Importer REST API examples¶
Mass configuring a directory of shapefiles¶
In order to initiate an import of the c:\data\tasmania directory into the existing tasmania workspace the following JSON will be POSTed to GeoServer:
{
"import": {
"targetWorkspace": {
"workspace": {
"name": "tasmania"
}
},
"data": {
"type": "directory",
"location": "C:/data/tasmania"
}
}
}
This curl command can be used for the purpose:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @import.json "http://localhost:8080/geoserver/rest/imports"
The importer will locate the files to be imported, and automatically prepare the tasks, returning the following response:
{
"import": {
"id": 9,
"href": "http://localhost:8080/geoserver/rest/imports/9",
"state": "PENDING",
"archive": false,
"targetWorkspace": {
"workspace": {
"name": "tasmania"
}
},
"data": {
"type": "directory",
"format": "Shapefile",
"location": "C:\\data\\tasmania",
"href": "http://localhost:8080/geoserver/rest/imports/9/data"
},
"tasks": [
{
"id": 0,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/0",
"state": "READY"
},
{
"id": 1,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/1",
"state": "READY"
},
{
"id": 2,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/2",
"state": "READY"
},
{
"id": 3,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/3",
"state": "READY"
}
]
}
}
After checking every task is ready, the import can be initiated by executing a POST on the import resource:
curl -u admin:geoserver -XPOST "http://localhost:8080/geoserver/rest/imports/9"
The resource can then be monitored for progress, and eventually final results:
curl -u admin:geoserver -XGET "http://localhost:8080/geoserver/rest/imports/9"
Which in case of successful import will look like:
{
"import": {
"id": 9,
"href": "http://localhost:8080/geoserver/rest/imports/9",
"state": "COMPLETE",
"archive": false,
"targetWorkspace": {
"workspace": {
"name": "tasmania"
}
},
"data": {
"type": "directory",
"format": "Shapefile",
"location": "C:\\data\\tasmania",
"href": "http://localhost:8080/geoserver/rest/imports/9/data"
},
"tasks": [
{
"id": 0,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/0",
"state": "COMPLETE"
},
{
"id": 1,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/1",
"state": "COMPLETE"
},
{
"id": 2,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/2",
"state": "COMPLETE"
},
{
"id": 3,
"href": "http://localhost:8080/geoserver/rest/imports/9/tasks/3",
"state": "COMPLETE"
}
]
}
}
Configuring a shapefile with no projection information¶
In this case, let’s assume we have a single shapefile, tasmania_cities.shp, that does not have the .prj anciliary file (the example is equally good for any case where the prj file contents cannot be matched to an official EPSG code).
We are going to post the following import definition:
{
"import": {
"targetWorkspace": {
"workspace": {
"name": "tasmania"
}
},
"data": {
"type": "file",
"file": "C:/data/tasmania/tasmania_cities.shp"
}
}
}
With the usual curl command:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @import.json "http://localhost:8080/geoserver/rest/imports"
The response in case the CRS is missing will be:
{
"import": {
"id": 13,
"href": "http://localhost:8080/geoserver/rest/imports/13",
"state": "PENDING",
"archive": false,
"targetWorkspace": {
"workspace": {
"name": "tasmania"
}
},
"data": {
"type": "file",
"format": "Shapefile",
"file": "tasmania_cities.shp"
},
"tasks": [
{
"id": 0,
"href": "http://localhost:8080/geoserver/rest/imports/13/tasks/0",
"state": "NO_CRS"
}
]
}
}
Drilling into the task layer we can see the srs information is missing:
{
"layer": {
"name": "tasmania_cities",
"href": "http://localhost:8080/geoserver/rest/imports/13/tasks/0/layer",
"title": "tasmania_cities",
"originalName": "tasmania_cities",
"nativeName": "tasmania_cities",
"bbox": {
"minx": 146.2910004483,
"miny": -43.85100181689,
"maxx": 148.2910004483,
"maxy": -41.85100181689
},
"attributes": [
