Miscellaneous tips and tricks

Export a process graph

You can export the underlying process graph of a DataCube, VectorCube, etc, to a standardized JSON format, which allows interoperability with other openEO tools.

For example, use print_json() to directly print the JSON representation in your interactive Jupyter or Python session:

>>> dump = cube.print_json()
  "process_graph": {
    "loadcollection1": {
      "process_id": "load_collection",

Or save it to a file, by getting the JSON representation first as a string with to_json():

# Export as JSON string
dump = cube.to_json()

# Write to file in `pathlib` style
export_path = pathlib.Path("path/to/export.json")
export_path.write_text(dump, encoding="utf8")

# Write to file in `open()` style
with open("path/to/export.json", encoding="utf8") as f:


Avoid using methods like flat_graph(), which are mainly intended for internal use. Not only are these methods subject to change, they also lead to representations with interoperability and reuse issues. For example, naively printing or automatic (repr) rendering of flat_graph() output will roughly look like JSON, but is in fact invalid: it uses single quotes (instead of double quotes) and booleans values are title-case (instead of lower case).

Execute a process graph directly from raw JSON

When you have a process graph in JSON format, as a string, a local file or a URL, you can execute/download it without converting it do a DataCube first. Just pass the string, path or URL directly to Connection.download(), Connection.execute() or Connection.create_job(). For example:

# `execute` with raw JSON string
        "add": {"process_id": "add", "arguments": {"x": 3, "y": 5}, "result": true}

# `download` with local path to JSON file

# `create_job` with URL to JSON file
job = connection.create_job("https://jsonbin.example/my/process-graph.json")