chore: add tpch q14-18 (#928)

tests/benchmark/tpch/q14.py

@@ -0,0 +1,25 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pathlib
+
+import benchmark.utils as utils
+import bigframes_vendored.tpch.queries.q14 as vendored_tpch_q14
+
+if __name__ == "__main__":
+    dataset_id, session, suffix = utils.get_tpch_configuration()
+    current_path = pathlib.Path(__file__).absolute()
+
+    utils.get_execution_time(
+        vendored_tpch_q14.q, current_path, suffix, dataset_id, session
+    )

tests/benchmark/tpch/q15.py

@@ -0,0 +1,25 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pathlib
+
+import benchmark.utils as utils
+import bigframes_vendored.tpch.queries.q15 as vendored_tpch_q15
+
+if __name__ == "__main__":
+    dataset_id, session, suffix = utils.get_tpch_configuration()
+    current_path = pathlib.Path(__file__).absolute()
+
+    utils.get_execution_time(
+        vendored_tpch_q15.q, current_path, suffix, dataset_id, session
+    )

tests/benchmark/tpch/q16.py

@@ -0,0 +1,25 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pathlib
+
+import benchmark.utils as utils
+import bigframes_vendored.tpch.queries.q16 as vendored_tpch_q16
+
+if __name__ == "__main__":
+    dataset_id, session, suffix = utils.get_tpch_configuration()
+    current_path = pathlib.Path(__file__).absolute()
+
+    utils.get_execution_time(
+        vendored_tpch_q16.q, current_path, suffix, dataset_id, session
+    )

tests/benchmark/tpch/q17.py

@@ -0,0 +1,25 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pathlib
+
+import benchmark.utils as utils
+import bigframes_vendored.tpch.queries.q17 as vendored_tpch_q17
+
+if __name__ == "__main__":
+    dataset_id, session, suffix = utils.get_tpch_configuration()
+    current_path = pathlib.Path(__file__).absolute()
+
+    utils.get_execution_time(
+        vendored_tpch_q17.q, current_path, suffix, dataset_id, session
+    )

tests/benchmark/tpch/q18.py

@@ -0,0 +1,25 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pathlib
+
+import benchmark.utils as utils
+import bigframes_vendored.tpch.queries.q18 as vendored_tpch_q18
+
+if __name__ == "__main__":
+    dataset_id, session, suffix = utils.get_tpch_configuration()
+    current_path = pathlib.Path(__file__).absolute()
+
+    utils.get_execution_time(
+        vendored_tpch_q18.q, current_path, suffix, dataset_id, session
+    )

third_party/bigframes_vendored/tpch/queries/q14.py

@@ -0,0 +1,34 @@
+# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q14.py
+
+from datetime import date
+
+import bigframes
+
+
+def q(dataset_id: str, session: bigframes.Session):
+    lineitem = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.LINEITEM",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+    part = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.PART",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+
+    var1 = date(1995, 9, 1)
+    var2 = date(1995, 10, 1)
+
+    merged = lineitem.merge(part, left_on="L_PARTKEY", right_on="P_PARTKEY")
+
+    filtered = merged[(merged["L_SHIPDATE"] >= var1) & (merged["L_SHIPDATE"] < var2)]
+
+    filtered["CONDI_REVENUE"] = (
+        filtered["L_EXTENDEDPRICE"] * (1 - filtered["L_DISCOUNT"])
+    ) * filtered["P_TYPE"].str.contains("PROMO").astype("Int64")
+
+    total_revenue = (filtered["L_EXTENDEDPRICE"] * (1 - filtered["L_DISCOUNT"])).sum()
+    promo_revenue = filtered["CONDI_REVENUE"].sum()
+
+    promo_revenue_percent = 100.00 * promo_revenue / total_revenue
+
+    _ = round(promo_revenue_percent, 2)

third_party/bigframes_vendored/tpch/queries/q15.py

@@ -0,0 +1,48 @@
+# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q15.py
+
+from datetime import date
+
+import bigframes
+import bigframes.pandas as bpd
+
+
+def q(dataset_id: str, session: bigframes.Session):
+    lineitem = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.LINEITEM",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+    supplier = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.SUPPLIER",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+
+    var1 = date(1996, 1, 1)
+    var2 = date(1996, 4, 1)
+
+    filtered_lineitem = lineitem[
+        (lineitem["L_SHIPDATE"] >= var1) & (lineitem["L_SHIPDATE"] < var2)
+    ]
+    filtered_lineitem["REVENUE"] = filtered_lineitem["L_EXTENDEDPRICE"] * (
+        1 - filtered_lineitem["L_DISCOUNT"]
+    )
+
+    grouped_revenue = (
+        filtered_lineitem.groupby("L_SUPPKEY", as_index=False)
+        .agg(TOTAL_REVENUE=bpd.NamedAgg(column="REVENUE", aggfunc="sum"))
+        .rename(columns={"L_SUPPKEY": "SUPPLIER_NO"})
+    )
+
+    joined_data = bpd.merge(
+        supplier, grouped_revenue, left_on="S_SUPPKEY", right_on="SUPPLIER_NO"
+    )
+
+    max_revenue = joined_data["TOTAL_REVENUE"].max()
+    max_revenue_suppliers = joined_data[joined_data["TOTAL_REVENUE"] == max_revenue]
+
+    max_revenue_suppliers["TOTAL_REVENUE"] = max_revenue_suppliers[
+        "TOTAL_REVENUE"
+    ].round(2)
+    q_final = max_revenue_suppliers[
+        ["S_SUPPKEY", "S_NAME", "S_ADDRESS", "S_PHONE", "TOTAL_REVENUE"]
+    ].sort_values("S_SUPPKEY")
+    q_final.to_gbq()

