A Distributed Real-time Database Index Algorithm Based on B+ Tree and Consistent Hashing

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1 Avalabl onln at Procda Engnrng 24 (2011) Intrnatonal Confrnc on Advancs n Engnrng A Dstrbutd Ral-tm Databas Indx Algorthm Basd on B+ Tr and Consstnt Hashng Xanhu L a, Cuhua Rn b, Mnglong Yu a,c, a * a Chna Raltm Databas CO.LTD., SGEPRI, , Chna b Chna Communcatons 2nd Navgatonal Burau 2nd Engnrng Co., Ltd., Chongqng, , Chna) c Softwar Insttut of Nanjng Unvrsty, Nanjng, 21000, Chna Abstract Ths papr proposd a novl mthod of Dstrbutd ral-tm databas ndx algorthm basd on B+ Tr and consstnt hash. In ordr to dtrmn th storag locaton of ach TAG pont n th dstrbutd nvronmnt, Frst of all, vry storag nod and ach TAG pont ar mappd to crcular hash spac. Scondly, crat a hash tabl of TAG pont n vry storag nod, whch rcord th poston of ndx n vry TAG pont. Fnally, a B+ Tr ndx ar stablshd to organz and mantan th hstorcal data of on TAG pont. Thortcal analyss and xprmntal rsults show th valdty of th proposd mthod Publshd by Elsvr Ltd. Slcton and/or pr-rvw undr rsponsblty of ICAE2011 Opn accss undr CC BY-NC-ND lcns. Kywords: Dstrbutd Systm, Ral-tm Databas, Hbrarchy Indx, Consstnt Hashng; 1. Introducton Wth th dvlopmnt of computr tchnology and nhancmnt of Automaton tchnology, thr has bn a lot of data accss and managmnt applcatons wth tm constrants, such as powr systm schdulng, ndustral control, scurts tradng, arospac, and so on. Ths applcatons oftn rqur ral-tm samplng of th montorng qupmnt to undrstand th systm ral-tm opraton status, whch wth a vry hgh acquston frquncy, such as 25 pr scond, 50 pr scond or vn 100 pr scond. At th sam tm, all th data wthn th spcfd tm must b savd compltly, thus th nd to mantan hug amounts of data. Also t calls for Data acquston, procss and mak th rght rspons wthn a dsgnatd tm or tm rang, wth a sgnfcant tm-snstv. Thr ar so massv, ral-tm, hgh-frquncy data that th tradtonal rlatonal databas s hard to mt th nds of th applcaton, whthr to stor or rtrv. In rcnt yars, wth th mrgnc of ral-tm databas, t s possbl to *Corrspondng author. Tl.: E-mal addrss: lxanhu@sgpr.sgcc.com.cn Publshd by Elsvr Ltd. do: /j.prong Opn accss undr CC BY-NC-ND lcns.

2 172 Xanhu L t al. / Procda Engnrng 24 (2011) mplmnt th functons of ths applcatons. And now ral-tm databas has bcom a rsarch hotspot [1]. Currntly, thr ar som matur ral-tm databas systm at hom and abroad, ncludng th OSIsoft s PI[2] and InStp's DNA[3] n th Untd Stats, HghSoon [4] and LRTDB [5] ral-tm databas n Chna. A ral-tm databas s spcally dsgnd to dal wth th data wth a charactrstc of tm squnc of databas managmnt systm, whch s usd for th storag and managmnt of th ral-tm, hgh frquncy and massv data abov mntond. At th sam tm, n ordr to mprov th systm scalablty, fault tolranc and rtrval spd, t s ncssary to mak th ral-tm databas dstrbutd, that s to say a dstrbutd ral-tm databas systm s ncssary. Just bcaus of th charactrstc of ral-tm, hgh frquncy, massv and dstrbutd of dstrbutd ral-tm databas systm, to gt a bttr ndx mthod s playng a crucal rol for ffcntly storng and rtrval. Basd on ths objctv, ths papr puts forward a dstrbutd ral-tm databas Hbrarchy ndx algorthm, frst of all, w usng th consstncy hash algorthm to mak sur th corrspondng rlatonshps btwn TAGs and storag nods. Thn, tak th TAG nam or ID as hash ky valu, w rcord th TAGs n ach storag nod wth a hash tabl to mantan th TAGs blong to t. Fnally, Construct a B+ Tr for ach TAG to ndx all th data of th TAG. By comparng svral ndx mthods n Exprmntal scton, t shows th valdty of th proposd mthod. 