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test_dataproc.py
2402 lines (2135 loc) · 90.9 KB
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test_dataproc.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import inspect
from unittest import mock
from unittest.mock import MagicMock, Mock, call
import pytest
from google.api_core.exceptions import AlreadyExists, NotFound
from google.api_core.retry import Retry
from google.cloud.dataproc_v1 import Batch, JobStatus
from airflow.exceptions import (
AirflowException,
AirflowProviderDeprecationWarning,
AirflowTaskTimeout,
TaskDeferred,
)
from airflow.models import DAG, DagBag
from airflow.providers.google.cloud.links.dataproc import (
DATAPROC_CLUSTER_LINK_DEPRECATED,
DATAPROC_JOB_LINK_DEPRECATED,
DataprocClusterLink,
DataprocJobLink,
DataprocWorkflowLink,
)
from airflow.providers.google.cloud.operators.dataproc import (
ClusterGenerator,
DataprocCreateBatchOperator,
DataprocCreateClusterOperator,
DataprocCreateWorkflowTemplateOperator,
DataprocDeleteBatchOperator,
DataprocDeleteClusterOperator,
DataprocGetBatchOperator,
DataprocInstantiateInlineWorkflowTemplateOperator,
DataprocInstantiateWorkflowTemplateOperator,
DataprocLink,
DataprocListBatchesOperator,
DataprocScaleClusterOperator,
DataprocSubmitHadoopJobOperator,
DataprocSubmitHiveJobOperator,
DataprocSubmitJobOperator,
DataprocSubmitPigJobOperator,
DataprocSubmitPySparkJobOperator,
DataprocSubmitSparkJobOperator,
DataprocSubmitSparkSqlJobOperator,
DataprocUpdateClusterOperator,
)
from airflow.providers.google.cloud.triggers.dataproc import (
DataprocBatchTrigger,
DataprocClusterTrigger,
DataprocDeleteClusterTrigger,
DataprocSubmitTrigger,
DataprocWorkflowTrigger,
)
from airflow.providers.google.common.consts import GOOGLE_DEFAULT_DEFERRABLE_METHOD_NAME
from airflow.serialization.serialized_objects import SerializedDAG
from airflow.utils.timezone import datetime
from airflow.version import version as airflow_version
from tests.test_utils.db import clear_db_runs, clear_db_xcom
cluster_params = inspect.signature(ClusterGenerator.__init__).parameters
AIRFLOW_VERSION = "v" + airflow_version.replace(".", "-").replace("+", "-")
DATAPROC_PATH = "airflow.providers.google.cloud.operators.dataproc.{}"
DATAPROC_TRIGGERS_PATH = "airflow.providers.google.cloud.triggers.dataproc.{}"
TASK_ID = "task-id"
GCP_PROJECT = "test-project"
GCP_REGION = "test-location"
GCP_CONN_ID = "test-conn"
IMPERSONATION_CHAIN = ["ACCOUNT_1", "ACCOUNT_2", "ACCOUNT_3"]
TEMPLATE_ID = "template_id"
CLUSTER_NAME = "cluster_name"
CONFIG = {
"gce_cluster_config": {
"zone_uri": "https://www.googleapis.com/compute/v1/projects/project_id/zones/zone",
"metadata": {"metadata": "data"},
"network_uri": "network_uri",
"subnetwork_uri": "subnetwork_uri",
"internal_ip_only": True,
"tags": ["tags"],
"service_account": "service_account",
"service_account_scopes": ["service_account_scopes"],
},
"master_config": {
"num_instances": 2,
"machine_type_uri": "projects/project_id/zones/zone/machineTypes/master_machine_type",
"disk_config": {"boot_disk_type": "master_disk_type", "boot_disk_size_gb": 128},
"image_uri": "https://www.googleapis.com/compute/beta/projects/"
"custom_image_project_id/global/images/custom_image",
},
"worker_config": {
"num_instances": 2,
"machine_type_uri": "projects/project_id/zones/zone/machineTypes/worker_machine_type",
"disk_config": {"boot_disk_type": "worker_disk_type", "boot_disk_size_gb": 256},
"image_uri": "https://www.googleapis.com/compute/beta/projects/"
"custom_image_project_id/global/images/custom_image",
},
"secondary_worker_config": {
"num_instances": 4,
"machine_type_uri": "projects/project_id/zones/zone/machineTypes/worker_machine_type",
"disk_config": {"boot_disk_type": "worker_disk_type", "boot_disk_size_gb": 256},
"is_preemptible": True,
"preemptibility": "SPOT",
