| # |
| # 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 |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # 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. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| |
| """Simple utility PTransforms. |
| """ |
| |
| # pytype: skip-file |
| |
| import collections |
| import contextlib |
| import random |
| import re |
| import threading |
| import time |
| import uuid |
| from typing import TYPE_CHECKING |
| from typing import Any |
| from typing import Iterable |
| from typing import List |
| from typing import Tuple |
| from typing import TypeVar |
| from typing import Union |
| |
| from apache_beam import coders |
| from apache_beam import typehints |
| from apache_beam.metrics import Metrics |
| from apache_beam.portability import common_urns |
| from apache_beam.portability.api import beam_runner_api_pb2 |
| from apache_beam.pvalue import AsSideInput |
| from apache_beam.transforms import window |
| from apache_beam.transforms.combiners import CountCombineFn |
| from apache_beam.transforms.core import CombinePerKey |
| from apache_beam.transforms.core import DoFn |
| from apache_beam.transforms.core import FlatMap |
| from apache_beam.transforms.core import Flatten |
| from apache_beam.transforms.core import GroupByKey |
| from apache_beam.transforms.core import Map |
| from apache_beam.transforms.core import MapTuple |
| from apache_beam.transforms.core import ParDo |
| from apache_beam.transforms.core import Windowing |
| from apache_beam.transforms.ptransform import PTransform |
| from apache_beam.transforms.ptransform import ptransform_fn |
| from apache_beam.transforms.timeutil import TimeDomain |
| from apache_beam.transforms.trigger import AccumulationMode |
| from apache_beam.transforms.trigger import Always |
| from apache_beam.transforms.userstate import BagStateSpec |
| from apache_beam.transforms.userstate import CombiningValueStateSpec |
| from apache_beam.transforms.userstate import TimerSpec |
| from apache_beam.transforms.userstate import on_timer |
| from apache_beam.transforms.window import NonMergingWindowFn |
| from apache_beam.transforms.window import TimestampCombiner |
| from apache_beam.transforms.window import TimestampedValue |
| from apache_beam.typehints.decorators import get_signature |
| from apache_beam.typehints.sharded_key_type import ShardedKeyType |
| from apache_beam.utils import windowed_value |
| from apache_beam.utils.annotations import deprecated |
| from apache_beam.utils.annotations import experimental |
| from apache_beam.utils.sharded_key import ShardedKey |
| |
| if TYPE_CHECKING: |
| from apache_beam import pvalue |
| from apache_beam.runners.pipeline_context import PipelineContext |
| |
| __all__ = [ |
| 'BatchElements', |
| 'CoGroupByKey', |
| 'Distinct', |
| 'Keys', |
| 'KvSwap', |
| 'Regex', |
| 'Reify', |
| 'RemoveDuplicates', |
| 'Reshuffle', |
| 'ToString', |
| 'Values', |
| 'WithKeys', |
| 'GroupIntoBatches' |
| ] |
| |
| K = TypeVar('K') |
| V = TypeVar('V') |
| T = TypeVar('T') |
| |
| |
| class CoGroupByKey(PTransform): |
| """Groups results across several PCollections by key. |
| |
| Given an input dict of serializable keys (called "tags") to 0 or more |
| PCollections of (key, value) tuples, it creates a single output PCollection |
| of (key, value) tuples whose keys are the unique input keys from all inputs, |
| and whose values are dicts mapping each tag to an iterable of whatever values |
| were under the key in the corresponding PCollection, in this manner:: |
| |
| ('some key', {'tag1': ['value 1 under "some key" in pcoll1', |
| 'value 2 under "some key" in pcoll1', |
| ...], |
| 'tag2': ... , |
| ... }) |
| |
| For example, given:: |
| |
| {'tag1': pc1, 'tag2': pc2, 333: pc3} |
| |
| where:: |
| |
| pc1 = [(k1, v1)] |
| pc2 = [] |
| pc3 = [(k1, v31), (k1, v32), (k2, v33)] |
| |
| The output PCollection would be:: |
| |
| [(k1, {'tag1': [v1], 'tag2': [], 333: [v31, v32]}), |
| (k2, {'tag1': [], 'tag2': [], 333: [v33]})] |
| |
| CoGroupByKey also works for tuples, lists, or other flat iterables of |
| PCollections, in which case the values of the resulting PCollections |
| will be tuples whose nth value is the list of values from the nth |
| PCollection---conceptually, the "tags" are the indices into the input. |
| Thus, for this input:: |
| |
| (pc1, pc2, pc3) |
| |
| the output would be:: |
| |
| [(k1, ([v1], [], [v31, v32]), |
| (k2, ([], [], [v33]))] |
| |
| Attributes: |
| **kwargs: Accepts a single named argument "pipeline", which specifies the |
| pipeline that "owns" this PTransform. Ordinarily CoGroupByKey can obtain |
| this information from one of the input PCollections, but if there are none |
| (or if there's a chance there may be none), this argument is the only way |
| to provide pipeline information, and should be considered mandatory. |
| """ |
| def __init__(self, *, pipeline=None): |
| self.pipeline = pipeline |
| |
| def _extract_input_pvalues(self, pvalueish): |
| try: |
| # If this works, it's a dict. |
| return pvalueish, tuple(pvalueish.values()) |
| except AttributeError: |
| # Cast iterables a tuple so we can do re-iteration. |
| pcolls = tuple(pvalueish) |
| return pcolls, pcolls |
| |
| def expand(self, pcolls): |
| if isinstance(pcolls, dict): |
| if all(isinstance(tag, str) and len(tag) < 10 for tag in pcolls.keys()): |
| # Small, string tags. Pass them as data. |
| pcolls_dict = pcolls |
| restore_tags = None |
| else: |
| # Pass the tags in the restore_tags closure. |
| tags = list(pcolls.keys()) |
| pcolls_dict = {str(ix): pcolls[tag] for (ix, tag) in enumerate(tags)} |
| restore_tags = lambda vs: { |
| tag: vs[str(ix)] |
