rs_workflows/on_demand_processing.md

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Prefect flows and tasks for on-demand processing

build_dask_dashboard_url_message(cluster_instance)

Build the Dask dashboard log message from the configured public gateway endpoint.

Source code in docs/rs-client-libraries/rs_workflows/on_demand_processing.py

def build_dask_dashboard_url_message(cluster_instance: str | None) -> str:
    """Build the Dask dashboard log message from the configured public gateway endpoint."""
    public_base = os.getenv("DASK_GATEWAY_PUBLIC", "")

    if not public_base or not cluster_instance:
        return "Dask cluster dashboard URL is unavailable"

    dashboard_url = f"{public_base.rstrip('/')}/clusters/{cluster_instance}/status"
    return f"Dask cluster dashboard URL: {dashboard_url}"

dpr_processing(dpr_input, retry_config=RetryConfig()) async

Prefect flow for dpr-process.

Parameters:

Name	Type	Description	Default
dpr_input	DprProcessIn	Input parameters for executing this flow	required
retry_config	RetryConfig	Staging retry config	RetryConfig()

Source code in docs/rs-client-libraries/rs_workflows/on_demand_processing.py

@flow(name="process-generic")
async def dpr_processing(
    dpr_input: DprProcessIn,
    retry_config: RetryConfig = RetryConfig(),  # type: ignore
):
    """
    Prefect flow for dpr-process.

    Args:
        dpr_input: Input parameters for executing this flow
        retry_config: Staging retry config
    """
    logger = get_run_logger()
    logger.info(f"Starting the DPR processing flow with processor: {dpr_input.processor_name}")
    # Init flow environment and opentelemetry span
    flow_env = FlowEnv(dpr_input.env)

    with flow_env.start_span(__name__, "dpr-processing"):

        # Create cluster info from JUPYTERHUB_API_TOKEN env var (only in cluster mode, read from the
        # prefect blocks) and Dask cluster label.
        cluster_info = ClusterInfo(
            jupyter_token=os.environ["JUPYTERHUB_API_TOKEN"] if prefect_utils.CLUSTER_MODE else "",
            cluster_label=dpr_input.dask_cluster_label,
            cluster_instance=dpr_input.dask_cluster_instance or "",
        )

        # read tasktable and construct list of processing units
        task_table: dict[str, Any] = flow_env.rs_client.get_dpr_client().get_process(
            dpr_input.processor_name,
            cluster_info,
        )

        # Persist the full task table as a Prefect artifact for later investigation.
        md = "# Task table\n\n```json\n" + json.dumps(task_table, indent=2) + "\n```"
        artifact_key_name: str = "dpr-task-table"
        await acreate_markdown_artifact(key=artifact_key_name, markdown=md, description="DPR task table")
        logger.info(f"📌 Artifact named '{artifact_key_name}' has been linked to this flow.")
        # Log the public Dask dashboard URL when the flow input provides the cluster instance.
        logger.info(build_dask_dashboard_url_message(cluster_info.cluster_instance))

        processing_mode = list(dpr_input.processing_mode) if dpr_input.processing_mode else None
        unit_list = build_unit_list(
            tasktable=task_table,
            pipeline=dpr_input.pipeline,
            unit=dpr_input.unit,
            processing_mode=processing_mode,
            external_variables={
                "start_datetime": dpr_input.start_datetime,
                "end_datetime": dpr_input.end_datetime,
                "reference_date": dpr_input.reference_date,
                "instrument_mode": dpr_input.instrument_mode,
                "satellite": dpr_input.satellite,
            },
        )

        tasks = []
        for unit in unit_list:
            # For each input_adfs element computed on STEP 1
            for input_adfs in unit["input_adfs"]:
                # For each specific input in case of multiplicity=one_per_input
                specific_input_name, product_stac_items = _resolve_specific_input_product_stac_items(
                    input_adfs,
                    task_table,
                    unit,
                    dpr_input.input_products,
                    flow_env.rs_client,
                )
                for specific_input_product_stac_item in product_stac_items:
                    if specific_input_product_stac_item:
                        logger.info(
                            f"Submitting {input_adfs['name']} ADFS task for input {specific_input_product_stac_item}",
                        )
                    tasks.append(
                        process_input_adfs.submit(
                            input_adfs,
                            dpr_input,
                            task_table,
                            (specific_input_name, specific_input_product_stac_item),
                            retry_config.staging_retries,
                            retry_config.staging_retry_delay,
                        ),
                    )

        try:
            aux_items: list[tuple[str, str, tuple[bool, ItemCollection]]] = [t.result() for t in tasks]
        except (RuntimeError, KeyError) as err:
            raise err
        # Set of ADFS. Each tuple includes the adfs name, type and the s3/https storage path
        source_items: list[Item] = []
        adfs: set[tuple[str, str, str]] = set()
        for name, adf_type, (status, item_collection) in aux_items:
            for item in item_collection.items:
                # list with links to be added in derived_from
                source_items.append(item)

                if status:
                    asset = next(iter(item.assets.values()))
                    logger.info(f"ADFS '{name}' of type '{adf_type}': {asset.href}")
                    adfs.add((name, adf_type, asset.href))
                else:
                    raise ValueError(f"The adf input files {next(iter(item.assets.values()))} was not correctly staged")
        # generate the dpr payload file
        task_future = generate_payload.submit(flow_env, unit_list, list(adfs), dpr_input)
        # get the payload generation result
        generated_payload_res = task_future.result()
        # create the generated payload as a dictionary, as it will be used for
        # the prefect artifact. the SecretStr will be masked here
        generated_payload_res_as_dict = generated_payload_res.dump()
        # create the YAML string first (synchronous). This will be used for writing both the artifact as well
        # as the tmp file
        # md = "# Payload file\n\n```json\n" + json.dumps(generated_payload_res_as_dict, indent=2) + "\n```"
        yaml_str = yaml.dump(generated_payload_res_as_dict, default_flow_style=False, sort_keys=False)
        # Write the payload as prefect artifact
        pretty_markdown = f"```yaml\n{yaml_str}\n```"
        artifact_key_name = "dpr-payload"
        await acreate_markdown_artifact(
            key=artifact_key_name,
            markdown=pretty_markdown,
            description="DPR Payload file",
        )
        logger.info(f"📌 Artifact named '{artifact_key_name}' has been linked to this flow.")

