哎,hello哎,你聽到我對吧?哦,no好的,那我就攻向屏幕了嗯,可以,可以那現在嗯,好sorry, I was on muteGood morning or good afternoon or good eveningdepending on where you arewait for a few more minutesthere should be a few more people joiningI see the Alibaba team are onare you good to be presenting the fine arts projectok, thanks Alexyeah, we are onso shall we just wait a few minutesor we just get startedum,let's maybe wait a couple of minutessurea few more people should be joining soonthank youalright, it's nearly five pastum, why don't we, why don't we beginhi, Erinhi, Alexok, let me get startedshare the screen firstcan everyone see meyepokah, hello everyonemy name is Ren Yuan Yuand I am from the Alibaba demoacademyand today with me also there are a fewmy colleagues Andy Taoand Siyuan onlinetoday I'm going to presentour recent work called one yardit is distributed in memoryimmutable data managerand we have planned to donate itto CNCF as a sandbox projectand we would like to hear morefrom the communityespecially from the six storagecommunityabout the feedbacks on our projectfeel free to interruptif like you got any questionsok, the first question is whyBowser, why we need likeanother data storage engineso the problem is as followsyou can seeyou can see from the likepi data is a defective standard for data analysispeople like building various data applicationsin Pythonand normally if like they may likemultipolar libraries or projectsin the pi data ecosystemfor different kinds of worksfor example if we want to do visualizationwe use matplot.lyand we use to analyzedataframes we use pandasand if we want to dolike enumerate calculationswe use numpyand we want to do machine learningwe use pytorch or tensorflowand all thoselibraries works together very nicelybecause sharing dataespecially intermediate resultsvery efficientbetween the systemsor librarieshere is an example like we want to passan array from numpyto pytorchbasically pytorch understandsthe data structure of numpyand understands all its metadataand for the payload partthe actual array partit just shares the same arraywith a pointerpassing the cpointerand its lengthbetween those two librariesit's very easyin this caseyou change the tensor positionzeroto minus oneand for theend array parton numpy it also changesbecause it shares memoryso in this way sharing databetween those two librariesbasically with zero copyzero actual copyand it's like zero actual costbut what if likefor some reasonwe cannot do thewe want to access the same piece of databut we cannot do themdo it on the same processor we may need to do themultiprocess processingon the same like datait is not as easyas the first examplelike we canbut it's still possiblewith plasma from the app.it arrowit's basically a local object storeusing shared memoryit has a Pythonalso comes with a Python clientpeople canget an objectbasically that object memoryjust mappedon to that processand use that memorylike you can access the databut for thelike a metadata partit's not as straightforwardas the first onebecause for the object store partonly like managecontinuous memoryfor each objectit requires everythingstore in a continuous memorya section of a continuous memorybut for themetadata partyou either you handle thatyourselfyou got the metadatabecause metadata typically does nottake much spaceso you take thedata partor you just serialize those metadataalong with the payload partthe actual payload parttogetherand put it as a single objectfor plasmajust like what app.it arrow doesthat's the two waysyou can solve the problemof sharing metadatabut you can still sharetheyou can still share the payload partwith plasmajust betweendifferent processes or runtimeson a single machinebut then likeif we want to handlea bigger applicationwe handle big datathe big data itselfcannot fit into a single machineandplus we want todo many different tasksdifferent workloads on the same piece of dataoron the resultof the differentworkloadswhat can we doin this case we wantlaboratekubernetesand also theproject we were doing in the vnrlet's lookinrealize big dataapplicationwhat we have done in Pythonlikeactualthe real lifebig data applicationsis actuallyvery verycomplexthey are involving many different tasksfor example wefrom the raw datawe need to do some ETLsto do the drawingsto do the transformationsand then they can feed that datawe may want to feed that datato a graph systemto do a community detectionfor example we do the label propagationand then wewant to feed that datato a deep learning systemlike tensorflow or pytorchto find towe try to learn some patternslearn somethingmodelsand finallywe want to usedo some visualizationon the classificationwe want todo some inspectionwhether this classificationmake senseif that's the pipelineyou seefor each workloadthere are some dedicated systemsand between the systemswe need to shuffle the datain a distributed file systemandno more like zerocovid sharing of the data anymoreokhere's the observationthebigly data application involvesmany systemsand each system sharesmedia dataeternal file systemand finallythis kind of workloadoftenoptines as a chain or a deckandlike each individual taskrequiresthe results produced by theprevious taskshere are the problemokbecause firsteven for the big datawe have distributed file systemthe building and production ready systemslike high tensorflowspark, pytorchthose are very hard to developwhy? becausewe need toconsider different kinds ofdifferent distributed file systemswe may need toconsiderwhat's the file formatdo we use csvfor tablesor orcsorpuppetsthere are so manyfile formatsnot mentioned for graphsthere's no standard way to store graphso we need to dump themas a tablethat table may lose lots of informationand very inefficientand likeyou need tocouple with this kind of inputoutput and the systemssecond sharingthis data with externalsystem of