Revert of Add field_specs to all metrics in luci-py

client/third_party/infra_libs/ts_mon/common/metrics.py

 # Copyright 2015 The Chromium Authors. All rights reserved.
 # Use of this source code is governed by a BSD-style license that can be
 # found in the LICENSE file.
 
 """Classes representing individual metrics that can be sent."""
 
 import copy
-import re
 
 from infra_libs.ts_mon.protos.current import metrics_pb2
 from infra_libs.ts_mon.protos.new import metrics_pb2 as new_metrics_pb2
 
 from infra_libs.ts_mon.common import distribution
 from infra_libs.ts_mon.common import errors
 from infra_libs.ts_mon.common import interface
 
 
 MICROSECONDS_PER_SECOND = 1000000
 
 
-class Field(object):
-  FIELD_NAME_PATTERN = re.compile(r'[A-Za-z_][A-Za-z0-9_]*')
-
-  allowed_python_types = None
-  v1_type = None
-  v2_type = None
-  v1_field = None
-  v2_field = None
-
-  def __init__(self, name):
-    if not self.FIELD_NAME_PATTERN.match(name):
-      raise errors.MetricDefinitionError(
-          'Invalid metric field name "%s" - must match the regex "%s"' % (
-                name, self.FIELD_NAME_PATTERN.pattern))
-
-    self.name = name
-
-  def validate_value(self, metric_name, value):
-    if not isinstance(value, self.allowed_python_types):
-      raise errors.MonitoringInvalidFieldTypeError(
-          metric_name, self.name, value)
-
-  def populate_proto_v1(self, proto, value):
-    setattr(proto, self.v1_field, value)
-
-  def populate_proto_v2(self, proto, value):
-    setattr(proto, self.v2_field, value)
-
-
-class StringField(Field):
-  allowed_python_types = basestring
-  v1_type = metrics_pb2.MetricsField.STRING
-  v2_type = new_metrics_pb2.MetricsDataSet.MetricFieldDescriptor.STRING
-  v1_field = 'string_value'
-  v2_field = 'string_value'
-
-
-class IntegerField(Field):
-  allowed_python_types = (int, long)
-  v1_type = metrics_pb2.MetricsField.INT
-  v2_type = new_metrics_pb2.MetricsDataSet.MetricFieldDescriptor.INT64
-  v1_field = 'int_value'
-  v2_field = 'int64_value'
-
-
-class BooleanField(Field):
-  allowed_python_types = bool
-  v1_type = metrics_pb2.MetricsField.BOOL
-  v2_type = new_metrics_pb2.MetricsDataSet.MetricFieldDescriptor.BOOL
-  v1_field = 'bool_value'
-  v2_field = 'bool_value'
-
-
 class Metric(object):
   """Abstract base class for a metric.
 
   A Metric is an attribute that may be monitored across many targets. Examples
   include disk usage or the number of requests a server has received. A single
   process may keep track of many metrics.
 
   Note that Metric objects may be initialized at any time (for example, at the
   top of a library), but cannot be sent until the underlying Monitor object
   has been set up (usually by the top-level process parsing the command line).
@@ skipping 14 matching lines @@
   * StringMetric for metrics with string value
   * BooleanMetric for metrics with boolean values
   * CounterMetric for metrics with monotonically increasing integer values
   * GaugeMetric for metrics with arbitrarily varying integer values
   * CumulativeMetric for metrics with monotonically increasing float values
   * FloatMetric for metrics with arbitrarily varying float values
 
   See http://go/inframon-doc for help designing and using your metrics.
   """
 
-  def __init__(self, name, description, field_spec, units=None):
+  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.
-      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 (int): the unit used to measure data for given
                    metric. Please use the attributes of MetricDataUnit to find
                    valid integer values for this argument.
     """
-    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)
+    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)
 
   @property
   def name(self):
     return self._name
 
   @property
   def start_time(self):
     return self._start_time
 
   def is_cumulative(self):
     raise NotImplementedError()
 
+  def __eq__(self, other):
+    return (self.name == other.name and
+            self._fields == other._fields and
+            type(self) == type(other))
+
   def unregister(self):
     interface.unregister(self)
 
