{"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\/sv\/blog\/administrirovanie\/data-mesh-kak-rabotat-s-dannymi-bez-monolita","title":{"rendered":"Data Mesh: hur man arbetar med data utan monoliter","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Hej, Habr! Vi p\u00e5 Dodo Pizza Engineering \u00e4lskar verkligen data (vem g\u00f6r inte det nuf\u00f6rtiden?). H\u00e4r kommer en historia om hur man samlar in all data i Dodo Pizza-v\u00e4rlden och ger varje anst\u00e4lld p\u00e5 f\u00f6retaget enkel tillg\u00e5ng till den massiva datam\u00e4ngden. Uppdraget med stj\u00e4rna: att bevara teamet Data Engineerings nerver.<\/p>\n<p>\n<img decoding=\"async\" alt=\"Data Mesh: hur man arbetar med data utan monoliter\" 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>Som riktiga Pljushkin samlar vi all m\u00f6jlig information om driften av v\u00e5ra pizzerior:<\/p>\n<p><\/p>\n<ul>\n<li>vi minns alla anv\u00e4ndares best\u00e4llningar; <\/li>\n<li>vi vet hur l\u00e5ng tid det tog att g\u00f6ra den allra f\u00f6rsta pizzan i Syktyvkar;<\/li>\n<li>vi ser hur l\u00e4nge en pizza kyls ner p\u00e5 v\u00e4rmehyllan i Voronezh just nu; <\/li>\n<li>vi lagrar data om avskrivning av produkter;<\/li>\n<li>och mycket, mycket mer. <\/li>\n<\/ul>\n<p><\/p>\n<p>Flera team ansvarar f\u00f6r datan p\u00e5 Dodo Pizza just nu, ett av dem \u00e4r teamet Data Engineering. Just nu st\u00e5r vi (det vill s\u00e4ga vi) inf\u00f6r uppgiften: att ge varje anst\u00e4lld i f\u00f6retaget enkel tillg\u00e5ng till denna massiva datam\u00e4ngd. <\/p>\n<p><\/p>\n<p>N\u00e4r vi b\u00f6rjade t\u00e4nka p\u00e5 hur vi skulle g\u00f6ra detta och b\u00f6rjade diskutera uppgiften, hittade vi en mycket intressant metod f\u00f6r datastyrning \u2013 <noindex><a rel=\"nofollow\" href=\"https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html\">Data Mesh<\/a><\/noindex> (i l\u00e4nken hittar du en fantastisk och omfattande artikel). Deras id\u00e9er passade mycket v\u00e4l in i v\u00e5r syn p\u00e5 hur vi vill bygga v\u00e5rt system. H\u00e4r i artikeln kommer v\u00e5r nyt\u00e4nkning av metoden och hur vi ser dess implementering i Dodo Pizza Engineering.<\/p>\n<p><\/p>\n<h2 id=\"chto-my-imeem-v-vidu-pod-dannymi\">Vad menar vi med \"data\"<\/h2>\n<p><\/p>\n<p>F\u00f6rst och fr\u00e4mst l\u00e5t oss definiera vad vi menar med data p\u00e5 Dodo Pizza Engineering:<\/p>\n<p><\/p>\n<ul>\n<li>H\u00e4ndelser som skickas av tj\u00e4nster (vi har en gemensam buss byggd med RabbitMQ);<\/li>\n<li>Poster i databasen (f\u00f6r oss \u00e4r det MySQL och CosmosDB);<\/li>\n<li>Clickstream fr\u00e5n mobilapplikationen och webbplatsen.<\/li>\n<\/ul>\n<p><\/p>\n<p>F\u00f6r att Dodo Pizza ska kunna anv\u00e4nda denna data och lita p\u00e5 den \u00e4r det viktigt att f\u00f6ljande villkor uppfylls:<\/p>\n<p><\/p>\n<ul>\n<li><strong>De m\u00e5ste vara hela.<\/strong> Vi m\u00e5ste vara s\u00e4kra p\u00e5 att vi inte \u00e4ndrar datan under dess bearbetning, lagring och visning. Om verksamheten inte kan lita p\u00e5 v\u00e5r data, kommer den inte att vara till n\u00e5gon nytta.<\/li>\n<li><strong>De m\u00e5ste ha tidsst\u00e4mplar och f\u00e5r inte skrivas \u00f6ver.