courseevolves huge IO costand sometimes those costs arenecessaryif we want tooptimize those tasks as a wholewe want tohigh-plan this kind of thingsthe jobs is very challengingso that's the motivationwe want to build a systemlike solve thoseproblemswe want to make big data systemeasy to buildand we want to reducethis kind of IO cost in theand finallywe want to open the opportunityfor cross-task optimizationsthat's thequest for one yardso what is one yardone yard is a distributed in-memoryorgestor for immutable dataand it supportsthe recovery memory data sharingbetween different systemsit comes with out of theboss high-level data constructionfor developing big data applicationsfor examplewe havetensors, we have data frameswe have distributed graphswe have likescalersand also like common data structureslike arrays, hash tables, etc.like those kind of data structuresare justlike come out of the boxand those kind of data structurescan be mapped to the memoryjust likenative objectslike c++ objectsyou can use that datalike you can do the local dataaccessjust like anative objectsand finallywe provide driversfor data partitioningio and checkpoint migration, etc.that means the big dataapplicationsdo not need to care ofthe computing engineis startedthe data is already therein one yardthe computing engine itselfdoes not need to caredo we needto load the datafrom an external file systemor it's coming fromanother streamor whateverso the one yard comes withdrivers that can do thisfor thethe application build on tophere is the architectureof one yardone yardobject consists ofthe beta payloadbasically thoseconsume much of the memoryand the metadataand the data payloadstoresoring in the shared memoryjust like a plasma datawe open a big chunkfor storing the payloadand themetadata in one yardis likethink through a clusterusing eccdand we currentlywe support data framesgraphs, tensors, etc.many kinds ofand the one yard demoninstancesaccess with IPCand RPC connectionsand the data payloadfor IPC connectionsso RPCs you can only accessmetadatafor IPCyou can just nap theshared memory into your processand we have comes withmany like platform driverswhich can provide certain functionalitiesto certain data formatsfor examplemigration or IO loadold data or unload the datasave data to an externalinstances, etc.herecould Icould I just ask a coupleof clarifying questions thereis theis thedatareplicatedacross the different instancesor is it shardedor is it just separate data setscurrentlyactuallywe are just shardingsharding the data or partitioning the datais for the commonbig data applicationswe also support replicationsfor examplei will cometo that laterfor examplebutthatfor exampleif the data is replicatedwe have two processing processlike working on the same piece of datato speed upor for some reasonlike we want to have a data backupin the memoryor for some reason we just dump the datato an external file systemand we free that datafrom the memorythat's all can be controlledby something called a driverand we can builddrivers to do thatdrivers can workwith Kubernetesand theapplications on topto decide thatso on the very low levelthe datafor the one year does not careliketheactually it does not really understandthewhat does the data meanbut because we havemanager and driverswe can easily plug in newkinds ofdata structures or typeson to likeone yard solikebasicallyto make sense of the datais more like a client thingor the application thingnot the one yard thingit just maintains the metadatado you have any concernsoveraging at CDat a certain scaledue to its brittlenessusing it to sync the data through the clusterhave you run intothe performanceyou mean the performance considerationwell not only performance considerationsbut just the additional trafficthat flows through at CDthat isn't part of its normal functionalityat scale it seems like that wouldyou could possibly run into issues therewe have down testedbut currently we deploy theetcdas a standalone clusteras a standalone workloadin addition to theetcd required by Kubernetesalso likeit's not a big problembecause we only useetcd formetadata and only if thatmetadata is consumedbycross different cans of applicationwe put that data toetcdwe maintain that dataas local as possiblelike if that data is notrequired by a remote walkerlike that metadataor that object is notrequired by a remote walkerwe try not tolike expose thatand our data is mostlylike immutable sothose kind of metadatakind of static unlessyou're creating something newthat does not happentoo frequentlyso that's not a problemlike tomanage traffic or something like thatok thankshere I will show an examplefor to accesslike globaldataframe in one waythat means like we have a data framebig table and we partitionthe table intomany chunksand forlike each chunk maylocate in one ofthe wire instancelike we have a client on onelike on top of one walkerfirst we connect wirethroughdomain socketand then welike try to getan object of somespecific IDwe can getframesand we can accesswe canaccess a chunklike local chunkwe can check whether the chunk is local or notand we can get a local chunkand then we canlike just use a chunk like normallike pandas chunkand we can dowe can inspect metadataso as each stepinvolves differentinvolve componentsand basically if the chunkis localyou can use a chunk just likenormal native objectandcurrently we have some integrationwith Kubernetesand we have a vision thatlike with one-year supportand the abilityof Kubernetes we canmaybe we canfind a newfor big data tasksfirst we will coverhow to deploy one-year in Kubernetesandhow we leverage the power of Kubernetesand furtherhave how weuseKubernetes ability toco-schedule dataand workloads on top of itlike just flashbackto the previous taskwe want to solvelikewe want toreplace first we replacethe decrease faster with one-yearok