分布式数据库综述资源-CSDN文库

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1Overview
2DistributedDatabaseConcepts
1NoSQLDatabases
2DatabaseTypesbyEntityType
2.1DistributedMemoryCaches
2.2Key/ValueDatabases
2.3"BigTable"Databases
2.4DocumentDatabases
2.5GraphDatabases
3CAPTheorem
4ColumnarDatabases
5ElasticityandBASE
6MapReduce
3SurveyofDistributedDatabaseTechnologies
1Overview
2Key/ValueDatabases
2.1Dynomite(OpenSource)
2.2Riak(BashoTechnologies)
2.3TokyoCabinet/Tyrant(OpenSource)
2.4Vertica(VerticaSystems)
2.5Voldemort(OpenSource)
3BigTableDatabases
3.1AzureTables(Microsoft)
3.2Cassandra(Apache)
3.3Datastore(Google)
3.4HBase(Apache)
3.5Hypertable(OpenSource)
3.6MckoiDDB(DiehlandAssociates)
3.7SimpleDB(Amazon)
4DocumentDatabases
4.1CouchDB(Apache)
4.2DovetailDB(MillstoneCreativeWorks)
4.3MongoDB(OpenSource)
4.4Terrastore(OpenSource)
4.5ThruDB(OpenSource)
5GraphDatabases
5.1AllegroGraphRDFStore(FranzInc.)
5.2InfoGrid(NetMeshInc.)
5.3HyperGraphDB(Kobrix)
5.4Neo4j(NeoTechnologies)
4HonorableMentions
1Alterian
2BerkeleyDB
3CStore
4Chordless
5CloudStoreKFS
6Coherence
7Db4o
8Drizzle
9Dynamo
10ForceDatabase
11GigaSpaces XAP
12Gremlin
13GoogleAppEngine
14GT.M
15HamsterDB
16Heroku
17Hive

Overview

This document, researched and authored by Randy Guck, provides a summary of

distributed databases. These are commercial products, open source projects, and

research technologies that support massive data storage (petabyte+) using an

architecture that distributes storage and processing across multiple servers.

These can be considered "Internet age" databases that are being used by Amazon,

Facebook, Google and the like to address performance and scalability

requirements that cannot be met by traditional relational databases. Due to

their contrast in priorities and architecture compared to relational databases,

these technologies are loosely referred to as "NoSQL" databases, though an

absence of SQL is not a requirement.

DistributedDatabaseConcepts

Thissectiondescribesconceptsthatconstitutethenatureofmoderndistributed

databases.

NoSQLDatabases

Meaning "no SQL", this is a term that casually describes the new breed of

databases that are appearing largely in response to the limitations of existing

relational databases. Strictly speaking, there is no reason any distributed

database couldn't support SQL, but the implication of the term NoSQL is that

relational databases are the antithesis of the goals of the modern, distributed

database.[1] For example, instead of supporting ACID transactions, which is the

mainstay transactional model for relational databases, many of the new-

generation databases support BASE principles (described separately). Virtually

all of the databases in this paper are NoSQL databases, though some support SQL

or an SQL-like query language.

ThereisnoprecisedefinitionofwhatconstitutesaNoSQLdatabase,butthesedatabases

tendtohavemostorallofthefollowingtendencies:

Schemaless:"Tables"don'thaveapredefinedschema.Recordshavea

variablenumberoffieldsthatcanvaryfromrecordtorecord.Record

contentsandsemanticsareenforcedbyapplications.

Sharednothingarchitecture:Insteadofusingacommonstoragepool

(e.g.,SAN),eachserverusesonlyitsownlocalstorage.Thisallowsstorage

tobeaccessedatlocaldiskspeedsinsteadofnetworkspeeds,anditallows

capacitytobeincreasedbyaddingmorenodes.Costisalsoreducedsince

commodityhardwarecanbeused.

Elasticity:Bothstorageandservercapacitycanbeaddedontheflyby

merelyaddingmoreservers.Nodowntimeisrequired.Whenanewnodeis

added,thedatabasebeginsgivingitsomethingtodoandrequeststofulfill.

Sharding:Insteadofviewingthestorageasamonolithicspace,records

arepartitionedintoshards.Usually,ashardissmallenoughtobemanaged

byasingleserver,thoughshardsareusuallyreplicated.Shardingcanbe

automatic(e.g.,anexistingshardsplitswhenitgetstoobig),orapplications

canassistindatashardingbyassigningeachrecordapartitionID.

Asynchronousreplication:ComparedtoRAIDstorage(mirroringand/or

striping)orsynchronousreplication,NoSQLdatabasesemployasynchronous

replication.Thisallowswritestocompletemorequicklysincetheydon't

dependonextranetworktraffic.Onesideeffectofthisstrategyisthatdatais

notimmediatelyreplicatedandcouldbelostincertainwindows.Also,locking

isusuallynotavailabletoprotectallcopiesofaspecificunitofdata.

