Barrett Strausser



  • Main Flow use left and right keys
  • Code, Configuration and Miscellany use up and down
  • If you want an overview hit 'ESC'


  • What is MapReduce
  • Setup on AWS
  • Basics of Mappers and Reducer by example
  • Another MapReduce Example
  • HIVE
  • PIG

Lots of material. Each topic deserves a more thorough treatment.

Some non-pythonic code. I tried to make things language agnostic

Map Reduce

  • An algorithm not a framework
  • Might better be called emit and aggregate
  • It is about selecting or projecting or MAPPING
  • And then aggregating or summing or REDUCING
  • It is functional.
  • You've seen it before. Who writes python...?
  • Grok the below nonsense for great learning....

map (k1,v1) => list((k2,v2))
reduce (k2,list(v2)) => list(v2) 

Example - Simple  Web Index

map (url,content) => list(word,url)
reduce (word,list(url)) => list((word,set(url)) 

  1. Input a webpage model of form (url,content)
  2. Map(url, content)  to (word, url)  by splitting content into words.  
  3. Reduce (word, list(url)) by emitting (word,set(url))

For the visual learners

Hadoop FrameWork

  •  Wait just a minute.... What was that sort phase

  •  Hadoop will GUARANTEE that all tuples with the same key will be reduced by the same reducer instance AND in sorted order

  • Meaning, you will have everything you need locally to aggregate on a GIVEN KEY

  • Hadoop WILL NOT GUARANTEE that a reducer receives only one key.

More INDepth

  • Input reader - reads from a stable source : HDFS, S3, SomeNoSql

  • Mapper - splits input into (key,value)

  • Partition Function  - Function(key) => Reducer label

  • Comparison - Sort Phase

  • Reduce - Applies a function to each input ONCE. May or may not output result.

More visuals


  • MapReduce + HDFS

  • HDFS = Hadoop File System 

  • I mostly get by ignoring the details of trackers, job nodes as I'm concerned with productivity than optimal settings

  • Your mileage may vary


  •  JobTracker to which client applications submit  jobs.

  • JobTracker is rack-aware

  • TaskTracker - Runs jobs in separate JVM

  • Data is 3-redundant

  • Heartbeat between JobTracker and TaskTracker


  • I prefer Amazon EMR (Elastic Map Reduce)

  • My data is already there (S3)

  • I can use tools I know (boto)

  • Integrates well with the other AWS products (dynamodb)

Aws Tool Setup

  • setup is by no means exhaustive
  • miminal steps needed
sudo vi ~/.bashrc || sudo vi ~/.bash_profile
export EC2_HOME = ...export EC2_PRIVATE_KEY=pk-foo.pemexport EC2_CERT=cert-foo.pem
export EC2_KEYPAIR=...export EC2_URL=...
$sudo apt-get install python,pip$pip install boto


  • We have (sensors,logs whatever) that output timeseries of records

  • Each record consist of three different signals (channels)

  • Our task is to estimate the statistical distribution of the channels given some belief about their distribution

  • WHY ALL THE MATH? Easy to check your answers

Example Schema

  • Machine Name = Identifier of Sensor. Ranging from 1-3
  • Record_Date = Timestamp of sensor reading.
  • Channel_1 = Normally Distributed N(i,1). i = machine id
  • Channel_2 = Exponentially Distributed with lambda =3
  • Channel_3 = Lognormally Distributed LN(0,2)

  Machine-1    2012-11-28 22:42:00   0.1722  0.108   6.2504
  Machine-2    2012-11-28 22:42:00   0.0185  0.3336  3.316
  Machine-3    2012-11-28 22:42:00   1.6843  0.2725  1.3314
  Machine-1    2012-11-28 22:42:00   0.1482  0.1422  0.3965

Model Code

records = []
with open('/home/me/Git/EMR-DEMO/code/resources/mapper_input','w+') as f:
    for i in range(10000):
        input = machine_name = " Machine-" + str((i % 3) + 1) + '\t'
        input += str(datetime.datetime.today().replace(second=0, microsecond=0)) + '\t'
        input +=  str(round(random.normalvariate((i % 3),1),4)) + '\t'
        input +=  str(round( random.expovariate(3),4) )+ '\t'
        input +=  str(round(random.lognormvariate(0,2),4)) + '\n'


