{"id":90783,"date":"2020-08-06T01:42:19","date_gmt":"2020-08-05T23:42:19","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1"},"modified":"2020-08-06T01:42:19","modified_gmt":"2020-08-05T23:42:19","slug":"kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/administrirovanie\/kak-bigquery-ot-google-demokratiziroval-analiz-dannyh-chast-1","title":{"rendered":"Cum a democratizat BigQuery de la Google analiza datelor. Partea 1.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><b><i>Bun\u0103, Habr! Chiar acum, OTUS a deschis \u00eenscrierea pentru noul flux al cursului <noindex><a rel=\"nofollow\" href=\"https:\/\/otus.pw\/wDZQ\/\">\u201eInginer de date\u201d<\/a><\/noindex>. \u00cen ajunul \u00eenceperii cursului, am preg\u0103tit \u00een mod tradi\u021bional pentru voi traducerea unui material interesant. <\/i><\/b><\/p>\n<p>\n\u00cen fiecare zi, peste o sut\u0103 de milioane de oameni viziteaz\u0103 Twitter pentru a afla ce se \u00eent\u00e2mpl\u0103 \u00een lume \u0219i pentru a discuta despre asta. Fiecare tweet \u0219i orice alt\u0103 ac\u021biune a utilizatorului genereaz\u0103 un eveniment disponibil pentru analiza intern\u0103 a datelor \u00een Twitter. Sute de angaja\u021bi analizeaz\u0103 \u0219i vizualizeaz\u0103 aceste date, iar \u00eembun\u0103t\u0103\u021birea experien\u021bei lor este prioritatea principal\u0103 pentru echipa Twitter Data Platform. <\/p>\n<p>Credem c\u0103 utilizatorii cu o gam\u0103 larg\u0103 de abilit\u0103\u021bi tehnice ar trebui s\u0103 aib\u0103 posibilitatea de a g\u0103si date \u0219i de a avea acces la instrumente de analiz\u0103 \u0219i vizualizare care func\u021bioneaz\u0103 bine, bazate pe SQL. Acest lucru ar permite unei noi grupi de utilizatori cu mai pu\u021bin background tehnic, inclusiv anali\u0219ti de date \u0219i manageri de produse, s\u0103 extrag\u0103 informa\u021bii din date, permi\u021b\u00e2ndu-le s\u0103 \u00een\u021beleag\u0103 mai bine \u0219i s\u0103 foloseasc\u0103 oportunit\u0103\u021bile Twitter. Astfel, democratiz\u0103m analiza datelor \u00een Twitter. <noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Pe m\u0103sur\u0103 ce ne \u00eembun\u0103t\u0103\u021bim instrumentele \u0219i capacit\u0103\u021bile pentru analiza intern\u0103 a datelor, am fost martorii unei \u00eembun\u0103t\u0103\u021biri a serviciului Twitter. Cu toate acestea, mai avem mult de crescut. Instrumentele curente, precum Scalding, necesit\u0103 expertiz\u0103 \u00een programare. Instrumentele de analiz\u0103 bazate pe SQL, cum ar fi Presto \u0219i Vertica, au probleme de performan\u021b\u0103 la scar\u0103 mare. De asemenea, avem o problem\u0103 cu distribu\u021bia datelor \u00eentre mai multe sisteme f\u0103r\u0103 acces constant la acestea.<\/p>\n<p>Anul trecut, am anun\u021bat despre <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.twitter.com\/engineering\/en_us\/topics\/infrastructure\/2018\/a-new-collaboration-with-google-cloud.html\">o nou\u0103 colaborare cu Google<\/a><\/noindex>, \u00een cadrul c\u0103reia transfer\u0103m p\u0103r\u021bi din <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.twitter.com\/engineering\/en_us\/topics\/infrastructure\/2019\/the-start-of-a-journey-into-the-cloud.html\">infrastructura noastr\u0103 de date<\/a><\/noindex> pe Google Cloud Platform (GCP). Am ajuns la concluzia c\u0103 instrumentele Google Cloud <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/products\/big-data\/\">Big