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chore: add benchmarks for read_gbq_colab (#1860)
* chore: add benchmarks for read_gbq_colab * correct project id * exclude error too * Delete tests/benchmark/read_gbq_colab/first_page.py_percentile_99.error * explain column selection for groupby
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‎tests/benchmark/.gitignore‎

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*.bytesprocessed
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*.bq_exec_time_seconds
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*.error
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*.local_exec_time_seconds
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*.query_char_count
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*.slotmillis

‎tests/benchmark/read_gbq_colab/aggregate_output.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pathlib
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import benchmark.utils as utils
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import bigframes.session
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PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE
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def aggregate_output(
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*, project_id, dataset_id, table_id, session: bigframes.session.Session
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):
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# TODO(tswast): Support alternative query if table_id is a local DataFrame,
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# e.g. "{local_inline}" or "{local_large}"
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df = session._read_gbq_colab(
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f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}"
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)
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# Simulate getting the first page, since we'll always do that first in the UI.
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df.shape
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next(iter(df.to_pandas_batches(page_size=PAGE_SIZE)))
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# To simulate very small rows that can only fit a boolean,
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# some tables don't have an integer column. If an integer column is available,
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# we prefer to group by that to get a more realistic number of groups.
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group_column = "col_int64_1"
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if group_column not in df.columns:
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group_column = "col_bool_0"
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# Simulate the user aggregating by a column and visualizing those results
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df_aggregated = (
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df.assign(rounded=df[group_column].astype("Int64").round(-9))
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.groupby("rounded")
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.sum()
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)
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df_aggregated.shape
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next(iter(df_aggregated.to_pandas_batches(page_size=PAGE_SIZE)))
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if __name__ == "__main__":
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(
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project_id,
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dataset_id,
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table_id,
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session,
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suffix,
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) = utils.get_configuration(include_table_id=True)
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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aggregate_output,
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current_path,
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suffix,
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project_id=project_id,
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dataset_id=dataset_id,
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table_id=table_id,
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session=session,
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)

‎tests/benchmark/read_gbq_colab/config.jsonl‎

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{"benchmark_suffix": "percentile_09", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_09", "ordered": false}
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{"benchmark_suffix": "percentile_19", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_19", "ordered": false}
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{"benchmark_suffix": "percentile_29", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_29", "ordered": false}
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{"benchmark_suffix": "percentile_39", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_39", "ordered": false}
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{"benchmark_suffix": "percentile_49", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_49", "ordered": false}
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{"benchmark_suffix": "percentile_59", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_59", "ordered": false}
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{"benchmark_suffix": "percentile_69", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_69", "ordered": false}
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{"benchmark_suffix": "percentile_79", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_79", "ordered": false}
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{"benchmark_suffix": "percentile_89", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_89", "ordered": false}
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{"benchmark_suffix": "percentile_99", "project_id": "bigframes-dev-perf", "dataset_id": "read_gbq_colab_benchmark", "table_id": "percentile_99", "ordered": false}

‎tests/benchmark/read_gbq_colab/dry_run.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pathlib
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import benchmark.utils as utils
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import bigframes.session
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def dry_run(*, project_id, dataset_id, table_id, session: bigframes.session.Session):
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# TODO(tswast): Support alternative query if table_id is a local DataFrame,
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# e.g. "{local_inline}" or "{local_large}"
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session._read_gbq_colab(
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f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}",
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dry_run=True,
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)
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if __name__ == "__main__":
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(
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project_id,
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dataset_id,
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table_id,
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session,
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suffix,
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) = utils.get_configuration(include_table_id=True)
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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dry_run,
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current_path,
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suffix,
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project_id=project_id,
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dataset_id=dataset_id,
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table_id=table_id,
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session=session,
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)

