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# http://www.apache.org/licenses/LICENSE-2.0
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# cython: language_level=3
# cython: profile=True
# pytype: skip-file
from __future__ import absolute_import
import collections
import time
from functools import reduce
from typing import FrozenSet
from typing import Hashable
from typing import List
from apache_beam.coders import coder_impl
from apache_beam.coders import coders
from apache_beam.metrics.cells import DistributionData
from apache_beam.metrics.cells import DistributionResult
from apache_beam.metrics.cells import GaugeData
from apache_beam.metrics.cells import GaugeResult
from apache_beam.portability import common_urns
from apache_beam.portability.api import metrics_pb2
SAMPLED_BYTE_SIZE_URN = (
common_urns.monitoring_info_specs.SAMPLED_BYTE_SIZE.spec.urn)
ELEMENT_COUNT_URN = common_urns.monitoring_info_specs.ELEMENT_COUNT.spec.urn
START_BUNDLE_MSECS_URN = (
common_urns.monitoring_info_specs.START_BUNDLE_MSECS.spec.urn)
PROCESS_BUNDLE_MSECS_URN = (
common_urns.monitoring_info_specs.PROCESS_BUNDLE_MSECS.spec.urn)
FINISH_BUNDLE_MSECS_URN = (
common_urns.monitoring_info_specs.FINISH_BUNDLE_MSECS.spec.urn)
TOTAL_MSECS_URN = common_urns.monitoring_info_specs.TOTAL_MSECS.spec.urn
USER_COUNTER_URN = common_urns.monitoring_info_specs.USER_SUM_INT64.spec.urn
USER_DISTRIBUTION_URN = (
common_urns.monitoring_info_specs.USER_DISTRIBUTION_INT64.spec.urn)
USER_GAUGE_URN = common_urns.monitoring_info_specs.USER_LATEST_INT64.spec.urn
USER_METRIC_URNS = set(
[USER_COUNTER_URN, USER_DISTRIBUTION_URN, USER_GAUGE_URN])
WORK_REMAINING_URN = common_urns.monitoring_info_specs.WORK_REMAINING.spec.urn
WORK_COMPLETED_URN = common_urns.monitoring_info_specs.WORK_COMPLETED.spec.urn
DATA_CHANNEL_READ_INDEX = (
common_urns.monitoring_info_specs.DATA_CHANNEL_READ_INDEX.spec.urn)
# TODO(ajamato): Implement the remaining types, i.e. Double types
# Extrema types, etc. See:
# https://s.apache.org/beam-fn-api-metrics
SUM_INT64_TYPE = common_urns.monitoring_info_types.SUM_INT64_TYPE.urn
DISTRIBUTION_INT64_TYPE = (
common_urns.monitoring_info_types.DISTRIBUTION_INT64_TYPE.urn)
LATEST_INT64_TYPE = common_urns.monitoring_info_types.LATEST_INT64_TYPE.urn
PROGRESS_TYPE = common_urns.monitoring_info_types.PROGRESS_TYPE.urn
COUNTER_TYPES = set([SUM_INT64_TYPE])
DISTRIBUTION_TYPES = set([DISTRIBUTION_INT64_TYPE])
GAUGE_TYPES = set([LATEST_INT64_TYPE])
# TODO(migryz) extract values from beam_fn_api.proto::MonitoringInfoLabels
PCOLLECTION_LABEL = (
common_urns.monitoring_info_labels.PCOLLECTION.label_props.name)
PTRANSFORM_LABEL = (
common_urns.monitoring_info_labels.TRANSFORM.label_props.name)
NAMESPACE_LABEL = (
common_urns.monitoring_info_labels.NAMESPACE.label_props.name)
NAME_LABEL = (common_urns.monitoring_info_labels.NAME.label_props.name)
[docs]def create_labels(ptransform=None, namespace=None, name=None, pcollection=None):
"""Create the label dictionary based on the provided values.
Args:
ptransform: The ptransform id used as a label.
pcollection: The pcollection id used as a label.
"""
labels = {}
if ptransform:
labels[PTRANSFORM_LABEL] = ptransform
if namespace:
labels[NAMESPACE_LABEL] = namespace
if name:
labels[NAME_LABEL] = name
if pcollection:
labels[PCOLLECTION_LABEL] = pcollection
return labels
[docs]def int64_user_counter(namespace, name, metric, ptransform=None):
# type: (...) -> metrics_pb2.MonitoringInfo
"""Return the counter monitoring info for the specifed URN, metric and labels.
Args:
urn: The URN of the monitoring info/metric.
metric: The payload field to use in the monitoring info or an int value.
ptransform: The ptransform id used as a label.
