The MapReduce EnvironmentDistributed File SystemsOverview of the DFS EcologyMapReduce and Hadoop

Jeffrey D. UllmanStanford University

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Distributed File SystemsChunkingReplicationDistribution on Racks

Commodity Clusters Datasets can be very large.

Tens to hundreds of terabytes. Cannot process on a single server.

Standard architecture emerging: Cluster of commodity Linux nodes (compute nodes). Gigabit Ethernet interconnect.

How to organize computations on this architecture? Mask issues such as hardware failure.

Cluster Architecture

Mem

Disk

CPU

Mem

Disk

CPU

…

Switch

Each rack contains 16-64 nodes

Mem

Disk

CPU

Mem

Disk

CPU

…

Switch

Switch1 Gbps between any pair of nodesin a rack

2-10 Gbps backbone between racks

Stable Storage First order problem: if nodes can fail, how can

we store data persistently? Answer: Distributed File System.

Provides global file namespace. Examples: Google GFS, Colossus; Hadoop HDFS.

Typical usage pattern: Huge files. Data is rarely updated in place. Reads and appends are common.

Distributed File System Chunk Servers.

File is split into contiguous chunks, typically 64MB. Each chunk replicated (usually 2x or 3x). Try to keep replicas in different racks. Alternative: Erasure coding.

Master Node for a file. Stores metadata, location of all chunks. Possibly replicated.

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Compute Nodes Organized into racks. Intra-rack connection typically gigabit speed. Inter-rack connection faster by a small factor.

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Racks of Compute Nodes

File

Chunks

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3-way replication offiles, with copies ondifferent racks.

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Above the DFSMapReduceKey-Value StoresSQL Implementations

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The New Stack

Distributed File System

MapReduce, e.g.Hadoop

Object Store (key-valuestore), e.g., BigTable,

Hbase, Cassandra

SQL Implementations,e.g., PIG (relational

algebra), HIVE

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MapReduce Systems MapReduce (Google) and open-source (Apache)

equivalent Hadoop. Important specialized parallel computing tool. Cope with compute-node failures.

Avoid restart of the entire job.

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Key-Value Stores BigTable (Google), Hbase, Cassandra (Apache),

Dynamo (Amazon). Each row is a key plus values over a flexible set of

columns. Each column component can be a set of values.

Example: Structure of the Web. Key is a URL. One column is a set of URL’s – those linked to the

page represented by the key. A second column is the set of URL’s linking to the key.

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SQL-Like Systems PIG – Yahoo! implementation of relational

algebra. Translates to a sequence of map-reduce

operations, using Hadoop. Hive – open-source (Apache) implementation

of a restricted SQL, called QL, over Hadoop.

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SQL-Like Systems – (2) Sawzall – Google implementation of parallel

select + aggregation, but using C++. Dremel – (Google) real restricted SQL, column

oriented store. F1 – (Google) row-oriented, conventional, but

massive scale. Scope – Microsoft implementation of restricted

SQL.

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MapReduceFormal DefinitionImplementationFault-ToleranceExamples: Word-Count, Join

MapReduce Input: a set of key/value pairs. User supplies two functions:

map(k,v) set(k1,v1) reduce(k1, list(v1)) set(v2)

Technically, the input consists of key-value pairs of some type, but usually only the value is important.

(k1,v1) is an intermediate key/value pair. Output is the set of (k1,v2) pairs.

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Map Tasks and Reduce Tasks MapReduce job =

Map function (inputs -> key-value pairs) + Reduce function (key and list of values -> outputs).

Map and Reduce Tasks apply Map or Reduce function to (typically) many of their inputs. Unit of parallelism.

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Behind the Scenes The Map tasks generate key-value pairs.

Each takes one or more chunks of input from the distributed file system.

The system takes all the key-value pairs from all the Map tasks and sorts them by key.

Then, it forms key-(list-of-associated-values) pairs and passes each key-(value-list) pair to one of the Reduce tasks.

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MapReduce Pattern

Maptasks

Reducetasks

InputfromDFS

Outputto DFS

“key”-value pairs

Example: Word Count We have a large file documents, which are

sequences of words. Count the number of times each distinct word

appears in the file.

