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GeoRocket - a high-performance cloud-native data store for geospatial files

Michel Kraemer and Eva Klien

(Submission #183)


The cloud has become the new standard platform for modern scalable and resilient applications and services. In the geospatial domain, the cloud can be used to build web-based geographic information systems and spatial data infrastructures making tools and data available to a broad audience. However, current implementations of OGC and INSPIRE services have a traditional client/server architecture and do not scale well in terms of data volume and number of users. Modern microservice architectures allow for creating cloud-native applications that leverage the possibilities of distributed computing. In a microservice architecture, a complex software is created by combining several small services, while each of them serves a specific purpose such as data storage, processing, or visualization.

One of the services that can be used in a microservice architecture to build a cloud-native application is GeoRocket, a high-performance data store for geospatial files. GeoRocket can store large INSPIRE datasets, GeoJSON files, 3D city models and any other XML-based geospatial data. It provides high-performance storage and supports multiple cloud-based back-ends such as Amazon S3, MongoDB, or distributed file systems (e.g. HDFS or Ceph). GeoRocket offers high-speed search features based on the popular Open-Source framework Elasticsearch and enables users to perform spatial queries and to search for attributes and metadata. GeoRocket has been designed from the start to run in the cloud. It is scalable and resilient, and can be used to create complex distributed applications and services that are fault-tolerant and cost-effective and, therefore, provide great benefits for users from the geospatial domain.

An example for a service we are currently creating based on GeoRocket is an implementation of the Web Feature Service 3.0 (WFS 3.0) draft standard. We aim to create a web service that is fast and scalable, and that can be operated at a cost-effective level by leveraging cloud technologies such as object storage and serverless computing. Based on the WFS we are also creating a web-based visualization for very large, heterogeneous spatial data sets which offers a level of interactivity that goes beyond the state of the art.


Topic Area:  [3.2] Tools and technologies
Abstract Type:  Oral Presentation

Additional Fields

Academic:   Yes
Data Provider:   Yes
Data User:   No
INSPIRE Implementer (IT):   Yes
INSPIRE newbies:   No
Policy Officers:   No
Public Administration (MS/Regional/Local):   No
Thematic specialists:   No

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