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GeoServer REST Configuration

Project description


geoserver-restconfig is a python library for manipulating a GeoServer instance via the GeoServer RESTConfig API.

Note: geoserver-restconfig is a fork of the old

The project is distributed under a MIT License .


pip install geoserver-restconfig

For developers:

git clone
cd geoserver-restconfig
python develop

Getting Help

There is a brief manual at . If you have questions, please ask them on the GeoServer Users mailing list: .

Please use the Github project at for any bug reports (and pull requests are welcome, but please include tests where possible.)

Sample Layer Creation Code

from geoserver.catalog import Catalog
cat = Catalog("http://localhost:8080/geoserver/rest")
topp = cat.get_workspace("topp")
shapefile_plus_sidecars = shapefile_and_friends("states")
# shapefile_and_friends should look on the filesystem to find a shapefile
# and related files based on the base path passed in
# shapefile_plus_sidecars == {
#    'shp': 'states.shp',
#    'shx': 'states.shx',
#    'prj': 'states.prj',
#    'dbf': 'states.dbf'
# }

# 'data' is required (there may be a 'schema' alternative later, for creating empty featuretypes)
# 'workspace' is optional (GeoServer's default workspace is used by... default)
# 'name' is required
ft = cat.create_featurestore(name, workspace=topp, data=shapefile_plus_sidecars)

Running Tests

Since the entire purpose of this module is to interact with GeoServer, the test suite is mostly composed of integration tests. These tests necessarily rely on a running copy of GeoServer, and expect that this GeoServer instance will be using the default data directory that is included with GeoServer. This data is also included in the GeoServer source repository as /data/release/. In addition, it is expected that there will be a postgres database available at postgres:password@localhost:5432/db. You can test connecting to this database with the psql command line client by running $ psql -d db -Upostgres -h localhost -p 5432 (you will be prompted interactively for the password.)

To override the assumed database connection parameters, the following environment variables are supported:




If present, psycopg will be used to verify the database connection prior to running the tests.

If provided, the following environment variables will be used to reset the data directory:


Location of git repository to read the clean data from. If only this option is provided git clean will be used to reset the data.


Optional location of the data dir geoserver will be running with. If provided, rsync will be used to reset the data.


Optional environment variable allowing the catalog test cases to automatically download and start a vanilla GeoServer WAR form the web. Be sure that there are no running services on HTTP port 8080.

Here are the commands that I use to reset before running the geoserver-restconfig tests:

$ cd ~/geoserver/src/web/app/
$ PGUSER=postgres dropdb db
$ PGUSER=postgres createdb db -T template_postgis
$ git clean -dxff -- ../../../data/release/
$ git checkout -f
$ MAVEN_OPTS="-XX:PermSize=128M -Xmx1024M" \
GEOSERVER_DATA_DIR=../../../data/release \
mvn jetty:run

At this point, GeoServer will be running foregrounded, but it will take a few seconds to actually begin listening for http requests. You can stop it with CTRL-C (but don’t do that until you’ve run the tests!) You can run the geoserver-restconfig tests with the following command:

$ python test

Instead of restarting GeoServer after each run to reset the data, the following should allow re-running the tests:

$ git clean -dxff -- ../../../data/release/
$ curl -XPOST --user admin:geoserver http://localhost:8080/geoserver/rest/reload

More Examples - Updated for GeoServer 2.4+

Loading the GeoServer catalog using geoserver-restconfig is quite easy. The example below allows you to connect to GeoServer by specifying custom credentials.

from geoserver.catalog import Catalog
cat = Catalog("http://localhost:8080/geoserver/rest/", "admin", "geoserver")

The code below allows you to filter which workspaces to return


You may also specify the workspaces as a proper list

cat.get_workspaces(names=["geosolutions", "topp"])

The code below allows you to filter which stores to return

cat.get_stores(names=["sf", "mosaic"], workspaces=["nurc", "topp", "sf"])

names and workspaces can either be a comma delimited string or a list. This is true for the get_workspaces, get_stores, get_resources, get_layergroups and get_styles.