‎tests/benchmark/read_gbq_colab/filter_output.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pathlib
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import benchmark.utils as utils
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import bigframes.session
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PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE
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def filter_output(
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*, project_id, dataset_id, table_id, session: bigframes.session.Session
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):
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# TODO(tswast): Support alternative query if table_id is a local DataFrame,
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# e.g. "{local_inline}" or "{local_large}"
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df = session._read_gbq_colab(
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f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}"
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)
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# Simulate getting the first page, since we'll always do that first in the UI.
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df.shape
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next(iter(df.to_pandas_batches(page_size=PAGE_SIZE)))
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# Simulate the user filtering by a column and visualizing those results
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df_filtered = df[df["col_bool_0"]]
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df_filtered.shape
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next(iter(df_filtered.to_pandas_batches(page_size=PAGE_SIZE)))
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if __name__ == "__main__":
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(
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project_id,
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dataset_id,
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table_id,
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session,
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suffix,
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) = utils.get_configuration(include_table_id=True)
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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filter_output,
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current_path,
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suffix,
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project_id=project_id,
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dataset_id=dataset_id,
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table_id=table_id,
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session=session,
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)

‎tests/benchmark/read_gbq_colab/first_page.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pathlib
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import benchmark.utils as utils
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import bigframes.session
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PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE
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def first_page(*, project_id, dataset_id, table_id, session: bigframes.session.Session):
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# TODO(tswast): Support alternative query if table_id is a local DataFrame,
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# e.g. "{local_inline}" or "{local_large}"
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df = session._read_gbq_colab(
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f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}"
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)
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# Get number of rows (to calculate number of pages) and the first page.
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df.shape
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next(iter(df.to_pandas_batches(page_size=PAGE_SIZE)))
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if __name__ == "__main__":
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(
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project_id,
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dataset_id,
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table_id,
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session,
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suffix,
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) = utils.get_configuration(include_table_id=True)
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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first_page,
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current_path,
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suffix,
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project_id=project_id,
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dataset_id=dataset_id,
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table_id=table_id,
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session=session,
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)

‎tests/benchmark/read_gbq_colab/last_page.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pathlib
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import benchmark.utils as utils
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import bigframes.session
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PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE
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def last_page(*, project_id, dataset_id, table_id, session: bigframes.session.Session):
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# TODO(tswast): Support alternative query if table_id is a local DataFrame,
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# e.g. "{local_inline}" or "{local_large}"
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df = session._read_gbq_colab(
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f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}"
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)
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# Get number of rows (to calculate number of pages) and then all pages.
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df.shape
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for _ in df.to_pandas_batches(page_size=PAGE_SIZE):
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pass
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if __name__ == "__main__":
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(
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project_id,
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dataset_id,
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table_id,
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session,
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suffix,
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) = utils.get_configuration(include_table_id=True)
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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last_page,
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current_path,
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suffix,
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project_id=project_id,
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dataset_id=dataset_id,
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table_id=table_id,
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session=session,
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)

‎tests/benchmark/read_gbq_colab/sort_output.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pathlib
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import benchmark.utils as utils
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import bigframes.session
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PAGE_SIZE = utils.READ_GBQ_COLAB_PAGE_SIZE
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def sort_output(
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*, project_id, dataset_id, table_id, session: bigframes.session.Session
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):
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# TODO(tswast): Support alternative query if table_id is a local DataFrame,
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# e.g. "{local_inline}" or "{local_large}"
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df = session._read_gbq_colab(
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f"SELECT * FROM `{project_id}`.{dataset_id}.{table_id}"
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)
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# Simulate getting the first page, since we'll always do that first in the UI.
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df.shape
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next(iter(df.to_pandas_batches(page_size=PAGE_SIZE)))
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# Simulate the user sorting by a column and visualizing those results
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sort_column = "col_int64_1"
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if sort_column not in df.columns:
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sort_column = "col_bool_0"
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df_sorted = df.sort_values(sort_column)
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df_sorted.shape
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next(iter(df_sorted.to_pandas_batches(page_size=PAGE_SIZE)))
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if __name__ == "__main__":
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(
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project_id,
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dataset_id,
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table_id,
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session,
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suffix,
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) = utils.get_configuration(include_table_id=True)
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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sort_output,
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current_path,
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suffix,
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project_id=project_id,
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dataset_id=dataset_id,
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table_id=table_id,
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session=session,
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)

‎tests/benchmark/utils.py‎

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import bigframes
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READ_GBQ_COLAB_PAGE_SIZE = 100
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def get_configuration(include_table_id=False):
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parser = argparse.ArgumentParser()
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def _initialize_session(ordered: bool):
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# TODO(tswast): add a flag to enable the polars semi-executor.
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context = bigframes.BigQueryOptions(
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location="US", ordering_mode="strict" if ordered else "partial"
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)

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