Add PDS-DS Q53-62

python/cudf_polars/cudf_polars/experimental/benchmarks/pdsds_queries/q53.py

@@ -0,0 +1,170 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Query 53."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import polars as pl
+
+from cudf_polars.experimental.benchmarks.utils import get_data
+
+if TYPE_CHECKING:
+    from cudf_polars.experimental.benchmarks.utils import RunConfig
+
+
+def duckdb_impl(run_config: RunConfig) -> str:
+    """Query 53."""
+    return """
+    SELECT *
+    FROM   (SELECT i_manufact_id,
+                   Sum(ss_sales_price)             sum_sales,
+                   Avg(Sum(ss_sales_price))
+                     OVER (
+                       partition BY i_manufact_id) avg_quarterly_sales
+            FROM   item,
+                   store_sales,
+                   date_dim,
+                   store
+            WHERE  ss_item_sk = i_item_sk
+                   AND ss_sold_date_sk = d_date_sk
+                   AND ss_store_sk = s_store_sk
+                   AND d_month_seq IN ( 1199, 1199 + 1, 1199 + 2, 1199 + 3,
+                                        1199 + 4, 1199 + 5, 1199 + 6, 1199 + 7,
+                                        1199 + 8, 1199 + 9, 1199 + 10, 1199 + 11 )
+                   AND ( ( i_category IN ( 'Books', 'Children', 'Electronics' )
+                           AND i_class IN ( 'personal', 'portable', 'reference',
+                                            'self-help' )
+                           AND i_brand IN ( 'scholaramalgamalg #14',
+                                            'scholaramalgamalg #7'
+                                            ,
+                                            'exportiunivamalg #9',
+                                                           'scholaramalgamalg #9' )
+                         )
+                          OR ( i_category IN ( 'Women', 'Music', 'Men' )
+                               AND i_class IN ( 'accessories', 'classical',
+                                                'fragrances',
+                                                'pants' )
+                               AND i_brand IN ( 'amalgimporto #1',
+                                                'edu packscholar #1',
+                                                'exportiimporto #1',
+                                                    'importoamalg #1' ) ) )
+            GROUP  BY i_manufact_id,
+                      d_qoy) tmp1
+    WHERE  CASE
+             WHEN avg_quarterly_sales > 0 THEN Abs (sum_sales - avg_quarterly_sales)
+                                               /
+                                               avg_quarterly_sales
+             ELSE NULL
+           END > 0.1
+    ORDER  BY avg_quarterly_sales,
+              sum_sales,
+              i_manufact_id
+    LIMIT 100;
+    """
+
+
+def polars_impl(run_config: RunConfig) -> pl.LazyFrame:
+    """Query 53."""
+    # Load tables
+    item = get_data(run_config.dataset_path, "item", run_config.suffix)
+    store_sales = get_data(run_config.dataset_path, "store_sales", run_config.suffix)
+    date_dim = get_data(run_config.dataset_path, "date_dim", run_config.suffix)
+    store = get_data(run_config.dataset_path, "store", run_config.suffix)
+    month_seq_list = list(range(1199, 1199 + 12))
+    grouped_data = (
+        store_sales.join(item, left_on="ss_item_sk", right_on="i_item_sk")
+        .join(date_dim, left_on="ss_sold_date_sk", right_on="d_date_sk")
+        .join(store, left_on="ss_store_sk", right_on="s_store_sk")
+        .filter(pl.col("d_month_seq").is_in(month_seq_list))
+        .filter(
+            # Books/Children/Electronics categories
+            (
+                (pl.col("i_category").is_in(["Books", "Children", "Electronics"]))
+                & (
+                    pl.col("i_class").is_in(
+                        ["personal", "portable", "reference", "self-help"]
+                    )
+                )
+                & (
+                    pl.col("i_brand").is_in(
+                        [
+                            "scholaramalgamalg #14",
+                            "scholaramalgamalg #7",
+                            "exportiunivamalg #9",
+                            "scholaramalgamalg #9",
+                        ]
+                    )
+                )
+            )
+            |
+            # Women/Music/Men categories
+            (
+                (pl.col("i_category").is_in(["Women", "Music", "Men"]))
+                & (
+                    pl.col("i_class").is_in(
+                        ["accessories", "classical", "fragrances", "pants"]
+                    )
+                )
+                & (
+                    pl.col("i_brand").is_in(
+                        [
+                            "amalgimporto #1",
+                            "edu packscholar #1",
+                            "exportiimporto #1",
+                            "importoamalg #1",
+                        ]
+                    )
+                )
+            )
+        )
+        .group_by(["i_manufact_id", "d_qoy"])
+        .agg([pl.col("ss_sales_price").sum().alias("sum_sales_raw")])
+        .with_columns(
+            [
+                pl.when(pl.col("sum_sales_raw").is_not_null())
+                .then(pl.col("sum_sales_raw"))
+                .otherwise(None)
+                .alias("sum(ss_sales_price)")
+            ]
+        )
+    )
+    non_null_data = grouped_data.filter(pl.col("i_manufact_id").is_not_null())
+    null_data = grouped_data.filter(pl.col("i_manufact_id").is_null())
+    manufacturer_averages = non_null_data.group_by("i_manufact_id").agg(
+        [pl.col("sum(ss_sales_price)").mean().alias("avg_quarterly_sales")]
+    )
+    non_null_result = non_null_data.join(
+        manufacturer_averages, on="i_manufact_id", how="left"
+    )
+    null_result = null_data.with_columns(
+        [pl.col("sum(ss_sales_price)").mean().alias("avg_quarterly_sales")]
+    )
+    inner_query = pl.concat([non_null_result, null_result]).select(
+        [
+            "i_manufact_id",
+            pl.col("sum(ss_sales_price)").alias("sum_sales"),
+            "avg_quarterly_sales",
+        ]
+    )
+    return (
+        inner_query.filter(
+            # Percentage deviation > 10%
+            pl.when(pl.col("avg_quarterly_sales") > 0)
+            .then(
+                (pl.col("sum_sales") - pl.col("avg_quarterly_sales")).abs()
+                / pl.col("avg_quarterly_sales")
+            )
+            .otherwise(None)
+            > 0.1
+        )
+        .select(["i_manufact_id", "sum_sales", "avg_quarterly_sales"])
+        .sort(
+            ["avg_quarterly_sales", "sum_sales", "i_manufact_id"],
+            nulls_last=True,
+            descending=[False, False, False],
+        )
+        .limit(100)
+    )

