Download - CS5950 Introduction to Cloud Computing SummerII 2015 · failures occurred (application failure, network outage, system crash, etc.) that triggered rerunning some portion of the job

Intro to Cloud Computing, July+August 2015

8/5/15

WiSe Lab www.cs.wmic.edu/wise

Ajay Gupta, WMU-CS 1

CS5950 Introduction to Cloud Computing

SummerII 2015 http://www.cs.wmich.edu/gupta/teaching/cs5950/sumII10cloudComputing/index.html

Ajay Gupta B239, CEAS

Computer Science Department Western Michigan University

[email protected]

276-3104

1 Intro to Cloud Computing 2015

WiSe Lab @ WMU www.cs.wmich.edu/wise

Acknowledgements

•  I have liberally borrowed these slides and material from a number of sources including –  Web –  MIT, Harvard, UMD, UCSD, UW, Clarkson, . . . –  Amazon, Google, IBM, Apache, ManjraSoft,

CloudBook, . . . •  Thanks to original authors including Ives, Dyer,

Lin, Dean, Buyya, Ghemawat, Fanelli, Bisciglia, Kimball, Michels-Slettvet,…

•  If I have missed any, its purely unintentional. My sincere appreciation to those authors and their creative mind.



Today’s Topics

•  More on – MapReduce – Distributed file system

•  Synchronization and coordinating partial results

•  Use of complex keys and values




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THE ARCHITECTURE OF MAPREDUCE

Intro to Cloud Computing 2015

4 WiSe Lab @ WMU www.cs.wmich.edu/wise

What is MapReduce? •  A famous distributed programming model •  In many circles, considered the key building block for

much of Google’s data analysis –  Example of usage rates for one language that compiled to

MapReduce, in 2010: –  … [O]n one dedicated Workqueue cluster with 1500 Xeon CPUs, there were 32,580 Sawzall

jobs launched, using an average of 220 machines each. While running those jobs, 18,636 failures occurred (application failure, network outage, system crash, etc.) that triggered rerunning some portion of the job. The jobs read a total of 3.2x1015 bytes of data (2.8PB) and wrote 9.9x1012 bytes (9.3TB).

– Other similar languages: Yahoo’s Pig Latin and Pig; Microsoft’s Dryad

•  Cloned in open source: Hadoop, http://hadoop.apache.org/ •  Many “successors” now – just google and find out!



The MapReduce programming model •  Simple distributed functional programming

primitives •  Modeled after Lisp primitives:

–  map (apply function to all items in a collection) and –  reduce (apply function to set of items with a common

key) •  We start with:

–  A user-defined function to be applied to all data, map: (key,value) à (key, value)

–  Another user-specified operation reduce: (key, {set of values}) à result

–  A set of n nodes, each with data •  All nodes run map on all of their data, producing

new data with keys –  This data is collected by key, then shuffled, and finally reduced –  Dataflow is through temp files on DFS




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Simple example: Word count

•  Goal: Given a set of documents, count how often each word occurs –  Input: Key-value pairs (document:lineNumber, text) –  Output: Key-value pairs (word, #occurrences) –  What should be the intermediate key-value pairs?

Intro to Cloud Computing

2015

map(String key, String value) { // key: document name, line no // value: contents of line }

reduce(String key, Iterator values) { }

for each word w in value: emit(w, "1")

// key: a word // values: a list of counts int result = 0; for each v in values: result += ParseInt(v); emit(key, result)




8

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9

Simple example: Word count

9 University of Pennsylvania

Mapper (1-2)

Mapper (3-4)

Mapper (5-6)

Mapper (7-8)

Reducer (A-G)

Reducer (H-N)

Reducer (O-U)

Reducer (V-Z)

(1, the apple)

(2, is an apple) (3, not an orange) (4, because the) (5, orange)

(6, unlike the apple) (7, is orange) (8, not green)

(the, 1)

(apple, 1)

(is, 1)

(apple, 1) (an, 1)

(not, 1)

(orange, 1)

(an, 1) (because, 1)

(the, 1) (orange, 1)

(unlike, 1)

(apple, 1)

(the, 1)

(is, 1)

(orange, 1)

(not, 1)

(green, 1)

(apple, 3) (an, 2)

(because, 1) (green, 1)

(is, 2) (not, 2)

(orange, 3) (the, 3)

(unlike, 1)

(apple, {1, 1, 1}) (an, {1, 1})

(because, {1}) (green, {1})

(is, {1, 1}) (not, {1, 1})

(orange, {1, 1, 1}) (the, {1, 1, 1})

(unlike, {1})

Each mapper receives some of the KV-pairs as input

The mappers process the KV-pairs one by one

Each KV-pair output by the mapper is sent to the reducer that is responsible for it

The reducers sort their input by key and group it

The reducers process their input one group at a time

1 2 3 4 5

Key range the node is responsible for



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MapReduce dataflow


Mapper

Mapper

Mapper

Mapper

Reducer

Reducer

Reducer

Reducer

Inpu

t dat

a

Out

put d

ata

"The Shuffle"

Intermediate (key,value) pairs

What is meant by a 'dataflow'? What makes this so scalable?


