#

# Licensed to the Apache Software Foundation (ASF) under one or more

# contributor license agreements. See the NOTICE file distributed with

# this work for additional information regarding copyright ownership.

# The ASF licenses this file to You under the Apache License, Version 2.0

# (the "License"); you may not use this file except in compliance with

# the License. You may obtain a copy of the License at

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# http://www.apache.org/licenses/LICENSE-2.0

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# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

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"""

User-facing classes for Metrics API.

The classes in this file allow users to define and use metrics to be collected

and displayed as part of their pipeline execution.

- Metrics - This class lets pipeline and transform writers create and access

metric objects such as counters, distributions, etc.

"""

# pytype: skip-file

# mypy: disallow-untyped-defs

import logging

import re

from typing import TYPE_CHECKING

from typing import Dict

from typing import FrozenSet

from typing import Iterable

from typing import Iterator

from typing import List

from typing import Optional

from typing import Set

from typing import Type

from typing import Union

from apache_beam.metrics import cells

from apache_beam.metrics.cells import HistogramCellFactory

from apache_beam.metrics.execution import MetricResult

from apache_beam.metrics.execution import MetricUpdater

from apache_beam.metrics.metricbase import BoundedTrie

from apache_beam.metrics.metricbase import Counter

from apache_beam.metrics.metricbase import Distribution

from apache_beam.metrics.metricbase import Gauge

from apache_beam.metrics.metricbase import Histogram

from apache_beam.metrics.metricbase import MetricName

from apache_beam.metrics.metricbase import StringSet

if TYPE_CHECKING:

from apache_beam.internal.metrics.metric import MetricLogger

from apache_beam.metrics.execution import MetricKey

from apache_beam.metrics.metricbase import Metric

from apache_beam.utils.histogram import BucketType

__all__ = ['Metrics', 'MetricsFilter', 'Lineage']

_LOGGER = logging.getLogger(__name__)

class Metrics(object):

"""Lets users create/access metric objects during pipeline execution."""

@staticmethod

def get_namespace(namespace: Union[Type, str]) -> str:

if isinstance(namespace, type):

return '{}.{}'.format(namespace.__module__, namespace.__name__)

elif isinstance(namespace, str):

return namespace

else:

raise ValueError('Unknown namespace type')

@staticmethod

def counter(

namespace: Union[Type, str], name: str) -> 'Metrics.DelegatingCounter':

"""Obtains or creates a Counter metric.

Args:

namespace: A class or string that gives the namespace to a metric

name: A string that gives a unique name to a metric

Returns:

A Counter object.

"""

namespace = Metrics.get_namespace(namespace)

return Metrics.DelegatingCounter(MetricName(namespace, name))

@staticmethod

def distribution(

namespace: Union[Type, str],

name: str,

process_wide: bool = False) -> 'Metrics.DelegatingDistribution':

"""Obtains or creates a Distribution metric.

Distribution metrics are restricted to integer-only distributions.

Args:

namespace: A class or string that gives the namespace to a metric

name: A string that gives a unique name to a metric

Returns:

A Distribution object.

"""

namespace = Metrics.get_namespace(namespace)

return Metrics.DelegatingDistribution(

MetricName(namespace, name), process_wide=process_wide)

@staticmethod

def gauge(

namespace: Union[Type, str],

name: str,

process_wide: bool = False) -> 'Metrics.DelegatingGauge':

"""Obtains or creates a Gauge metric.

Gauge metrics are restricted to integer-only values.

Args:

namespace: A class or string that gives the namespace to a metric

name: A string that gives a unique name to a metric

process_wide: Whether or not the metric is specific to the current bundle

or should be calculated for the entire process.

Returns:

A Gauge object.

"""

namespace = Metrics.get_namespace(namespace)

return Metrics.DelegatingGauge(

MetricName(namespace, name), process_wide=process_wide)

@staticmethod

def string_set(

namespace: Union[Type, str], name: str) -> 'Metrics.DelegatingStringSet':

"""Obtains or creates a String set metric.

String set metrics are restricted to string values.

Args:

namespace: A class or string that gives the namespace to a metric

name: A string that gives a unique name to a metric

Returns:

A StringSet object.

