[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r454391588
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,127 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count('_gen_attr_1),
+ * sum('_gen_attr_2)]
+ * output = ['key, 'cat1_cnt, 'total])
+ * Project(
+ *projectionList = ['key, if ('id > 1) 'cat1 else null, cast('value as
bigint)]
Review comment:
I mean to unify the implementations of the filter clause that are
handled by this rule. This case is not handled by this rule before your PR.
Sorry if I didn't make myself clear enough.
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r454327250
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,127 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count('_gen_attr_1),
+ * sum('_gen_attr_2)]
+ * output = ['key, 'cat1_cnt, 'total])
+ * Project(
+ *projectionList = ['key, if ('id > 1) 'cat1 else null, cast('value as
bigint)]
Review comment:
This rule should be skipped if there is only one distinct. Having a
filter or not shouldn't change it.
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r454326711
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,127 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count('_gen_attr_1),
+ * sum('_gen_attr_2)]
+ * output = ['key, 'cat1_cnt, 'total])
+ * Project(
+ *projectionList = ['key, if ('id > 1) 'cat1 else null, cast('value as
bigint)]
Review comment:
Is this necessary? The query can work fine even if we don't add this
Project in this rule, right?
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r453513546
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,126 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
Review comment:
Do we need to rewrite this query? The planner can handle single distinct
agg func AFAIK.
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r453511010
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,126 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count('_gen_attr_1),
+ * sum('_gen_attr_2)]
+ * output = ['key, 'cat1_cnt, 'total])
+ * Project(
+ *projectionList = ['key, if ('id > 1) 'cat1 else null, cast('value as
bigint)]
+ *output = ['key, '_gen_attr_1, '_gen_attr_2])
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Four example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ *functions = [sum('_gen_attr_3)]
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, cast('value as bigint)),
+ * ('key, if ('id > 1) 'cat1 else null, null, 1, null),
+ * ('key, null, 'cat2, 2, null)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * The rule consists of the two phases as follows:
+ *
+ * In the first phase, if the aggregate query exists filter clauses, project
the output of
+ * the child of the aggregate query:
+ * 1. Project the data. There are three aggregation groups in this query:
+ *i. the non-distinct group;
+ *ii. the distinct 'cat1 group;
+ *iii. the distinct 'cat2 group with filter clause.
+ *Because there is at least one group with filter clause (e.g. the
distinct 'cat2
+ *group with filter clause), then will project the data.
+ * 2. Avoid projections that may output the same attributes. There are three
aggregation groups
+ *in this query:
+ *i. the non-distinct 'cat1 group;
+ *ii. the distinct
[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r453510469
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,126 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count('_gen_attr_1),
+ * sum('_gen_attr_2)]
+ * output = ['key, 'cat1_cnt, 'total])
+ * Project(
+ *projectionList = ['key, if ('id > 1) 'cat1 else null, cast('value as
bigint)]
+ *output = ['key, '_gen_attr_1, '_gen_attr_2])
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Four example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ *functions = [sum('_gen_attr_3)]
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, cast('value as bigint)),
+ * ('key, if ('id > 1) 'cat1 else null, null, 1, null),
+ * ('key, null, 'cat2, 2, null)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * The rule consists of the two phases as follows:
+ *
+ * In the first phase, if the aggregate query exists filter clauses, project
the output of
+ * the child of the aggregate query:
+ * 1. Project the data. There are three aggregation groups in this query:
+ *i. the non-distinct group;
+ *ii. the distinct 'cat1 group;
+ *iii. the distinct 'cat2 group with filter clause.
+ *Because there is at least one group with filter clause (e.g. the
distinct 'cat2
Review comment:
When there is at least one aggregate function has the filter clause, we
add a project node on the input plan.