The code below allows you to create a FeatureType from a Shapefile

geosolutions = cat.get_workspace("geosolutions")
import geoserver.util
shapefile_plus_sidecars = geoserver.util.shapefile_and_friends("C:/work/geoserver-restconfig/test/data/states")
# shapefile_and_friends should look on the filesystem to find a shapefile
# and related files based on the base path passed in
# shapefile_plus_sidecars == {
#    'shp': 'states.shp',
#    'shx': 'states.shx',
#    'prj': 'states.prj',
#    'dbf': 'states.dbf'
# }
# 'data' is required (there may be a 'schema' alternative later, for creating empty featuretypes)
# 'workspace' is optional (GeoServer's default workspace is used by... default)
# 'name' is required
ft = cat.create_featurestore("test", shapefile_plus_sidecars, geosolutions)

It is possible to create JDBC Virtual Layers too. The code below allow to create a new SQL View called my_jdbc_vt_test defined by a custom sql.

from geoserver.catalog import Catalog
from import JDBCVirtualTable, JDBCVirtualTableGeometry, JDBCVirtualTableParam

cat = Catalog('http://localhost:8080/geoserver/rest/', 'admin', '****')
store = cat.get_store('postgis-geoserver')
geom = JDBCVirtualTableGeometry('newgeom','LineString','4326')
ft_name = 'my_jdbc_vt_test'
epsg_code = 'EPSG:4326'
sql = 'select ST_MakeLine(wkb_geometry ORDER BY waypoint) As newgeom, assetid, runtime from waypoints group by assetid,runtime'
keyColumn = None
parameters = None

jdbc_vt = JDBCVirtualTable(ft_name, sql, 'false', geom, keyColumn, parameters)
ft = cat.publish_featuretype(ft_name, store, epsg_code, jdbc_virtual_table=jdbc_vt)

The next example shows how to create a PostGIS JNDI datastore (connection_parameters come from another example. Settings might be different depending on your needs):

cat = Catalog('http://localhost:8080/geoserver/rest/', 'admin', '****')
datastore_name = 'sample_jndi_store'

dstore = cat.get_store(name = datastore_name, workspace=metadata[WS])
# Let's check that the store doesn't already exist
if ds_store is None:
    ws = 'my_workspace'
    dstore = cat.create_datastore(workspace=ws, name = datastore_name)
    connection_parameters= {
        'type': 'PostGIS (JNDI)',
        'schema': 'my_schema',
        'Estimated extends': 'true',
        'fetch size': '1000',
        'encode functions': 'true',
        'Expose primary keys': 'false',
        'Support on the fly geometry simplification': 'true',
        'Batch insert size': '1',
        'preparedStatements': 'false',
        'Support on the fly geometry simplification, preserving topology': 'true',
        'jndiReferenceName': 'java:comp/env/jdbc/geodb',
        'dbtype': 'postgis',
        'namespace': 'my_workspace',
        'Loose bbox': 'true'
    assert dstore.enabled
    return dstore

This example shows how to easily update a layer property. The same approach may be used with every catalog resource

ne_shaded = cat.get_layer("ne_shaded")

Deleting a store from the catalog requires to purge all the associated layers first. This can be done by doing something like this:

st = cat.get_store("ne_shaded")

Alternatively, you can delete a store as well as all the underlying layers in one shot, like this:

store = cat.get_store("ne_shaded")
cat.delete(store, purge=True, recurse=True)

There are some functionalities allowing to manage the ImageMosaic coverages. It is possible to create new ImageMosaics, add granules to them, and also read the coverages metadata, modify the mosaic Dimensions and finally query the mosaic granules and list their properties.