"""
labels = create_labels(ptransform=ptransform, namespace=namespace, name=name)
if isinstance(metric, int):
metric = coders.VarIntCoder().encode(metric)
return create_monitoring_info(
USER_COUNTER_URN, SUM_INT64_TYPE, metric, labels)
[docs]def int64_counter(urn, metric, ptransform=None, pcollection=None):
# type: (...) -> metrics_pb2.MonitoringInfo
"""Return the counter monitoring info for the specifed URN, metric and labels.
Args:
urn: The URN of the monitoring info/metric.
metric: The payload field to use in the monitoring info or an int value.
ptransform: The ptransform id used as a label.
pcollection: The pcollection id used as a label.
"""
labels = create_labels(ptransform=ptransform, pcollection=pcollection)
if isinstance(metric, int):
metric = coders.VarIntCoder().encode(metric)
return create_monitoring_info(urn, SUM_INT64_TYPE, metric, labels)
[docs]def int64_user_distribution(namespace, name, metric, ptransform=None):
"""Return the distribution monitoring info for the URN, metric and labels.
Args:
urn: The URN of the monitoring info/metric.
metric: The DistributionData for the metric.
ptransform: The ptransform id used as a label.
"""
labels = create_labels(ptransform=ptransform, namespace=namespace, name=name)
payload = _encode_distribution(
coders.VarIntCoder(), metric.count, metric.sum, metric.min, metric.max)
return create_monitoring_info(
USER_DISTRIBUTION_URN, DISTRIBUTION_INT64_TYPE, payload, labels)
[docs]def int64_distribution(urn, metric, ptransform=None, pcollection=None):
# type: (...) -> metrics_pb2.MonitoringInfo
"""Return a distribution monitoring info for the URN, metric and labels.
Args:
urn: The URN of the monitoring info/metric.
metric: The DistributionData for the metric.
ptransform: The ptransform id used as a label.
pcollection: The pcollection id used as a label.
"""
labels = create_labels(ptransform=ptransform, pcollection=pcollection)
payload = _encode_distribution(
coders.VarIntCoder(), metric.count, metric.sum, metric.min, metric.max)
return create_monitoring_info(urn, DISTRIBUTION_INT64_TYPE, payload, labels)
[docs]def int64_user_gauge(namespace, name, metric, ptransform=None):
# type: (...) -> metrics_pb2.MonitoringInfo
"""Return the gauge monitoring info for the URN, metric and labels.
Args:
namespace: User-defined namespace of counter.
name: Name of counter.
metric: The GaugeData containing the metrics.
ptransform: The ptransform id used as a label.
"""
labels = create_labels(ptransform=ptransform, namespace=namespace, name=name)
if isinstance(metric, GaugeData):
coder = coders.VarIntCoder()
value = metric.value
timestamp = metric.timestamp
else:
raise TypeError(
'Expected GaugeData metric type but received %s with value %s' %
(type(metric), metric))
payload = _encode_gauge(coder, timestamp, value)
return create_monitoring_info(
USER_GAUGE_URN, LATEST_INT64_TYPE, payload, labels)
[docs]def int64_gauge(urn, metric, ptransform=None):
# type: (...) -> metrics_pb2.MonitoringInfo
"""Return the gauge monitoring info for the URN, metric and labels.
Args:
urn: The URN of the monitoring info/metric.
metric: An int representing the value. The current time will be used for
the timestamp.
ptransform: The ptransform id used as a label.
"""
labels = create_labels(ptransform=ptransform)
if isinstance(metric, int):
value = metric
time_ms = int(time.time()) * 1000
else:
raise TypeError(
'Expected int metric type but received %s with value %s' %
(type(metric), metric))
coder = coders.VarIntCoder()
payload = coder.encode(time_ms) + coder.encode(value)
return create_monitoring_info(urn, LATEST_INT64_TYPE, payload, labels)
[docs]def create_monitoring_info(urn, type_urn, payload, labels=None):
# type: (...) -> metrics_pb2.MonitoringInfo
"""Return the gauge monitoring info for the URN, type, metric and labels.
Args:
urn: The URN of the monitoring info/metric.
type_urn: The URN of the type of the monitoring info/metric.
i.e. beam:metrics:sum_int_64, beam:metrics:latest_int_64.
payload: The payload field to use in the monitoring info.
labels: The label dictionary to use in the MonitoringInfo.
