Hi All,I was able to make it work by creating the PCollection with the numpy array. However, writing to BQ was impossible because it requested for the schema.The code:(p | "create all" >> beam.Create(_expression_[1:5,1:5])| "write all text" >> beam.io.WriteToText('gs://archs4/output/', file_name_suffix='.txt'))Is there a walk around for providing schema for beam.io.BigQuerySink?
Many thanks,EilaOn Mon, Apr 2, 2018 at 11:33 AM, OrielResearch Eila Arich-Landkof <eila@xxxxxxxxxxxxxxxxx> wrote:Hello all,I would like to try a different way to leverage Apache beam for H5 => BQ (file to table transfer).For my use case, I would like to read every 10K rows of H5 data (numpy array format), transpose them and write them to BQ 10K columns. 10K is BQ columns limit.My code is below and fires the following error (I might have missed something basic). I am not using beam.Create and trying to create a PCollection from the ParDo transfer. is this posssible? if not, what is the alternative for creating a PColleciton from numpy array? (if any)ERROR:root:Exception at bundle <apache_beam.runners.direct.bundle_factory._Bundle object at 0x7f00aad7b7a0>, due to an exception. Traceback (most recent call last): File "/usr/local/envs/py2env/lib/python2.7/site-packages/apache_beam/runners/direct/executor.py", line 307, in call side_input_values) File "/usr/local/envs/py2env/lib/python2.7/site-packages/apache_beam/runners/direct/executor.py", line 332, in attempt_call evaluator.start_bundle() File "/usr/local/envs/py2env/lib/python2.7/site-packages/apache_beam/runners/direct/transform_evaluator.py", line 540, in start_bundle self._applied_ptransform.inputs.windowing, AttributeError: 'PBegin' object has no attribute 'windowing' ERROR:root:Giving up after 4 attempts. WARNING:root:A task failed with exception: 'PBegin' object has no attribute 'windowing' WARNING:root:A task failed with exception: 'PBegin' object has no attribute 'windowing'Code:options = PipelineOptions()google_cloud_options = options.view_as(GoogleCloudOptions)google_cloud_options.project = 'orielresearch-188115'google_cloud_options.job_name = 'h5-to-bq-10K'google_cloud_options.staging_location = 'gs://archs4/staging'google_cloud_options.temp_location = 'gs://archs4/temp'options.view_as(StandardOptions).runner = 'DirectRunner'p = beam.Pipeline(options=options)class read10kRowsDoFn(beam.DoFn):def process(self, element,index):print(index)row_start = indexrow_end = index+10000# returns numpy array - numpy.ndarrayd = _expression_[row_start,row_end,:]np.transpose(d)return(d)#for i in range(0,_expression_.shape,10000):k=210 # allows creating unique labels for the runnerfor i in range(0,3,2): # testk+=1bigQuery_dataset_table_name=bigquery_dataset_name+'.'+bigquery_table_name+str(k)print(bigQuery_dataset_table_name)label_read_row = "read "+bigQuery_dataset_table_namelabel_write_col = "write "+bigQuery_dataset_table_name# is this possible to generate a PCollection with ParDo without create?(p | label_read_row >> beam.ParDo(read10kRowsDoFn(i))| label_write_col >> beam.io.Write(beam.io.BigQuerySink(bigQuery_dataset_table_name)))p.run().wait_until_finish() #fires an errorMany thanks,--
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