   @staticmethod
   def _map_units_to_string(units):
     """Map MetricsDataUnits to the corresponding string according to:
        http://unitsofmeasure.org/ucum.html because that's what the new proto
        requires."""
     if units in _UNITS_TO_STRING:
       return _UNITS_TO_STRING[units]
     else:
       return '{unknown}'
 
-  def _populate_data_set(self, data_set):
+  def _populate_data_set(self, data_set, fields):
     """Populate MetricsDataSet."""
     data_set.metric_name = '%s%s' % (interface.state.metric_name_prefix,
                                      self._name)
     data_set.description = self._description or ''
     data_set.annotations.unit = self._map_units_to_string(self._units)
 
     if self.is_cumulative():
       data_set.stream_kind = new_metrics_pb2.CUMULATIVE
     else:
       data_set.stream_kind = new_metrics_pb2.GAUGE
 
     self._populate_value_type(data_set)
-    self._populate_field_descriptors(data_set)
+    self._populate_field_descriptors(data_set, fields)
 
   def _populate_data(self, data, start_time, end_time, fields, value):
     """Populate a new metrics_pb2.MetricsData.
 
     Args:
       data_ (new_metrics_pb2.MetricsData): protocol buffer into
         which to populate the current metric values.
       start_time (int): timestamp in microseconds since UNIX epoch.
     """
     data.start_timestamp.seconds = int(start_time)
     data.end_timestamp.seconds = int(end_time)
 
     self._populate_fields_new(data, fields)
     self._populate_value_new(data, value)
 
   def serialize_to(self, metric_pb, start_time, fields, value, target):
     """Generate metrics_pb2.MetricsData messages for this metric.
 
     Args:
       metric_pb (metrics_pb2.MetricsData): protocol buffer into which
         to serialize the current metric values.
       start_time (int): timestamp in microseconds since UNIX epoch.
       target (Target): a Target to use.
     """
 
     metric_pb.metric_name_prefix = interface.state.metric_name_prefix
     metric_pb.name = self._name
-    metric_pb.description = self._description
+    if self._description is not None:
+      metric_pb.description = self._description
     if self._units is not None:
       metric_pb.units = self._units
 
     self._populate_value(metric_pb, value, start_time)
     self._populate_fields(metric_pb, fields)
 
     target._populate_target_pb(metric_pb)
 
-  def _populate_field_descriptors(self, data_set):
+  def _populate_field_descriptors(self, data_set, fields):
     """Populate `field_descriptor` in MetricsDataSet.
 
     Args:
-      data_set (new_metrics_pb2.MetricsDataSet): a data set protobuf to populate
+      data_set (new_metrics_pb2.MetricsDataSet): a data set protobuf to
+          populate
+      fields (list of (key, value) tuples): normalized metric fields
+
+    Raises:
+      MonitoringInvalidFieldTypeError: if a field has a value of unknown type
     """
-    for spec in self._field_spec:
+    field_type = new_metrics_pb2.MetricsDataSet.MetricFieldDescriptor
+    for key, value in fields:
       descriptor = data_set.field_descriptor.add()
-      descriptor.name = spec.name
-      descriptor.field_type = spec.v2_type
+      descriptor.name = key
+      if isinstance(value, basestring):
+        descriptor.field_type = field_type.STRING
+      elif isinstance(value, bool):
+        descriptor.field_type = field_type.BOOL
+      elif isinstance(value, int):
+        descriptor.field_type = field_type.INT64
+      else:
+        raise errors.MonitoringInvalidFieldTypeError(self._name, key, value)
 
-  def _populate_fields_new(self, data, field_values):
+  def _populate_fields_new(self, data, fields):
     """Fill in the fields attribute of a metric protocol buffer.
 