{
"name": "the_geom",
"binding": "org.locationtech.jts.geom.MultiPoint"
},
{
"name": "CITY_NAME",
"binding": "java.lang.String"
},
{
"name": "ADMIN_NAME",
"binding": "java.lang.String"
},
{
"name": "CNTRY_NAME",
"binding": "java.lang.String"
},
{
"name": "STATUS",
"binding": "java.lang.String"
},
{
"name": "POP_CLASS",
"binding": "java.lang.String"
}
],
"style": {
"name": "tasmania_tasmania_cities2",
"href": "http://localhost:8080/geoserver/rest/imports/13/tasks/0/layer/style"
}
}
}
The following PUT request will update the SRS:
curl -u admin:geoserver -XPUT -H "Content-type: application/json" -d @layerUpdate.json "http://localhost:8080/geoserver/rest/imports/13/tasks/0/layer/"
Where layerUpdate.json is:
{
layer : {
srs: "EPSG:4326"
}
}
Getting the import definition again, we’ll find it ready to execute:
{
"import": {
"id": 13,
"href": "http://localhost:8080/geoserver/rest/imports/13",
"state": "PENDING",
"archive": false,
"targetWorkspace": {
"workspace": {
"name": "tasmania"
}
},
"data": {
"type": "file",
"format": "Shapefile",
"file": "tasmania_cities.shp"
},
"tasks": [
{
"id": 0,
"href": "http://localhost:8080/geoserver/rest/imports/13/tasks/0",
"state": "READY"
}
]
}
}
A POST request will make it execute:
curl -u admin:geoserver -XPOST "http://localhost:8080/geoserver/rest/imports/13"
And eventually succeed:
{
"import": {
"id": 13,
"href": "http://localhost:8080/geoserver/rest/imports/13",
"state": "COMPLETE",
"archive": false,
"targetWorkspace": {
"workspace": {
"name": "tasmania"
}
},
"data": {
"type": "file",
"format": "Shapefile",
"file": "tasmania_cities.shp"
},
"tasks": [
{
"id": 0,
"href": "http://localhost:8080/geoserver/rest/imports/13/tasks/0",
"state": "COMPLETE"
}
]
}
}
Uploading a Shapefile to PostGIS¶
This example shows the process for uploading a Shapefile (in a zip file) to an existing PostGIS datastore (cite:postgis).
Create the import definition:
{
"import": {
"targetStore": {
"dataStore": {
"name": "postgis"
}
},
"targetWorkspace": {
"workspace": {
"name": "cite"
}
}
}
}
POST this definition to /geoserver/rest/imports:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @import.json "http://localhost:8080/geoserver/rest/imports"
The response will contain the import ID.
We now have an empty import with no tasks. To add a task, POST the shapefile to the list of tasks:
curl -u admin:geoserver -F name=myshapefile.zip -F filedata=@myshapefile.zip "http://localhost:8080/geoserver/rest/imports/14/tasks"
Since we sent a shapefile, importer assumes the target will be a shapefile store. To import to PostGIS, we will need to reset it. Create the following JSON file:
{
"dataStore": {
"name":"postgis"
}
}
PUT this file to /geoserver/rest/imports/14/tasks/0/target:
curl -u admin:geoserver -XPUT -H "Content-type: application/json" -d @target.json "http://localhost:8080/geoserver/rest/imports/14/tasks/0/target"
Finally, we execute the import by sending a POST to /geoserver/rest/imports/14:
curl -u admin:geoserver -XPOST "http://localhost:8080/geoserver/rest/imports/14"
Uploading a CSV file to PostGIS while transforming it¶
A remote sensing tool is generating CSV files with some locations and measurements, that we want to upload into PostGIS as a new spatial table. The CSV file looks as follows:
AssetID, SampleTime, Lat, Lon, Value
1, 2015-01-01T10:00:00, 10.00, 62.00, 15.2
1, 2015-01-01T11:00:00, 10.10, 62.11, 30.25
1, 2015-01-01T12:00:00, 10.20, 62.22, 41.2
1, 2015-01-01T13:00:00, 10.31, 62.33, 27.6
1, 2015-01-01T14:00:00, 10.41, 62.45, 12
First, we are going to create a empty import with an existing postgis store as the target:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @import.json "http://localhost:8080/geoserver/rest/imports"
Where import.json is:
{
"import": {
"targetWorkspace": {
"workspace": {
"name": "topp"
}
},
"targetStore": {
"dataStore": {
"name": "gttest"
}
}
}
}
Then, we are going to POST the csv file to the tasks list, in order to create an import task for it:
