def status(self, instance):
command = 'maprcli service list -node %s -json' % instance.fqdn()
with instance.remote() as remote:
ec, out = remote.execute_command(util._run_as('mapr', command))
node_processes = json.loads(out)['data']
for node_process in node_processes:
if node_process['name'] == self.name:
return Status.by_value(node_process['state'])
return Status.NOT_CONFIGURED
def status(self, instance):
command = 'maprcli service list -node %s -json' % instance.internal_ip
with instance.remote() as remote:
ec, out = remote.execute_command(util._run_as('mapr', command))
node_processes = json.loads(out)['data']
for node_process in node_processes:
if node_process['name'] == self.name:
return Status.by_value(node_process['state'])
return Status.NOT_CONFIGURED
def _get_start_command(self, cluster_context, run_as=None):
command_template = ("%(start_script)s 1 %(master_url)s"
" --webui-port %(web_ui_port)s")
args = {
"master_url": SPARK_MASTER.submit_url(cluster_context),
"start_script": self._get_start_script_path(cluster_context),
"web_ui_port": SPARK_SLAVE_UI_PORT,
}
return g._run_as(run_as, command_template % args)
def _get_start_command(self, cluster_context, run_as=None):
command_template = ("%(start_script)s 1 %(master_url)s"
" --webui-port %(web_ui_port)s")
args = {
"master_url": SPARK_MASTER.submit_url(cluster_context),
"start_script": self._get_start_script_path(cluster_context),
"web_ui_port": SPARK_SLAVE_UI_PORT,
}
return g._run_as(run_as, command_template % args)
def start(self, cluster_context, instances=None):
master_url = SPARK_MASTER.submit_url(cluster_context)
args = {
'spark_home': Spark().home_dir(cluster_context),
'start_slave': self._start_script,
'master_url': master_url,
}
command = g._run_as('mapr', '%(start_slave)s 1 %(master_url)s')
command = ('cd %(spark_home)s && ' + command) % args
g.execute_command(instances, command)
def start(self, cluster_context, instances=None):
master_url = SPARK_MASTER.submit_url(cluster_context)
args = {
'spark_home': Spark().home_dir(cluster_context),
'start_slave': self._start_script,
'master_url': master_url,
}
command = g._run_as('mapr', '%(start_slave)s 1 %(master_url)s')
command = ('cd %(spark_home)s && ' + command) % args
g.execute_command(instances, command)
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 _get_stop_command(self, cluster_context, run_as=None):
command_template = ("%(stop_script)s stop"
" org.apache.spark.deploy.worker.Worker 1")
args = {"stop_script": self._get_stop_script_path(cluster_context)}
return g._run_as(run_as, command_template % args)
def _get_stop_command(self, cluster_context, run_as=None):
command_template = ("%(stop_script)s stop"
" org.apache.spark.deploy.worker.Worker 1")
args = {"stop_script": self._get_stop_script_path(cluster_context)}
return g._run_as(run_as, command_template % args)
def chmod(remote, path, mode, recursive=True, run_as=None):
command = 'hadoop fs -chmod %(recursive)s %(mode)s %(path)s'
args = {'recursive': '-R' if recursive else '', 'path': path, 'mode': mode}
remote.execute_command(g._run_as(run_as, command % args))
def mkdir(remote, path, recursive=True, run_as=None):
command = 'hadoop fs -mkdir %(recursive)s %(path)s'
args = {'recursive': '-p' if recursive else '', 'path': path}
remote.execute_command(g._run_as(run_as, command % args))
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 chmod(remote, path, mode, recursive=True, run_as=None):
command = 'hadoop fs -chmod %(recursive)s %(mode)s %(path)s'
args = {'recursive': '-R' if recursive else '', 'path': path, 'mode': mode}
remote.execute_command(g._run_as(run_as, command % args))
def mkdir(remote, path, recursive=True, run_as=None):
command = 'hadoop fs -mkdir %(recursive)s %(path)s'
args = {'recursive': '-p' if recursive else '', 'path': path}
remote.execute_command(g._run_as(run_as, command % args))
