[SPARK-1938] [SQL] ApproxCountDistinctMergeFunction should return Int value. #893
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
Merged build triggered. |
|
Merged build started. |
|
Merged build finished. All automated tests passed. |
|
All automated tests passed. |
|
An Int can only hold 2**32 values so for a signed int (like the JVM uses) Can we make this a Long instead? On Tue, May 27, 2014 at 4:48 AM, UCB AMPLab [email protected]:
|
|
@ash211 Makes sense to me -- which doesn't necessarily mean a lot in this unfamiliar area of the code.... It looks to me like the dataType for each of CountDistinct, ApproxCountDistinctMerge and ApproxCountDistinct should be LongType. And while we are in this file, AggregateFunction#eval(input: Row): Any doesn't actually override anything and looks to me to be superfluous. |
|
yea it looks like we should definitely use a long for this. |
|
@ash211 @markhamstra @rxin Thank you for your comments. |
… ApproxCountDistinct LongType.
|
Merged build triggered. |
|
Merged build started. |
|
Merged build finished. All automated tests passed. |
|
All automated tests passed. |
|
Thanks. I've merged this in master & branch-1.0. |
asfgit
pushed a commit
that referenced
this pull request
May 28, 2014
… value. `ApproxCountDistinctMergeFunction` should return `Int` value because the `dataType` of `ApproxCountDistinct` is `IntegerType`. Author: Takuya UESHIN <[email protected]> Closes #893 from ueshin/issues/SPARK-1938 and squashes the following commits: 3970e88 [Takuya UESHIN] Remove a superfluous line. 5ad7ec1 [Takuya UESHIN] Make dataType for each of CountDistinct, ApproxCountDistinctMerge and ApproxCountDistinct LongType. cbe7c71 [Takuya UESHIN] Revert a change. fc3ac0f [Takuya UESHIN] Fix evaluated value type of ApproxCountDistinctMergeFunction to Int. (cherry picked from commit 9df8683) Signed-off-by: Reynold Xin <[email protected]>
pdeyhim
pushed a commit
to pdeyhim/spark-1
that referenced
this pull request
Jun 25, 2014
… value. `ApproxCountDistinctMergeFunction` should return `Int` value because the `dataType` of `ApproxCountDistinct` is `IntegerType`. Author: Takuya UESHIN <[email protected]> Closes apache#893 from ueshin/issues/SPARK-1938 and squashes the following commits: 3970e88 [Takuya UESHIN] Remove a superfluous line. 5ad7ec1 [Takuya UESHIN] Make dataType for each of CountDistinct, ApproxCountDistinctMerge and ApproxCountDistinct LongType. cbe7c71 [Takuya UESHIN] Revert a change. fc3ac0f [Takuya UESHIN] Fix evaluated value type of ApproxCountDistinctMergeFunction to Int.
xiliu82
pushed a commit
to xiliu82/spark
that referenced
this pull request
Sep 4, 2014
… value. `ApproxCountDistinctMergeFunction` should return `Int` value because the `dataType` of `ApproxCountDistinct` is `IntegerType`. Author: Takuya UESHIN <[email protected]> Closes apache#893 from ueshin/issues/SPARK-1938 and squashes the following commits: 3970e88 [Takuya UESHIN] Remove a superfluous line. 5ad7ec1 [Takuya UESHIN] Make dataType for each of CountDistinct, ApproxCountDistinctMerge and ApproxCountDistinct LongType. cbe7c71 [Takuya UESHIN] Revert a change. fc3ac0f [Takuya UESHIN] Fix evaluated value type of ApproxCountDistinctMergeFunction to Int.
wangyum
added a commit
that referenced
this pull request
May 26, 2023
* [CARMEL-5851] Push partial aggregate through join (#999)
* [CARMEL-5851] Push partial aggregate through join (#977)
* [CARMEL-5851] Make partial aggregation adaptive (#892)
* Make partial aggregation adaptive
* Fix codegen
* fix
* Support group only
* Fix test error
* Only support deterministic
* Add another config
* Fix data issue
* fix
* Remove isSupportPartialAgg
* Fix
* Deduplicate right side of left semi anti join (#893)
* DeduplicateRightSideOfLeftSemiAntiJoin
* Fix test
* Add test
* Introduce stats
* Fix
* PushPartialAggregationThroughJoin
* PushPartialAggregationThroughJoin
* isPartialAgg = true,
* push project through join
* Add PullOutGroupingExpressions and reduce changes
* val (leftProjectList, rightProjectList, remainingProjectList) =
split(projectList ++ join.condition.map(_.references.toSeq).getOrElse(Nil),
join.left, join.right)
* Fix java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression cannot be cast to org.apache.spark.sql.catalyst.expressions.NamedExpression
at org.apache.hive.jdbc.HiveStatement.execute(HiveStatement.java:297)
at org.apache.hive.jdbc.HiveStatement.executeQuery(HiveStatement.java:392)
at com.ebay.carmel.spark.BenchmarkAndVerifyResult$.$anonfun$main$1(BenchmarkAndVerifyResult.scala:156)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at scala.collection.immutable.List.map(List.scala:298)
at com.ebay.carmel.spark.BenchmarkAndVerifyResult$.main(BenchmarkAndVerifyResult.scala:144)
at com.ebay.carmel.spark.BenchmarkAndVerifyResult.main(BenchmarkAndVerifyResult.scala)
* Fix TPCDS q3 reuslt incorrect:
```
0: jdbc:hive2://10.211.174.151:10000/access_v> SELECT
. . . . . . . . . . . . . . . . . . . . . . .> dt.d_year,
. . . . . . . . . . . . . . . . . . . . . . .> item.i_brand_id brand_id,
. . . . . . . . . . . . . . . . . . . . . . .> item.i_brand brand,
. . . . . . . . . . . . . . . . . . . . . . .> SUM(cast(ss_ext_sales_price as decimal(17, 2))) sum_agg
. . . . . . . . . . . . . . . . . . . . . . .> FROM date_dim dt, store_sales, item
. . . . . . . . . . . . . . . . . . . . . . .> WHERE dt.d_date_sk = store_sales.ss_sold_date_sk
. . . . . . . . . . . . . . . . . . . . . . .> AND store_sales.ss_item_sk = item.i_item_sk
. . . . . . . . . . . . . . . . . . . . . . .> AND item.i_manufact_id = 128