##
File path:
[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r453509648
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,126 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count('_gen_attr_1),
+ * sum('_gen_attr_2)]
+ * output = ['key, 'cat1_cnt, 'total])
+ * Project(
+ *projectionList = ['key, if ('id > 1) 'cat1 else null, cast('value as
bigint)]
+ *output = ['key, '_gen_attr_1, '_gen_attr_2])
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Four example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ *functions = [sum('_gen_attr_3)]
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, cast('value as bigint)),
+ * ('key, if ('id > 1) 'cat1 else null, null, 1, null),
+ * ('key, null, 'cat2, 2, null)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * The rule consists of the two phases as follows:
+ *
+ * In the first phase, if the aggregate query exists filter clauses, project
the output of
+ * the child of the aggregate query:
+ * 1. Project the data. There are three aggregation groups in this query:
+ *i. the non-distinct group;
+ *ii. the distinct 'cat1 group;
+ *iii. the distinct 'cat2 group with filter clause.
Review comment:
This doesn't match the group. Maybe just make it general `the distinct
group without filter clause` and `the distinct group with filter clause`
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r453508040
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -102,23 +102,126 @@ import org.apache.spark.sql.types.IntegerType
* {{{
* Aggregate(
*key = ['key]
- *functions = [count(if (('gid = 1)) 'cat1 else null),
- * count(if (('gid = 2)) 'cat2 else null),
+ *functions = [count(if (('gid = 1)) '_gen_attr_1 else null),
+ * count(if (('gid = 2)) '_gen_attr_2 else null),
* first(if (('gid = 0)) 'total else null) ignore nulls]
*output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
* Aggregate(
- * key = ['key, 'cat1, 'cat2, 'gid]
- * functions = [sum('value) with FILTER('id > 1)]
- * output = ['key, 'cat1, 'cat2, 'gid, 'total])
+ * key = ['key, '_gen_attr_1, '_gen_attr_2, 'gid]
+ * functions = [sum('_gen_attr_3)]
+ * output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, 'total])
* Expand(
- *projections = [('key, null, null, 0, cast('value as bigint), 'id),
+ *projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
* ('key, 'cat1, null, 1, null, null),
* ('key, null, 'cat2, 2, null, null)]
- *output = ['key, 'cat1, 'cat2, 'gid, 'value, 'id])
+ *output = ['key, '_gen_attr_1, '_gen_attr_2, 'gid, '_gen_attr_3, 'id])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * Third example: single distinct aggregate function with filter clauses and
have
+ * not other distinct aggregate function (in sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count('_gen_attr_1),
+ * sum('_gen_attr_2)]
+ * output = ['key, 'cat1_cnt, 'total])
+ * Project(
+ *projectionList = ['key, if ('id > 1) 'cat1 else null, cast('value as
bigint)]
+ *output = ['key, '_gen_attr_1, '_gen_attr_2])
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Four example: single distinct aggregate function with filter clauses (in
sql):
Review comment:
`single` -> `more than one`?
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r451635926
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,75 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, cast('value as bigint)),
+ * ('key, if ('id > 1) 'cat1 else null, null, 1, null),
+ * ('key, null, 'cat2, 2, null)]
+ * output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'value])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * The rule consists of the two phases as follows:
+ *
+ * In the first phase, if the aggregate query with distinct aggregations and
+ * filter clauses, project the output of the child of the aggregate query:
+ * 1. Project the data. There are three aggregation groups in this query:
Review comment:
can you update the comment?
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r451635560
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,75 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
Review comment:
This plan rewriting LGTM. Shall we update the second example to make it
consistent with this example?
```
...
Aggregate(
key = ...
functions = [sum('value)]
output = ...
Expand(
projections = [('key, null, null, 0, if ('id > 1) cast('value as
bigint) else null, 'id),
(...),
(...)]
output = [...])
LocalTableScan [...]
```
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r451630462
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,75 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
Review comment:
We can list some builtin agg functions that ignore nulls, and omit the
FILTER for them.
