apache-beam

问题 What is the purpose of org.apache.beam.sdk.transforms.Reshuffle? In the documentation the purpose is defined as: A PTransform that returns a PCollection equivalent to its input but operationally provides some of the side effects of a GroupByKey, in particular preventing fusion of the surrounding transforms, checkpointing and deduplication by id. What is the benefit of preventing fusion of the surrounding transforms? I thought fusion is an optimization to prevent unnecessarily steps. Actual

问题 What is the purpose of org.apache.beam.sdk.transforms.Reshuffle? In the documentation the purpose is defined as: A PTransform that returns a PCollection equivalent to its input but operationally provides some of the side effects of a GroupByKey, in particular preventing fusion of the surrounding transforms, checkpointing and deduplication by id. What is the benefit of preventing fusion of the surrounding transforms? I thought fusion is an optimization to prevent unnecessarily steps. Actual

问题 I am pretty new working on Apache Beam , where in I am trying to write a pipeline to extract the data from Google BigQuery and write the data to GCS in CSV format using Python. Using beam.io.read(beam.io.BigQuerySource()) I am able to read the data from BigQuery but not sure how to write it to GCS in CSV format. Is there a custom function to achieve the same , could you please help me? import logging import apache_beam as beam PROJECT='project_id' BUCKET='project_bucket' def run(): argv = [ '

问题 I am pretty new working on Apache Beam , where in I am trying to write a pipeline to extract the data from Google BigQuery and write the data to GCS in CSV format using Python. Using beam.io.read(beam.io.BigQuerySource()) I am able to read the data from BigQuery but not sure how to write it to GCS in CSV format. Is there a custom function to achieve the same , could you please help me? import logging import apache_beam as beam PROJECT='project_id' BUCKET='project_bucket' def run(): argv = [ '

问题 I want to skip header line from a CSV file. As of now I'm removing the header manually before loading it to google storage. Below is my code : PCollection<String> financeobj =p.apply(TextIO.read().from("gs://storage_path/Financials.csv")); PCollection<ClassFinance> pojos5 = financeobj.apply(ParDo.of(new DoFn<String, ClassFinance>() { // converting String into classtype private static final long serialVersionUID = 1L; @ProcessElement public void processElement(ProcessContext c) { String[]

问题 The main object A python streaming pipeline in which I read the input from pub/sub. After the input is analyzed, two option are available: If x=1 -> insert If x=2 -> update Testing This can not be done using apache beam function, so you need to develop it using the 0.25 API of BigQuery (currently this is the version supported in Google Dataflow) The problem The inserted record are still in the BigQuery buffer, so the update statement fail: UPDATE or DELETE statement over table table would

问题 The main object A python streaming pipeline in which I read the input from pub/sub. After the input is analyzed, two option are available: If x=1 -> insert If x=2 -> update Testing This can not be done using apache beam function, so you need to develop it using the 0.25 API of BigQuery (currently this is the version supported in Google Dataflow) The problem The inserted record are still in the BigQuery buffer, so the update statement fail: UPDATE or DELETE statement over table table would

问题 I'm using Python SDK for Apache Beam. The values of the datatable and the schema are in the PCollection. This is the message I read from the PubSub: {"DEVICE":"rms005_m1","DATESTAMP":"2020-05-29 20:54:26.733 UTC","SINUMERIK__x_position":69.54199981689453,"SINUMERIK__y_position":104.31400299072266,"SINUMERIK__z_position":139.0850067138672} Then I want to write it to BigQuery using the values in the json message with the lambda function for the datatable and this function for the schema: def

问题 I have a Python Apache Beam batch pipeline running on Dataflow (runner v2) that reads in rows from a CSV file, where each row is a simple key,value . I want to group these elements by key into batches of 10 values each, and then feed each batch into the following ParDo transform to be wrapped in another key for partitioning. This (should) effectively allow me precise control of the distribution of elements into each partition. class ParseExamplesDoFn(beam.DoFn): def process(self, row):

问题 Take the official document 'Creating Templates' as an example: https://cloud.google.com/dataflow/docs/templates/creating-templates class WordcountOptions(PipelineOptions): @classmethod def _add_argparse_args(cls, parser): # Use add_value_provider_argument for arguments to be templatable # Use add_argument as usual for non-templatable arguments parser.add_value_provider_argument( '--input', default='gs://dataflow-samples/shakespeare/kinglear.txt', help='Path of the file to read from') parser