},
"software_config": {"properties": {"properties": "data"}, "optional_components": ["optional_components"]},
"lifecycle_config": {
"idle_delete_ttl": {"seconds": 60},
"auto_delete_time": "2019-09-12T00:00:00.000000Z",
},
"encryption_config": {"gce_pd_kms_key_name": "customer_managed_key"},
"autoscaling_config": {"policy_uri": "autoscaling_policy"},
"config_bucket": "storage_bucket",
"initialization_actions": [
{"executable_file": "init_actions_uris", "execution_timeout": {"seconds": 600}}
],
"endpoint_config": {},
}
VIRTUAL_CLUSTER_CONFIG = {
"kubernetes_cluster_config": {
"gke_cluster_config": {
"gke_cluster_target": "projects/project_id/locations/region/clusters/gke_cluster_name",
"node_pool_target": [
{
"node_pool": "projects/project_id/locations/region/clusters/gke_cluster_name/nodePools/dp", # noqa
"roles": ["DEFAULT"],
}
],
},
"kubernetes_software_config": {"component_version": {"SPARK": b"3"}},
},
"staging_bucket": "test-staging-bucket",
}
CONFIG_WITH_CUSTOM_IMAGE_FAMILY = {
"gce_cluster_config": {
"zone_uri": "https://www.googleapis.com/compute/v1/projects/project_id/zones/zone",
"metadata": {"metadata": "data"},
"network_uri": "network_uri",
"subnetwork_uri": "subnetwork_uri",
"internal_ip_only": True,
"tags": ["tags"],
"service_account": "service_account",
"service_account_scopes": ["service_account_scopes"],
},
"master_config": {
"num_instances": 2,
"machine_type_uri": "projects/project_id/zones/zone/machineTypes/master_machine_type",
"disk_config": {"boot_disk_type": "master_disk_type", "boot_disk_size_gb": 128},
"image_uri": "https://www.googleapis.com/compute/beta/projects/"
"custom_image_project_id/global/images/family/custom_image_family",
},
"worker_config": {
"num_instances": 2,
"machine_type_uri": "projects/project_id/zones/zone/machineTypes/worker_machine_type",
"disk_config": {"boot_disk_type": "worker_disk_type", "boot_disk_size_gb": 256},
"image_uri": "https://www.googleapis.com/compute/beta/projects/"
"custom_image_project_id/global/images/family/custom_image_family",
},
"secondary_worker_config": {
"num_instances": 4,
"machine_type_uri": "projects/project_id/zones/zone/machineTypes/worker_machine_type",
"disk_config": {"boot_disk_type": "worker_disk_type", "boot_disk_size_gb": 256},
"is_preemptible": True,
"preemptibility": "SPOT",
},
"software_config": {"properties": {"properties": "data"}, "optional_components": ["optional_components"]},
"lifecycle_config": {
"idle_delete_ttl": {"seconds": 60},
"auto_delete_time": "2019-09-12T00:00:00.000000Z",
},
"encryption_config": {"gce_pd_kms_key_name": "customer_managed_key"},
"autoscaling_config": {"policy_uri": "autoscaling_policy"},
"config_bucket": "storage_bucket",
"initialization_actions": [
{"executable_file": "init_actions_uris", "execution_timeout": {"seconds": 600}}
],
"endpoint_config": {
"enable_http_port_access": True,
},
}
LABELS = {"labels": "data", "airflow-version": AIRFLOW_VERSION}
LABELS.update({"airflow-version": "v" + airflow_version.replace(".", "-").replace("+", "-")})
CLUSTER = {"project_id": "project_id", "cluster_name": CLUSTER_NAME, "config": CONFIG, "labels": LABELS}
UPDATE_MASK = {
"paths": ["config.worker_config.num_instances", "config.secondary_worker_config.num_instances"]
}
TIMEOUT = 120
RETRY = mock.MagicMock(Retry)
RESULT_RETRY = mock.MagicMock(Retry)
METADATA = [("key", "value")]
REQUEST_ID = "request_id_uuid"
WORKFLOW_NAME = "airflow-dataproc-test"
WORKFLOW_TEMPLATE = {
"id": WORKFLOW_NAME,
"placement": {
"managed_cluster": {
"cluster_name": CLUSTER_NAME,
"config": CLUSTER,
}
},
"jobs": [{"step_id": "pig_job_1", "pig_job": {}}],
}
TEST_DAG_ID = "test-dataproc-operators"
DEFAULT_DATE = datetime(2020, 1, 1)
TEST_JOB_ID = "test-job"
TEST_WORKFLOW_ID = "test-workflow"
DATAPROC_JOB_LINK_EXPECTED = (
f"https://console.cloud.google.com/dataproc/jobs/{TEST_JOB_ID}?"
