def _add_metadata(
self, rows: beam.pvalue.PCollection[Row]
) -> beam.pvalue.PCollection[Row]:
"""Add ip metadata to a collection of roundtrip rows.
Args:
rows: beam.PCollection[Row]
Returns:
PCollection[Row]
The same rows as above with with additional metadata columns added.
"""
# PCollection[Tuple[DateIpKey,Row]]
rows_keyed_by_ip_and_date = (
rows
| 'key by ips and dates' >> beam.Map(lambda row: (make_date_ip_key(
row), row)).with_output_types(Tuple[DateIpKey, Row]))
# PCollection[DateIpKey]
# pylint: disable=no-value-for-parameter
ips_and_dates = (rows_keyed_by_ip_and_date
| 'get ip and date keys per row' >>
beam.Keys().with_output_types(DateIpKey))
# PCollection[DateIpKey]
deduped_ips_and_dates = (
# pylint: disable=no-value-for-parameter
ips_and_dates
| 'dedup' >> beam.Distinct().with_output_types(DateIpKey))
# PCollection[Tuple[date,List[ip]]]
grouped_ips_by_dates = (
deduped_ips_and_dates | 'group by date' >>
beam.GroupByKey().with_output_types(Tuple[str, Iterable[str]]))
# PCollection[Tuple[DateIpKey,Row]]
ips_with_metadata = (grouped_ips_by_dates
| 'get ip metadata' >> beam.FlatMapTuple(
self._add_ip_metadata).with_output_types(
Tuple[DateIpKey, Row]))
# PCollection[Tuple[Tuple[date,ip],Dict[input_name_key,List[Row]]]]
grouped_metadata_and_rows = (
({
IP_METADATA_PCOLLECTION_NAME: ips_with_metadata,
ROWS_PCOLLECION_NAME: rows_keyed_by_ip_and_date
}) | 'group by keys' >> beam.CoGroupByKey())
# PCollection[Row]
rows_with_metadata = (
grouped_metadata_and_rows | 'merge metadata with rows' >>
beam.FlatMapTuple(merge_metadata_with_rows).with_output_types(Row))
return rows_with_metadata
def _add_vantage_point_tags(
rows: beam.pvalue.PCollection[Row],
ips_with_metadata: beam.pvalue.PCollection[Tuple[DateIpKey, Row]]
) -> beam.pvalue.PCollection[Row]:
"""Add tags for vantage point IPs - resolver name (hostname/control/special) and country
Args:
rows: PCollection of measurement rows
ips_with_metadata: PCollection of dated ips with geo metadata
Returns:
PCollection of measurement rows with tag information added to the ip row
"""
# PCollection[Tuple[DateIpKey,Row]]
rows_keyed_by_ip_and_date = (
rows | 'add vp tags: key by ips and dates' >>
beam.Map(lambda row: (make_date_ip_key(row), row)).with_output_types(
Tuple[DateIpKey, Row]))
# PCollection[Tuple[Tuple[date,ip],Dict[input_name_key,List[Row]]]]
grouped_metadata_and_rows = (
({
IP_METADATA_PCOLLECTION_NAME: ips_with_metadata,
ROWS_PCOLLECION_NAME: rows_keyed_by_ip_and_date
}) | 'add vp tags: group by keys' >> beam.CoGroupByKey())
# PCollection[Row]
rows_with_metadata = (
grouped_metadata_and_rows | 'add vp tags: merge metadata with rows' >>
beam.FlatMapTuple(merge_metadata_with_rows).with_output_types(Row))
return rows_with_metadata
def test_flat_map_tuple_wrapper(self):
def tuple_map_fn(a: str, b: str, c: str) -> typehints.Iterable[str]:
return [a, b, c]
th = beam.FlatMapTuple(tuple_map_fn).get_type_hints()
self.assertEqual(th.input_types, ((str, str, str), {}))
self.assertEqual(th.output_types, ((str, ), {}))
def unflatten_rows(
rows: beam.pvalue.PCollection[Row]) -> beam.pvalue.PCollection[Row]:
"""Unflatten so that each row contains a array of answer IPs
Args:
rows: measurement rows with a single recieved ip
Returns:
measurement rows aggregated so they have an array of recieved responses
"""
# PCollection[Tuple[str,Row]]
keyed_by_measurement_id = (
rows | 'key by measurement id' >>
beam.Map(lambda row: (row['measurement_id'], row)).with_output_types(
Tuple[str, Row]))
# PCollection[Tuple[str,Iterable[Row]]]
grouped_by_measurement_id = (
keyed_by_measurement_id
| 'group by measurement id' >> beam.GroupByKey())
# PCollection[Row]
unflattened_rows = (
grouped_by_measurement_id | 'unflatten rows' >> beam.FlatMapTuple(
lambda k, v: _unflatten_satellite(v)).with_output_types(Row))
return unflattened_rows
def test_flat_map_tuple_wrapper(self):
# TODO(BEAM-8662): Also test with a fn that accepts default arguments.
def tuple_map_fn(a: str, b: str, c: str) -> typehints.Iterable[str]:
return [a, b, c]
th = beam.FlatMapTuple(tuple_map_fn).get_type_hints()
self.assertEqual(th.input_types, ((str, str, str), {}))
self.assertEqual(th.output_types, ((str, ), {}))
def test_flat_map_tuple_wrapper(self):
