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Case Study : Apple Rebuilds Siri
Backend Service Using Apache Mesos
Macy
Apple siri的挑戰
- 數以億計的iphone和ipad的語音查詢
- 每個服務群集都有數千個節點
What and Why using Mesos
- What Mesos
- Open Source
- 穩定、好擴充的分佈式資源管理架構
- Why Mesos
- more and more microservice,componets,container,
- node.js,python,java,mysql,mongodb,spark,
- batch process , long running service
- 為了解決多種資源類型(主要考慮CPU和MEM)的系統的公平資源分配問題,以及不同用戶對資
源有不同的需求
- 發展成熟
Mesos master的分配演算法 -
Dominant Resource Fairness(DFS)
- Berkeley AMPLab團隊提出DRF,一種針對不同資源類型的max-min fairness。
- 假設我們有10塊pizza,不同人想要不同數量的pizza,如何實現公平分pizza
- Henry(1) Ray(2) Kentpon(3.5) Josee(3.5)
- 如果一個人得到的資源少於平均分配,那他可以選擇不share,反之分到多於額
外的資源,則必須分享出給其他資源缺少的人
- 不能貪心謊報資源
- 如果任務完成,則不能多要額外資源。
- 需求大的用戶要獲得大於或等於需求小的用戶的資源
Apple siri backend architectureJ.A.R.V.I.S
使用Mesos會遇到的挑戰
- 需要花時間摸索他的source code, 和許多開發人員一起維護開發文件與定義檔,
不然就是等著別人釋出答案
- framwork涉及許多語言Mesos(C++)、Marathon(Scala)、Mesos-DNS
(Golang),但有很多framework可以滿足不同需求
- 花時間寫測試此mesos的架構是否通過
Action Item
- 整理出後端正在運行的程序
- 整理出鯨魚需要裝載的貨物
- 找一個適合我們的framework搭配Mesos
參考資料
- https://www.infoq.com/news/2015/05/mesos-powers-apple-siri
- http://www.slideshare.net/akirillov/data-processing-platforms-architectures-
with-spark-mesos-akka-cassandra-and-kafka
- https://mesosphere.com/blog/2015/04/23/apple-details-j-a-r-v-i-s-the-mesos-
framework-that-runs-siri/
- http://ithelp.ithome.com.tw/users/20103456/ironman/1063
- http://www.gegugu.com/2016/01/13/542.html

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20161216 case study : apple rebuilds siri backend service using apache mesos

Hinweis der Redaktion

  1. 系統越來越複雜 工作量越大 有長期的服務 也有批次處理的服務
  2. Mesos Master主要是可以接收slave所回報的目前資源狀態,並且會把資源的offer指定給frameowrk,在多台的master下正在執行active的master只會有一台,其它的master都為standby狀態,如果active的master當掉standby的master才會啟動起來。 slave會向zookeeper查詢確認Mesos Master在哪台的host上,然後就能連上Mesos Master,定時的回報目前有多少的資源給Mesos Master。 每一台slave都會定時的回報目前有多少的資源給Mesos Master,之後Mesos Master會把資源的offer提供給Framework,Framework可以接受和拒絕資源的offer。當執行Job時Frameowrk會指派要在哪幾台的slave上執行executor和tasks zookeeper是拿來做預防Mesos master當掉用的,一但Mesos master有問題之後zookeeper會透過選舉的方式,從Standby的master選出一個新的master 首先slave向master報告自己的資源信息,比如4個CPU等等,在計算之後發給(scheduler)框架1,框架1裡面有task queue,比如scheduler有兩個Job,scheduler分配給不同task不同的資源,把信息發給master,master再返還給對應的slave去跑task 是2 level scheduler的調度方式,和Kubernets不一樣
  3. 伯克利Berkeley AMPLab團隊的提出了Dominant Resource Fairness(DRF),一種針對不同資源類型的max-min fairness。並且在Mesos的設計和實現中評估了DRF,顯示了它可以比slot-based 公平調度算法得到更好的吞吐量。 DRF is a max-min fair algorithm for heterogeneous resources cpu memory io
  4. jarvis 是 Mesos的一個Framework 具備HA,Health Checks,服務發現等功能。如果某個Docker進程崩潰,jarvis會重新啟動同樣的進程。 蘋果使用Mesos成功使用75000個節點
  5. Mesos的核心職責是管理資源的抽象和任務的狀態 因為縮放是複雜的,並且依賴於應用程序,因此將負擔放在Mesos上沒有意義。相反,其他框架將提供此功能。一個新的縮放框架,或者一個當前端口。
  6. 優勢 設計相當精良的API。Pod,Service,Namespace是微服務的正確抽象。 充滿活力,快速推進的社區。一種語言,Golang。 劣勢 還很年輕,會遇到bug。還沒有有名的大公司大規模使用。Google本身還在使用BORG,而在撰寫本文時,遷移到Kubernetes的很多公司還沒有大型生產集群。支持Docker