Schema design for time series data - Cloud Bigtable

Schema design for time series data. This page describes schema design patterns for storing time series data in Cloud Bigtable. This page builds on Designing ... WhyGoogle close DiscoverwhyleadingbusinesseschooseGoogleCloud Whetheryourbusinessisearlyinitsjourneyorwellonitswaytodigitaltransformation,GoogleCloudcanhelpyousolveyourtoughestchallenges. Learnmore Keybenefits Overview Topreasonsbusinesseschooseus. Multicloud Runyourappswhereveryouneedthem. Trustandsecurity Keepyourdatasecureandcompliant. Globalinfrastructure BuildonthesameinfrastructureasGoogle. Datacloud Unifydataacrossyourorganization. Opencloud Scalewithopen,flexibletechnology. Sustainability Runonthecleanestcloudintheindustry. Productivityandcollaboration ConnectyourteamswithAI-poweredapps. Resources Events BrowseupcomingGoogleCloudevents. Blog Readourlatestproductnewsandstories. Customerstories Explorecasestudiesandvideos. Analystreports Readwhatindustryanalystssayaboutus. 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Atimeseriesisacollectionofdatathatconsistsofmeasurementsandthe timeswhenthemeasurementsarerecorded.Examplesoftimeseriesincludethe following: Theplotofmemoryusageonyourcomputer Temperatureovertimeonanewsreport Stockmarketpricesoveraperiodoftime Agoodschemaresultsinexcellentperformanceandscalability,andabadschema canleadtoapoorlyperformingsystem.However,nosingleschemadesign providesthebestfitforallusecases. Thepatternsdescribedonthispageprovideastartingpoint.Yourunique datasetandthequeriesyouplantousearethemostimportantthingsto considerasyoudesignaschemaforyourtime-seriesdata. Thebasicdesignpatternsforstoringtime-seriesdatain Bigtableareasfollows: Rowsaretimebuckets Newcolumnsfornewevents Newcellsfornewevents Rowsrepresentsingletimestamps Serializedcolumndata Unserializedcolumndata Dataforexamples Toillustratethedifferencesbetweenpatterns,theexamplesonthispageassume thatyouarestoringdataforanappthatrecordsthe measurementsthatweatherballoonstakeonceeveryminute.Weuseeventto meanasinglerequestthatwritesoneormultiplecellsatthe sametime.LocationIDscorrespondwithGoogleCloudregions. Measurement Example Timestampsonthispageareformattedlike`tYYYY-MM-DD-HHMM`for humanreadability.Inaproductiontable,timestampsareusually expressedasthenumberofmicrosecondssince1970-01-0100:00:00 UTC,like`1616264288050807`). Pressure(pascals) 94587 Temperature(Celsius) 9.5 Humidity(percentage) 65 Altitude(meters) 601 Relateddata Example BalloonID 3698 Location asia-southeast1 Timestamp1 t2021-03-05-1204 Timebuckets Inatimebucketpattern,eachrowinyourtablerepresentsa"bucket"oftime, suchasanhour,day,ormonth.Arowkeyincludesanon-timestampidentifier, suchasweek49,forthetimeperiodrecordedintherow,alongwithother identifyingdata. Thesizeofthebucketthatyouuse—suchasminute,hour,orday— dependsonthequeriesthatyouplantouseandon Bigtabledatasizelimits.Forinstance,if rowsthatcontainanhourofdataarebiggertherecommendedmaximumsize perrowof100MB,thenrowsthatrepresentahalfhour oraminuteareprobablyabetterchoice. Advantagesoftimebucketpatternsincludethefollowing: You'llseebetterperformance.Forexample,ifyoustore100 measurements,Bigtablewritesandreadsthosemeasurementsfaster iftheyareinonerowthaniftheyarein100rows. Datastoredinthiswayiscompressedmoreefficientlythandataintall, narrowtables. Disadvantagesincludethefollowing: Time-bucketschemadesignpatternsaremorecomplicatedthansingle-timestamp patternsandcantakemoretimeandefforttodevelop. Addingnewcolumnsfornewevents Inthistimebucketpattern,youwriteanewcolumntoarowforeachevent, storingthedatainthecolumnqualifierratherthanasacellvalue. Thismeansthatforeachcell,yousendthecolumnfamily,columnqualifier,and timestamp,butnovalue. Usingthispatternforthesampleweatherballoondata,eachrowcontainsall themeasurementsforasinglemetric,suchaspressure,forasingleweather balloon,overthecourseofaweek.Eachrowkeycontainsthelocation,balloon ID,metricthatyouarerecordingintherow,andaweeknumber.Everytimea balloonreportsitsdataforametric,youaddanewcolumntotherow.The columnqualifiercontainsthemeasurement,thepressureinPascals,forthe minuteidentifiedbythecelltimestamp. Inthisexample,afterthreeminutesarowmightlooklikethis: Rowkey 94558 94122 95992 us-west2#3698#pressure#week1 t2021-03-05-1200 t2021-03-05-1201 t2021-03-05-1202 Usecasesforthispatternincludethefollowing: Youdon'tneedtomeasurechangesinyourtimeseriesdata. Youwanttosavestoragespacebyusingcolumnqualifiersasdata. Addingnewcellsfornewevents Inthistimebucketpattern,youaddnewcellstoexistingcolumnswhenyou writeanewevent.Thispatternletsyoutakeadvantageof Bigtable'sabilitytoletyoustoremultipletimestampedcellsin agivenrowandcolumn.It'simportanttospecifygarbagecollectionruleswhen youusethispattern. Usingtheweatherballoondataasanexample,eachrowcontainsallthe measurementsforasingleweatherballoonoverthecourseofaweek.Therowkey prefixisanidentifierfortheweek,soyoucanreadanentireweek'sworthof dataformultipleballoonswithasinglequery.Theotherrowkeysegmentsare thelocationwheretheballoonoperatesandtheIDnumberfortheballoon.The tablehasonecolumnfamily,measurements,andthatcolumnfamilyhasone columnforeachtypeofmeasurement:pressure,temperature,humidity,and altitude. Everytimeaballoonsendsitsmeasurements,theapplication writesnewvaluestotherowthatholdsthecurrentweek'sdatafortheballoon, writingadditionaltimestampedcellstoeachcolumn.Attheendoftheweek, eachcolumnineachrowhasonemeasurementforeachminuteoftheweek,or 10,080cells(ifyourgarbagecollectionpolicyallowsit). Eachcolumnineachrowholdsameasurementforeachminuteoftheweek.Inthis case,afterthreeminutes,thefirsttwocolumnsinarowmightlooklikethis: Rowkey pressure temp asia-south2#3698#week1 94558(t2021-03-05-1200) 9.5(t2021-03-05-1200) 94122(t2021-03-05-1201) 9.4(t2021-03-05-1201) 95992(t2021-03-05-1202) 9.2(t2021-03-05-1202) Usecasesforthispatternincludethefollowing: Youwanttobeabletomeasurechangesinmeasurementsovertime. Single-timestamprows Inthispattern,youcreatearowforeachneweventormeasurementinsteadof addingcellstocolumnsinexistingrows.Therowkeysuffixisthetimestamp value.Tablesthatfollowthispatterntendtobetallandnarrow,andeach columninarowcontainsonlyonecell. Important:Toavoidhotspots,neveruseatimestampvalueasarowkeyprefix. Single-timestampserialized Inthispattern,youstoreallthedataforarowinasinglecolumnina serializedformatsuchasaprotocolbuffer(protobuf).Thisapproachis describedinmoredetailonDesigningyourschema. Forexample,ifyouusethispatterntostoretheweatherballoondata,your tablemightlooklikethisafterfourminutes: Rowkey measurements_blob us-west2#3698#2021-03-05-1200 protobuf_1 us-west2#3698#2021-03-05-1201 protobuf_2 us-west2#3698#2021-03-05-1202 protobuf_3 us-west2#3698#2021-03-05-1203 