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r451629863
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,75 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
Review comment:
We assume the agg func ignores null inputs, which may not be true. Shall
we rewrite it to `sum('value) with FILTER('value is not null)`?
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r451624190
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,75 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, cast('value as bigint)),
+ * ('key, if ('id > 1) 'cat1 else null, null, 1, null),
+ * ('key, null, 'cat2, 2, null)]
+ * output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'value])
Review comment:
can we keep the name `cat1`, `cat2`?
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r450729347
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,78 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, 'value),
+ * ('key, '_gen_distinct_1, null, 1, null),
+ * ('key, null, '_gen_distinct_2, 2, null)]
+ * output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'value])
+ * Project(
Review comment:
We can even merge this project with the expand, right?
```
* Expand(
* projections = [('key, null, null, 0, cast('value as bigint)),
* ('key, if ('id > 1) 'cat1 else null, null, 1,
null),
* ('key, null, 'cat2, 2, null)]
* output = ['key, 'cat1, 'cat2, 'gid, 'value])
```
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r450157642
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -148,24 +204,105 @@ object RewriteDistinctAggregates extends
Rule[LogicalPlan] {
val distinctAggs = exprs.flatMap { _.collect {
case ae: AggregateExpression if ae.isDistinct => ae
}}
-// We need at least two distinct aggregates for this rule because
aggregation
-// strategy can handle a single distinct group.
+// We need at least two distinct aggregates or a single distinct aggregate
with a filter for
+// this rule because aggregation strategy can handle a single distinct
group without a filter.
// This check can produce false-positives, e.g., SUM(DISTINCT a) &
COUNT(DISTINCT a).
-distinctAggs.size > 1
+distinctAggs.size > 1 || distinctAggs.exists(_.filter.isDefined)
}
def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
-case a: Aggregate if mayNeedtoRewrite(a.aggregateExpressions) => rewrite(a)
+case a: Aggregate if mayNeedtoRewrite(a.aggregateExpressions) =>
+ val expandAggregate = extractFiltersInDistinctAggregates(a)
+ rewriteDistinctAggregates(expandAggregate)
}
- def rewrite(a: Aggregate): Aggregate = {
+ private def extractFiltersInDistinctAggregates(a: Aggregate): Aggregate = {
+val aggExpressions = collectAggregateExprs(a)
+val (distinctAggExpressions, regularAggExpressions) =
aggExpressions.partition(_.isDistinct)
+if (distinctAggExpressions.exists(_.filter.isDefined)) {
+ // Constructs pairs between old and new expressions for regular
aggregates. Because we
+ // will construct a new `Aggregate` and the children of the distinct
aggregates will be
+ // changed to generated ones, we need to create new references to avoid
collisions between
+ // distinct and regular aggregate children.
+ val regularAggExprs =
regularAggExpressions.filter(_.children.exists(!_.foldable))
+ val regularFunChildren = regularAggExprs
+.flatMap(_.aggregateFunction.children.filter(!_.foldable))
+ val regularFilterAttrs = regularAggExprs.flatMap(_.filterAttributes)
+ val regularAggChildren = (regularFunChildren ++
regularFilterAttrs).distinct
+ val regularAggChildrenMap = regularAggChildren.map {
+case ne: NamedExpression => ne -> ne
+case other => other -> Alias(other, other.toString)()
+ }
+ val namedRegularAggChildren = regularAggChildrenMap.map(_._2)
+ val regularAggChildAttrLookup = regularAggChildrenMap.map { kv =>
+(kv._1, kv._2.toAttribute)
+ }.toMap
+ val regularAggPairs = regularAggExprs.map {
+case ae @ AggregateExpression(af, _, _, filter, _) =>
+ val newChildren = af.children.map(c =>
regularAggChildAttrLookup.getOrElse(c, c))
+ val raf =
af.withNewChildren(newChildren).asInstanceOf[AggregateFunction]
+ val filterOpt = filter.map(_.transform {
+case a: Attribute => regularAggChildAttrLookup.getOrElse(a, a)
+ })
+ val aggExpr = ae.copy(aggregateFunction = raf, filter = filterOpt)
+ (ae, aggExpr)
+ }
-// Collect all aggregate expressions.