python/cudf_polars/cudf_polars/experimental/benchmarks/pdsds_queries/q54.py

@@ -0,0 +1,159 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Query 54."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import polars as pl
+
+from cudf_polars.experimental.benchmarks.utils import get_data
+
+if TYPE_CHECKING:
+    from cudf_polars.experimental.benchmarks.utils import RunConfig
+
+
+def duckdb_impl(run_config: RunConfig) -> str:
+    """Query 54."""
+    return """
+    WITH my_customers
+         AS (SELECT DISTINCT c_customer_sk,
+                             c_current_addr_sk
+             FROM   (SELECT cs_sold_date_sk     sold_date_sk,
+                            cs_bill_customer_sk customer_sk,
+                            cs_item_sk          item_sk
+                     FROM   catalog_sales
+                     UNION ALL
+                     SELECT ws_sold_date_sk     sold_date_sk,
+                            ws_bill_customer_sk customer_sk,
+                            ws_item_sk          item_sk
+                     FROM   web_sales) cs_or_ws_sales,
+                    item,
+                    date_dim,
+                    customer
+             WHERE  sold_date_sk = d_date_sk
+                    AND item_sk = i_item_sk
+                    AND i_category = 'Sports'
+                    AND i_class = 'fitness'
+                    AND c_customer_sk = cs_or_ws_sales.customer_sk
+                    AND d_moy = 5
+                    AND d_year = 2000),
+         my_revenue
+         AS (SELECT c_customer_sk,
+                    Sum(ss_ext_sales_price) AS revenue
+             FROM   my_customers,
+                    store_sales,
+                    customer_address,
+                    store,
+                    date_dim
+             WHERE  c_current_addr_sk = ca_address_sk
+                    AND ca_county = s_county
+                    AND ca_state = s_state
+                    AND ss_sold_date_sk = d_date_sk
+                    AND c_customer_sk = ss_customer_sk
+                    AND d_month_seq BETWEEN (SELECT DISTINCT d_month_seq + 1
+                                             FROM   date_dim
+                                             WHERE  d_year = 2000
+                                                    AND d_moy = 5) AND
+                                            (SELECT DISTINCT
+                                            d_month_seq + 3
+                                             FROM   date_dim
+                                             WHERE  d_year = 2000
+                                                    AND d_moy = 5)
+             GROUP  BY c_customer_sk),
+         segments
+         AS (SELECT Cast(( revenue / 50 ) AS INT) AS segment
+             FROM   my_revenue)
+    SELECT segment,
+                   Count(*)     AS num_customers,
+                   segment * 50 AS segment_base
+    FROM   segments
+    GROUP  BY segment
+    ORDER  BY segment,
+              num_customers
+    LIMIT 100;
+    """
+
+
+def polars_impl(run_config: RunConfig) -> pl.LazyFrame:
+    """Query 54."""
+    catalog_sales = get_data(
+        run_config.dataset_path, "catalog_sales", run_config.suffix
+    )
+    web_sales = get_data(run_config.dataset_path, "web_sales", run_config.suffix)
+    store_sales = get_data(run_config.dataset_path, "store_sales", run_config.suffix)
+    item = get_data(run_config.dataset_path, "item", run_config.suffix)
+    date_dim = get_data(run_config.dataset_path, "date_dim", run_config.suffix)
+    customer = get_data(run_config.dataset_path, "customer", run_config.suffix)
+    customer_address = get_data(
+        run_config.dataset_path, "customer_address", run_config.suffix
+    )
+    store = get_data(run_config.dataset_path, "store", run_config.suffix)
+
+    cs_sales = catalog_sales.select(
+        [
+            pl.col("cs_sold_date_sk").alias("sold_date_sk"),
+            pl.col("cs_bill_customer_sk").alias("customer_sk"),
+            pl.col("cs_item_sk").alias("item_sk"),
+        ]
+    )
+    ws_sales = web_sales.select(
+        [
+            pl.col("ws_sold_date_sk").alias("sold_date_sk"),
+            pl.col("ws_bill_customer_sk").alias("customer_sk"),
+            pl.col("ws_item_sk").alias("item_sk"),
+        ]
+    )
+    cs_or_ws_sales = pl.concat([cs_sales, ws_sales])
+    my_customers = (
+        cs_or_ws_sales.join(date_dim, left_on="sold_date_sk", right_on="d_date_sk")
+        .join(item, left_on="item_sk", right_on="i_item_sk")
+        .join(customer, left_on="customer_sk", right_on="c_customer_sk")
+        .filter(
+            (pl.col("i_category") == "Sports")
+            & (pl.col("i_class") == "fitness")
+            & (pl.col("d_moy") == 5)
+            & (pl.col("d_year") == 2000)
+        )
+        .select([pl.col("customer_sk").alias("c_customer_sk"), "c_current_addr_sk"])
+        .unique()
+    )
+
+    my_revenue = (
+        my_customers.join(
+            customer_address, left_on="c_current_addr_sk", right_on="ca_address_sk"
+        )
+        .join(
+            store, left_on=["ca_county", "ca_state"], right_on=["s_county", "s_state"]
+        )
+        .join(store_sales, left_on="c_customer_sk", right_on="ss_customer_sk")
+        .join(date_dim, left_on="ss_sold_date_sk", right_on="d_date_sk")
+        .filter(pl.col("d_month_seq").is_between(1206, 1208))
+        .group_by("c_customer_sk")
+        .agg([pl.col("ss_ext_sales_price").sum().alias("revenue")])
+    )
+
+    segments = my_revenue.with_columns(
+        (pl.col("revenue") / 50.0).round(0).cast(pl.Int32).alias("segment")
+    ).select("segment")
+
+    return (
+        segments.group_by("segment")
+        .agg([pl.len().alias("num_customers")])
+        .with_columns((pl.col("segment") * 50).alias("segment_base"))
+        .select(
+            [
+                "segment",
+                pl.col("num_customers").cast(pl.Int64),
+                "segment_base",
+            ]
+        )
+        .sort(
+            ["segment", "num_customers"],
+            nulls_last=True,
+            descending=[False, False],
+        )
+        .limit(100)
+    )

