def _validate_output_tables(self, client, output_table_base_name,
sharding_config_path, append, is_main_output):
if (output_table_base_name !=
bigquery_util.get_table_base_name(output_table_base_name)):
raise ValueError(
('Output table cannot contain "{}". we reserve this '
'string to mark sharded output tables.').format(
bigquery_util.TABLE_SUFFIX_SEPARATOR))
project_id, dataset_id, table_id = bigquery_util.parse_table_reference(
output_table_base_name)
bigquery_util.raise_error_if_dataset_not_exists(
client, project_id, dataset_id)
all_output_tables = []
if is_main_output:
all_output_tables.append(
bigquery_util.compose_table_name(table_id,
SAMPLE_INFO_TABLE_SUFFIX))
sharding = variant_sharding.VariantSharding(sharding_config_path)
num_shards = sharding.get_num_shards()
# In case there is no residual in config we will ignore the last shard.
if not sharding.should_keep_shard(sharding.get_residual_index()):
num_shards -= 1
for i in range(num_shards):
table_suffix = sharding.get_output_table_suffix(i)
if table_suffix != bigquery_util.get_table_base_name(table_suffix):
raise ValueError(
('Table suffix cannot contain "{}" we reserve this '
'string to mark sharded output tables.').format(
bigquery_util.TABLE_SUFFIX_SEPARATOR))
all_output_tables.append(
bigquery_util.compose_table_name(table_id, table_suffix))
for output_table in all_output_tables:
if append:
if not bigquery_util.table_exist(client, project_id,
dataset_id, output_table):
raise ValueError(
'Table {}:{}.{} does not exist, cannot append to it.'.
format(project_id, dataset_id, output_table))
else:
if bigquery_util.table_exist(client, project_id, dataset_id,
output_table):
raise ValueError((
'Table {}:{}.{} already exists, cannot overwrite it. Please '
'set `--append True` if you want to append to it.'
).format(project_id, dataset_id, output_table))
def test_get_table_base_name(self):
without_suffix1 = 'project_id.dataset_id.table_id'
without_suffix2 = 'project_id:dataset_id.table_id'
self.assertEqual(without_suffix1,
bigquery_util.get_table_base_name(without_suffix1))
self.assertEqual(without_suffix2,
bigquery_util.get_table_base_name(without_suffix2))
with_suffix1 = without_suffix1 + '___chr1'
with_suffix2 = without_suffix2 + '___chr1'
self.assertEqual(without_suffix1,
bigquery_util.get_table_base_name(with_suffix1))
self.assertEqual(without_suffix2,
bigquery_util.get_table_base_name(with_suffix2))
with_two_suffixes1 = with_suffix1 + '___extra_suffix'
with_two_suffixes2 = with_suffix2 + '___extra_suffix'
self.assertEqual(without_suffix1,
bigquery_util.get_table_base_name(with_two_suffixes1))
self.assertEqual(without_suffix2,
bigquery_util.get_table_base_name(with_two_suffixes2))
def _bigquery_to_vcf_shards(
known_args, # type: argparse.Namespace
beam_pipeline_options, # type: pipeline_options.PipelineOptions
vcf_data_temp_folder, # type: str
header_file_path, # type: str
):
# type: (...) -> None
"""Runs BigQuery to VCF shards pipelines.
It reads the variants from BigQuery table, groups a collection of variants
within a contiguous region of the genome (the size of the collection is
adjustable through flag `--number_of_bases_per_shard`), sorts them, and then
writes to one VCF file. All VCF data files are saved in
`vcf_data_temp_folder`.
Also, it writes the meta info and data header with the sample names to
`vcf_header_file_path`.
"""
schema = _get_schema(known_args.input_table)
variant_query = _get_variant_query(known_args, schema)
logging.info('Processing BigQuery query %s:', variant_query)
project_id, dataset_id, table_id = bigquery_util.parse_table_reference(
known_args.input_table)
bq_variant_source = bigquery.BigQuerySource(query=variant_query,
validate=True,
use_standard_sql=True)
annotation_names = _extract_annotation_names(schema)
base_table_id = bigquery_util.get_table_base_name(table_id)
sample_query = _SAMPLE_INFO_QUERY_TEMPLATE.format(
PROJECT_ID=project_id,
DATASET_ID=dataset_id,
TABLE_NAME=bigquery_util.compose_table_name(base_table_id,
SAMPLE_INFO_TABLE_SUFFIX))
bq_sample_source = bigquery.BigQuerySource(query=sample_query,
validate=True,
use_standard_sql=True)
with beam.Pipeline(options=beam_pipeline_options) as p:
variants = (p
| 'ReadFromBigQuery ' >> beam.io.Read(bq_variant_source)
| bigquery_to_variant.BigQueryToVariant(annotation_names))
sample_table_rows = (
p
| 'ReadFromSampleTable' >> beam.io.Read(bq_sample_source))
if known_args.sample_names:
temp_sample_names = (p
| transforms.Create(known_args.sample_names,
reshuffle=False))
else:
# Get sample names from sample IDs in the variants and sort.
id_to_name_hash_table = (sample_table_rows
| 'SampleIdToNameDict' >>
sample_mapping_table.SampleIdToNameDict())
temp_sample_ids = (
variants
| 'CombineSampleIds' >> combine_sample_ids.SampleIdsCombiner(
known_args.preserve_sample_order))
temp_sample_names = (
temp_sample_ids
| 'GetSampleNames' >> sample_mapping_table.GetSampleNames(
beam.pvalue.AsSingleton(id_to_name_hash_table))
| 'CombineToList' >> beam.combiners.ToList()
| 'SortSampleNames' >> beam.ParDo(sorted))
name_to_id_hash_table = (
sample_table_rows
|
'SampleNameToIdDict' >> sample_mapping_table.SampleNameToIdDict())
sample_ids = (temp_sample_names
| 'GetSampleIds' >> sample_mapping_table.GetSampleIds(
beam.pvalue.AsSingleton(name_to_id_hash_table))
| 'CombineSortedSampleIds' >> beam.combiners.ToList())
sample_names = temp_sample_names | beam.combiners.ToList()
_ = (sample_names
| 'GenerateVcfDataHeader' >> beam.ParDo(
_write_vcf_header_with_sample_names, _VCF_FIXED_COLUMNS,
known_args.representative_header_file, header_file_path))
_ = (variants
| densify_variants.DensifyVariants(
beam.pvalue.AsSingleton(sample_ids))
| 'PairVariantWithKey' >> beam.Map(
_pair_variant_with_key, known_args.number_of_bases_per_shard)
| 'GroupVariantsByKey' >> beam.GroupByKey()
| beam.ParDo(_get_file_path_and_sorted_variants,
vcf_data_temp_folder)
| vcfio.WriteVcfDataLines(known_args.bq_uses_1_based_coordinate))
