Stactools-Pipelines

Stactools-Pipelines is a large scale, turnkey processing framework to create STAC metadata and cloud optimized formats for data stored on S3 using stactools packages.

Requirements

• Python>=3.9
• Docker
• tox
• awscli
• An IAM role with sufficient permissions for creating, destroying and modifying the relevant stack resources.

Developing Pipelines

A template pipeline structure for Sentinel 1 is included in the repo here.

To develop a new pipeline, create a directory in pipelines using a simple name for your pipeline dataset.

At a minimum include a

• requirements.txt With your application's dependencies.
• config.yaml Your pipeline's configuration settings.
• app.py A Lambda application with a handler function defined which consumes an SQSEvent creates a STAC Item and posts it to the ingestor.
• test_app.py A pytest based unit test file which exercises app.py.
• collection.py A Lambda application with a handler function which creates a STAC Collection and posts it to the ingestor.
• test_collection.py A pytest based unit test file which exercises collection.py.

config.yaml structure

• id Required The pipeline name. This should be the same as the pipeline's parent folder and should use _s for separators to support Python module discovery.

• compute Required Currently only the awslambda value is supported.

• ingestor_url Required The ingestor API's root path with the stage value included.

• secret_arn Required The secret manager ARN for using a Cognito JWKS implementation with the ingestor API.

• sns Optional The SNS topic to listen to for new granule notifications.

• inventory_location Optional The location of an S3 inventory that can be used by the pipeline to process and ingest existing granules. Include a historic.py file (and a test_historic.py) in your pipeline which implements a query_inventory, row_to_message_body and handler method to query the inventory and send the results to the processing queue. If provided, an athena table is created. Default is None. If provided, file_list can't be provided.

• historic_frequency Optional If an inventory_location is included the historic_frequency (how often in hours the historic.py is run) must also be included. A value of 0 indicates that the historic.py function will run a single time on deployment and process the entire inventory. If a value of > 0 is specified then an initial_chunk must also be specified. The pipeline will build a stack which uses these values to incrementally chunk through the inventory file with cron executions to process until the entire inventory has been processed.

• file_list Optional Location of a non-standard AWS inventory file. Default is None. If provided, no Athena table is created and the 'historic' lambda processes that list. If provided, inventory_location can't be provided.

Testing a Pipeline

Create an environment setting using your pipline name.

$ export PIPELINE=<Your pipeline name>

To run your pipeline unit tests

$ tox

Deploying a Pipeline

Deploying a pipeline will use the pipeline's config.yaml to deploy all the necessary resources for running the pipeline. Including STAC Collection and Item creation Lambdas and any queues or Athena tables that are required. If an sns was specified it will begin processing notifications as soon as deployment is complete .

Create a development virtual environment with

$ tox -e dev
$ source devenv/bin/activate

Create environment settings for your pipeline deployment

$ export PROJECT=<The project name for resource cost tracking>
$ export PIPELINE=<Your pipeline name>
$ export STAGE=<Environment stage>

With an AWS profile enabled with sufficient permissions build and push your pipeline image with

$ python image_builder.py

Deploy the infrastructure for your pipeline with

$ cdk deploy

Developing stactools-pipelines

To create a development virtual environment for core repository development use

$ tox -e dev
$ source devenv/bin/activate