Shulou(Shulou.com)06/03 Report--

Before learning any technology of spark, please correctly understand spark, you can refer to: correctly understand spark

We already know in http://7639240.blog.51cto.com/7629240/1966131 that a scala function is actually an interface in java, and the same is true for java8 lambda, and an lambda expression is an interface in java. Next, let's take a look at the simplest example of wordcount in spark, which is implemented with java8's non-lambda and lambda respectively:

1. Java spark wordcount programs that are not implemented by lambda:

Public class WordCount {public static void main (String [] args) {SparkConf conf = new SparkConf (). SetAppName ("appName"). SetMaster ("local"); JavaSparkContext sc = new JavaSparkContext (conf); / / JavaPairRDD inputRDD = sc.hadoopFile ("hdfs://master:9999/user/word.txt", / / TextInputFormat.class, LongWritable.class, Text.class) JavaRDD inputRDD = sc.textFile ("file:///Users/tangweiqun/test.txt"); JavaRDD wordsRDD = inputRDD.flatMap (new FlatMapFunction () {@ Override public Iterator call (String s) throws Exception {return Arrays.asList (s.split (")) .iterator ();}})) JavaPairRDD keyValueWordsRDD = wordsRDD.mapToPair (new PairFunction () {@ Override public Tuple2 call (String s) throws Exception {return new Tuple2 (s, 1);}}) JavaPairRDD wordCountRDD = keyValueWordsRDD.reduceByKey (new HashPartitioner (2), new Function2 () {@ Override public Integer call (Integer a, Integer b) throws Exception {return a + b;}}) / / delete File outputFile = new File ("/ Users/tangweiqun/wordcount") if the output file exists; if (outputFile.exists ()) {File [] files = outputFile.listFiles (); for (File file: files) {file.delete ();} outputFile.delete () } wordCountRDD.saveAsTextFile ("file:///Users/tangweiqun/wordcount"); System.out.println (wordCountRDD.collect ());}}"

2. Wordcount code of java8 lambda implementation

Public class WordCount {public static void main (String [] args) {SparkConf conf = new SparkConf (). SetAppName ("appName"). SetMaster ("local"); JavaSparkContext sc = new JavaSparkContext (conf); / / JavaPairRDD inputRDD = sc.hadoopFile ("hdfs://master:9999/user/word.txt", / / TextInputFormat.class, LongWritable.class, Text.class) JavaRDD inputRDD = sc.textFile ("file:///Users/tangweiqun/test.txt"); JavaRDD wordsRDD = inputRDD.flatMap (input-> Arrays.asList (input.split (")). Iterator ()); JavaPairRDD keyValueWordsRDD = wordsRDD.mapToPair (word-> new Tuple2 (word, 1)); JavaPairRDD wordCountRDD = keyValueWordsRDD.reduceByKey ((a, b)-> a + b) / / delete File outputFile = new File ("/ Users/tangweiqun/wordcount") if the output file exists; if (outputFile.exists ()) {File [] files = outputFile.listFiles (); for (File file: files) {file.delete ();} outputFile.delete () } wordCountRDD.saveAsTextFile ("file:///Users/tangweiqun/wordcount"); System.out.println (wordCountRDD.collect ());}}"

As you can see from the above, the implementation of lambda is more concise, and you can also see that a lambda function expression is a java interface.

The combineByKey we mentioned in http://7639240.blog.51cto.com/7629240/1966958 looks like this:

JavaPairRDD javaPairRDD = sc.parallelizePairs (Arrays.asList (new Tuple2 ("coffee", 1), new Tuple2 ("coffee", 2), new Tuple2 ("panda", 3), new Tuple2 ("coffee", 9), 2) / / when a new key is encountered in a partition, apply the function Function createCombiner = new Function () {@ Override public Tuple2 call (Integer value) throws Exception {return new Tuple2 (value, 1);} to the value corresponding to the partition. / when you encounter a key that has already applied the above createCombiner function in a partition, apply the function Function2 mergeValue = new Function2 () {@ Override public Tuple2 call (Tuple2 acc, Integer value) throws Exception {return new Tuple2 (acc._1 () + value, acc._2 () + 1) to the value corresponding to the key;}} / / apply this function Function2 mergeCombiners = new Function2 () {@ Override public Tuple2 call (Tuple2 acc1, Tuple2 acc2) throws Exception {return new Tuple2 (acc1._1 () + acc2._1 (), acc1._2 () + acc2._2 ());} when you need to aggregate data from different partitions. JavaPairRDD combineByKeyRDD = javaPairRDD.combineByKey (createCombiner, mergeValue, mergeCombiners); / / result: [(coffee, (12)), (panda, (3))] System.out.println ("combineByKeyRDD =" + combineByKeyRDD.collect ())

It can be written as the combineByKey implemented by lambda as follows:

JavaPairRDD javaPairRDD = sc.parallelizePairs (Arrays.asList (new Tuple2 ("coffee", 1), new Tuple2 ("coffee", 2), new Tuple2 ("panda", 3), new Tuple2 ("coffee", 9), 2); / / when a new key is encountered in a partition, apply this function Function createCombiner = value-> new Tuple2 (value, 1) to the value corresponding to that key / / when you encounter a key that has already applied the above createCombiner function in a partition, apply the function Function2 mergeValue = (acc, value)-> new Tuple2 (acc._1 () + value, acc._2 () + 1) to the value corresponding to the key. / / apply this function Function2 mergeCombiners = (acc1, acc2)-> new Tuple2 (acc1._1 () + acc2._1 (), acc1._2 () + acc2._2 ()) when you need to aggregate data from different partitions; JavaPairRDD combineByKeyRDD = javaPairRDD.combineByKey (createCombiner, mergeValue, mergeCombiners); / / result: [(coffee, (12)), (panda, (3))] System.out.println ("combineByKeyRDD =" + combineByKeyRDD.collect ())

If you want an in-depth systematic understanding of spark RDD api, you can refer to: detailed explanation of the principle of spark core RDD api

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