feat: GWAS Catalog harmonisation prototype

config/step/gwas_catalog_sumstat_preprocess.yaml

@@ -0,0 +1,3 @@
+_target_: otg.gwas_catalog_sumstat_preprocess.GWASCatalogSumstatsPreprocessStep
+raw_sumstats_path: ???
+out_sumstats_path: ???

src/airflow/dags/common_airflow.py

@@ -13,6 +13,7 @@
     DataprocSubmitJobOperator,
 )
 from airflow.utils.trigger_rule import TriggerRule
+from google.cloud import dataproc_v1
 
 if TYPE_CHECKING:
     from pathlib import Path
@@ -64,6 +65,7 @@ def create_cluster(
     master_machine_type: str = "n1-highmem-8",
     worker_machine_type: str = "n1-standard-16",
     num_workers: int = 2,
+    num_preemptible_workers: int = 0,
     num_local_ssds: int = 1,
     autoscaling_policy: str = GCP_AUTOSCALING_POLICY,
 ) -> DataprocCreateClusterOperator:
@@ -74,6 +76,7 @@ def create_cluster(
         master_machine_type (str): Machine type for the master node. Defaults to "n1-highmem-8".
         worker_machine_type (str): Machine type for the worker nodes. Defaults to "n1-standard-16".
         num_workers (int): Number of worker nodes. Defaults to 2.
+        num_preemptible_workers (int): Number of preemptible worker nodes. Defaults to 0.
         num_local_ssds (int): How many local SSDs to attach to each worker node, both primary and secondary. Defaults to 1.
         autoscaling_policy (str): Name of the autoscaling policy to use. Defaults to GCP_AUTOSCALING_POLICY.
 
@@ -88,6 +91,7 @@ def create_cluster(
         worker_machine_type=worker_machine_type,
         master_disk_size=500,
         worker_disk_size=500,
+        num_preemptible_workers=num_preemptible_workers,
         num_workers=num_workers,
         image_version=GCP_DATAPROC_IMAGE,
         enable_component_gateway=True,
@@ -303,3 +307,49 @@ def generate_dag(cluster_name: str, tasks: list[DataprocSubmitJobOperator]) -> A
         >> tasks
         >> delete_cluster(cluster_name)
     )
+
+
+def submit_pyspark_job_no_operator(
+    cluster_name: str,
+    step_id: str,
+    other_args: Optional[list[str]] = None,
+) -> None:
+    """Submits the Pyspark job to the cluster.
+
+    Args:
+        cluster_name (str): Cluster name
+        step_id (str): Step id
+        other_args (Optional[list[str]]): Other arguments to pass to the CLI step. Defaults to None.
+    """
+    # Create the job client.
+    job_client = dataproc_v1.JobControllerClient(
+        client_options={"api_endpoint": f"{GCP_REGION}-dataproc.googleapis.com:443"}
+    )
+
+    python_uri = f"{INITIALISATION_BASE_PATH}/{PYTHON_CLI}"
+    # Create the job config. 'main_jar_file_uri' can also be a
+    # Google Cloud Storage URL.
+    job_description = {
+        "placement": {"cluster_name": cluster_name},
+        "pyspark_job": {
+            "main_python_file_uri": python_uri,
+            "args": [f"step={step_id}"]
+            + (other_args if other_args is not None else [])
+            + [
+                f"--config-dir={CLUSTER_CONFIG_DIR}",
+                f"--config-name={CONFIG_NAME}",
+            ],
+            "properties": {
+                "spark.jars": "/opt/conda/miniconda3/lib/python3.10/site-packages/hail/backend/hail-all-spark.jar",
+                "spark.driver.extraClassPath": "/opt/conda/miniconda3/lib/python3.10/site-packages/hail/backend/hail-all-spark.jar",
+                "spark.executor.extraClassPath": "./hail-all-spark.jar",
+                "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
+                "spark.kryo.registrator": "is.hail.kryo.HailKryoRegistrator",
+            },
+        },
+    }
+    res = job_client.submit_job(
+        project_id=GCP_PROJECT, region=GCP_REGION, job=job_description
+    )
+    job_id = res.reference.job_id
+    print(f"Submitted job ID {job_id}.")

