2018-12-13 14:47:20 -05:00
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---
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layout: doc_page
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title: "Druid vs Elasticsearch"
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---
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2018-11-13 12:38:37 -05:00
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2018-12-12 23:42:12 -05:00
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# Druid vs Elasticsearch
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2015-05-05 17:07:32 -04:00
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2015-11-09 19:40:07 -05:00
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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.
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2015-05-05 17:07:32 -04:00
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2015-11-09 19:40:07 -05:00
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Elasticsearch is a search systems based on Apache Lucene. It provides full text search for schema-free documents
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and provides access to raw event level data. Elasticsearch is increasingly adding more support for analytics and aggregations.
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[Some members of the community](https://groups.google.com/forum/#!msg/druid-development/nlpwTHNclj8/sOuWlKOzPpYJ) have pointed out
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the resource requirements for data ingestion and aggregation in Elasticsearch is much higher than those of Druid.
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2015-05-05 17:07:32 -04:00
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2015-11-09 19:40:07 -05:00
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Elasticsearch also does not support data summarization/roll-up at ingestion time, which can compact the data that needs to be
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stored up to 100x with real-world data sets. This leads to Elasticsearch having greater storage requirements.
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Druid focuses on OLAP work flows. Druid is optimized for high performance (fast aggregation and ingestion) at low cost,
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and supports a wide range of analytic operations. Druid has some basic search support for structured event data, but does not support
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full text search. Druid also does not support completely unstructured data. Measures must be defined in a Druid schema such that
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summarization/roll-up can be done.
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