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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2023/02/07 07:20:00 UTC
[jira] [Commented] (SPARK-39851) Improve join stats estimation if one side can keep uniqueness
[ https://issues.apache.org/jira/browse/SPARK-39851?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17685104#comment-17685104 ]
Apache Spark commented on SPARK-39851:
--------------------------------------
User 'wankunde' has created a pull request for this issue:
https://github.com/apache/spark/pull/39923
> Improve join stats estimation if one side can keep uniqueness
> -------------------------------------------------------------
>
> Key: SPARK-39851
> URL: https://issues.apache.org/jira/browse/SPARK-39851
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.4.0
> Reporter: Yuming Wang
> Priority: Major
>
> {code:sql}
> SELECT i_item_sk ss_item_sk
> FROM item,
> (SELECT DISTINCT iss.i_brand_id brand_id,
> iss.i_class_id class_id,
> iss.i_category_id category_id
> FROM item iss) x
> WHERE i_brand_id = brand_id
> AND i_class_id = class_id
> AND i_category_id = category_id
> {code}
> Current:
> {noformat}
> == Optimized Logical Plan ==
> Project [i_item_sk#4 AS ss_item_sk#54], Statistics(sizeInBytes=370.8 MiB, rowCount=3.24E+7)
> +- Join Inner, (((i_brand_id#11 = brand_id#51) AND (i_class_id#13 = class_id#52)) AND (i_category_id#15 = category_id#53)), Statistics(sizeInBytes=1112.3 MiB, rowCount=3.24E+7)
> :- Project [i_item_sk#4, i_brand_id#11, i_class_id#13, i_category_id#15], Statistics(sizeInBytes=4.6 MiB, rowCount=2.02E+5)
> : +- Filter ((isnotnull(i_brand_id#11) AND isnotnull(i_class_id#13)) AND isnotnull(i_category_id#15)), Statistics(sizeInBytes=84.6 MiB, rowCount=2.02E+5)
> : +- Relation spark_catalog.default.item[i_item_sk#4,i_item_id#5,i_rec_start_date#6,i_rec_end_date#7,i_item_desc#8,i_current_price#9,i_wholesale_cost#10,i_brand_id#11,i_brand#12,i_class_id#13,i_class#14,i_category_id#15,i_category#16,i_manufact_id#17,i_manufact#18,i_size#19,i_formulation#20,i_color#21,i_units#22,i_container#23,i_manager_id#24,i_product_name#25] parquet, Statistics(sizeInBytes=85.2 MiB, rowCount=2.04E+5)
> +- Aggregate [brand_id#51, class_id#52, category_id#53], [brand_id#51, class_id#52, category_id#53], Statistics(sizeInBytes=2.6 MiB, rowCount=1.37E+5)
> +- Project [i_brand_id#62 AS brand_id#51, i_class_id#64 AS class_id#52, i_category_id#66 AS category_id#53], Statistics(sizeInBytes=3.9 MiB, rowCount=2.02E+5)
> +- Filter ((isnotnull(i_brand_id#62) AND isnotnull(i_class_id#64)) AND isnotnull(i_category_id#66)), Statistics(sizeInBytes=84.6 MiB, rowCount=2.02E+5)
> +- Relation spark_catalog.default.item[i_item_sk#55,i_item_id#56,i_rec_start_date#57,i_rec_end_date#58,i_item_desc#59,i_current_price#60,i_wholesale_cost#61,i_brand_id#62,i_brand#63,i_class_id#64,i_class#65,i_category_id#66,i_category#67,i_manufact_id#68,i_manufact#69,i_size#70,i_formulation#71,i_color#72,i_units#73,i_container#74,i_manager_id#75,i_product_name#76] parquet, Statistics(sizeInBytes=85.2 MiB, rowCount=2.04E+5)
> {noformat}
> Excepted:
> {noformat}
> == Optimized Logical Plan ==
> Project [i_item_sk#4 AS ss_item_sk#54], Statistics(sizeInBytes=2.3 MiB, rowCount=2.02E+5)
> +- Join Inner, (((i_brand_id#11 = brand_id#51) AND (i_class_id#13 = class_id#52)) AND (i_category_id#15 = category_id#53)), Statistics(sizeInBytes=7.0 MiB, rowCount=2.02E+5)
> :- Project [i_item_sk#4, i_brand_id#11, i_class_id#13, i_category_id#15], Statistics(sizeInBytes=4.6 MiB, rowCount=2.02E+5)
> : +- Filter ((isnotnull(i_brand_id#11) AND isnotnull(i_class_id#13)) AND isnotnull(i_category_id#15)), Statistics(sizeInBytes=84.6 MiB, rowCount=2.02E+5)
> : +- Relation spark_catalog.default.item[i_item_sk#4,i_item_id#5,i_rec_start_date#6,i_rec_end_date#7,i_item_desc#8,i_current_price#9,i_wholesale_cost#10,i_brand_id#11,i_brand#12,i_class_id#13,i_class#14,i_category_id#15,i_category#16,i_manufact_id#17,i_manufact#18,i_size#19,i_formulation#20,i_color#21,i_units#22,i_container#23,i_manager_id#24,i_product_name#25] parquet, Statistics(sizeInBytes=85.2 MiB, rowCount=2.04E+5)
> +- Aggregate [brand_id#51, class_id#52, category_id#53], [brand_id#51, class_id#52, category_id#53], Statistics(sizeInBytes=2.6 MiB, rowCount=1.37E+5)
> +- Project [i_brand_id#62 AS brand_id#51, i_class_id#64 AS class_id#52, i_category_id#66 AS category_id#53], Statistics(sizeInBytes=3.9 MiB, rowCount=2.02E+5)
> +- Filter ((isnotnull(i_brand_id#62) AND isnotnull(i_class_id#64)) AND isnotnull(i_category_id#66)), Statistics(sizeInBytes=84.6 MiB, rowCount=2.02E+5)
> +- Relation spark_catalog.default.item[i_item_sk#55,i_item_id#56,i_rec_start_date#57,i_rec_end_date#58,i_item_desc#59,i_current_price#60,i_wholesale_cost#61,i_brand_id#62,i_brand#63,i_class_id#64,i_class#65,i_category_id#66,i_category#67,i_manufact_id#68,i_manufact#69,i_size#70,i_formulation#71,i_color#72,i_units#73,i_container#74,i_manager_id#75,i_product_name#76] parquet, Statistics(sizeInBytes=85.2 MiB, rowCount=2.04E+5)
> {noformat}
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