2. Dstrbutd ral-tm databas framwork Thr ar two typ of nod n th dstrbutd ral-tm databas systm[6][7][8], on s th cntr control srvr namd NamSrvr, whch can xt only on n th whol systm. It s usd to storag th rlatd mtadata of th whol systm, such as th data storag srvr nformaton, data partng nformaton, accss control nformaton and so on. Anothr s th data storag srvr namd DataSrvr, whch can xt on or svral n th whol systm. And also t could b bult n dffrnt computr. Ths typ of nod s manly usd to data storag n dstrbutd ral-tm databas. NamSrvr Standby NamSrvr Actv P011 P02 P01 P03 P05 P04 P05 P03 P04 P03 P05 P02 P02 P01 P04 Fg.1 Dstrbutd ral-tm databas framwork dagram Whn th clnt wants to storag or rtrval data, frst of all, t snds a rqust to th NamSrvr to nqur th locaton of th actual data. And thn communcat wth th actual DataSrvr to do th rally data storag or rtrval. That s to say that actual data transmsson s btwn and DataSrvr. In ordr to mprov th avalablty and rlablty, w rct th NamSrvr wth Dual-Computr Hot- Standby. Normally, NamSrvr actv provd srvc. Onc NamSrvr Actv wth a fault occurs and stops provd srvc, NamSrvr Standby wll automatcally swtch to Actv mod and provdng srvc to nsur systm avalablty and rlablty. Takng TAG as unt, DataSrvr storag lots of data fls. In ordr to mprov th systm's usablty and fault tolranc, ach data fl n th whol dstrbutd ral-tm databas havng many cops. At th sam tm TAG s th unt of th dynamc load balancng. Through th analyss of th dynamc load of ach DataSrvr, NamSrvr dynamc adjustmnt th load

3 Xanhu L t al. / Procda Engnrng 24 (2011) n th whol dstrbutd ral-tm databas systm. Wth th hartbat mchansm, NamSrvr gt th opraton status of ach DataSrvr. Th Hartbat packag, whch contans DataSrvr s CPU, mmory, and dsk usag, s th bass for dynamc load balancng. Fgur 1 s a typcal cas of dstrbutd ral-tm databas framwork. 3. Hrarchy ndx 3.1. Data partton Along wth th ncrasng amount of data n dstrbutd ral-tm databas systms, how to bttr storag and managmnt th ncrasng data bcom th man ndx of dstrbutd ral-tm databas prformanc. A bttr mthod s to part th data of th systm [9]. To mt th prformanc of th systm rqurmnts, th whol systm data wll shar n many DataSrvr through th data partton, whch mak th data quantty b much smallr n vry DataSrvr. Crtanly, thr ar many knds of mthod to part th whol systm data. W can part data wth TAG ID. Of cours tm rang s a good choc. And somon tak data quantty as th dvson standard. In ths papr, w tak TAG ID as th dvson standard. In ordr to mprov th systm fault tolranc and mnmz th nod onln or nod offln, whch wll trggr to rhash, and thn a larg numbr of data wll mgrat among th whol systm DataSrvr. Combnd wth th company busnss, w choos th hash algorthm proposd n ltratur [10] [11] [12]. Wth ths mthod, th rmov/add a data nods always, t can b as small as possbl to chang th alrady xst ky mappng rlaton among th DataSrvr to mt th rqurmnts of monotoncty, balanc and sprad. Th stps of th data partton mthod proposd n ths papr as follows: 3.2. To construct hash spac A valu nto an n-bt ky, 0~2^n-1. Now magn mappng th rang nto a crcl, thn th ky wll b wrappd, and 0 wll b followd by 2^n-1. In ths papr, w tak n for 32. Thn th hash spac wll as show n fgur 2. (a) And w tak th map functon as: Ky = hash(objctid); HASH(DataSrvrIPPORT) DataSrvr6 DataSrvr5 DataSrvr3 DataSrvr2 DataSrvr1 DataSrvr7 Fg.2 (a) hash spac; (b) Th Dstrbuton of DataSrvr aftr mappng Map DataSrvr nto hash spac In th systm ntalzaton procss, w us a hash functon to gt all th DataSrvrs ky valus and map thm nto th hash spac. In ths papr, w assum that thr ar svn DataSrvrs xstng n th whol systm. Aftr th ntalzaton procss, thos DataSrvrs ar dstrbutd n th hash spac as show n Fgur 2. (b).

