def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
if spark_client.stop_job_app(args.job_id, args.app_name):
log.info("Stopped app {0}".format(args.app_name))
else:
log.error("App with name {0} does not exist or was already deleted")
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
log.info('-------------------------------------------')
log.info('spark cluster id: {}'.format(args.cluster_id))
log.info('username: {}'.format(args.username))
log.info('-------------------------------------------')
if args.ssh_key:
ssh_key = args.ssh_key
else:
ssh_key = spark_client.secrets_config.ssh_pub_key
ssh_key, password = utils.get_ssh_key_or_prompt(ssh_key, args.username, args.password, spark_client.secrets_config)
spark_client.create_user(
cluster_id=args.cluster_id,
username=args.username,
password=password,
ssh_key=ssh_key
)
if password:
log.info('password: %s', '*' * len(password))
elif ssh_key:
log.info('ssh public key: %s', ssh_key)
log.info('-------------------------------------------')
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
ssh_conf = SshConfig()
ssh_conf.merge(cluster_id=args.cluster_id,
username=args.username,
job_ui_port=args.jobui,
job_history_ui_port=args.jobhistoryui,
web_ui_port=args.webui,
jupyter_port=args.jupyter,
name_node_ui_port=args.namenodeui,
rstudio_server_port=args.rstudioserver,
host=args.host,
connect=args.connect)
http_prefix = 'http://localhost:'
log.info("-------------------------------------------")
log.info("spark cluster id: %s", ssh_conf.cluster_id)
log.info("open webui: %s%s", http_prefix, ssh_conf.web_ui_port)
log.info("open jobui: %s%s", http_prefix, ssh_conf.job_ui_port)
log.info("open jobhistoryui: %s%s", http_prefix,
ssh_conf.job_history_ui_port)
log.info("open jupyter: %s%s", http_prefix, ssh_conf.jupyter_port)
log.info("open namenodeui: %s%s", http_prefix,
ssh_conf.name_node_ui_port)
log.info("open rstudio server: %s%s", http_prefix,
ssh_conf.rstudio_server_port)
log.info("ssh username: %s", ssh_conf.username)
log.info("connect: %s", ssh_conf.connect)
log.info("-------------------------------------------")
# get ssh command
try:
ssh_cmd = utils.ssh_in_master(
client=spark_client,
cluster_id=ssh_conf.cluster_id,
webui=ssh_conf.web_ui_port,
jobui=ssh_conf.job_ui_port,
jobhistoryui=ssh_conf.job_history_ui_port,
namenodeui=ssh_conf.name_node_ui_port,
jupyter=ssh_conf.jupyter_port,
rstudioserver=ssh_conf.rstudio_server_port,
username=ssh_conf.username,
host=ssh_conf.host,
connect=ssh_conf.connect)
if not ssh_conf.connect:
log.info("")
log.info(
"Use the following command to connect to your spark head node:"
)
log.info("\t%s", ssh_cmd)
except batch_error.BatchErrorException as e:
if e.error.code == "PoolNotFound":
raise aztk.error.AztkError(
"The cluster you are trying to connect to does not exist.")
else:
raise
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
if args.tail:
utils.stream_logs(client=spark_client, cluster_id=args.cluster_id, application_name=args.app_name)
else:
app_logs = spark_client.get_application_log(cluster_id=args.cluster_id, application_name=args.app_name)
print(app_logs.log)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
job_conf = JobConfig()
job_conf.merge(args.job_id, args.job_conf)
aztk_applications = []
for application in job_conf.applications:
aztk_applications.append(
aztk.spark.models.ApplicationConfiguration(
name=application.get('name'),
application=application.get('application'),
application_args=application.get('application_args'),
main_class=application.get('main_class'),
jars=[],
py_files=[],
files=[],
driver_java_options=application.get('driver_java_options'),
driver_library_path=application.get('driver_library_path'),
driver_class_path=application.get('driver_class_path'),
driver_memory=application.get('driver_memory'),
executor_memory=application.get('executor_memory'),
driver_cores=application.get('driver_cores'),
executor_cores=application.get('executor_cores')))
# by default, load spark configuration files in .aztk/
spark_configuration = config.load_aztk_spark_config()
# overwrite with values in job_conf if they exist
if job_conf.spark_defaults_conf:
spark_configuration.spark_defaults_conf = job_conf.spark_defaults_conf
if job_conf.spark_env_sh:
spark_configuration.spark_env_sh = job_conf.spark_env_sh
if job_conf.core_site_xml:
spark_configuration.core_site_xml = job_conf.core_site_xml
job_configuration = aztk.spark.models.JobConfiguration(
id=job_conf.id,
applications=aztk_applications,
custom_scripts=job_conf.custom_scripts,
spark_configuration=spark_configuration,
vm_size=job_conf.vm_size,
