{"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\/ro\/blog\/administrirovanie\/data-mesh-kak-rabotat-s-dannymi-bez-monolita","title":{"rendered":"Data Mesh: cum s\u0103 lucrezi cu datele f\u0103r\u0103 un monolit","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Salut, Habr! La echipa Dodo Pizza Engineering, ne plac foarte mult datele (cine nu le iube\u0219te acum?). Acum va fi o poveste despre cum s\u0103 acumul\u0103m toate datele din lumea Dodo Pizza \u0219i s\u0103 oferim oric\u0103rui angajat al companiei acces convenabil la acest imens set de date. Sarcina cu stea: s\u0103 p\u0103str\u0103m nervii echipei de inginerie a datelor.<\/p>\n<p>\n<img decoding=\"async\" alt=\"Data Mesh: cum s\u0103 lucrezi cu datele f\u0103r\u0103 un monolit\" 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>Ca adev\u0103ra\u021bi Pliu\u0219kin, adun\u0103m diverse informa\u021bii despre activitatea pizz\u0103riilor noastre:<\/p>\n<p><\/p>\n<ul>\n<li>ne amintim toate comenzile utilizatorilor; <\/li>\n<li>\u0219tim c\u00e2t timp a durat prepararea primei pizza din Syktyvkar;<\/li>\n<li>vedem c\u00e2t timp pizza se r\u0103ce\u0219te pe raftul de \u00eenc\u0103lzire din Voronezh chiar acum; <\/li>\n<li>stoc\u0103m date despre desfacerile de produse;<\/li>\n<li>\u0219i multe altele. <\/li>\n<\/ul>\n<p><\/p>\n<p>Pentru gestionarea datelor, Dodo Pizza are \u00een prezent mai multe echipe, una dintre ele fiind echipa de inginerie a datelor. Acum, \u00een fa\u021ba noastr\u0103 (adic\u0103 a noastr\u0103) se afl\u0103 sarcina: s\u0103 oferim oric\u0103rui angajat al companiei acces convenabil la acest imens set de date. <\/p>\n<p><\/p>\n<p>C\u00e2nd am \u00eenceput s\u0103 ne g\u00e2ndim la cum s\u0103 facem acest lucru \u0219i am \u00eenceput s\u0103 discut\u0103m despre sarcin\u0103, am g\u0103sit o abordare foarte interesant\u0103 pentru gestionarea datelor - <noindex><a rel=\"nofollow\" href=\"https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html\">Data Mesh<\/a><\/noindex> (la link g\u0103si\u021bi un articol gastronomic uria\u0219). Ideile sale s-au potrivit foarte bine cu viziunea noastr\u0103 despre cum vrem s\u0103 construim sistemul nostru. \u00cen continuare, \u00een articolul nostru va fi revizuit\u0103 abordarea \u0219i cum vedem implementarea ei \u00een Dodo Pizza Engineering.<\/p>\n<p><\/p>\n<h2 id=\"chto-my-imeem-v-vidu-pod-dannymi\">Ce \u00een\u021belegem prin \u201edate\u201d<\/h2>\n<p><\/p>\n<p>Pentru \u00eenceput, s\u0103 ne clarific\u0103m ce \u00een\u021belegem prin date la Dodo Pizza Engineering:<\/p>\n<p><\/p>\n<ul>\n<li>Evenimentele pe care le trimit serviciile (avem un bus comun construit cu RabbitMQ);<\/li>\n<li>\u00cenregistr\u0103rile din baza de date (pentru noi aceasta este MySQL \u0219i CosmosDB);<\/li>\n<li>Clickstream de pe aplica\u021bia mobil\u0103 \u0219i site.<\/li>\n<\/ul>\n<p><\/p>\n<p>Pentru ca afacerea Dodo Pizza s\u0103 poat\u0103 utiliza aceste date \u0219i s\u0103 se bazeze pe ele, este important s\u0103 \u00eendeplineasc\u0103 urm\u0103toarele condi\u021bii:<\/p>\n<p><\/p>\n<ul>\n<li><strong>Acestea trebuie s\u0103 fie integrale.