90 lines
3.4 KiB
Python
90 lines
3.4 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 time
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from airflow import models
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from airflow.models.variable import Variable
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from airflow.operators import empty
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from airflow.providers.google.cloud.operators.dataproc import (
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DataprocCreateBatchOperator)
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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 = Variable.get("BQ_LOCATION")
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CURATED_BQ_DATASET = Variable.get("CURATED_BQ_DATASET")
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CURATED_GCS = Variable.get("CURATED_GCS")
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CURATED_PRJ = Variable.get("CURATED_PRJ")
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DP_KMS_KEY = Variable.get("DP_KMS_KEY", "")
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DP_REGION = Variable.get("DP_REGION")
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LAND_PRJ = Variable.get("LAND_PRJ")
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LAND_BQ_DATASET = Variable.get("LAND_BQ_DATASET")
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LAND_GCS = Variable.get("LAND_GCS")
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PHS_CLUSTER_NAME = Variable.get("PHS_CLUSTER_NAME")
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PROCESSING_GCS = Variable.get("PROCESSING_GCS")
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PROCESSING_PRJ = Variable.get("PROCESSING_PRJ")
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PROCESSING_SA = Variable.get("PROCESSING_SA")
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PROCESSING_SUBNET = Variable.get("PROCESSING_SUBNET")
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PROCESSING_VPC = Variable.get("PROCESSING_VPC")
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PYTHON_FILE_LOCATION = PROCESSING_GCS + "/pyspark_gcs2bq.py"
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PHS_CLUSTER_PATH = "projects/" + PROCESSING_PRJ + "/regions/" + DP_REGION + "/clusters/" + PHS_CLUSTER_NAME
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SPARK_BIGQUERY_JAR_FILE = "gs://spark-lib/bigquery/spark-bigquery-with-dependencies_2.13-0.29.0.jar"
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BATCH_ID = "batch-create-phs-" + str(int(time.time()))
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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_gcs2bq", # 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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start = empty.EmptyOperator(task_id='start', trigger_rule='all_success')
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end = empty.EmptyOperator(task_id='end', trigger_rule='all_success')
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create_batch = DataprocCreateBatchOperator(
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task_id="batch_create", project_id=PROCESSING_PRJ, batch_id=BATCH_ID,
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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,
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"subnetwork_uri": PROCESSING_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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"args": [
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LAND_GCS + "/customers.csv",
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CURATED_PRJ + ":" + CURATED_BQ_DATASET + ".customers",
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PROCESSING_GCS[5:]
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],
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"main_python_file_uri": PYTHON_FILE_LOCATION,
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"jar_file_uris": [SPARK_BIGQUERY_JAR_FILE]
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}
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})
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start >> create_batch >> end
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