4 174 Xanhu L t al. / Procda Engnrng 24 (2011) HASH(pontNam) HASH(DataSrvrIPPORT) HASH(pontNam) HASH(DataSrvrIPPORT) DataSrvr6 DataSrvr5 DataSrvr6 DataSrvr5 DataSrvr3 DataSrvr2 DataSrvr3 DataSrvr2 DataSrvr1 DataSrvr7 DataSrvr7 Fg.3 (a) th Ky valu dstrbuton of DataSrvr and TAG pont aftr mappng; (b) TAG th Ky valu dstrbuton aftr DataSrvr falur Map TAG pont nto DataSrvr In th procss of addng TAG pont, clnt snds rqust to NamSrvr. NamSrvr calculatd th MD5 valu of ths TAG pont accordng to rqust TAG pont s faturs dntfcaton cod (such as pont nam, pont ID). And thn map th MD5 valu to th hash spac wth th sam hash algorthm, lookng for DataSrvr n clockws drcton (hash ky valus ncras drcton), and th frst found DataSrvr s whr ths TAG pont data wll b storag. In ths papr, w suppos th whol systm xst 17TAG ponts(p1 ~ P17), thn aftr th stp of map DataSrvr nto hash spac, th dstrbuton of thos TAG pont n th hash spac as shown n fg.3. (a) And th dstrbuton of TAG ponts has shown n tabl 1. Tabl1 TAG pont stor n ach DataSrvr DataSrvr TAG pont DataSrvr TAG pont DataSrvr1 P10 DataSrvr5 P02,P09,P14,P16 DataSrvr2 P04,P12 DataSrvr6 P06,P11 DataSrvr3 P01,P07 DataSrvr7 P05,P08,P15 P03,P12,P Aftr ddatasrvr falur ovr Wth th consstnt hashng algorthm, whn a nw DataSrvr jon n or th xstng DataSrvr falur off, w nsur that nothng should to do wth that xcpt mgrat th falur DataSrvr data to th othr xst DataSrvr n hash spac. As shown n fgur 3 (b), whn DataSrvr1 falur off, w just nd to mgrat th data of DataSrvr1 to DataSrvr3, th othr componnts shouldn t b changd. Whn th clnt wants to nsrt or qury data, frstly, t snds rqust to NamSrvr to gt whr th TAG pont s. Ths s th frst stp of our hrarchy Indx mthod: mak sur whch DataSrvr storag th rqustd TAG pont s data TAG pont Indx Thr s a hash tabl nams PontHashTabl In ach DataSrvr ntrnal, whch rcord th dtald nformaton of vry TAG pont n ths DataSrvr. Th dtald pont nformaton nclud: pont nam, pont ID, th ndx locaton, that to say th root nod locaton of th tag pont B+ Tr, tc. PontHashTabl ralz lk that: map(nt PontID, PontConfgItm* tm). In PontConfgItm structur, rtcach pont to th corrspondng Cach of TAG pont. In Cach structur, thr s a pontr rawhst, pontng to th root nod of B+ Tr. Aftr gt th TAG pont s

5 Xanhu L t al. / Procda Engnrng 24 (2011) storag DataSrvr, th clnt thn communcats wth that DataSrvr. If rqustd to add pont, w map th TAG pont dtald nformaton to th approprat slot of th hash tabl PontHashTabl by hashng wth th pont charactrstcs dntfcaton cod, and thn storag th PontConfgItm of that pont to th PontHashTabl. Whl f rqustd to nsrt valu, DataSrvr calculat th hash valu of ths pont by usng th TAG pont nam or TAG pont ID, and gt th PontConfgItm from PontHashTabl. Thn w can gt th B+ Tr root nod locaton and travrs B+ Tr to fnd whr th nsrt data storag n. Ths s th scond stp of our hrarchy Indx mthod: mak sur th poston of TAG pont ndx. Th PontHashTabl shows as fgur 4. PontNam:P11 PontID: rtcach:1,25536 f o n tc n P j HASH(pontID) PontHashTabl Fg.4 DataSrvr hash tabl wth TAG ponts 3.4. Data ndx Aftr gt th poston of TAG pont ndx. Th B+ Tr s travrsd from roots nod f stor or rtrv data rqustd. And thn compar th rqustd data tm rang wth th B+ Tr nod. If tm rang match, thn travrsd th chld nod untl to th laf nod. And ths laf nod s th nod whch th rqustd data nsrt n or