. . . . . . . . . . . . . . . . . . . . . . .> AND dt.d_moy = 11
. . . . . . . . . . . . . . . . . . . . . . .> GROUP BY dt.d_year, item.i_brand, item.i_brand_id
. . . . . . . . . . . . . . . . . . . . . . .> ORDER BY dt.d_year, sum_agg DESC, brand_id, brand
. . . . . . . . . . . . . . . . . . . . . . .> LIMIT 10;
+---------+-----------+---------------------+--------------+
| d_year | brand_id | brand | sum_agg |
+---------+-----------+---------------------+--------------+
| 1998 | 2003001 | exportiimporto #1 | 43900603.69 |
| 1998 | 1002001 | importoamalg #1 | 35836273.32 |
| 1998 | 1004001 | edu packamalg #1 | 35775953.92 |
| 1998 | 5001001 | amalgscholar #1 | 35538345.92 |
| 1998 | 4001001 | amalgedu pack #1 | 35317861.64 |
| 1998 | 5004001 | edu packscholar #1 | 35302613.66 |
| 1998 | 3003001 | exportiexporti #1 | 35006929.11 |
| 1998 | 2004001 | edu packimporto #1 | 26473180.83 |
| 1998 | 4002001 | importoedu pack #1 | 26176292.12 |
| 1998 | 2002001 | importoimporto #1 | 26171441.74 |
+---------+-----------+---------------------+--------------+
10 rows selected (5.041 seconds)
0: jdbc:hive2://10.211.174.151:10000/access_v> SELECT
. . . . . . . . . . . . . . . . . . . . . . .> dt.d_year,
. . . . . . . . . . . . . . . . . . . . . . .> item.i_brand_id brand_id,
. . . . . . . . . . . . . . . . . . . . . . .> item.i_brand brand,
. . . . . . . . . . . . . . . . . . . . . . .> SUM(ss_ext_sales_price) sum_agg
. . . . . . . . . . . . . . . . . . . . . . .> FROM date_dim dt, store_sales, item
. . . . . . . . . . . . . . . . . . . . . . .> WHERE dt.d_date_sk = store_sales.ss_sold_date_sk
. . . . . . . . . . . . . . . . . . . . . . .> AND store_sales.ss_item_sk = item.i_item_sk
. . . . . . . . . . . . . . . . . . . . . . .> AND item.i_manufact_id = 128
. . . . . . . . . . . . . . . . . . . . . . .> AND dt.d_moy = 11
. . . . . . . . . . . . . . . . . . . . . . .> GROUP BY dt.d_year, item.i_brand, item.i_brand_id
. . . . . . . . . . . . . . . . . . . . . . .> ORDER BY dt.d_year, sum_agg DESC, brand_id, brand
. . . . . . . . . . . . . . . . . . . . . . .> LIMIT 10;
+---------+-----------+----------------------+--------------+
| d_year | brand_id | brand | sum_agg |
+---------+-----------+----------------------+--------------+
| 1998 | 2003001 | exportiimporto #1 | 15851205.06 |
| 1998 | 3003001 | exportiexporti #1 | 12790869.96 |
| 1998 | 5001001 | amalgscholar #1 | 12763633.47 |
| 1998 | 1004001 | edu packamalg #1 | 12603183.68 |
| 1998 | 4001001 | amalgedu pack #1 | 12268486.99 |
| 1998 | 5004001 | edu packscholar #1 | 11667142.19 |
| 1998 | 1002001 | importoamalg #1 | 11336379.88 |
| 1998 | 2004001 | edu packimporto #1 | 9165179.56 |
| 1998 | 2002001 | importoimporto #1 | 9148014.59 |
| 1998 | 4004001 | edu packedu pack #1 | 8314235.98 |
+---------+-----------+----------------------+--------------+
10 rows selected (8.508 seconds)
```
Try to fix BigInteger out of long range
22/04/23 00:08:59 ERROR Executor: Exception in task 294.3 in stage 3.0 of app application_1644958298137_48668 (TID 471)
java.lang.ArithmeticException: BigInteger out of long range
at java.math.BigInteger.longValueExact(BigInteger.java:4632)
at org.apache.spark.sql.types.Decimal.toUnscaledLong(Decimal.scala:220)
at org.apache.spark.sql.catalyst.expressions.UnsafeRow.setDecimal(UnsafeRow.java:281)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.agg_doAggregate_sum_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.agg_doConsume_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.agg_doAggregateWithKeysOutput_1$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.agg_doAggregateWithKeys_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:50)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:730)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at org.apache.spark.rdd.RDD$$anon$2.hasNext(RDD.scala:332)
at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:176)
at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
at org.apache.spark.scheduler.Task.run(Task.scala:129)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:486)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1391)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:489)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
* Fix tpcds q93 RuntimeException: Couldn't find sr_return_quantity#34 in [ss_item_sk#3,ss_customer_sk#4,ss_ticket_number#10L,sr_item_sk#26,sr_reason_sk#32,sr_ticket_number#33]
at scala.sys.package$.error(package.scala:30)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.$anonfun$applyOrElse$1(BoundAttribute.scala:81)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
... 217 more
* Fix bbensid q1 TreeNodeException:
```
Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: item_id#1077
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:75)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:324)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:324)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:329)
```
```sql
SELECT
u.user_cntry_id AS byr_cntry_id,
l.item_site_id AS list_site_id,
l.auct_type_code,
cat.sap_category_id AS sap_id,
bb.src_cre_dt,
COUNT(bb.item_id) AS blocked_bids,
COUNT(DISTINCT u.prmry_user_id) AS blocked_buyers
FROM
access_views.dw_tns_blkd_bid bb
INNER JOIN access_views.dw_users u ON (bb.byr_id = u.user_id)
INNER JOIN access_views.dw_lstg_item l ON (bb.item_id = l.item_id)
INNER JOIN access_views.dw_category_groupings cat ON (l.leaf_categ_id = cat.leaf_categ_id AND l.item_site_id = cat.site_id)
WHERE
bb.src_cre_dt BETWEEN '2021-07-12' AND '2021-07-15'
AND l.auct_end_dt >= '2021-07-12'
