def model_datastoreio():
"""Using a Read and Write transform to read/write to Cloud Datastore."""
import uuid
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.io.gcp.datastore.v1new.datastoreio import ReadFromDatastore
from apache_beam.io.gcp.datastore.v1new.datastoreio import WriteToDatastore
from apache_beam.io.gcp.datastore.v1new.types import Entity
from apache_beam.io.gcp.datastore.v1new.types import Key
from apache_beam.io.gcp.datastore.v1new.types import Query
project = 'my_project'
kind = 'my_kind'
query = Query(kind, project)
# [START model_datastoreio_read]
p = beam.Pipeline(options=PipelineOptions())
entities = p | 'Read From Datastore' >> ReadFromDatastore(query)
# [END model_datastoreio_read]
# [START model_datastoreio_write]
p = beam.Pipeline(options=PipelineOptions())
musicians = p | 'Musicians' >> beam.Create(
['Mozart', 'Chopin', 'Beethoven', 'Vivaldi'])
def to_entity(content):
key = Key([kind, str(uuid.uuid4())])
entity = Entity(key)
entity.set_properties({'content': content})
return entity
entities = musicians | 'To Entity' >> beam.Map(to_entity)
entities | 'Write To Datastore' >> WriteToDatastore(project)
def create_multi_datasource_reader(pipeline,
project,
namespace,
kinds,
keys_only=False):
if not kinds:
kinds = [None]
sources = []
for kind in kinds:
# namespace を指定しない(==None)と [default] namespace が使われる
query = Query(project=project, namespace=namespace, kind=kind)
if keys_only:
# see
# https://beam.apache.org/releases/pydoc/2.14.0/_modules/apache_beam/io/gcp/datastore/v1new/types.html#Query
# https://google-cloud-python.readthedocs.io/en/0.32.0/_modules/google/cloud/datastore/query.html#Query.keys_only
query.projection = ['__key__']
if not kind:
# kind を指定しない場合は明示的に __key__ asc でソートしないとエラーになる
query.order = ['__key__']
description = 'ReadFromDatastore kind={}'.format(kind if kind else "*")
s = pipeline | description >> ReadFromDatastore(query=query)
sources.append(s)
return sources
def run(argv=None, save_main_session=True):
"""Main entry point; defines and runs the wordcount pipeline."""
parser = argparse.ArgumentParser()
parser.add_argument('--input',
dest='input',
default='gs://wordcounttest2/data/datatest.txt',
help='Input file to process.')
parser.add_argument(
'--output',
dest='output',
# CHANGE 1/5: The Google Cloud Storage path is required
# for outputting the results.
default='gs://YOUR_OUTPUT_BUCKET/AND_OUTPUT_PREFIX',
help='Output file to write results to.')
known_args, pipeline_args = parser.parse_known_args(argv)
pipeline_args.extend([
# CHANGE 2/5: (OPTIONAL) Change this to DataflowRunner to
# run your pipeline on the Google Cloud Dataflow Service.
'--runner=DirectRunner',
# CHANGE 3/5: Your project ID is required in order to run your pipeline on
# the Google Cloud Dataflow Service.
'--project=SET_YOUR_PROJECT_ID_HERE',
# CHANGE 4/5: Your Google Cloud Storage path is required for staging local
# files.
'--staging_location=gs://YOUR_BUCKET_NAME/AND_STAGING_DIRECTORY',
# CHANGE 5/5: Your Google Cloud Storage path is required for temporary
# files.
'--temp_location=gs://YOUR_BUCKET_NAME/AND_TEMP_DIRECTORY',
'--job_name=your-wordcount-job',
])
# We use the save_main_session option because one or more DoFn's in this
# workflow rely on global context (e.g., a module imported at module level).
pipeline_options = PipelineOptions(pipeline_args)
pipeline_options.view_as(
SetupOptions).save_main_session = save_main_session
with beam.Pipeline(options=pipeline_options) as p:
# Read the text file[pattern] into a PCollection.
lines = p | ReadFromDatastore(known_args.input)
types = type(lines)
# Count the occurrences of each word.
counts = (lines
| 'Split' >> (beam.FlatMap(lambda x: re.findall(
r'[A-Za-z\']+', x)).with_output_types(unicode))
| 'PairWithOne' >> beam.Map(lambda x: (x, 1))
| 'GroupAndSum' >> beam.CombinePerKey(sum))
# Format the counts into a PCollection of strings.
def format_result(word_count):
(word, count) = word_count
return '%s: %s, %s' % (word, count, types)
output = counts | 'Format' >> beam.Map(format_result)
# Write the output using a "Write" transform that has side effects.
# pylint: disable=expression-not-assigned
output | WriteToText(known_args.output)
def read_from_datastore(project, user_options, pipeline_options):
"""Creates a pipeline that reads entities from Cloud Datastore."""
