fairspace-pipeline 0.1.0

Building blocks for creating a pipeline for Fairspace

Fairspace Pipeline

This repository contains the Fairspace Pipeline, a package that can be used as a base to create custom ETL pipelines for Fairspace.

Installation

Requires Python 3.12 or newer.

Installing from PyPI

python -m pip install fairspace-pipeline

Installing from sources

git clone https://github.com/thehyve/fairspace-pipeline.git
python -m build
python -m pip install .

Usage

Fairspace Pipeline cannot be used as a standalone package. It requires a custom implementation of several and specific configuration. Sections below describe the necessary steps to create a fully-functioning custom pipeline with reusable components and interfaces implementation.

Configuration

Create a .env file with the following environment variables:

# Keycloak
KEYCLOAK_CLIENT_ID="" # e.g. KEYCLOAK_CLIENT_ID="workspace-client"
KEYCLOAK_CLIENT_SECRET="" # e.g. KEYCLOAK_CLIENT_SECRET="********"
KEYCLOAK_USERNAME="" # e.g. KEYCLOAK_USERNAME="organisation-admin"
KEYCLOAK_PASSWORD="" # e.g. KEYCLOAK_PASSWORD="fairspace123"
KEYCLOAK_SERVER_URL="" # e.g. KEYCLOAK_SERVER_URL="https://my-keycloak-instance.com"
KEYCLOAK_REALM="" # e.g. KEYCLOAK_REALM="fairspace"

# Fairspace
FAIRSPACE_URL="" # e.g. FAIRSPACE_URL="https://my-fairspace-instance.com"

# Amazon S3
IS_AWS_S3=False # e.g. IS_AWS_S3=True if using AWS S3 bucket for source data
AWS_SOURCE_BUCKET_NAME="" # e.g. AWS_SOURCE_BUCKET_NAME="fairspace-metadata-source"
AWS_OUTPUT_BUCKET_NAME="" # e.g. AWS_OUTPUT_BUCKET_NAME="fairspace-metadata-output"

# Pipeline settings
TAXONOMIES_TTL_PATH="config/custom_taxonomies.ttl"
SOURCE_STUDIES_PATHS='' # e.g. '["./test_data/test_study_input1", "./test_data/test_study_input2"]'
OUTPUT_DATA_PATH="" # e.g. OUTPUT_DATA_PATH="./test_data/.output_data"

Next, implement a function that creates a FairspacePipelineConfig object using environment variables

def get_config() -> FairspacePipelineConfig:
    config = FairspacePipelineConfig()
    config.source_study_prefixes = json.loads(str(os.environ.get('SOURCE_STUDIES_PREFIXES', '[""]')))
    config.output_data_directory = os.environ.get('OUTPUT_DATA_PATH',
                                                  os.path.join(os.getcwd(), 'test_data/.output_data'))
    config.is_aws_s3 = os.getenv("IS_AWS_S3", 'False').lower() in ('true', '1', 't')
    if config.is_aws_s3:
        config.source_study_directories = json.loads(os.environ.get('SOURCE_STUDIES_PATHS'))
    else:
        config.source_study_directories = [os.path.join(os.getcwd(), val) for val in json.loads(
            os.environ.get('SOURCE_STUDIES_PATHS', '["test_data/source_data"]'))]
    config.source_bucket_name = os.environ.get('AWS_SOURCE_BUCKET_NAME')
    config.output_bucket_name = os.environ.get('AWS_OUTPUT_BUCKET_NAME')
    config.taxonomies_directory = os.environ.get(
        'TAXONOMIES_TTL_PATH',
        os.path.join(pathlib.Path(__file__).parent.absolute(), "config", "taxonomies.ttl")
    )
    return config

Taxonomies

Implement custom taxonomies graph class that extends the TaxonomiesGraph class. The class needs to:

• include all custom taxonomies as class attributes,
• define the TAXONOMY_PREFIX to be used in custom taxonomy terms URIs.

Sample implementation:

from fairspace_pipeline.graph.taxonomy_graph import TaxonomiesGraph

TAXONOMY_PREFIX = "https://fairspace.com/custom_ontology#"  # Prefix for custom taxonomies

class CustomTaxonomiesGraph(TaxonomiesGraph):
    def __init__(self, taxonomies_dir):
        super().__init__(taxonomies_dir, TAXONOMY_PREFIX)
        self.countries = self.query_taxonomy(taxonomy_name='Country')
        self.file_types = self.query_taxonomy(taxonomy_name='FileType')
        self.species = self.query_taxonomy(taxonomy_name='Species')
        self.gender = self.query_taxonomy(taxonomy_name='Gender')
        self.study_status = self.query_taxonomy(taxonomy_name='StudyStatus')
        self.study_phases = self.query_taxonomy(taxonomy_name='StudyPhase')
        self.sample_treatments = self.query_taxonomy(taxonomy_name='Treatment')

Mapping

Implement custom graph class that extends the FairspaceGraph class. It is a module for mapping data to RDF graphs specific to the custom data model, e.g.:

The class needs to implement create_graph() and get_file_suffix_processing_order() methods of the FairspaceGraph interface.

