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chore: add tpch q14-18 (#928)
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‎tests/benchmark/tpch/q14.py

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# Copyright 2024 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_vendored.tpch.queries.q14 as vendored_tpch_q14
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if __name__ == "__main__":
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dataset_id, session, suffix = utils.get_tpch_configuration()
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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vendored_tpch_q14.q, current_path, suffix, dataset_id, session
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)

‎tests/benchmark/tpch/q15.py

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# Copyright 2024 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_vendored.tpch.queries.q15 as vendored_tpch_q15
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if __name__ == "__main__":
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dataset_id, session, suffix = utils.get_tpch_configuration()
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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vendored_tpch_q15.q, current_path, suffix, dataset_id, session
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)

‎tests/benchmark/tpch/q16.py

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# Copyright 2024 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_vendored.tpch.queries.q16 as vendored_tpch_q16
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if __name__ == "__main__":
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dataset_id, session, suffix = utils.get_tpch_configuration()
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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vendored_tpch_q16.q, current_path, suffix, dataset_id, session
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)

‎tests/benchmark/tpch/q17.py

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# Copyright 2024 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_vendored.tpch.queries.q17 as vendored_tpch_q17
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if __name__ == "__main__":
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dataset_id, session, suffix = utils.get_tpch_configuration()
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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vendored_tpch_q17.q, current_path, suffix, dataset_id, session
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)

‎tests/benchmark/tpch/q18.py

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# Copyright 2024 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_vendored.tpch.queries.q18 as vendored_tpch_q18
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if __name__ == "__main__":
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dataset_id, session, suffix = utils.get_tpch_configuration()
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current_path = pathlib.Path(__file__).absolute()
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utils.get_execution_time(
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vendored_tpch_q18.q, current_path, suffix, dataset_id, session
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)

‎third_party/bigframes_vendored/tpch/queries/q14.py

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# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q14.py
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from datetime import date
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import bigframes
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def q(dataset_id: str, session: bigframes.Session):
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lineitem = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.LINEITEM",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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part = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.PART",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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var1 = date(1995, 9, 1)
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var2 = date(1995, 10, 1)
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merged = lineitem.merge(part, left_on="L_PARTKEY", right_on="P_PARTKEY")
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filtered = merged[(merged["L_SHIPDATE"] >= var1) & (merged["L_SHIPDATE"] < var2)]
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filtered["CONDI_REVENUE"] = (
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filtered["L_EXTENDEDPRICE"] * (1 - filtered["L_DISCOUNT"])
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) * filtered["P_TYPE"].str.contains("PROMO").astype("Int64")
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total_revenue = (filtered["L_EXTENDEDPRICE"] * (1 - filtered["L_DISCOUNT"])).sum()
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promo_revenue = filtered["CONDI_REVENUE"].sum()
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promo_revenue_percent = 100.00 * promo_revenue / total_revenue
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_ = round(promo_revenue_percent, 2)

‎third_party/bigframes_vendored/tpch/queries/q15.py

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# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q15.py
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from datetime import date
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import bigframes
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import bigframes.pandas as bpd
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def q(dataset_id: str, session: bigframes.Session):
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lineitem = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.LINEITEM",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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supplier = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.SUPPLIER",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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var1 = date(1996, 1, 1)
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var2 = date(1996, 4, 1)
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filtered_lineitem = lineitem[
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(lineitem["L_SHIPDATE"] >= var1) & (lineitem["L_SHIPDATE"] < var2)
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]
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filtered_lineitem["REVENUE"] = filtered_lineitem["L_EXTENDEDPRICE"] * (
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1 - filtered_lineitem["L_DISCOUNT"]
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)
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grouped_revenue = (
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filtered_lineitem.groupby("L_SUPPKEY", as_index=False)
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.agg(TOTAL_REVENUE=bpd.NamedAgg(column="REVENUE", aggfunc="sum"))
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.rename(columns={"L_SUPPKEY": "SUPPLIER_NO"})
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)
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joined_data = bpd.merge(
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supplier, grouped_revenue, left_on="S_SUPPKEY", right_on="SUPPLIER_NO"
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)
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max_revenue = joined_data["TOTAL_REVENUE"].max()
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max_revenue_suppliers = joined_data[joined_data["TOTAL_REVENUE"] == max_revenue]
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max_revenue_suppliers["TOTAL_REVENUE"] = max_revenue_suppliers[
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"TOTAL_REVENUE"
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].round(2)
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q_final = max_revenue_suppliers[
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["S_SUPPKEY", "S_NAME", "S_ADDRESS", "S_PHONE", "TOTAL_REVENUE"]
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].sort_values("S_SUPPKEY")
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q_final.to_gbq()

