Vector Data Backends

GeoTrellis supports two well-known distributed vector-feature stores: GeoMesa and GeoWave. A question that often arises in the vector processing world is: “Which should I use?” At first glance, it can be hard to tell the difference, apart from “one is Java and the other is Scala”. The real answer is, of course, “it depends”.

In the fall of 2016, our team was tasked with an official comparison of the two. It was our goal to increase awareness of their respective strengths and weaknesses, so that both teams can focus on their strengths during development, and the public can make an easier choice. We analysed a number of angles, including:

  • Feature set
  • Performance
  • Ease of use
  • Project maturity

The full report should be made public in Q1/Q2 of 2017.

While developing applications directly with these projects is quite a different experience, in terms of our GeoTrellis interfaces for each project (as a vector data backend), they support essentially the same feature set (GeoWave optionally supports reading/writing Raster layers).

Keep in mind that as of 2016 October 25, both of these GeoTrellis modules are still experimental.


import geotrellis.spark._

val instance: GeoMesaInstance(
  tableName = ...,
  instanceName = ...,
  zookeepers = ...,
  users = ...,
  password = ...,
  useMock = ...

val reader = new GeoMesaFeatureReader(instance)
val writer = new GeoMesaFeatureWriter(instance)

val id: LayerId = ...
val query: Query = ... /* GeoMesa query type */

val spatialFeatureType: SimpleFeatureType = ... /* from geomesa - see their docs */

/* for some generic D, following GeoTrellis `Feature[G, D]` */
val res: RDD[SimpleFeature] =[Point, D](


import geotrellis.spark._

val res: RDD[Feature[G, Map[String, Object]]] =
  zookeepers = ...,
  accumuloInstanceName = ...,
  accumuloInstanceUser = ...,
  accumuloInstancePass = ...,
  gwNamespace = ...,
  simpleFeatureType = ... /* from geowave */