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from __future__ import annotations
import os
import uuid
from datetime import datetime
from airflow import DAG
from airflow.providers.yandex.operators.yandexcloud_dataproc import (
DataprocCreateClusterOperator,
DataprocCreateHiveJobOperator,
DataprocCreateMapReduceJobOperator,
DataprocCreatePysparkJobOperator,
DataprocCreateSparkJobOperator,
DataprocDeleteClusterOperator,
)
# Name of the datacenter where Dataproc cluster will be created
from airflow.utils.trigger_rule import TriggerRule
# should be filled with appropriate ids
[docs]AVAILABILITY_ZONE_ID = 'ru-central1-c'
# Dataproc cluster jobs will produce logs in specified s3 bucket
[docs]S3_BUCKET_NAME_FOR_JOB_LOGS = ''
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = 'example_yandexcloud_dataproc_operator'
with DAG(
DAG_ID,
schedule=None,
start_date=datetime(2021, 1, 1),
tags=['example'],
) as dag:
[docs] create_cluster = DataprocCreateClusterOperator(
task_id='create_cluster',
zone=AVAILABILITY_ZONE_ID,
s3_bucket=S3_BUCKET_NAME_FOR_JOB_LOGS,
computenode_count=1,
computenode_max_hosts_count=5,
)
create_hive_query = DataprocCreateHiveJobOperator(
task_id='create_hive_query',
query='SELECT 1;',
)
create_hive_query_from_file = DataprocCreateHiveJobOperator(
task_id='create_hive_query_from_file',
query_file_uri='s3a://data-proc-public/jobs/sources/hive-001/main.sql',
script_variables={
'CITIES_URI': 's3a://data-proc-public/jobs/sources/hive-001/cities/',
'COUNTRY_CODE': 'RU',
},
)
create_mapreduce_job = DataprocCreateMapReduceJobOperator(
task_id='create_mapreduce_job',
main_class='org.apache.hadoop.streaming.HadoopStreaming',
file_uris=[
's3a://data-proc-public/jobs/sources/mapreduce-001/mapper.py',
's3a://data-proc-public/jobs/sources/mapreduce-001/reducer.py',
],
args=[
'-mapper',
'mapper.py',
'-reducer',
'reducer.py',
'-numReduceTasks',
'1',
'-input',
's3a://data-proc-public/jobs/sources/data/cities500.txt.bz2',
'-output',
f's3a://{S3_BUCKET_NAME_FOR_JOB_LOGS}/dataproc/job/results/{uuid.uuid4()}',
],
properties={
'yarn.app.mapreduce.am.resource.mb': '2048',
'yarn.app.mapreduce.am.command-opts': '-Xmx2048m',
'mapreduce.job.maps': '6',
},
)
create_spark_job = DataprocCreateSparkJobOperator(
task_id='create_spark_job',
main_jar_file_uri='s3a://data-proc-public/jobs/sources/java/dataproc-examples-1.0.jar',
main_class='ru.yandex.cloud.dataproc.examples.PopulationSparkJob',
file_uris=[
's3a://data-proc-public/jobs/sources/data/config.json',
],
archive_uris=[
's3a://data-proc-public/jobs/sources/data/country-codes.csv.zip',
],
jar_file_uris=[
's3a://data-proc-public/jobs/sources/java/icu4j-61.1.jar',
's3a://data-proc-public/jobs/sources/java/commons-lang-2.6.jar',
's3a://data-proc-public/jobs/sources/java/opencsv-4.1.jar',
's3a://data-proc-public/jobs/sources/java/json-20190722.jar',
],
args=[
's3a://data-proc-public/jobs/sources/data/cities500.txt.bz2',
f's3a://{S3_BUCKET_NAME_FOR_JOB_LOGS}/dataproc/job/results/${{JOB_ID}}',
],
properties={
'spark.submit.deployMode': 'cluster',
},
packages=['org.slf4j:slf4j-simple:1.7.30'],
repositories=['https://repo1.maven.org/maven2'],
exclude_packages=['com.amazonaws:amazon-kinesis-client'],
)
create_pyspark_job = DataprocCreatePysparkJobOperator(
task_id='create_pyspark_job',
main_python_file_uri='s3a://data-proc-public/jobs/sources/pyspark-001/main.py',
python_file_uris=[
's3a://data-proc-public/jobs/sources/pyspark-001/geonames.py',
],
file_uris=[
's3a://data-proc-public/jobs/sources/data/config.json',
],
archive_uris=[
's3a://data-proc-public/jobs/sources/data/country-codes.csv.zip',
],
args=[
's3a://data-proc-public/jobs/sources/data/cities500.txt.bz2',
f's3a://{S3_BUCKET_NAME_FOR_JOB_LOGS}/dataproc/job/results/${{JOB_ID}}',
],
jar_file_uris=[
's3a://data-proc-public/jobs/sources/java/dataproc-examples-1.0.jar',
's3a://data-proc-public/jobs/sources/java/icu4j-61.1.jar',
's3a://data-proc-public/jobs/sources/java/commons-lang-2.6.jar',
],
properties={
'spark.submit.deployMode': 'cluster',
},
packages=['org.slf4j:slf4j-simple:1.7.30'],
repositories=['https://repo1.maven.org/maven2'],
exclude_packages=['com.amazonaws:amazon-kinesis-client'],
)
delete_cluster = DataprocDeleteClusterOperator(
task_id='delete_cluster', trigger_rule=TriggerRule.ALL_DONE
)
create_cluster >> create_mapreduce_job >> create_hive_query >> create_hive_query_from_file
create_hive_query_from_file >> create_spark_job >> create_pyspark_job >> delete_cluster
from tests.system.utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "teardown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests.system.utils import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)
