#
# 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.
#
# pytype: skip-file
import logging
import re
import time
from typing import Optional
from typing import Tuple
from apache_beam import version as beam_version
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.runners.interactive import interactive_environment as ie
from apache_beam.runners.interactive.dataproc.types import ClusterMetadata
from apache_beam.runners.interactive.utils import obfuscate
from apache_beam.runners.interactive.utils import progress_indicated
try:
from google.cloud import dataproc_v1
from apache_beam.io.gcp import gcsfilesystem #pylint: disable=ungrouped-imports
except ImportError:
class UnimportedDataproc:
Cluster = None
dataproc_v1 = UnimportedDataproc()
_LOGGER = logging.getLogger(__name__)
# Name of the log file auto-generated by Dataproc. We use it to locate the
# startup output of the Flink daemon to retrieve master url and dashboard
# information.
DATAPROC_STAGING_LOG_NAME = 'dataproc-initialization-script-0_output'
# Home dir of os user yarn.
YARN_HOME = '/var/lib/hadoop-yarn'
# Configures the os user yarn to use gcloud as the docker credHelper.
# Also sets some taskmanager configurations for better parallelism.
# Finally starts the yarn application: flink cluster in session mode.
INIT_ACTION = """#!/bin/bash
sudo -u yarn gcloud auth configure-docker --quiet
readonly FLINK_INSTALL_DIR='/usr/lib/flink'
readonly MASTER_HOSTNAME="$(/usr/share/google/get_metadata_value attributes/dataproc-master)"
cat <<EOF >>${FLINK_INSTALL_DIR}/conf/flink-conf.yaml
taskmanager.memory.network.fraction: 0.2
taskmanager.memory.network.min: 64mb
taskmanager.memory.network.max: 1gb
EOF
sed -i \
"s/^taskmanager.network.numberOfBuffers: 2048/taskmanager.network.numberOfBuffers: 8192/" \
${FLINK_INSTALL_DIR}/conf/flink-conf.yaml
if [[ "${HOSTNAME}" == "${MASTER_HOSTNAME}" ]]; then
. /usr/bin/flink-yarn-daemon
fi
"""
[docs]class DataprocClusterManager:
"""Self-contained cluster manager that controls the lifecyle of a Dataproc
cluster connected by one or more pipelines under Interactive Beam.
"""
def __init__(self, cluster_metadata: ClusterMetadata) -> None:
"""Initializes the DataprocClusterManager with properties required
to interface with the Dataproc ClusterControllerClient.
"""
self.cluster_metadata = cluster_metadata
# Pipelines whose jobs are executed on the cluster.
self.pipelines = set()
self._cluster_client = dataproc_v1.ClusterControllerClient(
client_options={
'api_endpoint': \
f'{self.cluster_metadata.region}-dataproc.googleapis.com:443'
})
self._fs = gcsfilesystem.GCSFileSystem(PipelineOptions())
self._staging_directory = None
cache_dir = ie.current_env().options.cache_root
if not cache_dir.startswith('gs://'):
error_msg = (
'ib.options.cache_root needs to be a Cloud Storage '
'Bucket to cache source recording and PCollections in current '
f'interactive setup, instead \'{cache_dir}\' is assigned.')
_LOGGER.error(error_msg)
raise ValueError(error_msg)
self._cache_root = cache_dir.rstrip('/')
[docs] def stage_init_action(self) -> str:
"""Stages the initialization action script to GCS cache root to set up
Dataproc clusters.
Returns the staged gcs file path.
"""
# Versionizes the initialization action script.
init_action_ver = obfuscate(INIT_ACTION)
path = f'{self._cache_root}/dataproc-init-action-{init_action_ver}.sh'
if not self._fs.exists(path):
with self._fs.create(path) as bwriter:
bwriter.write(INIT_ACTION.encode())
return path
[docs] @progress_indicated
def create_cluster(self, cluster: dict) -> None:
"""Attempts to create a cluster using attributes that were
initialized with the DataprocClusterManager instance.