and then wewewewant the differentkind of workload toshare the datathrough the means of CRDsso they find the CRDsthey want to accessand we use one-yearto map the data tohis workersandfor example, we change theworkload as we usedataframe engine called Marshas integrationwith one-yearand it's builtby Alibabaandgrass system called Grascois also built on top ofone-yearwe directlyshare the data can be directly sharedthrough the CRDsand then we usecouple flow andthe graph analysiswe can still usethe one-yearto share becauseit talks in thelanguage of NDE arraysthe chance of NDE arraysand we havepycin sdk to easilypycin-basedlike librariesand in this waythe end-to-end big datatask is deployed on Kubernetesand the intermediate resultslike dataabstracted as CRDsand leave the in one-yearand into memoryand maybe we canhave a scheduler tooptimize the locality of the next joband if the jobis matched we canassist scheduler orwe initiate anotherjob to migratethe data for the alignmentor we repartition the datafor the alignmentI was just thinkingthat would bean ideal candidate fora custom controlleror a mutatingcontroller or somethingpotentiallysofirst wehow to deploy one-year on Kubernetesactually it's a little bitlikenot very straightforwardbecause one-year requiresIPC communication between one-yearserver port and the application portfor memorycurrentlywe deploy one-year as ademonsetso in this way we need toeitherusing a hosthouse for an IPCsocketor we needseparatevolume claimjust to putsocket thereand we have done the experimentit worksand as long as we canhave a domain socket mappedto different containerswe can bundle one-yearto the same portand the domain socket can be sharedand for deploymentof one-yearwejust said it could be deployed as ademonsetand we haveyou leverage the helpto install one-yearquickly install and deployon KubernetesandSecondly weone-year objectswe expose it asquantitative resourceslike basically if some jobrequires some kind of datathat's in themeans of one-year objectsit can look up throughthe Kubernetes CRDsandyeahthat'sjustwe have plans in progresslike to build aone-year operatorthat's be responsible for thedev ops of the one-yearon your ownkubernetes clusterand we want it to be responsiblemanaging the status of one-year clusterand managing the CRDsand provide thescaling and out capabilityof one-year on Kubernetesand to be responsibleif we wantsome kind of data checkpointingwe want some recoveryfor tolerance and sexualwe can use the one-yearoperatorfirst ahead we planto leveragethe schedule offunctionality of Kubernetesand then we wantlike useKubernetes tooperate the datahow the data is partitionedor how the data is migratedhow the data is replicatedhow the data finds the workloadand how the workload finds the datalikefirst the worker poddescribes the required one-yearobject in their specsand schedule tries to alignthe worker pod withrequired one-year objectsby retrieving the location specfrom the CRDsand then we can triggera data migrationall reputationto ensure that the podcan accept data they requirehere is an examplelike first we have a joblet's generate a partdata part oneand data part twoon the one-year instancewe want the way twoand it's part onepart two and they cometogether theymaybe theythey are part of a global graphsome graphglobal graph andfor the next jobwe have a opportunity tolike collocatemaybe we maybe want tolike put P2 and those P2togetherlike on the P1 part onepart two but if notif it's not possiblelike trigger a migrationlike to satisfythe requirementsand then thejob is launched the data thereand it can be directly mappedto their processhere is thesorryjust a couple of questions therethe data migrationthat would be happeningshared memory to shared memoryyesit's kind of by a driveractually driver is a special clienta special client that livesin the wired containerso basicallyit's in a separate processof the wired team but in the same containerit's just likeokay like for eachnote there'slike wired containerand there will be also a driverwe send itcommand so you need toorate this object to this objectto this instanceto just create a newobject hereand maybe removehere or we just keep it hereit depends on what commandit was givenso it's basically likethe driver isdoing very primitive jobslike it's a special clientor special applicationlike but it can providelikesome like for examplewe want to check pointing is thatwe want to save the dataa copy of data toto the disk we just likedown to the data to the diskthen we can move onthat's basicallythe meaning I feeland for the roadmapinterestingjust one other small commentwe hada projectpresenta couple of months backcalledthe datasetlife cycle managerit was an IBM projectwhichhadat leastnot the shared memory aspect butit had put together a processwhere you could have CRDsthat wouldidentifydatasetsand load them onto particular nodesspecifically forresearchandpotentially big data typeuse casesso I'm kind of wondering if maybe there isyeah yeah I thinkthat's a good idea actuallywe don't currently havelike bandwidthto like tovery deep on the scheduling partactually there's too muchwork to doactually I'm just thinkingthe model of one yard veryfeeds this kind ofthe abilityprovidedby other CNCF projectsand actually we are currentlylooking at another project calledfluidto kindof achieving the similarsince but I will definitelyalso look at the dataset projectbut I didn't pay attentionto the previous meetingswe will definitely checkthank you very muchvery cooland for the roadmapcurrently we have likeeverything build testingwe did that through thegithub actionswe havevarious data types supported alreadywe have arraysgraphsandthe data framesessential and we supportseveral likecomputation enginespy torch mars grascopeexcept for py torchthose two are from alibabaand we releasethe docker image on docker