docker_repo=job_conf.docker_repo,
max_dedicated_nodes=job_conf.max_dedicated_nodes,
max_low_pri_nodes=job_conf.max_low_pri_nodes,
subnet_id=job_conf.subnet_id,
worker_on_master=job_conf.worker_on_master)
#TODO: utils.print_job_conf(job_configuration)
spark_client.submit_job(job_configuration)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
cluster_conf = ClusterConfiguration()
cluster_conf.spark_configuration = load_aztk_spark_config()
# read cluster.yaml configuartion file, overwrite values with args
file_config, wait = config.read_cluster_config()
cluster_conf.merge(file_config)
cluster_conf.merge(
ClusterConfiguration(cluster_id=args.cluster_id,
vm_count=args.size,
vm_low_pri_count=args.size_low_pri,
vm_size=args.vm_size,
subnet_id=args.subnet_id,
user_configuration=UserConfiguration(
username=args.username,
password=args.password,
),
docker_repo=args.docker_repo))
wait = wait if args.wait is None else args.wait
user_configuration = cluster_conf.user_configuration
if user_configuration and user_configuration.username:
ssh_key, password = utils.get_ssh_key_or_prompt(
spark_client.secrets_config.ssh_pub_key,
user_configuration.username, user_configuration.password,
spark_client.secrets_config)
cluster_conf.user_configuration = aztk.spark.models.UserConfiguration(
username=user_configuration.username,
password=password,
ssh_key=ssh_key)
else:
cluster_conf.user_configuration = None
print_cluster_conf(cluster_conf, wait)
spinner = utils.Spinner()
spinner.start()
# create spark cluster
cluster = spark_client.create_cluster(cluster_conf, wait=wait)
spinner.stop()
if wait:
log.info("Cluster %s created successfully.", cluster.id)
else:
log.info("Cluster %s is being provisioned.", cluster.id)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
cluster_id = args.cluster_id
if not args.force:
confirmation_cluster_id = input(
"Please confirm the id of the cluster you wish to delete: ")
if confirmation_cluster_id != cluster_id:
log.error(
"Confirmation cluster id does not match. Please try again.")
return
if spark_client.delete_cluster(cluster_id):
log.info("Deleting cluster %s", cluster_id)
else:
log.error("Cluster with id '%s' doesn't exist or was already deleted.",
cluster_id)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
job_id = args.job_id
if not args.force:
# check if job exists before prompting for confirmation
spark_client.get_job(job_id)
confirmation_cluster_id = input(
"Please confirm the id of the cluster you wish to delete: ")
if confirmation_cluster_id != job_id:
log.error(
"Confirmation cluster id does not match. Please try again.")
return
if spark_client.delete_job(job_id):
log.info("Deleting Job %s", job_id)
else:
log.error("Job with id '%s' doesn't exist or was already deleted.",
job_id)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
result = spark_client.cluster_run(args.cluster_id, args.command)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
utils.print_applications(spark_client.list_applications(args.job_id))
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
# read cluster.yaml configuartion file, overwrite values with args
cluster_conf = ClusterConfig()
cluster_conf.merge(uid=args.cluster_id,
size=args.size,
size_low_pri=args.size_low_pri,
vm_size=args.vm_size,
subnet_id=args.subnet_id,
wait=args.wait,
username=args.username,
password=args.password,
docker_repo=args.docker_repo)
if cluster_conf.custom_scripts:
custom_scripts = []
for custom_script in cluster_conf.custom_scripts:
custom_scripts.append(
aztk.spark.models.CustomScript(script=custom_script['script'],
run_on=custom_script['runOn']))
else:
custom_scripts = None
if cluster_conf.file_shares:
file_shares = []
for file_share in cluster_conf.file_shares:
file_shares.append(
aztk.spark.models.FileShare(
storage_account_name=file_share['storage_account_name'],
storage_account_key=file_share['storage_account_key'],
file_share_path=file_share['file_share_path'],
mount_path=file_share['mount_path']))
else:
file_shares = None
if cluster_conf.username:
ssh_key, password = utils.get_ssh_key_or_prompt(
spark_client.secrets_config.ssh_pub_key, cluster_conf.username,
cluster_conf.password, spark_client.secrets_config)
user_conf = aztk.spark.models.UserConfiguration(
username=cluster_conf.username, password=password, ssh_key=ssh_key)
else:
user_conf = None
print_cluster_conf(cluster_conf)
spinner = utils.Spinner()
spinner.start()
# create spark cluster
cluster = spark_client.create_cluster(
aztk.spark.models.ClusterConfiguration(
cluster_id=cluster_conf.uid,
vm_count=cluster_conf.size,