# TODO(https://github.com/apache/beam/issues/19961): Also test with a fn
# that accepts default arguments.
def tuple_map_fn(a: str, b: str, c: str) -> typehints.Iterable[str]:
return [a, b, c]
th = beam.FlatMapTuple(tuple_map_fn).get_type_hints()
self.assertEqual(th.input_types, ((str, str, str), {}))
self.assertEqual(th.output_types, ((str, ), {}))
def expand(self, pcoll):
return (
pcoll
| "Start" >> beam.FlatMap(_start_stage, self.specs_by_target)
| "CreateTasks" >> beam.FlatMapTuple(_copy_tasks)
# prevent undesirable fusion
# https://stackoverflow.com/a/54131856/809705
| "Reshuffle" >> beam.Reshuffle()
| "CopyChunks" >> beam.MapTuple(_copy_chunk)
# prepare inputs for the next stage (if any)
| "Finish" >> beam.Distinct())
def execute_pipeline(pipeline, options):
messages = (
pipeline
| "read messages" >> beam.io.ReadFromPubSub(topic=options.input_topic)
| "parse to messages" >> beam.ParDo(ParseMessage()))
sessions = (messages
| "window" >> beam.WindowInto(beam.window.Sessions(15))
| "add key" >> beam.Map(lambda element:
(element.user, element.products))
| "group by user" >> beam.GroupByKey()
| "first flatten" >> beam.FlatMapTuple(flat_function)
| "second flatten" >> beam.FlatMapTuple(flat_function)
) | "log 1" >> beam.ParDo(Log())
products = (
sessions
| "add new key" >> beam.Map(lambda session: (session[1].id,
(session[1], session[0])))
|
"group by product" >> beam.GroupByKey()) | "log 2" >> beam.ParDo(Log())
def test_flat_map_tuple(self):
def f(a, b, y=None):
return a, b, y
expected = [(1, 2), (3, 4)] | beam.FlatMapTuple(f, y=5)
actual = [(1, 2), (3, 4)] | threadmap.FlatThreadMapTuple(f, y=5)
self.assertEqual(expected, actual)
actual = [(1, 2), (3, 4)] | threadmap.FlatThreadMapTuple(
f,
y=5,
num_threads=None,
)
self.assertEqual(expected, actual)
def add_received_ip_tags(
rows: beam.pvalue.PCollection[Row],
ips_with_metadata: beam.pvalue.PCollection[Tuple[DateIpKey, Row]]
) -> beam.pvalue.PCollection[Row]:
"""Add tags for answer ips (field received.ip) - asnum, asname, http, cert
Args:
rows: PCollection of measurement rows
ips_with_metadata: PCollection of dated ips with geo metadata
Returns:
PCollection of measurement rows with tag information added to the recieved.ip row
"""
# PCollection[Tuple[DateIpKey,Row]]
received_keyed_by_ip_and_date = (
rows | 'key by received ips and dates' >>
beam.Map(lambda row:
(_make_date_received_ip_key(row), row)).with_output_types(
Tuple[DateIpKey, Row]))
# Iterable[PCollection[Tuple[DateIpKey,Row]]]
partition_by_domain = (received_keyed_by_ip_and_date
| 'partition by domain' >> beam.Partition(
_get_domain_partition, NUM_DOMAIN_PARTITIONS))
collections = []
for i in range(0, NUM_DOMAIN_PARTITIONS):
elements = partition_by_domain[i]
# PCollection[Tuple[Tuple[date,ip],Dict[input_name_key,List[Row]]]]
grouped_received_metadata_and_rows = (({
IP_METADATA_PCOLLECTION_NAME: ips_with_metadata,
ROWS_PCOLLECION_NAME: elements
}) | f'group by received ip keys {i}' >> beam.CoGroupByKey())
# PCollection[Row]
domain_rows_with_tags = (
grouped_received_metadata_and_rows | f'tag received ips {i}' >>
beam.FlatMapTuple(lambda k, v: merge_metadata_with_rows(
k, v, field='received')).with_output_types(Row))
collections.append(domain_rows_with_tags)
# PCollection[Row]
rows_with_tags = (
collections
| 'merge domain collections' >> beam.Flatten().with_output_types(Row))
return rows_with_tags
def run_beam_pipeline(self, scan_type: str, incremental_load: bool,
job_name: str, table_name: str,
start_date: Optional[datetime.date],
end_date: Optional[datetime.date]) -> None:
"""Run a single apache beam pipeline to load json data into bigquery.
Args:
scan_type: one of 'echo', 'discard', 'http', 'https'
incremental_load: boolean. If true, only load the latest new data, if
false reload all data.
job_name: string name for this pipeline job.
table_name: dataset.table name like 'base.scan_echo'
start_date: date object, only files after or at this date will be read.
Mostly only used during development.
end_date: date object, only files at or before this date will be read.
Mostly only used during development.
Raises:
Exception: if any arguments are invalid or the pipeline fails.
"""
logging.getLogger().setLevel(logging.INFO)
pipeline_options = self._get_pipeline_options(scan_type, job_name)
gcs = GCSFileSystem(pipeline_options)
new_filenames = self._data_to_load(gcs, scan_type, incremental_load,
table_name, start_date, end_date)
if not new_filenames:
logging.info('No new files to load incrementally')
return
with beam.Pipeline(options=pipeline_options) as p:
# PCollection[Tuple[filename,line]]
lines = _read_scan_text(p, new_filenames)
# PCollection[Row]
rows = (
lines | 'flatten json' >>
beam.FlatMapTuple(_flatten_measurement).with_output_types(Row))
# PCollection[Row]
rows_with_metadata = self._add_metadata(rows)
self._write_to_bigquery(rows_with_metadata, table_name,
incremental_load)
def flatmap_tuple(test=None):
# [START flatmap_tuple]
import apache_beam as beam
def format_plant(icon, plant):
if icon:
yield '{}{}'.format(icon, plant)
with beam.Pipeline() as pipeline:
plants = (pipeline
| 'Gardening plants' >> beam.Create([
('🍓', 'Strawberry'),
('🥕', 'Carrot'),
('🍆', 'Eggplant'),
('🍅', 'Tomato'),
('🥔', 'Potato'),
(None, 'Invalid'),
])
| 'Format' >> beam.FlatMapTuple(format_plant)
| beam.Map(print))
# [END flatmap_tuple]
if test:
test(plants)
def CheckAggregation(inputs_and_expected, aggregation):
