def _default_opts(self):
return combine_dicts(
super(HadoopJobRunner, self)._default_opts(),
dict(
hadoop_tmp_dir='tmp/mrjob',
)
)
def _default_opts(self):
return combine_dicts(
super(MRJobBinRunner, self)._default_opts(),
dict(
read_logs=True,
)
)
def _add_runner_args_for_opt(parser, opt_name, include_deprecated=True):
"""Add switches for a single option (*opt_name*) to the given parser."""
conf = _RUNNER_OPTS[opt_name]
if conf.get('deprecated') and not include_deprecated:
return
switches = conf.get('switches') or []
for args, kwargs in switches:
kwargs = dict(kwargs)
deprecated_aliases = kwargs.pop('deprecated_aliases', None)
kwargs['dest'] = opt_name
if kwargs.get('action') == 'append':
kwargs['default'] = []
else:
kwargs['default'] = None
parser.add_argument(*args, **kwargs)
# add a switch for deprecated aliases
if deprecated_aliases and include_deprecated:
help = 'Deprecated alias%s for %s' % (
('es' if len(deprecated_aliases) > 1 else ''),
args[-1])
parser.add_argument(
*deprecated_aliases,
**combine_dicts(kwargs, dict(help=help)))
def _default_opts(self):
return combine_dicts(
super(MRJobBinRunner, self)._default_opts(),
dict(
sh_bin=['sh', '-ex'],
)
)
def default_options(self):
super_opts = super(HadoopRunnerOptionStore, self).default_options()
return combine_dicts(super_opts, {
'hadoop_home': os.environ.get('HADOOP_HOME'),
'hdfs_scratch_dir': 'tmp/mrjob',
'check_input_paths': True
})
def jobconf(self):
orig_jobconf = super(ZipNumClusterJob, self).jobconf()
custom_jobconf = {'mapreduce.job.reduces': self.options.shards,
'mapreduce.totalorderpartitioner.path': self.options.splitfile}
combined = combine_dicts(orig_jobconf, custom_jobconf)
return combined
def _default_opts(self):
return combine_dicts(
super(SparkMRJobRunner, self)._default_opts(),
dict(
cloud_part_size_mb=_DEFAULT_CLOUD_PART_SIZE_MB,
),
)
def _opts_combiners(cls):
"""Map from option name to a combine_*() function used to combine
values for that option. This allows us to specify that some options
are lists, or contain environment variables, or whatever."""
return combine_dicts(
super(HadoopJobRunner, cls)._opts_combiners(),
{"hadoop_bin": combine_paths, "hadoop_home": combine_paths, "hdfs_scratch_dir": combine_paths},
)
def test_hadoop_1(self):
updated, warnings = self.updated_and_warnings(
self.JOBCONF, '1.0')
self.assertEqual(updated,
combine_dicts(self.JOBCONF, {'user.name': 'dave'}))
self.assertIn('do not match hadoop version', warnings)
self.assertIn('mapreduce.job.user.name: user.name', warnings)
def paginate(self, **kwargs):
result = self.method(**kwargs)
values = result[self.result_key]
for page_start in range(0, len(values), self.page_size):
page = values[page_start:page_start + self.page_size]
yield combine_dicts(result, {self.result_key: page})
def _job_runner_kwargs_for_runner(self, runner_alias):
"""Helper method that powers the *_job_runner_kwargs()
methods."""