GROUP BY
1,2,3,4,5
```
* Fix bbensid q194 NPE:
```
spark.sql("create table t1(a bigint, b string) using parquet")
spark.sql("create table t2(x bigint, y string) using parquet")
spark.sql("insert into t1 values(1, 1), (2, 2)")
spark.sql("insert into t2 values(1, 1)")
sql("SELECT distinct COALESCE(t2.y, '100') AS rev_rollup2 FROM t1 left JOIN t2 ON t1.a = t2.x").collect().foreach(println)
sql("SELECT distinct rev_rollup2 FROM t1 left JOIN (select x,COALESCE(t2.y, '100') AS rev_rollup2 from t2) t2 ON t1.a = t2.x").collect().foreach(println)
```
```
0: jdbc:hive2://10.211.174.26:10000/access_vi> create table t1(a bigint, b string) using parquet;
+---------+
| Result |
+---------+
+---------+
No rows selected (0.816 seconds)
0: jdbc:hive2://10.211.174.26:10000/access_vi> create table t2(x bigint, y string) using parquet;
+---------+
| Result |
+---------+
+---------+
No rows selected (0.95 seconds)
0: jdbc:hive2://10.211.174.26:10000/access_vi> insert into t1 values(1, 1), (2, 2);
+---------+
| Result |
+---------+
+---------+
No rows selected (14.762 seconds)
0: jdbc:hive2://10.211.174.26:10000/access_vi> insert into t2 values(1, 1);
+---------+
| Result |
+---------+
+---------+
No rows selected (20.527 seconds)
0: jdbc:hive2://10.211.174.26:10000/access_vi> SELECT distinct COALESCE(t2.y, '100') AS rev_rollup2 FROM t1 left JOIN t2 ON t1.a = t2.x;
+--------------+
| rev_rollup2 |
+--------------+
| 100 |
| 1 |
+--------------+
2 rows selected (8.685 seconds)
0: jdbc:hive2://10.211.174.26:10000/access_vi> SELECT distinct rev_rollup2 FROM t1 left JOIN (select x,COALESCE(t2.y, '100') AS rev_rollup2 from t2) t2 ON t1.a = t2.x;
+--------------+
| rev_rollup2 |
+--------------+
| NULL |
| 1 |
+--------------+
2 rows selected (2.02 seconds)
```
* Enhance: OUTER joins are supported for group by without aggregate functions
* ColumnPruning and CollapseProject support PartialAggregate
* Fix bbendis q65 reuslt incorrect:
```sql
spark-sql> SELECT
> w.src_cre_dt,
> w.site_id,
> l.auct_type_code,
> w.vstr_yn_id,
> COUNT(w.item_id) AS watches,
> count(*)
> FROM
> access_views.dw_myebay_wtch_trk w
> INNER JOIN access_views.dw_lstg_item l ON (w.item_id = l.item_id)
> WHERE
> w.src_cre_dt BETWEEN '2021-07-08' AND '2021-07-15'
> AND l.auct_end_dt >= '2021-07-08'
> AND w.cnvrted_yn_id = 0
> GROUP BY
> 1,2,3,4
> ORDER by 1,2,3,4 limit 5;
```
2021-07-08 0 1 0 4929026 4930784
2021-07-08 0 7 0 711405 711413
2021-07-08 0 8 0 154 154
2021-07-08 0 9 0 6097525 6097948
2021-07-08 0 13 0 123415 123482
rewrite:
```sql
SELECT
w.src_cre_dt,
w.site_id,
l.auct_type_code,
w.vstr_yn_id,
COUNT(w.item_id) AS watches,
sum(w.cnt * l.cnt) AS watches2
FROM
(select src_cre_dt, site_id, vstr_yn_id, item_id, COUNT(item_id) as cnt from access_views.dw_myebay_wtch_trk where src_cre_dt BETWEEN '2021-07-08' AND '2021-07-15' and cnvrted_yn_id = 0 group by src_cre_dt, site_id, vstr_yn_id, item_id) w
INNER JOIN (select auct_type_code, item_id, COUNT(*) as cnt from access_views.dw_lstg_item where auct_end_dt >= '2021-07-08' group by auct_type_code, item_id) l ON (w.item_id = l.item_id)
GROUP BY
1,2,3,4
```
* Fix should not push count aggregate expression if groupingExpressions is empty:
```
spark-sql> create table t1(id int) using parquet;
spark-sql> select count(*) from t1;
0
spark-sql> select sum(0) from t1;
NULL
```
* Fix bbendis q323 RuntimeException:
```sql
0: jdbc:hive2://10.211.174.151:10000/access_v> SELECT
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> COALESCE(u.prmry_user_id, a.user_id) AS parent_uid,
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> CAST(modified_date AS DATE) AS modified_date
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> FROM
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> access_views.dw_user_past_aliases a
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> INNER JOIN access_views.dw_users u ON (a.user_id = u.user_id)
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> WHERE
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> a.alias_flag = '2'
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> GROUP BY
. . . . . . . . . . . . . . . . . . . . . . .>
. . . . . . . . . . . . . . . . . . . . . . .> 1,2
. . . . . . . . . . . . . . . . . . . . . . .> limit 1;
Error: Error running query: java.lang.RuntimeException: Couldn't find _groupingexpression#174653 in [_groupingexpression#174654] (state=,code=0)
```
* Refactor the code
* Support range join case:
```sql
use access_views;
CREATE TEMPORARY TABLE DATE_RANGE AS
(
SELECT
CAL_DT,
RETAIL_WEEK,
RETAIL_YEAR
, TRIM(CAST(RETAIL_YEAR AS INT)) || 'W' || TRIM(SUBSTR(CAST(CAST(RETAIL_WEEK+1000 AS INT) AS VARCHAR(20)), 3)) AS WEEK_ID
, RTL_WEEK_BEG_DT AS WEEK_BEG_DT
, RETAIL_WK_END_DATE AS WEEK_END_DT
, MONTH_BEG_DT, MONTH_END_DT, MONTH_ID
, QTR_BEG_DT, QTR_END_DT, QTR_ID
, YEAR_ID
FROM DW_CAL_DT
WHERE 1=1
AND CAL_DT BETWEEN DATE'2020-01-01' AND CURRENT_DATE
);
create temp table t11 using parquet as
SELECT D.CAL_DT,
COUNT(DISTINCT LSTG.ITEM_ID) ITEM_NUM
FROM DATE_RANGE D
INNER JOIN
(SELECT HOT.AUCT_START_DT,
HOT.AUCT_END_DT,
HOT.ITEM_ID
FROM ACCESS_VIEWS.DW_LSTG_ITEM HOT
INNER JOIN ACCESS_VIEWS.DW_CATEGORY_GROUPINGS CATE ON CATE.SITE_ID = HOT.ITEM_SITE_ID AND CATE.LEAF_CATEG_ID = HOT.LEAF_CATEG_ID
INNER JOIN ACCESS_VIEWS.SSA_CURNCY_PLAN_RATE_DIM FX ON FX.CURNCY_ID = HOT.LSTG_CURNCY_ID
INNER JOIN ACCESS_VIEWS.DW_ITEMS_SHIPPING SHIP ON HOT.ITEM_ID=SHIP.ITEM_ID AND HOT.ITEM_VRSN_ID=SHIP.ITEM_VRSN_ID
WHERE 1 = 1
AND HOT.AUCT_TYPE_CODE NOT IN (10,15)
AND HOT.ITEM_SITE_ID <> 223
AND CATE.SAP_CATEGORY_ID NOT IN (5,7,23,41,-999)) LSTG