# Split the test stream into a branch of to-be-processed elements, and
# a branch of expected results.
inputs, expected = (
inputs_and_expected
| beam.FlatMapTuple(lambda tag, value: [
beam.pvalue.TaggedOutput(tag, ('key1', value)),
beam.pvalue.TaggedOutput(tag, ('key2', value)),
]).with_outputs('input', 'expect'))
# Process the inputs with the given windowing to produce actual outputs.
outputs = (
inputs
| beam.MapTuple(lambda key, value: TimestampedValue(
(key, value), value))
| beam.WindowInto(window_fn,
trigger=trigger_fn,
accumulation_mode=accumulation_mode,
timestamp_combiner=timestamp_combiner)
| aggregation
| beam.MapTuple(_windowed_value_info_map_fn)
# Place outputs back into the global window to allow flattening
# and share a single state in Check.
| 'Global' >> beam.WindowInto(
beam.transforms.window.GlobalWindows()))
# Feed both the expected and actual outputs to Check() for comparison.
tagged_expected = (
expected
| beam.MapTuple(lambda key, value: (key, ('expect', value))))
tagged_outputs = (
outputs
| beam.MapTuple(lambda key, value: (key, ('actual', value))))
# pylint: disable=expression-not-assigned
([tagged_expected, tagged_outputs]
| beam.Flatten()
| beam.ParDo(Check(self.allow_out_of_order)))
def process_satellite_with_tags(
row_lines: beam.pvalue.PCollection[Tuple[str, str]],
tag_lines: beam.pvalue.PCollection[Tuple[str, str]]
) -> beam.pvalue.PCollection[Row]:
"""Process Satellite measurements and tags.
Args:
row_lines: Row objects
tag_lines: various
Returns:
PCollection[Row] of rows with tag metadata added
"""
# PCollection[Row]
rows = (row_lines | 'flatten json' >> beam.ParDo(
flatten.FlattenMeasurement()).with_output_types(Row))
# PCollection[Row]
tag_rows = (tag_lines | 'tag rows' >>
beam.FlatMapTuple(_read_satellite_tags).with_output_types(Row))
# PCollection[Row]
rows_with_metadata = _add_satellite_tags(rows, tag_rows)
return rows_with_metadata
def transform_data(train_data_file, test_data_file, working_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data using the CSV reader, and transform it using a
preprocessing pipeline that scales numeric data and converts categorical data
from strings to int64 values indices, by creating a vocabulary for each
category.
Args:
train_data_file: File containing training data
test_data_file: File containing test data
working_dir: Directory to write transformed data and metadata to
"""
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
# Since we are modifying some features and leaving others unchanged, we
# start by setting `outputs` to a copy of `inputs.
outputs = inputs.copy()
# Scale numeric columns to have range [0, 1].
for key in NUMERIC_FEATURE_KEYS:
outputs[key] = tft.scale_to_0_1(inputs[key])
for key in OPTIONAL_NUMERIC_FEATURE_KEYS:
# This is a SparseTensor because it is optional. Here we fill in a default
# value when it is missing.
sparse = tf.sparse.SparseTensor(inputs[key].indices,
inputs[key].values,
[inputs[key].dense_shape[0], 1])
dense = tf.sparse.to_dense(sp_input=sparse, default_value=0.)
# Reshaping from a batch of vectors of size 1 to a batch to scalars.
dense = tf.squeeze(dense, axis=1)
outputs[key] = tft.scale_to_0_1(dense)
# For all categorical columns except the label column, we generate a
# vocabulary, and convert the string feature to a one-hot encoding.
for key in CATEGORICAL_FEATURE_KEYS:
integerized = tft.compute_and_apply_vocabulary(
tf.strings.strip(inputs[key]),
num_oov_buckets=NUM_OOV_BUCKETS,
vocab_filename=key)
depth = (tft.experimental.get_vocabulary_size_by_name(key) +
NUM_OOV_BUCKETS)
one_hot_encoded = tf.one_hot(integerized,
depth=tf.cast(depth, tf.int32),
on_value=1.0,
off_value=0.0)
# This output is now one-hot encoded. If saving transformed data to disk,
# this can incur significant memory cost.
outputs[key] = tf.reshape(one_hot_encoded, [-1, depth])
# For the label column we provide the mapping from string to index.
table_keys = ['>50K', '<=50K']
with tf.init_scope():
initializer = tf.lookup.KeyValueTensorInitializer(
keys=table_keys,
values=tf.cast(tf.range(len(table_keys)), tf.int64),
key_dtype=tf.string,
value_dtype=tf.int64)
table = tf.lookup.StaticHashTable(initializer, default_value=-1)
# Remove trailing periods for test data when the data is read with tf.data.
label_str = tf.strings.regex_replace(inputs[LABEL_KEY], r'\.', '')
label_str = tf.strings.strip(label_str)
data_labels = table.lookup(label_str)
transformed_label = tf.one_hot(indices=data_labels,
depth=len(table_keys),
on_value=1.0,
off_value=0.0)
outputs[LABEL_KEY] = tf.reshape(transformed_label,
[-1, len(table_keys)])
return outputs
# The "with" block will create a pipeline, and run that pipeline at the exit
# of the block.
with beam.Pipeline() as pipeline:
