MAP REDUCE BASICS CHAPTER 2

Basics• Divide and conquer– Partition large problem into smaller subproblems– Worker work on subproblems in parallel• Threads in a core, cores in multi-core processor,

multiple processor in a machine, machines in a cluster– Combine intermediate results from worker to final result– Issues• How break up into smaller tasks• Assign tasks to workers• Workers get data needed• Coordinate synchronization among workers• Share partial results• Do all if SE errors and HW faults?

Basics

• MR – abstraction that hides system-level details from programmer

• Move code to data– Spread data across disks– DFS manages storage

Topics

• Functional programming• MapReduce• Distributed file system

Functional Programming Roots

• MapReduce = functional programming plus distributed processing on steroids – Not a new idea… dates back to the 50’s (or even 30’s)

• What is functional programming?– Computation as application of functions– Computation is evaluation of mathematical functions– Avoids state and mutable data– Emphasizes application of functions instead of changes in

state

Functional Programming Roots

• How is it different?– Traditional notions of “data” and “instructions” are not

applicable– Data flows are implicit in program– Different orders of execution are possible– Theoretical foundation provided by lambda calculus

• a formal system for function definition

• Exemplified by LISP, Scheme

Overview of Lisp

• Functions written in prefix notation

(+ 1 2) 3 (* 3 4) 12(sqrt ( + (* 3 3) (* 4 4))) 5(define x 3) x(* x 5) 15

Functions

• Functions = lambda expressions bound to variablesExample expressed with lambda:(+ 1 2) 3 λxλy.x+y

• Above expression is equivalent to:

• Once defined, function can be applied:

(define (foo x y) (sqrt (+ (* x x) (* y y))))

(define foo (lambda (x y) (sqrt (+ (* x x) (* y y)))))

(foo 3 4) 5

Functional Programming Roots

• Two important concepts in functional programming– Map: do something to everything in a list– Fold: combine results of a list in some way

Functional Programming Map

• Higher order functions – accept other functions as arguments– Map• Takes a function f and its argument, which is a list• applies to all elements in list• Returns a list as result

• Lists are primitive data types – [1 2 3 4 5]– [[a 1] [b 2] [c 3]]

Map/Fold in Action• Simple map example:

(map (lambda (x) (* x x)) [1 2 3 4 5]) [1 4 9 16 25]

Functional Programming Reduce

– Fold• Takes function g, which has 2 arguments: an initial

value and a list. • The g applied to initial value and 1st item in list• Result stored in intermediate variable• Intermediate variable and next item in list 2nd

application of g, etc.• Fold returns final value of intermediate variable

Map/Fold in Action• Simple map example:

• Fold examples:

• Sum of squares:

(map (lambda (x) (* x x)) [1 2 3 4 5]) [1 4 9 16 25]

(fold + 0 [1 2 3 4 5]) 15(fold * 1 [1 2 3 4 5]) 120

(define (sum-of-squares v) // where v is a list (fold + 0 (map (lambda (x) (* x x)) v)))

(sum-of-squares [1 2 3 4 5]) 55

Functional Programming Roots

• Use map/fold in combination• Map – transformation of dataset• Fold- aggregation operation• Can apply map in parallel• Fold – more restrictions, elements must be

brought together– Many applications do not require g be applied to

all elements of list, fold aggregations in parallel

Functional Programming Roots

• Map in MapReduce is same as in functional programming

• Reduce corresponds to fold• 2 stages:– User specified computation applied over all input,

can occur in parallel, return intermediate output– Output aggregated by another user-specified

computation

Mappers/Reducers

• Key-value pair (k,v) – basic data structure in MR

• Keys, values – int, strings, etc., user defined– e.g. keys – URLs, values – HTML content– e.g. keys – node ids, values – adjacency lists of

nodesMap: (k1, v1) -> [(k2, v2)]Reduce: (k2, [v2]) -> [(k3, v2)]

Where […] denotes a list

General Flow• Apply mapper to every input key-value pair stored in

DFS• Generate arbitrary number of intermediate (k,v)• Distributed group by operation (shuffle) on intermediate

keys• Sort intermediate results by key (not across reducers)• Aggregate intermediate results• Generate final output to DFS – one file per reducer

Map

Reduce

What function is implemented?