ON D.CAL_DT BETWEEN LSTG.AUCT_START_DT AND LSTG.AUCT_END_DT
GROUP BY 1;
```
* Fix bbendis q311 introduce 2 PartialAggregates:
```sql
== Optimized Logical Plan ==
Aggregate [session_start_dt#84199, site_id#84163, _groupingexpression#84731], [session_start_dt#84199, site_id#84163, count(if ((gid#84733 = 4)) CASE WHEN (av.`type_1` = 'sign_in_visit') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84737 else null) AS sign_in_visit#84095L, count(if ((gid#84733 = 5)) CASE WHEN (av.`type_1` IN ('sign_in_suc', 'reg_suc', 'gxo_suc')) THEN spark_catalog.ubi_t.ubi_event.`guid` END#84735 else null) AS access_succ#84096L, count(if ((gid#84733 = 6)) CASE WHEN (av.`type_1` = 'sign_in_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84736 else null) AS sign_in_succ#84097L, count(if ((gid#84733 = 1)) CASE WHEN (av.`type_1` = 'reg_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84739 else null) AS reg_succ#84098L, count(if ((gid#84733 = 2)) CASE WHEN (av.`type_1` = 'gxo_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84738 else null) AS gxo_succ#84099L, count(if ((gid#84733 = 3)) s165.`guid`#84734 else null) AS tot_visitors#84100L, _groupingexpression#84731 AS experience#84101], Statistics(sizeInBytes=2.96E+38 B)
+- Aggregate [session_start_dt#84199, site_id#84163, _groupingexpression#84731, s165.`guid`#84734, CASE WHEN (av.`type_1` IN ('sign_in_suc', 'reg_suc', 'gxo_suc')) THEN spark_catalog.ubi_t.ubi_event.`guid` END#84735, CASE WHEN (av.`type_1` = 'sign_in_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84736, CASE WHEN (av.`type_1` = 'sign_in_visit') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84737, CASE WHEN (av.`type_1` = 'gxo_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84738, CASE WHEN (av.`type_1` = 'reg_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84739, gid#84733], [session_start_dt#84199, site_id#84163, _groupingexpression#84731, s165.`guid`#84734, CASE WHEN (av.`type_1` IN ('sign_in_suc', 'reg_suc', 'gxo_suc')) THEN spark_catalog.ubi_t.ubi_event.`guid` END#84735, CASE WHEN (av.`type_1` = 'sign_in_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84736, CASE WHEN (av.`type_1` = 'sign_in_visit') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84737, CASE WHEN (av.`type_1` = 'gxo_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84738, CASE WHEN (av.`type_1` = 'reg_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84739, gid#84733], Statistics(sizeInBytes=5.70E+38 B)
+- Expand [Vector(session_start_dt#84199, site_id#84163, _groupingexpression#84731, null, null, null, null, null, CASE WHEN (type_1#84089 = reg_suc) THEN guid#80355 END, 1), Vector(session_start_dt#84199, site_id#84163, _groupingexpression#84731, null, null, null, null, CASE WHEN (type_1#84089 = gxo_suc) THEN guid#80355 END, null, 2), Vector(session_start_dt#84199, site_id#84163, _groupingexpression#84731, guid#84161, null, null, null, null, null, 3), Vector(session_start_dt#84199, site_id#84163, _groupingexpression#84731, null, null, null, CASE WHEN (type_1#84089 = sign_in_visit) THEN guid#80355 END, null, null, 4), Vector(session_start_dt#84199, site_id#84163, _groupingexpression#84731, null, CASE WHEN type_1#84089 IN (sign_in_suc,reg_suc,gxo_suc) THEN guid#80355 END, null, null, null, null, 5), Vector(session_start_dt#84199, site_id#84163, _groupingexpression#84731, null, null, CASE WHEN (type_1#84089 = sign_in_suc) THEN guid#80355 END, null, null, null, 6)], [session_start_dt#84199, site_id#84163, _groupingexpression#84731, s165.`guid`#84734, CASE WHEN (av.`type_1` IN ('sign_in_suc', 'reg_suc', 'gxo_suc')) THEN spark_catalog.ubi_t.ubi_event.`guid` END#84735, CASE WHEN (av.`type_1` = 'sign_in_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84736, CASE WHEN (av.`type_1` = 'sign_in_visit') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84737, CASE WHEN (av.`type_1` = 'gxo_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84738, CASE WHEN (av.`type_1` = 'reg_suc') THEN spark_catalog.ubi_t.ubi_event.`guid` END#84739, gid#84733], Statistics(sizeInBytes=5.70E+38 B)
+- Project [guid#84161, site_id#84163, session_start_dt#84199, guid#80355, type_1#84089, CASE WHEN (cobrand#84164 = 0) THEN dWeb WHEN (cobrand#84164 = 7) THEN FSoM WHEN ((cobrand#84164 = 6) AND primary_app_id#84182 IN (1462,2878)) THEN iOS WHEN ((cobrand#84164 = 6) AND (primary_app_id#84182 = 2571)) THEN Android WHEN ((cobrand#84164 = 6) AND (primary_app_id#84182 = 3564)) THEN mWeb ELSE Other END AS _groupingexpression#84731], Statistics(sizeInBytes=5.65E+37 B)
+- Join LeftOuter, ((((guid#80355 = guid#84161) AND (session_skey#84201L = session_skey#84162L)) AND (session_start_dt#84203 = session_start_dt#84199)) AND (site_id#84714 = cast(site_id#84163 as decimal(10,0)))), Statistics(sizeInBytes=6.43E+37 B)
:- Project [guid#84161, session_skey#84162L, site_id#84163, cobrand#84164, session_start_dt#84199, primary_app_id#84182], Statistics(sizeInBytes=11.9 GiB)
: +- Filter (((((isnotnull(exclude#84189) AND (exclude#84189 = 0)) AND isnotnull(session_start_dt#84199)) AND NOT cast(cobrand#84164 as int) IN (2,3,4,5,9)) AND (session_start_dt#84199 >= 2021-07-14)) AND (session_start_dt#84199 <= 2021-07-15)), Statistics(sizeInBytes=77.0 GiB)
: +- Relation p_soj_cl_t.clav_session[guid#84161,session_skey#84162L,site_id#84163,cobrand#84164,cguid#84165,buyer_site_id#84166,lndg_page_id#84167,start_timestamp#84168,end_timestamp#84169,exit_page_id#84170,valid_page_count#84171,gr_cnt#84172,gr_1_cnt#84173,vi_cnt#84174,homepage_cnt#84175,myebay_cnt#84176,signin_cnt#84177,min_sc_seqnum#84178,max_sc_seqnum#84179,signedin_user_id#84180,mapped_user_id#84181,primary_app_id#84182,agent_id#84183L,session_cntry_id#84184,... 15 more fields] parquet, Statistics(sizeInBytes=77.0 GiB)