src/airflow/dags/gwas_catalog_harmonisation.py

@@ -0,0 +1,117 @@
+"""Airflow DAG for the harmonisation part of the pipeline."""
+from __future__ import annotations
+
+import re
+import time
+from pathlib import Path
+from typing import Any
+
+import common_airflow as common
+from airflow.decorators import task
+from airflow.models.dag import DAG
+from airflow.providers.google.cloud.operators.gcs import GCSListObjectsOperator
+
+CLUSTER_NAME = "otg-gwascatalog-harmonisation"
+AUTOSCALING = "gwascatalog-harmonisation"
+
+SUMMARY_STATS_BUCKET_NAME = "open-targets-gwas-summary-stats"
+
+with DAG(
+    dag_id=Path(__file__).stem,
+    description="Open Targets Genetics — GWAS Catalog harmonisation",
+    default_args=common.shared_dag_args,
+    **common.shared_dag_kwargs,
+):
+    # List raw harmonised files from GWAS Catalog
+    list_inputs = GCSListObjectsOperator(
+        task_id="list_raw_harmonised",
+        bucket=SUMMARY_STATS_BUCKET_NAME,
+        prefix="raw-harmonised",
+        match_glob="**/*.h.tsv.gz",
+    )
+    # List parquet files that have been previously processed
+    list_outputs = GCSListObjectsOperator(
+        task_id="list_harmonised_parquet",
+        bucket=SUMMARY_STATS_BUCKET_NAME,
+        prefix="harmonised",
+        match_glob="**/_SUCCESS",
+    )
+
+    # Create list of pending jobs
+    @task(task_id="create_to_do_list")
+    def create_to_do_list(**kwargs: Any) -> Any:
+        """Create the to-do list of studies.
+
+        Args:
+            **kwargs (Any): Keyword arguments.
+
+        Returns:
+            Any: To-do list.
+        """
+        ti = kwargs["ti"]
+        raw_harmonised = ti.xcom_pull(
+            task_ids="list_raw_harmonised", key="return_value"
+        )
+        print("Number of raw harmonised files: ", len(raw_harmonised))
+        to_do_list = []
+        # Remove the ones that have been processed
+        parquets = ti.xcom_pull(task_ids="list_harmonised_parquet", key="return_value")
+        print("Number of parquet files: ", len(parquets))
+        for path in raw_harmonised:
+            match_result = re.search(
+                "raw-harmonised/(.*)/(GCST\d+)/harmonised/(.*)\.h\.tsv\.gz", path
+            )
+            if match_result:
+                study_id = match_result.group(2)
+                if f"harmonised/{study_id}.parquet/_SUCCESS" not in parquets:
+                    to_do_list.append(path)
+        print("Number of jobs to submit: ", len(to_do_list))
+        ti.xcom_push(key="to_do_list", value=to_do_list)
+
+    # Submit jobs to dataproc
+    @task(task_id="submit_jobs")
+    def submit_jobs(**kwargs: Any) -> None:
+        """Submit jobs to dataproc.
+
+        Args:
+            **kwargs (Any): Keyword arguments.
+        """
+        ti = kwargs["ti"]
+        todo = ti.xcom_pull(task_ids="create_to_do_list", key="to_do_list")
+        print("Number of jobs to submit: ", len(todo))
+        for i in range(len(todo)):
+            # Not to exceed default quota 400 jobs per minute
+            if i > 0 and i % 399 == 0:
+                time.sleep(60)
+            input_path = todo[i]
+            match_result = re.search(
+                "raw-harmonised/(.*)/(GCST\d+)/harmonised/(.*)\.h\.tsv\.gz", input_path
+            )
+            if match_result:
+                study_id = match_result.group(2)
+            print("Submitting job for study: ", study_id)
+            common.submit_pyspark_job_no_operator(
+                cluster_name=CLUSTER_NAME,
+                step_id="gwas_catalog_sumstat_preprocess",
+                other_args=[
+                    f"step.raw_sumstats_path=gs://{SUMMARY_STATS_BUCKET_NAME}/{input_path}",
+                    f"step.out_sumstats_path=gs://{SUMMARY_STATS_BUCKET_NAME}/harmonised/{study_id}.parquet",
+                ],
+            )
+
+    # list_inputs >>
+    (
+        [list_inputs, list_outputs]
+        >> create_to_do_list()
+        >> common.create_cluster(
+            CLUSTER_NAME,
+            autoscaling_policy=AUTOSCALING,
+            num_workers=8,
+            num_preemptible_workers=8,
+            master_machine_type="n1-highmem-64",
+            worker_machine_type="n1-standard-2",
+        )
+        >> common.install_dependencies(CLUSTER_NAME)
+        >> submit_jobs()
+        # >> common.delete_cluster(CLUSTER_NAME)
+    )