p = beam.Pipeline(options=pipeline_options)
# Create a query to read entities from datastore.
query = make_ancestor_query(project, user_options.kind,
user_options.namespace, user_options.ancestor)
# Read entities from Cloud Datastore into a PCollection.
lines = p | 'read from datastore' >> ReadFromDatastore(query)
# Count the occurrences of each word.
def count_ones(word_ones):
(word, ones) = word_ones
return word, sum(ones)
counts = (lines
| 'split' >> beam.ParDo(WordExtractingDoFn())
| 'pair_with_one' >> beam.Map(lambda x: (x, 1))
| 'group' >> beam.GroupByKey()
| 'count' >> beam.Map(count_ones))
# Format the counts into a PCollection of strings.
def format_result(word_count):
(word, count) = word_count
return '%s: %s' % (word, count)
output = counts | 'format' >> beam.Map(format_result)
# Write the output using a "Write" transform that has side effects.
# pylint: disable=expression-not-assigned
output | 'write' >> beam.io.WriteToText(
file_path_prefix=user_options.output,
num_shards=user_options.num_shards)
result = p.run()
# Wait until completion, main thread would access post-completion job results.
result.wait_until_finish()
return result
def main():
project = 'chromeperf'
options = PipelineOptions()
options.view_as(DebugOptions).add_experiment('use_beam_bq_sink')
options.view_as(GoogleCloudOptions).project = project
bq_export_options = options.view_as(BqExportOptions)
p = beam.Pipeline(options=options)
entities_read = Metrics.counter('main', 'entities_read')
failed_entity_transforms = Metrics.counter('main',
'failed_entity_transforms')
test_metadata_entities = (
p
| 'ReadFromDatastore(TestMetadata)' >> ReadFromDatastore(
Query(project=project, kind='TestMetadata')))
# TODO: fetch SparseDiagnostics entities and join with TestMetadata here for
# additional metadata.
test_metadata_rows = (
test_metadata_entities
| 'ConvertEntityToRow(TestMetadata)' >> beam.FlatMap(
ConvertEntity(TestMetadataEntityToRowDict, entities_read,
failed_entity_transforms)))
"""
CREATE TABLE `chromeperf.chromeperf_dashboard_data.test_metadata`
(test STRING NOT NULL,
internal_only BOOLEAN NOT NULL,
improvement_direction STRING,
units STRING,
has_rows BOOLEAN NOT NULL,
deprecated BOOLEAN NOT NULL,
description STRING,
unescaped_story_name STRING,
parent STRING,
bot_group STRING NOT NULL,
bot STRING NOT NULL,
measurement STRING NOT NULL,
)
CLUSTER BY bot_group, bot, measurement;
""" # pylint: disable=pointless-string-statement
bq_testmetadata_schema = {
'fields': [
# 'test' corresponds to the same column in the Rows export.
{
'name': 'test',
'type': 'STRING',
'mode': 'REQUIRED'
},
{
'name': 'internal_only',
'type': 'BOOLEAN',
'mode': 'REQUIRED'
},
{
'name': 'improvement_direction',
'type': 'STRING',
'mode': 'NULLABLE'
},
{
'name': 'units',
'type': 'STRING',
'mode': 'NULLABLE'
},
{
'name': 'has_rows',
'type': 'BOOLEAN',
'mode': 'REQUIRED'
},
{
'name': 'deprecated',
'type': 'BOOLEAN',
'mode': 'REQUIRED'
},
{
'name': 'description',
'type': 'STRING',
'mode': 'NULLABLE'
},
{
'name': 'unescaped_story_name',
'type': 'STRING',
'mode': 'NULLABLE'
},
{
'name': 'parent',
'type': 'STRING',
'mode': 'NULLABLE'
