[SPARK-2065] [EC2] give launched instances names #1043
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merging upstream updates
This update gives launched EC2 instances descriptive names by using instance tags. Launched instances now show up in the EC2 console with these names. I used `format()` with named parameters, which I believe is the recommended practice for string formatting in Python, but which doesn’t seem to be used elsewhere in the script.
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Caveat: I had trouble testing this with spot instances. For some reason, even when I set my spot price really high, I could not get Amazon to grant me spot instances. So, I expect the instance names to show up correctly with spot instances, but I was not able to test that myself. |
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Can one of the admins verify this patch? |
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Jenkins, test this please. |
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Merged build triggered. |
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Merged build started. |
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Merged build finished. All automated tests passed. |
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All automated tests passed. |
| @@ -418,6 +418,12 @@ def launch_cluster(conn, opts, cluster_name): | |||
| master_nodes = master_res.instances | |||
| print "Launched master in %s, regid = %s" % (zone, master_res.id) | |||
|
|
|||
| # Give the instances descriptive names | |||
| for master in master_nodes: | |||
| master.add_tag(key='Name', value='spark-{cn}-master-{iid}'.format(cn=cluster_name, iid=master.id)) | |||
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Do you mind wrapping this before 100 char wide? We don't yet enforce it, but we will soon enforce line length in Python as well. Also for line 425. Thanks!
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No problem! I made the change. Do you have any rules about whether the closing parenthesis should go on its own line or not?
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Do you mind running pep8 on this? Let's make sure that doesn't report any style violations. We will get pep8 integrated with Jenkins in the future. |
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Ah, I see. :) Thank you for the gentle pointer. I've fixed 2 PEP8 violations in my patch, as well as 1 PEP8 violation elsewhere. |
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Thanks. I am merging this in master. |
pdeyhim
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that referenced
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Jun 25, 2014
This update resolves [SPARK-2065](https://issues.apache.org/jira/browse/SPARK-2065). It gives launched EC2 instances descriptive names by using instance tags. Launched instances now show up in the EC2 console with these names. I used `format()` with named parameters, which I believe is the recommended practice for string formatting in Python, but which doesn’t seem to be used elsewhere in the script. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Author: nchammas <nicholas.chammas@gmail.com> Closes apache#1043 from nchammas/master and squashes the following commits: 69f6e22 [Nicholas Chammas] PEP8 fixes 2627247 [Nicholas Chammas] broke up lines before they hit 100 chars 6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names 69da6cf [nchammas] Merge pull request apache#1 from apache/master
nchammas
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xiliu82
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Sep 4, 2014
This update resolves [SPARK-2065](https://issues.apache.org/jira/browse/SPARK-2065). It gives launched EC2 instances descriptive names by using instance tags. Launched instances now show up in the EC2 console with these names. I used `format()` with named parameters, which I believe is the recommended practice for string formatting in Python, but which doesn’t seem to be used elsewhere in the script. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Author: nchammas <nicholas.chammas@gmail.com> Closes apache#1043 from nchammas/master and squashes the following commits: 69f6e22 [Nicholas Chammas] PEP8 fixes 2627247 [Nicholas Chammas] broke up lines before they hit 100 chars 6544b7e [Nicholas Chammas] [SPARK-2065] give launched instances names 69da6cf [nchammas] Merge pull request apache#1 from apache/master
wangyum
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that referenced
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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
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This update resolves SPARK-2065. It gives launched EC2 instances descriptive names by using instance tags. Launched instances now show up in the EC2 console with these names.
I used
format()with named parameters, which I believe is the recommended practice for string formatting in Python, but which doesn’t seem to be used elsewhere in the script.