  • Read the files in from STDIN

  • Split on '\t' to find the key and values

  • Your input format determines how you parse

  • Process the values however you wish

  • Emit a (key, value) pair by printing to STDOUT


#!/usr/bin/env python
# encoding: utf-8
import sys
import decimal

def some_function(sensor_record):

    numerical_record = []
    for record in sensor_record:
        scaled = decimal.Decimal(record).quantize(decimal.Decimal('.001'),rounding=decimal.ROUND_DOWN)

    return ''.join(map(lambda x: str(x) + ' ',numerical_record))

def record_cleaner(record):
    record = record.strip()
    (key,date,c1,c2,c3) = record.split('\t')
    values = [c1,c2,c3]
    return (key,values)

def process(count,record) :
    count += 1
    (key,values) =  record_cleaner(record)
    transformed_data = some_function(values)
    output = '%s\t%s:%s' % (key,count,transformed_data)
    print output

    return count

count = 0

for record in sys.stdin :
    count = process(count,record)


  • The reducer is very similar to the mapper

  • You read in from StdIn

  • You split on '\t' into key and value

  • You then process the records how you wish

  • Write the results to StdOut

Reducer WalkThrough

  • Start reading in records
import sys
import decimal
import math

machine_name = None key = '' machine = 'foo' print >> sys.stderr, 'Starting' for record in sys.stdin: if(record != None and record != '') : (k,i,v) = record_cleaner(record) if(machine_name == None): record_count = 0 running_sum = [0,0,0] machine_name = k if(machine_name != k): estimates = estimate_parameters(record_count,running_sum) print '%s\t%s:%s:%s:%s' % (machine_name,estimates[0],estimates[1],estimates[2],estimates[3]) machine_name = k #print >> sys.stderr, 'machine ' + machine_name record_count = 0 running_sum = [0,0,0] (record_count,running_sum) = process(record_count,running_sum,record) else: #print >> sys.stderr, 'processing %s' % record (record_count,running_sum) = process(record_count,running_sum,record) estimates = estimate_parameters(record_count,running_sum) print '%s\t%s:%s:%s:%s' % (machine_name,estimates[0],estimates[1],estimates[2],estimates[3])

Reducer WalkThrough

  • Core Logic
def process(record_count,running_sum,record):
    record_count += 1
    record = record_cleaner(record)
    data_tuple = record_to_datatuple(record[2])
    running_sum = compute_sum(running_sum,data_tuple)
    t = (record_count,running_sum)
    return t

Reducer WalkThrough

  • Split into key and value(s) based on file format
def record_cleaner(record):
    record = record.strip()
    split_record = record.split('\t')
    key = split_record[0]
    split_record = split_record[1].split(":")
    id = split_record[0]
    value = split_record[1]
    return (key,id,value)

Reducer WalkThrough

  • Process into a list and do some numerical manipulation
def record_to_datatuple(record):
    split_record = record.split(" ")
    numerical_record = []
    for record in split_record:
        scaled = decimal.Decimal(record).quantize(decimal.Decimal('.001'),rounding=decimal.ROUND_DOWN)

    return numerical_record

Reducer WalkThrough

  • Keep a running sum as we iterate
def compute_sum(running_sum,data_tuple):
        running_sum[0] += data_tuple[0]
        running_sum[1] += data_tuple[1]
        running_sum[2] += data_tuple[2]
    except :
        print >> sys.stderr, 'error in running sum with tuple %s' % data_tuple
    return running_sum

Reducer WalkThrough

  • Finally compute the estimates
def estimate_parameters(record_count,running_sum):

    normal_estimate = running_sum[0] / record_count
    exponential_estimate = 1 / (running_sum[1] /record_count)

    log_sum = running_sum[2]

    log_u =  log_sum / record_count

    log_numerator =  log_sum*log_sum - log_sum*log_u + log_u*log_u
    log_sigma = log_numerator / record_count
    return (normal_estimate,exponential_estimate,log_u,log_sigma)