Word Count Using MapReduce

map(key, value):// key: document name; value: text of document

FOR (each word w in value)emit(w, 1);

reduce(key, value-list):// key: a word; value: an iterator over value-list

result = 0;FOR (each count v on value-list)

result += v;emit(result);

Distributed Execution Overview

UserProgram

Worker

Worker

Master

Worker

Worker

Worker

fork fork fork

assignmap assign

reduce

readlocalwrite

remoteread,sort

OutputFile 0

OutputFile 1

writeChunk 0Chunk1Chunk 2

Input Data

Data Management Input and final output are stored in the

distributed file system. Scheduler tries to schedule Map tasks “close” to

physical storage location of input data – preferably at the same node.

Intermediate results are stored on local file storage of Map and Reduce workers.

The Master Task Maintain task status: (idle, active, completed). Idle tasks get scheduled as workers become

available. When a Map task completes, it sends the

Master the location and sizes of its intermediate files, one for each Reduce task.

Master pushes location of intermediates to Reduce tasks.

Master pings workers periodically to detect failures.

How Many Map and Reduce Tasks? Rule of thumb: Use several times more Map

tasks and Reduce tasks than the number of compute nodes available. Minimizes skew caused by different tasks taking

different amounts of time. One DFS chunk per Map task is common.

Combiners Often a Map task will produce many pairs of the

form (k,v1), (k,v2), … for the same key k. E.g., popular words in Word Count.

Can save communication time by applying Reduce function to values with the same key at the Map task. Called a combiner.

Works only if Reduce function is commutative and associative.

Partition Function We need to assure that records with the same

intermediate key end up at the same Reduce task.

System uses a default partition function e.g., hash(key) mod R, if there are R Reduce tasks.

Sometimes useful to override. Example: hash(hostname(URL)) mod R ensures URLs

from a host end up at the same Reduce task and therefore appear together in the output.

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Coping With Failures MapReduce is designed to deal with

compute nodes failing to execute a task. Re-executes failed tasks, not whole jobs. Failure modes:

1. Compute-node failure (e.g., disk crash).2. Rack communication failure.3. Software failures, e.g., a task requires Java n;

node has Java n-1.

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Things MapReduce is Good At1. Matrix-Matrix and Matrix-vector

multiplication. One step of the PageRank iteration was the original

application.2. Relational algebra operations.

We’ll do an example of the join.3. Many other “embarrassingly parallel”

operations.

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Review of Terminology Map-Reduce job =

Map function (inputs -> key-value pairs) + Reduce function (key and list of values -> outputs).

Map and Reduce Tasks apply Map or Reduce function to (typically) many of their inputs. Unit of parallelism.

Mapper = application of the Map function to a single input.

Reducer = application of the Reduce function to a single key-(list of values) pair.

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Example: Natural Join Join of R(A,B) with S(B,C) is the set of tuples

(a,b,c) such that (a,b) is in R and (b,c) is in S. Mappers need to send R(a,b) and S(b,c) to the

same reducer, so they can be joined there. Mapper output: key = B-value, value = relation

and other component (A or C). Example: R(1,2) -> (2, (R,1))

S(2,3) -> (2, (S,3))

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Mapping Tuples

Mapper for

R(1,2)R(1,2) (2, (R,1))

Mapper for

R(4,2)R(4,2)

Mapper for

S(2,3)S(2,3)

Mapper for

S(5,6)S(5,6)

(2, (R,4))

(2, (S,3))

(5, (S,6))

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Grouping Phase There is a reducer for each key. Every key-value pair generated by any mapper is

sent to the reducer for its key.

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Mapping Tuples

Mapper for

R(1,2)(2, (R,1))

Mapper for

R(4,2)

Mapper for

S(2,3)

Mapper for

S(5,6)

(2, (R,4))

(2, (S,3))

(5, (S,6))

Reducerfor B = 2

Reducerfor B = 5

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Constructing Value-Lists The input to each reducer is organized by the

system into a pair: The key. The list of values associated with that key.

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The Value-List Format

Reducerfor B = 2

Reducerfor B = 5

(2, [(R,1), (R,4), (S,3)])

(5, [(S,6)])

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The Reduce Function for Join Given key b and a list of values that are either

(R, ai) or (S, cj), output each triple (ai, b, cj). Thus, the number of outputs made by a reducer is

the product of the number of R’s on the list and the number of S’s on the list.

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Output of the Reducers

Reducerfor B = 2

Reducerfor B = 5

(2, [(R,1), (R,4), (S,3)])

(5, [(S,6)])

(1,2,3), (4,2,3)