hub and quayand we have integrated with helpfor the deploymentof the vignard operatorsandwe aim to furtherimprove its performanceeither by thosedata typesand also bythe basicprimitive operationson vignard such as create objector remove objectessential and alsowe plan to add morelanguage support such asgo-essentialand rust maybeand we maywant to look intohow to build the storagehierarchy such asfor like objects on memoriesin some device like on dpualso theesternal storage like local ssdsomething like thatwhether we can leverage thatand we want toeither build aschedulerplugging all like people can integrate with otherscheduler framework thatcan handle the data locality problemthe statusof the project currentlyis hostedon the github alibaba organd it got343stars asof yesterdayand we got33 issuesand10013pprand we have6 maintainers currentlycome from alibabaandbut wecome any contribution from the communityand we have a clear pathfor newcomer to becomemaintenorscurrently it's up to2.0 licenseand we haveeither issues, discussions, prsare we comeand we have a website to host the documentsfor the communitygovernance we have a clear pathfor newcomers to becomemaintenor we have designedmany good first issuesopen many good first issuesfor newcomersand before becomingmaintenor we is packedthe newcomer can submit5prsto one yardandhe can reach one of the existing maintainersand wewe are happy tohave a voteand a majority is requiredand it's justgo teamstuff andwe very welcomeas non-maintenors and contributorsand we knowmy building one yardit's likeactually it's notthe effortfrom one teamor just one vendor is not enoughand the community is the keyso we really welcomeas non-maintenors and contributorsand we expectmaintenor could spendat leastone fifth of his timeon the projectandthe enhancement decisionslikepropose as the issuesandwe canhave a vote bymaintenors andto develop it you canfeel free to sell for a signormaintenor is a signand the release cyclewe will follow the same wayto cut release packageslike forone year a major releaseand every two months we areminor release and patchversion every one or two weekendsand they planto distribute our firstmajor releasein Apriland we have alreadyreleased the packagesin like high-five videosdopper images and help chartsoh sorrythe wrong placewhyyeahactuallyMVR is a very naturalfeet to cloud native computinglike it providesefficient distributed data sharingand you know cloud native environmentand itall can be Oscar rated by Kubernetesand we find thatit's really excitingand we alreadymeeting existingabilities provided by many ofCNCL projectscurrently it's Kubernetes nativestilling outit comes with a scheduleplugging and we usehelp for deploymentas a clusterand we use ETCDand CRDs from Kube Kubernetesfor the metadata managementand we reallywant tolike IPCvindernutual to encouragecollaboration and innovationactually it's kind ofa foundationof like buildinglike new big data systemor making the existingsystem better soI think that's likesomething we wantpossibly we can get feedbackand confusion from the user communitythat things they can engageand we want towork with a sense of communityand we believe togetherwe can build the next generationcloud native paradigmfor big data applicationsthat's all for mypresentationany questionsthank you so muchthis was a really good presentationare there any questionsfrom anybody on the callperhaps LuisJaneI think this is greatI learned a lotactually from itand one of the things I look forward tois maybe a demo of itthat would be greatI can see the architectureI would like to see howit gets usedit gets usedmaybe we cando it in the next meetingif you guysbecause we were likefor many likeclover native's workespecially the scheduler partwe were just doing someveryearly poc versionproper concept versionand we haven'tget everythinglinked togethermaybe in next meetingwe can do a demolet's seehow the samemoreother than thatI guess we just have to go throughthe due diligencethat we have to go throughthe projectand check it outbut I amquite impressed with what you showedsoespecially all the memory thingssoand justto double checksoare youapplyingare you planning to apply for sandboxor incubation levelactuallyI want tolike hear from you guys said wellwe are playingfor the sandboxI think it's easierand our project only opensa few months agoonOctoberNovember I can't rememberit'spretty new and we only gotmentainers from Alibabaand we really want toget more like externalmentainerslike before we canmove to the next levelI think that's our planbut yeah incubatorno wewe are happybut we justit's not ready yetyeah I think the sandbox is correctyou need to have like end usersright Alex I believeI think they looks likewe don't want that yetfor end userswe have Alibabaof course but yeahwe need tonot yourself other end usersyeah yeah I understandyeah so basically sowe have manywe have two other open source projectslike build on topwe are already open sourcedby Alibabaand they got manywe got a few end usersI'm not sure whetherthat counts as our end users as wellI think yeahbut we just want to improve ityeah it's notenough yeahyeah it looks pretty decentfor sandbox projectyes I agreeit may not be enoughI'm just concernedI agreeon the amount of time the sandbox project has a lifetimeis there a limitbecause this project seems goodbut I'm just concerned that it may notcollect enoughend users or othercommunity membersis there a lifetime for sandboxor do we wait untilmaybe the team has morecontributorswellI think the projectis just at the rightat the right stage becauseyou're closeto the 1.0 releaseandusing sandboxtoincrease the number of maintainersand to build out the communityis perfectonce you're in sandboxthere are reviews every 6 monthsor sobut yeahI don't anticipate thatthat's going to be a problemyou can then make the decisionto