大规模数据处理 / 云计算 Lecture 2 – Mapreduce System

彭波北京大学信息科学技术学院

4/22/2011http://net.pku.edu.cn/~course/cs402/

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Jimmy LinUniversity of Maryland 课程建设 SEWMGroup

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Outline

• 2003 "The Google file system," in sosps. Bolton Landing, NY, USA: ACM Press, 2003.

• 2004 "MapReduce: Simplified Data Processing on Large Clusters," in Osdi, 2004,

MapReduce Basic

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Typical Large-Data Problem Iterate over a large number of records

Extract something of interest from each

Shuffle and sort intermediate results

Aggregate intermediate results

Generate final output

Key idea: provide a functional abstraction for these two operations

Map

Reduce

(Dean and Ghemawat, OSDI 2004)

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MapReduce Programmers specify two functions:

map (k, v) → <k’, v’>*reduce (k’, v’) → <k’ ’, v’ ’>* All values with the same key are sent to the same reducer

The execution framework handles everything else…

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mapmap map map

Shuffle and Sort: aggregate values by keys

reduce reduce reduce

k1 k2 k3 k4 k5 k6v1 v2 v3 v4 v5 v6

ba 1 2 c c3 6 a c5 2 b c7 8

a 1 5 b 2 7 c 2 3 6 8

r1 s1 r2 s2 r3 s3

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MapReduce Programmers specify two functions:

map (k, v) → <k’, v’>*reduce (k’, v’) → <k’, v’>* All values with the same key are sent to the same reducer

The execution framework handles everything else…

What’s “everything else”?

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MapReduce “Runtime” Handles scheduling

Assigns workers to map and reduce tasks

Handles “data distribution” Moves processes to data

Handles synchronization Gathers, sorts, and shuffles intermediate data

Handles errors and faults Detects worker failures and restarts

Everything happens on top of a distributed FS (later)

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MapReduce Programmers specify two functions:

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

The execution framework handles everything else… Not quite…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 Divides up key space for parallel reduce operationscombine (k’, v’) → <k’, v’>* Mini-reducers that run in memory after the map phase Used as an optimization to reduce network traffic

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combinecombine combine combine

ba 1 2 c 9 a c5 2 b c7 8

partition partition partition partition

mapmap map map

k1 k2 k3 k4 k5 k6v1 v2 v3 v4 v5 v6

ba 1 2 c c3 6 a c5 2 b c7 8

Shuffle and Sort: aggregate values by keys

reduce reduce reduce

a 1 5 b 2 7 c 2 9 8

r1 s1 r2 s2 r3 s3

c 2 3 6 8

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Two more details… Barrier between map and reduce phases

But we can begin copying intermediate data earlier

Keys arrive at each reducer in sorted order No enforced ordering across reducers

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“Hello World”: Word Count

Map(String docid, String text): for each word w in text: Emit(w, 1);

Reduce(String term, Iterator<Int> values): int sum = 0; for each v in values: sum += v; Emit(term, value);

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MapReduce can refer to… The programming model

The execution framework (aka “runtime”)

The specific implementation

Usage is usually clear from context!

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MapReduce Implementations Google has a proprietary implementation in C++

Bindings in Java, Python

Hadoop is an open-source implementation in Java An Apache project Large contribution of development led by Yahoo, used in

production Rapidly expanding software ecosystem

Lots of custom research implementations For GPUs, cell processors, etc.

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split 0

split 1

split 2

split 3

split 4

worker

worker

worker

worker

worker

Master

UserProgram

outputfile 0

outputfile 1

(1) submit

(2) schedule map (2) schedule reduce

(3) read(4) local write

(5) remote read(6) write

Inputfiles

Mapphase

Intermediate files(on local disk)

Reducephase

Outputfiles

Adapted from (Dean and Ghemawat, OSDI 2004)

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How do we get data to the workers?

Compute Nodes

NAS

SAN

What’s the problem here?

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Distributed File System Don’t move data to workers… move workers to the data!

Store data on the local disks of nodes in the cluster Start up the workers on the node that has the data local

Why? Not enough RAM to hold all the data in memory Disk access is slow, but disk throughput is reasonable

A distributed file system is the answer GFS (Google File System) for Google’s MapReduce HDFS (Hadoop Distributed File System) for Hadoop

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GFS: Assumptions Commodity hardware over “exotic” hardware

Scale “out”, not “up”

High component failure rates Inexpensive commodity components fail all the time

“Modest” number of huge files Multi-gigabyte files are common, if not encouraged

Files are write-once, mostly appended to Perhaps concurrently

Large streaming reads over random access High sustained throughput over low latency

GFS slides adapted from material by (Ghemawat et al., SOSP 2003)

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GFS: Design Decisions Files stored as chunks

Fixed size (64MB)

Reliability through replication Each chunk replicated across 3+ chunkservers

Single master to coordinate access, keep metadata Simple centralized management

No data caching Little benefit due to large datasets, streaming reads

Simplify the API Push some of the issues onto the client (e.g., data layout)

HDFS = GFS clone (same basic ideas)

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From GFS to HDFS Terminology differences:

GFS master = Hadoop namenode GFS chunkservers = Hadoop datanodes

Functional differences: No file appends in HDFS (planned feature) HDFS performance is (likely) slower

For the most part, we’ll use the Hadoop terminology…

Adapted from (Ghemawat et al., SOSP 2003)

(file name, block id)

(block id, block location)

instructions to datanode

datanode state(block id, byte range)

block data

HDFS namenode

HDFS datanode

Linux file system

…

HDFS datanode

Linux file system

…

File namespace/foo/bar

block 3df2

Application

HDFS Client

HDFS Architecture

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Namenode Responsibilities Managing the file system namespace:

Holds file/directory structure, metadata, file-to-block mapping, access permissions, etc.

Coordinating file operations: Directs clients to datanodes for reads and writes No data is moved through the namenode

Maintaining overall health: Periodic communication with the datanodes Block re-replication and rebalancing Garbage collection

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Putting everything together…

datanode daemon

Linux file system

…

tasktracker

slave node

datanode daemon

Linux file system

…

tasktracker

slave node

datanode daemon

Linux file system

…

tasktracker

slave node

namenode

namenode daemon

job submission node

jobtracker

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References 2003 "The Google file system," in sosps. Bolton Landing,

NY, USA: ACM Press, 2003. 2004 "MapReduce: Simplified Data Processing on Large

Clusters," in Osdi, 2004,

Q&A?Thanks you!