Modify BigQuery write mechanism to use the new WriteToBigQuery PTransform, and remove num_bigquery_write_shards flag.

Previously, issue #199 forced us to use a hack to shard the variants before they are written to BigQuery, which negatively affects the speed of the tool. With the implementation of the new sink, the flag is no longer need.

Author: tneymanov

docs/large_inputs.md

@@ -14,7 +14,6 @@ Default settings:
   --worker_machine_type <default n1-standard-1> \
   --disk_size_gb <default 250> \
   --worker_disk_type <default PD> \
-  --num_bigquery_write_shards <default 1> \
   --partition_config_path <default None> \
 ```
 
@@ -98,8 +97,7 @@ transforms (e.g. the sample name is repeated in every record in the BigQuery
 output rather than just being specified once as in the VCF header), you
 typically need 3 to 4 times the total size of the raw VCF files.
 
-In addition, if [merging](variant_merging.md) or
-[--num_bigquery_write_shards](#--num_bigquery_write_shards) is enabled, you may
+In addition, if [merging](variant_merging.md) is enabled, you may
 need more disk per worker (e.g. 500GB) as the same variants need to be
 aggregated together on one machine.
 
@@ -110,32 +108,14 @@ more expensive. However, when choosing a large machine (e.g. `n1-standard-16`),
 they can reduce cost as they can avoid idle CPU cycles due to disk IOPS
 limitations.
 
-As a result, we recommend using SSDs if [merging](variant_merge.md) or
-[--num_bigquery_write_shards](#--num_bigquery_write_shards) is enabled: these
-operations require "shuffling" the data (i.e. redistributing the data among
-workers), which require significant disk I/O.
+As a result, we recommend using SSDs if [merging](variant_merge.md) is enabled:
+these operations require "shuffling" the data (i.e. redistributing the data
+among workers), which require significant disk I/O.
 
 Set
 `--worker_disk_type compute.googleapis.com/projects//zones//diskTypes/pd-ssd`
 to use SSDs.
 
-### `--num_bigquery_write_shards`
-
-Currently, the write operation to BigQuery in Dataflow is performed as a
-postprocessing step after the main transforms are done. As a workaround for
-BigQuery write limitations (more details
-[here](https://github.com/googlegenomics/gcp-variant-transforms/issues/199)),
-we have added "sharding" when writing to BigQuery. This makes the data load
-to BigQuery significantly faster as it parallelizes the process and enables
-loading large (>5TB) data to BigQuery at once.
-
-As a result, we recommend setting `--num_bigquery_write_shards 20` when loading
-any data that has more than 1 billion rows (after merging) or 1TB of final
-output. You may use a smaller number of write shards (e.g. 5) when using
-[partitioned output](#--partition_config_path) as each partition also acts as a
-"shard". Note that using a larger value (e.g. 50) can cause BigQuery write to
-fail as there is a maximum limit on the number of concurrent writes per table.
-
 ### `--partition_config_path`
 
 Partitioning the output can save significant query costs once the data is in
@@ -146,4 +126,3 @@ partition).
 As a result, we recommend setting the partition config for very large data
 where possible. Please see the [documentation](partitioning.md) for more
 details.
-

gcp_variant_transforms/options/variant_transform_options.py

@@ -173,7 +173,8 @@ def add_arguments(self, parser):
     parser.add_argument(
         '--num_bigquery_write_shards',
         type=int, default=1,
-        help=('Before writing the final result to output BigQuery, the data is '
+        help=('Note: This flag is now deprecated and should not be used! '
+              'Before writing the final result to output BigQuery, the data is '
               'sharded to avoid a known failure for very large inputs (issue '
               '#199). Setting this flag to 1 will avoid this extra sharding.'
               'It is recommended to use 20 for loading large inputs without '

gcp_variant_transforms/testing/integration/vcf_to_bq_tests/huge_tests/test_1000_genomes.json

@@ -9,7 +9,6 @@
     "worker_machine_type": "n1-standard-64",
     "max_num_workers": "64",
     "num_workers": "20",
-    "num_bigquery_write_shards": "20",
     "assertion_configs": [
       {
         "query": ["NUM_ROWS_QUERY"],

gcp_variant_transforms/testing/integration/vcf_to_bq_tests/presubmit_tests/large_tests/option_optimize_for_large_inputs.json

@@ -9,7 +9,6 @@
     "worker_machine_type": "n1-standard-16",
     "max_num_workers": "20",
     "num_workers": "20",
-    "num_bigquery_write_shards": "2",
     "assertion_configs": [
       {
         "query": ["NUM_ROWS_QUERY"],
@@ -68,4 +67,3 @@
     ]
   }
 ]
-

gcp_variant_transforms/testing/integration/vcf_to_bq_tests/presubmit_tests/large_tests/platinum_no_merge.json