move to incubationonce you're readyok thank yousandbox is correctbrilliant okone thing I will do isI will sharethe recordingof the presentationand the deckwith the TOCat the next callso that they'll havesome backgroundof the project beforethey go intothe nextsandbox reviewthe TOChave a regularregular schedule nowwhere they dothey review all of the sandboxapplicationsin one go everyone to review two monthsI need to double checkwhen the next review isbut I'll find outand let you know as wellthank youif there's anything morewe can providemake it more solidjust let us knowany other questionsor comments for the teamalright I think we're goodwe also hadwe also hadanother item on the agendatocontinue to reviewthe DR documentthat Rafaelahad shared at last timebut unfortunatelyRafaela had to drop offsomething came upso he had to drop offfor that half-bustso ifI would strongly encourageif you have commentsor any other contentto feedback to that DR documentsto apply it to the documentto review the commentsin the next SIG meetingI'll make sure I review itcool thanks Liesit's a really good documentthat's coming along nicelydoes anybody elsehave any other itemsthey want toto raise or discussokay so we get 12 minutes backthanks everyonehave a good rest of your dayThanks謝謝


around we've got Paul McKessel here who's the CEO of Clustrix Paul thanks for coming on and Paul's actually one of the co-founders of Icelon systems just got bought out by EMC big data play we heard a lot about that two weeks ago didn't we John yeah I mean you're I'm psyched to have you on because you could talk about a couple different things from many perspectives one you've been an entrepreneur successful Icelon great founding of a huge exit you know billions of dollars old EMC you form Clustrix in 2006 again another startup raise a bunch of money you got clients you guys are very successful great reputation in the industry and you're sitting at the doorstep of this mega trend cloud and growth and applications so you know I think we can talk about a lot of things but you know first thing is as an entrepreneur how do you feel right now in this environment I mean you guys have raised like 30 million dollars you have customers you're producing product what's the state of the market in your mind for being an entrepreneur especially in big data I think there's never been a time better than right now what we have is a situation where folks are trying to build large-scale internet sites that are trying to grow to the size of the entire world for their customer base the challenge about how do you go from systems that were developed to be single server type of solutions to something that can scale across multiple boxes across rows of racks in a data center across multiple data centers how do you do that so it's all about from our perspective clustering scale out instead of having to scale up fault tolerance comes from clustering easy scalability adding nodes into a bio cluster for scalability on the fly or all key concepts things we did at Icelon for unstructured file storage and the things we're developing pioneering and have built at clustering for structured and you built a lot of that at Icelon into the into the software yes I mean that was really kind of the secret sauce and and and so now you're you're a cluster database innovator no so talk about why the world needs another database yeah sure so to date all of the relational database systems that are on the market are single instance kind of things are not truly clustered they're not truly scalable so what happens is your only option for growth is to buy a bigger more expensive piece of hardware get more specialized and get off of the commodity hardware curve and go into something very expensive to remain on these systems the other the alternative to that has been a very intensive development effort alone known largely in the industry of sharding where you essentially break your data apart into individual database systems and you lose all that relational context so everything that the database was doing for you falls apart in this new world so cholesterol for the first time brings the ability to scale out and maintain all of that relational database semantics that has been you know growing with the growth of data over the last 30 years so so tie that to this whole big data theme right because you you think about big data you think about you know shared nothing you think about distributed yeah storage and MapReduce and how do you guys fit and how do you add value yeah that's a great question so related to what MapReduce does for distribution of processing power on large data sets what Clustrix does is we bring the query to the data in a cluster instead of the data to the query so it's very much in that similar theme of parallel massively parallel processing across clustered elements the difference that Clustrix brings is that we are very focused on high performance oil TP so lots of high transaction rates lots and lots of concurrent users in a system that looks exactly like what used to be there but failed to scale so one of the interesting and very compelling parts of our product is that we also emulate and look just like a mySQL server on the wire so there's no need to change your application modify the way that it works do anything special to get scalability and fault tolerance just with Clustrix we slot right in and now you can scale and grow and this thing we were just talking about bringing the query to the data allows you to continually scale out to tens hundreds of nodes in a single cluster with no single point of failure and no asymptote in the scalability and so we have published a couple of different performance results that show linear scalability continuing to add nodes and it just keeps scaling excellent so now so I have to ask you so