{"id":31139,"date":"2019-10-31T21:39:40","date_gmt":"2019-10-31T18:39:40","guid":{"rendered":"https:\/\/prohoster.info\/blog\/monitorim-resursy-klasterov-kubernetes\/"},"modified":"2019-10-31T21:39:40","modified_gmt":"2019-10-31T18:39:40","slug":"monitorim-resursy-klasterov-kubernetes","status":"publish","type":"post","link":"https:\/\/prohoster.info\/lv\/blog\/administrirovanie\/monitorim-resursy-klasterov-kubernetes","title":{"rendered":"Uzraug\u0101m Kubernetes klasteru resursus","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"Uzraug\u0101m<\/a><\/noindex><\/p>\n

<\/p>\n

Es esmu izveidojis Kube Eagle \u2014 Prometheus eksport\u0113t\u0101ju. Izr\u0101d\u012bj\u0101s, ka t\u0101 ir lieliska lieta, kas pal\u012bdz lab\u0101k izprast mazo un vid\u0113jo klasteru resursus. Rezult\u0101t\u0101 es ietaup\u012bju vair\u0101kus simtus dol\u0101ru, jo izv\u0113l\u0113jos pareizos ma\u0161\u012bnu tipus un piel\u0101goju aplik\u0101ciju resursu ierobe\u017eojumus darba slodz\u0113m.<\/p>\n

<\/p>\n

Es past\u0101st\u012b\u0161u par priek\u0161roc\u012bb\u0101m Kube Eagle<\/a><\/noindex>, bet vispirms izskaidro\u0161u, k\u0101p\u0113c rad\u0101s probl\u0113mas un k\u0101d\u0113\u013c vajadz\u0113ja kvalitat\u012bvu monitoringu.<\/p>\n

<\/a><\/noindex><\/p>\n

Es p\u0101rvald\u012bju vair\u0101kus klasterus ar 4\u201350 mezgliem. Katr\u0101 klaster\u012b ir l\u012bdz 200 mikroservisiem un aplik\u0101cij\u0101m. Lai efekt\u012bv\u0101k izmantotu pieejamo aparat\u016bru, liel\u0101k\u0101 da\u013ca izvieto\u0161anas bija konfigur\u0113ta ar burstable operat\u012bvo atmi\u0146u un CPU resursiem. T\u0101d\u0113j\u0101di podi var\u0113ja izmantot pieejamos resursus, ja tas bija nepiecie\u0161ams, un taj\u0101 pa\u0161\u0101 laik\u0101 netrauc\u0113ja cit\u0101m aplik\u0101cij\u0101m \u0161aj\u0101 mezgl\u0101. Nu, vai tas nav lieliski?<\/p>\n

<\/p>\n

Un, lai gan klasteris pat\u0113r\u0113ja relat\u012bvi maz CPU (8%) un operat\u012bv\u0101s atmi\u0146as (40%), mums past\u0101v\u012bgi rad\u0101s probl\u0113mas ar podu izraid\u012b\u0161anu, kad tie m\u0113\u0123in\u0101ja piepras\u012bt vair\u0101k atmi\u0146as, nek\u0101 bija pieejams mezgl\u0101. Taj\u0101 laik\u0101 mums bija tikai viena panelis Kubernetes resursu monitor\u0113\u0161anai. \u0160\u0101da:<\/p>\n

<\/p>\n

\"Uzraug\u0101m<\/a><\/noindex>
\nGrafana panelis tikai ar cAdvisor metriem<\/em><\/p>\n

