Add PDS-DS Q63-72

python/cudf_polars/cudf_polars/experimental/benchmarks/pdsds_queries/q63.py

@@ -0,0 +1,139 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Query 63."""
+
+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 63."""
+    return """
+    SELECT *
+    FROM   (SELECT i_manager_id,
+                   Sum(ss_sales_price)            sum_sales,
+                   Avg(Sum(ss_sales_price))
+                     OVER (
+                       partition BY i_manager_id) avg_monthly_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 ( 1200, 1200 + 1, 1200 + 2, 1200 + 3,
+                                        1200 + 4, 1200 + 5, 1200 + 6, 1200 + 7,
+                                        1200 + 8, 1200 + 9, 1200 + 10, 1200 + 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_manager_id,
+                      d_moy) tmp1
+    WHERE  CASE
+             WHEN avg_monthly_sales > 0 THEN Abs (sum_sales - avg_monthly_sales) /
+                                             avg_monthly_sales
+             ELSE NULL
+           END > 0.1
+    ORDER  BY i_manager_id,
+              avg_monthly_sales,
+              sum_sales
+    LIMIT 100;
+    """
+
+
+def polars_impl(run_config: RunConfig) -> pl.LazyFrame:
+    """Query 63."""
+    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)
+
+    inner_query = (
+        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(
+                [1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211]
+            )
+            & (
+                (
+                    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",
+                        ]
+                    )
+                )
+                | (
+                    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_manager_id", "d_moy"])
+        .agg([pl.col("ss_sales_price").sum().alias("sum_sales")])
+        .with_columns(
+            pl.col("sum_sales").mean().over("i_manager_id").alias("avg_monthly_sales")
+        )
+    )
+
+    return (
+        inner_query.with_columns(
+            pl.when(pl.col("avg_monthly_sales") > 0)
+            .then(
+                (pl.col("sum_sales") - pl.col("avg_monthly_sales")).abs()
+                / pl.col("avg_monthly_sales")
+            )
+            .otherwise(None)
+            .alias("deviation")
+        )
+        .filter(pl.col("deviation") > 0.1)
+        .select(["i_manager_id", "sum_sales", "avg_monthly_sales"])
+        .sort(
+            ["i_manager_id", "avg_monthly_sales", "sum_sales"],
+            nulls_last=True,
+            descending=[False, False, False],
+        )
+        .limit(100)
+    )