# user can no longer silently ignore switches by overriding
# job_runner_kwargs()
return combine_dicts(
self._kwargs_from_switches(_allowed_keys(runner_alias)),
self.job_runner_kwargs(),
)
def test_hadoop_2(self):
updated, warnings = self.updated_and_warnings(
self.JOBCONF, '2.0')
self.assertEqual(updated,
combine_dicts(self.JOBCONF,
{'mapreduce.job.jar': 'a.jar'}))
self.assertIn('do not match hadoop version', warnings)
self.assertIn('mapred.jar: mapreduce.job.jar', warnings)
def _runner_kwargs(self):
# just use combine_dicts() and not combine_confs(); leave the
# magic to the runner
return combine_dicts(
self._non_option_kwargs(),
# don't screen out irrelevant opts (see #1898)
self._kwargs_from_switches(set(_RUNNER_OPTS)),
self._job_kwargs(),
)
def _default_opts(self):
return combine_dicts(
super(HadoopJobRunner, self)._default_opts(),
dict(
hadoop_tmp_dir='tmp/mrjob',
spark_deploy_mode='client',
spark_master='yarn',
)
)
def test_can_override_sort_values_from_job(self):
mr_job = MRSortValuesAndMore()
self.assertEqual(
mr_job.partitioner(),
'org.apache.hadoop.mapred.lib.HashPartitioner')
self.assertEqual(
mr_job.jobconf(),
combine_dicts(_SORT_VALUES_JOBCONF, MRSortValuesAndMore.JOBCONF))
def _jobconf_for_step(self, step_num):
"""Get the jobconf dictionary, optionally including step-specific
jobconf info.
Also translate jobconfs to the current Hadoop version, if necessary.
"""
step = self._get_step(step_num)
jobconf = combine_dicts(self._opts["jobconf"], step.get("jobconf"))
return add_translated_jobconf_for_hadoop_version(jobconf, self.get_hadoop_version())
def hadoop_job_runner_kwargs(self):
"""Keyword arguments to create create runners when
:py:meth:`make_runner` is called, when we run a job on EMR
(``-r hadoop``).
:return: map from arg name to value
Re-define this if you want finer control when running jobs on hadoop.
"""
return combine_dicts(self.job_runner_kwargs(), self._get_kwargs_from_opt_group(self.hadoop_opt_group))
def _hadoop_conf_args(self, step, step_num, num_steps):
"""Build a list of extra arguments to the hadoop binary.
This handles *cmdenv*, *hadoop_extra_args*, *hadoop_input_format*,
*hadoop_output_format*, *jobconf*, and *partitioner*.
This doesn't handle input, output, mappers, reducers, or uploading
files.
"""
assert 0 <= step_num < num_steps
args = []
jobconf = combine_dicts(self._opts['jobconf'], step.get('jobconf'))
# hadoop_extra_args
args.extend(self._opts['hadoop_extra_args'])
# new-style jobconf
version = self.get_hadoop_version()
# translate the jobconf configuration names to match
# the hadoop version
jobconf = add_translated_jobconf_for_hadoop_version(jobconf,
version)
if uses_generic_jobconf(version):
for key, value in sorted(jobconf.iteritems()):
if value is not None:
args.extend(['-D', '%s=%s' % (key, value)])
# old-style jobconf
else:
for key, value in sorted(jobconf.iteritems()):
if value is not None:
args.extend(['-jobconf', '%s=%s' % (key, value)])
# partitioner
if self._partitioner:
args.extend(['-partitioner', self._partitioner])
# cmdenv
for key, value in sorted(self._opts['cmdenv'].iteritems()):
args.append('-cmdenv')
args.append('%s=%s' % (key, value))
# hadoop_input_format
if (step_num == 0 and self._hadoop_input_format):
args.extend(['-inputformat', self._hadoop_input_format])
# hadoop_output_format
if (step_num == num_steps - 1 and self._hadoop_output_format):
args.extend(['-outputformat', self._hadoop_output_format])
return args
def test_no_special_logic_for_paths(self):
assert_equal(combine_dicts(
{'PATH': '/bin:/usr/bin',
'PYTHONPATH': '/usr/lib/python/site-packages',
'PS1': '> '},
{'PATH': '/home/dave/bin',
'PYTHONPATH': '/home/dave/python',
'CLASSPATH': '/home/dave/java',
'PS1': '\w> '}),
{'PATH': '/home/dave/bin',
'PYTHONPATH': '/home/dave/python',
'CLASSPATH': '/home/dave/java',
'PS1': '\w> '})
def _jobconf_for_step(self, step_num):
"""Get the jobconf dictionary, optionally including step-specific
jobconf info.