| for (ix, tag) in enumerate(tags) |
| } |
| else: |
| # Tags are tuple indices. |
| num_tags = len(pcolls) |
| pcolls_dict = {str(ix): pcolls[ix] for ix in range(num_tags)} |
| restore_tags = lambda vs: tuple(vs[str(ix)] for ix in range(num_tags)) |
| |
| result = ( |
| pcolls_dict | 'CoGroupByKeyImpl' >> _CoGBKImpl(pipeline=self.pipeline)) |
| if restore_tags: |
| return result | 'RestoreTags' >> MapTuple( |
| lambda k, vs: (k, restore_tags(vs))) |
| else: |
| return result |
| |
| |
| class _CoGBKImpl(PTransform): |
| def __init__(self, *, pipeline=None): |
| self.pipeline = pipeline |
| |
| def expand(self, pcolls): |
| # Check input PCollections for PCollection-ness, and that they all belong |
| # to the same pipeline. |
| for pcoll in pcolls.values(): |
| self._check_pcollection(pcoll) |
| if self.pipeline: |
| assert pcoll.pipeline == self.pipeline |
| |
| tags = list(pcolls.keys()) |
| |
| def add_tag(tag): |
| return lambda k, v: (k, (tag, v)) |
| |
| def collect_values(key, tagged_values): |
| grouped_values = {tag: [] for tag in tags} |
| for tag, value in tagged_values: |
| grouped_values[tag].append(value) |
| return key, grouped_values |
| |
| return ([ |
| pcoll |
| | 'Tag[%s]' % tag >> MapTuple(add_tag(tag)) |
| for (tag, pcoll) in pcolls.items() |
| ] |
| | Flatten(pipeline=self.pipeline) |
| | GroupByKey() |
| | MapTuple(collect_values)) |
| |
| |
| @ptransform_fn |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types(K) |
| def Keys(pcoll, label='Keys'): # pylint: disable=invalid-name |
| """Produces a PCollection of first elements of 2-tuples in a PCollection.""" |
| return pcoll | label >> MapTuple(lambda k, _: k) |
| |
| |
| @ptransform_fn |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types(V) |
| def Values(pcoll, label='Values'): # pylint: disable=invalid-name |
| """Produces a PCollection of second elements of 2-tuples in a PCollection.""" |
| return pcoll | label >> MapTuple(lambda _, v: v) |
| |
| |
| @ptransform_fn |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types(Tuple[V, K]) |
| def KvSwap(pcoll, label='KvSwap'): # pylint: disable=invalid-name |
| """Produces a PCollection reversing 2-tuples in a PCollection.""" |
| return pcoll | label >> MapTuple(lambda k, v: (v, k)) |
| |
| |
| @ptransform_fn |
| @typehints.with_input_types(T) |
| @typehints.with_output_types(T) |
| def Distinct(pcoll): # pylint: disable=invalid-name |
| """Produces a PCollection containing distinct elements of a PCollection.""" |
| return ( |
| pcoll |
| | 'ToPairs' >> Map(lambda v: (v, None)) |
| | 'Group' >> CombinePerKey(lambda vs: None) |
| | 'Distinct' >> Keys()) |
| |
| |
| @deprecated(since='2.12', current='Distinct') |
| @ptransform_fn |
| @typehints.with_input_types(T) |
| @typehints.with_output_types(T) |
| def RemoveDuplicates(pcoll): |
| """Produces a PCollection containing distinct elements of a PCollection.""" |
| return pcoll | 'RemoveDuplicates' >> Distinct() |
| |
| |
| class _BatchSizeEstimator(object): |
| """Estimates the best size for batches given historical timing. |
| """ |
| |
| _MAX_DATA_POINTS = 100 |
| _MAX_GROWTH_FACTOR = 2 |
| |
| def __init__( |
| self, |
| min_batch_size=1, |
| max_batch_size=10000, |
| target_batch_overhead=.05, |
| target_batch_duration_secs=1, |
| variance=0.25, |
| clock=time.time, |
| ignore_first_n_seen_per_batch_size=0): |
| if min_batch_size > max_batch_size: |
| raise ValueError( |
| "Minimum (%s) must not be greater than maximum (%s)" % |
| (min_batch_size, max_batch_size)) |
| if target_batch_overhead and not 0 < target_batch_overhead <= 1: |
| raise ValueError( |
| "target_batch_overhead (%s) must be between 0 and 1" % |
| (target_batch_overhead)) |
| if target_batch_duration_secs and target_batch_duration_secs <= 0: |
| raise ValueError( |
| "target_batch_duration_secs (%s) must be positive" % |
| (target_batch_duration_secs)) |
| if not (target_batch_overhead or target_batch_duration_secs): |
| raise ValueError( |
| "At least one of target_batch_overhead or " |
| "target_batch_duration_secs must be positive.") |
| if ignore_first_n_seen_per_batch_size < 0: |
| raise ValueError( |
| 'ignore_first_n_seen_per_batch_size (%s) must be non ' |
| 'negative' % (ignore_first_n_seen_per_batch_size)) |
| self._min_batch_size = min_batch_size |
| self._max_batch_size = max_batch_size |
| self._target_batch_overhead = target_batch_overhead |
| self._target_batch_duration_secs = target_batch_duration_secs |
| self._variance = variance |
| self._clock = clock |
| self._data = [] |
| self._ignore_next_timing = False |
| self._ignore_first_n_seen_per_batch_size = ( |
| ignore_first_n_seen_per_batch_size) |
| self._batch_size_num_seen = {} |
| self._replay_last_batch_size = None |
| |
| self._size_distribution = Metrics.distribution( |
| 'BatchElements', 'batch_size') |
| self._time_distribution = Metrics.distribution( |
| 'BatchElements', 'msec_per_batch') |
| # Beam distributions only accept integer values, so we use this to |
| # accumulate under-reported values until they add up to whole milliseconds. |
| # (Milliseconds are chosen because that's conventionally used elsewhere in |
| # profiling-style counters.) |
| self._remainder_msecs = 0 |
| |
| def ignore_next_timing(self): |
| """Call to indicate the next timing should be ignored. |
| |
| For example, the first emit of a ParDo operation is known to be anomalous |
| due to setup that may occur. |
| """ |
| self._ignore_next_timing = True |
| |
| @contextlib.contextmanager |
| def record_time(self, batch_size): |
| start = self._clock() |
| yield |
| elapsed = self._clock() - start |
| elapsed_msec = 1e3 * elapsed + self._remainder_msecs |
