Hadoop Streaming

TIP

Learn how to work with Hadoop Streaming.
Learn how to write Hadoop Streaming programs using python.

In this section, you will learn how to work with Hadoop Streaming, a tool to run any executable in Hadoop MapReduce. We will show how to count the frequency of different values of event-id for each patient event sequence file. The examples here are shown in Python code, but you will find that it's straightforward to adapt this concept to other languages.

Mapper and Reducer

Streaming works by passing a data mapper and reducer written in another programming language through standard input and output. Let's have a look at the source code[^1] for the mapper and reducer one at at time.

Mapper

The source code for the mapper is:

#!/usr/bin/env python

import sys

for line in sys.stdin:
    line        = line.strip()
    splits      = line.split(',')

    if len(splits) != 4:
        # ignore problemactic line
        continue

    # unwind the splits
    patient_id, event_name, date_offset, value = splits

    # emit key-value pair seperated by \t for all events
    print(event_name + '\\' + '1')

This script reads lines from standard input and with some simple processing outputs to standard output the event_name as the key and 1 as the value.

Reducer

The reducer is a little bit more complex. The output of the mapper will be shuffled by Hadoop framework's shuffle process (a part of MapReduce) and the reduder will get a list of key-value pairs. The MapReduce framework guarantees that all key-value pairs with the same key will go to same reducer instance.

The source code for the reducer is:

#!/usr/bin/env python

import sys

current_event = None
current_count = 0
event_name = None

for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()

    # parse the input we got from mapper.py
    event_name, count = line.split('\t', 1)

    # convert count (currently a string) to int
    try:
        count = int(count)
    except ValueError:
        # count was not a number, so silently
        # ignore/discard this line
        continue

    # line is sorted with key (event name)
    if current_event == event_name:
        # same key accumulate
        current_count += count
    else:
        # a new key to work on
        if current_event:
            # write result to STDOUT
            print('%s\t%s' % (current_event, current_count))
        current_count = count
        current_event = event_name

## do not forget to output the last event_name if needed!
if current_event == event_name:
    print('%s\t%s' % (current_event, current_count))

This script checks the boundaries of the sorted input and sums up values from same key.

How to run

Local test

Before running it in Hadoop, it's more convenient to test the code in shell using the cat and sort commands. You will need to navigate to the sample/hadoop-streaming folder. Then, run the below command in the shell:

cat data/* | python mapper.py | sort | python reducer.py

You will see results like:

DIAG0043        1
DIAG00845       8
DIAG0086        1
DIAG0088        4
DIAG01190       1
DIAG0201        1
DIAG0202        1
DIAG0204        1
DIAG0221        1
DIAG0232        1
...

Now that we've verified that it works as expected, we can run it in Hadoop.

Hadoop

We first need to put the data into HDFS, then run hadoop:

hdfs dfs -mkdir streaming-data
hdfs dfs -put data/* streaming-data
hadoop jar hadoop-streaming.jar \
   -files mapper.py,reducer.py \
   -mapper "python mapper.py" \
   -reducer "python reducer.py" \
   -input streaming-data \
   -output streaming-output

Now we check the results and clean up:

## check result
hdfs dfs -ls streaming-output
hdfs dfs -cat streaming-output/*

## clean up
hdfs dfs -rm -r streaming-output
hdfs dfs -rm -r streaming-data

Exercise

Update mapper and reducer to output diagnostic code occurred more than once

# Hadoop Streaming

# Mapper and Reducer

# Mapper

# Reducer

# How to run

# Local test

# Hadoop

# Further reading