DataFrame/Dataset groupBy行为/优化

Question

假设我们有DataFrame,df包含以下列:

名称,姓氏,大小,宽度,长度,重量

现在我们想要执行几个操作,例如我们想要创建一些包含Size和Width数据的DataFrame.

val df1 = df.groupBy("surname").agg( sum("size") )
val df2 = df.groupBy("surname").agg( sum("width") )

正如您所注意到的,其他列(如Length)不会在任何地方使用.Spark是否足够聪明,可以在洗牌阶段之前丢弃多余的列,还是随身携带？威尔跑:

val dfBasic = df.select("surname", "size", "width")

在分组之前以某种方式影响性能？

Answer 1

是的,它" 足够聪明 ".groupBy在a DataFrame上执行的操作与groupBy在普通RDD上执行的操作不同.在您描述的场景中,根本不需要移动原始数据.让我们创建一个小例子来说明:

val df = sc.parallelize(Seq(
   ("a", "foo", 1), ("a", "foo", 3), ("b", "bar", 5), ("b", "bar", 1)
)).toDF("x", "y", "z")

df.groupBy("x").agg(sum($"z")).explain

// == Physical Plan ==
// *HashAggregate(keys=[x#148], functions=[sum(cast(z#150 as bigint))])
// +- Exchange hashpartitioning(x#148, 200)
//    +- *HashAggregate(keys=[x#148], functions=[partial_sum(cast(z#150 as bigint))])
//       +- *Project [_1#144 AS x#148, _3#146 AS z#150]
//          +- *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, scala.Tuple3, true])._1, true, false) AS _1#144, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, scala.Tuple3, true])._2, true, false) AS _2#145, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#146]
//             +- Scan ExternalRDDScan[obj#143]

您可以在第一阶段进行投影,其中仅保留所需的列.下一个数据在本地汇总,最后在全球范围内传输和汇总.如果使用Spark <= 1.4,您将得到一些不同的答案输出,但一般结构应该完全相同.

最后,DAG可视化显示以上描述描述了实际工作:

类似地,Dataset.groupByKey后面跟着reduceGroups,包含map-side(ObjectHashAggregatewith partial_reduceaggregator)和reduce-side(ObjectHashAggregatewith reduceaggregatorreduction):

case class Foo(x: String, y: String, z: Int)

val ds = df.as[Foo]
ds.groupByKey(_.x).reduceGroups((x, y) => x.copy(z = x.z + y.z)).explain

// == Physical Plan ==
// ObjectHashAggregate(keys=[value#126], functions=[reduceaggregator(org.apache.spark.sql.expressions.ReduceAggregator@54d90261, Some(newInstance(class $line40.$read$$iw$$iw$Foo)), Some(class $line40.$read$$iw$$iw$Foo), Some(StructType(StructField(x,StringType,true), StructField(y,StringType,true), StructField(z,IntegerType,false))), input[0, scala.Tuple2, true]._1 AS value#128, if ((isnull(input[0, scala.Tuple2, true]._2) || None.equals)) null else named_struct(x, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, scala.Tuple2, true]._2)).x, true, false) AS x#25, y, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, scala.Tuple2, true]._2)).y, true, false) AS y#26, z, assertnotnull(assertnotnull(input[0, scala.Tuple2, true]._2)).z AS z#27) AS _2#129, newInstance(class scala.Tuple2), staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, $line40.$read$$iw$$iw$Foo, true])).x, true, false) AS x#25, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, $line40.$read$$iw$$iw$Foo, true])).y, true, false) AS y#26, assertnotnull(assertnotnull(input[0, $line40.$read$$iw$$iw$Foo, true])).z AS z#27, StructField(x,StringType,true), StructField(y,StringType,true), StructField(z,IntegerType,false), true, 0, 0)])
// +- Exchange hashpartitioning(value#126, 200)
//    +- ObjectHashAggregate(keys=[value#126], functions=[partial_reduceaggregator(org.apache.spark.sql.expressions.ReduceAggregator@54d90261, Some(newInstance(class $line40.$read$$iw$$iw$Foo)), Some(class $line40.$read$$iw$$iw$Foo), Some(StructType(StructField(x,StringType,true), StructField(y,StringType,true), StructField(z,IntegerType,false))), input[0, scala.Tuple2, true]._1 AS value#128, if ((isnull(input[0, scala.Tuple2, true]._2) || None.equals)) null else named_struct(x, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, scala.Tuple2, true]._2)).x, true, false) AS x#25, y, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, scala.Tuple2, true]._2)).y, true, false) AS y#26, z, assertnotnull(assertnotnull(input[0, scala.Tuple2, true]._2)).z AS z#27) AS _2#129, newInstance(class scala.Tuple2), staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, $line40.$read$$iw$$iw$Foo, true])).x, true, false) AS x#25, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(assertnotnull(input[0, $line40.$read$$iw$$iw$Foo, true])).y, true, false) AS y#26, assertnotnull(assertnotnull(input[0, $line40.$read$$iw$$iw$Foo, true])).z AS z#27, StructField(x,StringType,true), StructField(y,StringType,true), StructField(z,IntegerType,false), true, 0, 0)])
//       +- AppendColumns <function1>, newInstance(class $line40.$read$$iw$$iw$Foo), [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true, false) AS value#126]
//          +- *Project [_1#4 AS x#8, _2#5 AS y#9, _3#6 AS z#10]
//             +- *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, scala.Tuple3, true])._1, true, false) AS _1#4, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, scala.Tuple3, true])._2, true, false) AS _2#5, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#6]
//                +- Scan ExternalRDDScan[obj#3]

然而,其他方法KeyValueGroupedDataset可能类似于RDD.groupByKey.例如mapGroups(或flatMapGroups)不使用部分聚合.

ds.groupByKey(_.x)
  .mapGroups((_, iter) => iter.reduce((x, y) => x.copy(z = x.z + y.z)))
  .explain

//== Physical Plan ==
//*SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, $line15.$read$$iw$$iw$Foo, true]).x, true, false) AS x#37, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, $line15.$read$$iw$$iw$Foo, true]).y, true, false) AS y#38, assertnotnull(input[0, $line15.$read$$iw$$iw$Foo, true]).z AS z#39]
//+- MapGroups <function2>, value#32.toString, newInstance(class $line15.$read$$iw$$iw$Foo), [value#32], [x#8, y#9, z#10], obj#36: $line15.$read$$iw$$iw$Foo
//   +- *Sort [value#32 ASC NULLS FIRST], false, 0
//      +- Exchange hashpartitioning(value#32, 200)
//         +- AppendColumns <function1>, newInstance(class $line15.$read$$iw$$iw$Foo), [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true, false) AS value#32]
//            +- *Project [_1#4 AS x#8, _2#5 AS y#9, _3#6 AS z#10]
//               +- *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, scala.Tuple3, true])._1, true, false) AS _1#4, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, scala.Tuple3, true])._2, true, false) AS _2#5, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#6]
//                  +- Scan ExternalRDDScan[obj#3]