<\/p>\n

Ar \u0161\u0101du paneli mezgliem, kas pat\u0113r\u0113 daudz atmi\u0146as un CPU, redz\u0113t nav probl\u0113ma. Probl\u0113ma ir saprast, k\u0101ds ir c\u0113lonis. Lai podi paliktu sav\u0101 viet\u0101, var\u0113tu, protams, konfigur\u0113t garant\u0113tos resursus visiem podiem (piepras\u012btie resursi ir vien\u0101di ar ierobe\u017eojumu). Bet tas nav vispr\u0101t\u012bg\u0101kais aparat\u016bras izmanto\u0161ana. Klaster\u012b bija vair\u0101kas simti gigabaiti atmi\u0146as, ta\u010du da\u017eiem mezgliem tr\u016bka resursu, bet citiem bija saglab\u0101ju\u0161ies 4\u201310 GB.<\/p>\n

<\/p>\n

Izr\u0101d\u0101s, Kubernetes pl\u0101not\u0101js nevienm\u0113r\u012bgi sadal\u012bja darba slodzes pa pieejamajiem resursiem. Kubernetes pl\u0101not\u0101js \u0146em v\u0113r\u0101 da\u017e\u0101das konfigur\u0101cijas: affinit\u0101tes noteikumus, taints un tolerances, mezglu selektorus, kas var ierobe\u017eot pieejamos mezglus. Bet man\u0101 gad\u012bjum\u0101 nekas t\u0101ds nebija, un podi tika pl\u0101noti atbilsto\u0161i piepras\u012btajiem resursiem katr\u0101 mezgl\u0101.<\/p>\n

<\/p>\n

Poda izv\u0113l\u0113 bija mezgls, kuram bija visvair\u0101k br\u012bvo resursu un kur\u0161 atbilda piepras\u012bjuma pras\u012bb\u0101m. Mums izn\u0101ca, ka piepras\u012btie resursi mezglos nesakr\u012bt ar faktisko lietojumu, un \u0161eit pal\u012bgu sniedza Kube Eagle un t\u0101 resursu monitor\u0113\u0161anas iesp\u0113jas.<\/p>\n

<\/p>\n

Man gandr\u012bz visi Kubernetes klasteri tika uzraudz\u012bti tikai ar Node exporter<\/a><\/noindex> un Kube State Metrics<\/a><\/noindex>. Node Exporter nodro\u0161ina statistiku par ieeju-izeju un diska, CPU un operat\u012bv\u0101s atmi\u0146as izmanto\u0161anu, bet Kube State Metrics par\u0101da Kubernetes objektu metriku, piem\u0113ram, piepras\u012bjumus un ierobe\u017eojumus attiec\u012bb\u0101 uz CPU un atmi\u0146u.<\/p>\n

<\/p>\n

Mums ir j\u0101apvieno izmanto\u0161anas metri\u0137i ar piepras\u012bjumu un ierobe\u017eojumu metri\u0137iem Grafana, un tad m\u0113s ieg\u016bsim visu inform\u0101ciju par probl\u0113mu. Tas izklaus\u0101s vienk\u0101r\u0161i, bet paties\u012bb\u0101 \u0161ajos divos r\u012bkos eti\u0137etes tiek sauktas at\u0161\u0137ir\u012bgi, un da\u017eiem metri\u0137iem visp\u0101r nav metadatu eti\u0137e\u0161u. Kube Eagle visu izdar\u012bs pats, un panelis izskat\u0101s \u0161\u0101di:<\/p>\n

<\/p>\n

\"Uzraug\u0101m<\/a><\/noindex><\/p>\n

<\/p>\n

\"Uzraug\u0101m<\/a><\/noindex>
\nKube Eagle uzraudz\u012bbas panelis<\/a><\/noindex><\/em><\/p>\n

<\/p>\n

Mums izdev\u0101s atrisin\u0101t daudz resursu probl\u0113mu un ietaup\u012bt dzelzi:<\/p>\n