The geoserver-restconfig methods map the REST APIs for ImageMosaic

In order to create a new ImageMosaic layer, you can prepare a zip file containing the properties files for the mosaic configuration. Refer to the GeoTools ImageMosaic Plugin guide in order to get details on the mosaic configuration. The package contains an already configured zip file with two granules. You need to update or remove the file before creating the mosaic otherwise you will get an exception.

from geoserver.catalog import Catalog
cat = Catalog("http://localhost:8180/geoserver/rest")
cat.create_imagemosaic("NOAAWW3_NCOMultiGrid_WIND_test", "")

By defualt the cat.create_imagemosaic tries to configure the layer too. If you want to create the store only, you can specify the following parameter

cat.create_imagemosaic("NOAAWW3_NCOMultiGrid_WIND_test", "", "none")

In order to retrieve from the catalog the ImageMosaic coverage store you can do this

store = cat.get_store("NOAAWW3_NCOMultiGrid_WIND_test")

It is possible to add more granules to the mosaic at runtime. With the following method you can add granules already present on the machine local path.


The method below allows to send granules remotely via POST to the ImageMosaic. The granules will be uploaded and stored on the ImageMosaic index folder.


To delete an ImageMosaic store, you can follow the standard approach, by deleting the layers first. ATTENTION: at this time you need to manually cleanup the data dir from the mosaic granules and, in case you used a DB datastore, you must also drop the mosaic tables.

layer = cat.get_layer("NOAAWW3_NCOMultiGrid_WIND_test")

By default the ImageMosaic layer has not the coverage dimensions configured. It is possible using the coverage metadata to update and manage the coverage dimensions. ATTENTION: notice that the presentation parameters accepts only one among the following values {‘LIST’, ‘DISCRETE_INTERVAL’, ‘CONTINUOUS_INTERVAL’}

from import DimensionInfo
timeInfo = DimensionInfo("time", "true", "LIST", None, "ISO8601", None)
coverage.metadata = ({'dirName':'NOAAWW3_NCOMultiGrid_WIND_test_NOAAWW3_NCOMultiGrid_WIND_test', 'time': timeInfo})

Once the ImageMosaic has been configured, it is possible to read the coverages along with their granule schema and granule info.

from geoserver.catalog import Catalog
cat = Catalog("http://localhost:8180/geoserver/rest")
store = cat.get_store("NOAAWW3_NCOMultiGrid_WIND_test")
coverages = cat.mosaic_coverages(store)
schema = cat.mosaic_coverage_schema(coverages['coverages']['coverage'][0]['name'], store)
granules = cat.list_granules(coverages['coverages']['coverage'][0]['name'], store)

The granules details can be easily read by doing something like this:


When the mosaic grows up and starts having a huge set of granules, you may need to filter the granules query through a CQL filter on the coverage schema attributes.

granules = cat.list_granules(coverages['coverages']['coverage'][0]['name'], store, "time >= '2013-10-01T03:00:00.000Z'")
granules = cat.list_granules(coverages['coverages']['coverage'][0]['name'], store, "time >= '2013-10-01T03:00:00.000Z' AND run = 0")
granules = cat.list_granules(coverages['coverages']['coverage'][0]['name'], store, "location LIKE '%20131002T000000.tif'")

Creating layergroups

A layergroup can be setup by providing a list of layers and the related styles to the catalog. In the next example, a layergroup with 3 layers and their associated styles get created.

workspace = 'my_workspace'
layers_in_group = ['my_workspace:layer_1', 'my_workspace:layer_2', 'my_workspace:layer_3']
styles = ['my_workspace:style_1', 'my_workspace:style_2', 'my_workspace:style_3']
layergroup_name = 'test_layergroup'

layergroup = cat.create_layergroup(layergroup_name, layers_in_group, styles, workspace)
# Note that if no bounds are provided, GeoServer will automatically compute the layergroup bounding box

Nesting layergroups

A Layergroup can internally contain layergroups too. In the next example an additional layergroup containing a layer_4 simple layer plus the previously created test_layegroup will be created. This time, layer attributes need to be specified.

workspace = 'my_workspace'

layers = []
layers.append({'name':'my_workspace:layer_4', 'attributes':{'type':'layer'}})
layers.append({'name':'my_workspace:test_layergroup', 'attributes':{'type':'layerGroup'}})

# Not specifying the style for the nested layergroup
styles = []
layergroup_name = 'outer_layergroup'

outer_layergroup = cat.create_layergroup(layergroup_name, layers, styles, workspace)

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