<\/strong> Det betyder att vi alltid vill ha m\u00f6jlighet att g\u00e5 tillbaka och se datan fr\u00e5n den tidpunkten. Till exempel, att f\u00e5 veta hur m\u00e5nga pizzor som s\u00e5ldes den 8 juli 2018.<\/li>\n<li><strong>De m\u00e5ste vara p\u00e5litliga.<\/strong> Under data collection and storage, we must not only maintain integrity but also reliability. We cannot afford to lose data, time slices, because with them we lose the trust of our clients (both external and internal).<\/li>\n<li><strong>They must have a stable scheme \u2013 we write queries for this data.<\/strong> We would really prefer that with changes to the application code, during refactoring, they do not change so much that our queries cease to work. Those who write queries will never know that you made a refactor until everything breaks down completely. We wouldn\u2019t want to find out about this from clients.<\/li>\n<\/ul>\n<p><\/p>\n<p>Considering all these requirements, we concluded that data in Dodo is a product. Just like the public API of the service. Accordingly, the same team that owns the service should own the data. Additionally, changes to the data schema must always be backward-compatible.<\/p>\n<p><\/p>\n<h2 id=\"tradicionnyy-podhod--data-lake\">The traditional approach is Data Lake.<\/h2>\n<p><\/p>\n<p>To address the challenges of reliable storage and processing of big data, there is a traditional approach adopted by many companies that deal with such a pool of information \u2013 Data Lake. Within this approach, data engineers collect information from all system components and store it in one large repository (this could be, for instance, Hadoop, Azure Kusto, Apache Cassandra, or even a MySQL replica if the data fits in it). <\/p>\n<p><\/p>\n<p>Then these very engineers write queries against such a repository. The implementation of this approach at Dodo Pizza Engineering implies that the Data Engineering team will own the data schema in the analytical repository. <\/p>\n<p><\/p>\n<p>In this scenario, the team becomes very sad kittens, and here's why:<\/p>\n<p><\/p>\n<ul>\n<li>They must keep track of changes in <strong>ALL<\/strong> services within the company. And there are many of them, and changes occur quite frequently (on average, we merge ~100 pull requests per week, and many services do not make pull requests at all).<\/li>\n<li>When the data schema changes, the product owner and the team changing the data schema must wait for the Data Engineering to write the necessary code to support the changes. At the same time, it's been quite a while since we have been implementing features, and the situation where one team waits for another is quite rare. We do not want this to become a 'normal' part of the development process.<\/li>\n<li>They must be immersed in <strong>EVERY <\/strong> F\u00f6retagets aff\u00e4r. En pizzakedja kan verka som en enkel verksamhet, men s\u00e5 \u00e4r det bara till synes. Det \u00e4r mycket sv\u00e5rt att samla tillr\u00e4ckliga kompetenser i ett och samma team f\u00f6r att bygga en adekvat datamodell f\u00f6r hela f\u00f6retaget.<\/li>\n<li>Den utg\u00f6r en enskild felpunkt. Varje g\u00e5ng det beh\u00f6vs \u00e4ndra de data som tj\u00e4nsten returnerar eller skriva en f\u00f6rfr\u00e5gan, hamnar dessa uppgifter p\u00e5 Data Engineering-teamet. Resultatet blir att teamet har en \u00f6verbelastad backlogg.