| self._size_distribution.update(batch_size) |
| self._time_distribution.update(int(elapsed_msec)) |
| self._remainder_msecs = elapsed_msec - int(elapsed_msec) |
| # If we ignore the next timing, replay the batch size to get accurate |
| # timing. |
| if self._ignore_next_timing: |
| self._ignore_next_timing = False |
| self._replay_last_batch_size = batch_size |
| else: |
| self._data.append((batch_size, elapsed)) |
| if len(self._data) >= self._MAX_DATA_POINTS: |
| self._thin_data() |
| |
| def _thin_data(self): |
| # Make sure we don't change the parity of len(self._data) |
| # As it's used below to alternate jitter. |
| self._data.pop(random.randrange(len(self._data) // 4)) |
| self._data.pop(random.randrange(len(self._data) // 2)) |
| |
| @staticmethod |
| def linear_regression_no_numpy(xs, ys): |
| # Least squares fit for y = a + bx over all points. |
| n = float(len(xs)) |
| xbar = sum(xs) / n |
| ybar = sum(ys) / n |
| if xbar == 0: |
| return ybar, 0 |
| if all(xs[0] == x for x in xs): |
| # Simply use the mean if all values in xs are same. |
| return 0, ybar / xbar |
| b = ( |
| sum([(x - xbar) * (y - ybar) |
| for x, y in zip(xs, ys)]) / sum([(x - xbar)**2 for x in xs])) |
| a = ybar - b * xbar |
| return a, b |
| |
| @staticmethod |
| def linear_regression_numpy(xs, ys): |
| # pylint: disable=wrong-import-order, wrong-import-position |
| import numpy as np |
| from numpy import sum |
| n = len(xs) |
| if all(xs[0] == x for x in xs): |
| # If all values of xs are same then fallback to linear_regression_no_numpy |
| return _BatchSizeEstimator.linear_regression_no_numpy(xs, ys) |
| xs = np.asarray(xs, dtype=float) |
| ys = np.asarray(ys, dtype=float) |
| |
| # First do a simple least squares fit for y = a + bx over all points. |
| b, a = np.polyfit(xs, ys, 1) |
| |
| if n < 10: |
| return a, b |
| else: |
| # Refine this by throwing out outliers, according to Cook's distance. |
| # https://en.wikipedia.org/wiki/Cook%27s_distance |
| sum_x = sum(xs) |
| sum_x2 = sum(xs**2) |
| errs = a + b * xs - ys |
| s2 = sum(errs**2) / (n - 2) |
| if s2 == 0: |
| # It's an exact fit! |
| return a, b |
| h = (sum_x2 - 2 * sum_x * xs + n * xs**2) / (n * sum_x2 - sum_x**2) |
| cook_ds = 0.5 / s2 * errs**2 * (h / (1 - h)**2) |
| |
| # Re-compute the regression, excluding those points with Cook's distance |
| # greater than 0.5, and weighting by the inverse of x to give a more |
| # stable y-intercept (as small batches have relatively more information |
| # about the fixed overhead). |
| weight = (cook_ds <= 0.5) / xs |
| b, a = np.polyfit(xs, ys, 1, w=weight) |
| return a, b |
| |
| try: |
| # pylint: disable=wrong-import-order, wrong-import-position |
| import numpy as np |
| linear_regression = linear_regression_numpy |
| except ImportError: |
| linear_regression = linear_regression_no_numpy |
| |
| def _calculate_next_batch_size(self): |
| if self._min_batch_size == self._max_batch_size: |
| return self._min_batch_size |
| elif len(self._data) < 1: |
| return self._min_batch_size |
| elif len(self._data) < 2: |
| # Force some variety so we have distinct batch sizes on which to do |
| # linear regression below. |
| return int( |
| max( |
| min( |
| self._max_batch_size, |
| self._min_batch_size * self._MAX_GROWTH_FACTOR), |
| self._min_batch_size + 1)) |
| |
| # There tends to be a lot of noise in the top quantile, which also |
| # has outsided influence in the regression. If we have enough data, |
| # Simply declare the top 20% to be outliers. |
| trimmed_data = sorted(self._data)[:max(20, len(self._data) * 4 // 5)] |
| |
| # Linear regression for y = a + bx, where x is batch size and y is time. |
| xs, ys = zip(*trimmed_data) |
| a, b = self.linear_regression(xs, ys) |
| |
| # Avoid nonsensical or division-by-zero errors below due to noise. |
| a = max(a, 1e-10) |
| b = max(b, 1e-20) |
| |
| last_batch_size = self._data[-1][0] |
| cap = min(last_batch_size * self._MAX_GROWTH_FACTOR, self._max_batch_size) |
| |
| target = self._max_batch_size |
| |
| if self._target_batch_duration_secs: |
| # Solution to a + b*x = self._target_batch_duration_secs. |
| target = min(target, (self._target_batch_duration_secs - a) / b) |
| |
| if self._target_batch_overhead: |
| # Solution to a / (a + b*x) = self._target_batch_overhead. |
| target = min(target, (a / b) * (1 / self._target_batch_overhead - 1)) |
| |
| # Avoid getting stuck at a single batch size (especially the minimal |
| # batch size) which would not allow us to extrapolate to other batch |
| # sizes. |
| # Jitter alternates between 0 and 1. |
| jitter = len(self._data) % 2 |
| # Smear our samples across a range centered at the target. |
| if len(self._data) > 10: |
| target += int(target * self._variance * 2 * (random.random() - .5)) |
| |
| return int(max(self._min_batch_size + jitter, min(target, cap))) |
| |
| def next_batch_size(self): |
| # Check if we should replay a previous batch size due to it not being |
| # recorded. |
| if self._replay_last_batch_size: |
| result = self._replay_last_batch_size |
| self._replay_last_batch_size = None |
| else: |
| result = self._calculate_next_batch_size() |
| |
| seen_count = self._batch_size_num_seen.get(result, 0) + 1 |
| if seen_count <= self._ignore_first_n_seen_per_batch_size: |
| self.ignore_next_timing() |
| self._batch_size_num_seen[result] = seen_count |
| return result |
| |
| |
| class _GlobalWindowsBatchingDoFn(DoFn): |
| def __init__(self, batch_size_estimator): |
| self._batch_size_estimator = batch_size_estimator |
| |
| def start_bundle(self): |
| self._batch = [] |
| self._batch_size = self._batch_size_estimator.next_batch_size() |
| # The first emit often involves non-trivial setup. |
