def testStatsEntryId_VaryStringDimensions(self):
"""Ensures StatsEntries with different str dimensions get different ids."""
entry1 = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.INT,
fields_values=[
stats_values.StatsStoreFieldValue(
field_type=rdf_stats.MetricFieldDefinition.FieldType.
STR,
str_value="dim1"),
stats_values.StatsStoreFieldValue(
field_type=rdf_stats.MetricFieldDefinition.FieldType.
STR,
str_value="dim2")
],
int_value=42),
timestamp=_one_second_timestamp)
# Clone entry1 and change a single dimension value.
entry2 = stats_values.StatsStoreEntry()
entry2.FromDict(entry1.ToPrimitiveDict())
entry2.metric_value.fields_values[0].str_value = "dim3"
self.assertEqual(
db_utils.GenerateStatsEntryId(entry1),
b"\xb8'\x1cv\xf9`\x90\xd1\x9d#{\x8a'y\xd8E\x0bx\x1b6f\xe6\x8d\x16\xb95"
b"\x0011uy\xf9")
self.assertEqual(
db_utils.GenerateStatsEntryId(entry2),
b"\xa4\xcb\x95*\xe4\x8aclM&@\xde\xba\x17\xec\x02\xc6i\xea\xc0\x1a{bQ"
b"\xabr~w}Z\xb9\x99")
def testStatsEntryId_VaryIntDimensions(self):
"""Ensures StatsEntries with different int dimensions get different ids."""
entry1 = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.INT,
fields_values=[
stats_values.StatsStoreFieldValue(
field_type=rdf_stats.MetricFieldDefinition.FieldType.
INT,
int_value=11),
stats_values.StatsStoreFieldValue(
field_type=rdf_stats.MetricFieldDefinition.FieldType.
INT,
int_value=12)
],
int_value=42),
timestamp=_one_second_timestamp)
# Clone entry1 and change a single dimension value.
entry2 = stats_values.StatsStoreEntry()
entry2.FromDict(entry1.ToPrimitiveDict())
entry2.metric_value.fields_values[0].int_value = 13
self.assertEqual(
db_utils.GenerateStatsEntryId(entry1),
b"pl\xc31\x1a\xf8\xd5\xfe\xe1\xc6\x10gR\x10f\x0c\xb8\xd3\x96_\xa3`\x19"
b"\xf2\x15\xc5\xa0\x8d\xbbu\xf1&")
self.assertEqual(
db_utils.GenerateStatsEntryId(entry2),
b"L \x1d+\xd5<g\\\xa8\xa4\x97\xdd\xe8^\x88\xac\xc7\xbej\xae\xff\xd5S"
b"\x10\xce\xec\x82a\xe5_\xe1\x1c")
def _GenerateStatsEntriesForMultiDimensionalMetric(process_id, metric_name,
metadata, timestamp):
"""Generates StatsStoreEntries for the given multi-dimensional metric.
Args:
process_id: Process identifier to use for for the generated entries.
metric_name: Name of the multi-dimensional metric.
metadata: MetricMetadata for the metric.
timestamp: Timestamp to use for the generated entries.
Returns:
A list of StatsStoreEntries containing current values for the metric's
dimensions.
"""
stats_entries = []
for raw_field_values in stats_collector_instance.Get().GetMetricFields(
metric_name):
_ValidateFieldValues(raw_field_values, metadata)
metric_value = stats_values.StatsStoreValue()
for i, raw_field_value in enumerate(raw_field_values):
field_value = stats_values.StatsStoreFieldValue()
field_value.SetValue(raw_field_value,
metadata.fields_defs[i].field_type)
metric_value.fields_values.Append(field_value)
raw_metric_value = stats_collector_instance.Get().GetMetricValue(
metric_name, fields=raw_field_values)
metric_value.SetValue(raw_metric_value, metadata.value_type)
stats_entries.append(
stats_values.StatsStoreEntry(process_id=process_id,
metric_name=metric_name,
metric_value=metric_value,
timestamp=timestamp))
return stats_entries
def testDuplicateStatsEntryWrite_MultiDimensional(self):
"""Tests errors raised when writing duplicate multi-dimensional entries."""
self._SetUpStatsTest()
duplicate_entry = stats_values.StatsStoreEntry()
duplicate_entry.FromDict(_multi_dim_entry.ToPrimitiveDict())
duplicate_entry.metric_value.int_value = 43
with self.assertRaises(db.DuplicateMetricValueError):
self.db.WriteStatsStoreEntries([duplicate_entry])
def testStatsEntryId_IgnoreMetricValues(self):
"""Ensures metric values have no influence id generation."""