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
# Create a TFXIO to read the census data with the schema. To do this we
# need to list all columns in order since the schema doesn't specify the
# order of columns in the csv.
# We first read CSV files and use BeamRecordCsvTFXIO whose .BeamSource()
# accepts a PCollection[bytes] because we need to patch the records first
# (see "FixCommasTrainData" below). Otherwise, tfxio.CsvTFXIO can be used
# to both read the CSV files and parse them to TFT inputs:
# csv_tfxio = tfxio.CsvTFXIO(...)
# raw_data = (pipeline | 'ToRecordBatches' >> csv_tfxio.BeamSource())
csv_tfxio = tfxio.BeamRecordCsvTFXIO(
physical_format='text',
column_names=ORDERED_CSV_COLUMNS,
schema=_SCHEMA)
# Read in raw data and convert using CSV TFXIO. Note that we apply
# some Beam transformations here, which will not be encoded in the TF
# graph since we don't do the from within tf.Transform's methods
# (AnalyzeDataset, TransformDataset etc.). These transformations are just
# to get data into a format that the CSV TFXIO can read, in particular
# removing spaces after commas.
raw_data = (pipeline
| 'ReadTrainData' >> beam.io.ReadFromText(
train_data_file, coder=beam.coders.BytesCoder())
| 'FixCommasTrainData' >>
beam.Map(lambda line: line.replace(b', ', b','))
| 'DecodeTrainData' >> csv_tfxio.BeamSource())
# Combine data and schema into a dataset tuple. Note that we already used
# the schema to read the CSV data, but we also need it to interpret
# raw_data.
raw_dataset = (raw_data, csv_tfxio.TensorAdapterConfig())
# The TFXIO output format is chosen for improved performance.
transformed_dataset, transform_fn = (
raw_dataset | tft_beam.AnalyzeAndTransformDataset(
preprocessing_fn, output_record_batches=True))
# Transformed metadata is not necessary for encoding.
transformed_data, _ = transformed_dataset
# Extract transformed RecordBatches, encode and write them to the given
# directory.
coder = RecordBatchToExamplesEncoder()
_ = (transformed_data
| 'EncodeTrainData' >>
beam.FlatMapTuple(lambda batch, _: coder.encode(batch))
| 'WriteTrainData' >> beam.io.WriteToTFRecord(
os.path.join(working_dir,
TRANSFORMED_TRAIN_DATA_FILEBASE)))
# Now apply transform function to test data. In this case we remove the
# trailing period at the end of each line, and also ignore the header line
# that is present in the test data file.
raw_test_data = (pipeline
| 'ReadTestData' >> beam.io.ReadFromText(
test_data_file,
skip_header_lines=1,
coder=beam.coders.BytesCoder())
| 'FixCommasTestData' >>
beam.Map(lambda line: line.replace(b', ', b','))
| 'RemoveTrailingPeriodsTestData' >>
beam.Map(lambda line: line[:-1])
| 'DecodeTestData' >> csv_tfxio.BeamSource())
raw_test_dataset = (raw_test_data, csv_tfxio.TensorAdapterConfig())
# The TFXIO output format is chosen for improved performance.
transformed_test_dataset = (
(raw_test_dataset, transform_fn)
| tft_beam.TransformDataset(output_record_batches=True))
# Transformed metadata is not necessary for encoding.
transformed_test_data, _ = transformed_test_dataset
# Extract transformed RecordBatches, encode and write them to the given
# directory.
_ = (
transformed_test_data
| 'EncodeTestData' >>
beam.FlatMapTuple(lambda batch, _: coder.encode(batch))
| 'WriteTestData' >> beam.io.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)))
# Will write a SavedModel and metadata to working_dir, which can then
# be read by the tft.TFTransformOutput class.
_ = (transform_fn
|
'WriteTransformFn' >> tft_beam.WriteTransformFn(working_dir))
def run(argv=None, save_main_session=True):
"""
Main entry point; defines and runs the kuill pipeline.
"""
parser = argparse.ArgumentParser()
sp = parser.add_subparsers()
#
get_characters_parser = sp.add_parser("get_characters")
get_characters_parser.add_argument("--output", default="characters.csv")
get_characters_parser.set_defaults(command=get_characters_command)
get_species_parser = sp.add_parser("get_species")
get_species_parser.add_argument("--output", default="species.csv")
get_species_parser.set_defaults(command=get_species_command)
pipeline_parser = sp.add_parser("pipeline")
pipeline_parser.add_argument(
'--characters', type=str,
required=True,
help='Path to an input file.')
pipeline_parser.add_argument(
'--species', type=str,
required=True,
help='Path to an input file.')
pipeline_parser.add_argument(
'--output', type=str,
required=True,
help='Path to the output file(s).')
pipeline_parser.set_defaults(command=None)
args, pipeline_args = parser.parse_known_args(argv)
if args.command:
# handle get_data_command
args.command(args)
return
options = PipelineOptions(pipeline_args)
options.view_as(SetupOptions).save_main_session = save_main_session
def join_data(k, v):
logging.debug("join_data: %s", (k, v))
import itertools
return itertools.product(v['characters'], v['species'])
def merge_data(row):
logging.debug("merge_data: %s", row)
character_data, species_data = row
character_data.update(species_data)
del character_data['species_id']
return character_data
def by_all_appearances(row):
logging.debug("by_appearances: %s", row)
return (row["appearances"], row)
def appearance_key_for_element(element):
"""
element is a dictionary of character data
return the value of the "appearances" key
"""
return element["appearances"]
def height_key_for_element(element):
"""
element is a dictionary of character data
return the value of the "appearances" key
"""
return element["height"]
def format_csv(data):
lines = ["{height},{appearances},{name},{species}".format(
**row) for row in data]
return "\n".join(lines)
def resplit_data(data):
"""
data is a list with one element, a list of rows?