@@ -7,7 +7,6 @@
     "worker_machine_type": "n1-standard-16",
     "max_num_workers": "20",
     "num_workers": "20",
-    "num_bigquery_write_shards": "20",
     "assertion_configs": [
       {
         "query": ["NUM_ROWS_QUERY"],

gcp_variant_transforms/transforms/variant_to_bigquery.py

@@ -16,7 +16,6 @@
 
 from __future__ import absolute_import
 
-import random
 from typing import Dict, List  # pylint: disable=unused-import
 
 import apache_beam as beam
@@ -29,7 +28,6 @@
 from gcp_variant_transforms.libs import processed_variant
 from gcp_variant_transforms.libs import vcf_field_conflict_resolver
 from gcp_variant_transforms.libs.variant_merge import variant_merge_strategy  # pylint: disable=unused-import
-from gcp_variant_transforms.transforms import limit_write
 
 
 # TODO(samanvp): remove this hack when BQ custom sink is added to Python SDK,
@@ -75,7 +73,6 @@ def __init__(
       update_schema_on_append=False,  # type: bool
       allow_incompatible_records=False,  # type: bool
       omit_empty_sample_calls=False,  # type: bool
-      num_bigquery_write_shards=1,  # type: int
       null_numeric_value_replacement=None  # type: int
       ):
     # type: (...) -> None
@@ -99,8 +96,6 @@ def __init__(
 +       schema if there is a mismatch.
       omit_empty_sample_calls: If true, samples that don't have a given call
         will be omitted.
-      num_bigquery_write_shards: If > 1, we will limit number of sources which
-        are used for writing to the output BigQuery table.
       null_numeric_value_replacement: the value to use instead of null for
         numeric (float/int/long) lists. For instance, [0, None, 1] will become
         [0, `null_numeric_value_replacement`, 1]. If not set, the value will set
@@ -125,7 +120,6 @@ def __init__(
 
     self._allow_incompatible_records = allow_incompatible_records
     self._omit_empty_sample_calls = omit_empty_sample_calls
-    self._num_bigquery_write_shards = num_bigquery_write_shards
     if update_schema_on_append:
       bigquery_util.update_bigquery_schema_on_append(self._schema.fields,
                                                      self._output_table)
@@ -136,35 +130,14 @@ def expand(self, pcoll):
             self._bigquery_row_generator,
             self._allow_incompatible_records,
             self._omit_empty_sample_calls))
-    if self._num_bigquery_write_shards > 1:
-      # We split data into self._num_bigquery_write_shards random partitions
-      # and then write each part to final BQ by appending them together.
-      # Combined with LimitWrite transform, this will avoid the BQ failure.
-      bq_row_partitions = bq_rows | beam.Partition(
-          lambda _, n: random.randint(0, n - 1),
-          self._num_bigquery_write_shards)
-      bq_writes = []
-      for i in range(self._num_bigquery_write_shards):
-        bq_rows = (bq_row_partitions[i] | 'LimitWrite' + str(i) >>
-                   limit_write.LimitWrite(_WRITE_SHARDS_LIMIT))
-        bq_writes.append(
-            bq_rows | 'WriteToBigQuery' + str(i) >>
-            beam.io.Write(beam.io.BigQuerySink(
+    return (bq_rows
+            | 'WriteToBigQuery' >> beam.io.WriteToBigQuery(
                 self._output_table,
                 schema=self._schema,
                 create_disposition=(
                     beam.io.BigQueryDisposition.CREATE_IF_NEEDED),
                 write_disposition=(
-                    beam.io.BigQueryDisposition.WRITE_APPEND))))
-      return bq_writes
-    else:
-      return (bq_rows
-              | 'WriteToBigQuery' >> beam.io.Write(beam.io.BigQuerySink(
-                  self._output_table,
-                  schema=self._schema,
-                  create_disposition=(
-                      beam.io.BigQueryDisposition.CREATE_IF_NEEDED),
-                  write_disposition=(
-                      beam.io.BigQueryDisposition.WRITE_APPEND
-                      if self._append
-                      else beam.io.BigQueryDisposition.WRITE_TRUNCATE))))
+                    beam.io.BigQueryDisposition.WRITE_APPEND
+                    if self._append
+                    else beam.io.BigQueryDisposition.WRITE_TRUNCATE),
+                method=beam.io.WriteToBigQuery.Method.STREAMING_INSERTS))

gcp_variant_transforms/vcf_to_bq.py

@@ -475,7 +475,6 @@ def run(argv=None):
                update_schema_on_append=known_args.update_schema_on_append,
                allow_incompatible_records=known_args.allow_incompatible_records,
                omit_empty_sample_calls=known_args.omit_empty_sample_calls,
-               num_bigquery_write_shards=known_args.num_bigquery_write_shards,
                null_numeric_value_replacement=(
                    known_args.null_numeric_value_replacement)))