<\/li>\n<\/ul>\n<p><\/p>\n<blockquote><p>Det visar sig att teamet befinner sig i sk\u00e4rningspunkten av ett stort antal behov och knappast kan tillfredsst\u00e4lla dem. Samtidigt kommer de att vara i st\u00e4ndig tidsn\u00f6d och stress. Det vill vi verkligen undvika. D\u00e4rf\u00f6r m\u00e5ste vi t\u00e4nka p\u00e5 hur vi kan l\u00f6sa dessa problem och samtidigt f\u00e5 m\u00f6jlighet att analysera data.<\/p><\/blockquote>\n<p><\/p>\n<h2 id=\"peretekaya-ot-data-lake-k-data-mesh\">Fr\u00e5n Data Lake till Data Mesh<\/h2>\n<p><\/p>\n<p>Som tur \u00e4r har inte bara vi st\u00e4llt denna fr\u00e5ga. Faktum \u00e4r att denna typ av problem redan har l\u00f6sts inom industrin (halleluja!). Men inom ett annat omr\u00e5de: distribution av applikationer. Ja, jag pratar om DevOps-ansatsen, d\u00e4r teamet best\u00e4mmer hur produkten som de skapar ska distribueras. <\/p>\n<p><\/p>\n<p>En liknande ansats f\u00f6r att l\u00f6sa Data Lake-problem f\u00f6reslogs av Zhamak Dehghani, konsult p\u00e5 ThoughtWorks. Genom att observera hur liknande problem hanteras av Netflix och Spotify skrev hon en fantastisk artikel <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>(l\u00e4nken till den var i b\u00f6rjan av artikeln). Huvudid\u00e9erna som vi tog med oss fr\u00e5n den \u00e4r:<\/p>\n<p><\/p>\n<ul>\n<li>Dela upp den stora Data Lake i datadom\u00e4ner, vilka liknar dom\u00e4ner inom domain-driven design. Varje dom\u00e4n \u00e4r en liten bounded context.<\/li>\n<li>Feature Team som ansvarar f\u00f6r DDD-dom\u00e4ner ansvarar ocks\u00e5 f\u00f6r motsvarande datadom\u00e4ner. De lagrar strukturen, g\u00f6r \u00e4ndringar i den, laddar upp data i den. De vet ocks\u00e5 hur man \u00e4ndrar datainl\u00e4sningen utan att n\u00e5got g\u00e5r s\u00f6nder n\u00e4r applikationen \u00e4ndras. Kunskapen f\u00f6rsvinner inte. F\u00f6r att \u00f6ppna data beh\u00f6ver de inte g\u00e5 n\u00e5gonstans. Teamet har hela utvecklingscykeln, fr\u00e5n att \u00e4ndra operationella data till att tillhandah\u00e5lla analytiska data till tredje part. Ett team har fullt ansvar f\u00f6r allt som r\u00f6r dom\u00e4nen (b\u00e5de aff\u00e4rsdoma\u0308nen och datadom\u00e4nerna).<\/li>\n<li>Data Engineer \u2013 en roll inom Feature Team. Det beh\u00f6ver inte vara en separat person, men det \u00e4r absolut n\u00f6dv\u00e4ndigt att teamet har denna kompetens.<\/li>\n<\/ul>\n<p><\/p>\n<h2 id=\"a-v-eto-vremya-komanda-data-engineering\">\u0410 \u0432 \u044d\u0442\u043e \u0432\u0440\u0435\u043c\u044f \u043a\u043e\u043c\u0430\u043d\u0434\u0430 Data Engineering&#8230;<\/h2>\n<p><\/p>\n<p>Om vi t\u00e4nker oss att allt detta kan genomf\u00f6ras med ett fingerkn\u00e4pp, kvarst\u00e5r det att besvara tv\u00e5 fr\u00e5gor:<\/p>\n<p><\/p>\n<p><strong>Vad kommer Data Engineering-teamet att syssla med nu?