"""
logging.debug("resplit_data: %s", data)
for row in data:
yield row
with beam.Pipeline(options=options) as p:
char_inputs = (
p
| 'ReadCharInputText' >> beam.io.ReadFromText(
args.characters, skip_header_lines=1)
)
spec_inputs = (
p
| 'ReadSpecInputText' >> beam.io.ReadFromText(
args.species, skip_header_lines=1)
)
characters = (
char_inputs
| "parse chars" >> beam.ParDo(ParseCharacterFn())
| "key_char" >> beam.Map(
lambda c: (c["species_id"], c))
)
species = (
spec_inputs
| beam.ParDo(ParseSpeciesFn())
| "key_spec" >> beam.Map(
lambda s: (s["species_id"], s))
)
joined = (
{"characters": characters,
"species": species}
| beam.CoGroupByKey()
| beam.FlatMapTuple(join_data)
)
merged = (
joined
| beam.Map(merge_data)
)
top = (
merged
| "top by appearances" >> beam.combiners.Top.Of(
10, key=appearance_key_for_element)
| "re-split for height" >> beam.FlatMap(resplit_data)
| "top by height" >> beam.combiners.Top.Of(
10, key=height_key_for_element)
)
output = ( # noqa
top
| "format_csv" >> beam.Map(format_csv)
| 'WriteCharacterData' >> beam.io.WriteToText(args.output)
)
def expand(self, pcoll): return pcoll | beam.FlatMapTuple(self._split_chunks)
def expand(self, pcoll):
return (
pcoll
| beam.Create(list(self.pattern.items()))
| beam.FlatMapTuple(self._open_chunks)
)
def expand(self,
lifts: beam.pvalue.PCollection) -> beam.pvalue.PCollection:
"""Takes top k and bottom k x values (sorted by lift) per slice and y value.
Args:
lifts: A PCollection of tuples of the form: (
_SlicedFeatureKey(slice_key, x_path),
_LiftInfo(x, y, lift, xy_count, x_count, y_count)).
Returns:
A PCollection resulting from a group by with the keys of the form
(slice_key, x_path) and a stream of values of the form
(y, y_count, [(x, lift, xy_count, x_count)], in which the stream of values
has been limited to the top k and bottom k elements per key.
"""
def move_y_info_to_key(key, value):
slice_key, x_path = key
return (_LiftSeriesKey(slice_key=slice_key,
x_path=x_path,
y=value.y,
y_count=value.y_count),
_LiftValue(x=value.x,
lift=value.lift,
xy_count=value.xy_count,
x_count=value.x_count))
# Push y_* into key so that we get per-slice, per-x-path, per-y top and
# bottom k when calling {Largest,Smallest}PerKey.
# (_LiftSequenceKey(slice, x_path, y, y_count),
# _LiftValue(x, lift, xy_count, x_count))
lifts = lifts | 'MoveYToKey' >> beam.MapTuple(move_y_info_to_key)
top_key = operator.attrgetter('lift', 'x')
if self._top_k_per_y:
# (_LiftSequenceKey(slice, x_path, y, y_count),
# [_LiftValue(x, lift, xy_count, x_count)])
top_k = (lifts
| 'TopK' >> beam.transforms.combiners.Top.PerKey(
n=self._top_k_per_y, key=top_key))
if self._bottom_k_per_y:
# (_LiftSequenceKey(slice, x_path, y, y_count),
# [_LiftValue(x, lift, xy_count, x_count)])
bottom_k = (lifts
| 'BottomK' >> beam.transforms.combiners.Top.PerKey(
n=self._bottom_k_per_y, reverse=True, key=top_key))
if self._top_k_per_y and self._bottom_k_per_y:
# (_LiftSeriesKey(slice, x_path, y, y_count),
# [_LiftValue(x, lift, xy_count, x_count)])
grouped_lifts = (
(top_k, bottom_k)
| 'MergeTopAndBottom' >> beam.Flatten()
| 'FlattenTopAndBottomLifts' >>
beam.FlatMapTuple(lambda k, vs: ((k, v) for v in vs))
| 'ReGroupTopAndBottom' >> beam.GroupByKey())
elif self._top_k_per_y:
grouped_lifts = top_k
elif self._bottom_k_per_y:
grouped_lifts = bottom_k
else:
grouped_lifts = lifts | 'GroupByYs' >> beam.GroupByKey()
def move_y_info_to_value(key, lift_values):
return (_SlicedFeatureKey(key.slice_key, key.x_path),
_LiftSeries(y=key.y,
y_count=key.y_count,
lift_values=lift_values))
# (_SlicedFeatureKey(slice, x_path),
# _LiftSeries(y, y_count, [_LiftValue(x, lift, xy_count, x_count)]))
return (grouped_lifts
| 'MoveYInfoToValue' >> beam.MapTuple(move_y_info_to_value))
#pipeline2.py: Separate subject with grade from a PCollection
import apache_beam as beam
def my_format(sub, marks):
yield '{}\t{}'.format(sub, marks)
with beam.Pipeline() as pipeline:
plants = (pipeline
| 'Subjects' >> beam.Create([
('English', 'A'),
('Maths', 'B+'),
('Science', 'A-'),
('French', 'A'),
('Arts', 'A+'),
])
| 'Format subjects with marks' >> beam.FlatMapTuple(my_format)
| beam.Map(print))
def post_processing_satellite(
rows: beam.pvalue.PCollection[Row]) -> beam.pvalue.PCollection[Row]:
"""Run post processing on Satellite v1 data (calculate confidence, verify interference).