<\/strong> Trebuie s\u0103 fim siguri c\u0103 nu modific\u0103m datele \u00een timpul proces\u0103rii, stoc\u0103rii \u0219i afi\u0219\u0103rii lor. Dac\u0103 afacerea nu poate avea \u00eencredere \u00een datele noastre, atunci acestea nu vor avea nicio valoare.<\/li>\n<li><strong>Acestea trebuie s\u0103 aib\u0103 marcaj temporal \u0219i s\u0103 nu fie suprascrise.<\/strong> Aceasta \u00eenseamn\u0103 c\u0103 dorim s\u0103 avem posibilitatea de a reveni \u00een orice moment \u0219i de a examina datele din acel interval de timp. De exemplu, s\u0103 \u0219tim c\u00e2te pizza au fost v\u00e2ndute pe 8 iulie 2018.<\/li>\n<li><strong>Acestea trebuie s\u0103 fie fiabile.<\/strong> \u00cen procesul de colectare \u0219i stocare a datelor, trebuie s\u0103 p\u0103str\u0103m nu doar integritatea, ci \u0219i fiabilitatea. Nu putem pierde date, feronestim, pentru c\u0103 odat\u0103 cu acestea pierdem \u00eencrederea clien\u021bilor no\u0219tri (at\u00e2t externi, c\u00e2t \u0219i interni).<\/li>\n<li><strong>Acestea trebuie s\u0103 aib\u0103 un schelet stabil \u2013 scriem interog\u0103ri pentru aceste date.<\/strong> Nu ne-ar pl\u0103cea s\u0103 se schimbe at\u00e2t de mult, \u00eenc\u00e2t interog\u0103rile noastre s\u0103 nu mai func\u021bioneze, odat\u0103 cu modific\u0103rile codului aplica\u021biei \u0219i cu refactorizarea. Cine scrie interog\u0103rile nu va \u0219ti niciodat\u0103 c\u0103 a\u021bi f\u0103cut refactorizare p\u00e2n\u0103 c\u00e2nd totul nu se va strica complet. Nu ne dorim s\u0103 afl\u0103m asta de la clien\u021bi.<\/li>\n<\/ul>\n<p><\/p>\n<p>Av\u00e2nd \u00een vedere toate aceste cerin\u021be, am ajuns la concluzia c\u0103 datele \u00een Dodo reprezint\u0103 un produs. La fel ca API-ul public al serviciului. Astfel, echipa care de\u021bine datele ar trebui s\u0103 fie aceea\u0219i echip\u0103 care de\u021bine serviciul. De asemenea, schimb\u0103rile \u00een schema datelor ar trebui s\u0103 fie \u00eentotdeauna compatibile cu versiunile anterioare.<\/p>\n<p><\/p>\n<h2 id=\"tradicionnyy-podhod--data-lake\">Abordarea tradi\u021bional\u0103 \u2013 Data Lake<\/h2>\n<p><\/p>\n<p>Pentru a rezolva sarcinile de stocare \u0219i procesare sigur\u0103 a Big Data, exist\u0103 o abordare tradi\u021bional\u0103, acceptat\u0103 \u00een multe companii care lucreaz\u0103 cu un astfel de volum de informa\u021bii \u2013 Data Lake. \u00cen cadrul acestei abord\u0103ri, inginerii de date colecteaz\u0103 informa\u021biile din toate componentele sistemului \u0219i le stocheaz\u0103 \u00eentr-un singur depozit mare (aceasta poate fi, de exemplu, Hadoop, Azure Kusto, Apache Cassandra sau chiar un replica MySQL, dac\u0103 datele \u00eencap \u00een ea). <\/p>\n<p><\/p>\n<p>Apoi, ace\u0219ti ingineri scriu interog\u0103ri pentru acest depozit. Implementarea acestei abord\u0103ri \u00een Dodo Pizza Engineering presupune c\u0103 echipa de Data Engineering va de\u021bine schema de date \u00een depozitul analitic. <\/p>\n<p><\/p>\n<p>\u00cen acest caz, echipa devine foarte trist\u0103 \u0219i iat\u0103 de ce:<\/p>\n<p><\/p>\n<ul>\n<li>Ea trebuie s\u0103 urm\u0103reasc\u0103 schimb\u0103rile \u00een <strong>TOATE<\/strong> serviciile din cadrul companiei. \u0218i acestea sunt multe, iar schimb\u0103rile sunt foarte multe (\u00een medie, noi fuzion\u0103m ~100 pull requests pe s\u0103pt\u0103m\u00e2n\u0103, iar multe servicii nu efectueaz\u0103 deloc pull requests).