Args:
cluster: Dictionary representing Dataproc cluster. Read more about the
schema for clusters here:
https://cloud.google.com/python/docs/reference/dataproc/latest/google.cloud.dataproc_v1.types.Cluster
"""
if self.cluster_metadata.master_url:
return
try:
self._cluster_client.create_cluster(
request={
'project_id': self.cluster_metadata.project_id,
'region': self.cluster_metadata.region,
'cluster': cluster
})
except Exception as e:
if e.code == 409:
_LOGGER.info(
'Cluster %s already exists. Continuing...',
self.cluster_metadata.cluster_name)
elif e.code == 403:
_LOGGER.error(
'Due to insufficient project permissions, '
'unable to create cluster: %s',
self.cluster_metadata.cluster_name)
raise ValueError(
'You cannot create a cluster in project: {}'.format(
self.cluster_metadata.project_id))
elif e.code == 501:
_LOGGER.error(
'Invalid region provided: %s', self.cluster_metadata.region)
raise ValueError(
'Region {} does not exist!'.format(self.cluster_metadata.region))
else:
_LOGGER.error(
'Unable to create cluster: %s', self.cluster_metadata.cluster_name)
raise e
else:
_LOGGER.info(
'Cluster created successfully: %s',
self.cluster_metadata.cluster_name)
self._staging_directory = self.get_staging_location()
master_url, dashboard = self.get_master_url_and_dashboard()
self.cluster_metadata.master_url = master_url
self.cluster_metadata.dashboard = dashboard
[docs] def create_flink_cluster(self) -> None:
"""Calls _create_cluster with a configuration that enables FlinkRunner."""
init_action_path = self.stage_init_action()
cluster = {
'project_id': self.cluster_metadata.project_id,
'cluster_name': self.cluster_metadata.cluster_name,
'config': {
'software_config': {
# TODO(https://github.com/apache/beam/issues/21527): Uncomment
# these lines when a Dataproc image is released with previously
# missing dependencies.
# 'image_version': ie.current_env().clusters.
# DATAPROC_IMAGE_VERSION,
'optional_components': ['DOCKER', 'FLINK'],
'properties': {
# Enforces HOME dir for user yarn.
'yarn:yarn.nodemanager.user-home-dir': YARN_HOME
}
},
'initialization_actions': [{
'executable_file': init_action_path
}],
'gce_cluster_config': {
'metadata': {
'flink-start-yarn-session': 'false'
},
'service_account_scopes': [
'https://www.googleapis.com/auth/cloud-platform'
]
},
'master_config': {
# There must be 1 and only 1 instance of master.
'num_instances': 1
},
'worker_config': {},
'endpoint_config': {
'enable_http_port_access': True
}
},
'labels': {
'goog-dataflow-notebook': beam_version.__version__.replace(
'.', '_')
}
}
# Additional gce_cluster_config.
gce_cluster_config = cluster['config']['gce_cluster_config']
if self.cluster_metadata.subnetwork:
gce_cluster_config['subnetwork_uri'] = self.cluster_metadata.subnetwork
# Additional InstanceGroupConfig for master and workers.
master_config = cluster['config']['master_config']
worker_config = cluster['config']['worker_config']
if self.cluster_metadata.num_workers:
worker_config['num_instances'] = self.cluster_metadata.num_workers
if self.cluster_metadata.machine_type:
master_config['machine_type_uri'] = self.cluster_metadata.machine_type
worker_config['machine_type_uri'] = self.cluster_metadata.machine_type
self.create_cluster(cluster)
[docs] def cleanup(self) -> None:
"""Deletes the cluster that uses the attributes initialized
with the DataprocClusterManager instance."""
try:
self._cluster_client.delete_cluster(
request={
'project_id': self.cluster_metadata.project_id,
'region': self.cluster_metadata.region,
'cluster_name': self.cluster_metadata.cluster_name,
})
self.cleanup_staging_files()
except Exception as e:
if e.code == 403:
_LOGGER.error(
'Due to insufficient project permissions, '
'unable to clean up the default cluster: %s',
self.cluster_metadata.cluster_name)
raise ValueError(
'You cannot delete a cluster in project: {}'.format(
self.cluster_metadata.project_id))
elif e.code == 404:
_LOGGER.error(
'Cluster does not exist: %s', self.cluster_metadata.cluster_name)
raise ValueError(
'Cluster was not found: {}'.format(
self.cluster_metadata.cluster_name))
else:
_LOGGER.error(
'Failed to delete cluster: %s', self.cluster_metadata.cluster_name)
raise e
[docs] def get_cluster_details(self) -> dataproc_v1.Cluster:
"""Gets the Dataproc_v1 Cluster object for the current cluster manager."""
