnified Observability DatabaseReal-time OLAP Analytics Database
Value ModelMulti-Value (supports complex data structures)Multi-Value (dimensions and metrics)
Multi-model SupportMetrics, Logs & Traces in one databasePrimarily event/fact data for analytics
Query LanguagesSQL & PromQL (dual interface)SQL & Native JSON API
Ingestion ProtocolsSQL
gRPC
InfluxDB Line Protocol
Prometheus Remote Storage
OpenTelemetry
HTTP API
Kafka
Kinesis
Pulsar
HTTP
Batch files
Data RetentionFlexible TTL policies with tiered storageSegment-based retention with automated expiration
Continuous AggregationBuilt-in SQL aggregation, Pipeline ETL engine & Flow streaming computationRoll-ups and pre-aggregation at ingestion time
Use CasesUnified observability, real-time analytics, IoT monitoring, edge computingBusiness intelligence, user-facing analytics, interactive dashboards
ArchitectureCloud-native distributed with compute-storage separationMicroservices architecture (Broker, Historical, Middle Manager)
Storage FormatApache Parquet (columnar, compressed)Time-partitioned segments in datasources
Storage ScalabilityObject storage integration with unlimited capacityDeep storage with automatic tier management
High AvailabilityNative clustering with automatic failoverDeep storage with coordinator-based failover
LicenseApache 2.0Apache 2.0
Deployment OptionsSingle-node, cluster, Kubernetes-native, edge-to-cloud with unified APIMulti-component deployment (brokers, historicals, coordinators)
Operational ComplexitySingle unified system with simplified Kubernetes operationsComplex multi-service orchestration
Written LanguageRust (memory safety, SIMD optimizations)Java (ecosystem compatibility, no native SIMD)

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