{"id":52728,"date":"2019-11-15T00:00:00","date_gmt":"2019-11-14T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/data-mesh-kak-rabotat-s-dannymi-bez-monolita"},"modified":"2020-02-18T14:00:31","modified_gmt":"2020-02-18T11:00:31","slug":"data-mesh-kak-rabotat-s-dannymi-bez-monolita","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hr\/blog\/administrirovanie\/data-mesh-kak-rabotat-s-dannymi-bez-monolita","title":{"rendered":"Data Mesh: kako raditi s podacima bez monolita","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Pozdrav, Habr! U Dodo Pizza Engineering jako volimo podatke (a tko ih danas ne voli?). Ovo je pri\u010da o tome kako prikupiti sve podatke svijeta Dodo Pizze i omogu\u0107iti svakom zaposleniku tvrtke lak pristup tom skupu podataka. Zadatak pod zvjezdicom: sa\u010duvati \u017eivce tima za obradu podataka.<\/p>\n<p>\n<img decoding=\"async\" alt=\"Data Mesh: kako raditi s podacima bez monolita\" src=\"\/wp-content\/uploads\/2019\/11\/f796b2caaa9dce1ad5e5317dd7d79b16.jpeg\" style=\"display:block;margin: 0 auto;\" \/><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Kao pravi Plu\u0161kin, skupljamo razne informacije o radu na\u0161ih pizzerija:<\/p>\n<p><\/p>\n<ul>\n<li>pamtimo sve narud\u017ebe korisnika; <\/li>\n<li>znamo koliko je vremena pro\u0161lo od izrade prve pizze u Syktyvkaru;<\/li>\n<li>vidimo koliko vremena pizza hladi na toploj polici u Vorone\u017eu upravo sada; <\/li>\n<li>\u010duvamo podatke o otpisu namirnica;<\/li>\n<li>i mnogo toga drugog. <\/li>\n<\/ul>\n<p><\/p>\n<p>Za rad s podacima u Dodo Pizzi trenutno je odgovorno nekoliko timova, a jedan od njih je tim za obradu podataka. Sada je pred nama zada\u0107a: omogu\u0107iti svakom zaposleniku tvrtke lak pristup tom skupu podataka. <\/p>\n<p><\/p>\n<p>Kada smo po\u010deli razmi\u0161ljati o tome kako to u\u010diniti i zapo\u010deli raspravu, prona\u0161li smo vrlo zanimljiv pristup upravljanju podacima \u2013 <noindex><a rel=\"nofollow\" href=\"https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html\">Data Mesh<\/a><\/noindex> (u linku \u0107ete prona\u0107i ogroman sjajan \u010dlanak). Njegove ideje su se vrlo dobro uklopile u na\u0161e predstave o tome kako \u017eelimo izgraditi na\u0161 sustav. U nastavku \u010dlanka bit \u0107e na\u0161e preispitivanje pristupa i kako vidimo njegovu implementaciju u Dodo Pizza Engineering.<\/p>\n<p><\/p>\n<h2 id=\"chto-my-imeem-v-vidu-pod-dannymi\">\u0160to podrazumijevamo pod 'podacima'<\/h2>\n<p><\/p>\n<p>Za po\u010detak, definirajmo \u0161to podrazumijevamo pod podacima u Dodo Pizza Engineering:<\/p>\n<p><\/p>\n<ul>\n<li>Doga\u0111aji koje \u0161alju servisi (imamo zajedni\u010dku sabirnicu izgra\u0111enu pomo\u0107u RabbitMQ);<\/li>\n<li>Zapisi unutar baze podataka (za nas su to MySQL i CosmosDB);<\/li>\n<li>Clickstream s mobilne aplikacije i web stranice.<\/li>\n<\/ul>\n<p><\/p>\n<p>Kako bi Dodo Pizza mogla koristiti ove podatke i oslanjati se na njih, va\u017eno je da se ispune sljede\u0107i uvjeti:<\/p>\n<p><\/p>\n<ul>\n<li><strong>Oni moraju biti cjeloviti.<\/strong> Moramo biti sigurni da ne mijenjamo podatke tijekom obrade, pohrane i prikaza. Ako posao ne mo\u017ee vjerovati na\u0161im podacima, ne\u0107emo imati nikakve koristi od njih.<\/li>\n<li><strong>Moru imati vremensku oznaku i ne smiju se brisati.<\/strong> To zna\u010di da \u017eelimo imati mogu\u0107nost da se bilo kada vratimo i pogledamo podatke iz tog vremenskog razdoblja. Na primjer, saznati koliko je pizza prodano 8. srpnja 2018. godine.<\/li>\n<li><strong>Moru biti pouzdani.