04/19/23 EECS 584, Fall 2011 1

MapReduce: Simplied Data Processing on Large Clusters

Yunxing Dai, Huan Feng

Real world problem

Count the number of occurences of each word in a huge collections of word lists.– sample input: seven book of Harry Potter

Real world problem

Count the number of occurences of each word in a huge collections of word lists.

Word Occurences

The 15414

Good 5435

Never 6546

Tie 694

... ....

Possible solution

Hash table– each entry is key value pair,

(word, occurrence)– scan all the file, put each word into the hash

table

Real world problem--follow up

What if you are given a huge set of files and have access to a large set of machines?

Problem with hash table:– low concurrency – hard to scale

• one node fail, restart all work

MapReduce solution

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Map primitive

Idea from functional language Given a function, apply the function to all

element INDIVIDUALLY in the list, combine the result into a new list

e.g. increment each elems from a list by 1

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Reduce primitive

Idea from functional language Apply a function to all elems from a list,

combine them into a single resule e.g. calculate the sum of a list

Map reduce solution--Single node

Map each single word into a (key, value) pair.– "Good" -> ("Good", 1)

Put together all the pairs that have the same key. Input these pairs to a reduce program. Add the value together – [("Good", 1), ("Good", 1), ("Good", 1)] -> 3

Map reduce solution

Files

in_01

in_02

in_03

in_04

in_05

...

...

...

...

Map reduce solution

Files

in_01

in_02

in_03

in_04

in_05

...

...

...

...

pairs

(The, 1)

(Good, 1)

(The, 1)

(Bad, 1)

(Not, 1)

(The, 1)

...

Map

Map reduce solution

Files

in_01

in_02

in_03

in_04

in_05

...

...

...

...

pairs

(The, 1)

(Good, 1)

(The, 1)

(Bad, 1)

(Not, 1)

(The, 1)

...

Maplist of pairs

[(The, 1), (The, 1), (The, 1)...]

[(Good, 1), (Good, 1), (Good, 1)...]

[(Is, 1), (Is, 1), (Is, 1)...]

[(Therofiery, 1)]

[(Bad, 1), (Bad, 1), (Bad, 1)...]

....

...

...

...

Merge and Sort

Map reduce solution

What if now we are given a huge number of files.

And a large number of machines.

It can be scalable!

Map can be applied to different part of input in parallel.

If part of map tasks failed, just need to restart them instead of restarting all.

Map reduce solution-scalable version

Map : split the file into several parts, apply map function to every part of them.

Shuffle : distribute intemediate result into different buckets according to the hash value of key, assign buckets to several reducers. Each reducer sort the pairs by key.

Reduce : apply the reduce function to all the elements that have the same key and produce the result.

MapReduce

Gerneralized Software framework Users are only responsible for provide

two functions : map and reduce Easy to scale to large amount of

machines.

Split the input files into several pieces

Each piece is assigned to one worker(mapper)

Before sorting, the key value pairs are hashed by key into R buckets.

(The, 1), (Good, 1), (The 1), (Never, 1)

(The, 1), (Good, 1), (The 1), (Never, 1)

(Is, 1), (Is, 1), (Tie, 1), (Work, 1)

(The, 1), (Good, 1), (The 1), (Never, 1)

(Is, 1), (Is, 1), (Tie, 1), (Work, 1)

Each bucket is read by one worker(reducer), then sort and produce the results.

Master program, control the process and assign work to workers

Fault tolerance

worker fail : simply assigned the another worker to do it.

master fail : restart the whole work

Implementation details

Locality – Take location information of input files into

account– assigned a map task to closest machine of

the input data.

Implementation details

Backup tasks – abnormal machines in a task lengthen the total

time.– When a task is almost finished, duplicate

remaining tasks as back-up tasks.– Whenever a primary or a backup execution is

done, mark the task as finished.

TASK1 TASK2 TASK3

In progress

Completed

Implementation details

Backup tasks – abnormal machines in a task lengthen the total

time.– When a task is almost finished, duplicate

remaining tasks as back-up tasks.– Whenever a primary or a backup execution is

done, mark the task as finished.

TASK1 TASK2 TASK3

In progress

Completed

Implementation details

Backup tasks – abnormal machines in a task lengthen the total

time.– When a task is almost finished, duplicate

remaining tasks as back-up tasks.– Whenever a primary or a backup execution is

done, mark the task as finished.

TASK1 TASK2 TASK3

In progress

CompletedTASK1

Back Up

Implementation details

Backup tasks – abnormal machines in a task lengthen the total

time.– When a task is almost finished, duplicate

remaining tasks as back-up tasks.– Whenever a primary or a backup execution is

done, mark the task as finished.

TASK1 TASK2 TASK3

In progress

CompletedTASK1

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Useful Extensions

Partitioning Functions– hash-based or range-based– self-defined partition function

Combiner Function (similar to Reduce Function)– <any, 1> <a, 1> <any, 1> <any ,1>– resolve significant repetition in intermediate outputs

Skipping Bad Records– errors or bugs

– acceptable to ignore a few records

Local Execution– help facilitate debugging, profiling and testing

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Performance & Evaluation

Cluster Configuration– 1800 nodes– 2×2GHz, 4GB memory, 2×160GB IDE, Gb Ethernet link– 2-level tree-shaped switched network

Grep – in 1010 100-byte records– M = 15000 R = 1– take ~150 seconds

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Performance & Evaluation (Sort)

Sort– 1010 100-byte records– M = 15000, R = 4000– Normal, No-Backup, 200 tasks killed

A few things to note– Input rate is higher than the shuffle & output rate– no backup, execution flow is similar except the long tail– kill tasks, the tasks restarted & the rate drop to zero

04/19/23 EECS 584, Fall 2011 37

Application of MapReduce

Broadly applicable– large-scale machine learning problems– clustering problems for Google News– extraction of data & properties– graph computations

Large-Scale Indexing– The indexing code is simpler, smaller (~3800 to ~700)– Indexing process is much easier to operate & easy spead up

MapReduce & Parallel DBMS

MapReduce is not novel at all– a entriely new paradigm?

MapReduce is a step backwards – no schema– no high-level access language

MapReduce Is a poor implementation– no Index– overlook skew– lots of P2P Network traffic in the shuffle phase

Missing features– indexes, updates, transactions

Not compatible to DBMS Tools

MapReduce & Parallel DBMS

MapReduce & Parallel DBMS

Parallel database – has significant performance advantage– take a lot of time to tune and setup– are not general enough (UDFs, UDTs)– SQL is not that easy & straightforward

MapReduce– easy to setup & easy to program– scalable & fault-tolerant– bruteforce solution

What is MapReduce

A parallel programming model /data processing paradigm rather than a complete DBMS– Does not target everything DBMS targets– It's simple but it works

Works for those who– have a lot of data (of some specific type)– UDTs and UDFs are complex to tune– would rather program in sequencial language rather than

SQL– no need to index data because data change all the time– do not need to pay

Questions?