BASEinsteadofACID:NoSQLdatabasesemphasizeperformanceand

availability.ThisrequiresprioritizingthecomponentsoftheCAPtheorem

(describedelsewhere)thattendstomaketrueACIDtransactionsimplausible.

1.↑TopreventthemisunderstandingthatSQLsomehowcannotbeused,some

haveredefinedNoSQLasmeaning"notonlySQL".

DatabaseTypesbyEntityType

The new breed of NoSQL databases can be categorized several ways. This section

proposes a taxonomy that describes databases from lower to high levels of

functionality based on the type of entity that each supports. An entity could be

a tuple, record, document, or something else. Generally, more functionality is

available with the more complex entity types. Strictly speaking, however, higher

level databases are not necessarily supersets of the database "below" them.

DistributedMemoryCaches

AmemorycachesitsontopofapersistentstoresuchasaSQLdatabase,cachingmost

recentlyuseddata,typicallyrecordsorfieldvalues.Atahighlevel,amemorycacheisjust

ahashtable:everyvalueisaccessedviaakey.TheopensourceprojectMemcached

wasoneofthefirsttoexpandthememorycachemodelandcreatethenotionof

distributedcaching.Itallowsarequestononenodetofetchthevaluefromanynodein

thenetwork.Consequently,thetotalsizeoftheinmemorycacheisthesumofthe

memorycacheonallnodes.Usingcommodityhardware,morenodescanbeaddedto

increasethecachesizeandthereforeoverallperformanceoftheapplication.Intendedto

acceleratewebapplications,Memcacheddemonstratesthatanydataintensiveapplication

canenjoysignificantperformanceandscalabilitygainsusingthedistributedcache

architecture.

Memcachedisthemostusedopensourcedistributedcache,butthereareseveral

commercialdistributedcachesthatprovidesimilarfunctionality.ExamplesareOracle's

Coherence,GigaStore'sXAPInMemoryGrid,andIBM'sWebSphereeXtremeScale.

Distributedcachesprompteddeveloperstoviewdistributeddatabasesinanewway,by

invertingtheconceptofadistributedcache.Insteadofprovidingacachinglayertoan

existingdatastore,somenewdistributeddatabasestreatthedistributedhashtableasthe

database,backedbyapersistentstore.Suchimplementationsdonotrequireabackend

SQLdatabasebutinsteadprovidetheirownpersistence.Thisgivesrisetothekey/value

database.

Key/ValueDatabases

These databases provide an efficient key to value persistent map. Compared to

more sophisticated databases, they are very limited because they provide only a

single way to efficiently access values. Auxiliary paths to rows must be managed

externally, e.g., via Lucene or an application-managed index. Most of the

databases described in this paper are key/value stores at their core, often

providing additional functionality for access by secondary values.

Examplesofkey/valuedatabaseswithoutadditionalindexingfacilitiesare:

BerkeleyDB

HBase

MemcacheDB

Redis

SimpleDB

TokyoCabinet/Tyrant

Voldemort

Riak

"BigTable"Databases

These are also called recordoriented or

tabular databases

. The term bigtable has

become popular due to Google's proprietary

BigTable

implementation. Similar to

relational databases, a big table database consists of multiple tables, each

containing a set of addressable

rows

. Each row consists of a set of values that

are considered columns. However, due to the scalability and storage

requirements imposed by the applications for which they are used, big table

databases differ from relational tables in several important ways:

Eachrowcanhaveadifferentsetofcolumns.Althoughallrowsinagiventable

mayberequiredtohaveapredefinedsetofcolumngroups,individualrowscan

differbyspecificcolumnswithinthesegroups.Thismeansthatatablecanhave

differentvaluesfromrowtorow.Furthermore,thecolumnsdonotneedtobepre

definedinaschemabutinsteadcanbeaddeddynamically.

Tablesareintendedtohavemanymorecolumnsthanatypicalrelational

database.Arowcouldcontainthousandsofcolumns.Somebigtable

databasessupportmillionsofcolumnvaluesperrow.

Allbigtabledatabasesupportcompoundvalues,whichmeans,comparetoa

key/valuestore,arecordcanhavemultiplefields.However,someonlysupport

scalarvalueswithinarecord,requiringBLOBsandotherlargevaluestobestored

separately.

Rowsaretypicallyversioned.Thismeansthatmultiplecopiesofthesamerow

mayexist(i.e.,withthesamerowID).Rowsaretypicallyversionedbyasystem

assignedtimestamp.

Datastorageistypicallydividedintoshards,whichareindependently

managed.

Sometimesonlyupdatestoasinglerowareconsideredatomic.Inthis

case,multi-rowupdatesareperformedinseparatetransactions,andinter-

rowconsistencyishandledthroughsoft-transactiontechniques.Alternatively,

abigtablemayallowmulti-rowupdatesbutonlywhenrestrictedtorowsin

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