+- Union, Statistics(sizeInBytes=5.05E+27 B)
:- Aggregate [session_start_dt#84203, guid#80355, session_skey#84201L, site_id#84205, _groupingexpression#84732], [session_start_dt#84203, guid#80355, session_skey#84201L, cast(site_id#84205 as decimal(10,0)) AS site_id#84714, _groupingexpression#84732 AS type_1#84089], Statistics(sizeInBytes=8.0 TiB)
: +- Project [GUID#80355, SESSIONSKEY#80356L AS SESSION_SKEY#84201L, cast(concat(substr(dt#80385, 0, 4), -, substr(dt#80385, 5, 2), -, substr(dt#80385, 7, 2)) as date) AS SESSION_START_DT#84203, SITEID#80361 AS SITE_ID#84205, CASE WHEN ((PAGEID#80363 IN (4853,2487283,2487285) AND (RDT#80376 = 0)) OR PAGEID#80363 IN (2050445,2050533)) THEN sign_in_visit WHEN PAGEID#80363 IN (2052190,2053938) THEN reg_suc WHEN PAGEID#80363 IN (4852,2051246,2266111) THEN sign_in_suc END AS _groupingexpression#84732], Statistics(sizeInBytes=7.5 TiB)
: +- Filter (((((isnotnull(SESSIONSKEY#80356L) AND isnotnull(cast(SITEID#80361 as decimal(10,0)))) AND isnotnull(guid#80355)) AND (cast(concat(substr(dt#80385, 0, 4), -, substr(dt#80385, 5, 2), -, substr(dt#80385, 7, 2)) as date) >= 2021-07-14)) AND (cast(concat(substr(dt#80385, 0, 4), -, substr(dt#80385, 5, 2), -, substr(dt#80385, 7, 2)) as date) <= 2021-07-15)) AND ((((((PAGEID#80363 = 4853) AND (PAGENAME#80364 = signin2)) AND ((lower(HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,sgnTabClick)) = signin) OR isnull(lower(HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,sgnTabClick))))) OR (PAGEID#80363 IN (2052190,2053938) AND (lower(HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,type)) = reg_confirm))) OR (((PAGEID#80363 = 4852) AND isnotnull(cast(HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,uid) as decimal(18,0)))) AND isnull(HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,sgnFastFYPReset)))) OR (((((PAGEID#80363 = 2266111) AND (cast(HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,sgnStatus) as int) = 0)) AND (HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,sgnChannelType) IN (0,2) AND NOT (HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,authMethod) = guest_id_token))) OR PAGEID#80363 IN (2050445,2050533,2051246)) OR (PAGEID#80363 IN (2487283,2487285) AND NOT (HiveSimpleUDF#com.ebay.hadoop.udf.soj.SojTagFetcher(APPLICATIONPAYLOAD#80370,SigninRedirect) = V4))))), Statistics(sizeInBytes=46.7 TiB)
: +- Relation ubi_t.ubi_event[guid#80355,sessionskey#80356L,seqnum#80357,sessionstartdt#80358L,sojdatadt#80359L,clickid#80360,siteid#80361,version#80362,pageid#80363,pagename#80364,refererhash#80365L,eventtimestamp#80366L,urlquerystring#80367,clientdata#80368,cookies#80369,applicationpayload#80370,webserver#80371,referrer#80372,userid#80373,itemid#80374L,flags#80375,rdt#80376,regu#80377,sqr#80378,... 8 more fields] parquet, Statistics(sizeInBytes=46.7 TiB)
+- Aggregate [sess_session_start_dt#84669, sess_guid#84665, sess_session_skey#84666L, sess_site_id#84670], [sess_session_start_dt#84669 AS session_start_dt#84090, sess_guid#84665 AS guid#84091, sess_session_skey#84666L AS session_skey#84092L, cast(sess_site_id#84670 as decimal(10,0)) AS site_id#84715, gxo_suc AS type_1#84094], Statistics(sizeInBytes=5.05E+27 B)
+- Project [sess_guid#84665, sess_session_skey#84666L, sess_session_start_dt#84669, sess_site_id#84670], Statistics(sizeInBytes=3.56E+27 B)
+- Join Inner, ((((item_id#84428 = item_id#84639) AND (transaction_id#84436 = transaction_id#84640)) AND (auct_end_dt#84429 = auct_end_dt#84641)) AND (created_dt#84440 = created_dt#84650)), Statistics(sizeInBytes=7.12E+27 B)
:- PartialAggregate [item_id#84428, auct_end_dt#84429, transaction_id#84436, created_dt#84440], [item_id#84428, auct_end_dt#84429, transaction_id#84436, created_dt#84440], Statistics(sizeInBytes=206.1 TiB)
: +- Project [item_id#84428, auct_end_dt#84429, transaction_id#84436, created_dt#84440], Statistics(sizeInBytes=206.1 TiB)
: +- Join LeftOuter, (cast(lstg_curncy_id#84474 as decimal(9,0)) = curncy_id#72721), Statistics(sizeInBytes=309.2 TiB)
: :- PartialAggregate [item_id#84428, auct_end_dt#84429, transaction_id#84436, created_dt#84440, lstg_curncy_id#84474], [item_id#84428, auct_end_dt#84429, transaction_id#84436, created_dt#84440, lstg_curncy_id#84474], Statistics(sizeInBytes=330.8 GiB)
: : +- Project [item_id#84428, auct_end_dt#84429, transaction_id#84436, created_dt#84440, lstg_curncy_id#84474], Statistics(sizeInBytes=330.8 GiB)
: : +- Filter ((((((((isnotnull(created_dt#84440) AND isnotnull(auct_end_dt#84429)) AND isnotnull(CHECKOUT_FLAGS4#84486)) AND (created_dt#84440 >= 2021-07-14)) AND (created_dt#84440 <= 2021-07-16)) AND (auct_end_dt#84429 >= 2021-07-14)) AND isnotnull(item_id#84428)) AND isnotnull(transaction_id#84436)) AND ((cast(CHECKOUT_FLAGS4#84486 as bigint) & 2) > 0)), Statistics(sizeInBytes=10.2 TiB)
: : +- Relation gdw_tables.dw_checkout_trans[item_id#84428,auct_end_dt#84429,site_id#84430,leaf_categ_id#84431,seller_id#84432,slr_cntry_id#84433,buyer_id#84434,byr_cntry_id#84435,transaction_id#84436,shipping_address_id#84437,sale_type#84438,created_time#84439,created_dt#84440,last_modified#84441,last_modified_dt#84442,checkout_flags#84443,checkout_status#84444,checkout_status_details#84445,payment_method#84446,shipping_fee#84447,shipping_xfee#84448,tax#84449,tax_state#84450,instruction_flag#84451,... 110 more fields] parquet, Statistics(sizeInBytes=10.2 TiB)