‎third_party/bigframes_vendored/tpch/queries/q16.py

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# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q16.py
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import bigframes
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import bigframes.pandas as bpd
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def q(dataset_id: str, session: bigframes.Session):
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part = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.PART",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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partsupp = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.PARTSUPP",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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supplier = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.SUPPLIER",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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var1 = "Brand#45"
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supplier = supplier[
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supplier["S_COMMENT"].str.contains("Customer.*Complaints", regex=True)
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]["S_SUPPKEY"]
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q_filtered = part.merge(partsupp, left_on="P_PARTKEY", right_on="PS_PARTKEY")
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q_filtered = q_filtered[q_filtered["P_BRAND"] != var1]
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q_filtered = q_filtered[~q_filtered["P_TYPE"].str.contains("MEDIUM POLISHED")]
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q_filtered = q_filtered[q_filtered["P_SIZE"].isin([49, 14, 23, 45, 19, 3, 36, 9])]
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final_df = q_filtered[~q_filtered["PS_SUPPKEY"].isin(supplier)]
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grouped = final_df.groupby(["P_BRAND", "P_TYPE", "P_SIZE"], as_index=False)
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result = grouped.agg(
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SUPPLIER_CNT=bpd.NamedAgg(column="PS_SUPPKEY", aggfunc="nunique")
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)
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q_final = result.sort_values(
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by=["SUPPLIER_CNT", "P_BRAND", "P_TYPE", "P_SIZE"],
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ascending=[False, True, True, True],
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)
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q_final.to_gbq()

‎third_party/bigframes_vendored/tpch/queries/q17.py

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# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q17.py
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import bigframes
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import bigframes.pandas as bpd
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def q(dataset_id: str, session: bigframes.Session):
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lineitem = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.LINEITEM",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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part = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.PART",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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VAR1 = "Brand#23"
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VAR2 = "MED BOX"
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filtered_part = part[(part["P_BRAND"] == VAR1) & (part["P_CONTAINER"] == VAR2)]
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q1 = bpd.merge(
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filtered_part, lineitem, how="left", left_on="P_PARTKEY", right_on="L_PARTKEY"
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)
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grouped = (
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q1.groupby("P_PARTKEY", as_index=False)
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.agg(AVG_QUANTITY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="mean"))
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.rename(columns={"P_PARTKEY": "KEY"})
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)
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grouped["AVG_QUANTITY"] = grouped["AVG_QUANTITY"] * 0.2
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q_final = bpd.merge(grouped, q1, left_on="KEY", right_on="P_PARTKEY")
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q_final = q_final[q_final["L_QUANTITY"] < q_final["AVG_QUANTITY"]]
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q_final = bpd.DataFrame(
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{"AVG_YEARLY": [(q_final["L_EXTENDEDPRICE"].sum() / 7.0).round(2)]}
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)
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q_final.to_gbq()

‎third_party/bigframes_vendored/tpch/queries/q18.py

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# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q18.py
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import typing
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import bigframes
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import bigframes.pandas as bpd
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def q(dataset_id: str, session: bigframes.Session):
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customer = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.CUSTOMER",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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lineitem = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.LINEITEM",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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orders = session.read_gbq(
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f"bigframes-dev-perf.{dataset_id}.ORDERS",
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index_col=bigframes.enums.DefaultIndexKind.NULL,
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)
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var1 = 300
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q1 = lineitem.groupby("L_ORDERKEY", as_index=False).agg(
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SUM_QUANTITY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum")
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)
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q1 = q1[q1["SUM_QUANTITY"] > var1]
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filtered_orders = orders.merge(
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q1, left_on="O_ORDERKEY", right_on="L_ORDERKEY", how="inner"
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)
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result = filtered_orders.merge(
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lineitem, left_on="O_ORDERKEY", right_on="L_ORDERKEY"
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)
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result = result.merge(customer, left_on="O_CUSTKEY", right_on="C_CUSTKEY")
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final_result = result.groupby(
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["C_NAME", "C_CUSTKEY", "O_ORDERKEY", "O_ORDERDATE", "O_TOTALPRICE"],
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as_index=False,
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).agg(COL6=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum"))
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final_result = final_result.rename(columns={"O_ORDERDATE": "O_ORDERDAT"})
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final_result = typing.cast(bpd.DataFrame, final_result).sort_values(
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["O_TOTALPRICE", "O_ORDERDAT"], ascending=[False, True]
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)
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q_final = final_result.head(100)
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q_final.to_gbq()

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