<\/strong> U procesu prikupljanja i pohrane podataka moramo paziti ne samo na cjelovitost, nego i na pouzdanost. Ne smijemo gubiti podatke, vremenske dijelove, jer zajedno s njima gubimo povjerenje na\u0161ih klijenata (i vanjskih i unutarnjih).<\/li>\n<li><strong>Moraju biti sa stabilnom shemom \u2013 pi\u0161emo upite za te podatke.<\/strong> Zbilja bismo voljeli da se promjenom koda aplikacije, refaktorizacijom, ne promijene toliko da na\u0161i upiti prestanu raditi. Onaj tko pi\u0161e upite nikad ne sazna da ste napravili refaktorizaciju dok se sve ne pokvari. Ne bismo voljeli doznati to od klijenata.<\/li>\n<\/ul>\n<p><\/p>\n<p>Uzimaju\u0107i u obzir sve ove zahtjeve, do\u0161li smo do zaklju\u010dka da su podaci u Dodo proizvod. Isti kao javno API usluge. Prema tome, podacima treba upravljati istom ekipom koja upravlja uslugom. Tako\u0111er, promjene sheme podataka uvijek moraju biti unazad kompatibilne.<\/p>\n<p><\/p>\n<h2 id=\"tradicionnyy-podhod--data-lake\">Tradicionalni pristup \u2013 Data Lake<\/h2>\n<p><\/p>\n<p>Za rje\u0161avanje zadataka pouzdane pohrane i obrade velikih podataka postoji tradicionalni pristup, prihva\u0107en u mnogim firmama koje rade s takvim skupom informacija \u2013 Data Lake. U okviru ovog pristupa, in\u017eenjeri za podatke prikupljaju informacije sa svih komponenti sustava i pohranjuju ih u jedno veliko skladi\u0161te (to mo\u017ee biti, primjerice, Hadoop, Azure Kusto, Apache Cassandra ili \u010dak MySQL replikacija, ako se podaci mogu smjestiti u nju). <\/p>\n<p><\/p>\n<p>Nakon toga, ti isti in\u017eenjeri pi\u0161u upite za to skladi\u0161te. Implementacija ovog pristupa u Dodo Pizza Engineering podrazumijeva da \u0107e tim Data Engineeringa upravljati shemom podataka u analiti\u010dkom skladi\u0161tu. <\/p>\n<p><\/p>\n<p>U tom scenariju, tim postaje vrlo tu\u017eni ma\u010di\u0107i i evo za\u0161to:<\/p>\n<p><\/p>\n<ul>\n<li>Mora pratiti promjene u <strong>SVIM<\/strong> uslugama unutar tvrtke. A njih je mnogo i promjena je jako puno (u prosjeku spajamo ~100 pull zahtjeva tjedno, pri \u010demu mnoge usluge uop\u0107e ne rade pull zahtjeve).<\/li>\n<li>Prilikom promjene sheme podataka, proizvodni vlasnik i tim koji mijenja shemu podataka moraju \u010dekati da Data Engineering napi\u0161e kod potreban za podr\u0161ku promjenama. U me\u0111uvremenu, imamo situaciju u kojoj jedna ekipa \u010deka drugu \u2013 \u0161to je vrlo rijetko. I ne \u017eelimo da to postane 'normalan' dio procesa razvoja.<\/li>\n<li>Mora biti uronjena u <strong>CJELI <\/strong> tvrtke. Mre\u017ea pizzerija izgleda kao jednostavan posao, ali to je samo privid. Veoma je te\u0161ko okupiti u jednom timu dovoljno kompetencija za izgradnju adekvatnog modela podataka za cijelu tvrtku.<\/li>\n<li>Ona je jedinstvena to\u010dka neuspjeha. Svaki put kada je potrebno promijeniti podatke koje servis vra\u0107a ili napisati upit, sve te zadatke prebacuje se timu za in\u017eenjering podataka. Na kraju, tim ima preoptere\u0107en backlog.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>Ispada da se tim nalazi na raskri\u017eju ogromnog broja potreba i vjerojatno ih ne\u0107e mo\u0107i zadovoljiti. Uz to, bit \u0107e u stalnoj jurnjavi i stresu. Ne \u017eelimo to. Stoga moramo razmi\u0161ljati o tome kako rije\u0161iti te probleme i pritom dobiti mogu\u0107nost analize podataka.