Big Data

Hadoop Platform and Application Framework. Content and exercises from Coursera - University of California, San Diego

Map Reduce

Generate input data

> sh map-reduce/data_generator/data_generator.script

Running without hadoop stack (local)

> cat map-reduce/input/join2_gen* | map-reduce/join2_mapper.py | sort | map-reduce/join2_reducer.py

Running with hadoop stack (remote - cloudera)

> hdfs dfs -put map-reduce/input /user/cloudera/input
> hdfs dfs -ls /user/cloudera/input
> hdfs dfs -cat /user/cloudera/input/join2_genchanA.txt

> hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar \
   -input /user/cloudera/input \
   -output /user/cloudera/output \
   -mapper map-reduce/join2_mapper.py \
   -reducer map-reduce/join2_reducer.py

Spark

Wordcount exercise using PySpark

You will need to enter in the PySpark console:

> sudo easy_install ipython==1.2.1

> PYSPARK_DRIVER_PYTHON=ipython pyspark

Reading: Resiliant Destributed Datasets

Spark> fileA = sc.textFile("input/map-reduce/join1_FileA.txt")
Spark> fileA.collect()
Spark>  Out[]: [u'able,991', u'about,11', u'burger,15', u'actor,22']

Spark> fileB = sc.textFile("input/map-reduce/join1_FileB.txt")
Spark> fileB.collect()
Spark> Out[29]:
   [u'Jan-01 able,5',
     u'Feb-02 about,3',
     u'Mar-03 about,8',
     u'Apr-04 able,13',
     u'Feb-22 actor,3',
     u'Feb-23 burger,5',
     u'Mar-08 burger,2',
     u'Dec-15 able,100']

Loading code into PySpark console:

Spark> load spark/wordcount.py

Testing code:

Spark> test_splitA()
Spark> True
Spark> test_splitB()
Spark> True

Applying split_fileA function to dataset A:

Spark> fileA_data = fileA.map(split_fileA)
Spark> fileA_data.collect()

Out[74]: [(u'able', 991), (u'about', 11), (u'burger', 15), (u'actor', 22)]

Applying split_fileB function to dataset B:

Spark> fileB_data = fileB.map(split_fileB)
Spark> fileB_data.collect()

Out[72]:
  [(u'able', u'Jan-01 5'),
   (u'about', u'Feb-02 3'),
   (u'about', u'Mar-03 8'),
   (u'able', u'Apr-04 13'),
   (u'actor', u'Feb-22 3'),
   (u'burger', u'Feb-23 5'),
   (u'burger', u'Mar-08 2'),
   (u'able', u'Dec-15 100')]

Join A and B:

Spark> fileB_joined_fileA = fileB_data.join(fileA_data)
Spark> fileB_joined_fileA.collect()

Out[76]: [(u'about', (u'Feb-02 3', 11)), ...]