| self._batch_size_estimator.ignore_next_timing() |
| |
| def process(self, element): |
| self._batch.append(element) |
| if len(self._batch) >= self._batch_size: |
| with self._batch_size_estimator.record_time(self._batch_size): |
| yield self._batch |
| self._batch = [] |
| self._batch_size = self._batch_size_estimator.next_batch_size() |
| |
| def finish_bundle(self): |
| if self._batch: |
| with self._batch_size_estimator.record_time(self._batch_size): |
| yield window.GlobalWindows.windowed_value(self._batch) |
| self._batch = None |
| self._batch_size = self._batch_size_estimator.next_batch_size() |
| |
| |
| class _WindowAwareBatchingDoFn(DoFn): |
| |
| _MAX_LIVE_WINDOWS = 10 |
| |
| def __init__(self, batch_size_estimator): |
| self._batch_size_estimator = batch_size_estimator |
| |
| def start_bundle(self): |
| self._batches = collections.defaultdict(list) |
| self._batch_size = self._batch_size_estimator.next_batch_size() |
| # The first emit often involves non-trivial setup. |
| self._batch_size_estimator.ignore_next_timing() |
| |
| def process(self, element, window=DoFn.WindowParam): |
| self._batches[window].append(element) |
| if len(self._batches[window]) >= self._batch_size: |
| with self._batch_size_estimator.record_time(self._batch_size): |
| yield windowed_value.WindowedValue( |
| self._batches[window], window.max_timestamp(), (window, )) |
| del self._batches[window] |
| self._batch_size = self._batch_size_estimator.next_batch_size() |
| elif len(self._batches) > self._MAX_LIVE_WINDOWS: |
| window, _ = sorted( |
| self._batches.items(), |
| key=lambda window_batch: len(window_batch[1]), |
| reverse=True)[0] |
| with self._batch_size_estimator.record_time(self._batch_size): |
| yield windowed_value.WindowedValue( |
| self._batches[window], window.max_timestamp(), (window, )) |
| del self._batches[window] |
| self._batch_size = self._batch_size_estimator.next_batch_size() |
| |
| def finish_bundle(self): |
| for window, batch in self._batches.items(): |
| if batch: |
| with self._batch_size_estimator.record_time(self._batch_size): |
| yield windowed_value.WindowedValue( |
| batch, window.max_timestamp(), (window, )) |
| self._batches = None |
| self._batch_size = self._batch_size_estimator.next_batch_size() |
| |
| |
| @typehints.with_input_types(T) |
| @typehints.with_output_types(List[T]) |
| class BatchElements(PTransform): |
| """A Transform that batches elements for amortized processing. |
| |
| This transform is designed to precede operations whose processing cost |
| is of the form |
| |
| time = fixed_cost + num_elements * per_element_cost |
| |
| where the per element cost is (often significantly) smaller than the fixed |
| cost and could be amortized over multiple elements. It consumes a PCollection |
| of element type T and produces a PCollection of element type List[T]. |
| |
| This transform attempts to find the best batch size between the minimim |
| and maximum parameters by profiling the time taken by (fused) downstream |
| operations. For a fixed batch size, set the min and max to be equal. |
| |
| Elements are batched per-window and batches emitted in the window |
| corresponding to its contents. |
| |
| Args: |
| min_batch_size: (optional) the smallest number of elements per batch |
| max_batch_size: (optional) the largest number of elements per batch |
| target_batch_overhead: (optional) a target for fixed_cost / time, |
| as used in the formula above |
| target_batch_duration_secs: (optional) a target for total time per bundle, |
| in seconds |
| variance: (optional) the permitted (relative) amount of deviation from the |
| (estimated) ideal batch size used to produce a wider base for |
| linear interpolation |
| clock: (optional) an alternative to time.time for measuring the cost of |
| donwstream operations (mostly for testing) |
| """ |
| def __init__( |
| self, |
| min_batch_size=1, |
| max_batch_size=10000, |
| target_batch_overhead=.05, |
| target_batch_duration_secs=1, |
| variance=0.25, |
| clock=time.time): |
| self._batch_size_estimator = _BatchSizeEstimator( |
| min_batch_size=min_batch_size, |
| max_batch_size=max_batch_size, |
| target_batch_overhead=target_batch_overhead, |
| target_batch_duration_secs=target_batch_duration_secs, |
| variance=variance, |
| clock=clock) |
| |
| def expand(self, pcoll): |
| if getattr(pcoll.pipeline.runner, 'is_streaming', False): |
| raise NotImplementedError("Requires stateful processing (BEAM-2687)") |
| elif pcoll.windowing.is_default(): |
| # This is the same logic as _GlobalWindowsBatchingDoFn, but optimized |
| # for that simpler case. |
| return pcoll | ParDo( |
| _GlobalWindowsBatchingDoFn(self._batch_size_estimator)) |
| else: |
| return pcoll | ParDo(_WindowAwareBatchingDoFn(self._batch_size_estimator)) |
| |
| |
| class _IdentityWindowFn(NonMergingWindowFn): |
| """Windowing function that preserves existing windows. |
| |
| To be used internally with the Reshuffle transform. |
| Will raise an exception when used after DoFns that return TimestampedValue |
| elements. |
| """ |
| def __init__(self, window_coder): |
| """Create a new WindowFn with compatible coder. |
| To be applied to PCollections with windows that are compatible with the |
| given coder. |
| |
| Arguments: |
| window_coder: coders.Coder object to be used on windows. |
| """ |
| super().__init__() |
| if window_coder is None: |
| raise ValueError('window_coder should not be None') |
| self._window_coder = window_coder |
| |
| def assign(self, assign_context): |
| if assign_context.window is None: |
| raise ValueError( |
| 'assign_context.window should not be None. ' |
| 'This might be due to a DoFn returning a TimestampedValue.') |
