| --- |
| id: druid-vs-elasticsearch |
| title: "Apache Druid vs Elasticsearch" |
| --- |
| |
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| |
| |
| We are not experts on search systems, if anything is incorrect about our portrayal, please let us know on the mailing list or via some other means. |
| |
| Elasticsearch is a search system based on Apache Lucene. It provides full text search for schema-free documents |
| and provides access to raw event level data. Elasticsearch is increasingly adding more support for analytics and aggregations. |
| [Some members of the community](https://groups.google.com/forum/#!msg/druid-development/nlpwTHNclj8/sOuWlKOzPpYJ) have pointed out |
| the resource requirements for data ingestion and aggregation in Elasticsearch is much higher than those of Druid. |
| |
| Elasticsearch also does not support data summarization/roll-up at ingestion time, which can compact the data that needs to be |
| stored up to 100x with real-world data sets. This leads to Elasticsearch having greater storage requirements. |
| |
| Druid focuses on OLAP work flows. Druid is optimized for high performance (fast aggregation and ingestion) at low cost, |
| and supports a wide range of analytic operations. Druid has some basic search support for structured event data, but does not support |
| full text search. Druid also does not support completely unstructured data. Measures must be defined in a Druid schema such that |
| summarization/roll-up can be done. |
