Apache spark session

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Transcript of Apache spark session

Sandeep GiriHadoop

Apache

A fast and general engine for large-scale data processing.

• Really fast Hadoop • 100x faster than Hadoop MapReduce in memory, • 10x faster on disk.

• Builds on similar paradigms as Hadoop • Integrated with Hadoop

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Apache

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wget http://d3kbcqa49mib13.cloudfront.net/spark-1.1.0-bin-hadoop2.4.tgz

tar zxvf spark-1.1.0-bin-hadoop2.4.tgz && rm spark-1.1.0-bin-hadoop2.4.tgz;

mv spark-1.1.0-bin-hadoop2.4 /usr/lib/

cd /usr/lib;

ln -s spark-1.1.0-bin-hadoop2.4/ spark

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/usr/lib/spark/bin/pyspark

INSTALLING ON YARNAlready Installed on hadoop1.knowbigdata.com

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SPARK - CONCEPTS - RESILIENT DISTRIBUTED DATASETA collection of elements partitioned across cluster

lines = sc.textFile('hdfs://hadoop1.knowbigdata.com/user/student/sgiri/wordcount/input/big.txt')

• RDD Can be persisted in memory • RDD Auto recover from node failures • Can have any data type but has a special dataset type for key-value • Supports two type of operations: transformation and action • Each Element of RDD across cluster is run through map function

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SPARK - TRANSFORMATIONS

JavaRDD<Integer> lineLengths = lines.map(new Function<String, Integer>() { public Integer call(String s) { return s.length(); } });

Creates a new dataset

persist()cache()

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SPARK - TRANSFORMATIONSmap(func)

Return a new distributed dataset formed by passing each element of the source through a function func.

Analogous to foreach of pig.

filter(func)Return a new dataset formed by selecting those

elements of the source on which func returns true.

flatMap( func)

Similar to map, but each input item can be mapped to 0 or more output items

groupByKey ([numTasks])

When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs.

See More: sample, union, intersection, distinct, groupByKey, reduceByKey, sortByKey,join https://spark.apache.org/docs/latest/api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html

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SPARK - ACTIONS

int totalLength = lineLengths.reduce( new Function2<Integer, Integer, Integer>() { public Integer call(Integer a, Integer b) { return a + b; } });

Return value to the driver

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SPARK - ACTIONS

reduce(func)Aggregate elements of dataset using a function:

• Takes 2 arguments and returns one • Commutative and associative for parallelism

count() Return the number of elements in the dataset.

collect() Return all elements of dataset as an array at driver. Used for small output.

take(n)Return an array with the first n elements of the dataset.

Not Parallel.

See More: first(), takeSample(), takeOrdered(), saveAsTextFile(path) https://spark.apache.org/docs/latest/api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html

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SPARK - EXAMPLE - REDUCE SUM FUNCTION//Single Node lines = ["san giri g", "san giri", "giri", "bhagwat kumar", "mr. shashank sharma", "anto"] lineLengths = [11, 9, 4, 14, 20, 4] sum = ??? !//Node1 lines = ["san giri g", "san giri", "giri"] lineLengths = [11, 9, 4] !totalLength = [20, 4] totalLength = 24 //sum or min or max or sqrt(a*a + b*b) !//Node2 lines = ["bhagwat kumar"] lineLengths = [14] totalLength = 14 !//Node3 lines = ["mr. shashank sharma", "anto"] lineLengths = [20, 4] totalLength = 24 !!//Driver Node lineLengths = [24, 14, 24] lineLength = [38, 24] lineLength = [62]

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SPARK - SHARED MEMORY

Broadcast<int[]> broadcastVar = sc.broadcast(new int[] {1, 2, 3}); broadcastVar.value(); // returns [1, 2, 3]

Broadcast Variables

Broadcast() broadcast.value()

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SPARK - SHARED MEMORY

Accumulator<Integer> accum = sc.accumulator(0); sc.parallelize(Arrays.asList(1, 2, 3, 4)).foreach(x -> accum.add(x)); accum.value(); // returns 10

Accumulators

+= 10 += 20

• are only “added” to • through associative operation • assoc.: (2+3)+4=2+(3+4)=9

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!#Import regular expression import re; !#load file lines = sc.textFile('hdfs://hadoop1.knowbigdata.com/user/student/sgiri/wordcount/input/big.txt') !#Split line into multiple lines fm = lines.flatMap(lambda lines: lines.split(" ")); !#Keep only alphanumerics m = fm.map(lambda word: ( re.sub(r"[^A-Za-z0-9]*", ""), word.lower()), 1)) !#Run Reduce counts = m.reduceByKey(lambda a, b: a + b) counts.count(); counts.saveAsTextFile('hdfs://hadoop1.knowbigdata.com/user/student/sgiri/wordcount/output/spark')

Word Count example

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import re; lines = sc.textFile('hdfs://hadoop1.knowbigdata.com/user/student/sgiri/wordcount/input/big.txt'); common = sc.broadcast({"a":1, "an":1, "the":1, "this":1, "that":1, "of":1, "is":1}); accum = sc.accumulator(0); !fm = lines.flatMap(lambda lines: lines.split(" ")); m = fm.map( lambda word: ( re.sub( r"[^A-Za-z0-9]*", "", word.lower() ), 1) ) !def filterfunc(k): accum.add(1); return k[0] not in common.value; !cleaned = m.filter(filterfunc); cleaned.take(10) counts = cleaned.reduceByKey(lambda a, b: a + b) counts.count(); counts.saveAsTextFile('hdfs://hadoop1.knowbigdata.com/user/student/sgiri/wordcount/output/spark')

WordCount with Accumulator and broadcast