Example: unigram (word count)

• (docid, doc) on DFS, doc is text• Mapper tokenizes (docid, doc), emits (k,v) for

every word – (word, 1)• Execution framework all same keys brought

together in reducer• Reducer – sums all counts (of 1) for word• Each reduce writes to one file• Words within file sorted, file same # words• Can use output as input to another MR

Combine - Bandwidth Optimization

• Issue: large number of key-value pairs– Example – word count (word, 1)– If copy across network intermediate data > input

• Solution: use Combiner functions– allow local aggregation (after mapper) before shuffle sort

• Word Count - Aggregate (count each word locally)– intermediate = # unique words

– Executed on same machine as mapper – no output from other mappers

– Results in a “mini-reduce” right after the map phase– (k,v) of same type as input/output– If operation associative and commutative, reduce can be combiner

• Reduces key-value pairs to save bandwidth

Partitioners – Load Balance

• Issue: Intermediate results all on one reducer• Solution: use Partitioner functions – divide up intermediate key space and assign (k,v) to

reducers– Specifies task to which copy (k,v)– Reducer processes keys in sorted order– Partitioner computes hash value of key, takes mod of

value with # reducers• Hopefully same number of each to each reducer

• But may be- Zipfian

MapReduce• Programmers specify two functions:

map (k, v) → <k’, v’>*reduce (k’, v’) → <k’, v’>*– All v’ with the same k’ are reduced together

• Usually, programmers also specify:partition (k’, number of partitions ) → partition for k’– Often a simple hash of the key, e.g. hash(k’) mod n– Allows reduce operations for different keys in parallel

• Implementations:– Google has a proprietary implementation in C++– Hadoop is an open source implementation in Java (lead by

Yahoo)

Its not just Map and Reduce• Apply mapper to every input key-value pair stored in

DFS• Generate arbitrary number of intermediate (k,v)• Aggregate locally • Assign to reducers• Distributed group by operation (shuffle) on intermediate

keys• Sort intermediate results by key (not across reducers)• Aggregate intermediate results• Generate final output to DFS – one file per reducer

Map

Reduce

Combine

Partition

Execution Framework

• MapReduce program (job) contains• Code for mappers• Combiners • Partitioners• Code for reducers• Configuration parameters (where is input, store output)

– Execution framework takes care of everything else– Developer submits job to submission node of

cluster (jobtracker)

Recall these problems?

• How do we assign work units to workers?• What if we have more work units than workers?• What if workers need to share partial results?• How do we aggregate partial results?• How do we know all the workers have finished?• What if workers die?

Execution Framework

• Scheduling– Job divided into tasks (certain block of (k,v) pairs)– Can have 1000s jobs need to be assigned– May exceed number that can run concurrently– Task queue– Coordination among tasks from different jobs

Execution Framework

• Speculative execution• Map phase only as fast as?– slowest map task• Problem: Stragglers, flaky hardware• Solution: Use speculative execution:–Exact copy of same task on different machine–Uses result of fastest task in attempt to finish–Better for map or reduce?–Can improve running time by 44% (Google)–Doesn’t help if skewed in distributed of values

Execution Framework

• Data/code co-location– Execute near data– It not possible must stream data• Try to keep within same rack

Execution Framework

• Synchronization– Concurrently running processes join up– Intermediate (k,v) grouped by key, copy intermediate data over network, shuffle/sort

• Number of copy operations? Worst case:–M X R copy operations

• Each mapper may send intermediate results to every reducer

– Reduce computation cannot start until all mappers finished, (k,v) shuffled/sorted• Differs from functional programming

– Can copy intermediate (k,v) over network to reducer when mapper finishes

Execution Framework

• Error/fault handling– The norm– Disk failures, RAM errors, datacenter outages– Software errors– Corrupted data