Also translate jobconfs to the current Hadoop version, if necessary.
"""
step = self._get_step(step_num)
jobconf = combine_dicts(self._opts["jobconf"], step.get("jobconf"))
# if user is using the wrong jobconfs, add in the correct ones
self._update_jobconf_for_hadoop_version(jobconf, self.get_hadoop_version())
return jobconf
def test_can_override_sort_values_from_cmd_line(self):
mr_job = MRSortValues(
['--partitioner', 'org.pants.FancyPantsPartitioner',
'--jobconf', 'stream.num.map.output.key.fields=lots'])
self.assertEqual(
mr_job.partitioner(),
'org.pants.FancyPantsPartitioner')
self.assertEqual(
mr_job.jobconf(),
combine_dicts(_SORT_VALUES_JOBCONF,
{'stream.num.map.output.key.fields': 'lots'}))
def _default_opts(self):
return combine_dicts(
super(DataprocJobRunner, self)._default_opts(),
dict(
bootstrap_python=True,
check_cluster_every=_DEFAULT_CHECK_CLUSTER_EVERY,
cleanup=['CLUSTER', 'JOB', 'LOCAL_TMP'],
cloud_fs_sync_secs=_DEFAULT_CLOUD_FS_SYNC_SECS,
image_version=_DEFAULT_IMAGE_VERSION,
instance_type=_DEFAULT_INSTANCE_TYPE,
master_instance_type=_DEFAULT_INSTANCE_TYPE,
num_core_instances=_DATAPROC_MIN_WORKERS,
num_task_instances=0,
)
)
def emr_job_runner_kwargs(self):
args = super(DownloadToS3, self).emr_job_runner_kwargs()
# set up AWS credentials on EMR instances
access_key = os.environ['AWS_ACCESS_KEY_ID']
secret = os.environ['AWS_SECRET_ACCESS_KEY']
args['cmdenv'] = combine_dicts(args['cmdenv'], {'AWS_ACCESS_KEY_ID': access_key, 'AWS_SECRET_ACCESS_KEY': secret})
# install pip, aws-cli, and boto
args['bootstrap_cmds'] = combine_lists(args['bootstrap_cmds'],
['sysctl -w "net.ipv4.tcp_window_scaling=0"',
'sudo apt-get install python-pip',
'sudo pip install awscli',
'sudo pip install boto'])
return args
def _default_opts(self):
return combine_dicts(
super(HadoopInTheCloudJobRunner, self)._default_opts(),
dict(
cloud_part_size_mb=100, # 100 MB
max_mins_idle=_DEFAULT_MAX_MINS_IDLE,
# don't use a list because it makes it hard to read option
# values when running in verbose mode. See #1284
ssh_bind_ports=xrange(40001, 40841),
ssh_tunnel=False,
ssh_tunnel_is_open=False,
# ssh_bin isn't included here. For example, the Dataproc
# runner launches ssh through the gcloud util
),
)
def default_options(self):
super_opts = super(RunnerOptionStore, self).default_options()
try:
owner = getpass.getuser()
except:
owner = None
return combine_dicts(super_opts, {
'base_tmp_dir': tempfile.gettempdir(),
'bootstrap_mrjob': True,
'cleanup': ['ALL'],
'cleanup_on_failure': ['NONE'],
'hadoop_version': '0.20',
'owner': owner,
})
def job_runner_kwargs(self):
"""Keyword arguments used to create runners when
:py:meth:`make_runner` is called.
:return: map from arg name to value
Re-define this if you want finer control of runner initialization.
You might find :py:meth:`mrjob.conf.combine_dicts` useful if you
want to add or change lots of keyword arguments.