<\/li>\n<li>C\u00e2nd schema de date se schimb\u0103, Product Owner-ul \u0219i echipa care modific\u0103 schema de date trebuie s\u0103 a\u0219tepte p\u00e2n\u0103 Data Engineering finalizeaz\u0103 codul necesar pentru a sus\u021bine modific\u0103rile. \u00cen acela\u0219i timp, avem de mult timp proiecte \u00een derulare, iar situa\u021bia \u00een care o echip\u0103 a\u0219teapt\u0103 o alt\u0103 echip\u0103 este foarte rar\u0103. \u0218i nu dorim ca aceasta s\u0103 devin\u0103 o parte \u201enormal\u0103\u201d a procesului de dezvoltare.<\/li>\n<li>Ea trebuie s\u0103 fie implicat\u0103 \u00een <strong>\u00ceNTREG <\/strong> afacerea companiei. Re\u021beaua de pizzerii pare o afacere simpl\u0103, dar doar a\u0219a pare. Este foarte dificil s\u0103 aduni \u00een aceea\u0219i echip\u0103 suficiente competen\u021be pentru a construi un model de date adecvat pentru \u00eentreaga companie.<\/li>\n<li>Este un singur punct de e\u0219ec. De fiecare dat\u0103 c\u00e2nd trebuie s\u0103 schimbi datele pe care le returneaz\u0103 serviciul sau s\u0103 scrii o interogare, toate aceste sarcini ajung la echipa de Data Engineering. \u00cen final, echipa se confrunt\u0103 cu un backlog supra\u00eenc\u0103rcat.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>Astfel, echipa se afl\u0103 la intersec\u021bia unei cantit\u0103\u021bi enorme de nevoi \u0219i probabil nu va putea s\u0103 le satisfac\u0103. \u00cen plus, se va afla \u00eentr-o stare constant\u0103 de criz\u0103 \u0219i stres. Nu ne dorim a\u0219a ceva. Prin urmare, trebuie s\u0103 ne g\u00e2ndim la cum s\u0103 rezolv\u0103m aceste probleme \u0219i s\u0103 ob\u021binem, totodat\u0103, capacitatea de a analiza datele.<\/p><\/blockquote>\n<p><\/p>\n<h2 id=\"peretekaya-ot-data-lake-k-data-mesh\">Trec\u00e2nd de la Data Lake la Data Mesh<\/h2>\n<p><\/p>\n<p>Din fericire, aceast\u0103 \u00eentrebare nu ne-a fost adresat\u0103 doar nou\u0103. De fapt, o astfel de problem\u0103 a fost deja rezolvat\u0103 \u00een industrie (aleluia!). Doar c\u0103 \u00eentr-un alt domeniu: desf\u0103\u0219urarea aplica\u021biilor. Da, m\u0103 refer la abordarea DevOps, unde echipa stabile\u0219te cum trebuie s\u0103 fie desf\u0103\u0219urat produsul pe care \u00eel creeaz\u0103. <\/p>\n<p><\/p>\n<p>O abordare similar\u0103 pentru rezolvarea problemelor Data Lake a fost propus\u0103 de Zhamak Dehghani, consultant ThoughtWorks. Observ\u00e2nd cum abord\u0103ri similare sunt folosite de Netflix \u0219i Spotify, ea a scris un articol uimitor <noindex><a rel=\"nofollow\" href=\"https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html\">How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh<\/a><\/noindex>(linkul c\u0103tre acest articol a fost la \u00eenceputul articolului). Principalele idei pe care le-am extras din acesta sunt:<\/p>\n<p><\/p>\n<ul>\n<li>\u00cemp\u0103r\u021birea unui Data Lake mare \u00een domenii de date care se aseam\u0103n\u0103 foarte mult cu domeniile din design-ul bazat pe domenii. Fiecare domeniu este un mic bounded context.