     Args:
       metric (metrics_pb2.MetricsData): a metrics protobuf to populate
-      field_values (tuple): field values
+      fields (list of (key, value) tuples): normalized metric fields
+
+    Raises:
+      MonitoringInvalidFieldTypeError: if a field has a value of unknown type
     """
-    for spec, value in zip(self._field_spec, field_values):
+    for key, value in fields:
       field = data.field.add()
-      field.name = spec.name
-      spec.populate_proto_v2(field, value)
+      field.name = key
+      if isinstance(value, basestring):
+        field.string_value = value
+      elif isinstance(value, bool):
+        field.bool_value = value
+      elif isinstance(value, int):
+        field.int64_value = value
+      else:
+        raise errors.MonitoringInvalidFieldTypeError(self._name, key, value)
 
-  def _populate_fields(self, metric, field_values):
+  def _populate_fields(self, metric, fields):
     """Fill in the fields attribute of a metric protocol buffer.
 
     Args:
       metric (metrics_pb2.MetricsData): a metrics protobuf to populate
-      field_values (tuple): field values
+      fields (list of (key, value) tuples): normalized metric fields
+
+    Raises:
+      MonitoringInvalidFieldTypeError: if a field has a value of unknown type
     """
-    for spec, value in zip(self._field_spec, field_values):
+    for key, value in fields:
       field = metric.fields.add()
-      field.name = spec.name
-      field.type = spec.v1_type
-      spec.populate_proto_v1(field, value)
+      field.name = key
+      if isinstance(value, basestring):
+        field.type = metrics_pb2.MetricsField.STRING
+        field.string_value = value
+      elif isinstance(value, bool):
+        field.type = metrics_pb2.MetricsField.BOOL
+        field.bool_value = value
+      elif isinstance(value, int):
+        field.type = metrics_pb2.MetricsField.INT
+        field.int_value = value
+      else:
+        raise errors.MonitoringInvalidFieldTypeError(self._name, key, value)
 
-  def _validate_fields(self, fields):
-    """Checks the correct number and types of field values were provided.
+  def _normalize_fields(self, fields):
+    """Merges the fields with the default fields and returns something hashable.
 
     Args:
-      fields (dict): A dict of field values given by the user, or None.
+      fields (dict): A dict of fields passed by the user, or None.
 
     Returns:
-      fields' values as a tuple, in the same order as the field_spec.
+      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:
-      WrongFieldsError: if you provide a different number of fields to those
-        the metric was defined with.
-      MonitoringInvalidFieldTypeError: if the field value was the wrong type for
-        the field spec.
+      MonitoringTooManyFieldsError: if there are more than seven metric fields
     """
-    fields = fields or {}
+    if fields is None:
+      return self._normalized_fields
 
-    if not isinstance(fields, dict):
-      raise ValueError('fields should be a dict, got %r (%s)' % (
-          fields, type(fields)))
+    all_fields = copy.copy(self._fields)
+    all_fields.update(fields)
 
-    if sorted(fields) != self._sorted_field_names:
-      raise errors.WrongFieldsError(
-          self.name, fields.keys(), self._sorted_field_names)
+    if len(all_fields) > 7:
+      raise errors.MonitoringTooManyFieldsError(self._name, all_fields)
 
-    for spec in self._field_spec:
-      spec.validate_value(self.name, fields[spec.name])
-
-    return tuple(fields[spec.name] for spec in self._field_spec)
+    return tuple(sorted(all_fields.iteritems()))
 
   def _populate_value(self, metric, value, start_time):
     """Fill in the the data values of a metric protocol buffer.
 
     Args:
       metric (metrics_pb2.MetricsData): a metrics protobuf to populate
       value (see concrete class): the value of the metric to be set
       start_time (int): timestamp in microseconds since UNIX epoch.
     """
     raise NotImplementedError()
@@ skipping 17 matching lines @@
     raise NotImplementedError()
 
   def set(self, value, fields=None, target_fields=None):
     """Set a new value for this metric. Results in sending a new value.
 
     The subclass should do appropriate type checking on value and then call
     self._set_and_send_value.
 
     Args:
       value (see concrete class): the value of the metric to be set
-      fields (dict): metric field values
+      fields (dict): additional metric fields to complement those on self
       target_fields (dict): overwrite some of the default target fields
     """
     raise NotImplementedError()
 
   def get(self, fields=None, target_fields=None):
     """Returns the current value for this metric.
 