storag accordng to th rqust typ, storag or nsrt data. Ths s th thrd stp of our hrarchy Indx mthod: To dtrmn whr to gt or put th rqust data. In th DataNod structur of B+ Tr, w mak som changs. To lnk all of th DataNod n th sam B+ Tr wth prv and nxt pontrs, and mak t lk a doubly lnkd lst wth whch t can ncras th spd of batch rtrval. Each TAG pont s B+ Tr ndx structur shows n Fgur 5. o N x a ta d StartTm RawHst In D EndTm Prv Nxt Data Root. m t T r t a S r t n P o. [ ] m d T E n [ ] [ ] Fg.5 B+ Tr ndx structur dagram n DataSrvr sd of dstrbutd ral-tm databas 4. Exprmntal rsults and analyss In ths scton, w compar th nsrt and qury ffcncy among thr dffrnt ndx mthods n th platform of HghSoon, whch s th man product of Chna Raltm Databas CO.LTD. For suprorty of partton, w can rfr to [6-10]. Ths xprmnt focusd on gt th nsrt and rtrval ffcncy among dffrnt data ndx mthod. Comparson of nsrt and rtrv prformanc among B+ Tr, RB- tr and T- tr. Th tst platform s basd on ponts of 20 mllon TAG ponts; nsrt 10 mllon of vnts to ach

6 176 Xanhu L t al. / Procda Engnrng 24 (2011) TAG pont. Th rsults shown n Tabl 2, th unt for th mllon vnts pr scond, can b sn from th tabl, B+ Tr as a larg amount of data n th prsstnt systm has bttr prformanc. Tabl 2 Insrt and rtrv prformanc comparson among four data ntrpolaton ndx structur Functon B+ Tr Rd-Black Tr T- Tr Batch nsrt Cross scton nsrt qury Concluson Ths papr prsnts a nw data ndx mthod of dstrbutd ral-tm databas. Through th ntroducton of th Consstnt hashng algorthm, w can dtrmn th ral-tm databas data fragmntaton ruls n dstrbutd nvronmnt. Usng of hashtabls and B+ Tr ndx mthod, w can mantan th data for ach TAG pont. Exprmntal comparson of th proposd mthod wth som common mthods btwn nsrt and rtrval ffcncy, and th proposd mthod s provd bttr. As th company s man markt s th powr systm, th nxt stp s manly to do paramtr tunng on th ndx to adapt to dffrnt ndustrs nsrt and rtrval ffcncy rqurmnts. Rfrncs [1].Bn Kao, Hctor Garca-Molna. An ovrvw of ral-tm databas systms[r]: Tch. Rport of Prncton Unvrsty, Stanford Unvrsty [2].OSI,PI_Systm_Standards[EB/OL]. [3].InStp, dna_ovrvw[eb/ol]. [4].CRD,HghSoon.[EB/OL]. HghSoon. [5].LUCULENT,LRTDB[EB/OL]. LRTDB. [6].Fay Chang, Jffry Dan, Sanjay Ghmawat, tc. Bgtabl: A Dstrbutd Storag Systm for Structurd Data[J]. Journal of ACM Transactons on Computr Systms (TOCS). TOCS Hompag archv Volum 26 Issu 2, Jun 2008, ACM Nw York, USA. [7].Sanjay Ghmawat, Howard Goboff, Shun-Tak Lung. Th Googl fl systm[j]. Procdng of SOSP '03 Procdngs of th nntnth ACM symposum on Opratng systms prncpls, Volum 37 Issu 5, Dcmbr 2003, ACM Nw York, NY, USA. [8].Jffry Dan, Sanjay Ghmawat. MapRduc: smplfd data procssng on larg clustrs[c]. Communcatons of th ACM - 50th annvrsary ssu: CACM Hompag archv. Volum 51 Issu 1, January ACM Nw York, NY, USA. [9].Wkpda, Shard( databas archtctur)[eb/ol]. [10].Davd Kargr, Erc Lhman, tc. Consstnt Hashng and Random Trs: Dstrbutd Cachng Protocls for Rlvng Hot Spots on th World Wd Wb[C]: Procdngs of th twnty-nnth annual ACM symposum on Thory of computng, Nw York, 1997[C]. [11].Guspp Dcanda, Dnz Hastorun, Madan Jampan, tc. Dynamo: amazon's hghly avalabl ky-valu stor[c]. Procdng of twnty-frst ACM SIGOPS symposum on Opratng systms prncpls, V.41 Issu 6, ACM Nw York, USA. [12].THE CODE PROJECT, Consstnt hashng [CP].