: +- PartialAggregate [curncy_id#72721], [curncy_id#72721], Statistics(sizeInBytes=957.0 B)
: +- Project [curncy_id#72721], Statistics(sizeInBytes=957.0 B)
: +- Filter isnotnull(curncy_id#72721), Statistics(sizeInBytes=4.4 KiB)
: +- Relation gdw_tables.ssa_curncy_plan_rate_dim[CURNCY_ID#72721,CURNCY_PLAN_RATE#72722,CRE_DATE#72723,CRE_USER#72724,UPD_DATE#72725,UPD_USER#72726] parquet, Statistics(sizeInBytes=4.4 KiB)
+- PartialAggregate [item_id#84639, transaction_id#84640, auct_end_dt#84641, created_dt#84650, sess_guid#84665, sess_session_skey#84666L, sess_session_start_dt#84669, sess_site_id#84670], [item_id#84639, transaction_id#84640, auct_end_dt#84641, created_dt#84650, sess_guid#84665, sess_session_skey#84666L, sess_session_start_dt#84669, sess_site_id#84670], Statistics(sizeInBytes=28.6 TiB)
+- PartialAggregate [item_id#84639, transaction_id#84640, auct_end_dt#84641, created_dt#84650, sess_guid#84665, sess_session_skey#84666L, sess_session_start_dt#84669, sess_site_id#84670], [item_id#84639, transaction_id#84640, auct_end_dt#84641, created_dt#84650, sess_guid#84665, sess_session_skey#84666L, sess_session_start_dt#84669, sess_site_id#84670], Statistics(sizeInBytes=28.6 TiB)
+- Project [item_id#84639, transaction_id#84640, auct_end_dt#84641, created_dt#84650, sess_guid#84665, sess_session_skey#84666L, sess_session_start_dt#84669, sess_site_id#84670], Statistics(sizeInBytes=28.6 TiB)
+- Filter ((((((((((((isnotnull(sess_session_start_dt#84669) AND isnotnull(created_dt#84650)) AND isnotnull(auct_end_dt#84641)) AND (created_dt#84650 >= 2021-07-14)) AND (created_dt#84650 <= 2021-07-16)) AND (sess_session_start_dt#84669 >= 2021-07-14)) AND (sess_session_start_dt#84669 <= 2021-07-15)) AND (auct_end_dt#84641 >= 2021-07-14)) AND isnotnull(item_id#84639)) AND isnotnull(transaction_id#84640)) AND isnotnull(sess_session_skey#84666L)) AND isnotnull(cast(sess_site_id#84670 as decimal(10,0)))) AND isnotnull(sess_guid#84665)), Statistics(sizeInBytes=318.1 TiB)
+- Relation p_soj_cl_t.checkout_metric_item[item_id#84639,transaction_id#84640,auct_end_dt#84641,item_site_id#84642,trans_site_id#84643,auct_type_code#84644,leaf_categ_id#84645,seller_id#84646,buyer_id#84647,seller_country_id#84648,buyer_country_id#84649,created_dt#84650,created_time#84651,item_price#84652,quantity#84653,lstg_curncy_exchng_rate#84654,lstg_curncy_id#84655,ck_wacko_yn#84656,variation_id#84657,version_id#84658,app_id#84659,format_flags64#84660L,auct_start_dt#84661,leaf_categ_id2#84662,... 51 more fields] parquet, Statistics(sizeInBytes=318.1 TiB)
```
* Fix tpcds q24a DecimalAggregates issue:
```
=== Applying Rule org.apache.spark.sql.catalyst.optimizer.DecimalAggregates ===
Subquery false Subquery false
+- Aggregate [CheckOverflow((0.050000 * promote_precision(avg(netpaid#166))), DecimalType(24,8), true) AS (CAST(0.05 AS DECIMAL(21,6)) * CAST(avg(netpaid) AS DECIMAL(21,6)))#176] +- Aggregate [CheckOverflow((0.050000 * promote_precision(avg(netpaid#166))), DecimalType(24,8), true) AS (CAST(0.05 AS DECIMAL(21,6)) * CAST(avg(netpaid) AS DECIMAL(21,6)))#176]
! +- Aggregate [c_last_name#121, c_first_name#120, s_store_name#66, ca_state#138, s_state#85, i_color#107, i_current_price#95, i_manager_id#110, i_units#108, i_size#105], [sum(ss_net_paid#37, None) AS netpaid#166] +- Aggregate [c_last_name#121, c_first_name#120, s_store_name#66, ca_state#138, s_state#85, i_color#107, i_current_price#95, i_manager_id#110, i_units#108, i_size#105], [MakeDecimal(sum(UnscaledValue(ss_net_paid#37), None),17,2) AS netpaid#166]
+- Project [ss_net_paid#37, s_store_name#66, s_state#85, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110, c_first_name#120, c_last_name#121, ca_state#138] +- Project [ss_net_paid#37, s_store_name#66, s_state#85, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110, c_first_name#120, c_last_name#121, ca_state#138]
+- Join Inner, ((s_zip#86 = ca_zip#139) AND (c_birth_country#126 = upper(ca_country#140))) +- Join Inner, ((s_zip#86 = ca_zip#139) AND (c_birth_country#126 = upper(ca_country#140)))
:- Project [s_store_name#66, s_state#85, s_zip#86, ss_net_paid#37, c_first_name#120, c_last_name#121, c_birth_country#126, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110] :- Project [s_store_name#66, s_state#85, s_zip#86, ss_net_paid#37, c_first_name#120, c_last_name#121, c_birth_country#126, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110]
: +- Join Inner, ((ss_item_sk#19 = sr_item_sk#42) AND (ss_ticket_number#26L = sr_ticket_number#49L)) : +- Join Inner, ((ss_item_sk#19 = sr_item_sk#42) AND (ss_ticket_number#26L = sr_ticket_number#49L))
: :- Project [s_store_name#66, s_state#85, s_zip#86, ss_item_sk#19, ss_ticket_number#26L, ss_net_paid#37, c_first_name#120, c_last_name#121, c_birth_country#126, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110] : :- Project [s_store_name#66, s_state#85, s_zip#86, ss_item_sk#19, ss_ticket_number#26L, ss_net_paid#37, c_first_name#120, c_last_name#121, c_birth_country#126, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110]
: : +- Join Inner, (ss_item_sk#19 = i_item_sk#90) : : +- Join Inner, (ss_item_sk#19 = i_item_sk#90)
: : :- Project [s_store_name#66, s_state#85, s_zip#86, ss_item_sk#19, ss_ticket_number#26L, ss_net_paid#37, c_first_name#120, c_last_name#121, c_birth_country#126] : : :- Project [s_store_name#66, s_state#85, s_zip#86, ss_item_sk#19, ss_ticket_number#26L, ss_net_paid#37, c_first_name#120, c_last_name#121, c_birth_country#126]
: : : +- Join Inner, (ss_customer_sk#20 = c_customer_sk#112) : : : +- Join Inner, (ss_customer_sk#20 = c_customer_sk#112)