Job Context

  • Great. Let's burn some dust.
import boto
from boto.s3.key import Key
from boto.emr.step import StreamingStep

#you will need your own bucket
root_path = '/home/me/Git/EMR-DEMO/code/'
s3 = boto.connect_s3()
emr_demo_bucket = s3.create_bucket('bearrito.demos.emr')

Job Context

  • Upload all our scripts
json_records = Key(emr_demo_bucket)
json_records.key = "input/hive/mapper_input"
json_records.set_contents_from_filename(root_path + 'resources/mapper_input')

input_key = Key(emr_demo_bucket)
input_key.key = "input/0/mapper_input"
input_key.set_contents_from_filename( root_path + 'resources/mapper_input')

mapper_key = Key(emr_demo_bucket)
mapper_key.key = "scripts/mapper_script.py"
mapper_key.set_contents_from_filename(root_path + 'src/EMRDemoMapper.py')

reducer_key = Key(emr_demo_bucket)
reducer_key.key = "scripts/reducer_script.py"
reducer_key.set_contents_from_filename( root_path +  'src/EMRDemoReducer.py')

Job Context

  • Run and Monitor the Job.
demo_step = StreamingStep(name ='EMR Demo Example'

emr = boto.connect_emr()
jobid = emr.run_jobflow(name="EMR Example",log_uri='s3://bearrito.demos.logs',steps = [demo_step])

status = emr.describe_jobflow(jobid)

#log into AWS to monitor further


  • The last example was focused on aggregation
  • What about a search example
  • Given a target record find the machine that generated the record closest to that record
  • Our approach is going to work in phases

  1. Given inputs, emit a (machine_name, distance) pair
  2. Foreach machine_name emit the smallest distance
  3. Collapse the set of min distance onto a single key
  4. Loop over and emit the smallest.

Mapper Phase 0

  • Given a target emit a machine name and distance.
#!/usr/bin/env python
# encoding: utf-8
import decimal
import math
import sys

def record_to_decimal(sensor_record):
    numerical_record = []
    for record in sensor_record:
        scaled = decimal.Decimal(record).quantize(decimal.Decimal('.001'),rounding=decimal.ROUND_DOWN)
    return numerical_record

def record_cleaner(record):
    record = record.strip()
    (super_key,record_date,c1,c2,c3) = record.split('\t')
    key = super_key[super_key.index('Machine'):]
    values = [c1,c2,c3]
    return (key,values)

def compute_distance(target,data):
    d0 = (target[0] - data[0])**2
    d1 = (target[1] - data[1])**2
    d2 = (target[2] - data[2])**2

    return math.sqrt(d0 + d1 + d2)

def process(target,record) :
    (key,values) =  record_cleaner(record)
    transformed_data = record_to_decimal(values)
    distance = compute_distance(target,transformed_data)
    output = '%s\t%s' % (key,distance)
    print output

target = (decimal.Decimal(-1.53),decimal.Decimal(0.144),decimal.Decimal(1.99))
for record in sys.stdin:

Reducer Phase 0

  • For a given key(machine) emit the closest record
#!/usr/bin/env python
# encoding: utf-8
import decimal
import sys

def process(min_machine,min_distance,record) :
    (key,value) = record.split("\t")
    data = decimal.Decimal(value).quantize(decimal.Decimal('.001'),rounding=decimal.ROUND_DOWN)
    if(min_machine == None) :
        min_machine = key
        min_distance = None

    if(min_machine != key) :
        print "%s\t%s" % (min_machine,min_distance)
        min_machine = key
        min_distance = None

    if(min_distance == None or data < min_distance) :
        min_distance = data

    return (min_machine,min_distance)

min_distance = None
min_machine = None
for record in sys.stdin :
    (min_machine,min_distance) = process(min_machine,min_distance,record)
print "%s\t%s" % (min_machine,min_distance)

Mapper Phase 1

  • Given all the min keys, project them onto a common key.
#!/usr/bin/env python
# encoding: utf-8
import sys
for record in sys.stdin :
     (key,value) = record.split('\t')
     print "%s\t%s:%s" % ('agg',key,value)

Reducer Phase 1

  • Loop over all min keys and emit the smallest

#!/usr/bin/env python
# encoding: utf-8
import decimal
import sys

def process(min_machine,min,record) :
    try :