Args:
rows: PCollection of measurement rows
Returns:
PCollection of measurement rows with confidence and verify fields
"""
def _total_tags(key: Tuple[str, str],
row: Row) -> Tuple[Tuple[str, str], int]:
total_tags = 0
for tag_type in flatten_satellite.SATELLITE_TAGS:
if tag_type != 'ip':
type_tags = {
ans[tag_type]
for ans in row['received'] if ans.get(tag_type)
}
total_tags += len(type_tags)
return (key, total_tags)
def _flat_rows_controls(key: Any, value: Row) -> Iterator[Tuple[Row, int]]: # pylint: disable=unused-argument
num_control_tags = 0
if len(value['control']) > 0:
num_control_tags = value['control'][0]
for row in value['test']:
yield (row, num_control_tags)
# Partition rows into test measurements and control measurements
# 'anomaly' is None for control measurements
# PCollection[Tuple[Tuple[str, str], Row]], PCollection[Tuple[Tuple[str, str], Row]]
rows, controls = (rows | 'key by dates and domains' >>
beam.Map(lambda row: ((row['date'], row['domain']), row))
| 'partition test and control' >> beam.Partition(
lambda row, p: int(row[1]['anomaly'] is None), 2))
# PCollection[Tuple[Tuple[str, str], int]]
num_ctags = controls | 'calculate # control tags' >> beam.MapTuple(
_total_tags)
# PCollection[Row]
post = ({
'test': rows,
'control': num_ctags
} | 'group rows and # control tags by keys' >> beam.CoGroupByKey()
| 'flatmap to (row, # control tags)' >>
beam.FlatMapTuple(_flat_rows_controls)
| 'calculate confidence' >> beam.MapTuple(_calculate_confidence) |
'verify interference' >> beam.Map(_verify).with_output_types(Row))
# PCollection[Row]
# pylint: disable=no-value-for-parameter
controls = (controls
| 'unkey control' >> beam.Values().with_output_types(Row))
# PCollection[Row]
post = ((post, controls) | 'flatten test and control' >> beam.Flatten())
return post
def run(self):
"""Returns a PCollection of audit errors aggregated from all models.
Returns:
PCollection. A PCollection of audit errors discovered during the
audit.
Raises:
ValueError. When the `datastoreio` option, which provides the
PTransforms for performing datastore IO operations, is None.
"""
existing_models, deleted_models = (
self.pipeline
| 'Get all models' >> ndb_io.GetModels(
datastore_services.query_everything(), self.datastoreio_stub)
| 'Partition by model.deleted' >> (
beam.Partition(lambda model, _: int(model.deleted), 2))
)
models_of_kind_by_index = (
existing_models
# NOTE: Partition returns a statically-sized list of PCollections.
# Creating partitions is wasteful when there are fewer items than
# there are partitions, like in our unit tests. In exchange, in
# production the job will be able to take advantage of the high
# parallelizability of PCollections, which are designed for enormous
# datasets and parallel processing.
#
# Alternatively, we could have used GroupBy. However, that returns
# an _iterable_ of items rather than a PCollection, and so it is
# vulnerable to out-of-memory errors.
#
# Since this job is concerned with running audits on EVERY MODEL IN
# STORAGE, Partition is the clear winner regardless of the overhead
# we'll see in unit tests.
| 'Split models into parallelizable PCollections' >> beam.Partition(
lambda m, _, kinds: kinds.index(job_utils.get_model_kind(m)),
# NOTE: Partition requires a hard-coded number of slices; it
# cannot be used with dynamic numbers generated in a pipeline.
# KIND_BY_INDEX is a constant tuple so that requirement is
# satisfied in this case.
len(KIND_BY_INDEX), KIND_BY_INDEX)
)
existing_key_count_pcolls = []
missing_key_error_pcolls = []
audit_error_pcolls = [
deleted_models
| 'Apply ValidateDeletedModel on deleted models' >> (
beam.ParDo(base_validation.ValidateDeletedModel()))
]
model_groups = python_utils.ZIP(KIND_BY_INDEX, models_of_kind_by_index)
for kind, models_of_kind in model_groups:
audit_error_pcolls.extend(models_of_kind | ApplyAuditDoFns(kind))
if kind in ALL_MODEL_KINDS_REFERENCED_BY_PROPERTIES:
existing_key_count_pcolls.append(
models_of_kind | GetExistingModelKeyCounts(kind))
if kind in ID_REFERENCING_PROPERTIES_BY_KIND_OF_POSSESSOR:
missing_key_error_pcolls.extend(
models_of_kind | GetMissingModelKeyErrors(kind))
existing_key_counts = (
existing_key_count_pcolls
| 'Flatten PCollections of existing key counts' >> beam.Flatten()
)
missing_key_errors = (
missing_key_error_pcolls
| 'Flatten PCollections of missing key errors' >> beam.Flatten()
)
audit_error_pcolls.append(
(existing_key_counts, missing_key_errors)
| 'Group counts and errors by key' >> beam.CoGroupByKey()
| 'Filter keys without any errors' >> (
beam.FlatMapTuple(self._get_model_relationship_errors))
)
return audit_error_pcolls | 'Combine audit results' >> beam.Flatten()
def transform_data(working_dir):
"""Transform the data and write out as a TFRecord of Example protos.
Read in the data from the positive and negative examples on disk, and
transform it using a preprocessing pipeline that removes punctuation,
tokenizes and maps tokens to int64 values indices.