<\/p>\n

    \n
  1. Da\u017ei izstr\u0101d\u0101t\u0101ji nezin\u0101ja, cik daudz resursu nepiecie\u0161ams mikroservisiem (vai vienk\u0101r\u0161i par to nenogurdin\u0101ja). Mums nebija ar ko atrast nepareizos resursu piepras\u012bjumus - tam ir j\u0101zina pat\u0113ri\u0146\u0161 plus piepras\u012bjumi un ierobe\u017eojumi. Tagad vi\u0146i redz Prometheus metri\u0137us, uzrauga faktisk\u0101s izmanto\u0161anas l\u012bmeni un piel\u0101go piepras\u012bjumus un ierobe\u017eojumus.<\/li>\n
  2. JVM lietojumprogrammas pat\u0113r\u0113 tik daudz operat\u012bv\u0101s atmi\u0146as, cik vien var. Atkritumu sav\u0101c\u0113js atbr\u012bvo atmi\u0146u tikai tad, ja tiek izmantots vair\u0101k nek\u0101 75%. Un, t\u0101 k\u0101 liel\u0101kajai da\u013cai servisu ir burstable atmi\u0146a, to vienm\u0113r aiz\u0146\u0113ma JVM. T\u0101p\u0113c visi \u0161ie Java servisi pat\u0113r\u0113ja daudz vair\u0101k operat\u012bv\u0101s atmi\u0146as, nek\u0101 tika gaid\u012bts.<\/li>\n
  3. Da\u017eas lietojumprogrammas piepras\u012bja p\u0101r\u0101k daudz atmi\u0146as, un Kubernetes \u012bstenot\u0101js nedeva \u0161\u012bs noder\u012bbas citiem lietojumiem, kaut ar\u012b t\u0101m faktiski bija vair\u0101k br\u012bvas atmi\u0146as nek\u0101 cit\u0101m noder\u012bb\u0101m. Viens izstr\u0101d\u0101t\u0101js nejau\u0161i pievienoja lieku ciparu pie piepras\u012bjuma un aiz\u0146\u0113ma lielu operat\u012bv\u0101s atmi\u0146as gabalu: 20 GB viet\u0101 2. Neviens pat nepaman\u012bja. Lietojumprogrammai bija 3 replikas, t\u0101p\u0113c cieta 3 noder\u012bbas.<\/li>\n
  4. M\u0113s ievies\u0101m resursu ierobe\u017eojumus, p\u0101rlik\u0101m podus ar pareiziem piepras\u012bjumiem un ieguv\u0101m ide\u0101lu dzelzs izmanto\u0161anas l\u012bdzsvaru vis\u0101s noder\u012bb\u0101s. Da\u017eas noder\u012bbas var\u0113ja pat sl\u0113gt. P\u0113c tam m\u0113s redz\u0113j\u0101m, ka mums ir nepareizas ma\u0161\u012bnas (v\u0113rstas uz CPU, nevis atmi\u0146u). M\u0113s main\u012bj\u0101m tipu un izdz\u0113\u0161am v\u0113l da\u017eas noder\u012bbas.<\/li>\n<\/ol>\n

    <\/p>\n

    Secin\u0101jumi<\/h3>\n

    <\/p>\n

    Ar burstable resursiem klaster\u012b j\u016bs efekt\u012bv\u0101k izmantojat eso\u0161o aparat\u016bru, ta\u010du Kubernetes pl\u0101not\u0101js pl\u0101no podus atbilsto\u0161i resursu piepras\u012bjumiem, un tas ir riskanti. Lai nogalin\u0101tu divus za\u0137us: un probl\u0113mas nov\u0113rstu, un resursus izmantotu piln\u012bb\u0101, - nepiecie\u0161ams labs uzraudz\u012bbas r\u012bks. Tie\u0161i tam Kube Eagle b\u016bs noder\u012bgs. Kube Eagle<\/a><\/noindex> (Prometheus eksport\u0113t\u0101js un Grafana uzraudz\u012bbas panelis).<\/p>\n

    Avots: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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[…]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":23107,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-31139","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Uzraug\u0101m Kubernetes klusteru resursus | ProHoster","description":"","canonical_url":"https:\/\/prohoster.info\/lv\/blog\/administrirovanie\/monitorim-resursy-klasterov-kubernetes","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"lv_LV","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c 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