<\/p><\/blockquote>\n<p><\/p>\n<h2 id=\"peretekaya-ot-data-lake-k-data-mesh\">Prelazak s Data Lake-a na Data Mesh<\/h2>\n<p><\/p>\n<p>Na sre\u0107u, ovo pitanje postavljali su si ne samo mi. U stvarnosti, sli\u010dan problem ve\u0107 je rije\u0161en u industriji (aleluja!). Samo u drugom podru\u010dju: implementaciji aplikacija. Da, govorim o DevOps pristupu, gdje tim odre\u0111uje kako treba implementirati proizvod koji stvaraju. <\/p>\n<p><\/p>\n<p>Sli\u010dan pristup rje\u0161avanju problema Data Lake-a predlo\u017eila je Zhamak Dehghani, konzultantica u ThoughtWorks-u. Promatraju\u0107i kako sli\u010dne zadatke rje\u0161avaju Netflix i Spotify, napisala je sjajan \u010dlanak <noindex><a rel=\"nofollow\" href=\"https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html\">Kako prije\u0107i s monolitnog Data Lake-a na distribuirani Data Mesh<\/a><\/noindex>(link na njega bio je na po\u010detku \u010dlanka). Glavne ideje koje smo iz njega izveli su:<\/p>\n<p><\/p>\n<ul>\n<li>Podijeliti veliki Data Lake na domene podataka, koje su vrlo sli\u010dne domenama vo\u0111enim domenama (domain-driven design). Svaka domena je mali bounded context.<\/li>\n<li>Feature Team-ovi koji su odgovorni za DDD domene tako\u0111er su odgovorni za odgovaraju\u0107e domene podataka. Oni \u010duvaju shemu, unose u nju promjene, u\u010ditavaju podatke u nju. Uz to, sami sve znaju: kako promijeniti u\u010ditavanje podataka i ni\u0161ta ne slomiti kada se aplikacija mijenja. Znanja ne odlaze nikamo. Da bi otvorili podatke, ne trebaju nikuda i\u0107i. Sam tim vodi potpuni ciklus razvoja od promjene operativnih podataka do pru\u017eanja analiti\u010dkih podataka tre\u0107im stranama. Jedan tim posjeduje sve povezano s domenom (kao poslovnom domenom, tako i domenom podataka).<\/li>\n<li>Data Engineer \u2013 uloga unutar Feature Team-a. Ne mora biti posebna osoba, ali je obavezno da tim posjeduje tu kompetenciju.<\/li>\n<\/ul>\n<p><\/p>\n<h2 id=\"a-v-eto-vremya-komanda-data-engineering\">A u to vrijeme tim za Data Engineering&#8230;<\/h2>\n<p><\/p>\n<p>Ako zamislimo da se sve ovo ostvaruje s prstohvatom, ostaje nam samo da odgovorimo na dva pitanja:<\/p>\n<p><\/p>\n<p><strong>\u010cime \u0107e se sada baviti tim Data Engineeringa?<\/strong> U Dodo Pizza Engineering ve\u0107 postoji tim za platformu\/SRE. Njihov zadatak je omogu\u0107iti programerima alate za jednostavno postavljanje servisa. Tim Data Engineeringa \u0107e igrati istu ulogu, ali za podatke. <\/p>\n<p><\/p>\n<p>Pretvaranje operativnih podataka u analiti\u010dke je slo\u017een proces. U\u010diniti analiti\u010dke podatke dostupnima cijeloj tvrtki jo\u0161 je slo\u017eenije. Tim Data Engineeringa \u0107e se baviti rje\u0161avanjem tih problema. <\/p>\n<p><\/p>\n<p>Namjeravamo pru\u017eiti Feature Teamu prikladan skup alata i praksi, zahvaljuju\u0107i kojima \u0107e mo\u0107i objavljivati podatke iz svojih servisa za ostatak tvrtke. Tako\u0111er, mi \u0107emo biti odgovorni za zajedni\u010dke infrastrukturne dijelove data pipelinea (redovi, pouzdano skladi\u0161te, klasteri za izvo\u0111enje transformacija nad podacima). <\/p>\n<p><\/p>\n<p><strong>Kako \u0107e vje\u0161tine Data Engineera postati prisutne unutar Feature Teama?<\/strong> S Feature Teamom je slo\u017eenije. Naravno, mogli bismo poku\u0161ati zaposliti po jednog Data Engineera u svaki od na\u0161ih timova. No, to je jako te\u0161ko. Te\u0161ko je prona\u0107i osobu s dobrim pozadinom u obradi podataka i uvjeriti je da radi unutar proizvodnih timova. <\/p>\n<p><\/p>\n<p>Velika prednost Doda je to \u0161to volimo interno obrazovanje. Tako da je na\u0161 plan trenutno ovakav: tim Data Engineeringa po\u010dinje objavljivati podatke nekih servisa, pati, mu\u010di se, ali nastavlja jesti kaktus. \u010cim shvatimo da imamo spreman proces za objavljivanje, po\u010dinjemo pri\u010dati o tome u Feature Teamu. <\/p>\n<p><\/p>\n<p>Imamo nekoliko na\u010dina kako to u\u010diniti: <\/p>\n<p><\/p>\n<ol>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/dodopizzaio\/blog\/462033\/\">DevForum<\/a><\/noindex>, na