Differences in MapReduce Implementations

• Hadoop (Apache) vs. Google– Hadoop - Values arbitrarily ordered, can change key

in reducer– Google – program can specify 2ndary sort, can’t

change key in reducer• Hadoop– Programmer can specify number of map tasks, but

framework makes final decision– In reduce, programmer specified number of tasks is

used

Hadoop

• Careful using external resources (e.g. bottleneck querying SQL DB)

• Mappers can emit arbitrary number of intermediate (k,v), can be of different type

• Reduce can emit artibtraty number of final (k,v) and can be of different type than intermediate (k,v)

• Different from functional programming, can have side effects (state change internal – may cause problems, external may write to files)

• MapReduce can have no reduce, but must have mapper– Can just pass identity function to reducer– May not have any input (compute pi)

Other Sources

• Other source can serve as source/destination for data from MapReduce– Google – BigTable– Hbase – BigTable clone– Hadoop – integrated RDB with parallel processing,

can write to DB tables

Distributed File System (DFS)

• In HPC, storage distinct from computation• NAS (network attached storage) and SAN are common– Separate, dedicated nodes for storage

• Fetch, load, process, write• Bottleneck– Higher performance networks $$ (10G Ethernet), special

purpose interconnects $$$ (InfiniBand)• $$ increases non-linearly

– In GFS Computation and storage not distinct components

Hadoop Distributed File System - HDFS• GFS supports proprietary MapReduce• HDFS – supports Hadoop• Don’t have to run GFS on DFS, but misses

advantages• Difference in GFS and HDFS vs. DFS:– Adapted to large data processing– divide user data into chunks/blocks - LARGE– Replicate these across the local disk nodes in

cluster– Master-slave architecture

HDFS vs GFS (Google File System)

• Difference in HDFS:– Master-slave architecture• GFS: Master (master), slave (chunkserver)• HDFS: master (namenode), slave (datanode)

– Master – namespace (metadata, directory structure, file to block mapping, location of blocks, access permission)

– Slaves – manage actual data blocks– Client contacts namespace, gets data from slaves, 3 copies of

each block, etc.– Block is 64 MB– Initially Files were immutable – once closed cannot be

modified

HDFS

• Namenode– Namespace management– Coordinate file operations

• Lazy garbage collection

– Maintain file system health• Heartbeats, under-replication, balancing

• Supports subset of POSIX API, pushed to application

• No Security

Hadoop Cluster Architecture

• HDFS namenode runs daemon• Job submission node runs jobtracker – point of contact run MapReduce– Monitors progress of MapReduce jobs,

coordinates Mappers and reducers• Slaves run tasktracker– Runs users code, datanode daemon, serve HDFS

data– Send heartbeat messages to jobtracker

Hadoop Cluster Architecture

• Number of reduce tasks depends on reducers specified by programmer

• Number of map tasks depends on– Hint from programmer– Number of input files– Number of HDFS data blocks of files

Hadoop Cluster Architecture

• Map tasks assigned– (k,v) called input split• Input splits computed automatically• Aligned on HDFS boundaries so associated with single

block, simplifies scheduling• Data locality, if not stream across network (same rack if

possible)

• How can we use MapReduce to solve problems?

Hadoop Cluster Architecture

• Mappers in Hadoop– Javaobjects with a MAP method– Mapper object instantiated for every map task by tasktracker– Life cycle – instantiation, hook in API for program specified

code• Mappers can load state, static data sources, dictionaries, etc.

– After initialization: MAP method called by framework on all (k,v) in input split

– Method calls within same Java object, can preserve state across multiple (k,v) in same task

– Can run programmer specified termination code

Hadoop Cluster Architecture

• Reducers in Hadoop– Execution similar to that of mappers• Instantiation, initialization, framework calls REDUCE

method with intermediate key and iterator over all key values• Intermediate keys in sorted order• Can preserve state across multiple intermediate keys

CAP Theorem

• Consistency, availability, partition tolerance• Cannot satisfy all 3• Partitioning unavoidable in large data systems,

must trade off availability and consistency– If master fails, system is unavailable so consistent!– If multiple masters, more available, but inconsistent

• Workaround to single namenode– Warm standby namenode– Hadoop community working on it