def test_create_workflow_dir(self, get_remote, uuid4):
job = mock.Mock()
job.name = "job"
# This is to mock "with remote.get_remote(instance) as r"
remote_instance = mock.Mock()
get_remote.return_value.__enter__ = mock.Mock(return_value=remote_instance)
remote_instance.execute_command = mock.Mock()
remote_instance.execute_command.return_value = 0, "standard out"
uuid4.return_value = "generated_uuid"
job_utils.create_workflow_dir("where", "/tmp/somewhere", job, "uuid")
remote_instance.execute_command.assert_called_with("mkdir -p /tmp/somewhere/job/uuid")
remote_instance.execute_command.reset_mock()
job_utils.create_workflow_dir("where", "/tmp/somewhere", job)
remote_instance.execute_command.assert_called_with("mkdir -p /tmp/somewhere/job/generated_uuid")
def test_create_workflow_dir(self, get_remote, uuid4):
job = mock.Mock()
job.name = "job"
# This is to mock "with remote.get_remote(instance) as r"
remote_instance = mock.Mock()
get_remote.return_value.__enter__ = mock.Mock(
return_value=remote_instance)
remote_instance.execute_command = mock.Mock()
remote_instance.execute_command.return_value = 0, "standard out"
uuid4.return_value = "generated_uuid"
job_utils.create_workflow_dir("where", "/tmp/somewhere", job, "uuid")
remote_instance.execute_command.assert_called_with(
"mkdir -p /tmp/somewhere/job/uuid")
remote_instance.execute_command.reset_mock()
job_utils.create_workflow_dir("where", "/tmp/somewhere", job)
remote_instance.execute_command.assert_called_with(
"mkdir -p /tmp/somewhere/job/generated_uuid")
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
input_source, output_source = job_utils.get_data_sources(job_execution,
job)
for data_source in [input_source, output_source]:
if data_source and data_source.type == 'hdfs':
h.configure_cluster_for_hdfs(self.cluster, data_source)
break
hdfs_user = self.plugin.get_hdfs_user()
# TODO(tmckay): this should probably be "get_namenode"
# but that call does not exist in the plugin api now.
# However, other engines may need it.
oozie_server = self.plugin.get_oozie_server(self.cluster)
wf_dir = job_utils.create_workflow_dir(oozie_server, job, hdfs_user)
job_utils.upload_job_files(oozie_server, wf_dir, job, hdfs_user)
wf_xml = workflow_factory.get_workflow_xml(
job, self.cluster, job_execution, input_source, output_source)
path_to_workflow = self._upload_workflow_file(oozie_server, wf_dir,
wf_xml, hdfs_user)
job_params = self._get_oozie_job_params(hdfs_user,
path_to_workflow)
client = self._get_client()
oozie_job_id = client.add_job(x.create_hadoop_xml(job_params),
job_execution)
client.run_job(job_execution, oozie_job_id)
return oozie_job_id
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
# This will be a dictionary of tuples, (native_url, runtime_url)
# keyed by data_source id
data_source_urls = {}
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs,
job_execution.id,
data_source_urls,
self.cluster)
)
job_execution = conductor.job_execution_update(
ctx, job_execution,
{"data_source_urls": job_utils.to_url_dict(data_source_urls)})
# Now that we've recorded the native urls, we can switch to the
# runtime urls
data_source_urls = job_utils.to_url_dict(data_source_urls,
runtime=True)
job_utils.prepare_cluster_for_ds(additional_sources,
self.cluster, updated_job_configs,
data_source_urls)
# It is needed in case we are working with Spark plugin
self.plugin_params['master'] = (
self.plugin_params['master'] % {'host': self.master.hostname()})
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(self.master, '/tmp/spark-edp',
job, job_execution.id, "700")
paths, builtin_paths = self._upload_job_files(
self.master, wf_dir, job, updated_job_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) if p.startswith(wf_dir) else p
for p in paths]
builtin_paths = [os.path.basename(p) for p in builtin_paths]
cmd = self._build_command(wf_dir, paths, builtin_paths,
updated_job_configs)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
# The redirects of stdout and stderr will preserve output in the wf_dir
with remote.get_remote(self.master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod u+rwx,g+rx,o+rx %s" % wf_dir)
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!"