-val aggExpressions = a.aggregateExpressions.flatMap { e =>
- e.collect {
-case ae: AggregateExpression => ae
+ // Constructs pairs between old and new expressions for distinct
aggregates, too.
+ val distinctAggExprs = distinctAggExpressions.filter(e =>
e.children.exists(!_.foldable))
+ val (projections, distinctAggPairs) = distinctAggExprs.map {
+case ae @ AggregateExpression(af, _, _, filter, _) =>
+ // First, In order to reduce costs, it is better to handle the
filter clause locally.
+ // e.g. COUNT (DISTINCT a) FILTER (WHERE id > 1), evaluate expression
+ // If(id > 1) 'a else null first, and use the result as output.
+ // Second, If at least two DISTINCT aggregate expression which may
references the
+ // same attributes. We need to construct the generated attributes so
as the output not
+ // lost. e.g. SUM (DISTINCT a), COUNT (DISTINCT a) FILTER (WHERE id
> 1) will output
+ // attribute '_gen_distinct-1 and attribute '_gen_distinct-2 instead
of two 'a.
+ // Note: The illusionary mechanism may result in at least two
distinct groups, so we
+ // still need to call `rewrite`.
+ val unfoldableChildren = af.children.filter(!_.foldable)
+ // Expand projection
+ val projectionMap = unfoldableChildren.map {
+case e if filter.isDefined =>
+ val ife = If(filter.get, e, nullify(e))
+ e -> Alias(ife,
s"_gen_distinct_${NamedExpression.newExprId.id}")()
+case e => e -> Alias(e,
[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r450157305
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,63 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, 'value),
+ * ('key, '_gen_distinct_1, null, 1, null),
+ * ('key, null, '_gen_distinct_2, 2, null)]
+ * output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'value])
+ * Project(
+ *projectList = ['key, if ('id > 1) 'cat1 else null, 'cat2,
cast('value as bigint)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'value])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * The rule consists of the two phases as follows:
+ *
+ * In the first phase, expands data for the distinct aggregates where filter
clauses exist:
Review comment:
Please explain "what" first, not "why".
> Guaranteed to compute filter clauses in the first aggregate locally.
This is not a "what".
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r450007512
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -148,24 +204,105 @@ object RewriteDistinctAggregates extends
Rule[LogicalPlan] {
val distinctAggs = exprs.flatMap { _.collect {
case ae: AggregateExpression if ae.isDistinct => ae
}}
-// We need at least two distinct aggregates for this rule because
aggregation
-// strategy can handle a single distinct group.
+// We need at least two distinct aggregates or a single distinct aggregate
with a filter for
+// this rule because aggregation strategy can handle a single distinct
group without a filter.
// This check can produce false-positives, e.g., SUM(DISTINCT a) &
COUNT(DISTINCT a).
-distinctAggs.size > 1
+distinctAggs.size > 1 || distinctAggs.exists(_.filter.isDefined)
}
def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
-case a: Aggregate if mayNeedtoRewrite(a.aggregateExpressions) => rewrite(a)
+case a: Aggregate if mayNeedtoRewrite(a.aggregateExpressions) =>
+ val expandAggregate = extractFiltersInDistinctAggregates(a)
+ rewriteDistinctAggregates(expandAggregate)
}
- def rewrite(a: Aggregate): Aggregate = {
+ private def extractFiltersInDistinctAggregates(a: Aggregate): Aggregate = {
+val aggExpressions = collectAggregateExprs(a)
+val (distinctAggExpressions, regularAggExpressions) =
aggExpressions.partition(_.isDistinct)
+if (distinctAggExpressions.exists(_.filter.isDefined)) {
+ // Constructs pairs between old and new expressions for regular
aggregates. Because we
+ // will construct a new `Aggregate` and the children of the distinct
aggregates will be
+ // changed to generated ones, we need to create new references to avoid
collisions between
+ // distinct and regular aggregate children.