<\/strong> Inom Dodo Pizza Engineering finns det redan ett plattforms-\/SRE-team. Deras uppgift \u00e4r att ge utvecklarna verktyg f\u00f6r enkel distribution av tj\u00e4nster. Data Engineering-teamet kommer att ha en liknande roll, men f\u00f6r data. <\/p>\n<p><\/p>\n<p>Att omvandla operativa data till analytiska \u00e4r en komplex process. Att g\u00f6ra de analytiska data tillg\u00e4ngliga f\u00f6r hela f\u00f6retaget \u00e4r \u00e4nnu sv\u00e5rare. Det \u00e4r dessa problem som Data Engineering-teamet kommer att fokusera p\u00e5. <\/p>\n<p><\/p>\n<p>Vi planerar att ge Feature Team ett bekv\u00e4mt verktygspaket och metoder, s\u00e5 att de kan publicera data fr\u00e5n sina tj\u00e4nster till resten av f\u00f6retaget. Vi kommer ocks\u00e5 att vara ansvariga f\u00f6r de gemensamma infrastrukturella delarna av data-pipeline (k\u00f6er, p\u00e5litlig lagring, kluster f\u00f6r datatransformationer). <\/p>\n<p><\/p>\n<p><strong>Hur kommer Data Engineer-kunskaper att uppst\u00e5 inom Feature Team?<\/strong> Det \u00e4r mer komplicerat med Feature Team. Sj\u00e4lvklart skulle vi kunna f\u00f6rs\u00f6ka anst\u00e4lla en Data Engineer i varje team. Men det \u00e4r v\u00e4ldigt sv\u00e5rt. Att hitta n\u00e5gon med en bra bakgrund inom databehandling och \u00f6vertyga dem att arbeta i produktteam \u00e4r en utmaning. <\/p>\n<p><\/p>\n<p>En stor f\u00f6rdel med Dodo \u00e4r att vi \u00e4lskar intern utbildning. S\u00e5 nu \u00e4r v\u00e5r plan denna: Data Engineering-teamet b\u00f6rjar publicera data fr\u00e5n vissa tj\u00e4nster, de gr\u00e5ter, sticks, men forts\u00e4tter att \u00e4ta kaktusen. S\u00e5 snart vi f\u00f6rst\u00e5r att vi har en f\u00e4rdig process f\u00f6r publicering kommer vi att b\u00f6rja ber\u00e4tta om den f\u00f6r Feature Team. <\/p>\n<p><\/p>\n<p>Vi har flera s\u00e4tt att g\u00f6ra detta: <\/p>\n<p><\/p>\n<ol>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/dodopizzaio\/blog\/462033\/\">DevForum<\/a><\/noindex>, d\u00e4r vi kommer att ber\u00e4tta hur den process vi har skapat ser ut, vilka verktyg som finns och hur de kan anv\u00e4ndas mest effektivt. <\/li>\n<li>Att tala p\u00e5 DevForum kommer att hj\u00e4lpa oss att samla in feedback fr\u00e5n produktutvecklare. Efter detta kan vi g\u00e5 med i produktteam och hj\u00e4lpa dem att l\u00f6sa problem med datpublicering, organisera tr\u00e4ning f\u00f6r team. <\/li>\n<\/ol>\n<p><\/p>\n<h2 id=\"potreblenie-dannyh\">Konsumtion av data<\/h2>\n<p><\/p>\n<p>Just nu har jag pratat mycket om publicering av data. Men det finns ocks\u00e5 konsumtion. Vad har vi f\u00f6r information om det?<\/p>\n<p><\/p>\n<p>Vi har ett fantastiskt BI-team som skriver mycket komplexa rapporter f\u00f6r f\u00f6rvaltningsbolaget. Inom Dodo IS finns det m\u00e5nga rapporter f\u00f6r v\u00e5ra partner som hj\u00e4lper dem att styra pizzerior. I v\u00e5r nya modell ser vi dem som datakonsumenter, som har sina egna datadom\u00e4ner. Det \u00e4r faktiskt konsumenterna som kommer att ansvara f\u00f6r sina egna dom\u00e4ner. Ibland kan konsumentens dom\u00e4n beskrivas med en enda fr\u00e5ga i analyslagret \u2013 och det \u00e4r bra. Men vi f\u00f6rst\u00e5r att detta inte alltid kommer att fungera. D\u00e4rf\u00f6r vill vi att den plattform vi skapar f\u00f6r produktteam ocks\u00e5 ska kunna anv\u00e4ndas av datakonsumenter (eftersom det i fallet med rapporterna inom Dodo IS \u2013 kommer att vara samma team). <\/p>\n<p><\/p>\n<p>S\u00e5 h\u00e4r ser vi p\u00e5 arbete med data inom Dodo Pizza Engineering. Vi ser fram emot att l\u00e4sa era tankar om detta i kommentarerna.<\/p>\n<p>K\u00e4lla: <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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