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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)