"""

namespace = Metrics.get_namespace(namespace)

return Metrics.DelegatingStringSet(MetricName(namespace, name))

@staticmethod

def bounded_trie(

namespace: Union[Type, str],

name: str) -> 'Metrics.DelegatingBoundedTrie':

"""Obtains or creates a Bounded Trie metric.

Args:

namespace: A class or string that gives the namespace to a metric

name: A string that gives a unique name to a metric

Returns:

A BoundedTrie object.

"""

namespace = Metrics.get_namespace(namespace)

return Metrics.DelegatingBoundedTrie(MetricName(namespace, name))

@staticmethod

def histogram(

namespace: Union[Type, str],

name: str,

bucket_type: 'BucketType',

logger: Optional['MetricLogger'] = None) -> 'Metrics.DelegatingHistogram':

"""Obtains or creates a Histogram metric.

Args:

namespace: A class or string that gives the namespace to a metric

name: A string that gives a unique name to a metric

bucket_type: A type of bucket used in a histogram. A subclass of

apache_beam.utils.histogram.BucketType

logger: MetricLogger for logging locally aggregated metric

Returns:

A Histogram object.

"""

namespace = Metrics.get_namespace(namespace)

return Metrics.DelegatingHistogram(

MetricName(namespace, name), bucket_type, logger)

class DelegatingHistogram(Histogram):

"""Metrics Histogram that Delegates functionality to MetricsEnvironment."""

def __init__(

self,

metric_name: MetricName,

bucket_type: 'BucketType',

logger: Optional['MetricLogger']) -> None:

super().__init__(metric_name)

self.metric_name = metric_name

self.cell_type = HistogramCellFactory(bucket_type)

self.logger = logger

self.updater = MetricUpdater(self.cell_type, self.metric_name)

def update(self, value: object) -> None:

self.updater(value)

if self.logger:

self.logger.update(self.cell_type, self.metric_name, value)

class DelegatingCounter(Counter):

"""Metrics Counter that Delegates functionality to MetricsEnvironment."""

def __init__(

self, metric_name: MetricName, process_wide: bool = False) -> None:

super().__init__(metric_name)

self.inc = MetricUpdater( # type: ignore[method-assign]

cells.CounterCell,

metric_name,

default_value=1,

process_wide=process_wide)

class DelegatingDistribution(Distribution):

"""Metrics Distribution Delegates functionality to MetricsEnvironment."""

def __init__(

self, metric_name: MetricName, process_wide: bool = False) -> None:

super().__init__(metric_name)

self.update = MetricUpdater(cells.DistributionCell, metric_name, process_wide=process_wide) # type: ignore[method-assign]

class DelegatingGauge(Gauge):

"""Metrics Gauge that Delegates functionality to MetricsEnvironment."""

def __init__(

self, metric_name: MetricName, process_wide: bool = False) -> None:

super().__init__(metric_name)

self.set = MetricUpdater( # type: ignore[method-assign]

cells.GaugeCell,

metric_name,

process_wide=process_wide)

class DelegatingStringSet(StringSet):

"""Metrics StringSet that Delegates functionality to MetricsEnvironment."""

def __init__(self, metric_name: MetricName) -> None:

super().__init__(metric_name)

self.add = MetricUpdater(cells.StringSetCell, metric_name) # type: ignore[method-assign]

class DelegatingBoundedTrie(BoundedTrie):

"""Metrics StringSet that Delegates functionality to MetricsEnvironment."""

def __init__(self, metric_name: MetricName) -> None:

super().__init__(metric_name)

self.add = MetricUpdater(cells.BoundedTrieCell, metric_name) # type: ignore[method-assign]

class MetricResults(object):

COUNTERS = "counters"

DISTRIBUTIONS = "distributions"

GAUGES = "gauges"

STRINGSETS = "string_sets"

BOUNDED_TRIES = "bounded_tries"

HISTOGRAMS = "histograms"

