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Speed-up Parquet data generation #10

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Speed-up Parquet data generation #10

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19 changes: 12 additions & 7 deletions dlio_benchmark/data_generator/parquet_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -303,6 +303,16 @@ def generate(self):
writer_target = pa.BufferOutputStream()

with pq.ParquetWriter(writer_target, schema, compression=compression) as writer:
# Generate all column data upfront for the entire file.
# This reduces function call overhead from (num_batches * num_columns)
# to just num_columns calls to gen_random_tensor.
if self.parquet_columns:
full_columns = self._generate_batch_columns(self.num_samples, rng)
else:
full_columns = self._generate_legacy_batch(elem_size, self.num_samples, rng)

full_table = pa.table(full_columns)

num_batches = (
self.num_samples + self.generation_batch_size - 1
) // self.generation_batch_size
Expand All @@ -312,13 +322,8 @@ def generate(self):
batch_end = min(batch_start + self.generation_batch_size, self.num_samples)
current_batch_size = batch_end - batch_start

# rng advances per batch — each batch gets unique data.
if self.parquet_columns:
columns = self._generate_batch_columns(current_batch_size, rng)
else:
columns = self._generate_legacy_batch(elem_size, current_batch_size, rng)

batch_table = pa.table(columns)
# Slice the pre-generated table for this batch (zero-copy).
batch_table = full_table.slice(batch_start, current_batch_size)
writer.write_table(batch_table, row_group_size=self.row_group_size)

if not is_local:
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