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GeoMesa is an open source suite of tools that enables large-scale geospatial querying and analytics on distributed computing systems. GeoMesa provides spatio-temporal indexing on top of the Accumulo, HBase, Google Bigtable and Cassandra databases for massive storage of point, line, and polygon data. GeoMesa also provides near real time stream processing of spatio-temporal data by layering spatial semantics on top of Apache Kafka. Through GeoServer, GeoMesa facilitates integration with a wide range of existing mapping clients over standard OGC (Open Geospatial Consortium) APIs and protocols such as WFS and WMS. GeoMesa supports Apache Spark for custom distributed geospatial analytics.

LocationTech GeoMesa is a member of the LocationTech working group of the Eclipse Foundation.

Join the Community

  • Join the chat at
  • GeoMesa Users and Dev mailing lists
  • GeoMesa JIRA for issue tracking



Current release: 3.4.1

     HBase | Accumulo | Cassandra | Kafka | Redis | FileSystem | Bigtable

Verifying Downloads

Downloads hosted on GitHub include SHA-256 hashes and gpg signatures (.asc files). To verify a download using gpg, import the appropriate key:

$ gpg2 --keyserver hkp:// --recv-keys CD24F317

Then verify the file:

$ gpg2 --verify geomesa-accumulo_2.12-3.4.1-bin.tar.gz.asc geomesa-accumulo_2.12-3.4.1-bin.tar.gz

The keys currently used for signing are:

Key ID Name
CD24F317 Emilio Lahr-Vivaz <elahrvivaz(-at-)>
1E679A56 James Hughes <jnh5y(-at-)>

Maven Integration


GeoMesa is hosted on Maven Central. To include it as a dependency, add the desired modules, for example:


GeoMesa depends on several third-party libraries that are only available in separate repositories. To include GeoMesa in your project, add the following repositories to your pom:

  <!-- geotools -->
  <!-- confluent -->

Nightly Snapshots

Snapshot versions are published nightly to the Eclipse repository:


Spark Runtimes

GeoMesa publishes spark-runtime JARs for integration with Spark environments like Databricks. These shaded JARs include all the required dependencies in a single artifact. When importing through Maven, all transitive dependencies can be excluded. There are Spark runtime JARs available for most of the different DataStore implementations:

      <!-- if groupId wildcards are not supported, the two main ones are jline:* and org.geotools:* -->

These JARs are also included in the Downloads bundles, above.

sbt Integration

Similarly, integration with sbt is straightforward:

// Add necessary resolvers
resolvers ++= Seq(
  "osgeo" at "",
  "confluent" at ""

// Select desired modules
libraryDependencies ++= Seq(
  "org.locationtech.geomesa" %% "geomesa-utils" % "3.4.1"

Building from Source

Development version: 3.5.0-SNAPSHOT

     Build Status Build Status


Use Git to download the source code. Navigate to the destination directory, then run:

git clone
cd geomesa

The project is built using Maven. To build, run:

mvn clean install

The full build takes quite a while. To speed it up, you may skip tests and use multiple threads. GeoMesa also provides the script build/mvn, which is a wrapper around Maven that downloads and runs Zinc, a fast incremental compiler:

build/mvn clean install -T8 -DskipTests

If the Zinc build fails with an error finding "javac", try setting the JAVA_HOME environment variable to point to the root of your JDK. Example from a Mac:

JAVA_HOME="/Library/Java/JavaVirtualMachines/jdk1.8.0_51.jdk/Contents/Home" build/mvn clean install

Scala Cross Build

To build for a different Scala version (e.g. 2.11), run the following script, then build as normal:

./build/ 2.11