curl -u admin:geoserver -F name=test -F filedata=@values.csv "http://localhost:8080/geoserver/rest/imports/0/tasks"
And we are going to get back a new task definition, with a notification that the CRS is missing:
{
"task": {
"id": 0,
"href": "http://localhost:8080/geoserver/rest/imports/16/tasks/0",
"state": "NO_CRS",
"updateMode": "CREATE",
"data": {
"type": "file",
"format": "CSV",
"file": "values.csv"
},
"target": {
"href": "http://localhost:8080/geoserver/rest/imports/16/tasks/0/target",
"dataStore": {
"name": "values",
"type": "CSV"
}
},
"progress": "http://localhost:8080/geoserver/rest/imports/16/tasks/0/progress",
"layer": {
"name": "values",
"href": "http://localhost:8080/geoserver/rest/imports/16/tasks/0/layer"
},
"transformChain": {
"type": "vector",
"transforms": [
]
}
}
}
As before, we are going to force the CRS by updating the layer:
curl -u admin:geoserver -XPUT -H "Content-type: application/json" -d @layerUpdate.json "http://localhost:8080/geoserver/rest/imports/0/tasks/0/layer/"
Where layerUpdate.json is:
{
layer : {
srs: "EPSG:4326"
}
}
Then, we are going to create a transformation mapping the Lat/Lon columns to a point:
{
"type": "AttributesToPointGeometryTransform",
"latField": "Lat",
"lngField": "Lon"
}
The above will be uploaded to GeoServer as follows:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @toPoint.json "http://localhost:8080/geoserver/rest/imports/0/tasks/0/transforms"
Now the import is ready to run, and we’ll execute it using:
curl -u admin:geoserver -XPOST "http://localhost:8080/geoserver/rest/imports/0"
If all goes well the new layer is created in PostGIS and registered in GeoServer as a new layer.
In case the features in the CSV need to be appended to an existing layer a PUT request against the task might be performed, changing its updateMode from “CREATE” to “APPEND”. Changing it to “REPLACE” instead will preserve the layer, but remove the old conents and replace them with the newly uploaded ones.
Uploading and optimizing a GeoTiff with ground control points¶
A data supplier is periodically providing GeoTiffs that we need to configure in GeoServer. The GeoTIFF is referenced via Ground Control Points, is organized by stripes, and has no overviews. The objective is to rectify, optimize and publish it via the importer.
First, we are going to create a empty import with no store as the target:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @import.json "http://localhost:8080/geoserver/rest/imports"
Where import.json is:
{
"import": {
"targetWorkspace": {
"workspace": {
"name": "sf"
}
}
}
}
Then, we are going to POST the GeoTiff file to the tasks list, in order to create an import task for it:
curl -u admin:geoserver -F name=test -F filedata=@box_gcp_fixed.tif "http://localhost:8080/geoserver/rest/imports/0/tasks"
We are then going to append the transformations to rectify (gdalwarp), retile (gdal_translate) and add overviews (gdaladdo) to it:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @warp.json "http://localhost:8080/geoserver/rest/imports/0/tasks/0/transforms"
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @gtx.json "http://localhost:8080/geoserver/rest/imports/0/tasks/0/transforms"
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @gad.json "http://localhost:8080/geoserver/rest/imports/0/tasks/0/transforms"
warp.json is:
{
"type": "GdalWarpTransform",
"options": [ "-t_srs", "EPSG:4326"]
}
gtx.json is:
{
"type": "GdalTranslateTransform",
"options": [ "-co", "TILED=YES", "-co", "BLOCKXSIZE=512", "-co", "BLOCKYSIZE=512"]
}
gad.json is:
{
"type": "GdalAddoTransform",
"options": [ "-r", "average"],
"levels" : [2, 4, 8, 16]
}
Now the import is ready to run, and we’ll execute it using:
curl -u admin:geoserver -XPOST "http://localhost:8080/geoserver/rest/imports/0"
A new layer box_gcp_fixed layer will appear in GeoServer, with an underlying GeoTiff file ready
for web serving.