protobuf_4 Advantagesofthispatternincludethefollowing: Storageefficiency Speed Disadvantagesincludethefollowing: Theinabilitytoretrieveonlycertaincolumnswhenyoureadthedata Theneedtodeserializethedataafterit'sread Usecasesforthispatternincludethefollowing: Youarenotsurehowyouwillquerythedataoryourqueriesmight fluctuate. Yourneedtokeepcostsdownoutweighsyourneedtobeabletofilterdata beforeyouretrieveitfromBigtable. Eacheventcontainssomanymeasurementsthatyoumightexceedthe 100MBper-rowlimitifyoustorethedatainmultiple columns. Single-timestampunserialized Inthispattern,youstoreeacheventinitsownrow,evenifyouarerecording onlyonemeasurement.Thedatainthecolumnsisnotserialized. Advantagesofthispatternincludethefollowing: Itisgenerallyeasiertoimplementthanatime-bucketpattern. Youmightspendlesstimerefiningyourschemabeforeusing it. Disadvantagesofthispatternoftenoutweightheadvantages: Bigtableislessperformantwiththispattern. Datastoredthiswayisnotasefficientlycompressedasdatainwider columns. Evenwhenthetimestampisattheendoftherowkey,thispatterncanresult inhotspots. Usecasesforthispatternincludethefollowing: Youwanttoalwaysretrieveallcolumnsbutonlyaspecifiedrangeof timestamps,butyouhaveareasonnottostorethedatainaserialized structure. Youwanttostoreanunboundednumberofevents. Usingtheweatherballoonexampledata,thecolumnfamilyand columnqualifiersarethesameastheexampleusingtimebucketsandnewcells. Inthispattern,however,everysetofreportedmeasurementsforeachweather ballooniswrittentoanewrow.Thefollowingtableshowsfiverowsthatare writtenusingthispattern: Rowkey pressure temperature humidity altitude us-west2#3698#2021-03-05-1200 94558 9.6 61 612 us-west2#3698#2021-03-05-1201 94122 9.7 62 611 us-west2#3698#2021-03-05-1202 95992 9.5 58 602 us-west2#3698#2021-03-05-1203 96025 9.5 66 598 us-west2#3698#2021-03-05-1204 96021 9.6 63 624 Additionalstrategies Ifyouneedtosendmultipledifferentqueriesforthesamedataset,consider storingyourdatainmultipletables,eachwitharowkeydesignedforoneof thequeries. Youcanalsocombinepatternsinsomecases.Forexample,youcanstore serializeddatainrowsthatrepresent timebuckets,aslongasyoudon'tlettherowsbecometoobig. What'snext Reviewthestepsinvolvedinplanningaschema. Understandthebestpracticesfordesigningaschema. ReadabouttheperformanceyoucanexpectfromBigtable. ExplorethediagnosticcapabilitiesofKeyVisualizer. Workthroughatutorialonmonitoringtime-seriesdata withOpenTSDBandGoogleCloud. Sendfeedback Exceptasotherwisenoted,thecontentofthispageislicensedundertheCreativeCommonsAttribution4.0License,andcodesamplesarelicensedundertheApache2.0License.Fordetails,seetheGoogleDevelopersSitePolicies.JavaisaregisteredtrademarkofOracleand/oritsaffiliates. Lastupdated2022-03-23UTC. [{ "type":"thumb-down", "id":"hardToUnderstand", "label":"Hardtounderstand" },{ "type":"thumb-down", "id":"incorrectInformationOrSampleCode", "label":"Incorrectinformationorsamplecode" },{ "type":"thumb-down", "id":"missingTheInformationSamplesINeed", "label":"Missingtheinformation/samplesIneed" },{ "type":"thumb-down", "id":"otherDown", "label":"Other" }] [{ "type":"thumb-up", "id":"easyToUnderstand", "label":"Easytounderstand" },{ "type":"thumb-up", "id":"solvedMyProblem", "label":"Solvedmyproblem" },{ "type":"thumb-up", "id":"otherUp", "label":"Other" }] Needtotellusmore? 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