Spark operator: RDD basic conversion operation (7) -zipWithIndex, zipWithUniqueId

ZipWithIndex

Def zipWithIndex (): RDD [(T, Long)]
This function combines the elements in the RDD with the ID (index number) of the element in the RDD into a key / value pair. 
    

  1.   Scala> var rdd2 = sc.makeRDD (Seq ("A", "B", "R", "D", "F"), 2)
    2. 

  2.  Rdd2: org.apache.spark.rdd.RDD [String] = ParallelCollectionRDD [34] at makeRDD at: 21
    3. 

  3.  
    4. 

  4.  Scala> rdd2.zipWithIndex (). Collect
    5. 

  5.  (B, 1), (R, 2), (D, 3), (F, 4))
    6. 

  6.  
    7.

ZipWithUniqueId

Def zipWithUniqueId (): RDD [(T, Long)]
This function combines RDD elements and a unique ID into key / value pairs. The unique ID generation algorithm is as follows:
The unique ID of the first element in each partition is: the partition index number,
The unique ID of the Nth element in each partition is: (the unique ID value of the previous element) + (the total number of RDD partitions)
Look at the following example: 
    

  1.  (A), "B", "C", "D", "E", "F"), 2)
    2. 

  2.  Rdd1: org.apache.spark.rdd.RDD [String] = ParallelCollectionRDD [44] at makeRDD at   :twenty one
    3. 

  3.  // rdd1 has two partitions,
    4. 

  4. 

    5. 

  5.  Scala> rdd1.zipWithUniqueId (). Collect
    6. 

  6.  (B, 2), (C, 4), (D, 1), (E, 3), (F, 5))
    7. 

  7.  // The total number of partitions is 2
    8. 

  8.  // The first partition has the first element ID of 0 and the first partition ID of the second partition
    9. 

  9.  // The first partition has the second element ID of 0 + 2 = 2, the first partition has the third element ID of 2 + 2 = 4
    10. 

  10.  // The second partition has the second element ID of 1 + 2 = 3, the second partition has the third element ID of 3 + 2 = 5
    11. 

  11. 

    12.

Commentaires

Enregistrer un commentaire

Posts les plus consultés de ce blog

Controlling Parallelism in Spark by controlling the input partitions by controlling the input partitions

Image
It is useful to be able to control the degree of parallelism when using Spark. Spark provides a very convenient method to increase the degree of parallelism which should be adequate in practice. This blog entry of the "Inside Spark" series describes the knobs Spark uses to control the degree of parallelism. Controlling the number of partitions in the " local " mode Spark is designed ground up to enable unit testing. To control the degree of parallelism in the local mode simply utilize the function JavaRDD<String> org.apache.spark.api.java.JavaSparkContext.parallelize(List<String> list, int numSlices) The following snippet of code demonstrates this usage- public class SparkControlPartitionSizeLocally { public static void main ( String [] args ) { JavaSparkContext sc = new JavaSparkContext ( "local" , "localpartitionsizecontrol" ); String input = "four score and sev
Lire la suite

Spark performance optimization: shuffle tuning

Image
Article directory 1 shuffle tuning 1.1 Summary of tuning 1.2 Overview of ShuffleManager Development 1.3 HashShuffleManager operating principle 1.3.1 Unoptured HashShuffleManager 1.3.2 Optimized HashShuffleManager 1.4 SortShuffleManager operating principle 1.4.1 General operating mechanism 1.4.2 bypass running mechanism 1.5 shuffle related parameters tuning 1.5.1 spark.shuffle.file.buffer 1.5.2 spark.reducer.maxSizeInFlight 1.5.3 spark.shuffle.io.maxRetries 1.5.4 spark.shuffle.io.retryWait 1.5.5 spark.shuffle.memoryFraction 1.5.6 spark.shuffle.manager 1.5.7 spark.shuffle.sort.bypassMergeThreshold 1.5.8 spark.shuffle.consolidateFiles 2 write in the last words Shuffle Summary of tuning Most of the performance of Spark operations is mainly consumed in the shuffle link, because the link contains a large number of disk IO, serialization, network data transmission and othe
Lire la suite

Spark optimization

Image
One operation and maintenance 1. Master hang up, standby restart is also invalid Master defaults to 512M of memory, when the task in the cluster is particularly high, it will hang, because the master will read each task event log log to generate spark ui, the memory will naturally OOM, you can run the log See that the master of the start through the HA will naturally fail for this reason. solve Increase the Master's memory spark-env.sh , set in the master node spark-env.sh : export SPARK_DAEMON_MEMORY 10g # 根据你的实际情况 Reduce the job information stored in the Master memory spark.ui.retainedJobs 500 # 默认都是1000 spark.ui.retainedStages 500 Hang up or suspend Sometimes we will see the web node in the web ui disappear or in the dead state, the task of running the node will report a variety of lost worker errors, causing the same reasons and the above, worker memory to save a lot of ui The information leads to gc when the heartbeat is lost
Lire la suite