int_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.INT,
int_value=42),
timestamp=_one_second_timestamp)
float_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.FLOAT,
float_value=4.2),
timestamp=_one_second_timestamp)
# TODO: String gauges are deprecated.
str_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.DEPRECATED_STR,
str_value="foo"),
timestamp=_one_second_timestamp)
distribution_entry = stats_values.StatsStoreEntry(
process_id="test_process",
metric_name="test_metric",
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.DISTRIBUTION,
distribution_value=rdf_stats.Distribution()),
timestamp=_one_second_timestamp)
expected_id = (
b"\x8e\xf4\xe7\xdb\x03\x01}sB\x97\x98\x957\x18\x02U\xb0\xe6x\x9f"
b"\x97Xfs/C\xedT\xd3\x89N\xe5")
self.assertEqual(db_utils.GenerateStatsEntryId(int_entry), expected_id)
self.assertEqual(db_utils.GenerateStatsEntryId(float_entry),
expected_id)
self.assertEqual(db_utils.GenerateStatsEntryId(str_entry), expected_id)
self.assertEqual(db_utils.GenerateStatsEntryId(distribution_entry),
expected_id)
def testWriteStatsStoreEntriesValidation(self):
self._SetUpFakeStatsContext()
# Create StatsStoreEntries with incorrect field values.
bad_single_dim_entry = stats_values.StatsStoreEntry()
bad_single_dim_entry.FromDict(_multi_dim_entry.ToPrimitiveDict())
bad_single_dim_entry.metric_name = _SINGLE_DIM_COUNTER
bad_multi_dim_entry = stats_values.StatsStoreEntry()
bad_multi_dim_entry.FromDict(_single_dim_entry1.ToPrimitiveDict())
bad_multi_dim_entry.metric_name = _MULTI_DIM_COUNTER
with self.assertRaises(ValueError) as cm:
self.db.WriteStatsStoreEntries([bad_single_dim_entry])
self.assertEqual(
"Value for metric single_dim_counter had 2 field values, yet the "
"metric was defined to have 0 fields.", cm.exception.message)
with self.assertRaises(ValueError) as cm:
self.db.WriteStatsStoreEntries([bad_multi_dim_entry])
self.assertEqual(
"Value for metric multi_dim_counter had 0 field values, yet the "
"metric was defined to have 2 fields.", cm.exception.message)
def _GenerateStatsEntryForSingleDimensionalMetric(process_id, metric_name,
metadata, timestamp):
"""Generates a StatsStoreEntry for the given single-dimensional metric.
Args:
process_id: Process identifier to use for for the generated entry.
metric_name: Name of the single-dimensional metric.
metadata: MetricMetadata for the metric.
timestamp: Timestamp to use for the generated entry.
Returns:
A StatsStoreEntry containing the current value of the metric.
"""
raw_metric_value = stats_collector_instance.Get().GetMetricValue(
metric_name)
metric_value = stats_values.StatsStoreValue()
metric_value.SetValue(raw_metric_value, metadata.value_type)
return stats_values.StatsStoreEntry(process_id=process_id,
metric_name=metric_name,
metric_value=metric_value,
timestamp=timestamp)
from grr_response_core.stats import stats_utils
from grr_response_server import db
from grr_response_server import stats_values
_TEST_PROCESS_ID = "test_process"
_SINGLE_DIM_COUNTER = "single_dim_counter"
_MULTI_DIM_COUNTER = "multi_dim_counter"
# RDF protobufs used for testing.
#
# TODO(user): Refactor rel-db tests so that component-specific test-mixin
# classes do not have to worry about defining attributes/methods that conflict
# with those of other mixin classes.
_single_dim_entry1 = stats_values.StatsStoreEntry(
process_id=_TEST_PROCESS_ID,
metric_name=_SINGLE_DIM_COUNTER,
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.INT, int_value=42),
timestamp=rdfvalue.RDFDatetime.FromSecondsSinceEpoch(1))
_single_dim_entry2 = stats_values.StatsStoreEntry(
process_id=_TEST_PROCESS_ID,
metric_name=_SINGLE_DIM_COUNTER,
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.INT, int_value=42),
timestamp=rdfvalue.RDFDatetime.FromSecondsSinceEpoch(2))
_single_dim_entry3 = stats_values.StatsStoreEntry(
process_id=_TEST_PROCESS_ID,
metric_name=_SINGLE_DIM_COUNTER,
metric_value=stats_values.StatsStoreValue(
value_type=rdf_stats.MetricMetadata.ValueType.INT, int_value=42),
timestamp=rdfvalue.RDFDatetime.FromSecondsSinceEpoch(3))
_multi_dim_entry = stats_values.StatsStoreEntry(