More examples •  Distributed grep – all lines matching a pattern

–  Map: filter by pattern –  Reduce: output set

•  Count URL access frequency –  Map: output each URL as key, with count 1 –  Reduce: sum the counts

•  Reverse web-link graph – Map: output (target,source) pairs when link

to target found in source – Reduce: concatenates values and emits

(target,list(source)) •  Inverted index

– Map: Emits (word,documentID) – Reduce: Combines these into

(word,list(documentID)) 11



Common mistakes to avoid •  Mapper and reducer should be stateless

–  Don't use static variables - after map + reduce return, they should remember nothing about the processed data!

–  Reason: No guarantees about which key-value pairs will be processed by which workers!

•  Don't try to do your own I/O! –  Don't try to write to files in the file system –  The MapReduce framework does the I/O

for you (usually):

•  All the incoming data will be fed as arguments to map and reduce

•  Any data your functions produce should be output via emit

12

HashMap h = new HashMap(); map(key, value) { if (h.contains(key)) { h.add(key,value); emit(key, "X"); } } Wrong!

map(key, value) { File foo = new File("xyz.txt"); while (true) { s = foo.readLine(); ... } } Wrong!




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More common mistakes to avoid

•  Mapper must not map too much data to the same key –  In particular, don't map everything to the same key!!

Otherwise the reduce worker will be overwhelmed! –  It's okay if some reduce workers have more work than others

•  Example: In WordCount, the reduce worker that works on the key 'and' has a lot more work than the reduce worker that works on 'argute'.


map(key, value) { emit("FOO", key + " " + value); }

reduce(key, value[]) { /* do some computation on all the values */ }

Wrong!


Designing MapReduce algorithms •  Key decision: What should be done by map, and what by reduce?

–  map can do something to each individual key-value pair, but it can't look at other key-value pairs

•  Example: Filtering out key-value pairs we don't need –  map can emit more than one intermediate key-value pair for each

incoming key-value pair •  Example: Incoming data is text, map produces (word,1) for

each word –  reduce can aggregate data; it can look at multiple values, as

long as map has mapped them to the same (intermediate) key •  Example: Count the number of words, add up the total cost, ...

•  Need to get the intermediate format right! –  If reduce needs to look at several values together, map

must emit them using the same key!




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More details on the MapReduce data flow

15

Data partitions by key

Map computation partitions

Reduce computation partitions

Redistribution by output’s key ("shuffle")

Coordinator

University of Pennsylvania

(Default MapReduce uses a file system)



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Some additional details •  To make this work, we need a few more parts…

•  The file system (distributed across all nodes): –  Stores the inputs, outputs, and temporary results

•  The driver program (executes on one node): –  Specifies where to find the inputs and the outputs –  Specifies what mapper and reducer to use –  Can customize behavior of the execution

•  The runtime system (controls nodes): –  Supervises the execution of tasks –  Esp. JobTracker

16 Intro to Cloud Computing


Some details •  Fewer computation partitions than data partitions

–  All data is accessible via a distributed filesystem with replication

–  Worker nodes produce data in key order (makes it easy to merge)

–  The master is responsible for scheduling, keeping all nodes busy

–  The master knows how many data partitions there are, which have completed – atomic commits to disk

•  Locality: Master tries to do work on nodes that have replicas of the data

•  Master can deal with stragglers (slow machines) by re-executing their tasks somewhere else

17 Intro to Cloud Computing


What if a worker crashes? •  We rely on the file system being shared across all the

nodes •  Two types of (crash) faults:

–  Node wrote its output and then crashed •  Here, the file system is likely to have a copy of

the complete output –  Node crashed before finishing its output

•  The JobTracker sees that the job isn’t making progress, and restarts the job elsewhere on the system

•  (Of course, we have fewer nodes to do work…) •  But what if the master crashes?




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Other challenges •  Locality

–  Try to schedule map task on machine that already has data

•  Task granularity –  How many map tasks? How many reduce tasks?

•  Dealing with stragglers –  Schedule some backup tasks

•  Saving bandwidth –  E.g., with combiners

•  Handling bad records –  "Last gasp" packet with current sequence number



Stay tuned

Next you will learn about: Programming in MapReduce (cont.)