},
# bot_group, bot, and measurement correspond to same columns in the
# Rows export.
{
'name': 'bot_group',
'type': 'STRING',
'mode': 'REQUIRED'
},
{
'name': 'bot',
'type': 'STRING',
'mode': 'REQUIRED'
},
{
'name': 'measurement',
'type': 'STRING',
'mode': 'REQUIRED'
},
],
}
def TableNameFn(unused_element):
return '{project}:{dataset}.test_metadata{suffix}'.format(
project=project,
dataset=bq_export_options.dataset.get(),
suffix=bq_export_options.table_suffix)
_ = (
test_metadata_rows
| 'WriteToBigQuery(test_metadata)' >> beam.io.WriteToBigQuery(
TableNameFn,
schema=bq_testmetadata_schema,
method=beam.io.WriteToBigQuery.Method.FILE_LOADS,
write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE,
create_disposition=beam.io.BigQueryDisposition.CREATE_NEVER,
# Cluster by the same columns as the Rows export, so that efficient
# queries work the same way with this table (and to make efficient
# joins with that table simpler).
additional_bq_parameters={
'clustering': {
'fields': ['bot_group', 'bot', 'measurement']
}
}))
result = p.run()
result.wait_until_finish()
PrintCounters(result)
def run(argv=None):
"""Main entry point."""
parser = argparse.ArgumentParser()
parser.add_argument('--kind',
dest='kind',
default='writereadtest',
help='Datastore Kind')
parser.add_argument('--num_entities',
dest='num_entities',
type=int,
required=True,
help='Number of entities to write')
parser.add_argument('--limit',
dest='limit',
type=int,
help='Limit of number of entities to write')
known_args, pipeline_args = parser.parse_known_args(argv)
pipeline_options = PipelineOptions(pipeline_args)
gcloud_options = pipeline_options.view_as(GoogleCloudOptions)
job_name = gcloud_options.job_name
kind = known_args.kind
num_entities = known_args.num_entities
project = gcloud_options.project
# Pipeline 1: Create and write the specified number of Entities to the
# Cloud Datastore.
ancestor_key = Key([kind, str(uuid.uuid4())], project=project)
_LOGGER.info('Writing %s entities to %s', num_entities, project)
p = new_pipeline_with_job_name(pipeline_options, job_name, '-write')
_ = (
p
| 'Input' >> beam.Create(list(range(num_entities)))
| 'To String' >> beam.Map(str)
|
'To Entity' >> beam.Map(EntityWrapper(kind, ancestor_key).make_entity)
| 'Write to Datastore' >> WriteToDatastore(project))
p.run()
query = Query(kind=kind, project=project, ancestor=ancestor_key)
# Optional Pipeline 2: If a read limit was provided, read it and confirm
# that the expected entities were read.
if known_args.limit is not None:
_LOGGER.info(
'Querying a limited set of %s entities and verifying count.',
known_args.limit)
p = new_pipeline_with_job_name(pipeline_options, job_name,
'-verify-limit')
query.limit = known_args.limit
entities = p | 'read from datastore' >> ReadFromDatastore(query)
assert_that(entities | beam.combiners.Count.Globally(),
equal_to([known_args.limit]))
p.run()
query.limit = None
# Pipeline 3: Query the written Entities and verify result.
_LOGGER.info('Querying entities, asserting they match.')
p = new_pipeline_with_job_name(pipeline_options, job_name, '-verify')
entities = p | 'read from datastore' >> ReadFromDatastore(query)
assert_that(entities | beam.combiners.Count.Globally(),
equal_to([num_entities]))
p.run()
# Pipeline 4: Delete Entities.
_LOGGER.info('Deleting entities.')
p = new_pipeline_with_job_name(pipeline_options, job_name, '-delete')
entities = p | 'read from datastore' >> ReadFromDatastore(query)
_ = (entities
| 'To Keys' >> beam.Map(lambda entity: entity.key)
| 'delete entities' >> DeleteFromDatastore(project))
p.run()
# Pipeline 5: Query the written Entities, verify no results.
_LOGGER.info(
'Querying for the entities to make sure there are none present.')
p = new_pipeline_with_job_name(pipeline_options, job_name,
'-verify-deleted')
entities = p | 'read from datastore' >> ReadFromDatastore(query)
assert_that(entities | beam.combiners.Count.Globally(), equal_to([0]))
p.run()
import argparse
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.io.gcp.datastore.v1new.datastoreio import ReadFromDatastore
from apache_beam.io.gcp.datastore.v1new.types import Query
parser = argparse.ArgumentParser()
known_args, pipeline_args = parser.parse_known_args(None)
pipeline_options = PipelineOptions(pipeline_args)
project = pipeline_options.view_as(GoogleCloudOptions).project
# define the pipeline steps
p = beam.Pipeline(options=pipeline_options)
data = p | 'Read from Datastore' >> ReadFromDatastore(
query=Query('natality-guid', project, limit=5))
scored = data | 'Print' >> beam.Map(print)
# run the pipeline
result = p.run()
result.wait_until_finish()