try:
return self._cluster_client.get_cluster(
request={
'project_id': self.cluster_metadata.project_id,
'region': self.cluster_metadata.region,
'cluster_name': self.cluster_metadata.cluster_name
})
except Exception as e:
if e.code == 403:
_LOGGER.error(
'Due to insufficient project permissions, '
'unable to retrieve information for cluster: %s',
self.cluster_metadata.cluster_name)
raise ValueError(
'You cannot view clusters in project: {}'.format(
self.cluster_metadata.project_id))
elif e.code == 404:
_LOGGER.error(
'Cluster does not exist: %s', self.cluster_metadata.cluster_name)
raise ValueError(
'Cluster was not found: {}'.format(
self.cluster_metadata.cluster_name))
else:
_LOGGER.error(
'Failed to get information for cluster: %s',
self.cluster_metadata.cluster_name)
raise e
[docs] def wait_for_cluster_to_provision(self) -> None:
while self.get_cluster_details().status.state.name == 'CREATING':
time.sleep(15)
[docs] def get_staging_location(self) -> str:
"""Gets the staging bucket of an existing Dataproc cluster."""
try:
self.wait_for_cluster_to_provision()
cluster_details = self.get_cluster_details()
bucket_name = cluster_details.config.config_bucket
gcs_path = 'gs://' + bucket_name + '/google-cloud-dataproc-metainfo/'
for file in self._fs._list(gcs_path):
if self.cluster_metadata.cluster_name in file.path:
# this file path split will look something like:
# ['gs://.../google-cloud-dataproc-metainfo/{staging_dir}/',
# '-{node-type}/dataproc-initialization-script-0_output']
return file.path.split(self.cluster_metadata.cluster_name)[0]
except Exception as e:
_LOGGER.error(
'Failed to get %s cluster staging bucket.',
self.cluster_metadata.cluster_name)
raise e
[docs] def parse_master_url_and_dashboard(self, line: str) -> Tuple[str, str]:
"""Parses the master_url and YARN application_id of the Flink process from
an input line. The line containing both the master_url and application id
is always formatted as such:
{text} Found Web Interface {master_url} of application
'{application_id}'.\\n
Truncated example where '...' represents additional text between segments:
... google-dataproc-startup[000]: ... activate-component-flink[0000]:
...org.apache.flink.yarn.YarnClusterDescriptor... [] -
Found Web Interface example-master-url:50000 of application
'application_123456789000_0001'.
Returns the flink_master_url and dashboard link as a tuple."""
cluster_details = self.get_cluster_details()
yarn_endpoint = cluster_details.config.endpoint_config.http_ports[
'YARN ResourceManager']
segment = line.split('Found Web Interface ')[1].split(' of application ')
master_url = segment[0]
application_id = re.sub('\'|.\n', '', segment[1])
dashboard = re.sub(
'/yarn/',
'/gateway/default/yarn/proxy/' + application_id + '/',
yarn_endpoint)
return master_url, dashboard
[docs] def get_master_url_and_dashboard(self) -> Tuple[Optional[str], Optional[str]]:
"""Returns the master_url of the current cluster."""
startup_logs = []
for file in self._fs._list(self._staging_directory):
if DATAPROC_STAGING_LOG_NAME in file.path:
startup_logs.append(file.path)
for log in startup_logs:
content = self._fs.open(log)
for line in content.readlines():
decoded_line = line.decode()
if 'Found Web Interface' in decoded_line:
return self.parse_master_url_and_dashboard(decoded_line)
return None, None
[docs] def cleanup_staging_files(self) -> None:
if self._staging_directory:
staging_files = [
file.path for file in self._fs._list(self._staging_directory)
]
self._fs.delete(staging_files)
if self._cache_root:
cache_files = [file.path for file in self._fs._list(self._cache_root)]
self._fs.delete(cache_files)