     Subclasses should never use this to get a value, modify it and set it again.
     Instead use _incr with a modify_fn.
     """
     return interface.state.store.get(
-        self.name, self._validate_fields(fields), target_fields)
+        self.name, self._normalize_fields(fields), target_fields)
 
   def get_all(self):
     return interface.state.store.iter_field_values(self.name)
 
   def reset(self):
     """Clears the values of this metric.  Useful in unit tests.
 
     It might be easier to call ts_mon.reset_for_unittest() in your setUp()
     method instead of resetting every individual metric.
     """
 
     interface.state.store.reset_for_unittest(self.name)
 
   def _set(self, fields, target_fields, value, enforce_ge=False):
-    interface.state.store.set(
-        self.name, self._validate_fields(fields), target_fields,
-        value, enforce_ge=enforce_ge)
+    interface.state.store.set(self.name, self._normalize_fields(fields),
+                              target_fields, value, enforce_ge=enforce_ge)
 
   def _incr(self, fields, target_fields, delta, modify_fn=None):
-    interface.state.store.incr(
-        self.name, self._validate_fields(fields), target_fields,
-        delta, modify_fn=modify_fn)
+    interface.state.store.incr(self.name, self._normalize_fields(fields),
+                               target_fields, delta, modify_fn=modify_fn)
 
 
 class StringMetric(Metric):
   """A metric whose value type is a string."""
 
   def _populate_value(self, metric, value, start_time):
     metric.string_value = value
 
   def _populate_value_new(self, data, value):
     data.string_value = value
@@ skipping 26 matching lines @@
     if not isinstance(value, bool):
       raise errors.MonitoringInvalidValueTypeError(self._name, value)
     self._set(fields, target_fields, value)
 
   def is_cumulative(self):
     return False
 
 
 class NumericMetric(Metric):  # pylint: disable=abstract-method
   """Abstract base class for numeric (int or float) metrics."""
+  # TODO(agable): Figure out if there's a way to send units with these metrics.
 
   def increment(self, fields=None, target_fields=None):
     self._incr(fields, target_fields, 1)
 
   def increment_by(self, step, fields=None, target_fields=None):
     self._incr(fields, target_fields, step)
 
 
 class CounterMetric(NumericMetric):
   """A metric whose value type is a monotonically increasing integer."""
 
-  def __init__(self, name, description, field_spec=None, start_time=None,
+  def __init__(self, name, fields=None, start_time=None, description=None,
                units=None):
     super(CounterMetric, self).__init__(
-        name, description, field_spec, units=units)
+        name, fields=fields, description=description, units=units)
     self._start_time = start_time
 
   def _populate_value(self, metric, value, start_time):
     metric.counter = value
     metric.start_timestamp_us = int(start_time * MICROSECONDS_PER_SECOND)
 
   def _populate_value_new(self, data, value):
     data.int64_value = value
 
   def _populate_value_type(self, data_set):
@@ skipping 30 matching lines @@
       raise errors.MonitoringInvalidValueTypeError(self._name, value)
     self._set(fields, target_fields, value)
 
   def is_cumulative(self):
     return False
 
 
 class CumulativeMetric(NumericMetric):
   """A metric whose value type is a monotonically increasing float."""
 
-  def __init__(self, name, description, field_spec=None, start_time=None,
+  def __init__(self, name, fields=None, start_time=None, description=None,
                units=None):
     super(CumulativeMetric, self).__init__(
-        name, description, field_spec, units=units)
+        name, fields=fields, description=description, units=units)
     self._start_time = start_time
 
   def _populate_value(self, metric, value, start_time):
     metric.cumulative_double_value = value
     metric.start_timestamp_us = int(start_time * MICROSECONDS_PER_SECOND)
 
   def _populate_value_new(self, data, value):
     data.double_value = value
 
   def _populate_value_type(self, data_set):
@@ skipping 22 matching lines @@
 
   def set(self, value, fields=None, target_fields=None):
     if not isinstance(value, (float, int)):
       raise errors.MonitoringInvalidValueTypeError(self._name, value)
     self._set(fields, target_fields, float(value))
 
   def is_cumulative(self):
     return False
 
 
-class _DistributionMetricBase(Metric):
+class DistributionMetric(Metric):
   """A metric that holds a distribution of values.
 