third_party/bigframes_vendored/tpch/queries/q16.py

@@ -0,0 +1,44 @@
+# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q16.py
+
+import bigframes
+import bigframes.pandas as bpd
+
+
+def q(dataset_id: str, session: bigframes.Session):
+    part = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.PART",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+    partsupp = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.PARTSUPP",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+    supplier = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.SUPPLIER",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+
+    var1 = "Brand#45"
+
+    supplier = supplier[
+        supplier["S_COMMENT"].str.contains("Customer.*Complaints", regex=True)
+    ]["S_SUPPKEY"]
+
+    q_filtered = part.merge(partsupp, left_on="P_PARTKEY", right_on="PS_PARTKEY")
+    q_filtered = q_filtered[q_filtered["P_BRAND"] != var1]
+    q_filtered = q_filtered[~q_filtered["P_TYPE"].str.contains("MEDIUM POLISHED")]
+    q_filtered = q_filtered[q_filtered["P_SIZE"].isin([49, 14, 23, 45, 19, 3, 36, 9])]
+
+    final_df = q_filtered[~q_filtered["PS_SUPPKEY"].isin(supplier)]
+
+    grouped = final_df.groupby(["P_BRAND", "P_TYPE", "P_SIZE"], as_index=False)
+    result = grouped.agg(
+        SUPPLIER_CNT=bpd.NamedAgg(column="PS_SUPPKEY", aggfunc="nunique")
+    )
+
+    q_final = result.sort_values(
+        by=["SUPPLIER_CNT", "P_BRAND", "P_TYPE", "P_SIZE"],
+        ascending=[False, True, True, True],
+    )
+
+    q_final.to_gbq()

third_party/bigframes_vendored/tpch/queries/q17.py

@@ -0,0 +1,40 @@
+# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q17.py
+
+import bigframes
+import bigframes.pandas as bpd
+
+
+def q(dataset_id: str, session: bigframes.Session):
+    lineitem = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.LINEITEM",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+    part = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.PART",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+
+    VAR1 = "Brand#23"
+    VAR2 = "MED BOX"
+
+    filtered_part = part[(part["P_BRAND"] == VAR1) & (part["P_CONTAINER"] == VAR2)]
+    q1 = bpd.merge(
+        filtered_part, lineitem, how="left", left_on="P_PARTKEY", right_on="L_PARTKEY"
+    )
+
+    grouped = (
+        q1.groupby("P_PARTKEY", as_index=False)
+        .agg(AVG_QUANTITY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="mean"))
+        .rename(columns={"P_PARTKEY": "KEY"})
+    )
+    grouped["AVG_QUANTITY"] = grouped["AVG_QUANTITY"] * 0.2
+
+    q_final = bpd.merge(grouped, q1, left_on="KEY", right_on="P_PARTKEY")
+
+    q_final = q_final[q_final["L_QUANTITY"] < q_final["AVG_QUANTITY"]]
+
+    q_final = bpd.DataFrame(
+        {"AVG_YEARLY": [(q_final["L_EXTENDEDPRICE"].sum() / 7.0).round(2)]}
+    )
+
+    q_final.to_gbq()

third_party/bigframes_vendored/tpch/queries/q18.py

@@ -0,0 +1,51 @@
+# Contains code from https://github.com/pola-rs/tpch/blob/main/queries/polars/q18.py
+
+import typing
+
+import bigframes
+import bigframes.pandas as bpd
+
+
+def q(dataset_id: str, session: bigframes.Session):
+    customer = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.CUSTOMER",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+    lineitem = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.LINEITEM",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+    orders = session.read_gbq(
+        f"bigframes-dev-perf.{dataset_id}.ORDERS",
+        index_col=bigframes.enums.DefaultIndexKind.NULL,
+    )
+
+    var1 = 300
+
+    q1 = lineitem.groupby("L_ORDERKEY", as_index=False).agg(
+        SUM_QUANTITY=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum")
+    )
+    q1 = q1[q1["SUM_QUANTITY"] > var1]
+
+    filtered_orders = orders.merge(
+        q1, left_on="O_ORDERKEY", right_on="L_ORDERKEY", how="inner"
+    )
+
+    result = filtered_orders.merge(
+        lineitem, left_on="O_ORDERKEY", right_on="L_ORDERKEY"
+    )
+    result = result.merge(customer, left_on="O_CUSTKEY", right_on="C_CUSTKEY")
+
+    final_result = result.groupby(
+        ["C_NAME", "C_CUSTKEY", "O_ORDERKEY", "O_ORDERDATE", "O_TOTALPRICE"],
+        as_index=False,
+    ).agg(COL6=bpd.NamedAgg(column="L_QUANTITY", aggfunc="sum"))
+
+    final_result = final_result.rename(columns={"O_ORDERDATE": "O_ORDERDAT"})
+
+    final_result = typing.cast(bpd.DataFrame, final_result).sort_values(
+        ["O_TOTALPRICE", "O_ORDERDAT"], ascending=[False, True]
+    )
+
+    q_final = final_result.head(100)
+    q_final.to_gbq()