f"region={GCP_REGION}&project={GCP_PROJECT}"
)
DATAPROC_CLUSTER_LINK_EXPECTED = (
f"https://console.cloud.google.com/dataproc/clusters/{CLUSTER_NAME}/monitoring?"
f"region={GCP_REGION}&project={GCP_PROJECT}"
)
DATAPROC_WORKFLOW_LINK_EXPECTED = (
f"https://console.cloud.google.com/dataproc/workflows/instances/{GCP_REGION}/{TEST_WORKFLOW_ID}?"
f"project={GCP_PROJECT}"
)
DATAPROC_JOB_CONF_EXPECTED = {
"resource": TEST_JOB_ID,
"region": GCP_REGION,
"project_id": GCP_PROJECT,
"url": DATAPROC_JOB_LINK_DEPRECATED,
}
DATAPROC_JOB_EXPECTED = {
"job_id": TEST_JOB_ID,
"region": GCP_REGION,
"project_id": GCP_PROJECT,
}
DATAPROC_CLUSTER_CONF_EXPECTED = {
"resource": CLUSTER_NAME,
"region": GCP_REGION,
"project_id": GCP_PROJECT,
"url": DATAPROC_CLUSTER_LINK_DEPRECATED,
}
DATAPROC_CLUSTER_EXPECTED = {
"cluster_id": CLUSTER_NAME,
"region": GCP_REGION,
"project_id": GCP_PROJECT,
}
DATAPROC_WORKFLOW_EXPECTED = {
"workflow_id": TEST_WORKFLOW_ID,
"region": GCP_REGION,
"project_id": GCP_PROJECT,
}
BATCH_ID = "test-batch-id"
BATCH = {
"spark_batch": {
"jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
"main_class": "org.apache.spark.examples.SparkPi",
},
}
def assert_warning(msg: str, warnings):
assert any(msg in str(w) for w in warnings)
class DataprocTestBase:
@classmethod
def setup_class(cls):
cls.dagbag = DagBag(dag_folder="/dev/null", include_examples=False)
cls.dag = DAG(TEST_DAG_ID, default_args={"owner": "airflow", "start_date": DEFAULT_DATE})
def setup_method(self):
self.mock_ti = MagicMock()
self.mock_context = {"ti": self.mock_ti}
self.extra_links_manager_mock = Mock()
self.extra_links_manager_mock.attach_mock(self.mock_ti, "ti")
def tearDown(self):
self.mock_ti = MagicMock()
self.mock_context = {"ti": self.mock_ti}
self.extra_links_manager_mock = Mock()
self.extra_links_manager_mock.attach_mock(self.mock_ti, "ti")
@classmethod
def tearDownClass(cls):
clear_db_runs()
clear_db_xcom()
class DataprocJobTestBase(DataprocTestBase):
@classmethod
def setup_class(cls):
cls.extra_links_expected_calls = [
call.ti.xcom_push(execution_date=None, key="conf", value=DATAPROC_JOB_CONF_EXPECTED),
call.hook().wait_for_job(job_id=TEST_JOB_ID, region=GCP_REGION, project_id=GCP_PROJECT),
]
class DataprocClusterTestBase(DataprocTestBase):
@classmethod
def setup_class(cls):
super().setup_class()
cls.extra_links_expected_calls_base = [
call.ti.xcom_push(execution_date=None, key="dataproc_cluster", value=DATAPROC_CLUSTER_EXPECTED)
]
class TestsClusterGenerator:
def test_image_version(self):
with pytest.raises(ValueError) as ctx:
ClusterGenerator(
custom_image="custom_image",
image_version="image_version",
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
)
assert "custom_image and image_version" in str(ctx.value)
def test_custom_image_family_error_with_image_version(self):
with pytest.raises(ValueError) as ctx:
ClusterGenerator(
image_version="image_version",
custom_image_family="custom_image_family",
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
)
assert "image_version and custom_image_family" in str(ctx.value)
def test_custom_image_family_error_with_custom_image(self):
with pytest.raises(ValueError) as ctx:
ClusterGenerator(
custom_image="custom_image",
custom_image_family="custom_image_family",
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
)
assert "custom_image and custom_image_family" in str(ctx.value)
def test_nodes_number(self):
with pytest.raises(AssertionError) as ctx:
ClusterGenerator(
num_workers=0, num_preemptible_workers=0, project_id=GCP_PROJECT, cluster_name=CLUSTER_NAME
)
assert "num_workers == 0 means single" in str(ctx.value)
def test_build(self):
generator = ClusterGenerator(
project_id="project_id",
num_workers=2,
