93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
#!/usr/bin/env python
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# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import datetime
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import time
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import os
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from airflow import models
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from airflow.providers.google.cloud.operators.dataproc import (
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DataprocCreateBatchOperator, DataprocDeleteBatchOperator, DataprocGetBatchOperator, DataprocListBatchesOperator
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)
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from airflow.utils.dates import days_ago
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# --------------------------------------------------------------------------------
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# Get variables
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# --------------------------------------------------------------------------------
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BQ_LOCATION = os.environ.get("BQ_LOCATION")
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CURATED_BQ_DATASET = os.environ.get("CURATED_BQ_DATASET")
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CURATED_GCS = os.environ.get("CURATED_GCS")
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CURATED_PRJ = os.environ.get("CURATED_PRJ")
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DP_KMS_KEY = os.environ.get("DP_KMS_KEY", "")
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DP_REGION = os.environ.get("DP_REGION")
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GCP_REGION = os.environ.get("GCP_REGION")
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LAND_PRJ = os.environ.get("LAND_PRJ")
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LAND_BQ_DATASET = os.environ.get("LAND_BQ_DATASET")
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LAND_GCS = os.environ.get("LAND_GCS")
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PHS_CLUSTER_NAME = os.environ.get("PHS_CLUSTER_NAME")
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PROCESSING_GCS = os.environ.get("PROCESSING_GCS")
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PROCESSING_PRJ = os.environ.get("PROCESSING_PRJ")
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PROCESSING_SA_DP = os.environ.get("PROCESSING_SA_DP")
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PROCESSING_SA_SUBNET = os.environ.get("PROCESSING_SUBNET")
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PROCESSING_SA_VPC = os.environ.get("PROCESSING_VPC")
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PYTHON_FILE_LOCATION = "gs://"+PROCESSING_GCS+"/pyspark_sort.py"
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PHS_CLUSTER_PATH = "projects/"+PROCESSING_PRJ+"/regions/"+DP_REGION+"/clusters/"+PHS_CLUSTER_NAME
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default_args = {
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# Tell airflow to start one day ago, so that it runs as soon as you upload it
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"start_date": days_ago(1),
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"region": DP_REGION,
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}
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with models.DAG(
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"dataproc_batch_operators", # The id you will see in the DAG airflow page
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default_args=default_args, # The interval with which to schedule the DAG
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schedule_interval=None, # Override to match your needs
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) as dag:
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create_batch = DataprocCreateBatchOperator(
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task_id="batch_create",
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project_id=PROCESSING_PRJ,
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batch={
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"environment_config": {
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"execution_config": {
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"service_account": PROCESSING_SA_DP,
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"subnetwork_uri": PROCESSING_SA_SUBNET
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},
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"peripherals_config": {
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"spark_history_server_config":{
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"dataproc_cluster": PHS_CLUSTER_PATH
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}
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}
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},
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"pyspark_batch": {
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"main_python_file_uri": PYTHON_FILE_LOCATION,
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}
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},
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batch_id="batch-create-phs-"+str(int(time.time())),
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)
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list_batches = DataprocListBatchesOperator(
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task_id="list-all-batches",
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)
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get_batch = DataprocGetBatchOperator(
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task_id="get_batch",
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batch_id="batch-create-phs",
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)
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create_batch >> list_batches >> get_batch |