: : : :- Project [s_store_name#66, s_state#85, s_zip#86, ss_item_sk#19, ss_customer_sk#20, ss_ticket_number#26L, ss_net_paid#37] : : : :- Project [s_store_name#66, s_state#85, s_zip#86, ss_item_sk#19, ss_customer_sk#20, ss_ticket_number#26L, ss_net_paid#37]
: : : : +- Join Inner, (ss_store_sk#24 = s_store_sk#61) : : : : +- Join Inner, (ss_store_sk#24 = s_store_sk#61)
: : : : :- Project [s_store_sk#61, s_store_name#66, s_state#85, s_zip#86] : : : : :- Project [s_store_sk#61, s_store_name#66, s_state#85, s_zip#86]
: : : : : +- Filter ((((s_market_id#71 = 8) AND isnotnull(s_market_id#71)) AND isnotnull(s_zip#86)) AND isnotnull(s_store_sk#61)) : : : : : +- Filter ((((s_market_id#71 = 8) AND isnotnull(s_market_id#71)) AND isnotnull(s_zip#86)) AND isnotnull(s_store_sk#61))
: : : : : +- Relation hermes_tpcds5t.store[s_store_sk#61,s_store_id#62,s_rec_start_date#63,s_rec_end_date#64,s_closed_date_sk#65,s_store_name#66,s_number_employees#67,s_floor_space#68,s_hours#69,s_manager#70,s_market_id#71,s_geography_class#72,s_market_desc#73,s_market_manager#74,s_division_id#75,s_division_name#76,s_company_id#77,s_company_name#78,s_street_number#79,s_street_name#80,s_street_type#81,s_suite_number#82,s_city#83,s_county#84,... 5 more fields] parquet : : : : : +- Relation hermes_tpcds5t.store[s_store_sk#61,s_store_id#62,s_rec_start_date#63,s_rec_end_date#64,s_closed_date_sk#65,s_store_name#66,s_number_employees#67,s_floor_space#68,s_hours#69,s_manager#70,s_market_id#71,s_geography_class#72,s_market_desc#73,s_market_manager#74,s_division_id#75,s_division_name#76,s_company_id#77,s_company_name#78,s_street_number#79,s_street_name#80,s_street_type#81,s_suite_number#82,s_city#83,s_county#84,... 5 more fields] parquet
: : : : +- Project [ss_item_sk#19, ss_customer_sk#20, ss_store_sk#24, ss_ticket_number#26L, ss_net_paid#37] : : : : +- Project [ss_item_sk#19, ss_customer_sk#20, ss_store_sk#24, ss_ticket_number#26L, ss_net_paid#37]
: : : : +- Filter (((isnotnull(ss_customer_sk#20) AND isnotnull(ss_store_sk#24)) AND isnotnull(ss_ticket_number#26L)) AND isnotnull(ss_item_sk#19)) : : : : +- Filter (((isnotnull(ss_customer_sk#20) AND isnotnull(ss_store_sk#24)) AND isnotnull(ss_ticket_number#26L)) AND isnotnull(ss_item_sk#19))
: : : : +- Relation hermes_tpcds5t.store_sales[ss_sold_time_sk#18,ss_item_sk#19,ss_customer_sk#20,ss_cdemo_sk#21,ss_hdemo_sk#22,ss_addr_sk#23,ss_store_sk#24,ss_promo_sk#25,ss_ticket_number#26L,ss_quantity#27,ss_wholesale_cost#28,ss_list_price#29,ss_sales_price#30,ss_ext_discount_amt#31,ss_ext_sales_price#32,ss_ext_wholesale_cost#33,ss_ext_list_price#34,ss_ext_tax#35,ss_coupon_amt#36,ss_net_paid#37,ss_net_paid_inc_tax#38,ss_net_profit#39,ss_sold_date_sk#40] parquet : : : : +- Relation hermes_tpcds5t.store_sales[ss_sold_time_sk#18,ss_item_sk#19,ss_customer_sk#20,ss_cdemo_sk#21,ss_hdemo_sk#22,ss_addr_sk#23,ss_store_sk#24,ss_promo_sk#25,ss_ticket_number#26L,ss_quantity#27,ss_wholesale_cost#28,ss_list_price#29,ss_sales_price#30,ss_ext_discount_amt#31,ss_ext_sales_price#32,ss_ext_wholesale_cost#33,ss_ext_list_price#34,ss_ext_tax#35,ss_coupon_amt#36,ss_net_paid#37,ss_net_paid_inc_tax#38,ss_net_profit#39,ss_sold_date_sk#40] parquet
: : : +- Project [c_customer_sk#112, c_first_name#120, c_last_name#121, c_birth_country#126] : : : +- Project [c_customer_sk#112, c_first_name#120, c_last_name#121, c_birth_country#126]
: : : +- Filter (isnotnull(c_birth_country#126) AND isnotnull(c_customer_sk#112)) : : : +- Filter (isnotnull(c_birth_country#126) AND isnotnull(c_customer_sk#112))
: : : +- Relation hermes_tpcds5t.customer[c_customer_sk#112,c_customer_id#113,c_current_cdemo_sk#114,c_current_hdemo_sk#115,c_current_addr_sk#116,c_first_shipto_date_sk#117,c_first_sales_date_sk#118,c_salutation#119,c_first_name#120,c_last_name#121,c_preferred_cust_flag#122,c_birth_day#123,c_birth_month#124,c_birth_year#125,c_birth_country#126,c_login#127,c_email_address#128,c_last_review_date#129] parquet : : : +- Relation hermes_tpcds5t.customer[c_customer_sk#112,c_customer_id#113,c_current_cdemo_sk#114,c_current_hdemo_sk#115,c_current_addr_sk#116,c_first_shipto_date_sk#117,c_first_sales_date_sk#118,c_salutation#119,c_first_name#120,c_last_name#121,c_preferred_cust_flag#122,c_birth_day#123,c_birth_month#124,c_birth_year#125,c_birth_country#126,c_login#127,c_email_address#128,c_last_review_date#129] parquet
: : +- Project [i_item_sk#90, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110] : : +- Project [i_item_sk#90, i_current_price#95, i_size#105, i_color#107, i_units#108, i_manager_id#110]
: : +- Filter isnotnull(i_item_sk#90) : : +- Filter isnotnull(i_item_sk#90)
: : +- Relation hermes_tpcds5t.item[i_item_sk#90,i_item_id#91,i_rec_start_date#92,i_rec_end_date#93,i_item_desc#94,i_current_price#95,i_wholesale_cost#96,i_brand_id#97,i_brand#98,i_class_id#99,i_class#100,i_category_id#101,i_category#102,i_manufact_id#103,i_manufact#104,i_size#105,i_formulation#106,i_color#107,i_units#108,i_container#109,i_manager_id#110,i_product_name#111] parquet : : +- Relation hermes_tpcds5t.item[i_item_sk#90,i_item_id#91,i_rec_start_date#92,i_rec_end_date#93,i_item_desc#94,i_current_price#95,i_wholesale_cost#96,i_brand_id#97,i_brand#98,i_class_id#99,i_class#100,i_category_id#101,i_category#102,i_manufact_id#103,i_manufact#104,i_size#105,i_formulation#106,i_color#107,i_units#108,i_container#109,i_manager_id#110,i_product_name#111] parquet
: +- Project [sr_item_sk#42, sr_ticket_number#49L] : +- Project [sr_item_sk#42, sr_ticket_number#49L]
: +- Filter (isnotnull(sr_ticket_number#49L) AND isnotnull(sr_item_sk#42)) : +- Filter (isnotnull(sr_ticket_number#49L) AND isnotnull(sr_item_sk#42))