zone="zone",
network_uri="network_uri",
subnetwork_uri="subnetwork_uri",
internal_ip_only=True,
tags=["tags"],
storage_bucket="storage_bucket",
init_actions_uris=["init_actions_uris"],
init_action_timeout="10m",
metadata={"metadata": "data"},
custom_image="custom_image",
custom_image_project_id="custom_image_project_id",
autoscaling_policy="autoscaling_policy",
properties={"properties": "data"},
optional_components=["optional_components"],
num_masters=2,
master_machine_type="master_machine_type",
master_disk_type="master_disk_type",
master_disk_size=128,
worker_machine_type="worker_machine_type",
worker_disk_type="worker_disk_type",
worker_disk_size=256,
num_preemptible_workers=4,
preemptibility="Spot",
region="region",
service_account="service_account",
service_account_scopes=["service_account_scopes"],
idle_delete_ttl=60,
auto_delete_time=datetime(2019, 9, 12),
auto_delete_ttl=250,
customer_managed_key="customer_managed_key",
)
cluster = generator.make()
assert CONFIG == cluster
def test_build_with_custom_image_family(self):
generator = ClusterGenerator(
project_id="project_id",
num_workers=2,
zone="zone",
network_uri="network_uri",
subnetwork_uri="subnetwork_uri",
internal_ip_only=True,
tags=["tags"],
storage_bucket="storage_bucket",
init_actions_uris=["init_actions_uris"],
init_action_timeout="10m",
metadata={"metadata": "data"},
custom_image_family="custom_image_family",
custom_image_project_id="custom_image_project_id",
autoscaling_policy="autoscaling_policy",
properties={"properties": "data"},
optional_components=["optional_components"],
num_masters=2,
master_machine_type="master_machine_type",
master_disk_type="master_disk_type",
master_disk_size=128,
worker_machine_type="worker_machine_type",
worker_disk_type="worker_disk_type",
worker_disk_size=256,
num_preemptible_workers=4,
preemptibility="Spot",
region="region",
service_account="service_account",
service_account_scopes=["service_account_scopes"],
idle_delete_ttl=60,
auto_delete_time=datetime(2019, 9, 12),
auto_delete_ttl=250,
customer_managed_key="customer_managed_key",
enable_component_gateway=True,
)
cluster = generator.make()
assert CONFIG_WITH_CUSTOM_IMAGE_FAMILY == cluster
class TestDataprocClusterCreateOperator(DataprocClusterTestBase):
def test_deprecation_warning(self):
with pytest.warns(AirflowProviderDeprecationWarning) as warnings:
op = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_name="cluster_name",
num_workers=2,
zone="zone",
)
assert_warning("Passing cluster parameters by keywords", warnings)
assert op.project_id == GCP_PROJECT
assert op.cluster_name == "cluster_name"
assert op.cluster_config["worker_config"]["num_instances"] == 2
assert "zones/zone" in op.cluster_config["master_config"]["machine_type_uri"]
@mock.patch(DATAPROC_PATH.format("Cluster.to_dict"))
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute(self, mock_hook, to_dict_mock):
self.extra_links_manager_mock.attach_mock(mock_hook, "hook")
mock_hook.return_value.create_cluster.result.return_value = None
create_cluster_args = {
"region": GCP_REGION,
"project_id": GCP_PROJECT,
"cluster_name": CLUSTER_NAME,
"request_id": REQUEST_ID,
"retry": RETRY,
"timeout": TIMEOUT,
"metadata": METADATA,
"cluster_config": CONFIG,
"labels": LABELS,
"virtual_cluster_config": None,
}
expected_calls = [
*self.extra_links_expected_calls_base,
call.hook().create_cluster(**create_cluster_args),
]
op = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
labels=LABELS,
cluster_name=CLUSTER_NAME,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
request_id=REQUEST_ID,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context=self.mock_context)
mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