"""
return combine_dicts(
self._non_option_kwargs(),
self._kwargs_from_switches(_allowed_keys('base')),
self._job_kwargs(),
)
def default_options(self):
super_opts = super(RunnerOptionStore, self).default_options()
try:
owner = getpass.getuser()
except:
owner = None
return combine_dicts(super_opts, {
'check_input_paths': True,
'cleanup': ['ALL'],
'cleanup_on_failure': ['NONE'],
'local_tmp_dir': tempfile.gettempdir(),
'owner': owner,
'sh_bin': ['sh', '-ex'],
'strict_protocols': True,
})
def test_no_special_logic_for_paths(self):
self.assertEqual(
combine_dicts(
{"PATH": "/bin:/usr/bin", "PYTHONPATH": "/usr/lib/python/site-packages", "PS1": "> "},
{
"PATH": "/home/dave/bin",
"PYTHONPATH": "/home/dave/python",
"CLASSPATH": "/home/dave/java",
"PS1": "\w> ",
},
),
{
"PATH": "/home/dave/bin",
"PYTHONPATH": "/home/dave/python",
"CLASSPATH": "/home/dave/java",
"PS1": "\w> ",
},
)
def jobconf(self):
"""``-D`` args to pass to hadoop streaming. This should be a map
from property name to value.
By default, this combines :option:`jobconf` options from the command
lines with :py:attr:`JOBCONF`, with command line arguments taking
precedence.
We also blank out ``mapred.output.key.comparator.class``
and ``mapred.text.key.comparator.options`` to prevent interference
from :file:`mrjob.conf`.
:py:attr:`SORT_VALUES` *can* be overridden by :py:attr:`JOBCONF`, the
command line, and step-specific ``jobconf`` values.
For example, if you know your values are numbers, and want to sort
them in reverse, you could do::
SORT_VALUES = True
JOBCONF = {
'mapred.output.key.comparator.class':
'org.apache.hadoop.mapred.lib.KeyFieldBasedComparator',
'mapred.text.key.comparator.options': '-k1 -k2nr',
}
If you want to re-define this, it's strongly recommended that do
something like this, so as not to inadvertently disable
the :option:`jobconf` option::
def jobconf(self):
orig_jobconf = super(MyMRJobClass, self).jobconf()
custom_jobconf = ...
return mrjob.conf.combine_dicts(orig_jobconf, custom_jobconf)
"""
# combine job and runner jobconf
unfiltered_jobconf = combine_dicts(self.JOBCONF, self.options.jobconf)
# turn booleans into the Java equivalent ("false", not "False")
return {
k: json.dumps(v) if not isinstance(v, string_types) else v
for k, v in unfiltered_jobconf.items() if v is not None
}
def default_options(self):
super_opts = super(RunnerOptionStore, self).default_options()
try:
owner = getpass.getuser()
except:
owner = None
return combine_dicts(
super_opts,
{
"check_input_paths": True,
"cleanup": ["ALL"],
"cleanup_on_failure": ["NONE"],
"local_tmp_dir": tempfile.gettempdir(),
"owner": owner,
"sh_bin": ["sh", "-ex"],
"strict_protocols": True,
},
)
def _default_opts(self):
return combine_dicts(
super(SparkMRJobRunner, self)._default_opts(),
dict(cloud_part_size_mb=_DEFAULT_CLOUD_PART_SIZE_MB, ),
)
class DataprocRunnerOptionStore(RunnerOptionStore):
ALLOWED_KEYS = RunnerOptionStore.ALLOWED_KEYS.union(set([
'gcp_project',
'cluster_id',
'region',
'zone',