John was asking you about entrepreneurialism before are you going to hire a CEO or a VP of engineering that's what I want to know oh that's an interesting yeah so my car today CEO VP of engineering we'll see how things program so I'm watching you my kids out there I told you before my kids out there I gotta give a shout out to Roman Roman Volante he's a he's a he's a math whiz he's an engineer I'm like look this is the time to really go hard after the alpha geek stuff right look at this successful entrepreneur it's a math environment yeah math trivets very true and there has been a change I think in recent years you know driven largely by the success of things like my combinator to really push more towards technical based leadership in technical companies and that seems to make a lot of sense and continues to drive innovation and make product fit and company fit align with what people and you're seeing a lot more of that right where you've got technical people now you know coming in and actually running companies you you can't go the other way you can't have a business person go into technology that would be a mess but but totally different disciplines I mean a VP of engineering I mean you focused on products you focus on timelines and prioritization true and and I guess there's some of that obviously in the business side but contrast the skill sets yeah yeah that's a great question I mean I have to give a lot of credit for the navigation of that roadmap right from an engineer engineering background towards you know technology leadership and then company leadership to our our investors or VCs Sequoia Capital US venture partners ATA ventures the folks at Y Combinator people like Don Listwin who's on our boards been very helpful in that and even my co-founder at Iceland Sujal Patel was you know very very helpful and sort of showing me that white combination investor is an investor in cluster access at the beginning or they come in later very beginning yeah okay so you're one of the first classes of the white combination second class yeah Sergei Sergei Sergei sorry off who's my co-founder was at cluster was the one in the second like on later what about the the role of databases now I mean I see with open source I mean anyone can walk into white comedy now they're handing out money for being in class hundred thousand dollars you know so it kind of brings this you know new new collegiate environment to entrepreneurship but you know open source like my sequel and all the you know memcache all these tools they're great I can start stuff I can start a fire and build up with some momentum launching app on iTunes and in launch and have somewhat scale right right you guys address that right so like my challenge is that our blog gets bigger and bigger you know it's all open source it's like shit I gotta rewrite the whole thing I don't want to take it out of production right I don't have fives in databases yeah and exactly that was why we decided to support the the my sequel protocol on the wire so that people can do things like start with a lamp stack right a more traditional open source stack and when you get to the point where scalability becomes an issue clusterics can show up and and slot right in without having to change the application so you can still run essentially a lamp stack but now you get scalability and fault tolerance using clusterics instead of my sequel portion so you guys typical startup you do a lot of good work you'd probably do improve the concepts early on yeah some good tech and good team I'll see biased engineering since you're the CEO sure they got all better bonuses than the other guys but talk about the clients you're announcing you guys just recently announced tuckled here your colleague sure yeah you guys have announced some serious customers yeah that's right now some AOL yeah it's that net box that net photo box and I offer and then we have a number of other ones that that that have become customers since those announcements and we will continue to make and so you get the customers but you had it for a while finally they get the legal signs off on right and you can say things can you talk about them and like what is AOL how are they using because that's not a startup I mean it's still at scale right how are they leveraging clusterics to say a startup that's a girl sure I think I think it's the AOL stuff might be a little sensitive for us to discuss but I can certainly talk about their whole product plans on the web yesterday right sure and and actually you know what I think I would prefer to do is let those guys talk about their use of clusterics on their own blogs because a lot of these folks are starting to they are talking about it they're starting to talk about the not not AOL but the other ones and so we should we should maybe follow up and go back through that so a lot of this is you know technology leadership on their part also right having the foresight to realize where the technology that they had been living with was sort of falling apart not scaling up growing and looking for alternatives and finding clusterics as the alternative that really allowed them to get through their their roadblocks you know it exhibits an incredible amount of foresight and due diligence on their part to even you know find clusterics at the time that they were doing that so let's we were relatively under the radar so without talking specifics in terms of companies can you talk about some use cases and sure help the audience go through a case of their how do you how should they expect to use it what are they going to do to actually bring in and adopt it yeah absolutely so to infrastructure needs to grind typical use case is you've got a application that has some amount of relational data in it so you've got users in your database that are doing things