mock_hook.return_value.create_cluster.assert_called_once_with(**create_cluster_args)
# Test whether xcom push occurs before create cluster is called
self.extra_links_manager_mock.assert_has_calls(expected_calls, any_order=False)
to_dict_mock.assert_called_once_with(mock_hook().wait_for_operation())
self.mock_ti.xcom_push.assert_called_once_with(
key="dataproc_cluster",
value=DATAPROC_CLUSTER_EXPECTED,
execution_date=None,
)
@mock.patch(DATAPROC_PATH.format("Cluster.to_dict"))
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute_in_gke(self, mock_hook, to_dict_mock):
self.extra_links_manager_mock.attach_mock(mock_hook, "hook")
mock_hook.return_value.create_cluster.return_value = None
create_cluster_args = {
"region": GCP_REGION,
"project_id": GCP_PROJECT,
"cluster_name": CLUSTER_NAME,
"request_id": REQUEST_ID,
"retry": RETRY,
"timeout": TIMEOUT,
"metadata": METADATA,
"cluster_config": None,
"labels": LABELS,
"virtual_cluster_config": VIRTUAL_CLUSTER_CONFIG,
}
expected_calls = [
*self.extra_links_expected_calls_base,
call.hook().create_cluster(**create_cluster_args),
]
op = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
labels=LABELS,
cluster_name=CLUSTER_NAME,
project_id=GCP_PROJECT,
virtual_cluster_config=VIRTUAL_CLUSTER_CONFIG,
request_id=REQUEST_ID,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context=self.mock_context)
mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
mock_hook.return_value.create_cluster.assert_called_once_with(**create_cluster_args)
# Test whether xcom push occurs before create cluster is called
self.extra_links_manager_mock.assert_has_calls(expected_calls, any_order=False)
to_dict_mock.assert_called_once_with(mock_hook().wait_for_operation())
self.mock_ti.xcom_push.assert_called_once_with(
key="dataproc_cluster",
value=DATAPROC_CLUSTER_EXPECTED,
execution_date=None,
)
@mock.patch(DATAPROC_PATH.format("Cluster.to_dict"))
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute_if_cluster_exists(self, mock_hook, to_dict_mock):
mock_hook.return_value.create_cluster.side_effect = [AlreadyExists("test")]
mock_hook.return_value.get_cluster.return_value.status.state = 0
op = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
labels=LABELS,
cluster_name=CLUSTER_NAME,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
request_id=REQUEST_ID,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context=self.mock_context)
mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
mock_hook.return_value.create_cluster.assert_called_once_with(
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
labels=LABELS,
cluster_name=CLUSTER_NAME,
request_id=REQUEST_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
virtual_cluster_config=None,
)
mock_hook.return_value.get_cluster.assert_called_once_with(
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
)
to_dict_mock.assert_called_once_with(mock_hook.return_value.get_cluster.return_value)
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute_if_cluster_exists_do_not_use(self, mock_hook):
mock_hook.return_value.create_cluster.side_effect = [AlreadyExists("test")]
mock_hook.return_value.get_cluster.return_value.status.state = 0
op = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
labels=LABELS,
cluster_name=CLUSTER_NAME,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
request_id=REQUEST_ID,
use_if_exists=False,
)
with pytest.raises(AlreadyExists):
op.execute(context=self.mock_context)
@mock.patch(DATAPROC_PATH.format("DataprocCreateClusterOperator._wait_for_cluster_in_deleting_state"))
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute_if_cluster_exists_in_error_state(self, mock_hook, mock_wait_for_deleting):