'image_version',
'check_cluster_every',
'instance_type',
'master_instance_type',
'core_instance_type',
'task_instance_type',
'num_core_instances',
'num_task_instances',
'cloud_fs_sync_secs',
'cloud_tmp_dir',
'bootstrap',
'bootstrap_python',
'max_hours_idle',
]))
COMBINERS = combine_dicts(RunnerOptionStore.COMBINERS, {
'bootstrap': combine_lists,
'cloud_tmp_dir': combine_paths,
})
DEFAULT_FALLBACKS = {
'core_instance_type': 'instance_type',
'task_instance_type': 'instance_type'
}
def __init__(self, alias, opts, conf_paths):
super(DataprocRunnerOptionStore, self).__init__(
alias, opts, conf_paths)
# Dataproc requires a master and >= 2 core instances
# num_core_instances refers ONLY to number of CORE instances and does
# NOT include the required 1 instance for master
# In other words, minimum cluster size is 3 machines, 1 master and 2
# "num_core_instances" workers
if self['num_core_instances'] < _DATAPROC_MIN_WORKERS:
raise DataprocException(
'Dataproc expects at LEAST %d workers' % _DATAPROC_MIN_WORKERS)
for varname, fallback_varname in self.DEFAULT_FALLBACKS.items():
self[varname] = self[varname] or self[fallback_varname]
if self['core_instance_type'] != self['task_instance_type']:
raise DataprocException(
'Dataproc v1 expects core/task instance types to be identical')
def default_options(self):
super_opts = super(DataprocRunnerOptionStore, self).default_options()
return combine_dicts(super_opts, {
'bootstrap_python': True,
'image_version': _DEFAULT_IMAGE_VERSION,
'check_cluster_every': _DEFAULT_CHECK_CLUSTER_EVERY,
'instance_type': _DEFAULT_INSTANCE_TYPE,
'master_instance_type': _DEFAULT_INSTANCE_TYPE,
'num_core_instances': _DATAPROC_MIN_WORKERS,
'num_task_instances': 0,
'cloud_fs_sync_secs': _DEFAULT_CLOUD_FS_SYNC_SECS,
'max_hours_idle': _DEFAULT_MAX_HOURS_IDLE,
'sh_bin': ['/bin/sh', '-ex'],
'cleanup': ['CLUSTER', 'JOB', 'LOCAL_TMP']
})
def _default_opts(self):
return combine_dicts(
super(MRJobBinRunner, self)._default_opts(),
dict(sh_bin=['sh', '-ex'], ))
def test_later_values_take_precedence(self):
self.assertEqual(
combine_dicts({'TMPDIR': '/tmp', 'HOME': '/home/dave'},
{'TMPDIR': '/var/tmp'}),
{'TMPDIR': '/var/tmp', 'HOME': '/home/dave'})
class SimRunnerOptionStore(RunnerOptionStore):
COMBINERS = combine_dicts(RunnerOptionStore.COMBINERS, {
'cmdenv': combine_local_envs,
})
def test_None_value(self):
self.assertEqual(
combine_dicts({'USER': '******', 'TERM': 'xterm'}, {'USER': None}),
{'TERM': 'xterm', 'USER': None})
def _runner_kwargs(self):
return combine_dicts(
self._non_option_kwargs(),
self._kwargs_from_switches(self._runner_opt_names()),
self._job_kwargs(),
)
def test_cleared_value(self):
self.assertEqual(
combine_dicts({'USER': '******', 'TERM': 'xterm'},
{'USER': ClearedValue('caleb')}),
{'TERM': 'xterm', 'USER': '******'})
def test_skip_None(self):
self.assertEqual(combine_dicts(None, {'USER': '******'}, None,
{'TERM': 'xterm'}, None),
{'USER': '******', 'TERM': 'xterm'})
class RunnerOptionStore(OptionStore):