with other users so they might be trading objects or selling things to one another or doing things like having permissions on different objects different videos or photos that they're allowed to see so it's about sharing and information and relationships through the social graph that is a lot of what initially brings folks to to clusterics so what happens is the application is is using the relational database to maintain all of these relational pieces of information and that those queries are usually done through SQL which is one of the interfaces that we support and what happens is over time that database now gets too loaded to be able to be run there's too much pressure being put on the database will be run on a single box anymore and this is why you see folks when they start to grow when their website start to really grow in scale they slow down can't handle the traffic half failures get hung up that kind of thing so clusterics is designed to alleviate all that pressure with really no change at all to the application no change to the way that they work with the systems adding clusterics is as simple as plugging in the boxes turning them on and connecting to it as if it was a database so when you say plug in the boxes you're talking about it comes into the appliance that's correct yes so so talk about why appliance and yeah and take us to a lot of IT guys there's a lot there's real benefits yeah you know there's some concerns talk through some of that dialogue yeah actually Aaron Passey our CTO who by the way was also the chief architect at Icelon has a great post on our blog about why an appliance and I'll just sort of run quickly through that so an appliance allows us to have all the way from the lowest level drivers in the operating system all the way up through the software stack to the very top interface of the database be completely optimized for that piece of hardware completely optimized for performance and even for failure characteristics so what happens when when the storage system deals with a drive failure that kind of thing recovery yeah our system handles all of that and and knows what to expect at different points along the way when disks fail or networks fail that kind of thing so what you get from clusters is a system that understands out of the box its own environment and can react to things that happen in the course of in the course of infrastructure build on the course of life so then additionally there's not any question about you know what kind of hardware are you going to have to requisition and what kind of operating system patches and patches and all that stuff we we take out of all that stuff and just make it very very simple it's designed to be the easiest thing in the world to use install it let it scale and be fault tolerant for you and then you can move on to other things so most of these most of these folks what they really want to do is develop their application add new features for their users and worrying about the database layer is something that should have been solved and hadn't been and so we really attack this problem based on the failure that we saw in the industry to address so it's another point of management but it's just that it's one point of management as opposed to all these bespoke that's exactly right great so just on cluster x where do you see the cluster x in the next five years I mean you see it what's your vision I mean you have see started it because you had a passion for this whole database and growth side of its scale what do you see in five years so vision yeah I mean so I so long as a company that we started for unstructured data for file storage you know we went public after five or five and a half years and then emcee made a very great you know intelligent purchase on their part in December bought Iceland for just over two billion dollars I know you're biased but I agree by the way I actually think it was a great purchase yeah so I'm really really smart and in the unstructured data space that made a lot of sense so in the structured relational data space we see cluster x doing the exact same thing we have by many analysts opinion a much larger market in the relational data space than in the file space more intelligence that needs to go into the query planning of relational database queries as opposed to simpler file queries and so so there is I mean people are mashing up the database environment who go like say Facebook for example they do a lot of things with the photos of the haystack projects from well-known about so the system software is a hack in a way but now it can't be a hack you need to have the scale out mentality around the structured that's right not just the unstructured but like that's right the real production that's right and and you know we see ourselves as preventing people from having to go through the kinds of growing pains some of the earlier pioneers had to go through some of the folks that they're right there on some of the folks at Facebook they had to do incredible you know feats of engineering to be able to get through that stuff through those growth growth phases and we see ourselves as enabling other folks to be able to get through that without having can they still code can they still code so you save so you basically save a lot of time benefit to the your client and risk and and diversion and all kinds of things we say them and so can they still code with your system so if I want to do a little bit of Facebook hacking like environment like for my environment we still do that sure absolutely yeah absolutely the the just because the database is now scalable doesn't mean that there aren't more interesting things you can continue to do on top in the in the application stack what's your assessment of big data I mean we ask all the CEOs that come in here what's big data I mean