% (wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + self.master.id,
edp.JOB_STATUS_RUNNING,
{'spark-path': wf_dir})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(_("Spark job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") %
{'status': ret, 'stdout': stdout})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
# This will be a dictionary of tuples, (native_url, runtime_url)
# keyed by data_source id
data_source_urls = {}
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs,
job_execution.id,
data_source_urls,
self.cluster)
)
job_execution = conductor.job_execution_update(
ctx, job_execution,
{"data_source_urls": job_utils.to_url_dict(data_source_urls)})
# Now that we've recorded the native urls, we can switch to the
# runtime urls
data_source_urls = job_utils.to_url_dict(data_source_urls,
runtime=True)
# We'll always run the driver program on the master
master = plugin_utils.get_instance(self.cluster, "nimbus")
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(master, '/tmp/storm-edp', job,
job_execution.id, "700")
paths = self._upload_job_files(master, wf_dir, job,
updated_job_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) for p in paths]
app_jar = paths.pop(0)
job_class = updated_job_configs["configs"]["edp.java.main_class"]
topology_name = self._generate_topology_name(job.name)
# Launch the storm job using storm jar
host = master.hostname()
args = updated_job_configs.get('args', [])
args = " ".join([arg for arg in args])
if args:
args = " " + args
cmd = (
'%(storm_jar)s -c nimbus.host=%(host)s %(job_jar)s '
'%(main_class)s %(topology_name)s%(args)s' % (
{
"storm_jar": "/usr/local/storm/bin/storm jar",
"main_class": job_class,
"job_jar": app_jar,
"host": host,
"topology_name": topology_name,
"args": args
}))
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
# The redirects of stdout and stderr will preserve output in the wf_dir
with remote.get_remote(master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!"
% (wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# topology_name@instance_id as the job id
# We know the job is running so return "RUNNING"
return (topology_name + "@" + master.id,
edp.JOB_STATUS_RUNNING,
{'storm-path': wf_dir})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(_("Storm job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") %
{'status': ret, 'stdout': stdout})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(
job_execution.job_configs))
# We'll always run the driver program on the master
master = plugin_utils.get_instance(self.cluster,
spark.SPARK_MASTER.ui_name)
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(master, '/tmp/spark-edp', job,
job_execution.id, "700")
paths, builtin_paths = self._upload_job_files(master, wf_dir, job,
updated_job_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) for p in paths]
builtin_paths = [os.path.basename(p) for p in builtin_paths]
# TODO(tmckay): for now, paths[0] is always assumed to be the app
# jar and we generate paths in order (mains, then libs).
# When we have a Spark job type, we can require a "main" and set
# the app jar explicitly to be "main"
app_jar = paths.pop(0)
job_class = updated_job_configs["configs"]["edp.java.main_class"]
# If we uploaded builtins then we are using a wrapper jar. It will
# be the first one on the builtin list and the original app_jar needs
# to be added to the 'additional' jars
if builtin_paths:
wrapper_jar = builtin_paths.pop(0)
wrapper_class = 'org.openstack.sahara.edp.SparkWrapper'
wrapper_xml = self._upload_wrapper_xml(master, wf_dir,
updated_job_configs)
wrapper_args = "%s %s" % (wrapper_xml, job_class)
additional_jars = ",".join([app_jar] + paths + builtin_paths)
else:
wrapper_jar = wrapper_class = wrapper_args = ""
additional_jars = ",".join(paths)
# All additional jars are passed with the --jars option
if additional_jars:
additional_jars = " --jars " + additional_jars
# Launch the spark job using spark-submit and deploy_mode = client
cluster_context = self._get_cluster_context(self.cluster)
spark_home_dir = spark.Spark().home_dir(cluster_context)
# TODO(tmckay): we need to clean up wf_dirs on long running clusters
# TODO(tmckay): probably allow for general options to spark-submit
args = updated_job_configs.get('args', [])
args = " ".join([su.inject_swift_url_suffix(arg) for arg in args])
submit_args = {
"spark_submit": "%s/bin/spark-submit" % spark_home_dir,
"addnl_jars": additional_jars,
"master_url": spark.SPARK_MASTER.submit_url(cluster_context),
"args": args
}
if wrapper_jar and wrapper_class:
# Substrings which may be empty have spaces
# embedded if they are non-empty
submit_args.update({