mock_hook.return_value.create_cluster.side_effect = [AlreadyExists("test")]
cluster_status = mock_hook.return_value.get_cluster.return_value.status
cluster_status.state = 0
cluster_status.State.ERROR = 0
mock_wait_for_deleting.return_value.get_cluster.side_effect = [NotFound]
op = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
labels=LABELS,
cluster_name=CLUSTER_NAME,
delete_on_error=True,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
request_id=REQUEST_ID,
)
with pytest.raises(AirflowException):
op.execute(context=self.mock_context)
mock_hook.return_value.diagnose_cluster.assert_called_once_with(
region=GCP_REGION, project_id=GCP_PROJECT, cluster_name=CLUSTER_NAME
)
mock_hook.return_value.delete_cluster.assert_called_once_with(
region=GCP_REGION, project_id=GCP_PROJECT, cluster_name=CLUSTER_NAME
)
@mock.patch(DATAPROC_PATH.format("Cluster.to_dict"))
@mock.patch(DATAPROC_PATH.format("exponential_sleep_generator"))
@mock.patch(DATAPROC_PATH.format("DataprocCreateClusterOperator._create_cluster"))
@mock.patch(DATAPROC_PATH.format("DataprocCreateClusterOperator._get_cluster"))
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute_if_cluster_exists_in_deleting_state(
self,
mock_hook,
mock_get_cluster,
mock_create_cluster,
mock_generator,
to_dict_mock,
):
cluster_deleting = mock.MagicMock()
cluster_deleting.status.state = 0
cluster_deleting.status.State.DELETING = 0
cluster_running = mock.MagicMock()
cluster_running.status.state = 0
cluster_running.status.State.RUNNING = 0
mock_create_cluster.side_effect = [AlreadyExists("test"), cluster_running]
mock_generator.return_value = [0]
mock_get_cluster.side_effect = [cluster_deleting, NotFound("test")]
op = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
labels=LABELS,
cluster_name=CLUSTER_NAME,
delete_on_error=True,
gcp_conn_id=GCP_CONN_ID,
)
op.execute(context=self.mock_context)
calls = [mock.call(mock_hook.return_value), mock.call(mock_hook.return_value)]
mock_get_cluster.assert_has_calls(calls)
mock_create_cluster.assert_has_calls(calls)
to_dict_mock.assert_called_once_with(cluster_running)
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
@mock.patch(DATAPROC_TRIGGERS_PATH.format("DataprocAsyncHook"))
def test_create_execute_call_defer_method(self, mock_trigger_hook, mock_hook):
mock_hook.return_value.create_cluster.return_value = None
operator = DataprocCreateClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
labels=LABELS,
cluster_name=CLUSTER_NAME,
delete_on_error=True,
metadata=METADATA,
gcp_conn_id=GCP_CONN_ID,
impersonation_chain=IMPERSONATION_CHAIN,
retry=RETRY,
timeout=TIMEOUT,
deferrable=True,
)
with pytest.raises(TaskDeferred) as exc:
operator.execute(mock.MagicMock())
mock_hook.assert_called_once_with(
gcp_conn_id=GCP_CONN_ID,
impersonation_chain=IMPERSONATION_CHAIN,
)
mock_hook.return_value.create_cluster.assert_called_once_with(
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_config=CONFIG,
request_id=None,
labels=LABELS,
cluster_name=CLUSTER_NAME,
virtual_cluster_config=None,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
)
mock_hook.return_value.wait_for_operation.assert_not_called()
assert isinstance(exc.value.trigger, DataprocClusterTrigger)
assert exc.value.method_name == GOOGLE_DEFAULT_DEFERRABLE_METHOD_NAME
@pytest.mark.need_serialized_dag
def test_create_cluster_operator_extra_links(dag_maker, create_task_instance_of_operator):
ti = create_task_instance_of_operator(
DataprocCreateClusterOperator,
dag_id=TEST_DAG_ID,
execution_date=DEFAULT_DATE,
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
delete_on_error=True,
gcp_conn_id=GCP_CONN_ID,
)
serialized_dag = dag_maker.get_serialized_data()
deserialized_dag = SerializedDAG.from_dict(serialized_dag)
