def create_table_info_dimension(cls, exploration_strategy):
   # If the user has specified a specific set of table formats to run against, then
   # use those. Otherwise, load from the workload test vectors.
   if pytest.config.option.table_formats:
     table_formats = list()
     for tf in pytest.config.option.table_formats.split(','):
       dataset = get_dataset_from_workload(cls.get_workload())
       table_formats.append(TableFormatInfo.create_from_string(dataset, tf))
     tf_dimensions = ImpalaTestDimension('table_format', *table_formats)
   else:
     tf_dimensions = load_table_info_dimension(cls.get_workload(), exploration_strategy)
   # If 'skip_hbase' is specified or the filesystem is isilon, s3 or local, we don't
   # need the hbase dimension.
   if pytest.config.option.skip_hbase or TARGET_FILESYSTEM.lower() \
       in ['s3', 'isilon', 'local', 'abfs', 'adls']:
     for tf_dimension in tf_dimensions:
       if tf_dimension.value.file_format == "hbase":
         tf_dimensions.remove(tf_dimension)
         break
   return tf_dimensions
Esempio n. 2
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 def create_table_info_dimension(cls, exploration_strategy):
   # If the user has specified a specific set of table formats to run against, then
   # use those. Otherwise, load from the workload test vectors.
   if pytest.config.option.table_formats:
     table_formats = list()
     for tf in pytest.config.option.table_formats.split(','):
       dataset = get_dataset_from_workload(cls.get_workload())
       table_formats.append(TableFormatInfo.create_from_string(dataset, tf))
     tf_dimensions = ImpalaTestDimension('table_format', *table_formats)
   else:
     tf_dimensions = load_table_info_dimension(cls.get_workload(), exploration_strategy)
   # If 'skip_hbase' is specified or the filesystem is isilon, s3 or local, we don't
   # need the hbase dimension.
   if pytest.config.option.skip_hbase or TARGET_FILESYSTEM.lower() \
       in ['s3', 'isilon', 'local', 'adls']:
     for tf_dimension in tf_dimensions:
       if tf_dimension.value.file_format == "hbase":
         tf_dimensions.remove(tf_dimension)
         break
   return tf_dimensions