"driver_cp": self.get_driver_classpath(),
"wrapper_class": wrapper_class,
"wrapper_jar": wrapper_jar,
"wrapper_args": wrapper_args,
})
submit_cmd = ('%(spark_submit)s%(driver_cp)s'
' --class %(wrapper_class)s%(addnl_jars)s'
' --master %(master_url)s'
' %(wrapper_jar)s %(wrapper_args)s %(args)s')
else:
submit_args.update({
"job_class": job_class,
"app_jar": app_jar,
})
submit_cmd = ('%(spark_submit)s --class %(job_class)s'
'%(addnl_jars)s --master %(master_url)s'
' %(app_jar)s %(args)s')
submit_cmd = g._run_as('mapr', submit_cmd % submit_args)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
# The redirects of stdout and stderr will preserve output in the wf_dir
with master.remote() as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s && ./launch_command %s > /dev/null 2>&1 & echo $!" %
(wf_dir, submit_cmd),
raise_when_error=False)
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + master.id, edp.JOB_STATUS_RUNNING, {
'spark-path': wf_dir
})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(
_("Spark job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") % {
'status': ret,
'stdout': stdout
})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
indep_params = {}
data_source_urls = {}
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(
job_execution.job_configs, job_execution.id, data_source_urls)
)
job_execution = conductor.job_execution_update(
ctx, job_execution, {"data_source_urls": data_source_urls})
for data_source in additional_sources:
if data_source and data_source.type == 'hdfs':
h.configure_cluster_for_hdfs(self.cluster, data_source)
break
# It is needed in case we are working with Spark plugin
self.plugin_params['master'] = (
self.plugin_params['master'] % {'host': self.master.hostname()})
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(self.master, '/tmp/spark-edp',
job, job_execution.id, "700")
paths, builtin_paths = self._upload_job_files(
self.master, wf_dir, job, updated_job_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) for p in paths]
builtin_paths = [os.path.basename(p) for p in builtin_paths]
# TODO(tmckay): for now, paths[0] is always assumed to be the app
# jar and we generate paths in order (mains, then libs).
# When we have a Spark job type, we can require a "main" and set
# the app jar explicitly to be "main"
indep_params["app_jar"] = paths.pop(0)
indep_params["job_class"] = (
updated_job_configs["configs"]["edp.java.main_class"])
# If we uploaded builtins then we are using a wrapper jar. It will
# be the first one on the builtin list and the original app_jar needs
# to be added to the 'additional' jars
if builtin_paths:
indep_params["wrapper_jar"] = builtin_paths.pop(0)
indep_params["wrapper_class"] = (
'org.openstack.sahara.edp.SparkWrapper')
wrapper_xml = self._upload_wrapper_xml(self.master,
wf_dir,
updated_job_configs)
indep_params["wrapper_args"] = "%s %s" % (
wrapper_xml, indep_params["job_class"])
indep_params["addnl_jars"] = ",".join(
[indep_params["app_jar"]] + paths + builtin_paths)
else:
indep_params["addnl_jars"] = ",".join(paths)
# All additional jars are passed with the --jars option
if indep_params["addnl_jars"]:
indep_params["addnl_jars"] = (
" --jars " + indep_params["addnl_jars"])
# Launch the spark job using spark-submit and deploy_mode = client
# TODO(tmckay): we need to clean up wf_dirs on long running clusters
# TODO(tmckay): probably allow for general options to spark-submit
indep_params["args"] = updated_job_configs.get('args', [])
indep_params["args"] = " ".join([su.inject_swift_url_suffix(arg)
for arg in indep_params["args"]])
if indep_params.get("args"):
indep_params["args"] = (" " + indep_params["args"])
mutual_dict = self.plugin_params.copy()
mutual_dict.update(indep_params)
if mutual_dict.get("wrapper_jar"):
# Substrings which may be empty have spaces
# embedded if they are non-empty
cmd = (
'%(spark-user)s%(spark-submit)s%(driver-class-path)s'
' --class %(wrapper_class)s%(addnl_jars)s'
' --master %(master)s'
' --deploy-mode %(deploy-mode)s'
' %(wrapper_jar)s %(wrapper_args)s%(args)s') % dict(
mutual_dict)
else:
cmd = (
'%(spark-user)s%(spark-submit)s'
' --class %(job_class)s%(addnl_jars)s'
' --master %(master)s'
' --deploy-mode %(deploy-mode)s'
' %(app_jar)s%(args)s') % dict(
mutual_dict)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
# The redirects of stdout and stderr will preserve output in the wf_dir
with remote.get_remote(self.master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod u+rwx,g+rx,o+rx %s" % wf_dir)
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!"