<\/li>\n<li>Echipele de func\u021bionalitate (Feature Team) care sunt responsabile pentru domeniile DDD sunt, de asemenea, responsabile pentru domeniile corespunz\u0103toare de date. Ele p\u0103streaz\u0103 schema, fac modific\u0103ri \u0219i \u00eencarc\u0103 datele \u00een ea. \u00cen plus, \u0219tiu totul: cum s\u0103 schimbe \u00eenc\u0103rcarea datelor f\u0103r\u0103 a provoca defecte atunci c\u00e2nd aplica\u021bia se modific\u0103. Cuno\u0219tin\u021bele nu dispar nic\u0103ieri. Pentru a accesa datele, nu trebuie s\u0103 mearg\u0103 nic\u0103ieri. Echipa \u00een sine gestioneaz\u0103 \u00eentregul ciclu de dezvoltare, de la modificarea datelor opera\u021bionale la furnizarea datelor analitice c\u0103tre ter\u021bi. O singur\u0103 echip\u0103 de\u021bine tot ce \u021bine de domeniu (at\u00e2t domeniul de afaceri, c\u00e2t \u0219i domeniul de date).<\/li>\n<li>Data Engineer \u2013 un rol \u00een cadrul echipei de func\u021bionalitate. Nu trebuie neap\u0103rat s\u0103 fie o persoan\u0103 separat\u0103, dar este esen\u021bial ca echipa s\u0103 aib\u0103 aceast\u0103 competen\u021b\u0103.<\/li>\n<\/ul>\n<p><\/p>\n<h2 id=\"a-v-eto-vremya-komanda-data-engineering\">\u00centre timp, echipa de Data Engineering...<\/h2>\n<p><\/p>\n<p>Dac\u0103 ne imagin\u0103m c\u0103 totul se realizeaz\u0103 cu un simplu click, r\u0103m\u00e2ne s\u0103 r\u0103spundem la dou\u0103 \u00eentreb\u0103ri:<\/p>\n<p><\/p>\n<p><strong>Ce va face acum echipa de Data Engineering?<\/strong> \u00cen Dodo Pizza Engineering exist\u0103 deja o echip\u0103 de platform\u0103\/SRE. Sarcina acesteia este de a oferi dezvoltatorilor instrumente pentru desf\u0103\u0219urarea simpl\u0103 a serviciilor. Echipa de Data Engineering va \u00eendeplini aceea\u0219i rol, dar pentru date. <\/p>\n<p><\/p>\n<p>Transformarea datelor opera\u021bionale \u00een date analitice este un proces complex. A face datele analitice accesibile pentru \u00eentreaga companie este \u0219i mai complicat. Echipa de Data Engineering se va ocupa exact de aceste probleme. <\/p>\n<p><\/p>\n<p>Ne propunem s\u0103 oferim echipei Feature Team un set convenabil de instrumente \u0219i practici, care s\u0103 le permit\u0103 s\u0103 publice date din serviciul lor pentru restul companiei. De asemenea, vom fi responsabili pentru p\u0103r\u021bile infrastructurale generale ale pipeline-ului de date (cozi, stocare sigur\u0103, clustere pentru efectuarea transform\u0103rilor asupra datelor). <\/p>\n<p><\/p>\n<p><strong>Cum vor ap\u0103rea abilit\u0103\u021bile Data Engineer \u00een interiorul echipei Feature Team?