Data<\/a><\/noindex> ne pot ajuta \u00een ini\u021biativele noastre de democratizare a analizei, vizualiz\u0103rii \u0219i \u00eenv\u0103\u021b\u0103rii automate \u00een Twitter:<\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery\/\">BigQuery<\/a><\/noindex>: un depozit de date corporative cu un motor SQL bazat pe <noindex><a rel=\"nofollow\" href=\"https:\/\/ai.google\/research\/pubs\/pub36632\">Dremel<\/a><\/noindex>, renumit pentru rapiditate, simplitate \u0219i care se descurc\u0103 cu <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery-ml\/docs\/bigqueryml-intro\">\u00eenv\u0103\u021barea automat\u0103<\/a><\/noindex>.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/datastudio.google.com\/overview\">Data Studio:<\/a><\/noindex> un instrument pentru vizualizarea datelor mari cu func\u021bii de colaborare, asem\u0103n\u0103toare cu Google Docs.<\/li>\n<\/ul>\n<p>\nDin acest articol ve\u021bi \u00eenv\u0103\u021ba despre experien\u021ba noastr\u0103 cu aceste instrumente: ce am realizat, ce am \u00eenv\u0103\u021bat \u0219i ce vom face \u00een continuare. Acum ne vom concentra pe analiza de tip batch \u0219i interactiv\u0103. Analiza \u00een timp real o vom discuta \u00een articolul urm\u0103tor.<\/p>\n<h2>Istoria depozitelor de date \u00een Twitter<\/h2>\n<p>\n\u00cenainte de a ne aprofunda \u00een BigQuery, merit\u0103 s\u0103 reamintim pe scurt istoria depozitelor de date \u00een Twitter. \u00cen 2011, analiza datelor \u00een Twitter era realizat\u0103 \u00een Vertica \u0219i Hadoop. Pentru a construi lucr\u0103rile MapReduce ale Hadoop, am folosit Pig. \u00cen 2012 am \u00eenlocuit Pig cu Scalding, care avea un API Scala cu avantaje precum posibilitatea de a crea pipeline-uri complexe \u0219i u\u0219urin\u021ba \u00een testare. Totu\u0219i, pentru mul\u021bi anali\u0219ti de date \u0219i manageri de produs, care se sim\u021beau mai confortabil lucr\u00e2nd cu SQL, aceasta reprezenta o curb\u0103 de \u00eenv\u0103\u021bare destul de abrupt\u0103. Aproximativ \u00een 2016 am \u00eenceput s\u0103 folosim Presto ca interfa\u021b\u0103 SQL pentru datele Hadoop. Spark oferea o interfa\u021b\u0103 Python, ceea ce \u00eel f\u0103cea o alegere bun\u0103 pentru cercetarea ad hoc a datelor \u0219i \u00eenv\u0103\u021barea automat\u0103.<\/p>\n<p>\u00cencep\u00e2nd din 2018, am folosit urm\u0103toarele instrumente pentru analiza \u0219i vizualizarea datelor:<\/p>\n<ul>\n<li>Scalding pentru pipeline-uri de produc\u021bie<\/li>\n<li>Scalding \u0219i Spark pentru analize ad hoc \u0219i \u00eenv\u0103\u021bare automat\u0103<\/li>\n<li>Vertica \u0219i Presto pentru analize SQL ad hoc \u0219i interactive <\/li>\n<li>Druid pentru interac\u021biune mic\u0103, exploratorie \u0219i acces cu \u00eent\u00e2rziere mic\u0103 la metricile de serii temporale<\/li>\n<li>Tableau, Zeppelin \u0219i Pivot pentru vizualizarea datelor<\/li>\n<\/ul>\n<p>\nAm descoperit c\u0103, de\u0219i aceste instrumente ofer\u0103 capabilit\u0103\u021bi foarte puternice, ne-am confruntat cu dificult\u0103\u021bi \u00een a face aceste capabilit\u0103\u021bi accesibile pentru un public mai larg \u00een Twitter. Extinz\u00e2nd platforma noastr\u0103 cu ajutorul Google Cloud, ne concentr\u0103m pe simplificarea instrumentelor noastre analitice pentru \u00eentreaga comunitate Twitter.