+ val regularAggExprs =
regularAggExpressions.filter(_.children.exists(!_.foldable))
+ val regularFunChildren = regularAggExprs
+.flatMap(_.aggregateFunction.children.filter(!_.foldable))
+ val regularFilterAttrs = regularAggExprs.flatMap(_.filterAttributes)
+ val regularAggChildren = (regularFunChildren ++
regularFilterAttrs).distinct
+ val regularAggChildrenMap = regularAggChildren.map {
+case ne: NamedExpression => ne -> ne
+case other => other -> Alias(other, other.toString)()
+ }
+ val namedRegularAggChildren = regularAggChildrenMap.map(_._2)
+ val regularAggChildAttrLookup = regularAggChildrenMap.map { kv =>
+(kv._1, kv._2.toAttribute)
+ }.toMap
+ val regularAggPairs = regularAggExprs.map {
+case ae @ AggregateExpression(af, _, _, filter, _) =>
+ val newChildren = af.children.map(c =>
regularAggChildAttrLookup.getOrElse(c, c))
+ val raf =
af.withNewChildren(newChildren).asInstanceOf[AggregateFunction]
+ val filterOpt = filter.map(_.transform {
+case a: Attribute => regularAggChildAttrLookup.getOrElse(a, a)
+ })
+ val aggExpr = ae.copy(aggregateFunction = raf, filter = filterOpt)
+ (ae, aggExpr)
+ }
-// Collect all aggregate expressions.
-val aggExpressions = a.aggregateExpressions.flatMap { e =>
- e.collect {
-case ae: AggregateExpression => ae
+ // Constructs pairs between old and new expressions for distinct
aggregates, too.
+ val distinctAggExprs = distinctAggExpressions.filter(e =>
e.children.exists(!_.foldable))
+ val (projections, distinctAggPairs) = distinctAggExprs.map {
+case ae @ AggregateExpression(af, _, _, filter, _) =>
+ // First, In order to reduce costs, it is better to handle the
filter clause locally.
+ // e.g. COUNT (DISTINCT a) FILTER (WHERE id > 1), evaluate expression
+ // If(id > 1) 'a else null first, and use the result as output.
+ // Second, If at least two DISTINCT aggregate expression which may
references the
+ // same attributes. We need to construct the generated attributes so
as the output not
+ // lost. e.g. SUM (DISTINCT a), COUNT (DISTINCT a) FILTER (WHERE id
> 1) will output
+ // attribute '_gen_distinct-1 and attribute '_gen_distinct-2 instead
of two 'a.
+ // Note: The illusionary mechanism may result in at least two
distinct groups, so we
+ // still need to call `rewrite`.
+ val unfoldableChildren = af.children.filter(!_.foldable)
+ // Expand projection
+ val projectionMap = unfoldableChildren.map {
+case e if filter.isDefined =>
+ val ife = If(filter.get, e, nullify(e))
+ e -> Alias(ife,
s"_gen_distinct_${NamedExpression.newExprId.id}")()
+case e => e -> Alias(e,
[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r450003275
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,63 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, 'value),
+ * ('key, '_gen_distinct_1, null, 1, null),
+ * ('key, null, '_gen_distinct_2, 2, null)]
+ * output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'value])
+ * Project(
+ *projectList = ['key, if ('id > 1) 'cat1 else null, 'cat2,
cast('value as bigint)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'value])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * The rule consists of the two phases as follows:
+ *
+ * In the first phase, expands data for the distinct aggregates where filter
clauses exist:
Review comment:
what do you mean by this paragraph?