Adding a new granule into an existing mosaic¶
A data supplier is periodically providing new time based imagery that we need to add into an existing mosaic in GeoServer. The imagery is in GeoTiff format, and lacks a good internal structure, which needs to be aligned with the one into the other images.
First, we are going to create a import with an indication of where the granule is located, and the target store:
curl -u admin:geoserver -XPOST -H “Content-type: application/json” -d @import.json “http://localhost:8080/geoserver/rest/imports”
Where import.json is:
{
"import": {
"targetWorkspace": {
"workspace": {
"name": "topp"
}
},
"data": {
"type": "file",
"file": "/home/aaime/devel/gisData/ndimensional/data/world/world.200407.3x5400x2700.tiff"
},
"targetStore": {
"dataStore": {
"name": "bluemarble"
}
}
}
}
We are then going to append the transformations to harmonize the file with the rest of the mosaic:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @gtx.json "http://localhost:8080/geoserver/rest/imports/0/tasks/0/transforms"
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @gad.json "http://localhost:8080/geoserver/rest/imports/0/tasks/0/transforms"
gtx.json is:
{
"type": "GdalTranslateTransform",
"options": [ "-co", "TILED=YES"]
}
gad.json is:
{
"type": "GdalAddoTransform",
"options": [ "-r", "average"],
"levels" : [2, 4, 8, 16, 32, 64, 128]
}
Now the import is ready to run, and we’ll execute it using:
curl -u admin:geoserver -XPOST "http://localhost:8080/geoserver/rest/imports/0"
The new granule will be ingested into the mosaic, and will thus be available for time based requests.
Asynchronously fetching and importing data from a remote server¶
We assume a remote FTP server contains multiple shapefiles that we need to import in GeoServer as new layers. The files are large, and the server has much better bandwith than the client, so it’s best if GeoServer performs the data fetching on its own.
In this case a asynchronous request using remote data will be the best fit:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @import.json "http://localhost:8080/geoserver/rest/imports?async=true"
Where import.json is:
{
"import": {
"targetWorkspace": {
"workspace": {
"name": "topp"
}
},
"data": {
"type": "remote",
"location": "ftp://myserver/data/bc_shapefiles",
"username": "dan",
"password": "secret"
}
}
}
The request will return immediately with an import context in “INIT” state, and it will remain in such state until the data is fetched and the tasks created. Once the state switches to “PENDING” the import will be ready for execution. Since there is a lot of shapefiles to process, also the import run will be done in asynchronous mode:
curl -u admin:geoserver -XPOST "http://localhost:8080/geoserver/rest/imports/0?async=true"
The response will return immediately in this case as well, and the progress can be followed as the tasks in the import switch state.
Importing and optimizing a large image with a single request¶
A large image appears every now and then on a mounted disk share, the image needs to be optimized and imported into GeoServer as a new layer. Since the source is large and we need to copy it on the local disk where the data dir resides, a “remote” data is the right tool for the job, an asynchronous execution is also recommended to avoid waiting on a possibly large command. In this case the request will also contains the “exec=true” parameter to force the importer an immediate execution of the command.
The request will then look as follows:
curl -u admin:geoserver -XPOST -H "Content-type: application/json" -d @import.json "http://localhost:8080/geoserver/rest/imports?async=true&exec=true"
Where import.json is:
{
"import": {
"targetWorkspace": {
"workspace": {
"name": "topp"
}
},
"data": {
"type": "remote",
"location": "\/mnt\/remoteDisk\/bluemarble.tiff"
},
"transforms": [
{
"type": "GdalTranslateTransform",
"options": [
"-co", "TILED=YES",
"-co", "COMPRESS=JPEG",
"-co", "JPEG_QUALITY=85",
"-co", "PHOTOMETRIC=YCBCR"
]
},
{
"type": "GdalAddoTransform",
"options": [
"-r",
"average",
"--config", "COMPRESS_OVERVIEW", "JPEG",
"--config", "PHOTOMETRIC_OVERVIEW", "YCBCR"
],
"levels": [ 2, 4, 8, 16, 32, 64 ]
}
]
}
}
Given the request is asynchronous, the client will have to poll the server in order to check if the initialization and execution have succeded.