: +- Relation hermes_tpcds5t.store_returns[sr_return_time_sk#41,sr_item_sk#42,sr_customer_sk#43,sr_cdemo_sk#44,sr_hdemo_sk#45,sr_addr_sk#46,sr_store_sk#47,sr_reason_sk#48,sr_ticket_number#49L,sr_return_quantity#50,sr_return_amt#51,sr_return_tax#52,sr_return_amt_inc_tax#53,sr_fee#54,sr_return_ship_cost#55,sr_refunded_cash#56,sr_reversed_charge#57,sr_store_credit#58,sr_net_loss#59,sr_returned_date_sk#60] parquet : +- Relation hermes_tpcds5t.store_returns[sr_return_time_sk#41,sr_item_sk#42,sr_customer_sk#43,sr_cdemo_sk#44,sr_hdemo_sk#45,sr_addr_sk#46,sr_store_sk#47,sr_reason_sk#48,sr_ticket_number#49L,sr_return_quantity#50,sr_return_amt#51,sr_return_tax#52,sr_return_amt_inc_tax#53,sr_fee#54,sr_return_ship_cost#55,sr_refunded_cash#56,sr_reversed_charge#57,sr_store_credit#58,sr_net_loss#59,sr_returned_date_sk#60] parquet
+- Project [ca_state#138, ca_zip#139, ca_country#140] +- Project [ca_state#138, ca_zip#139, ca_country#140]
+- Filter (isnotnull(ca_country#140) AND isnotnull(ca_zip#139)) +- Filter (isnotnull(ca_country#140) AND isnotnull(ca_zip#139))
+- Relation hermes_tpcds5t.customer_address[ca_address_sk#130,ca_address_id#131,ca_street_number#132,ca_street_name#133,ca_street_type#134,ca_suite_number#135,ca_city#136,ca_county#137,ca_state#138,ca_zip#139,ca_country#140,ca_gmt_offset#141,ca_location_type#142] parquet +- Relation hermes_tpcds5t.customer_address[ca_address_sk#130,ca_address_id#131,ca_street_number#132,ca_street_name#133,ca_street_type#134,ca_suite_number#135,ca_city#136,ca_county#137,ca_state#138,ca_zip#139,ca_country#140,ca_gmt_offset#141,ca_location_type#142] parquet
```
* 1. Fix tpcds q82 can't add runtime filter
2. Fix Statistics issue
* Fix a bug
* Support avg
* 1. Support push down if AggregateExpression contains complex expressions
2. Deduplicate and reorder aggregate expressions to find more ReuseExchanges
* Fix TPC-DS v2.7 q57 and q67a can't re-use exchange issue:
```scala
class TPCDSV2_7_PlanStabilityWithStatsSuite extends PlanStabilitySuite with TPCDSBase {
override def injectStats: Boolean = true
override val goldenFilePath: String =
new File(baseResourcePath, s"approved-plans-v2_7").getAbsolutePath
Seq(
// "q5a", "q6", "q10a", "q11", "q12", "q14", "q14a",
// "q18a",
"q51a",
"q57",
"q67a").foreach { q =>
test(s"check simplified sf100 (tpcds-v2.7.0/$q)") {
println(s"=================${q}")
testQuery("tpcds-v2.7.0", q, ".sf100")
}
}
// test("check simplified sf100 (tpcds-v2.7.0/)") {
// testQuery("tpcds-v2.7.0", "q57", ".sf100")
// }
}
```
* Fix bbensid2 q12 java.lang.RuntimeException: Couldn't find date_confirm#25512
```
java.sql.SQLException: Error running query: java.lang.RuntimeException: Couldn't find date_confirm#25512 in sum#31527L,sum#31528L,count#31529L,sum#31530L,user_site_id#25498,half_origin_user#31347,_groupingexpression#31516,_groupingexpression#31517,_groupingexpression#31518,_groupingexpression#31519,pushed_count(user_id#25495)#31520L,pushed_count(date_confirm#25512)#31521L,pushed_sum(CASE WHEN CASE WHEN (flagsex6#25555 = -999) THEN false ELSE (((cast(flagsex6#25555 as bigint) & 65536) >= 1) <=> true) END THEN 1 ELSE 0 END, None)#31522L,site_name#30052,cnt#31525L
at org.apache.hive.jdbc.HiveStatement.execute(HiveStatement.java:297)
at org.apache.hive.jdbc.HiveStatement.executeQuery(HiveStatement.java:392)
at com.ebay.carmel.spark.BenchmarkAndVerifyResult$.$anonfun$main$1(BenchmarkAndVerifyResult.scala:162)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at scala.collection.immutable.List.map(List.scala:298)
at com.ebay.carmel.spark.BenchmarkAndVerifyResult$.main(BenchmarkAndVerifyResult.scala:146)
at com.ebay.carmel.spark.BenchmarkAndVerifyResult.main(BenchmarkAndVerifyResult.scala)
```
https://jirap.corp.ebay.com/browse/CARMEL-5966
* Merge code from Apache Spark
* Split Aggregate to Partial Agg and Final Agg.
* Enhance supportPushedAgg to do not downgrade tpcds q4 performance
* Do not downgrade bbendis 367 performance:
```
MAX(CASE WHEN sojlib.soj_extract_flag(sojlib.soj_nvl(e.soj, 'cflgs'), 15) = 1 THEN 1 ELSE 0 END) AS gbh_yn,
```
* Do not push if it is contains count distinct
* Simplify the code
* Fix bug
* Fix: org.apache.spark.sql.hive.execution.ObjectHashAggregateSuite.randomized aggregation test - [with distinct] - without grouping keys - with empty input
Error Message
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: c3#7123
Stacktrace
sbt.ForkMain$ForkError: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: c3#7123
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:75)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:324)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:324)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:329)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:414)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:252)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:412)
* Simplify the code
* Add config spark.sql.optimizer.partialAggregationOptimization.enabled
* Fix test
* Add tests
* Add partialAggregationOptimization.benefitRatio and partialAggregationOptimization.fallbackReductionRatio
* Port [SPARK-39248][SQL] Improve divide performance for decimal type
* fix
* Support sum(1)
* Aggregate expression's references from Alias
* sync code
* fix test
* fix
* Fix avg data issue
* Fix test error
* fix
* 1. Only push down if has benefit
2. Introduce FinalAggregate
* Fix
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
ApproxCountDistinctMergeFunctionshould returnIntvalue because thedataTypeofApproxCountDistinctisIntegerType.