kojem \u0107emo objasniti kako izgleda proces koji smo stvorili, koji alati postoje i kako ih naju\u010dinkovitije koristiti. <\/li>\n<li>Izlaganje na DevForumu pomo\u0107i \u0107e nam prikupiti povratne informacije od produktnih programera. Nakon toga, mo\u0107i \u0107emo se pridru\u017eiti proizvodnim timovima i pomo\u0107i im u rje\u0161avanju zadatka objavljivanja podataka, organizirati obuke za timove. <\/li>\n<\/ol>\n<p><\/p>\n<h2 id=\"potreblenie-dannyh\">Potro\u0161nja podataka<\/h2>\n<p><\/p>\n<p>Sada sam puno govorio o objavljivanju podataka. No, postoji i potro\u0161nja. \u0160to je s tim pitanjem?<\/p>\n<p><\/p>\n<p>Imamo sjajan BI tim koji pi\u0161e vrlo slo\u017eene izvje\u0161taje za upravlja\u010dku kompaniju. Unutar Dodo IS-a postoji mnogo izvje\u0161taja za na\u0161e partnere koji im poma\u017eu u upravljanju pizzerijama. U na\u0161em novom modelu, razmi\u0161ljamo o njima kao o potro\u0161a\u010dima podataka, koji imaju svoje domene podataka. Potro\u0161a\u010di \u0107e biti odgovorni za svoje vlastite domene. Ponekad se domena potro\u0161a\u010da mo\u017ee opisati jednim upitom u analiti\u010dkom skladi\u0161tu \u2013 i to je dobro. No, shva\u0107amo da to ne\u0107e uvijek funkcionirati. Zato \u017eelimo da platforma koju \u0107emo stvoriti za produkcijske timove bude tako\u0111er upotrebljiva za potro\u0161a\u010de podataka (jer u slu\u010daju izvje\u0161taja unutar Dodo IS-a \u2013 to \u0107e biti isti timovi). <\/p>\n<p><\/p>\n<p>Ovako vidimo rad s podacima u Dodo Pizza Engineering. Rado \u0107emo pro\u010ditati va\u0161e misli o tome u komentarima.<\/p>\n<p>Izvor: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/dodopizzaio\/blog\/475476\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041f\u0440\u0438\u0432\u0435\u0442, \u0425\u0430\u0431\u0440! \u041c\u044b \u0432 Dodo Pizza Engineering \u043e\u0447\u0435\u043d\u044c \u043b\u044e\u0431\u0438\u043c \u0434\u0430\u043d\u043d\u044b\u0435 (\u0430 \u043a\u0442\u043e \u0438\u0445 \u0441\u0435\u0439\u0447\u0430\u0441 \u043d\u0435 \u043b\u044e\u0431\u0438\u0442?). \u0421\u0435\u0439\u0447\u0430\u0441 \u0431\u0443\u0434\u0435\u0442 \u0438\u0441\u0442\u043e\u0440\u0438\u044f \u043e \u0442\u043e\u043c, \u043a\u0430\u043a \u043d\u0430\u043a\u043e\u043f\u0438\u0442\u044c \u0432\u0441\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u043c\u0438\u0440\u0430 Dodo Pizza \u0438 \u0434\u0430\u0442\u044c \u043b\u044e\u0431\u043e\u043c\u0443 \u0441\u043e\u0442\u0440\u0443\u0434\u043d\u0438\u043a\u0443 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 \u0443\u0434\u043e\u0431\u043d\u044b\u0439 \u0434\u043e\u0441\u0442\u0443\u043f \u043a \u044d\u0442\u043e\u043c\u0443 \u043c\u0430\u0441\u0441\u0438\u0432\u0443 \u0434\u0430\u043d\u043d\u044b\u0445. \u0417\u0430\u0434\u0430\u0447\u0430 \u043f\u043e\u0434 \u0437\u0432\u0451\u0437\u0434\u043e\u0447\u043a\u043e\u0439: \u0441\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u043d\u0435\u0440\u0432\u044b \u043a\u043e\u043c\u0430\u043d\u0434\u044b Data Engineering. \u041a\u0430\u043a \u043d\u0430\u0441\u0442\u043e\u044f\u0449\u0438\u0435 \u041f\u043b\u044e\u0448\u043a\u0438\u043d\u044b, \u043c\u044b \u043a\u043e\u043f\u0438\u043c \u0432\u0441\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u0443\u044e \u0438\u043d\u0444\u0443 \u043e [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":52729,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-52728","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.4 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041f\u0440\u0438\u0432\u0435\u0442, \u0425\u0430\u0431\u0440! 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