<\/p>\n<h2>Depozitul de date BigQuery de la Google <\/h2>\n<p>\nC\u00e2teva echipe de pe Twitter au integrat deja BigQuery \u00een unele dintre fluxurile lor de produc\u021bie. Folosindu-ne de experien\u021ba lor, am \u00eenceput s\u0103 evalu\u0103m capacit\u0103\u021bile BigQuery pentru toate scenariile de utilizare Twitter. Scopul nostru a fost s\u0103 oferim BigQuery \u00eentregii companii, precum \u0219i s\u0103 o standardiz\u0103m \u0219i s\u0103 o suport\u0103m \u00een cadrul setului de instrumente Data Platform. Acest lucru a fost dificil din multe motive. A trebuit s\u0103 dezvolt\u0103m o infrastructur\u0103 pentru a gestiona eficient volume mari de date, sprijinind gestionarea datelor la nivelul \u00eentregii companii, asigur\u00e2nd un control de acces adecvat \u0219i men\u021bin\u00e2nd confiden\u021bialitatea clien\u021bilor. De asemenea, a fost necesar s\u0103 cre\u0103m sisteme pentru distribuirea resurselor, monitorizarea \u0219i gestionarea pl\u0103\u021bilor, astfel \u00eenc\u00e2t echipele s\u0103 poat\u0103 utiliza BigQuery eficient.<\/p>\n<p>\u00cen noiembrie 2018, am lansat versiunea alpha a BigQuery \u0219i Data Studio pentru \u00eentreaga companie. Am oferit angaja\u021bilor de la Twitter unele dintre cele mai folosite tabele cu date personale curate. BigQuery a fost utilizat de peste 250 de utilizatori din echipe diverse, inclusiv inginerie, finan\u021be \u0219i marketing. Recent, ei au efectuat aproximativ 8.000 de interog\u0103ri, proces\u00e2nd aproximativ 100 PB pe lun\u0103, f\u0103r\u0103 a include interog\u0103rile programate. Primind feedback extrem de pozitiv, am decis s\u0103 mergem \u00eenainte \u0219i s\u0103 oferim BigQuery ca resurs\u0103 principal\u0103 pentru interac\u021biunea cu datele \u00een Twitter.<\/p>\n<p>Iat\u0103 schema arhitecturii noastre de nivel \u00eenalt pentru depozitul de date Google BigQuery. <\/p>\n<p><img decoding=\"async\" alt=\"Cum a democratizat BigQuery de la Google analiza datelor. Partea 1.\" src=\"\/wp-content\/uploads\/2020\/08\/6a3e0b43752ebbac641c43519c8c2fba.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\nCopiem datele din clusterele Hadoop locale \u00een Google Cloud Storage (GCS), folosind un instrument intern Cloud Replicator. Apoi folosim Apache Airflow pentru a construi fluxuri de lucru care utilizeaz\u0103 \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery\/docs\/reference\/bq-cli-reference#bq_load\">bq_load<\/a><\/noindex>\u201d pentru a \u00eenc\u0103rca datele din GCS \u00een BigQuery. Folosim Presto pentru a interoga seturile de date Parquet sau Thrift-LZO din GCS. BQ Blaster este un instrument intern Scalding pentru a \u00eenc\u0103rca seturi de date HDFS Vertica \u0219i Thrift-LZO \u00een BigQuery.<\/p>\n<p>\u00cen sec\u021biunile urm\u0103toare, vom discuta despre abordarea noastr\u0103 \u0219i cuno\u0219tin\u021bele \u00een ceea ce prive\u0219te u\u0219urin\u021ba de utilizare, performan\u021ba, gestionarea datelor, fiabilitatea sistemului \u0219i costurile.<\/p>\n<h4>Facilitatea de utilizare<\/h4>\n<p>\nAm descoperit c\u0103 utilizatorii au g\u0103sit u\u0219or s\u0103 \u00eenceap\u0103 cu BigQuery, deoarece nu necesita instalarea de software, iar utilizatorii au putut accesa printr-o interfa\u021b\u0103 web intuitiv\u0103. Cu toate acestea, utilizatorii trebuiau s\u0103 se familiarizeze cu unele func\u021bii GCP \u0219i conceptele sale, inclusiv resurse precum proiecte, seturi de date \u0219i tabele. Am dezvoltat materiale educa\u021bionale \u0219i tutoriale pentru a ajuta utilizatorii s\u0103 \u00eenceap\u0103. Odat\u0103 ce au dob\u00e2ndit o \u00een\u021belegere de baz\u0103, utilizatorii au \u00eenceput s\u0103 navigheze u\u0219or prin seturile de date, s\u0103 vizualizeze schema \u0219i datele tabelelor, s\u0103 execute interog\u0103ri simple \u0219i s\u0103 vizualizeze rezultatele \u00een Data Studio.