% (wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + self.master.id,
edp.JOB_STATUS_RUNNING,
{'spark-path': wf_dir})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(_("Spark job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") %
{'status': ret, 'stdout': stdout})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs)
)
for data_source in additional_sources:
if data_source and data_source.type == 'hdfs':
h.configure_cluster_for_hdfs(self.cluster, data_source)
break
# We'll always run the driver program on the master
master = plugin_utils.get_instance(self.cluster, "master")
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(master, '/tmp/spark-edp', job,
job_execution.id, "700")
paths, builtin_paths = self._upload_job_files(
master, wf_dir, job, updated_job_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) for p in paths]
builtin_paths = [os.path.basename(p) for p in builtin_paths]
# TODO(tmckay): for now, paths[0] is always assumed to be the app
# jar and we generate paths in order (mains, then libs).
# When we have a Spark job type, we can require a "main" and set
# the app jar explicitly to be "main"
app_jar = paths.pop(0)
job_class = updated_job_configs["configs"]["edp.java.main_class"]
# If we uploaded builtins then we are using a wrapper jar. It will
# be the first one on the builtin list and the original app_jar needs
# to be added to the 'additional' jars
if builtin_paths:
wrapper_jar = builtin_paths.pop(0)
wrapper_class = 'org.openstack.sahara.edp.SparkWrapper'
wrapper_xml = self._upload_wrapper_xml(master,
wf_dir,
updated_job_configs)
wrapper_args = "%s %s" % (wrapper_xml, job_class)
additional_jars = ",".join([app_jar] + paths + builtin_paths)
else:
wrapper_jar = wrapper_class = wrapper_args = ""
additional_jars = ",".join(paths)
# All additional jars are passed with the --jars option
if additional_jars:
additional_jars = " --jars " + additional_jars
# Launch the spark job using spark-submit and deploy_mode = client
host = master.hostname()
port = c_helper.get_config_value("Spark", "Master port", self.cluster)
spark_submit = os.path.join(
c_helper.get_config_value("Spark",
"Spark home",
self.cluster),
"bin/spark-submit")
# TODO(tmckay): we need to clean up wf_dirs on long running clusters
# TODO(tmckay): probably allow for general options to spark-submit
args = updated_job_configs.get('args', [])
args = " ".join([su.inject_swift_url_suffix(arg) for arg in args])
if args:
args = " " + args
if wrapper_jar and wrapper_class:
# Substrings which may be empty have spaces
# embedded if they are non-empty
cmd = (
'%(spark_submit)s%(driver_cp)s'
' --class %(wrapper_class)s%(addnl_jars)s'
' --master spark://%(host)s:%(port)s'
' %(wrapper_jar)s %(wrapper_args)s%(args)s') % (
{
"spark_submit": spark_submit,
"driver_cp": self.get_driver_classpath(),
"wrapper_class": wrapper_class,
"addnl_jars": additional_jars,
"host": host,
"port": port,
"wrapper_jar": wrapper_jar,
"wrapper_args": wrapper_args,
"args": args
})
else:
cmd = (
'%(spark_submit)s --class %(job_class)s%(addnl_jars)s'
' --master spark://%(host)s:%(port)s %(app_jar)s%(args)s') % (
{
"spark_submit": spark_submit,
"job_class": job_class,
"addnl_jars": additional_jars,
"host": host,
"port": port,
"app_jar": app_jar,
"args": args
})
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
# The redirects of stdout and stderr will preserve output in the wf_dir
with remote.get_remote(master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!"