<\/strong> Cu echipa Feature Team este mai complicat. Desigur, am putea \u00eencerca s\u0103 angaj\u0103m c\u00e2te un Data Engineer \u00een fiecare dintre echipele noastre. Dar este foarte complicat. S\u0103 g\u0103se\u0219ti o persoan\u0103 cu un background solid \u00een procesarea datelor \u0219i s\u0103 o convingi s\u0103 lucreze \u00een cadrul echipei de produs este dificil. <\/p>\n<p><\/p>\n<p>Un mare avantaj pentru Dodo este c\u0103 ne place formarea intern\u0103. A\u0219a c\u0103 planul nostru acum este: echipa de Data Engineering \u00eencepe s\u0103 publice date din anumite servicii, pl\u00e2nge, se \u00een\u021beap\u0103, dar continu\u0103 s\u0103 m\u0103n\u00e2nce cactus. Odat\u0103 ce vom \u00een\u021belege c\u0103 avem un proces gata pentru publicare, vom \u00eencepe s\u0103 vorbim despre el \u00een echipa Feature Team. <\/p>\n<p><\/p>\n<p>Avem c\u00e2teva modalit\u0103\u021bi de a face acest lucru: <\/p>\n<p><\/p>\n<ol>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/dodopizzaio\/blog\/462033\/\">DevForum<\/a><\/noindex>, pe care vom explica cum arat\u0103 procesul pe care l-am creat, ce instrumente exist\u0103 \u0219i cum pot fi folosite cel mai eficient. <\/li>\n<li>Prezentarea la DevForum ne va ajuta s\u0103 adun\u0103m feedback de la dezvoltatorii de produs. Dup\u0103 aceasta, putem s\u0103 ne al\u0103tur\u0103m echipelor de produs \u0219i s\u0103 le ajut\u0103m s\u0103 rezolve problemele legate de publicarea datelor, organiz\u00e2nd antrenamente pentru echipe. <\/li>\n<\/ol>\n<p><\/p>\n<h2 id=\"potreblenie-dannyh\">Consumarea datelor<\/h2>\n<p><\/p>\n<p>Acum am vorbit mult despre publicarea datelor. Dar exist\u0103 \u0219i consumul. Ce este de spus \u00een aceast\u0103 privin\u021b\u0103?<\/p>\n<p><\/p>\n<p>Avem o echip\u0103 BI minunat\u0103 care elaboreaz\u0103 rapoarte foarte complexe pentru compania de management. \u00cen interiorul Dodo IS exist\u0103 multe rapoarte pentru partenerii no\u0219tri, care \u00eei ajut\u0103 s\u0103 gestioneze pizzeriile. \u00cen noul nostru model, ne g\u00e2ndim la ei ca la consumatori de date care au propriile domenii de date. Iar ace\u0219ti consumatori vor fi responsabili pentru propriile lor domenii. Uneori, domeniul consumatorului poate fi descris printr-o singur\u0103 interogare \u00een depozitul analitic \u2013 \u0219i este bine. Dar \u00een\u021belegem c\u0103 acest lucru nu va func\u021biona \u00eentotdeauna. De aceea, ne dorim ca platforma pe care o vom crea pentru echipele de produse s\u0103 poat\u0103 fi folosit\u0103 \u0219i de consumatorii de date (\u00een cazul rapoartelor din interiorul Dodo IS, vor fi acelea\u0219i echipe). <\/p>\n<p><\/p>\n<p>A\u0219a vedem noi lucrul cu datele \u00een Dodo Pizza Engineering. A\u0219tept\u0103m cu interes p\u0103rerile voastre despre acest subiect \u00een comentarii.<\/p>\n<p>Sursa: <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 5.0.2.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041f\u0440\u0438\u0432\u0435\u0442, \u0425\u0430\u0431\u0440! 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