Elasticsearch vs PostGIS: What are the differences?
Elasticsearch: Open Source, Distributed, RESTful Search Engine. Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack); PostGIS: Open source spatial database. PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.
Elasticsearch belongs to "Search as a Service" category of the tech stack, while PostGIS can be primarily classified under "Database Tools".
Some of the features offered by Elasticsearch are:
- Distributed and Highly Available Search Engine.
- Multi Tenant with Multi Types.
- Various set of APIs including RESTful
On the other hand, PostGIS provides the following key features:
- Processing and analytic functions for both vector and raster data for splicing, dicing, morphing, reclassifying, and collecting/unioning with the power of SQL
- raster map algebra for fine-grained raster processing
- Spatial reprojection SQL callable functions for both vector and raster data
"Powerful api" is the primary reason why developers consider Elasticsearch over the competitors, whereas "De facto GIS in SQL" was stated as the key factor in picking PostGIS.
Elasticsearch and PostGIS are both open source tools. Elasticsearch with 42.4K GitHub stars and 14.2K forks on GitHub appears to be more popular than PostGIS with 645 GitHub stars and 246 GitHub forks.
According to the StackShare community, Elasticsearch has a broader approval, being mentioned in 2003 company stacks & 979 developers stacks; compared to PostGIS, which is listed in 53 company stacks and 15 developer stacks.
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