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About ElasticSearch
Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene, designed for horizontal scalability, reliability, and real-time search capabilities. As the core component of the Elastic Stack (ELK/EEK), Elasticsearch excels at full-text search, structured data queries, and complex analytics across massive datasets. It uses a JSON-based document model with automatic indexing and supports distributed search across clusters. Elasticsearch is widely adopted for log analysis, application performance monitoring, business analytics, and enterprise search applications where fast, flexible data retrieval is essential.
Click here to read the full report of GreptimeDB vs. ElasticSearch log benchmark.
GreptimeDB vs. ElasticSearch
Feature/AspectGreptimeDBElasticsearch
Data ModelUnified Observability DatabaseDocument-oriented Search Engine
Value ModelMulti-Value (supports complex data structures)Document-based (JSON with flexible schema)
Multi-model SupportMetrics, Logs & Traces in one databasePrimarily documents (requires separate systems for metrics/traces)
Ingestion ProtocolsSQL
gRPC
InfluxDB Line Protocol
Prometheus Remote Storage
OpenTelemetry
HTTP API
RESTful HTTP API
Bulk API
Beats agents
Logstash pipelines
Query LanguagesSQL & PromQL (dual interface)Query DSL (JSON-based)
SQL (via X-Pack)
Data RetentionFlexible TTL policies with automatic tieringIndex lifecycle management (ILM) policies
Continuous AggregationBuilt-in SQL aggregation, Pipeline ETL engine & Flow streaming computationAggregations framework (bucket, metric, pipeline)
Use CasesUnified observability, real-time analytics, IoT monitoring, edge computingFull-text search, log analysis, application monitoring, enterprise search
ArchitectureCloud-native distributed with compute-storage separationMaster-data node cluster architecture with sharding
Storage FormatApache Parquet (columnar, compressed)Lucene segments with inverted indexes
Search CapabilitiesTime-series optimized with SQL and PromQL queriesAdvanced full-text search with relevance scoring
Indexing StrategyAutomatic time-based partitioning and indexingInverted indexes with dynamic mapping
Performance FocusOptimized for time-series analytics and real-time queriesOptimized for search speed and complex aggregations
LicenseApache 2.0Elastic License v2 (source available)
Deployment ComplexitySingle system for observability stackRequires Elastic Stack components for complete solution
Resource RequirementsEfficient memory usage for time-series workloadsHigh memory requirements for indexing and caching
Query PerformanceSub-second analytical queries on time-series dataFast text search, variable performance on analytical queries
Written LanguageRust (memory safety, performance)Java (JVM ecosystem, mature tooling)

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