Sample implementation:

from fairspace_pipeline.graph.fairspace_graph import FairspaceGraph

CUSTOM_NS = Namespace("https://custom.com/ontology#")

class CustomGraph(FairspaceGraph):
    def __init__(self, taxonomies_graph: CustomTaxonomiesGraph):
        super().__init__(taxonomies_graph)

    def map_study(self, study_dict: dict) -> Study:
        # Implement custom study processing logic
        pass

    def map_sample(self, sample_dict: dict) -> Sample:
        # Implement custom sample processing logic
        pass

    def map_subject(self, subject_dict: dict) -> Subject:
        # Implement custom subject processing logic
        pass

    def create_study_graph(self, data: dict, prefix: str) -> Graph:
        graph = self.create_new_graph()
        studies = self.map_studies(data, prefix)
        for study in studies:
            graph.add((study.uri, RDF.type, CUSTOM_NS.Study))
            if study.label is not None:
                graph.add((study.uri, RDFS.label, Literal(study.label)))
            if study.identifier is not None:
                graph.add((study.uri, DCTERMS.identifier, Literal(study.identifier)))
            ...
        return graph

    def create_sample_graph(self, data: dict, prefix: str) -> Graph:
        graph = self.create_new_graph()
        samples = self.map_sample(data, prefix)
        for sample in samples:
            ...
        return graph
            
    def create_subject_graph(self, data: dict, prefix: str) -> Graph:
        graph = self.create_new_graph()
        subjects = self.map_subject(data, prefix)
        for subject in subjects:
            ...
        return graph
            
    def create_graph(self, file_path: str, data: dict, prefix: str) -> Graph:
        if str(file_path).endswith(STUDY_MANIFEST_FILE_SUFFIX):
            return self.create_study_graph(data, prefix)
        if str(file_path).endswith(SAMPLE_INFORMATION_SUFFIX):
            return self.create_sample_graph(data, prefix)
        ...
        return self.create_subjects_data_files_graph(data, prefix)

    # Define the order of processing files by file suffix in the source study directory
    # e.g. ['_study.json', '_sample_information.json']
    def get_file_suffix_processing_order(self) -> list[str]:
        return [STUDY_MANIFEST_FILE_SUFFIX, SAMPLE_INFORMATION_SUFFIX]

Pipeline runner

Define main function to run the pipeline.

from fairspace_pipeline.pipeline import FairspacePipeline, FairspacePipelineConfig

def main():
    load_dotenv()

    parser = argparse.ArgumentParser()
    parser.add_argument("-i", "--init", action="store_true")
    parser.add_argument("-p", "--process", action="store_true")
    parser.add_argument("-u", "--upload", action="store_true")
    parser.add_argument("-r", "--reindex", action="store_true")
    parser.add_argument("-d", "--delete", action="store_true")
    parser.add_argument("-c", "--compact", action="store_true")
    parser.add_argument("-ms", "--maintenance_status", action="store_true")
    args = parser.parse_args()

    config = get_config()

    taxonomies_graph = CustomTaxonomiesGraph(config.taxonomies_directory)
    graph = CustomGraph(config, taxonomies_graph)

    pipeline = FairspacePipeline(config, graph)
    pipeline.run(args.init, args.process, args.upload, args.delete, args.reindex, args.compact, args.maintenance_status)


if __name__ == '__main__':
    main()

Optional implementation

If a custom processing needs to be applied, implement a custom IO handler class that extends the IOHandler class, following examples of the LocalIOHandler class and the S3IOHandler class.

Running the complete pipeline

When all the components are implemented, the pipeline can be run with the following command:

python main.py --init --process --upload --compact

Include only the options that you need:

• --init or -i - prepare required roles for the configured user and upload taxonomies
• --process or -p - read the input source data, transform to Fairspace model and save to the configured output directory
• --upload or -u - upload the transform data into Fairspace
• --reindex or -r - reindex Fairspace views database
• --compact or -c - compact Fairspace Jena database to reduce the size
• --maintenance_status or -ms - get the maintenance status to see if reindexing or compacting is in progress