[@NaukriEngineering] Apache Spark

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Apache Spark Riya Singhal

Transcript of [@NaukriEngineering] Apache Spark

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Apache Spark

Riya Singhal

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Agenda● What is Big Data?

● What is the solution of Big data?

● How Apache Spark can help us?

● Apache Spark advantages over Hadoop MapReduce

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What is Big Data?● Lots of Data (Terabytes or Petabytes).

● Large and complex.

● Difficult to deal using Relational Databases.

● Challenges faced in - searching, storing, transfer, analysis, visualisation.

● Require Parallel processing on 100s of machines.

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Hadoop MapReduce● Allows distributed processing of large datasets across clusters.

● It is open source database management with scale out storage and distributed

processing.

● Characteristics:

○ Economical

○ Scalable

○ Reliable

○ Flexible

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MapReduce● Map - Data is converted into tuples (key/value pair).

● Reduce - Takes input from map and combines input from map to form smaller set of

tuples.

● Advantages

○ Scale data○ Parallel Processing○ Fast○ Built in fault tolerant

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MapReduce

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Shortcomings of MapReduce1. Slow for Iterative Jobs.

2. Slow for Interactive Ad-hoc queries.

3. Operations - Forces task be of type Map and Reduce.

4. Difficult to program - Even simple join operations also require extensive code.

Lacks data sharing. Data sharing done through stable storage (HDFS)→ slow.

Slow due to replication and Disk I/O but it is essential for fault tolerance.

Can we use memory? How will it be fault tolerant?

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Apache Spark● Developed in 2009 by UC Berkeley.

● Processing engine.

● Used for speed, ease of use, and sophisticated analytics.

● It is based on Hadoop MapReduce but it extends MapReduce for performing

more types of computations.

● Spark participated in Daytona Gray category, Spark sorted 100 TB of data (1

trillion records) the same data three time faster using ten times fewer

machines as compared to Hadoop.

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Apache Spark● Improves efficiency through

○ In-memory data sharing.

○ General computation graph.

● Improves usability through

○ Rich APIs in Java, Scala, Python.

○ Interactive Shell.

HOW ??

Upto 100x faster in memory and 10x faster on disk

Upto 2-5x less code

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Resilient Distributed Dataset (RDD)● Fundamental Data Structure of Apache Spark.

● Read-only collection of objects partitioned across a set of machines.

● Perform In-memory Computation.

● Build on transformation operations like map, filter etc.

● Fault tolerant through lineage.

● Features: ○ Immutable○ Parallel○ Cacheable○ Lazy Evaluated

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Resilient Distributed Dataset (RDD)Two types of operation can be performed:

● Transformation○ Create new RDD from existing RDD.

○ Creates DAG.

○ Lazily evaluated.

○ Increases efficiency by not returning large dataset.

○ Eg. GroupByKey, ReduceByKey, filter.

● Action○ All queries are executed.

○ Performs computation.

○ Returns result to driver program.

○ Eg. collect, count, take.

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Ready for some programming…..

(using python)

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Creating RDD# Creates a list of animal.animals = ['cat', 'dog', 'elephant', 'cat', 'mouse', ’cat’]

# Parallelize method is used to create RDD from list. Here “animalRDD” is created.#sc is Object of Spark Context.animalRDD = sc.parallelize(animals)

# Since RDD is lazily evaluated, to print it we perform an action operation, i.e. collect() which is used to print the RDD.print animalRDD.collect()

Output - ['cat', 'dog', 'elephant', 'cat', 'mouse', 'cat']

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Creating RDD from file#The file words.txt has names of animals through which animalsRDD is made.

animalsRDD = sc.textFile('/path/to/file/words.txt')

#collect() is the action operation.

print animalsRDD.collect()

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Map operation on RDD‘’’’’ To count the frequency of animals, we make (key/value) pair - (animal,1) for all the animals and then perform reduce operation which counts all the values.Lambda is used to write inline functions in python.‘’’’’mapRDD = animalRDD.map(lambda x:(x,1))

print mapRDD.collect()

Output - [('cat',1), ('dog',1), ('elephant',1), ('cat',1), ('mouse',1), ('cat',1)]

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Reduce operation on RDD‘’’’’ reduceByKey is used to perform reduce operation on same key. So in its arguments, we have defined a function to add the values for same key. Hence, we get the count of animals.‘’’’’reduceRDD = mapRDD.reduceByKey(lambda x,y:x+y)

print reduceRDD.collect()

Output - [('cat',3), ('dog',1), ('elephant',1), ('mouse',1)]

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Filter operation on RDD‘’’’’ Filter all the animals obtained from reducedRDD with count greater than 2. x is a tuple made of (animal, count), i.e. x[0]=animal name and x[1]=count of animal. Therefore we filter the reduceRDD based on x[1]>2.‘’’’’filterRDD = reduceRDD.filter(lambda x:x[1]>2)

print filterRDD.collect()

Output - [('cat',3)]

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Please refer http://spark.apache.org/docs/latest/programming-guide.html for

more about programming in Apache Spark.

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APACHE SPARK

VS

HADOOP MAPREDUCE

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Spark vs. Hadoop● Performance

○ Spark better as it does in-memory computation. ○ Hadoop is good for one pass ETL jobs and where data does not fit in memory.

● Ease of use○ Spark is easier to program and provides API in Java, Scala, R, Python.○ Spark has an interactive mode.○ Hadoop MapReduce is more difficult to program but many tools are available to

make it easier.

● Cost○ Spark is cost effective according to benchmark, though staffing can be costly.

● Compatibility○ Compatibility to data types and data sources is the same for both.

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Spark vs. Hadoop● Data Processing

○ Spark can perform real time processing and batch processing.

○ Hadoop MapReduce is good for batch processing. Hadoop requires storm for real

time processing, Giraph for graph processing, Mahout for machine learning.

● Fault tolerant

○ Hadoop MapReduce is slightly more tolerant.

● Caching

○ Spark can cache the input data.

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Applications Companies that uses Hadoop and Spark are:

● Hadoop - Hadoop is used good for static operation.

○ Dell, IBM, Cloudera, AWS and many more.

● Spark○ Real-time marketing campaign, online product recommendations etc.

○ eBay, Amazon, Yahoo, Nokia and many more.○ Data mining 40x times faster than Hadoop (Conviva).○ Traffic Prediction via EM (Mobile Millennium).○ DNA Sequence Analysis (SNAP).○ Twitter Spam Classification (Monarch).

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Apache Spark helping companies grow in their business

● Spark Helps Pinterest Identify Trends - Using Spark, Pinterest is able to

identify—and react to—developing trends as they happen.

● Netflix Leans on Spark for Personalization Aid - Netflix uses Spark to support

real-time stream processing for online recommendations and data monitoring.

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Libraries of Apache Spark Spark provides libraries to provide generality. We can combine these libraries seamlessly in the same application to provide more functionality.

Libraries provided by Apache Spark are:

1. Spark Streaming - It supports scalable and fault tolerant processing of streaming data.

2. Spark SQL - It allows spark to work with structured data.3. Spark MLlib - It provides scalable machine learning library and has machine

learning and statistical algorithms. 4. Spark GraphX - It is used to compute graphs over data.

Refer http://spark.apache.org/docs/latest/ for more information.

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THANK YOU