        (key,value) = record.split('\t')
        (machine,distance) = value.split(':')
        data = decimal.Decimal(distance).quantize(decimal.Decimal('.001'),rounding=decimal.ROUND_DOWN)

        if(min == None or data < min) :
            min = data
            min_machine = machine
    except :
        a = 1
    return (min_machine,min)

min = None
min_machine = None

for record in sys.stdin :
    if(record != None or record != ""):
        (min_machine,min) = process(min_machine,min,record)

print "%s\t%s" % (min_machine,min)

Job Setup

  • Setup our code
  • Notice the boostrap step
#!/usr/bin/env python
# encoding: utf-8
import boto
from boto.s3.key import Key
from boto.emr.step import StreamingStep
from boto.emr.bootstrap_action import BootstrapAction

root_path = '/home/barrett/Git/EMR-DEMO/code/'

s3 = boto.connect_s3()
emr_demo_bucket = s3.create_bucket('bearrito.demos.emr')

input_key = Key(emr_demo_bucket)
input_key.key = "input/0/mapper_input"
input_key.set_contents_from_filename(root_path + 'resources/mapper_input')

mapper_key = Key(emr_demo_bucket)

mapper_key.key = "scripts/bootstrap.sh"
mapper_key.set_contents_from_filename(root_path + 'src/BootStrap.sh')

bootstrap_step = BootstrapAction("bootstrap.sh",'s3://bearrito.demos.emr/scripts/bootstrap.sh',None)

mapper_key.key = "scripts/mapper_nearest_0.py"
mapper_key.set_contents_from_filename(root_path + 'src/EMRNearestMapper0.py')

mapper_key.key = "scripts/mapper_nearest_1.py"
mapper_key.set_contents_from_filename(root_path + 'src/EMRNearestMapper1.py')

reducer_key = Key(emr_demo_bucket)
reducer_key.key = "scripts/reducer_nearest_0.py"
reducer_key.set_contents_from_filename(root_path + 'src/EMRNearestReducer0.py')

reducer_key.key = "scripts/reducer_nearest_1.py"
reducer_key.set_contents_from_filename(root_path + 'src/EMRNearestReducer1.py')

nearest_0 = StreamingStep(name ='EMR First Phase'

nearest_1 = StreamingStep(name ='EMR Second Phase'

emr = boto.connect_emr()
jobid = emr.run_jobflow(name="EMR Two Phase"
                        ,steps = [nearest_0,nearest_1]

status = emr.describe_jobflow(jobid)


  • Need to ensure that we have correct runtime deps
wget http://python.org/ftp/python/2.7.2/Python-2.7.2.tar.bz2
tar jfx Python-2.7.2.tar.bz2
cd Python-2.7.2
./configure --with-threads --enable-shared
sudo make install
sudo ln -s /usr/local/lib/libpython2.7.so.1.0 /usr/lib/
sudo ln -s /usr/local/lib/libpython2.7.so /usr/

Break TIME

  • Any questions so far?

  • We've put in a lot of work so far for not much to show
  1. Quirky syntax of mapper and reducer
  2. Creating input and output locations on S3
  3. Managing jobs directly

  • We are going to look at abstractions that make this easier

HIVE Motivation

  • Let's backup
  • What does the below do in your SQL version
SELECT sum(channel_1), count(channel_1)
FROM sensor_records
GROUPBY machine_name
  • It probably groups records in a recordset by a field
  • Then computes the sum of a field and counts the number of records

map Reduce Formulation

  • FROM sensor_records => Defining the S3 Bucket or HDFS location.
  • GROUPBY machine_name => Becomes the MAP phase or emitting records with machine_name as key 
  • Example => ('Machine-1', .003)
  • SELECT => Becomes REDUCE phase with aggregating based on keys

  • We've already done this....with much code and overhead


  • Hive is an abstraction layer on top of HADOOP that allows for SQL like syntax and semantics.

  • Hides much of the gory details 

  • Very configurable but generally works out of the box.