% (wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + master.id,
edp.JOB_STATUS_RUNNING,
{'spark-path': wf_dir})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(_("Spark job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") %
{'status': ret, 'stdout': stdout})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
indep_params = {}
data_source_urls = {}
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs,
job_execution.id,
data_source_urls))
job_execution = conductor.job_execution_update(
ctx, job_execution, {"data_source_urls": data_source_urls})
for data_source in additional_sources:
if data_source and data_source.type == 'hdfs':
h.configure_cluster_for_hdfs(self.cluster, data_source)
break
# It is needed in case we are working with Spark plugin
self.plugin_params['master'] = (self.plugin_params['master'] % {
'host': self.master.hostname()
})
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(self.master, '/tmp/spark-edp',
job, job_execution.id, "700")
paths, builtin_paths = self._upload_job_files(self.master, wf_dir, job,
updated_job_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) for p in paths]
builtin_paths = [os.path.basename(p) for p in builtin_paths]
# TODO(tmckay): for now, paths[0] is always assumed to be the app
# jar and we generate paths in order (mains, then libs).
# When we have a Spark job type, we can require a "main" and set
# the app jar explicitly to be "main"
indep_params["app_jar"] = paths.pop(0)
indep_params["job_class"] = (
updated_job_configs["configs"]["edp.java.main_class"])
# If we uploaded builtins then we are using a wrapper jar. It will
# be the first one on the builtin list and the original app_jar needs
# to be added to the 'additional' jars
if builtin_paths:
indep_params["wrapper_jar"] = builtin_paths.pop(0)
indep_params["wrapper_class"] = (
'org.openstack.sahara.edp.SparkWrapper')
wrapper_xml = self._upload_wrapper_xml(self.master, wf_dir,
updated_job_configs)
indep_params["wrapper_args"] = "%s %s" % (
wrapper_xml, indep_params["job_class"])
indep_params["addnl_files"] = wrapper_xml
indep_params["addnl_jars"] = ",".join(
[indep_params["wrapper_jar"]] + paths + builtin_paths)
else:
indep_params["addnl_jars"] = ",".join(paths)
# All additional jars are passed with the --jars option
if indep_params["addnl_jars"]:
indep_params["addnl_jars"] = (" --jars " +
indep_params["addnl_jars"])
# Launch the spark job using spark-submit and deploy_mode = client
# TODO(tmckay): we need to clean up wf_dirs on long running clusters
# TODO(tmckay): probably allow for general options to spark-submit
indep_params["args"] = updated_job_configs.get('args', [])
indep_params["args"] = " ".join(
[su.inject_swift_url_suffix(arg) for arg in indep_params["args"]])
if indep_params.get("args"):
indep_params["args"] = (" " + indep_params["args"])
mutual_dict = self.plugin_params.copy()
mutual_dict.update(indep_params)
if mutual_dict.get("wrapper_jar"):
# Substrings which may be empty have spaces
# embedded if they are non-empty
cmd = ('%(spark-user)s%(spark-submit)s%(driver-class-path)s'
' --files %(addnl_files)s'
' --class %(wrapper_class)s%(addnl_jars)s'
' --master %(master)s'
' --deploy-mode %(deploy-mode)s'
' %(app_jar)s %(wrapper_args)s%(args)s') % dict(mutual_dict)
else:
cmd = ('%(spark-user)s%(spark-submit)s'
' --class %(job_class)s%(addnl_jars)s'
' --master %(master)s'
' --deploy-mode %(deploy-mode)s'
' %(app_jar)s%(args)s') % dict(mutual_dict)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
# The redirects of stdout and stderr will preserve output in the wf_dir
with remote.get_remote(self.master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod u+rwx,g+rx,o+rx %s" % wf_dir)
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!" %
(wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + self.master.id,
edp.JOB_STATUS_RUNNING, {
'spark-path': wf_dir
})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(
_("Spark job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") % {
'status': ret,
'stdout': stdout
})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
# This will be a dictionary of tuples, (native_url, runtime_url)
# keyed by data_source id
data_source_urls = {}
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs,
job_execution.id,
data_source_urls,
self.cluster)
)
job_execution = conductor.job_execution_update(
ctx, job_execution,
{"data_source_urls": job_utils.to_url_dict(data_source_urls)})
# Now that we've recorded the native urls, we can switch to the
# runtime urls
data_source_urls = job_utils.to_url_dict(data_source_urls,
runtime=True)
for data_source in additional_sources:
if data_source and data_source.type == 'hdfs':
h.configure_cluster_for_hdfs(self.cluster, data_source)
break
# It is needed in case we are working with Spark plugin
self.plugin_params['master'] = (
self.plugin_params['master'] % {'host': self.master.hostname()})
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(self.master, '/tmp/spark-edp',
job, job_execution.id, "700")
paths, builtin_paths = self._upload_job_files(
self.master, wf_dir, job, updated_job_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) if p.startswith(wf_dir) else p
for p in paths]
builtin_paths = [os.path.basename(p) for p in builtin_paths]
cmd = self._build_command(wf_dir, paths, builtin_paths,
updated_job_configs)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
# The redirects of stdout and stderr will preserve output in the wf_dir
with remote.get_remote(self.master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod u+rwx,g+rx,o+rx %s" % wf_dir)
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!"