<\/p>\n<p>Obiectivul nostru cu privire la introducerea datelor \u00een BigQuery a fost s\u0103 asigur\u0103m o \u00eenc\u0103rcare fluid\u0103 a seturilor de date HDFS sau GCS cu un singur clic. Am considerat <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/composer\/\">Cloud Composer<\/a><\/noindex> (Airflow gestionat), dar nu am reu\u0219it s\u0103-l utiliz\u0103m din cauza modelului nostru de securitate \u201eDomain Restricted Sharing\u201d (mai multe informa\u021bii \u00een sec\u021biunea \u201eManagementul datelor\u201d de mai jos). Am experimentat utilizarea Google Data Transfer Service (DTS) pentru organizarea sarcinilor de lucru BigQuery. De\u0219i DTS se configura rapid, nu era flexibil pentru construirea de fluxuri de lucru cu dependen\u021be. Pentru versiunea noastr\u0103 alfa, am creat propriul mediu Apache Airflow \u00een GCE \u0219i ne preg\u0103tim s\u0103-l punem \u00een produc\u021bie \u0219i s\u0103 sus\u021binem mai multe surse de date, precum Vertica.<\/p>\n<p>Pentru a transforma datele \u00een BigQuery, utilizatorii creeaz\u0103 fluxuri de date SQL simple, utiliz\u00e2nd interog\u0103ri planificate. Pentru fluxuri de lucru complexe, multi-etap\u0103 cu dependen\u021be, pl\u0103nuim s\u0103 utiliz\u0103m fie infrastructura noastr\u0103 Airflow, fie Cloud Composer, \u00eempreun\u0103 cu <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/dataflow\/\">Cloud Dataflow<\/a><\/noindex>.<\/p>\n<h4>Performan\u021b\u0103<\/h4>\n<p>\nBigQuery este conceput pentru interog\u0103ri SQL de uz general care proceseaz\u0103 volume mari de date. Nu este destinat interog\u0103rilor cu laten\u021b\u0103 sc\u0103zut\u0103 \u0219i l\u0103\u021bimi de band\u0103 mari necesare pentru o baz\u0103 de date tranzac\u021bional\u0103 sau analizei seriilor temporale cu laten\u021b\u0103 sc\u0103zut\u0103, implementat\u0103 <noindex><a rel=\"nofollow\" href=\"https:\/\/druid.apache.org\/\">Apache Druid<\/a><\/noindex>. Pentru interog\u0103rile analitice interactive, utilizatorii no\u0219tri a\u0219teapt\u0103 un timp de r\u0103spuns de mai pu\u021bin de un minut. A trebuit s\u0103 proiect\u0103m utilizarea BigQuery astfel \u00eenc\u00e2t s\u0103 corespund\u0103 acestor a\u0219tept\u0103ri. Pentru a asigura o performan\u021b\u0103 previzibil\u0103 pentru utilizatorii no\u0219tri, am folosit func\u021bionalit\u0103\u021bile BigQuery disponibile pentru clien\u021bii cu plat\u0103 fix\u0103, care permit de\u021bin\u0103torilor de proiecte s\u0103 rezerve sloturi minime pentru interog\u0103rile lor. <noindex><a rel=\"nofollow\" href=\"https:\/\/cloud.google.com\/bigquery\/docs\/slots\">Slot<\/a><\/noindex> BigQuery este unitatea de putere de calcul necesar\u0103 pentru a executa interog\u0103ri SQL. <\/p>\n<p>Am analizat peste 800 de interog\u0103ri, proces\u00e2nd aproximativ 1 TB de date fiecare, \u0219i am descoperit c\u0103 timpul mediu de execu\u021bie a fost de 30 de secunde. De asemenea, am aflat c\u0103 performan\u021ba depinde foarte mult de utilizarea sloturilor noastre \u00een diferite proiecte \u0219i sarcini. A trebuit s\u0103 delimit\u0103m clar rezervele noastre de sloturi pentru produc\u021bie \u0219i cele ad hoc, pentru a men\u021bine performan\u021ba \u00een scenariile de utilizare a produc\u021biei \u0219i analizei interaktive. Aceasta a avut un impact semnificativ asupra designului nostru pentru rezervarea sloturilor \u0219i ierarhia proiectelor.