| return [assign_context.window] |
| |
| def get_window_coder(self): |
| return self._window_coder |
| |
| |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types(Tuple[K, V]) |
| class ReshufflePerKey(PTransform): |
| """PTransform that returns a PCollection equivalent to its input, |
| but operationally provides some of the side effects of a GroupByKey, |
| in particular checkpointing, and preventing fusion of the surrounding |
| transforms. |
| |
| ReshufflePerKey is experimental. No backwards compatibility guarantees. |
| """ |
| def expand(self, pcoll): |
| windowing_saved = pcoll.windowing |
| if windowing_saved.is_default(): |
| # In this (common) case we can use a trivial trigger driver |
| # and avoid the (expensive) window param. |
| globally_windowed = window.GlobalWindows.windowed_value(None) |
| MIN_TIMESTAMP = window.MIN_TIMESTAMP |
| |
| def reify_timestamps(element, timestamp=DoFn.TimestampParam): |
| key, value = element |
| if timestamp == MIN_TIMESTAMP: |
| timestamp = None |
| return key, (value, timestamp) |
| |
| def restore_timestamps(element): |
| key, values = element |
| return [ |
| globally_windowed.with_value((key, value)) if timestamp is None else |
| window.GlobalWindows.windowed_value((key, value), timestamp) |
| for (value, timestamp) in values |
| ] |
| else: |
| |
| # typing: All conditional function variants must have identical signatures |
| def reify_timestamps( # type: ignore[misc] |
| element, timestamp=DoFn.TimestampParam, window=DoFn.WindowParam): |
| key, value = element |
| # Transport the window as part of the value and restore it later. |
| return key, windowed_value.WindowedValue(value, timestamp, [window]) |
| |
| def restore_timestamps(element): |
| key, windowed_values = element |
| return [wv.with_value((key, wv.value)) for wv in windowed_values] |
| |
| ungrouped = pcoll | Map(reify_timestamps).with_output_types(Any) |
| |
| # TODO(BEAM-8104) Using global window as one of the standard window. |
| # This is to mitigate the Dataflow Java Runner Harness limitation to |
| # accept only standard coders. |
| ungrouped._windowing = Windowing( |
| window.GlobalWindows(), |
| triggerfn=Always(), |
| accumulation_mode=AccumulationMode.DISCARDING, |
| timestamp_combiner=TimestampCombiner.OUTPUT_AT_EARLIEST) |
| result = ( |
| ungrouped |
| | GroupByKey() |
| | FlatMap(restore_timestamps).with_output_types(Any)) |
| result._windowing = windowing_saved |
| return result |
| |
| |
| @typehints.with_input_types(T) |
| @typehints.with_output_types(T) |
| class Reshuffle(PTransform): |
| """PTransform that returns a PCollection equivalent to its input, |
| but operationally provides some of the side effects of a GroupByKey, |
| in particular checkpointing, and preventing fusion of the surrounding |
| transforms. |
| |
| Reshuffle adds a temporary random key to each element, performs a |
| ReshufflePerKey, and finally removes the temporary key. |
| |
| Reshuffle is experimental. No backwards compatibility guarantees. |
| """ |
| def expand(self, pcoll): |
| # type: (pvalue.PValue) -> pvalue.PCollection |
| return ( |
| pcoll |
| | 'AddRandomKeys' >> Map(lambda t: (random.getrandbits(32), t)). |
| with_input_types(T).with_output_types(Tuple[int, T]) |
| | ReshufflePerKey() |
| | 'RemoveRandomKeys' >> Map(lambda t: t[1]).with_input_types( |
| Tuple[int, T]).with_output_types(T)) |
| |
| def to_runner_api_parameter(self, unused_context): |
| # type: (PipelineContext) -> Tuple[str, None] |
| return common_urns.composites.RESHUFFLE.urn, None |
| |
| @staticmethod |
| @PTransform.register_urn(common_urns.composites.RESHUFFLE.urn, None) |
| def from_runner_api_parameter( |
| unused_ptransform, unused_parameter, unused_context): |
| return Reshuffle() |
| |
| |
| def fn_takes_side_inputs(fn): |
| try: |
| signature = get_signature(fn) |
| except TypeError: |
| # We can't tell; maybe it does. |
| return True |
| |
| return ( |
| len(signature.parameters) > 1 or any( |
| p.kind == p.VAR_POSITIONAL or p.kind == p.VAR_KEYWORD |
| for p in signature.parameters.values())) |
| |
| |
| @ptransform_fn |
| def WithKeys(pcoll, k, *args, **kwargs): |
| """PTransform that takes a PCollection, and either a constant key or a |
| callable, and returns a PCollection of (K, V), where each of the values in |
| the input PCollection has been paired with either the constant key or a key |
| computed from the value. The callable may optionally accept positional or |
| keyword arguments, which should be passed to WithKeys directly. These may |
| be either SideInputs or static (non-PCollection) values, such as ints. |
| """ |
| if callable(k): |
| if fn_takes_side_inputs(k): |
| if all(isinstance(arg, AsSideInput) |
| for arg in args) and all(isinstance(kwarg, AsSideInput) |
| for kwarg in kwargs.values()): |
| return pcoll | Map( |
| lambda v, |
| *args, |
| **kwargs: (k(v, *args, **kwargs), v), |
| *args, |
| **kwargs) |
| return pcoll | Map(lambda v: (k(v, *args, **kwargs), v)) |
| return pcoll | Map(lambda v: (k(v), v)) |
| return pcoll | Map(lambda v: (k, v)) |
| |
| |
| @experimental() |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types(Tuple[K, Iterable[V]]) |
| class GroupIntoBatches(PTransform): |
| """PTransform that batches the input into desired batch size. Elements are |
| buffered until they are equal to batch size provided in the argument at which |
| point they are output to the output Pcollection. |
| |
| Windows are preserved (batches will contain elements from the same window) |
| |
| GroupIntoBatches is experimental. Its use case will depend on the runner if |
| it has support of States and Timers. |
| """ |