@staticmethod

def _matches_name(filter: 'MetricsFilter', metric_key: 'MetricKey') -> bool:

if ((filter.namespaces and

metric_key.metric.namespace not in filter.namespaces) or

(filter.names and metric_key.metric.name not in filter.names)):

return False

else:

return True

@staticmethod

def _is_sub_list(needle: List[str], haystack: List[str]) -> bool:

"""True iff `needle` is a sub-list of `haystack` (i.e. a contiguous slice

of `haystack` exactly matches `needle`"""

needle_len = len(needle)

haystack_len = len(haystack)

for i in range(0, haystack_len - needle_len + 1):

if haystack[i:i + needle_len] == needle:

return True

return False

@staticmethod

def _matches_sub_path(actual_scope: str, filter_scope: str) -> bool:

"""True iff the '/'-delimited pieces of filter_scope exist as a sub-list

of the '/'-delimited pieces of actual_scope"""

return bool(

actual_scope and MetricResults._is_sub_list(

filter_scope.split('/'), actual_scope.split('/')))

@staticmethod

def _matches_scope(filter: 'MetricsFilter', metric_key: 'MetricKey') -> bool:

if not filter.steps:

return True

for step in filter.steps:

if MetricResults._matches_sub_path(metric_key.step, step):

return True

return False

@staticmethod

def matches(

filter: Optional['MetricsFilter'], metric_key: 'MetricKey') -> bool:

if filter is None:

return True

if (MetricResults._matches_name(filter, metric_key) and

MetricResults._matches_scope(filter, metric_key)):

return True

return False

def query(

self,

filter: Optional['MetricsFilter'] = None

) -> Dict[str, List['MetricResult']]:

"""Queries the runner for existing user metrics that match the filter.

It should return a dictionary, with lists of each kind of metric, and

each list contains the corresponding kind of MetricResult. Like so:

{

"counters": [MetricResult(counter_key, committed, attempted), ...],

"distributions": [MetricResult(dist_key, committed, attempted), ...],

"gauges": [], // Empty list if nothing matched the filter.

"string_sets": [] [MetricResult(string_set_key, committed, attempted),

...]

}

The committed / attempted values are DistributionResult / GaugeResult / int

/ set objects.

"""

raise NotImplementedError

class MetricsFilter(object):

"""Simple object to filter metrics results.

If filters by matching a result's step-namespace-name with three internal

sets. No execution/matching logic is added to this object, so that it may

be used to construct arguments as an RPC request. It is left for runners

to implement matching logic by themselves.

Note: This class only supports user defined metrics.

"""

def __init__(self) -> None:

self._names: Set[str] = set()

self._namespaces: Set[str] = set()

self._steps: Set[str] = set()

@property

def steps(self) -> FrozenSet[str]:

return frozenset(self._steps)

@property

def names(self) -> FrozenSet[str]:

return frozenset(self._names)

@property

def namespaces(self) -> FrozenSet[str]:

return frozenset(self._namespaces)

def with_metric(self, metric: 'Metric') -> 'MetricsFilter':

name = metric.metric_name.name or ''

namespace = metric.metric_name.namespace or ''

return self.with_name(name).with_namespace(namespace)

def with_name(self, name: str) -> 'MetricsFilter':

return self.with_names([name])

def with_names(self, names: Iterable[str]) -> 'MetricsFilter':

if isinstance(names, str):

raise ValueError('Names must be a collection, not a string')

self._names.update(names)

return self

def with_namespace(self, namespace: Union[Type, str]) -> 'MetricsFilter':

return self.with_namespaces([namespace])

def with_namespaces(

self, namespaces: Iterable[Union[Type, str]]) -> 'MetricsFilter':

if isinstance(namespaces, str):

raise ValueError('Namespaces must be an iterable, not a string')

self._namespaces.update([Metrics.get_namespace(ns) for ns in namespaces])

return self

def with_step(self, step: str) -> 'MetricsFilter':

return self.with_steps([step])

def with_steps(self, steps: Iterable[str]) -> 'MetricsFilter':

if isinstance(steps, str):

raise ValueError('Steps must be an iterable, not a string')

self._steps.update(steps)

return self

class Lineage:

"""Standard collection of metrics used to record source and sinks information

for lineage tracking."""