# Test cases for this class live in tests.test_option_store rather than
# tests.test_runner.
ALLOWED_KEYS = OptionStore.ALLOWED_KEYS.union(
set([
'base_tmp_dir',
'bootstrap_mrjob',
'cleanup',
'cleanup_on_failure',
'cmdenv',
'hadoop_extra_args',
'hadoop_streaming_jar',
'hadoop_version',
'interpreter',
'jobconf',
'label',
'owner',
'python_archives',
'python_bin',
'setup',
'setup_cmds',
'setup_scripts',
'sh_bin',
'steps_interpreter',
'steps_python_bin',
'upload_archives',
'upload_files',
]))
COMBINERS = combine_dicts(
OptionStore.COMBINERS, {
'base_tmp_dir': combine_paths,
'cmdenv': combine_envs,
'hadoop_extra_args': combine_lists,
'interpreter': combine_cmds,
'jobconf': combine_dicts,
'python_archives': combine_path_lists,
'python_bin': combine_cmds,
'setup': combine_lists,
'setup_cmds': combine_lists,
'setup_scripts': combine_path_lists,
'sh_bin': combine_cmds,
'steps_interpreter': combine_cmds,
'steps_python_bin': combine_cmds,
'upload_archives': combine_path_lists,
'upload_files': combine_path_lists,
})
def __init__(self, alias, opts, conf_paths):
"""
:param alias: Runner alias (e.g. ``'local'``)
:param opts: Options from the command line
:param conf_paths: Either a file path or an iterable of paths to config
files
"""
super(RunnerOptionStore, self).__init__()
# sanitize incoming options and issue warnings for bad keys
opts = self.validated_options(opts,
'Got unexpected keyword arguments: %s')
unsanitized_opt_dicts = load_opts_from_mrjob_confs(
alias, conf_paths=conf_paths)
for path, mrjob_conf_opts in unsanitized_opt_dicts:
self.cascading_dicts.append(
self.validated_options(
mrjob_conf_opts,
'Got unexpected opts from %s: %%s' % path))
self.cascading_dicts.append(opts)
if (len(self.cascading_dicts) > 2
and all(len(d) == 0 for d in self.cascading_dicts[2:-1])
and (len(conf_paths or []) > 0 or len(opts) == 0)):
log.warning('No configs specified for %s runner' % alias)
self.populate_values_from_cascading_dicts()
self._validate_cleanup()
self._fix_interp_options()
log.debug('Active configuration:')
log.debug(pprint.pformat(self))
def default_options(self):
super_opts = super(RunnerOptionStore, self).default_options()
try:
owner = getpass.getuser()
except:
owner = None
return combine_dicts(
super_opts, {
'base_tmp_dir': tempfile.gettempdir(),
'bootstrap_mrjob': True,
'cleanup': ['ALL'],
'cleanup_on_failure': ['NONE'],
'hadoop_version': '0.20',
'owner': owner,
'sh_bin': ['sh'],
})
def _validate_cleanup(self):
# old API accepts strings for cleanup
# new API wants lists
for opt_key in ('cleanup', 'cleanup_on_failure'):
if isinstance(self[opt_key], basestring):
self[opt_key] = [self[opt_key]]
def validate_cleanup(error_str, opt_list):
for choice in opt_list:
if choice not in CLEANUP_CHOICES:
raise ValueError(error_str % choice)
if 'NONE' in opt_list and len(set(opt_list)) > 1:
raise ValueError('Cannot clean up both nothing and something!')
cleanup_error = ('cleanup must be one of %s, not %%s' %
', '.join(CLEANUP_CHOICES))
validate_cleanup(cleanup_error, self['cleanup'])
cleanup_failure_error = (
'cleanup_on_failure must be one of %s, not %%s' %
', '.join(CLEANUP_CHOICES))
validate_cleanup(cleanup_failure_error, self['cleanup_on_failure'])
def _fix_interp_options(self):
if not self['steps_python_bin']:
self['steps_python_bin'] = (self['python_bin'] or [sys.executable]
or ['python'])
if not self['python_bin']:
self['python_bin'] = ['python']
if not self['steps_interpreter']:
if self['interpreter']:
self['steps_interpreter'] = self['interpreter']
else:
self['steps_interpreter'] = self['steps_python_bin']
if not self['interpreter']:
self['interpreter'] = self['python_bin']
def _default_opts(cls):
"""A dictionary giving the default value of options."""
return combine_dicts(super(LocalMRJobRunner, cls)._default_opts(), {
# prefer whatever interpreter we're currently using
'python_bin': [sys.executable or 'python'],
})
def default_options(self):
super_opts = super(HadoopRunnerOptionStore, self).default_options()
return combine_dicts(super_opts, {
'hadoop_tmp_dir': 'tmp/mrjob',
})
def jobconf(self):
"""``-D`` args to pass to hadoop streaming. This should be a map
from property name to value.