everyone's it's like web 2.0 yeah everyone's got a different definition what's your your take or someone sees you the cocktail party yeah you know tell me about this big data thing what's going on with right when we've talked a lot today about the internet sites because that's our initial focus but beyond that even in a lot of the more traditional enterprises and and things like medical research genomics in a lot of the places financial markets things like high-frequency trading right you're talking about being able to capture and manage as much data as you can because it's not just the individual pieces that have value it's the it's the aggregate in total picture of what is happening in whatever segment you're tracking that adds the real value right so that is why and we're reaching the point now with the size and speed of these computer technologies that we can actually hold that in our hands and manage it and control it right so that is where big data is coming from that's where it's continuing to go internet is just one space out of the entire swath is really being enabled by where things are at so what's going to happen in your mind final question Mike at the top of the stack is I mean ultimately virtualization all these kind of plumbing activities enable the next layer up database middleware sure we're simplifying it but but that's what you're providing you're enabling up scale yeah so and data is a big part of that and center yeah we're trying to let people get out of the business of worrying about their own what's going to happen update the stack applications I see iTunes is a great you know app stores a poster child for growth angry birds where people using it business is gonna do that what's gonna what's your vision for the next you know five years or so in the top of stack virtualization at the desktop and application integration what's your angle on that yeah sure I mean we see lots of different things with so for example dealing with file storage in an inter-corporation how do you pass files back and forth between your different offices make sure they're tracked secure all that kind of stuff was traditionally an in-house IT managers thing to deal with right there's in some cases the real bane of their job right but now with folks like a box net providing secure file storage between folks and focusing on enterprise as a large part of their target we see we see people being able to sort of out this is IT as a service yeah exactly IT is exactly right IT is a that's a big trend you see that done deal absolutely you see that happening absolutely yep cool okay we are


What would you say the biggest objections or struggles or challenges that coaches have in swimming? Their biggest problem really is being able to do a good job of balancing.  But your six practices a week, two hours in the morning, two hours in the evening, plus weight training because like you want to coach these elite kids, and it's like I don't think you have to be doing that to make 60 grand a year. You could probably be doing it a different way, incorporating more coaches. I think they're just afraid to, afraid of like letting go of the program. Also, we have an issue here too with everything being like a non-profit. So when you do like stuff here and it's a non-profit, you have to have like some, most of the time you have to have a board and when you have a board, the board's made up of other people sometimes and they can like fire you. So you know like your job's on the edge there and so balancing that on how you want to organize your structure. Technically ours is a non-profit, but like me and my dad are on the board, you know, really not going to fire myself, which I don't know if that's highly legal or not, but no one's, the IRS isn't going to like come knocking down my door on, you know, oh my god, you're the board of a non-profit and it's like, yeah, I make a small amount of money and we zero out every year to take all my money. So I think they just, they fear, they fear that they're going to, they just can't sustain it. Like most of them are high school coaches that do it after school. And if they are full-time swim coach, then not a lot of full-time learn to swim coaches. There's not a lot of, there's like no full-time water polo coaches. Like that like does exist. That's my goal, right? That's my goal. It's like, I'm going to be a full-time water polo coach and make all my money coaching club water polo, which I've never met anyone that does that. It just doesn't make you enough money because people haven't thought about how to make money off of it, right? Like there's just either you have, you just, people just have not strategized to do it.  There's no like system in place on how to like do it full-time. That's the big thing and which I think people could do if they structured it properly. They just don't structure it properly and, you know, try to, you know, get the, again, also people don't like own their own pools, which would be like a huge factor.  You'd have to have your club rocking at like probably like a million a year I would think to like maintain a pool and do everything. Which would be, which isn't unfathomable if you have enough kids and you're turning the right process is totally, totally possible to do. But yeah, those are the, they, I think a lot of coaches get burned out because it's the, it's their second job. So that's pretty much most of it. I mean, I imagine that's in a lot of sports coaches are getting burned out because their second job in the camping. No, no, no professional water pool coaches here in the United States. Another is in Europe because they have clubs. So Hungary, Spain, Italy, they have professional clubs. So you can actually professional coach work for a club, but they don't do that here. So, okay. So when you, when you talk about