Managing Dependencies

•  Remember: Mappers run in isolation –  You have no idea in what order the mappers run –  You have no idea on what node the mappers run –  You have no idea when each mapper finishes

•  Tools for synchronization: –  Ability to hold state in reducer across multiple key-

value pairs –  Sorting function for keys –  Partitioner –  Cleverly-constructed data structures




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Motivating Example

•  Term co-occurrence matrix for a text collection –  M = N x N matrix (N = vocabulary size) –  mij: number of times i and j co-occur in some context

(for concreteness, let’s say context = sentence)

•  Why? –  Distributional profiles as a way of measuring semantic

distance –  Semantic distance useful for many language

processing tasks

“You shall know a word by the company it keeps” (Firth, 1957)

e.g., Mohammad and Hirst (EMNLP, 2006) 22 Intro to Cloud Computing 2015


MapReduce: Large Counting Problems

•  Term co-occurrence matrix for a text collection = specific instance of a large counting problem –  A large event space (number of terms) –  A large number of events (the collection itself) –  Goal: keep track of interesting statistics about the

events •  Basic approach

–  Mappers generate partial counts –  Reducers aggregate partial counts

How do we aggregate partial counts efficiently?



First Try: “Pairs”

•  Each mapper takes a sentence: – Generates all co-occurring term pairs – For all pairs, emit (a, b) → count

•  Reducers sum up counts associated with these pairs

•  Use combiners!

Note: in all my slides for this example, I denote a key-value pair as k → v




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“Pairs” Analysis

•  Advantages – Easy to implement, easy to understand

•  Disadvantages – Lots of pairs to sort and shuffle around (upper

bound?)



Another Try: “Stripes” •  Idea: group together pairs into an associative array

•  Each mapper takes a sentence: –  Generate all co-occurring term pairs –  For each term, emit a → { b: countb, c: countc, d: countd … }

•  Reducers perform element-wise sum of associative arrays

(a, b) → 1 (a, c) → 2 (a, d) → 5 (a, e) → 3 (a, f) → 2

a → { b: 1, c: 2, d: 5, e: 3, f: 2 }

a → { b: 1, d: 5, e: 3 } a → { b: 1, c: 2, d: 2, f: 2 } a → { b: 2, c: 2, d: 7, e: 3, f: 2 }

+



“Stripes” Analysis

•  Advantages – Far less sorting and shuffling of key-value

pairs – Can make better use of combiners

•  Disadvantages – More difficult to implement – Underlying object is more heavyweight – Fundamental limitation in terms of size of

event space 27 Intro to Cloud Computing



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Cluster size: 38 cores Data Source: Associated Press Worldstream (APW) of the English Gigaword Corpus (v3), which contains 2.27 million documents (1.8 GB compressed, 5.7 GB uncompressed)



Conditional Probabilities

•  How do we compute conditional probabilities from counts?

•  How do we do this with MapReduce?

∑==

')',(count),(count

)(count),(count)|(

BBABA

ABAABP



P(B|A): “Pairs”approach

For this to work: – Must emit extra (a, *) for every bn in mapper – Must make sure all a’s get sent to same

reducer (use Partitioner) – Must make sure (a, *) comes first (define sort

order)

(a, b1) → 3 (a, b2) → 12 (a, b3) → 7 (a, b4) → 1 …

(a, *) → 32

(a, b1) → 3 / 32 (a, b2) → 12 / 32 (a, b3) → 7 / 32 (a, b4) → 1 / 32 …

Reducer holds this value in memory




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P(B|A): “Stripes” approach

•  Easy! – One pass to compute (a, *) – Another pass to directly compute P(B|A)

a → {b1:3, b2 :12, b3 :7, b4 :1, … }



Synchronization in Hadoop •  Approach 1: turn synchronization into an ordering

problem –  Sort keys into correct order of computation –  Partition key space so that each reducer gets the appropriate set

of partial results –  Hold state in reducer across multiple key-value pairs to perform

computation –  Illustrated by the “pairs” approach

•  Approach 2: construct data structures that “bring the pieces together” –  Each reducer receives all the data it needs to complete the

computation –  Illustrated by the “stripes” approach



Issues and Tradeoffs

•  Number of key-value pairs –  Object creation overhead –  Time for sorting and shuffling pairs across the

network •  Size of each key-value pair

–  De/serialization overhead

•  Combiners make a big difference! –  RAM vs. disk and network –  Arrange data to maximize opportunities to aggregate

partial results




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Data Types in Hadoop Writable Defines a de/serialization protocol.

Every data type in Hadoop is a Writable.

WritableComparable Defines a sort order. All keys must be of this type (but not values).

IntWritable LongWritable Text …

Concrete classes for different data types.



Complex Data Types in Hadoop •  How do you implement complex data types? •  The easiest way:

–  Encoded it as Text, e.g., (a, b) = “a:b” –  Use regular expressions to parse and extract data –  Works, but pretty hack-ish

•  The hard way: –  Define a custom implementation of WritableComparable –  Must implement: readFields, write, compareTo –  Computationally efficient, but slow for rapid prototyping

•  Alternatives: –  Cloud9 offers two other choices: Tuple and JSON



Next time

•  Info retrievals •  Examples




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Questions?



Download - CS5950 Introduction to Cloud Computing SummerII 2015 · failures occurred (application failure, network outage, system crash, etc.) that triggered rerunning some portion of the job

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