LINEAGE_NAMESPACE = "lineage"

SOURCE = "sources_v2"

SINK = "sinks_v2"

_METRICS = {

SOURCE: Metrics.bounded_trie(LINEAGE_NAMESPACE, SOURCE),

SINK: Metrics.bounded_trie(LINEAGE_NAMESPACE, SINK)

}

def __init__(self, label: str) -> None:

"""Create a Lineage with valid label (:data:`~Lineage.SOURCE` or

:data:`~Lineage.SINK`)

"""

self.metric = Lineage._METRICS[label]

@classmethod

def sources(cls) -> 'Lineage':

return cls(Lineage.SOURCE)

@classmethod

def sinks(cls) -> 'Lineage':

return cls(Lineage.SINK)

_RESERVED_CHARS = re.compile(r'[:\s.]')

@staticmethod

def wrap_segment(segment: str) -> str:

"""Wrap segment to valid segment name.

Specifically, If there are reserved chars (colon, whitespace, dot), escape

with backtick. If the segment is already wrapped, return the original.

"""

if segment.startswith("`") and segment.endswith("`"): return segment

if Lineage._RESERVED_CHARS.search(segment):

return "`" + segment + "`"

return segment

@staticmethod

def get_fq_name(

system: str, *segments: str, subtype: Optional[str] = None) -> str:

"""Assemble fully qualified name

(`FQN <https://cloud.google.com/data-catalog/docs/fully-qualified-names>`_).

Format:

- `system:segment1.segment2`

- `system:subtype:segment1.segment2`

- `system:`segment1.with.dots:colons`.segment2`

This helper method is for internal and testing usage only.

"""

segs = '.'.join(map(Lineage.wrap_segment, segments))

if subtype:

return ':'.join((system, subtype, segs))

return ':'.join((system, segs))

@staticmethod

def _get_fqn_parts(

system: str,

*segments: str,

subtype: Optional[str] = None,

last_segment_sep: Optional[str] = None) -> Iterator[str]:

yield system + ':'

if subtype:

yield subtype + ':'

if segments:

for segment in segments[:-1]:

yield segment + '.'

if last_segment_sep:

sub_segments = segments[-1].split(last_segment_sep)

for sub_segment in sub_segments[:-1]:

yield sub_segment + last_segment_sep

yield sub_segments[-1]

else:

yield segments[-1]

def add(

self,

system: str,

*segments: str,

subtype: Optional[str] = None,

last_segment_sep: Optional[str] = None) -> None:

"""

Adds the given details as Lineage.

For asset level lineage the resource location should be specified as

Dataplex FQN, see

https://cloud.google.com/data-catalog/docs/fully-qualified-names

Example of adding FQN components:

- `add("system", "segment1", "segment2")`

- `add("system", "segment1", "segment2", subtype="subtype")`

Example of adding a FQN:

- `add("system:segment1.segment2")`

- `add("system:subtype:segment1.segment2")`

The first positional argument serves as system, if full segments are

provided, or the full FQN if it is provided as a single argument.

"""

self.add_raw(

*self._get_fqn_parts(

system,

*segments,

subtype=subtype,

last_segment_sep=last_segment_sep))

def add_raw(self, *rollup_segments: str) -> None:

"""Adds the given fqn as lineage.

`rollup_segments` should be an iterable of strings whose concatenation

is a valid Dataplex FQN. In particular, this means they will often have

trailing delimiters.

"""

self.metric.add(rollup_segments)

@staticmethod

def query(results: MetricResults,

label: str,

truncated_marker: str = '*') -> Set[str]:

if not label in Lineage._METRICS:

raise ValueError("Label {} does not exist for Lineage", label)

response = results.query(

MetricsFilter().with_namespace(Lineage.LINEAGE_NAMESPACE).with_name(

label))[MetricResults.BOUNDED_TRIES]

result = set()

for metric in response:

for fqn in metric.committed.flattened():

result.add(''.join(fqn[:-1]) + (truncated_marker if fqn[-1] else ''))

for fqn in metric.attempted.flattened():

result.add(''.join(fqn[:-1]) + (truncated_marker if fqn[-1] else ''))

return result