deserialized_task = deserialized_dag.task_dict[TASK_ID]
# Assert operator links for serialized DAG
assert serialized_dag["dag"]["tasks"][0]["_operator_extra_links"] == [
{"airflow.providers.google.cloud.links.dataproc.DataprocClusterLink": {}}
]
# Assert operator link types are preserved during deserialization
assert isinstance(deserialized_task.operator_extra_links[0], DataprocClusterLink)
# Assert operator link is empty when no XCom push occurred
assert ti.task.get_extra_links(ti, DataprocClusterLink.name) == ""
# Assert operator link is empty for deserialized task when no XCom push occurred
assert deserialized_task.get_extra_links(ti, DataprocClusterLink.name) == ""
ti.xcom_push(key="dataproc_cluster", value=DATAPROC_CLUSTER_EXPECTED)
# Assert operator links are preserved in deserialized tasks after execution
assert deserialized_task.get_extra_links(ti, DataprocClusterLink.name) == DATAPROC_CLUSTER_LINK_EXPECTED
# Assert operator links after execution
assert ti.task.get_extra_links(ti, DataprocClusterLink.name) == DATAPROC_CLUSTER_LINK_EXPECTED
class TestDataprocClusterScaleOperator(DataprocClusterTestBase):
@classmethod
def setup_class(cls):
super().setup_class()
cls.extra_links_expected_calls_base = [
call.ti.xcom_push(execution_date=None, key="conf", value=DATAPROC_CLUSTER_CONF_EXPECTED)
]
def test_deprecation_warning(self):
with pytest.warns(AirflowProviderDeprecationWarning) as warnings:
DataprocScaleClusterOperator(task_id=TASK_ID, cluster_name=CLUSTER_NAME, project_id=GCP_PROJECT)
assert_warning("DataprocUpdateClusterOperator", warnings)
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute(self, mock_hook):
self.extra_links_manager_mock.attach_mock(mock_hook, "hook")
mock_hook.return_value.update_cluster.result.return_value = None
cluster_update = {
"config": {"worker_config": {"num_instances": 3}, "secondary_worker_config": {"num_instances": 4}}
}
update_cluster_args = {
"project_id": GCP_PROJECT,
"region": GCP_REGION,
"cluster_name": CLUSTER_NAME,
"cluster": cluster_update,
"graceful_decommission_timeout": {"seconds": 600},
"update_mask": UPDATE_MASK,
}
expected_calls = [
*self.extra_links_expected_calls_base,
call.hook().update_cluster(**update_cluster_args),
]
op = DataprocScaleClusterOperator(
task_id=TASK_ID,
cluster_name=CLUSTER_NAME,
project_id=GCP_PROJECT,
region=GCP_REGION,
num_workers=3,
num_preemptible_workers=4,
graceful_decommission_timeout="10m",
gcp_conn_id=GCP_CONN_ID,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context=self.mock_context)
mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
mock_hook.return_value.update_cluster.assert_called_once_with(**update_cluster_args)
# Test whether xcom push occurs before cluster is updated
self.extra_links_manager_mock.assert_has_calls(expected_calls, any_order=False)
self.mock_ti.xcom_push.assert_called_once_with(
key="conf",
value=DATAPROC_CLUSTER_CONF_EXPECTED,
execution_date=None,
)
@pytest.mark.need_serialized_dag
def test_scale_cluster_operator_extra_links(dag_maker, create_task_instance_of_operator):
ti = create_task_instance_of_operator(
DataprocScaleClusterOperator,
dag_id=TEST_DAG_ID,
execution_date=DEFAULT_DATE,
task_id=TASK_ID,
cluster_name=CLUSTER_NAME,
project_id=GCP_PROJECT,
region=GCP_REGION,
num_workers=3,
num_preemptible_workers=2,
graceful_decommission_timeout="2m",
gcp_conn_id=GCP_CONN_ID,
)
serialized_dag = dag_maker.get_serialized_data()
deserialized_dag = SerializedDAG.from_dict(serialized_dag)
deserialized_task = deserialized_dag.task_dict[TASK_ID]
# Assert operator links for serialized DAG
assert serialized_dag["dag"]["tasks"][0]["_operator_extra_links"] == [
