grouped_df = grouped_df[
grouped_df["cnt"] > q01_viewed_together_count].reset_index(drop=True)
### 2017 rows after filteration at sf-100
### should scale till sf-100k
grouped_df = grouped_df.repartition(npartitions=1).persist()
## converting to strings because of issue
# https://github.com/rapidsai/gpu-bdb/issues/36
grouped_df["item_sk_1"] = grouped_df["item_sk_1"].astype("str")
grouped_df["item_sk_2"] = grouped_df["item_sk_2"].astype("str")
grouped_df = grouped_df.map_partitions(lambda df: df.sort_values(
by=["cnt", "item_sk_1", "item_sk_2"], ascending=[False, True, True]))
grouped_df = grouped_df.reset_index(drop=True)
### below is just 100 rows so should fit on `cudf` context
grouped_df = grouped_df.head(q01_limit)
### writing to int to ensure same values
grouped_df["item_sk_1"] = grouped_df["item_sk_1"].astype("int32")
grouped_df["item_sk_2"] = grouped_df["item_sk_2"].astype("int32")
return grouped_df
if __name__ == "__main__":
from bdb_tools.cluster_startup import attach_to_cluster
import cudf
import dask_cudf
config = gpubdb_argparser()
client, bc = attach_to_cluster(config)
run_query(config=config, client=client, query_func=main)
WHERE wcs_item_sk = {q02_item_sk}
)
SELECT sd.wcs_item_sk as item_sk_1,
count(sd.wcs_item_sk) as cnt
FROM session_df sd
INNER JOIN item_df id
ON sd.wcs_user_sk = id.wcs_user_sk
AND sd.session_id = id.session_id
AND sd.wcs_item_sk <> {q02_item_sk}
GROUP BY sd.wcs_item_sk
ORDER BY cnt desc
LIMIT {q02_limit}
"""
result = bc.sql(last_query)
result["item_sk_2"] = q02_item_sk
result_order = ["item_sk_1", "item_sk_2", "cnt"]
result = result[result_order]
del session_df
bc.drop_table("session_df")
return result
if __name__ == "__main__":
config = gpubdb_argparser()
client, bc = attach_to_cluster(config, create_blazing_context=True)
run_query(config=config,
client=client,
query_func=main,
blazing_context=bc)
