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Spark:How to use join method?


Spark:How to use join method?

By : user2953393
Date : November 20 2020, 01:01 AM
I wish this help you You need to enable to enable Spark implicit convertions by importing org.apache.spark.SparkContext._ to access extra functions available on RDDs of (key, value) pairs.
code :
import org.apache.spark.SparkContext._

val p1: RDD[(K, V)] = ...
val p2: RDD[(K, W)] = ...
val joined: RDD[(K, (V, W))] = p1.join(p2)


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Spark : join method with generic RDD

Spark : join method with generic RDD


By : farhan
Date : March 29 2020, 07:55 AM
will be helpful for those in need Finally, I've managed to solve this by using a ClassTag. Just like in Java, types are erased at runtime, therefore the compiler is not able to make sure that an RDD(T,P) can be implicitly converted to another RDD(T,P). To fix that, we can use a ClassTag which is basically syntaxic sugar for keeping a type information during runtime :
code :
  def intersect[T:ClassTag](left: RDD[Article], right: RDD[Article])(by: Article => T) = {
    val a: RDD[(T, Article)] = left.map(t => (by(t),t))
    val b: RDD[(T, Article)] = right.map(t => (by(t),t))
    a.join(b).map { case (attr, (leftItem, rightItem)) => leftItem }
  }
implicit class RichRDD[T:ClassTag](rdd: RDD[T]) {
    def intersect[P:ClassTag](that: RDD[T])(by: T => P) = {
        val a: RDD[(P, T)] = rdd.map(t => (by(t),t))
        val b: RDD[(P, T)] = that.map(t => (by(t),t))
        a.join(b).map { case (attr, (leftItem, rightItem)) => leftItem 
    }
}
spark sql join performance issue with mongo-spark and spark-redshift connectors

spark sql join performance issue with mongo-spark and spark-redshift connectors


By : wellwood
Date : March 29 2020, 07:55 AM
this will help So after a lot of investigation we came to know that 90% of data in table2 had either email or phonenumber null and I had missed to handle joins on null values in the query.
So that was the main problem for this performance bottleneck.
(Spark skewed join) How to join two large Spark RDDs with highly duplicated keys without memory issues?

(Spark skewed join) How to join two large Spark RDDs with highly duplicated keys without memory issues?


By : jartigas
Date : March 29 2020, 07:55 AM
seems to work fine Actually, this is a standard problem in Spark called "skewed join": one of the sides of the join is skewed, meaning some of its keys are much more frequent that others. Some answers that didn't work out for me can be found here.
The strategy I used is inspired by the GraphFrame.skewedJoin() method defined here and its use in ConnectedComponents.skewedJoin() here. The join will be performed by joining the most frequent keys using a broadcast join and the less frequent keys using a standard join.
Java Spark : Spark Bug Workaround for Datasets Joining with unknow Join Column Names

Java Spark : Spark Bug Workaround for Datasets Joining with unknow Join Column Names


By : Saurav Anand
Date : March 29 2020, 07:55 AM
should help you out I am using Spark 2.3.1 with Java. , Try to call this method:
code :
private static Dataset<Row> cloneDataset(Dataset<Row> ds) {
    List<Column> filterColumns = new ArrayList<>();
    List<String> filterColumnsNames = new ArrayList<>();
    scala.collection.Iterator<StructField> it = ds.exprEnc().schema().toIterator();
    while (it.hasNext()) {
        String columnName = it.next().name();
        filterColumns.add(ds.col(columnName));
        filterColumnsNames.add(columnName);
    }
    ds = ds.select(JavaConversions.asScalaBuffer(filterColumns).seq()).toDF(scala.collection.JavaConverters.asScalaIteratorConverter(filterColumnsNames.iterator()).asScala().toSeq());
    return ds;
}
df1 = cloneDataset(df1);
df2 = cloneDataset(df2);
final Dataset<Row> join = df1.join(df2, columns_seq);
// or ( based on Nakeuh comment )
final Dataset<Row> join = cloneDataset(df1.join(df2, columns_seq)); 
Spark Java except method vs. leftanti join returning inconsistent results, possible bug?

Spark Java except method vs. leftanti join returning inconsistent results, possible bug?


By : poweruser
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Seems that .except() acts as EXCEPT DISTINCT operation as of 2.3.0. The previous documentations where EXCEPT is mentioned are incorrect as the behaviour was always EXCEPT DISTINCT
If you compare plans of leftanti
code :
== Physical Plan ==
*BroadcastHashJoin [value#1], [value#4], LeftAnti, BuildRight
:- LocalTableScan [value#1]
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, string, true]))
   +- LocalTableScan [value#4]
== Physical Plan ==
*HashAggregate(keys=[value#1], functions=[])
+- Exchange hashpartitioning(value#1, 200)
   +- *HashAggregate(keys=[value#1], functions=[])
      +- *BroadcastHashJoin [coalesce(value#1, )], [coalesce(value#4, )], LeftAnti, BuildRight, (value#1 <=> value#4)
         :- LocalTableScan [value#1]
         +- BroadcastExchange HashedRelationBroadcastMode(List(coalesce(input[0, string, true], )))
            +- LocalTableScan [value#4]
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