def __init__(self,
name,
description,
field_spec,
units=None,
target_type=None):
"""Create an instance of a Metric.
Args:
name (str): the file-like name of this metric
description (string): help string for the metric. Should be enough to
know what the metric is about.
field_spec (list): a list of Field subclasses to define the fields that
are allowed on this metric. Pass a list of either
StringField, IntegerField or BooleanField here.
units (string): the unit used to measure data for given metric. Some
common units are pre-defined in the MetricsDataUnits
class.
target_type (type): the subclass of google.protobuf.message.Message that
represents the target type.
"""
field_spec = field_spec or []
self._name = name.lstrip('/')
if not isinstance(description, basestring):
raise errors.MetricDefinitionError(
'Metric description must be a string')
if not description:
raise errors.MetricDefinitionError(
'Metric must have a description')
if (not isinstance(field_spec, (list, tuple))
or any(not isinstance(x, Field) for x in field_spec)):
raise errors.MetricDefinitionError(
'Metric constructor takes a list of Fields, or None')
if len(field_spec) > 12:
# Monarch imposes a limit of a cardinality of 5000 for a single metric
# (see http://shortn/_WBupjZf2of).
# If a user set 12 fields, and each of those is just a boolean field
# with two possible values, the _lower limit_ on the cardinality of
# that metric is 2^12, or 4096.
# Note that since a combination of 5 built-in fields is fixed, we do
# not need to count them.
raise errors.MonitoringTooManyFieldsError(self._name, field_spec)
if target_type and not (inspect.isclass(target_type)
and issubclass(target_type, message.Message)):
raise errors.MetricDefinitionError(
'Metric target type must be a class (not an instance of a class) '
'and that must be a subclass of google.protobuf.message.Message.'
)
self._start_time = None
self._field_spec = field_spec
self._sorted_field_names = sorted(x.name for x in field_spec)
self._description = description
self._units = units
self._target_type = target_type
self._enable_cumulative_set = False
interface.register(self)
def __init__(self, name, fields=None, description=None):
"""Create an instance of a Metric.
Args:
name (str): the file-like name of this metric
fields (dict): a set of key-value pairs to be set as default metric fields
description (string): help string for the metric. Should be enough to
know what the metric is about.
"""
self._name = name.lstrip('/')
self._start_time = None
fields = fields or {}
if len(fields) > 7:
raise errors.MonitoringTooManyFieldsError(self._name, fields)
self._fields = fields
self._normalized_fields = self._normalize_fields(self._fields)
self._description = description
interface.register(self)
def __init__(self, name, description, field_spec, units=None):
"""Create an instance of a Metric.
Args:
name (str): the file-like name of this metric
description (string): help string for the metric. Should be enough to
know what the metric is about.
field_spec (list): a list of Field subclasses to define the fields that
are allowed on this metric. Pass a list of either
StringField, IntegerField or BooleanField here.
units (string): the unit used to measure data for given metric. Some
common units are pre-defined in the MetricsDataUnits
class.
"""
field_spec = field_spec or []
self._name = name.lstrip('/')
if not isinstance(description, basestring):
raise errors.MetricDefinitionError(
'Metric description must be a string')
if not description:
raise errors.MetricDefinitionError(
'Metric must have a description')
if (not isinstance(field_spec, (list, tuple))
or any(not isinstance(x, Field) for x in field_spec)):
raise errors.MetricDefinitionError(
'Metric constructor takes a list of Fields, or None')
if len(field_spec) > 7:
raise errors.MonitoringTooManyFieldsError(self._name, field_spec)
self._start_time = None
self._field_spec = field_spec
self._sorted_field_names = sorted(x.name for x in field_spec)
self._description = description
self._units = units
interface.register(self)
def _normalize_fields(self, fields):
"""Merges the fields with the default fields and returns something hashable.
Args:
fields (dict): A dict of fields passed by the user, or None.
Returns:
A tuple of (key, value) tuples, ordered by key. This whole tuple is used
as the key in the self._values dict to identify the cell for a value.
Raises:
MonitoringTooManyFieldsError: if there are more than seven metric fields
"""
if fields is None:
return self._normalized_fields
all_fields = copy.copy(self._fields)
all_fields.update(fields)
if len(all_fields) > 7:
raise errors.MonitoringTooManyFieldsError(self._name, all_fields)
return tuple(sorted(all_fields.iteritems()))
def __init__(self, name, fields=None, description=None, units=None):
"""Create an instance of a Metric.
Args:
name (str): the file-like name of this metric
fields (dict): a set of key-value pairs to be set as default metric fields
description (string): help string for the metric. Should be enough to
know what the metric is about.
units (int): the unit used to measure data for given
metric. Please use the attributes of MetricDataUnit to find
valid integer values for this argument.
"""
self._name = name.lstrip('/')
self._start_time = None
fields = fields or {}
if len(fields) > 7:
raise errors.MonitoringTooManyFieldsError(self._name, fields)
self._fields = fields
self._normalized_fields = self._normalize_fields(self._fields)
self._description = description
self._units = units
interface.register(self)