python/cudf_polars/cudf_polars/experimental/benchmarks/pdsds_queries/q55.py

@@ -0,0 +1,64 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Query 55."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import polars as pl
+
+from cudf_polars.experimental.benchmarks.utils import get_data
+
+if TYPE_CHECKING:
+    from cudf_polars.experimental.benchmarks.utils import RunConfig
+
+
+def duckdb_impl(run_config: RunConfig) -> str:
+    """Query 55."""
+    return """
+    SELECT i_brand_id              brand_id,
+                   i_brand                 brand,
+                   Sum(ss_ext_sales_price) ext_price
+    FROM   date_dim,
+           store_sales,
+           item
+    WHERE  d_date_sk = ss_sold_date_sk
+           AND ss_item_sk = i_item_sk
+           AND i_manager_id = 33
+           AND d_moy = 12
+           AND d_year = 1998
+    GROUP  BY i_brand,
+              i_brand_id
+    ORDER  BY ext_price DESC,
+              i_brand_id
+    LIMIT 100;
+    """
+
+
+def polars_impl(run_config: RunConfig) -> pl.LazyFrame:
+    """Query 55."""
+    date_dim = get_data(run_config.dataset_path, "date_dim", run_config.suffix)
+    store_sales = get_data(run_config.dataset_path, "store_sales", run_config.suffix)
+    item = get_data(run_config.dataset_path, "item", run_config.suffix)
+    return (
+        store_sales.join(date_dim, left_on="ss_sold_date_sk", right_on="d_date_sk")
+        .join(item, left_on="ss_item_sk", right_on="i_item_sk")
+        .filter(
+            (pl.col("i_manager_id") == 33)
+            & (pl.col("d_moy") == 12)
+            & (pl.col("d_year") == 1998)
+        )
+        .group_by(["i_brand", "i_brand_id"])
+        .agg(pl.col("ss_ext_sales_price").sum().alias("ext_price"))
+        .select(
+            [
+                pl.col("i_brand_id").alias("brand_id"),
+                pl.col("i_brand").alias("brand"),
+                pl.col("ext_price"),
+            ]
+        )
+        .sort(["ext_price", "brand_id"], descending=[True, False], nulls_last=True)
+        .limit(100)
+    )