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r449090476
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,63 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, 'value),
+ * ('key, '_gen_distinct_1, null, 1, null),
+ * ('key, null, '_gen_distinct_2, 2, null)]
+ * output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'value])
+ * Expand(
Review comment:
Then we can merge the `Project` with the above `Expand`
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r449088934
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
##
@@ -118,7 +118,63 @@ import org.apache.spark.sql.types.IntegerType
* LocalTableScan [...]
* }}}
*
- * The rule does the following things here:
+ * Third example: single distinct aggregate function with filter clauses (in
sql):
+ * {{{
+ * SELECT
+ * COUNT(DISTINCT cat1) FILTER (WHERE id > 1) as cat1_cnt,
+ * COUNT(DISTINCT cat2) as cat2_cnt,
+ * SUM(value) AS total
+ * FROM
+ *data
+ * GROUP BY
+ *key
+ * }}}
+ *
+ * This translates to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ *key = ['key]
+ *functions = [COUNT(DISTINCT 'cat1) with FILTER('id > 1),
+ * COUNT(DISTINCT 'cat2),
+ * sum('value)]
+ *output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * LocalTableScan [...]
+ * }}}
+ *
+ * This rule rewrites this logical plan to the following (pseudo) logical plan:
+ * {{{
+ * Aggregate(
+ * key = ['key]
+ * functions = [count(if (('gid = 1)) '_gen_distinct_1 else null),
+ * count(if (('gid = 2)) '_gen_distinct_2 else null),
+ * first(if (('gid = 0)) 'total else null) ignore nulls]
+ * output = ['key, 'cat1_cnt, 'cat2_cnt, 'total])
+ * Aggregate(
+ *key = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid]
+ *functions = [sum('value)]
+ *output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'total])
+ * Expand(
+ * projections = [('key, null, null, 0, 'value),
+ * ('key, '_gen_distinct_1, null, 1, null),
+ * ('key, null, '_gen_distinct_2, 2, null)]
+ * output = ['key, '_gen_distinct_1, '_gen_distinct_2, 'gid, 'value])
+ * Expand(
Review comment:
This doesn't need to be an `Expand`: you just have one project list, and
we can just use `Project`.
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r449072745
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
##
@@ -216,15 +216,21 @@ abstract class AggregateFunction extends Expression {
def toAggregateExpression(): AggregateExpression =
toAggregateExpression(isDistinct = false)
/**
- * Wraps this [[AggregateFunction]] in an [[AggregateExpression]] and sets
`isDistinct`
- * flag of the [[AggregateExpression]] to the given value because
+ * Wraps this [[AggregateFunction]] in an [[AggregateExpression]] with
`isDistinct`
+ * flag and `filter` option of the [[AggregateExpression]] to the given
value because
* [[AggregateExpression]] is the container of an [[AggregateFunction]],
aggregation mode,
- * and the flag indicating if this aggregation is distinct aggregation or
not.
+ * the flag indicating if this aggregation is distinct aggregation or not
and filter option.
Review comment:
ditto, `and the optional 'filter'`
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[GitHub] [spark] cloud-fan commented on a change in pull request #27428: [SPARK-30276][SQL] Support Filter expression allows simultaneous use of DISTINCT
cloud-fan commented on a change in pull request #27428:
URL: https://github.com/apache/spark/pull/27428#discussion_r449072337
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
##
@@ -216,15 +216,21 @@ abstract class AggregateFunction extends Expression {
def toAggregateExpression(): AggregateExpression =
toAggregateExpression(isDistinct = false)
/**
- * Wraps this [[AggregateFunction]] in an [[AggregateExpression]] and sets
`isDistinct`
- * flag of the [[AggregateExpression]] to the given value because
+ * Wraps this [[AggregateFunction]] in an [[AggregateExpression]] with
`isDistinct`
+ * flag and `filter` option of the [[AggregateExpression]] to the given
value because
Review comment:
`filter option` looks weird, how about `and an optional 'filter'`?
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