"""
return metrics_pb2.MonitoringInfo(
urn=urn, type=type_urn, labels=labels or dict(), payload=payload)
[docs]def is_counter(monitoring_info_proto):
"""Returns true if the monitoring info is a coutner metric."""
return monitoring_info_proto.type in COUNTER_TYPES
[docs]def is_gauge(monitoring_info_proto):
"""Returns true if the monitoring info is a gauge metric."""
return monitoring_info_proto.type in GAUGE_TYPES
[docs]def is_distribution(monitoring_info_proto):
"""Returns true if the monitoring info is a distrbution metric."""
return monitoring_info_proto.type in DISTRIBUTION_TYPES
[docs]def is_user_monitoring_info(monitoring_info_proto):
"""Returns true if the monitoring info is a user metric."""
return monitoring_info_proto.urn in USER_METRIC_URNS
[docs]def parse_namespace_and_name(monitoring_info_proto):
"""Returns the (namespace, name) tuple of the URN in the monitoring info."""
# Remove the URN prefix which indicates that it is a user counter.
if is_user_monitoring_info(monitoring_info_proto):
labels = monitoring_info_proto.labels
return labels[NAMESPACE_LABEL], labels[NAME_LABEL]
# If it is not a user counter, just use the first part of the URN, i.e. 'beam'
split = monitoring_info_proto.urn.split(':', 1)
return split[0], split[1]
[docs]def get_step_name(monitoring_info_proto):
"""Returns a step name for the given monitoring info or None if step name
cannot be specified."""
# Right now only metrics that have a PTRANSFORM are taken into account
return monitoring_info_proto.labels.get(PTRANSFORM_LABEL)
[docs]def to_key(monitoring_info_proto):
# type: (metrics_pb2.MonitoringInfo) -> FrozenSet[Hashable]
"""Returns a key based on the URN and labels.
This is useful in maps to prevent reporting the same MonitoringInfo twice.
"""
key_items = list(monitoring_info_proto.labels.items()) # type: List[Hashable]
key_items.append(monitoring_info_proto.urn)
return frozenset(key_items)
[docs]def sum_payload_combiner(payload_a, payload_b):
coder = coders.VarIntCoder()
return coder.encode(coder.decode(payload_a) + coder.decode(payload_b))
[docs]def distribution_payload_combiner(payload_a, payload_b):
coder = coders.VarIntCoder()
(count_a, sum_a, min_a, max_a) = _decode_distribution(coder, payload_a)
(count_b, sum_b, min_b, max_b) = _decode_distribution(coder, payload_b)
return _encode_distribution(
coder,
count_a + count_b,
sum_a + sum_b,
min(min_a, min_b),
max(max_a, max_b))
_KNOWN_COMBINERS = {
SUM_INT64_TYPE: sum_payload_combiner,
DISTRIBUTION_INT64_TYPE: distribution_payload_combiner,
}
[docs]def consolidate(metrics, key=to_key):
grouped = collections.defaultdict(list)
for metric in metrics:
grouped[key(metric)].append(metric)
for values in grouped.values():
if len(values) == 1:
yield values[0]
else:
combiner = _KNOWN_COMBINERS.get(values[0].type)
if combiner:
def merge(a, b):
# pylint: disable=cell-var-from-loop
return metrics_pb2.MonitoringInfo(
urn=a.urn,
type=a.type,
labels=dict((label, value) for label,
value in a.labels.items()
if b.labels.get(label) == value),
payload=combiner(a.payload, b.payload))
yield reduce(merge, values)
else:
for value in values:
yield value
def _decode_gauge(coder, payload):
"""Returns a tuple of (timestamp, value)."""
timestamp_coder = coders.VarIntCoder().get_impl()
stream = coder_impl.create_InputStream(payload)
time_ms = timestamp_coder.decode_from_stream(stream, True)
return (time_ms / 1000.0, coder.get_impl().decode_from_stream(stream, True))
def _encode_gauge(coder, timestamp, value):
timestamp_coder = coders.VarIntCoder().get_impl()
stream = coder_impl.create_OutputStream()
timestamp_coder.encode_to_stream(int(timestamp * 1000), stream, True)
coder.get_impl().encode_to_stream(value, stream, True)
return stream.get()
def _decode_distribution(value_coder, payload):
"""Returns a tuple of (count, sum, min, max)."""
count_coder = coders.VarIntCoder().get_impl()
value_coder = value_coder.get_impl()
stream = coder_impl.create_InputStream(payload)
return (
count_coder.decode_from_stream(stream, True),
value_coder.decode_from_stream(stream, True),
value_coder.decode_from_stream(stream, True),
value_coder.decode_from_stream(stream, True))
def _encode_distribution(value_coder, count, sum, min, max):
count_coder = coders.VarIntCoder().get_impl()
value_coder = value_coder.get_impl()
stream = coder_impl.create_OutputStream()
count_coder.encode_to_stream(count, stream, True)
value_coder.encode_to_stream(sum, stream, True)
value_coder.encode_to_stream(min, stream, True)
value_coder.encode_to_stream(max, stream, True)
return stream.get()