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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.
Click here to read the full report of GreptimeDB vs. ElasticSearch log benchmark.
GreptimeDB vs. ElasticSearch
| Feature/Aspect | GreptimeDB | Elasticsearch |
|---|---|---|
| Data Model | Unified Observability Database | Document-oriented Search Engine |
| Value Model | Multi-Value (supports complex data structures) | Document-based (JSON with flexible schema) |
| Multi-model Support | Metrics, Logs & Traces in one database | Primarily documents (requires separate systems for metrics/traces) |
| Ingestion Protocols | SQL gRPC InfluxDB Line Protocol Prometheus Remote Storage OpenTelemetry Loki Push API Elasticsearch Bulk API HTTP API | RESTful HTTP API Bulk API Beats agents Logstash pipelines |
| Query Languages | SQL & PromQL (dual interface) | Query DSL (JSON-based) SQL (via X-Pack) |
| Data Retention | Flexible TTL policies with automatic tiering | Index lifecycle management (ILM) policies |
| Continuous Aggregation | Built-in SQL aggregation, Pipeline ETL engine & Flow streaming computation | Aggregations framework (bucket, metric, pipeline) |
| Use Cases | Unified observability, real-time analytics, IoT monitoring, edge computing | Full-text search, log analysis, application monitoring, enterprise search |
| Architecture | Cloud-native distributed with compute-storage separation | Master-data node cluster architecture with sharding |
| Storage Format | Apache Parquet (columnar, compressed) | Lucene segments with inverted indexes |
| Search Capabilities | Time-series optimized with SQL and PromQL queries | Advanced full-text search with relevance scoring |
| Indexing Strategy | Automatic time-based partitioning and indexing | Inverted indexes with dynamic mapping |
| Performance Focus | Optimized for time-series analytics and real-time queries | Optimized for search speed and complex aggregations |
| License | Apache 2.0 | Elastic License v2 (source available) |
| Deployment Complexity | Single system for observability stack | Requires Elastic Stack components for complete solution |
| Resource Requirements | Efficient memory usage for time-series workloads | High memory requirements for indexing and caching |
| Query Performance | Sub-second analytical queries on time-series data | Fast text search, variable performance on analytical queries |
| Written Language | Rust (memory safety, performance) | Java (JVM ecosystem, mature tooling) |
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