Correct. Trading away scalability for increased performance is not an
option for the standard Spark API.
On Tue, Jun 9, 2015 at 3:05 AM, Daniel Darabos <
daniel.dara...@lynxanalytics.com> wrote:
> It would be even faster to load the data on the driver and sort it there
> without using Spark :). Using reduce() is cheating, because it only works
> as long as the data fits on one machine. That is not the targeted use case
> of a distributed computation system. You can repeat your test with more
> data (that doesn't fit on one machine) to see what I mean.
>
> On Tue, Jun 9, 2015 at 8:30 AM, raggy <raghav0110...@gmail.com> wrote:
>
>> For a research project, I tried sorting the elements in an RDD. I did
>> this in
>> two different approaches.
>>
>> In the first method, I applied a mapPartitions() function on the RDD, so
>> that it would sort the contents of the RDD, and provide a result RDD that
>> contains the sorted list as the only record in the RDD. Then, I applied a
>> reduce function which basically merges sorted lists.
>>
>> I ran these experiments on an EC2 cluster containing 30 nodes. I set it up
>> using the spark ec2 script. The data file was stored in HDFS.
>>
>> In the second approach I used the sortBy method in Spark.
>>
>> I performed these operation on the US census data(100MB) found here
>>
>> A single lines looks like this
>>
>> 9, Not in universe, 0, 0, Children, 0, Not in universe, Never married, Not
>> in universe or children, Not in universe, White, All other, Female, Not in
>> universe, Not in universe, Children or Armed Forces, 0, 0, 0, Nonfiler,
>> Not
>> in universe, Not in universe, Child <18 never marr not in subfamily, Child
>> under 18 never married, 1758.14, Nonmover, Nonmover, Nonmover, Yes, Not in
>> universe, 0, Both parents present, United-States, United-States,
>> United-States, Native- Born in the United States, 0, Not in universe, 0,
>> 0,
>> 94, - 50000.
>> I sorted based on the 25th value in the CSV. In this line that is 1758.14.
>>
>> I noticed that sortBy performs worse than the other method. Is this the
>> expected scenario? If it is, why wouldn't the mapPartitions() and reduce()
>> be the default sorting approach?
>>
>> Here is my implementation
>>
>> public static void sortBy(JavaSparkContext sc){
>> JavaRDD<String> rdd = sc.textFile("/data.txt",32);
>> long start = System.currentTimeMillis();
>> rdd.sortBy(new Function<String, Double>(){
>>
>> @Override
>> public Double call(String v1) throws Exception {
>> // TODO Auto-generated method stub
>> String [] arr = v1.split(",");
>> return Double.parseDouble(arr[24]);
>> }
>> }, true, 9).collect();
>> long end = System.currentTimeMillis();
>> System.out.println("SortBy: " + (end - start));
>> }
>>
>> public static void sortList(JavaSparkContext sc){
>> JavaRDD<String> rdd = sc.textFile("/data.txt",32);
>> //parallelize(l,
>> 8);
>> long start = System.currentTimeMillis();
>> JavaRDD<LinkedList<Tuple2<Double, String>>> rdd3 =
>> rdd.mapPartitions(new FlatMapFunction<Iterator<String>,
>> LinkedList<Tuple2<Double, String>>>(){
>>
>> @Override
>> public Iterable<LinkedList<Tuple2<Double, String>>>
>> call(Iterator<String> t)
>> throws Exception {
>> // TODO Auto-generated method stub
>> LinkedList<Tuple2<Double, String>> lines = new
>> LinkedList<Tuple2<Double, String>>();
>> while(t.hasNext()){
>> String s = t.next();
>> String arr1[] = s.split(",");
>> Tuple2<Double, String> t1 = new Tuple2<Double,
>> String>(Double.parseDouble(arr1[24]),s);
>> lines.add(t1);
>> }
>> Collections.sort(lines, new IncomeComparator());
>> LinkedList<LinkedList<Tuple2<Double, String>>> list = new
>> LinkedList<LinkedList<Tuple2<Double, String>>>();
>> list.add(lines);
>> return list;
>> }
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Different-Sorting-RDD-methods-in-Apache-Spark-tp23214.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
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>