| def __init__( |
| self, batch_size, max_buffering_duration_secs=None, clock=time.time): |
| """Create a new GroupIntoBatches. |
| |
| Arguments: |
| batch_size: (required) How many elements should be in a batch |
| max_buffering_duration_secs: (optional) How long in seconds at most an |
| incomplete batch of elements is allowed to be buffered in the states. |
| The duration must be a positive second duration and should be given as |
| an int or float. Setting this parameter to zero effectively means no |
| buffering limit. |
| clock: (optional) an alternative to time.time (mostly for testing) |
| """ |
| self.params = _GroupIntoBatchesParams( |
| batch_size, max_buffering_duration_secs) |
| self.clock = clock |
| |
| def expand(self, pcoll): |
| input_coder = coders.registry.get_coder(pcoll) |
| return pcoll | ParDo( |
| _pardo_group_into_batches( |
| input_coder, |
| self.params.batch_size, |
| self.params.max_buffering_duration_secs, |
| self.clock)) |
| |
| def to_runner_api_parameter( |
| self, |
| unused_context # type: PipelineContext |
| ): # type: (...) -> Tuple[str, beam_runner_api_pb2.GroupIntoBatchesPayload] |
| return ( |
| common_urns.group_into_batches_components.GROUP_INTO_BATCHES.urn, |
| self.params.get_payload()) |
| |
| @staticmethod |
| @PTransform.register_urn( |
| common_urns.group_into_batches_components.GROUP_INTO_BATCHES.urn, |
| beam_runner_api_pb2.GroupIntoBatchesPayload) |
| def from_runner_api_parameter(unused_ptransform, proto, unused_context): |
| return GroupIntoBatches(*_GroupIntoBatchesParams.parse_payload(proto)) |
| |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types( |
| typehints.Tuple[ |
| ShardedKeyType[typehints.TypeVariable(K)], # type: ignore[misc] |
| typehints.Iterable[typehints.TypeVariable(V)]]) |
| class WithShardedKey(PTransform): |
| """A GroupIntoBatches transform that outputs batched elements associated |
| with sharded input keys. |
| |
| By default, keys are sharded to such that the input elements with the same |
| key are spread to all available threads executing the transform. Runners may |
| override the default sharding to do a better load balancing during the |
| execution time. |
| """ |
| def __init__( |
| self, batch_size, max_buffering_duration_secs=None, clock=time.time): |
| """Create a new GroupIntoBatches with sharded output. |
| See ``GroupIntoBatches`` transform for a description of input parameters. |
| """ |
| self.params = _GroupIntoBatchesParams( |
| batch_size, max_buffering_duration_secs) |
| self.clock = clock |
| |
| _shard_id_prefix = uuid.uuid4().bytes |
| |
| def expand(self, pcoll): |
| key_type, value_type = pcoll.element_type.tuple_types |
| sharded_pcoll = pcoll | Map( |
| lambda key_value: ( |
| ShardedKey( |
| key_value[0], |
| # Use [uuid, thread id] as the shard id. |
| GroupIntoBatches.WithShardedKey._shard_id_prefix + bytes( |
| threading.get_ident().to_bytes(8, 'big'))), |
| key_value[1])).with_output_types( |
| typehints.Tuple[ |
| ShardedKeyType[key_type], # type: ignore[misc] |
| value_type]) |
| return ( |
| sharded_pcoll |
| | GroupIntoBatches( |
| self.params.batch_size, |
| self.params.max_buffering_duration_secs, |
| self.clock)) |
| |
| def to_runner_api_parameter( |
| self, |
| unused_context # type: PipelineContext |
| ): # type: (...) -> Tuple[str, beam_runner_api_pb2.GroupIntoBatchesPayload] |
| return ( |
| common_urns.composites.GROUP_INTO_BATCHES_WITH_SHARDED_KEY.urn, |
| self.params.get_payload()) |
| |
| @staticmethod |
| @PTransform.register_urn( |
| common_urns.composites.GROUP_INTO_BATCHES_WITH_SHARDED_KEY.urn, |
| beam_runner_api_pb2.GroupIntoBatchesPayload) |
| def from_runner_api_parameter(unused_ptransform, proto, unused_context): |
| return GroupIntoBatches.WithShardedKey( |
| *_GroupIntoBatchesParams.parse_payload(proto)) |
| |
| |
| class _GroupIntoBatchesParams: |
| """This class represents the parameters for |
| :class:`apache_beam.utils.GroupIntoBatches` transform, used to define how |
| elements should be batched. |
| """ |
| def __init__(self, batch_size, max_buffering_duration_secs): |
| self.batch_size = batch_size |
| self.max_buffering_duration_secs = ( |
| 0 |
| if max_buffering_duration_secs is None else max_buffering_duration_secs) |
| self._validate() |
| |
| def __eq__(self, other): |
| if other is None or not isinstance(other, _GroupIntoBatchesParams): |
| return False |
| return ( |
| self.batch_size == other.batch_size and |
| self.max_buffering_duration_secs == other.max_buffering_duration_secs) |
| |
| def _validate(self): |
| assert self.batch_size is not None and self.batch_size > 0, ( |
| 'batch_size must be a positive value') |
| assert ( |
| self.max_buffering_duration_secs is not None and |
| self.max_buffering_duration_secs >= 0), ( |
| 'max_buffering_duration must be a non-negative value') |
| |
| def get_payload(self): |
| return beam_runner_api_pb2.GroupIntoBatchesPayload( |
| batch_size=self.batch_size, |
| max_buffering_duration_millis=int( |
| self.max_buffering_duration_secs * 1000)) |
| |
| @staticmethod |
| def parse_payload( |
| proto # type: beam_runner_api_pb2.GroupIntoBatchesPayload |
| ): |
| return proto.batch_size, proto.max_buffering_duration_millis / 1000 |
| |
| |
| def _pardo_group_into_batches( |
| input_coder, batch_size, max_buffering_duration_secs, clock=time.time): |
| ELEMENT_STATE = BagStateSpec('values', input_coder) |
| COUNT_STATE = CombiningValueStateSpec('count', input_coder, CountCombineFn()) |
| WINDOW_TIMER = TimerSpec('window_end', TimeDomain.WATERMARK) |
| BUFFERING_TIMER = TimerSpec('buffering_end', TimeDomain.REAL_TIME) |
| |
| class _GroupIntoBatchesDoFn(DoFn): |
| def process( |
| self, |