        # re-create the generated payload as a dictionary, as it will be used for
        # the payload file to upload to S3. here, the secrets are revealed
        generated_payload_res_with_secrets = generated_payload_res.dump(reveal_secrets=True)
        yaml_str = yaml.dump(generated_payload_res_with_secrets, default_flow_style=False, sort_keys=False)
        # upload the config payload file to S3
        tmp_dir = std_tempfile.gettempdir()
        tmp_file_path = os.path.join(tmp_dir, f"dpr_payload_{datetime.datetime.now().timestamp()}.yaml")
        async with await anyio.open_file(tmp_file_path, "w", encoding="utf-8") as tmp_file:
            await tmp_file.write(yaml_str)
            # flush to be extra-safe
            await tmp_file.flush()
        logger.debug(f"Writing the payload to file :\n {dpr_input.s3_payload_file}")
        await prefect_utils.s3_upload_file(tmp_file_path, dpr_input.s3_payload_file)

        # clean up the temp payload file
        await anyio.Path(tmp_file_path).unlink()

        # Run the DPR processor
        processed_items = run_processor.submit(
            flow_env.serialize(),
            dpr_input.processor_name,
            generated_payload_res,
            cluster_info,
            dpr_input.s3_payload_file,
            dpr_input.input_products,
            wait_for=[task_future],
        )
        try:
            processed_items.result()
        finally:
            prefect_utils.s3_delete(dpr_input.s3_payload_file)

        # add derived_from link
        processed = processed_items.result()
        logger.debug(f"processed_items: {processed}")

        for processed_item in processed:
            processed_item.stac_item.add_derived_from(*source_items)

        # Publish processed items to the catalog
        published = catalog_flow.publish.submit(
            flow_env.serialize(),
            dpr_input.generated_product_to_collection_identifier,
            processed_items,
        )

        # Wait for last task to end.
        # NOTE: use .result() and not .wait() to unwrap and propagate exceptions, if any.
        published.result()  # type: ignore[unused-coroutine]

        return

process_input_adfs(input_adfs, dpr_input, task_table, specific_input_product=(None, None), staging_retries=3, staging_retry_delay=60) async

Stage the ADFS inputs described in the task table for one processing unit input.

The task iterates through the ordered alternatives defined for a given ADFS input and stops at the first alternative that produces staged items.

For each alternative, the task: - builds the final AUX CQL2 request from the task table definition - resolves the target AUX collection identifier - runs AUX staging with retries - normalizes archived outputs when the staged assets still point to compressed archives - updates the catalog entries after normalization so downstream payload generation sees the final asset hrefs

Returns:

Type	Description
tuple[str, str, tuple[bool, ItemCollection]]	tuple[str, str, tuple[bool, ItemCollection]]: The input ADFS name and type together with the original staging status/item collection tuple shape expected by downstream code.

Raises:

Type	Description
RuntimeError	If no alternative returns staged data, or if the task table content cannot be read as expected.

Source code in docs/rs-client-libraries/rs_workflows/on_demand_processing.py

@task(name="Process input ADFS")
async def process_input_adfs(
    input_adfs: dict[str, Any],
    dpr_input: DprProcessIn,
    task_table: dict[str, Any],
    specific_input_product: tuple[str | None, Item | None] = (None, None),
    staging_retries: int = 3,
    staging_retry_delay: int = 60,
) -> tuple[str, str, tuple[bool, ItemCollection]]:
    """
    Stage the ADFS inputs described in the task table for one processing unit input.

    The task iterates through the ordered alternatives defined for a given ADFS
    input and stops at the first alternative that produces staged items.

    For each alternative, the task:
    - builds the final AUX CQL2 request from the task table definition
    - resolves the target AUX collection identifier
    - runs AUX staging with retries
    - normalizes archived outputs when the staged assets still point to
      compressed archives
    - updates the catalog entries after normalization so downstream payload
      generation sees the final asset hrefs

    Returns:
        tuple[str, str, tuple[bool, ItemCollection]]:
            The input ADFS name and type together with the original staging status/item
            collection tuple shape expected by downstream code.

    Raises:
        RuntimeError:
            If no alternative returns staged data, or if the task table content
            cannot be read as expected.
    """
    logger = get_run_logger()
    logger.info(f"🚧 Starting processing input ADFS for {input_adfs}")
    try:
        # For each "alternative" ( get it following the "order" )
        for alternative in input_adfs.get("alternatives", []):
            result = await _stage_input_adfs_alternative(
                alternative,
                input_adfs,
                dpr_input,
                task_table,
                specific_input_product,
                staging_retries,
                staging_retry_delay,
            )
            if result is not None:
                return result

        raise RuntimeError(f"Searching for adfs input {input_adfs['name']} did not return any result")

    except KeyError as kerr:
        raise RuntimeError(
            f"Unable to read / process tasktable and build cql2-json for: {json.dumps(input_adfs)}",
        ) from kerr