Getting STARTEd

  • I'm going to run this in interactive mode.
  • Plenty of docs on this. RTFM and start a job.
  • Then do.
ssh -i /home/barrett/EC2/MyKey.pem hadoop@ec2-.xxx.xxx.xx.xx.compute.amazonaws.com
  • Make sure your key is CHMOD 600 and you use the master IP
  • Hive CONSOLE

    • Start  a hive console 
            hadoop@ec2$ hive
            hive> show tables;
            Time Take: 12.02 seocnds
  • You should not have any tables yet
  • PartiTIONS

    • I performed a step I didn't show you
    • I partitioned my sensor records by machine name
    • I have an S3 bucket that looks like :
    • Each directory has a file that only has records for that machine name. 

    Table Creation

    • Why have partitions? Hortizontal Partitioning like regular SQL. Now do...
    CREATE EXTERNAL TABLE sensor_records (
    dq_machine_name string, record_date string, channel_1 float, channel_2 float, channel_3 float)
    PARTITIONED BY (machine_name string)
    LOCATION 's3://bearrito.demos.emr/input/hive/Sensor/';
  • This creates a table with the given schema.
  • You can use most of the atomic types you are familiar with
  • Table Creation

    • We partition by machine_name. Note that the partition column has the form column=value from our S3 bucket. So since our S3 bucket had machine_name =Machine-1 etc. We must use machine_name as the partition column.
    • We indicate the fields are separated the tab char
    • And we point the location at the root of partition folders. Now run..


    SELECT COUNT(*) FROM sensor_records;
    //These won't launch hadoop jobs. 
    //No real computation to perform when partitioned.
    SELECT * FROM sensor_records LIMIT 10;
    SELECT * FROM sensor_records  
    WHERE machine_name = 'Machine-1' LIMIT 10;
    // This will launch jobs. Why is that?
    SELECT * FROM sensor_records  
    WHERE machine_name = 'Machine-1' AND channel_1 < 0.0  LIMIT 10;
    //Since we know the distribution of the channels by machine 
    //we can easily check that aggregation is correct
    // Can anyone guess the result set? Think about the math.
    SELECT machine_name, AVG(channel_1) FROM sensor_records  GROUP BY machine_name;


    Pig Motivation

    • Different compuation model than HIVE
    • Hive is conceptually more SQL like
    • PIG is more of a data flow language

    • Pig allows for a dataflow pipeline
    • Allows for splits in the pipeline
    • Reads and Writes more like a programming language so appeals in that way

    Starting PIG

    • Start an interactive job like with hive
    • Logon to master node
    $ pig
    > pwd
    > cd s3://bearrito.demos.emr/input/0
    > ls
  • Start a session
  • Look around
  • Loading data

    • Import our data
    • Describe it
    • Illustrate it

    SENSOR_RECORDS = LOAD 's3://bearrito.demos.emr/input/0'
    >as (machine_name:chararray,record_date:chararray,channel_1:float,channel_2:float,channel_3:floa
  • You have the primitive types you would expect
  • You haven't evaluated any statements yet
  • Only defined the flow
  • Queries

    • Grouping 



  • Notice the use of the SENSOR_RECORDS bag in the last command
  • You sometimes need to rely on 'illustrate tbl_name'
  • Note the use of the FOREACH...GENERATE
  • This is a key construct
  • Everything is still lazy
  • Queries

    • Sampling + Filtering 


  • Syntax is usually VERB RELATION PREDICATE
  • Queries

    • Bags
    • Collapsing values to tuples and expanding back
    • Fields aren't necessarily atomic. 

    >GENERATE machine_name,
    >TOTUPLE (channel_1,channel_2,channel_3) as channel_tuple;

    >GENERATE machine_name, FLATTEN(channel_tuple) ;


    • Showing syntax is boring for me and you
    • Read the docs at http://pig.apache.org/docs/r0.11.0/basic.html
    • It basically reads and writes like SQL


    • DUMP 




    Whats NEXT

    •  Come to my next talk!

    • Setting up Hadoop and PIG Locally

    • Optimizing PIG

    • I'll cover User Defined Functions in PIG

    • Testing UDF's in PIG

    • The coolest thing since sliced bread : SCALDING

    pittsburgh nosql _ mapreduce

    By bearrito

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