Spark Paimon 中为什么我指定的分区没有下推

背景

最近在使用 Paimon 的时候遇到了一件很有意思的事情写的 SQL 居然读取的数据不下推明明是分区表但是却全量扫描了。
目前使用的版本信息如下:
Spark 3.5.0
Paimon 0.6.0
paimon的建表语句如下

CREATE TABLE `table_demo`(
  `user_id` string COMMENT 'from deserializer' 
  )
PARTITIONED BY ( 
  `dt` string COMMENT '日期, yyyyMMdd', 
  `hour` string COMMENT '小时, HH')
ROW FORMAT SERDE 
  'org.apache.paimon.hive.PaimonSerDe' 
STORED BY 
  'org.apache.paimon.hive.PaimonStorageHandler' 
WITH SERDEPROPERTIES ( 
  'serialization.format'='1')
LOCATION
  'xxxx'
TBLPROPERTIES (
  'bucket'='50', 
  'bucketing_version'='2', 
  'bukect-key'='user_id', 
  'file.format'='parquet', 
  'merge-engine'='partial-update', 
  'partial-update.ignore-delete'='true', 
  'primary-key'='user_id', 
  'transient_lastDdlTime'='1701679855', 
  'write-only'='false')

查询的SQL如下

select * from 
table_demo
where dt =20231212
and hour =10
limit 100;

注意我们这里写的dt是整数类型而表中定义的是字符串类型

结论及解决方法

结论

具体的原因是Spark DSv2中的规则 V2ScanRelationPushDown.pushDownFilters 对于 Cast类型转换表达式不会传递到DataSource端所以只会在读取完Source转换进行过滤
这种情况下对于文件的读取IO会增大但是对于shuffle等操作是不会有性能的影响的。

解决方法

对于分区字段来说我们在写SQL对分区字段进行过滤的时候保持和分区字段类型一致

分析

错误写法分析

针对于错误的写法,也就是导致读取全量数据的写法我们分析一下首先是类型转换阶段在Spark中对于类型不匹配的问题spark会用规则进行转换具体的规则是
CombinedTypeCoercionRule,
在日志中可以看到

=== Applying Rule org.apache.spark.sql.catalyst.analysis.TypeCoercionBase$CombinedTypeCoercionRule ===
 'GlobalLimit 100                                                                                   'GlobalLimit 100
 +- 'LocalLimit 100                                                                                     +- 'LocalLimit 100
    +- 'Project [*]                                                                                        +- 'Project [*]
!      +- 'Filter ((dt#520 = 20231212) AND (hour#521 = 10))                                                   +- Filter ((cast(dt#520 as int) = 20231212) AND (cast(hour#521 as int) = 10))
          +- SubqueryAlias spark_catalog.default.table_demo                                                      +- SubqueryAlias spark_catalog.default.table_demo
             +- RelationV2[user_id#497, dt#520, hour#521] spark_catalog.default.table_demo                          +- RelationV2[user_id#497,dt#520, hour#521] spark_catalog.default.table_demo
           

通过以上规则我们可以看到 过滤条件(dt#520 = 20231212) AND (hour#521 = 10) 转换为了 (cast(dt#520 as int) = 20231212) AND (cast(hour#521 as int) = 10)

接着再经过以下规则V2ScanRelationPushDown的洗礼我们可以看到如下日志

12-13 13:52:58 763  INFO (org.apache.spark.sql.execution.datasources.v2.V2ScanRelationPushDown:60) - 
Pushing operators to table_demo
Pushed Filters: IsNotNull(dt), IsNotNull(hour)
Post-Scan Filters: (cast(dt#520 as int) = 20231212),(cast(hour#521 as int) = 10)
12-13 13:52:58 723  INFO (org.apache.paimon.spark.PaimonScanBuilder:62) - pushFilter log: IsNotNull(dt),IsNotNull(hour)
12-13 13:52:58 823  INFO (org.apache.spark.sql.execution.datasources.v2.V2ScanRelationPushDown:60) - 
Output: user_id#497, dt#520, hour#521
         