<\/p>\n<p><i>Despre gestionarea datelor, func\u021bionalitate \u0219i costul sistemelor, vom discuta \u00een zilele urm\u0103toare \u00een a doua parte a traducerii, iar acum invit\u0103m pe to\u021bi cei interesa\u021bi la <noindex><a rel=\"nofollow\" href=\"https:\/\/otus.pw\/wDZQ\/\">webinarul live gratuit<\/a><\/noindex>, \u00een cadrul c\u0103ruia ve\u021bi putea afla detalii despre curs \u0219i de asemenea s\u0103 adresa\u021bi \u00eentreb\u0103ri expertului nostru \u2014 Egor Mate\u0219uk (Senior Data Engineer, MaximaTelecom). <\/i><\/p>\n<p><\/p>\n<h2>Cite\u0219te mai mult:<\/h2>\n<p><\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/501380\/\">Data Build Tool sau ce leg\u0103tur\u0103 exist\u0103 \u00eentre Data Warehouse \u0219i Smoothie<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/502324\/\">\u00cencercare \u00een Delta Lake: aplicarea for\u021bat\u0103 \u0219i evolu\u021bia schemei<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/503132\/\">Apache Parquet de mare vitez\u0103 pe Python cu Apache Arrow<\/a><\/noindex><\/li>\n<\/ul>\n<p>Sursa: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/otus\/blog\/513780\/\">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! \u041f\u0440\u044f\u043c\u043e \u0441\u0435\u0439\u0447\u0430\u0441 \u0432 OTUS \u043e\u0442\u043a\u0440\u044b\u0442 \u043d\u0430\u0431\u043e\u0440 \u043d\u0430 \u043d\u043e\u0432\u044b\u0439 \u043f\u043e\u0442\u043e\u043a \u043a\u0443\u0440\u0441\u0430 \u00abData Engineer\u00bb. \u0412 \u043f\u0440\u0435\u0434\u0434\u0432\u0435\u0440\u0438\u0438 \u0441\u0442\u0430\u0440\u0442\u0430 \u043a\u0443\u0440\u0441\u0430 \u043c\u044b \u0442\u0440\u0430\u0434\u0438\u0446\u0438\u043e\u043d\u043d\u043e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u0438\u043b\u0438 \u0434\u043b\u044f \u0432\u0430\u0441 \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u043e\u0433\u043e \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u0430. \u041a\u0430\u0436\u0434\u044b\u0439 \u0434\u0435\u043d\u044c \u0431\u043e\u043b\u0435\u0435 \u0441\u0442\u0430 \u043c\u0438\u043b\u043b\u0438\u043e\u043d\u043e\u0432 \u0447\u0435\u043b\u043e\u0432\u0435\u043a \u043f\u043e\u0441\u0435\u0449\u0430\u044e\u0442 Twitter, \u0447\u0442\u043e\u0431\u044b \u0443\u0437\u043d\u0430\u0442\u044c, \u0447\u0442\u043e \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442 \u0432 \u043c\u0438\u0440\u0435, \u0438 \u043e\u0431\u0441\u0443\u0434\u0438\u0442\u044c \u044d\u0442\u043e. \u041a\u0430\u0436\u0434\u044b\u0439 \u0442\u0432\u0438\u0442 \u0438 \u043b\u044e\u0431\u043e\u0435 \u0434\u0440\u0443\u0433\u043e\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u044e\u0442 \u0441\u043e\u0431\u044b\u0442\u0438\u0435, \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u043e\u0435 \u0434\u043b\u044f \u0432\u043d\u0443\u0442\u0440\u0435\u043d\u043d\u0435\u0433\u043e [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":90784,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-90783","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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