vm_low_pri_count=cluster_conf.size_low_pri,
vm_size=cluster_conf.vm_size,
subnet_id=cluster_conf.subnet_id,
custom_scripts=custom_scripts,
file_shares=file_shares,
docker_repo=cluster_conf.docker_repo,
spark_configuration=load_aztk_spark_config(),
user_configuration=user_conf),
wait=cluster_conf.wait)
spinner.stop()
if cluster_conf.wait:
log.info("Cluster %s created successfully.", cluster.id)
else:
log.info("Cluster %s is being provisioned.", cluster.id)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
spark_client.stop_job(args.job_id)
print("Stopped Job {0}".format(args.job_id))
def execute(_: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
clusters = spark_client.list_clusters()
utils.print_clusters(clusters)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
utils.print_jobs(spark_client.list_jobs())
import subprocess
from datetime import datetime
from azure.batch.models import BatchErrorException
import aztk.spark
from aztk.error import AztkError
from cli import config
dt = datetime.now()
time = dt.microsecond
base_job_id = "job-{}".format(time)
# load secrets
# note: this assumes secrets are set up in .aztk/secrets
spark_client = aztk.spark.Client(config.load_aztk_screts())
def test_submit_job():
test_id = "submit-"
try:
app1 = aztk.spark.models.ApplicationConfiguration(
name="pipy100",
application="examples/src/main/python/pi.py",
application_args=[100])
app2 = aztk.spark.models.ApplicationConfiguration(
name="pipy101",
application="examples/src/main/python/pi.py",
application_args=[100])
job_configuration = aztk.spark.models.JobConfiguration(
id=test_id + base_job_id,
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
app_logs = spark_client.get_job_application_log(args.job_id, args.app_name)
print(app_logs.log)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
cluster_id = args.cluster_id
cluster = spark_client.get_cluster(cluster_id)
utils.print_cluster(spark_client, cluster)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
utils.print_application(
spark_client.get_application(args.job_id, args.app_name))
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
utils.print_job(spark_client, spark_client.get_job(args.job_id))
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
spark_client.cluster_copy(cluster_id=args.cluster_id,
source_path=args.source_path,
destination_path=args.dest_path)
def execute(args: typing.NamedTuple):
spark_client = aztk.spark.Client(config.load_aztk_screts())
jars = []
py_files = []
files = []
if args.jars is not None:
jars = args.jars.replace(' ', '').split(',')
if args.py_files is not None:
py_files = args.py_files.replace(' ', '').split(',')
if args.files is not None:
files = args.files.replace(' ', '').split(',')
log.info("-------------------------------------------")
log.info("Spark cluster id: %s", args.cluster_id)
log.info("Spark app name: %s", args.name)
log.info("Wait for app completion: %s", args.wait)
if args.main_class is not None:
log.info("Entry point class: %s", args.main_class)
if jars:
log.info("JARS: %s", jars)
if py_files:
log.info("PY_Files: %s", py_files)
if files:
log.info("Files: %s", files)
if args.driver_java_options is not None:
log.info("Driver java options: %s", args.driver_java_options)
if args.driver_library_path is not None:
log.info("Driver library path: %s", args.driver_library_path)
if args.driver_class_path is not None:
log.info("Driver class path: %s", args.driver_class_path)
if args.driver_memory is not None:
log.info("Driver memory: %s", args.driver_memory)
if args.executor_memory is not None:
log.info("Executor memory: %s", args.executor_memory)
if args.driver_cores is not None:
log.info("Driver cores: %s", args.driver_cores)
if args.executor_cores is not None:
log.info("Executor cores: %s", args.executor_cores)
log.info("Application: %s", args.app)
log.info("Application arguments: %s", args.app_args)
log.info("-------------------------------------------")
spark_client.submit(
cluster_id=args.cluster_id,
application = aztk.spark.models.Application(
name=args.name,
application=args.app,
application_args=args.app_args,
main_class=args.main_class,
jars=jars,
py_files=py_files,
files=files,
driver_java_options=args.driver_java_options,
driver_library_path=args.driver_library_path,
driver_class_path=args.driver_class_path,
driver_memory=args.driver_memory,
executor_memory=args.executor_memory,
driver_cores=args.driver_cores,
executor_cores=args.executor_cores,
max_retry_count=args.max_retry_count
),
wait=False
)
if args.wait:
utils.stream_logs(client=spark_client, cluster_id=args.cluster_id, application_name=args.name)