   By default buckets are chosen from a geometric progression, each bucket being
   approximately 1.59 times bigger than the last.  In practice this is suitable
   for many kinds of data, but you may want to provide a FixedWidthBucketer or
   GeometricBucketer with different parameters."""
 
   CANONICAL_SPEC_TYPES = {
       2: metrics_pb2.PrecomputedDistribution.CANONICAL_POWERS_OF_2,
       10**0.2: metrics_pb2.PrecomputedDistribution.CANONICAL_POWERS_OF_10_P_0_2,
       10: metrics_pb2.PrecomputedDistribution.CANONICAL_POWERS_OF_10,
   }
 
-  def __init__(self, name, description, field_spec=None, is_cumulative=True,
-               bucketer=None, start_time=None, units=None):
-    super(_DistributionMetricBase, self).__init__(
-        name, description, field_spec, units=units)
+  def __init__(self, name, is_cumulative=True, bucketer=None, fields=None,
+               start_time=None, description=None, units=None):
+    super(DistributionMetric, self).__init__(
+        name, fields=fields, description=description, units=units)
     self._start_time = start_time
 
     if bucketer is None:
       bucketer = distribution.GeometricBucketer()
 
     self._is_cumulative = is_cumulative
     self.bucketer = bucketer
 
   def _populate_value(self, metric, value, start_time):
     pb = metric.distribution
@@ skipping 92 matching lines @@
     if self._is_cumulative:
       raise TypeError(
           'Cannot set() a cumulative DistributionMetric (use add() instead)')
 
     if not isinstance(value, distribution.Distribution):
       raise errors.MonitoringInvalidValueTypeError(self._name, value)
 
     self._set(fields, target_fields, value)
 
   def is_cumulative(self):
-    return self._is_cumulative
+    raise NotImplementedError()  # Keep this class abstract.
 
 
-class CumulativeDistributionMetric(_DistributionMetricBase):
+class CumulativeDistributionMetric(DistributionMetric):
   """A DistributionMetric with is_cumulative set to True."""
 
-  def __init__(self, name, description, field_spec=None, bucketer=None,
-               units=None):
+  def __init__(self, name, bucketer=None, fields=None,
+               description=None, units=None):
     super(CumulativeDistributionMetric, self).__init__(
-        name, description, field_spec,
+        name,
         is_cumulative=True,
         bucketer=bucketer,
+        fields=fields,
+        description=description,
         units=units)
 
+  def is_cumulative(self):
+    return True
 
-class NonCumulativeDistributionMetric(_DistributionMetricBase):
+
+class NonCumulativeDistributionMetric(DistributionMetric):
   """A DistributionMetric with is_cumulative set to False."""
 
-  def __init__(self, name, description, field_spec=None, bucketer=None,
-               units=None):
+  def __init__(self, name, bucketer=None, fields=None,
+               description=None, units=None):
     super(NonCumulativeDistributionMetric, self).__init__(
-        name, description, field_spec,
+        name,
         is_cumulative=False,
         bucketer=bucketer,
+        fields=fields,
+        description=description,
         units=units)
 
+  def is_cumulative(self):
+    return False
+
 
 class MetaMetricsDataUnits(type):
   """Metaclass to populate the enum values of metrics_pb2.MetricsData.Units."""
   def __new__(mcs, name, bases, attrs):
     attrs.update(metrics_pb2.MetricsData.Units.items())
     return super(MetaMetricsDataUnits, mcs).__new__(mcs, name, bases, attrs)
 
 
 class MetricsDataUnits(object):
   """An enumeration class for units of measurement for Metrics data.
@@ skipping 12 matching lines @@
     MetricsDataUnits.KILOBYTES: 'kBy',
     MetricsDataUnits.MEGABYTES: 'MBy',
     MetricsDataUnits.GIGABYTES: 'GBy',
     MetricsDataUnits.KIBIBYTES: 'kiBy',
     MetricsDataUnits.MEBIBYTES: 'MiBy',
     MetricsDataUnits.GIBIBYTES: 'GiBy',
     MetricsDataUnits.AMPS: 'A',
     MetricsDataUnits.MILLIAMPS : 'mA',
     MetricsDataUnits.DEGREES_CELSIUS: 'Cel'
 }