By default, this combines :option:`jobconf` options from the command
lines with :py:attr:`JOBCONF`, with command line arguments taking
precedence.
We also blank out ``mapred.output.key.comparator.class``
and ``mapred.text.key.comparator.options`` to prevent interference
from :file:`mrjob.conf`.
:py:attr:`SORT_VALUES` *can* be overridden by :py:attr:`JOBCONF`, the
command line, and step-specific ``jobconf`` values.
For example, if you know your values are numbers, and want to sort
them in reverse, you could do::
SORT_VALUES = True
JOBCONF = {
'mapred.output.key.comparator.class':
'org.apache.hadoop.mapred.lib.KeyFieldBasedComparator',
'mapred.text.key.comparator.options': '-k1 -k2nr',
}
If you want to re-define this, it's strongly recommended that do
something like this, so as not to inadvertently disable
the :option:`jobconf` option::
def jobconf(self):
orig_jobconf = super(MyMRJobClass, self).jobconf()
custom_jobconf = ...
return mrjob.conf.combine_dicts(orig_jobconf, custom_jobconf)
"""
# deal with various forms of bad behavior by users
unfiltered_jobconf = combine_dicts(self.JOBCONF, self.options.jobconf)
filtered_jobconf = {}
def format_hadoop_version(v_float):
if v_float >= 1.0:
# e.g. 1.0
return '%.1f' % v_float
else:
# e.g. 0.20
return '%.2f' % v_float
for key in unfiltered_jobconf:
unfiltered_val = unfiltered_jobconf[key]
filtered_val = unfiltered_val
# boolean values need to be lowercased
if isinstance(unfiltered_val, bool):
if unfiltered_val:
filtered_val = 'true'
else:
filtered_val = 'false'
# TODO: why would a jobconf variable be named 'hadoop_version'?
# hadoop_version should be a string
elif (key == 'hadoop_version'
and isinstance(unfiltered_val, float)):
log.warning('hadoop_version should be a string, not %s' %
unfiltered_val)
filtered_val = format_hadoop_version(unfiltered_val)
filtered_jobconf[key] = filtered_val
return filtered_jobconf
def test_deleted_value(self):
self.assertEqual(
combine_dicts({
'USER': '******',
'TERM': 'xterm'
}, {'USER': ClearedValue(None)}), {'TERM': 'xterm'})
def _opts_combiners(cls):
# on windows, PYTHONPATH should use ;, not :
return combine_dicts(
super(LocalMRJobRunner, cls)._opts_combiners(),
{'cmdenv': combine_local_envs})
def _opt_combiners(self):
"""Combine *cmdenv* with :py:func:`~mrjob.conf.combine_local_envs`"""
return combine_dicts(
super(SimMRJobRunner, self)._opt_combiners(),
dict(cmdenv=combine_local_envs),
)
class RunnerOptionStore(OptionStore):
# Test cases for this class live in tests.test_option_store rather than
# tests.test_runner.
ALLOWED_KEYS = OptionStore.ALLOWED_KEYS.union(
set([
'bootstrap_mrjob',
'check_input_paths',
'cleanup',
'cleanup_on_failure',
'cmdenv',
'hadoop_version',
'interpreter',
'jobconf',
'label',
'local_tmp_dir',
'owner',
'python_archives',
'python_bin',
'setup',
'setup_cmds',
'setup_scripts',
'sh_bin',
'steps_interpreter',
'steps_python_bin',
'strict_protocols',
'upload_archives',
'upload_files',
]))
COMBINERS = combine_dicts(
OptionStore.COMBINERS, {
'cmdenv': combine_envs,
'interpreter': combine_cmds,
'jobconf': combine_dicts,
'local_tmp_dir': combine_paths,
'python_archives': combine_path_lists,
'python_bin': combine_cmds,
'setup': combine_lists,
'setup_cmds': combine_lists,
'setup_scripts': combine_path_lists,
'sh_bin': combine_cmds,
'steps_interpreter': combine_cmds,
'steps_python_bin': combine_cmds,
'upload_archives': combine_path_lists,
'upload_files': combine_path_lists,
})
DEPRECATED_ALIASES = {
'base_tmp_dir': 'local_tmp_dir',
}
def __init__(self, alias, opts, conf_paths):
"""
:param alias: Runner alias (e.g. ``'local'``)
:param opts: Keyword args to runner's constructor (usually from the
command line).
