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chore: fix read_gbq_colab benchmark #1872

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Merged
tswast merged 2 commits into from
Jun 30, 2025
Merged

chore: fix read_gbq_colab benchmark #1872

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3404361
chore: fix `read_gbq_colab` benchmark
tswast Jun 30, 2025
3e72aec
fix for filter bench
tswast Jun 30, 2025
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17 changes: 3 additions & 14 deletions scripts/create_read_gbq_colab_benchmark_tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,18 +42,6 @@
17486432.0,
1919625975.0,
],
"num_materialized_or_scanned_rows": [
0.0,
6.0,
100.0,
4955.0,
23108.0,
139504.0,
616341.0,
3855698.0,
83725698.0,
5991998082.0,
],
"avg_row_bytes": [
0.00014346299635435792,
0.005370969708923197,
Expand Down Expand Up @@ -524,10 +512,11 @@ def main():
for i in range(num_percentiles):
percentile = TABLE_STATS["percentile"][i]
avg_row_bytes_raw = TABLE_STATS["avg_row_bytes"][i]
num_rows_raw = TABLE_STATS["num_materialized_or_scanned_rows"][i]
table_bytes_raw = TABLE_STATS["materialized_or_scanned_bytes"][i]

target_table_bytes = max(1, int(math.ceil(table_bytes_raw)))
target_row_bytes = max(1, int(math.ceil(avg_row_bytes_raw)))
num_rows = max(1, int(math.ceil(num_rows_raw)))
num_rows = max(1, int(math.ceil(target_table_bytes / target_row_bytes)))

table_name = f"percentile_{percentile:02d}"
print(f"\n--- Processing Table: {table_name} ---")
Expand Down
2 changes: 1 addition & 1 deletion tests/benchmark/read_gbq_colab/aggregate_output.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ def aggregate_output(
df_aggregated = (
df.assign(rounded=df[group_column].astype("Int64").round(-9))
.groupby("rounded")
.sum()
.sum(numeric_only=True)
)

df_aggregated.shape
Expand Down
12 changes: 10 additions & 2 deletions tests/benchmark/read_gbq_colab/filter_output.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
import pathlib

import benchmark.utils as utils
import pytest

import bigframes.session

Expand All @@ -35,8 +36,15 @@ def filter_output(

# Simulate the user filtering by a column and visualizing those results
df_filtered = df[df["col_bool_0"]]
df_filtered.shape
next(iter(df_filtered.to_pandas_batches(page_size=PAGE_SIZE)))
rows, _ = df_filtered.shape

# It's possible we don't have any pages at all, since we filtered out all
# matching rows.
if rows == 0:
with pytest.raises(StopIteration):
next(iter(df_filtered.to_pandas_batches(page_size=PAGE_SIZE)))
else:
next(iter(df_filtered.to_pandas_batches(page_size=PAGE_SIZE)))


if __name__ == "__main__":
Expand Down