| element, |
| window=DoFn.WindowParam, |
| element_state=DoFn.StateParam(ELEMENT_STATE), |
| count_state=DoFn.StateParam(COUNT_STATE), |
| window_timer=DoFn.TimerParam(WINDOW_TIMER), |
| buffering_timer=DoFn.TimerParam(BUFFERING_TIMER)): |
| # Allowed lateness not supported in Python SDK |
| # https://beam.apache.org/documentation/programming-guide/#watermarks-and-late-data |
| window_timer.set(window.end) |
| element_state.add(element) |
| count_state.add(1) |
| count = count_state.read() |
| if count == 1 and max_buffering_duration_secs > 0: |
| # This is the first element in batch. Start counting buffering time if a |
| # limit was set. |
| # pylint: disable=deprecated-method |
| buffering_timer.set(clock() + max_buffering_duration_secs) |
| if count >= batch_size: |
| return self.flush_batch(element_state, count_state, buffering_timer) |
| |
| @on_timer(WINDOW_TIMER) |
| def on_window_timer( |
| self, |
| element_state=DoFn.StateParam(ELEMENT_STATE), |
| count_state=DoFn.StateParam(COUNT_STATE), |
| buffering_timer=DoFn.TimerParam(BUFFERING_TIMER)): |
| return self.flush_batch(element_state, count_state, buffering_timer) |
| |
| @on_timer(BUFFERING_TIMER) |
| def on_buffering_timer( |
| self, |
| element_state=DoFn.StateParam(ELEMENT_STATE), |
| count_state=DoFn.StateParam(COUNT_STATE), |
| buffering_timer=DoFn.TimerParam(BUFFERING_TIMER)): |
| return self.flush_batch(element_state, count_state, buffering_timer) |
| |
| def flush_batch(self, element_state, count_state, buffering_timer): |
| batch = [element for element in element_state.read()] |
| if not batch: |
| return |
| key, _ = batch[0] |
| batch_values = [v for (k, v) in batch] |
| element_state.clear() |
| count_state.clear() |
| buffering_timer.clear() |
| yield key, batch_values |
| |
| return _GroupIntoBatchesDoFn() |
| |
| |
| class ToString(object): |
| """ |
| PTransform for converting a PCollection element, KV or PCollection Iterable |
| to string. |
| """ |
| |
| # pylint: disable=invalid-name |
| @staticmethod |
| def Element(): |
| """ |
| Transforms each element of the PCollection to a string. |
| """ |
| return 'ElementToString' >> Map(str) |
| |
| @staticmethod |
| def Iterables(delimiter=None): |
| """ |
| Transforms each item in the iterable of the input of PCollection to a |
| string. There is no trailing delimiter. |
| """ |
| if delimiter is None: |
| delimiter = ',' |
| return ( |
| 'IterablesToString' >> |
| Map(lambda xs: delimiter.join(str(x) for x in xs)).with_input_types( |
| Iterable[Any]).with_output_types(str)) |
| |
| # An alias for Iterables. |
| Kvs = Iterables |
| |
| |
| class Reify(object): |
| """PTransforms for converting between explicit and implicit form of various |
| Beam values.""" |
| @typehints.with_input_types(T) |
| @typehints.with_output_types(T) |
| class Timestamp(PTransform): |
| """PTransform to wrap a value in a TimestampedValue with it's |
| associated timestamp.""" |
| @staticmethod |
| def add_timestamp_info(element, timestamp=DoFn.TimestampParam): |
| yield TimestampedValue(element, timestamp) |
| |
| def expand(self, pcoll): |
| return pcoll | ParDo(self.add_timestamp_info) |
| |
| @typehints.with_input_types(T) |
| @typehints.with_output_types(T) |
| class Window(PTransform): |
| """PTransform to convert an element in a PCollection into a tuple of |
| (element, timestamp, window), wrapped in a TimestampedValue with it's |
| associated timestamp.""" |
| @staticmethod |
| def add_window_info( |
| element, timestamp=DoFn.TimestampParam, window=DoFn.WindowParam): |
| yield TimestampedValue((element, timestamp, window), timestamp) |
| |
| def expand(self, pcoll): |
| return pcoll | ParDo(self.add_window_info) |
| |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types(Tuple[K, V]) |
| class TimestampInValue(PTransform): |
| """PTransform to wrap the Value in a KV pair in a TimestampedValue with |
| the element's associated timestamp.""" |
| @staticmethod |
| def add_timestamp_info(element, timestamp=DoFn.TimestampParam): |
| key, value = element |
| yield (key, TimestampedValue(value, timestamp)) |
| |
| def expand(self, pcoll): |
| return pcoll | ParDo(self.add_timestamp_info) |
| |
| @typehints.with_input_types(Tuple[K, V]) |
| @typehints.with_output_types(Tuple[K, V]) |
| class WindowInValue(PTransform): |
| """PTransform to convert the Value in a KV pair into a tuple of |
| (value, timestamp, window), with the whole element being wrapped inside a |
| TimestampedValue.""" |
| @staticmethod |
| def add_window_info( |
| element, timestamp=DoFn.TimestampParam, window=DoFn.WindowParam): |
| key, value = element |
| yield TimestampedValue((key, (value, timestamp, window)), timestamp) |
| |
| def expand(self, pcoll): |
| return pcoll | ParDo(self.add_window_info) |
| |
| |
| class Regex(object): |
| """ |
| PTransform to use Regular Expression to process the elements in a |
| PCollection. |
| """ |
| |
| ALL = "__regex_all_groups" |
| |
| @staticmethod |
| def _regex_compile(regex): |
| """Return re.compile if the regex has a string value""" |
| if isinstance(regex, str): |
| regex = re.compile(regex) |
| return regex |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(str) |
| @ptransform_fn |
| def matches(pcoll, regex, group=0): |
| """ |
| Returns the matches (group 0 by default) if zero or more characters at the |
| beginning of string match the regular expression. To match the entire |
| string, add "$" sign at the end of regex expression. |
| |
| Group can be integer value or a string value. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| group: (optional) name/number of the group, it can be integer or a string |