:param conf_paths: An iterable of paths to config files
"""
super(RunnerOptionStore, self).__init__()
# sanitize incoming options and issue warnings for bad keys
opts = self.validated_options(opts)
unsanitized_opt_dicts = load_opts_from_mrjob_confs(
alias, conf_paths=conf_paths)
for path, mrjob_conf_opts in unsanitized_opt_dicts:
self.cascading_dicts.append(
self.validated_options(mrjob_conf_opts,
from_where=(' from %s' % path)))
self.cascading_dicts.append(opts)
if (len(self.cascading_dicts) > 2
and all(len(d) == 0 for d in self.cascading_dicts[2:-1])
and (len(conf_paths or []) > 0)):
log.warning('No configs specified for %s runner' % alias)
self.populate_values_from_cascading_dicts()
log.debug('Active configuration:')
log.debug(pprint.pformat(self))
def default_options(self):
super_opts = super(RunnerOptionStore, self).default_options()
try:
owner = getpass.getuser()
except:
owner = None
return combine_dicts(
super_opts, {
'check_input_paths': True,
'cleanup': ['ALL'],
'cleanup_on_failure': ['NONE'],
'local_tmp_dir': tempfile.gettempdir(),
'owner': owner,
'sh_bin': ['sh', '-ex'],
'strict_protocols': True,
})
def validated_options(self, opts, from_where=''):
opts = super(RunnerOptionStore,
self).validated_options(opts, from_where)
self._fix_cleanup_opt('cleanup', opts, from_where)
self._fix_cleanup_opt('cleanup_on_failure', opts, from_where)
return opts
def _fix_cleanup_opt(self, opt_key, opts, from_where=''):
if opts.get(opt_key) is None:
return
opt_list = opts[opt_key]
# runner expects list of string, not string
if isinstance(opt_list, string_types):
opt_list = [opt_list]
if 'NONE' in opt_list and len(set(opt_list)) > 1:
raise ValueError('Cannot clean up both nothing and something!')
def handle_cleanup_opt(opt):
if opt in CLEANUP_CHOICES:
return opt
if opt in _CLEANUP_DEPRECATED_ALIASES:
aliased_opt = _CLEANUP_DEPRECATED_ALIASES[opt]
# TODO: don't do this when option value is None
log.warning(
'Deprecated %s option %s%s has been renamed to %s' %
(opt_key, opt, from_where, aliased_opt))
return aliased_opt
raise ValueError('%s must be one of %s, not %s' %
(opt_key, ', '.join(CLEANUP_CHOICES), opt))
opt_list = [handle_cleanup_opt(opt) for opt in opt_list]
opts[opt_key] = opt_list
def test_empty(self):
assert_equal(combine_dicts(), {})
def test_empty(self):
self.assertEqual(combine_dicts(), {})
def default_options(self):
# don't bootstrap mrjob by default when running locally
super_opts = super(SimRunnerOptionStore, self).default_options()
return combine_dicts(super_opts, {
'bootstrap_mrjob': False,
})
def default_options(self):
super_opts = super(LocalRunnerOptionStore, self).default_options()
return combine_dicts(super_opts, {
# prefer whatever interpreter we're currently using
'python_bin': [sys.executable or 'python'],
})
def _default_opts(self):
return combine_dicts(
super(MRJobBinRunner, self)._default_opts(),
dict(read_logs=True, ))