Args:
working_dir: Directory to read shuffled data from and write transformed data
and metadata to.
"""
with beam.Pipeline() as pipeline:
with tft_beam.Context(
temp_dir=os.path.join(working_dir, TRANSFORM_TEMP_DIR)):
tfxio_train_data = tfxio.TFExampleRecord(file_pattern=os.path.join(
working_dir, SHUFFLED_TRAIN_DATA_FILEBASE + '*'),
schema=SCHEMA)
train_data = (pipeline |
'TFXIORead[Train]' >> tfxio_train_data.BeamSource())
tfxio_test_data = tfxio.TFExampleRecord(file_pattern=os.path.join(
working_dir, SHUFFLED_TEST_DATA_FILEBASE + '*'),
schema=SCHEMA)
test_data = (pipeline
| 'TFXIORead[Test]' >> tfxio_test_data.BeamSource())
def preprocessing_fn(inputs):
"""Preprocess input columns into transformed columns."""
review = inputs[REVIEW_KEY]
# Here tf.compat.v1.string_split behaves differently from
# tf.strings.split.
review_tokens = tf.compat.v1.string_split(review, DELIMITERS)
review_indices = tft.compute_and_apply_vocabulary(
review_tokens, top_k=VOCAB_SIZE)
# Add one for the oov bucket created by compute_and_apply_vocabulary.
review_bow_indices, review_weight = tft.tfidf(
review_indices, VOCAB_SIZE + 1)
return {
REVIEW_KEY: review_bow_indices,
REVIEW_WEIGHT_KEY: review_weight,
LABEL_KEY: inputs[LABEL_KEY]
}
# Transformed metadata is not necessary for encoding.
# The TFXIO output format is chosen for improved performance.
(transformed_train_data, _), transform_fn = (
(train_data, tfxio_train_data.TensorAdapterConfig())
| 'AnalyzeAndTransform' >> tft_beam.AnalyzeAndTransformDataset(
preprocessing_fn, output_record_batches=True))
transformed_test_data, _ = (
((test_data, tfxio_test_data.TensorAdapterConfig()),
transform_fn)
| 'Transform' >>
tft_beam.TransformDataset(output_record_batches=True))
# Extract transformed RecordBatches, encode and write them to the given
# directory.
coder = tfxio.RecordBatchToExamplesEncoder()
_ = (transformed_train_data
| 'EncodeTrainData' >>
beam.FlatMapTuple(lambda batch, _: coder.encode(batch))
| 'WriteTrainData' >> beam.io.WriteToTFRecord(
os.path.join(working_dir,
TRANSFORMED_TRAIN_DATA_FILEBASE)))
_ = (
transformed_test_data
| 'EncodeTestData' >>
beam.FlatMapTuple(lambda batch, _: coder.encode(batch))
| 'WriteTestData' >> beam.io.WriteToTFRecord(
os.path.join(working_dir, TRANSFORMED_TEST_DATA_FILEBASE)))
# Will write a SavedModel and metadata to two subdirectories of
# working_dir, given by tft.TRANSFORM_FN_DIR and
# tft.TRANSFORMED_METADATA_DIR respectively.
_ = (transform_fn
|
'WriteTransformFn' >> tft_beam.WriteTransformFn(working_dir))
def run(self) -> beam.PCollection[job_run_result.JobRunResult]:
"""Returns a PCollection of results from the skill migration.
Returns:
PCollection. A PCollection of results from the skill migration.
"""
unmigrated_skill_models = (
self.pipeline
| 'Get all non-deleted skill models' >>
(ndb_io.GetModels(skill_models.SkillModel.get_all()))
# Pylint disable is needed because pylint is not able to correctly
# detect that the value is passed through the pipe.
| 'Add skill model ID' >> beam.WithKeys( # pylint: disable=no-value-for-parameter
lambda skill_model: skill_model.id))
skill_summary_models = (
self.pipeline
| 'Get all non-deleted skill summary models' >>
(ndb_io.GetModels(skill_models.SkillSummaryModel.get_all()))
# Pylint disable is needed because pylint is not able to correctly
# detect that the value is passed through the pipe.
| 'Add skill summary ID' >> beam.WithKeys( # pylint: disable=no-value-for-parameter
lambda skill_summary_model: skill_summary_model.id))
migrated_skill_results = (unmigrated_skill_models
| 'Transform and migrate model' >>
beam.MapTuple(self._migrate_skill))
migrated_skills = (
migrated_skill_results
| 'Filter oks' >>
beam.Filter(lambda result_item: result_item.is_ok())
|
'Unwrap ok' >> beam.Map(lambda result_item: result_item.unwrap()))
migrated_skill_job_run_results = (
migrated_skill_results
| 'Generate results for migration' >>
(job_result_transforms.ResultsToJobRunResults('SKILL PROCESSED')))
skill_changes = (unmigrated_skill_models
| 'Generate skill changes' >> beam.FlatMapTuple(
self._generate_skill_changes))
skill_objects_list = (
{
'skill_model': unmigrated_skill_models,
'skill_summary_model': skill_summary_models,
'skill': migrated_skills,
'skill_changes': skill_changes
}
| 'Merge objects' >> beam.CoGroupByKey()
| 'Get rid of ID' >> beam.Values() # pylint: disable=no-value-for-parameter
| 'Remove unmigrated skills' >> beam.Filter(
lambda x: len(x['skill_changes']) > 0 and len(x['skill']) > 0)
| 'Reorganize the skill objects' >> beam.Map(
lambda objects: {
'skill_model': objects['skill_model'][0],
'skill_summary_model': objects['skill_summary_model'][0],
'skill': objects['skill'][0],
'skill_changes': objects['skill_changes']
}))
skill_objects_list_job_run_results = (
skill_objects_list
| 'Transform skill objects into job run results' >>
(job_result_transforms.CountObjectsToJobRunResult('SKILL MIGRATED')
))
cache_deletion_job_run_results = (
skill_objects_list
| 'Delete skill from cache' >>
beam.Map(lambda skill_object: self._delete_skill_from_cache(
skill_object['skill']))
| 'Generate results for cache deletion' >>
(job_result_transforms.ResultsToJobRunResults('CACHE DELETION')))
skill_models_to_put = (
skill_objects_list
| 'Generate skill models to put' >>
beam.FlatMap(lambda skill_objects: self._update_skill(
skill_objects['skill_model'],
skill_objects['skill'],
skill_objects['skill_changes'],
)))
skill_summary_models_to_put = (
skill_objects_list
| 'Generate skill summary models to put' >>
beam.Map(lambda skill_objects: self._update_skill_summary(
skill_objects['skill'], skill_objects['skill_summary_model'])))
unused_put_results = (
(skill_models_to_put, skill_summary_models_to_put)
| 'Merge models' >> beam.Flatten()
| 'Put models into the datastore' >> ndb_io.PutModels())
return (
(cache_deletion_job_run_results, migrated_skill_job_run_results,
skill_objects_list_job_run_results)
| beam.Flatten())
def run(self) -> beam.PCollection[job_run_result.JobRunResult]:
"""Returns a PCollection of results from the story migration.