{"airflow.providers.google.cloud.links.dataproc.DataprocLink": {}}
]
# Assert operator link types are preserved during deserialization
assert isinstance(deserialized_task.operator_extra_links[0], DataprocLink)
# Assert operator link is empty when no XCom push occurred
assert ti.task.get_extra_links(ti, DataprocLink.name) == ""
# Assert operator link is empty for deserialized task when no XCom push occurred
assert deserialized_task.get_extra_links(ti, DataprocLink.name) == ""
ti.xcom_push(
key="conf",
value=DATAPROC_CLUSTER_CONF_EXPECTED,
)
# Assert operator links are preserved in deserialized tasks after execution
assert deserialized_task.get_extra_links(ti, DataprocLink.name) == DATAPROC_CLUSTER_LINK_EXPECTED
# Assert operator links after execution
assert ti.task.get_extra_links(ti, DataprocLink.name) == DATAPROC_CLUSTER_LINK_EXPECTED
class TestDataprocClusterDeleteOperator:
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute(self, mock_hook):
op = DataprocDeleteClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
request_id=REQUEST_ID,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context={})
mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
mock_hook.return_value.delete_cluster.assert_called_once_with(
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
cluster_uuid=None,
request_id=REQUEST_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
)
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
@mock.patch(DATAPROC_TRIGGERS_PATH.format("DataprocAsyncHook"))
def test_create_execute_call_defer_method(self, mock_trigger_hook, mock_hook):
mock_hook.return_value.create_cluster.return_value = None
operator = DataprocDeleteClusterOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
cluster_name=CLUSTER_NAME,
request_id=REQUEST_ID,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
impersonation_chain=IMPERSONATION_CHAIN,
deferrable=True,
)
with pytest.raises(TaskDeferred) as exc:
operator.execute(mock.MagicMock())
mock_hook.assert_called_once_with(
gcp_conn_id=GCP_CONN_ID,
impersonation_chain=IMPERSONATION_CHAIN,
)
mock_hook.return_value.delete_cluster.assert_called_once_with(
project_id=GCP_PROJECT,
region=GCP_REGION,
cluster_name=CLUSTER_NAME,
cluster_uuid=None,
request_id=REQUEST_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
)
mock_hook.return_value.wait_for_operation.assert_not_called()
assert isinstance(exc.value.trigger, DataprocDeleteClusterTrigger)
assert exc.value.method_name == GOOGLE_DEFAULT_DEFERRABLE_METHOD_NAME
class TestDataprocSubmitJobOperator(DataprocJobTestBase):
@mock.patch(DATAPROC_PATH.format("DataprocHook"))
def test_execute(self, mock_hook):
xcom_push_call = call.ti.xcom_push(
execution_date=None, key="dataproc_job", value=DATAPROC_JOB_EXPECTED
)
wait_for_job_call = call.hook().wait_for_job(
job_id=TEST_JOB_ID, region=GCP_REGION, project_id=GCP_PROJECT, timeout=None
)
job = {}
mock_hook.return_value.wait_for_job.return_value = None
mock_hook.return_value.submit_job.return_value.reference.job_id = TEST_JOB_ID
self.extra_links_manager_mock.attach_mock(mock_hook, "hook")
op = DataprocSubmitJobOperator(
task_id=TASK_ID,
region=GCP_REGION,
project_id=GCP_PROJECT,
job=job,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
request_id=REQUEST_ID,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context=self.mock_context)
mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
# Test whether xcom push occurs before polling for job
assert self.extra_links_manager_mock.mock_calls.index(
xcom_push_call
) < self.extra_links_manager_mock.mock_calls.index(
wait_for_job_call
), "Xcom push for Job Link has to be done before polling for job status"
mock_hook.return_value.submit_job.assert_called_once_with(