| value. Defaults to 0, meaning the entire matched string will be |
| returned. |
| """ |
| regex = Regex._regex_compile(regex) |
| |
| def _process(element): |
| m = regex.match(element) |
| if m: |
| yield m.group(group) |
| |
| return pcoll | FlatMap(_process) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(List[str]) |
| @ptransform_fn |
| def all_matches(pcoll, regex): |
| """ |
| Returns all matches (groups) if zero or more characters at the beginning |
| of string match the regular expression. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| """ |
| regex = Regex._regex_compile(regex) |
| |
| def _process(element): |
| m = regex.match(element) |
| if m: |
| yield [m.group(ix) for ix in range(m.lastindex + 1)] |
| |
| return pcoll | FlatMap(_process) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(Tuple[str, str]) |
| @ptransform_fn |
| def matches_kv(pcoll, regex, keyGroup, valueGroup=0): |
| """ |
| Returns the KV pairs if the string matches the regular expression, deriving |
| the key & value from the specified group of the regular expression. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| keyGroup: The Regex group to use as the key. Can be int or str. |
| valueGroup: (optional) Regex group to use the value. Can be int or str. |
| The default value "0" returns entire matched string. |
| """ |
| regex = Regex._regex_compile(regex) |
| |
| def _process(element): |
| match = regex.match(element) |
| if match: |
| yield (match.group(keyGroup), match.group(valueGroup)) |
| |
| return pcoll | FlatMap(_process) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(str) |
| @ptransform_fn |
| def find(pcoll, regex, group=0): |
| """ |
| Returns the matches if a portion of the line matches the Regex. Returns |
| the entire group (group 0 by default). Group can be integer value or a |
| string value. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| group: (optional) name of the group, it can be integer or a string value. |
| """ |
| regex = Regex._regex_compile(regex) |
| |
| def _process(element): |
| r = regex.search(element) |
| if r: |
| yield r.group(group) |
| |
| return pcoll | FlatMap(_process) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(Union[List[str], List[Tuple[str, str]]]) |
| @ptransform_fn |
| def find_all(pcoll, regex, group=0, outputEmpty=True): |
| """ |
| Returns the matches if a portion of the line matches the Regex. By default, |
| list of group 0 will return with empty items. To get all groups, pass the |
| `Regex.ALL` flag in the `group` parameter which returns all the groups in |
| the tuple format. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| group: (optional) name of the group, it can be integer or a string value. |
| outputEmpty: (optional) Should empty be output. True to output empties |
| and false if not. |
| """ |
| regex = Regex._regex_compile(regex) |
| |
| def _process(element): |
| matches = regex.finditer(element) |
| if group == Regex.ALL: |
| yield [(m.group(), m.groups()[0]) for m in matches |
| if outputEmpty or m.groups()[0]] |
| else: |
| yield [m.group(group) for m in matches if outputEmpty or m.group(group)] |
| |
| return pcoll | FlatMap(_process) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(Tuple[str, str]) |
| @ptransform_fn |
| def find_kv(pcoll, regex, keyGroup, valueGroup=0): |
| """ |
| Returns the matches if a portion of the line matches the Regex. Returns the |
| specified groups as the key and value pair. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| keyGroup: The Regex group to use as the key. Can be int or str. |
| valueGroup: (optional) Regex group to use the value. Can be int or str. |
| The default value "0" returns entire matched string. |
| """ |
| regex = Regex._regex_compile(regex) |
| |
| def _process(element): |
| matches = regex.finditer(element) |
| if matches: |
| for match in matches: |
| yield (match.group(keyGroup), match.group(valueGroup)) |
| |
| return pcoll | FlatMap(_process) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(str) |
| @ptransform_fn |
| def replace_all(pcoll, regex, replacement): |
| """ |
| Returns the matches if a portion of the line matches the regex and |
| replaces all matches with the replacement string. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| replacement: the string to be substituted for each match. |
| """ |
| regex = Regex._regex_compile(regex) |
| return pcoll | Map(lambda elem: regex.sub(replacement, elem)) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(str) |
| @ptransform_fn |
| def replace_first(pcoll, regex, replacement): |
| """ |
| Returns the matches if a portion of the line matches the regex and replaces |
| the first match with the replacement string. |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| replacement: the string to be substituted for each match. |
| """ |
| regex = Regex._regex_compile(regex) |
| return pcoll | Map(lambda elem: regex.sub(replacement, elem, 1)) |
| |
| @staticmethod |
| @typehints.with_input_types(str) |
| @typehints.with_output_types(List[str]) |
| @ptransform_fn |
| def split(pcoll, regex, outputEmpty=False): |
| """ |
| Returns the list string which was splitted on the basis of regular |
| expression. It will not output empty items (by defaults). |
| |
| Args: |
| regex: the regular expression string or (re.compile) pattern. |
| outputEmpty: (optional) Should empty be output. True to output empties |
| and false if not. |
| """ |
| regex = Regex._regex_compile(regex) |
| outputEmpty = bool(outputEmpty) |
| |
| def _process(element): |
| r = regex.split(element) |
| if r and not outputEmpty: |
| r = list(filter(None, r)) |
| yield r |
| |
| return pcoll | FlatMap(_process) |