Returns:
PCollection. A PCollection of results from the story migration.
"""
unmigrated_story_models = (
self.pipeline
| 'Get all non-deleted story models' >> (
ndb_io.GetModels(story_models.StoryModel.get_all()))
# Pylint disable is needed because pylint is not able to correctly
# detect that the value is passed through the pipe.
| 'Add story keys' >> beam.WithKeys( # pylint: disable=no-value-for-parameter
lambda story_model: story_model.id)
)
story_summary_models = (
self.pipeline
| 'Get all non-deleted story summary models' >> (
ndb_io.GetModels(story_models.StorySummaryModel.get_all()))
# Pylint disable is needed because pylint is not able to correctly
# detect that the value is passed through the pipe.
| 'Add story summary keys' >> beam.WithKeys( # pylint: disable=no-value-for-parameter
lambda story_summary_model: story_summary_model.id)
)
topics = (
self.pipeline
| 'Get all non-deleted topic models' >> (
ndb_io.GetModels(topic_models.TopicModel.get_all()))
| 'Transform model into domain object' >> beam.Map(
topic_fetchers.get_topic_from_model)
# Pylint disable is needed because pylint is not able to correctly
# detect that the value is passed through the pipe.
| 'Add topic keys' >> beam.WithKeys( # pylint: disable=no-value-for-parameter
lambda topic: topic.id)
)
topic_id_to_topic = beam.pvalue.AsDict(topics)
migrated_story_results = (
unmigrated_story_models
| 'Transform and migrate model' >> beam.MapTuple(
self._migrate_story, topic_id_to_topic=topic_id_to_topic)
)
migrated_stories = (
migrated_story_results
| 'Filter oks' >> beam.Filter(
lambda result_item: result_item.is_ok())
| 'Unwrap ok' >> beam.Map(
lambda result_item: result_item.unwrap())
)
migrated_story_job_run_results = (
migrated_story_results
| 'Generate results for migration' >> (
job_result_transforms.ResultsToJobRunResults('STORY PROCESSED'))
)
story_changes = (
unmigrated_story_models
| 'Generate story changes' >> beam.FlatMapTuple(
self._generate_story_changes)
)
story_objects_list = (
{
'story_model': unmigrated_story_models,
'story_summary_model': story_summary_models,
'story': migrated_stories,
'story_change': story_changes
}
| 'Merge objects' >> beam.CoGroupByKey()
| 'Get rid of ID' >> beam.Values() # pylint: disable=no-value-for-parameter
| 'Remove unmigrated stories' >> beam.Filter(
lambda x: len(x['story_change']) > 0 and len(x['story']) > 0)
| 'Reorganize the story objects' >> beam.Map(lambda objects: {
'story_model': objects['story_model'][0],
'story_summary_model': objects['story_summary_model'][0],
'story': objects['story'][0],
'story_change': objects['story_change'][0]
})
)
story_objects_list_job_run_results = (
story_objects_list
| 'Transform story objects into job run results' >> (
job_result_transforms.CountObjectsToJobRunResult(
'STORY MIGRATED'))
)
cache_deletion_job_run_results = (
story_objects_list
| 'Delete story from cache' >> beam.Map(
lambda story_objects: self._delete_story_from_cache(
story_objects['story']))
| 'Generate results for cache deletion' >> (
job_result_transforms.ResultsToJobRunResults('CACHE DELETION'))
)
story_models_to_put = (
story_objects_list
| 'Generate story models to put' >> beam.FlatMap(
lambda story_objects: self._update_story(
story_objects['story_model'],
story_objects['story'],
story_objects['story_change'],
))
)
story_summary_models_to_put = (
story_objects_list
| 'Generate story summary models to put' >> beam.Map(
lambda story_objects: self._update_story_summary(
story_objects['story'],
story_objects['story_summary_model']
))
)
unused_put_results = (
(story_models_to_put, story_summary_models_to_put)
| 'Merge models' >> beam.Flatten()
| 'Put models into the datastore' >> ndb_io.PutModels()
)
return (
(
cache_deletion_job_run_results,
migrated_story_job_run_results,
story_objects_list_job_run_results
)
| beam.Flatten()
)