% (wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + self.master.id,
edp.JOB_STATUS_RUNNING,
{'spark-path': wf_dir})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(_("Spark job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") %
{'status': ret, 'stdout': stdout})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
# This will be a dictionary of tuples, (native_url, runtime_url)
# keyed by data_source id
data_source_urls = {}
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs,
job_execution.id,
data_source_urls,
self.cluster))
job_execution = conductor.job_execution_update(
ctx, job_execution,
{"data_source_urls": job_utils.to_url_dict(data_source_urls)})
# Now that we've recorded the native urls, we can switch to the
# runtime urls
data_source_urls = job_utils.to_url_dict(data_source_urls,
runtime=True)
job_utils.prepare_cluster_for_ds(additional_sources, self.cluster,
updated_job_configs, data_source_urls)
# We'll always run the driver program on the master
master = plugin_utils.get_instance(self.cluster, "nimbus")
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(master, '/tmp/storm-edp', job,
job_execution.id, "700")
paths = self._upload_job_files(master, wf_dir, job,
updated_job_configs)
topology_name = self._set_topology_name(job_execution, job.name)
# Launch the storm job using storm jar
host = master.hostname()
cmd = self._build_command(paths, updated_job_configs, host,
topology_name)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
ret, stdout = self._execute_remote_job(master, wf_dir, cmd)
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# topology_name@instance_id as the job id
# We know the job is running so return "RUNNING"
return (topology_name + "@" + master.id, edp.JOB_STATUS_RUNNING, {
'storm-path': wf_dir
})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(
_("Storm job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") % {
'status': ret,
'stdout': stdout
})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
proxy_configs = job_execution.job_configs.get('proxy_configs')
# We'll always run the driver program on the master
master = plugin_utils.get_instance(self.cluster, "master")
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(master, '/tmp/spark-edp', job,
job_execution.id)
paths = job_utils.upload_job_files(master,
wf_dir,
job,
libs_subdir=False,
proxy_configs=proxy_configs)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) for p in paths]
# TODO(tmckay): for now, paths[0] is always assumed to be the app
# jar and we generate paths in order (mains, then libs).
# When we have a Spark job type, we can require a "main" and set
# the app jar explicitly to be "main"
app_jar = paths.pop(0)
# The rest of the paths will be passed with --jars
additional_jars = ",".join(paths)
if additional_jars:
additional_jars = "--jars " + additional_jars
# Launch the spark job using spark-submit and deploy_mode = client
host = master.hostname()
port = c_helper.get_config_value("Spark", "Master port", self.cluster)
spark_submit = os.path.join(
c_helper.get_config_value("Spark", "Spark home", self.cluster),
"bin/spark-submit")
job_class = job_execution.job_configs.configs["edp.java.main_class"]
# TODO(tmckay): we need to clean up wf_dirs on long running clusters
# TODO(tmckay): probably allow for general options to spark-submit
args = " ".join(job_execution.job_configs.get('args', []))
# The redirects of stdout and stderr will preserve output in the wf_dir
cmd = "%s %s --class %s %s --master spark://%s:%s %s" % (
spark_submit, app_jar, job_class, additional_jars, host, port,
args)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
with remote.get_remote(master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!" %
(wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + master.id, edp.JOB_STATUS_RUNNING, {
'spark-path': wf_dir
})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(
_("Spark job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") % {
'status': ret,
'stdout': stdout
})
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
# We'll always run the driver program on the master
master = plugin_utils.get_instance(self.cluster, "master")
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(master, '/tmp/spark-edp', job,
job_execution.id)
paths = job_utils.upload_job_files(master, wf_dir, job,
libs_subdir=False)
# We can shorten the paths in this case since we'll run out of wf_dir
paths = [os.path.basename(p) for p in paths]