12-13 13:52:58 837  INFO (org.apache.spark.sql.catalyst.rules.PlanChangeLogger:60) - 
=== Applying Rule org.apache.spark.sql.execution.datasources.v2.V2ScanRelationPushDown ===
 InsertIntoHadoopFsRelationCommand ], Overwrite, [user_id,  dt, hour]                                                                        InsertIntoHadoopFsRelationCommand xxx, false, Parquet, [path=xxx], Overwrite, [user_id, dt, hour] 
 +- WriteFiles                                                                                                                                    +- WriteFiles
    +- Repartition 1, true                                                                                                                           +- Repartition 1, true
       +- GlobalLimit 100                                                                                                                               +- GlobalLimit 100
          +- LocalLimit 100                                                                                                                                +- LocalLimit 100
!            +- Filter ((isnotnull(dt#520) AND isnotnull(hour#521)) AND ((cast(dt#520 as int) = 20231212) AND (cast(hour#521 as int) = 10)))                   +- Filter ((cast(dt#520 as int) = 20231212) AND (cast(hour#521 as int) = 10))
!               +- RelationV2[user_id#497, dt#520, hour#521] spark_catalog.default.table_demo table_demo                                                                     +- RelationV2[user_id#497, dt#520, hour#521] spark_catalog.default.table_demo   table_demo
   

这里只有过滤条件 isnotnull(dt#520) AND isnotnull(hour#521) 被下推到了 DataSource。
从现象来看确实分区的过滤条件没有推到DataSource端, 我们来分析一下该规则的数据流

V2ScanRelationPushDown.pushDownFilters
       ||
       \/
PushDownUtils.pushFilters
       ||
       \/
DataSourceStrategy.translateFilterWithMappin
       ||
       \/
translateLeafNodeFilter

具体到translateLeafNodeFilter 方法

  private def translateLeafNodeFilter(
      predicate: Expression,
      pushableColumn: PushableColumnBase): Option[Filter] = predicate match {
    case expressions.EqualTo(pushableColumn(name), Literal(v, t)) =>
      Some(sources.EqualTo(name, convertToScala(v, t)))
    case expressions.EqualTo(Literal(v, t), pushableColumn(name)) =>
      Some(sources.EqualTo(name, convertToScala(v, t)))
    ...
    case _ => None

这里没有对Cast表达式进行处理所以说最后返回的就是不能下推的处理而 Paimon datasouce那边,具体的类为PaimonBaseScanBuilder

  override def pushFilters(filters: Array[Filter]): Array[Filter] = {

这里传进来的filters实参 就不存在 (cast(dt#520 as int) = 20231212) AND (cast(hour#521 as int) = 10) 这个过滤条件,所以就不会下推到Paimon中去

其实不仅仅是对于Paimon Source, 其他的source也会有这个问题。

正确学法分析

正确的SQL如下

select * from 
table_demo
where dt ='20231212'
and hour ='10'
limit 100;

运行如上SQL我们可以看到如下日志

12-14 14:22:42 328  INFO (org.apache.paimon.spark.PaimonScanBuilder:62) - pushFilter log: IsNotNull(dt),IsNotNull(hour),EqualTo(dt,20231212),EqualTo(hour,10)
12-14 14:22:42 405  INFO (org.apache.spark.sql.execution.datasources.v2.V2ScanRelationPushDown:60) - 
Pushing operators to table_demo
Pushed Filters: IsNotNull(dt), IsNotNull(hour), EqualTo(dt,20231212), EqualTo(hour,10)
Post-Scan Filters: 

=== Applying Rule org.apache.spark.sql.execution.datasources.v2.V2ScanRelationPushDown ===
 InsertIntoHadoopFsRelationCommand xxx, false, Parquet, [path=xxx], Overwrite, [user_id, dt, hour]                       InsertIntoHadoopFsRelationCommand xxx, false, Parquet, [path=xxx], Overwrite, [user_id, dt, hour]
 +- WriteFiles                                                                                                            +- WriteFiles
    +- Repartition 1, true                                                                                                   +- Repartition 1, true
       +- GlobalLimit 100                                                                                                       +- GlobalLimit 100
          +- LocalLimit 100                                                                                                        +- LocalLimit 100
!            +- Filter ((isnotnull(dt#1330) AND isnotnull(hour#1331)) AND ((dt#1330 = 20231212) AND (hour#1331 = 10)))               +- RelationV2[user_id#1307,  dt#1330, hour#1331] table_demo
!               +- RelationV2[user_id#1307,  dt#1330, hour#1331] spark_catalog.ad_dwd.table_demo table_demo                           
   

可以看到经过了规则转换 所有的过滤条件都下推到了DataSource了,但是具体的下推还得在DataSource进一步处理才能保证真正的下推