# TODO(tmckay): for now, paths[0] is always assumed to be the app
# jar and we generate paths in order (mains, then libs).
# When we have a Spark job type, we can require a "main" and set
# the app jar explicitly to be "main"
app_jar = paths.pop(0)
# The rest of the paths will be passed with --jars
additional_jars = ",".join(paths)
if additional_jars:
additional_jars = "--jars " + additional_jars
# Launch the spark job using spark-submit and deploy_mode = client
host = master.hostname()
port = c_helper.get_config_value("Spark", "Master port", self.cluster)
spark_submit = os.path.join(
c_helper.get_config_value("Spark",
"Spark home",
self.cluster),
"bin/spark-submit")
job_class = job_execution.job_configs.configs["edp.java.main_class"]
# TODO(tmckay): we need to clean up wf_dirs on long running clusters
# TODO(tmckay): probably allow for general options to spark-submit
args = " ".join(job_execution.job_configs.get('args', []))
# The redirects of stdout and stderr will preserve output in the wf_dir
cmd = "%s %s --class %s %s --master spark://%s:%s %s" % (
spark_submit,
app_jar,
job_class,
additional_jars,
host,
port,
args)
# If an exception is raised here, the job_manager will mark
# the job failed and log the exception
with remote.get_remote(master) as r:
# Upload the command launch script
launch = os.path.join(wf_dir, "launch_command")
r.write_file_to(launch, self._job_script())
r.execute_command("chmod +x %s" % launch)
ret, stdout = r.execute_command(
"cd %s; ./launch_command %s > /dev/null 2>&1 & echo $!"
% (wf_dir, cmd))
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# pid@instance_id as the job id
# We know the job is running so return "RUNNING"
return (stdout.strip() + "@" + master.id,
edp.JOB_STATUS_RUNNING,
{'spark-path': wf_dir})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError("Spark job execution failed. Exit status = %s, "
"stdout = %s" % (ret, stdout))
def run_job(self, job_execution):
ctx = context.ctx()
job = conductor.job_get(ctx, job_execution.job_id)
# This will be a dictionary of tuples, (native_url, runtime_url)
# keyed by data_source id
data_source_urls = {}
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs,
job_execution.id,
data_source_urls,
self.cluster)
)
job_execution = conductor.job_execution_update(
ctx, job_execution,
{"data_source_urls": job_utils.to_url_dict(data_source_urls)})
# Now that we've recorded the native urls, we can switch to the
# runtime urls
data_source_urls = job_utils.to_url_dict(data_source_urls,
runtime=True)
job_utils.prepare_cluster_for_ds(additional_sources,
self.cluster, updated_job_configs,
data_source_urls)
# We'll always run the driver program on the master
master = plugin_utils.get_instance(self.cluster, "nimbus")
# TODO(tmckay): wf_dir should probably be configurable.
# The only requirement is that the dir is writable by the image user
wf_dir = job_utils.create_workflow_dir(master, '/tmp/storm-edp', job,
job_execution.id, "700")
paths = self._upload_job_files(master, wf_dir, job,
updated_job_configs)
topology_name = self._set_topology_name(job_execution, job.name)
# Launch the storm job using storm jar
host = master.hostname()
cmd = self._build_command(paths, updated_job_configs, host,
topology_name)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
ret, stdout = self._execute_remote_job(master, wf_dir, cmd)
if ret == 0:
# Success, we'll add the wf_dir in job_execution.extra and store
# topology_name@instance_id as the job id
# We know the job is running so return "RUNNING"
return (topology_name + "@" + master.id,
edp.JOB_STATUS_RUNNING,
{'storm-path': wf_dir})
# Hmm, no execption but something failed.
# Since we're using backgrounding with redirect, this is unlikely.
raise e.EDPError(_("Storm job execution failed. Exit status = "
"%(status)s, stdout = %(stdout)s") %
{'status': ret, 'stdout': stdout})
