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Track B-3 解構大數據架構 - 大數據系統的伺服器與網路資源規劃

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講師:Dell Enterprise Technologist 李俊邦 (CP Li)

Publié dans : Technologie
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Track B-3 解構大數據架構 - 大數據系統的伺服器與網路資源規劃

  1. 1. 解構⼤大數據架構 ⼤大數據系統的伺服器與網路資源規劃 “How to eat an elephant – one byte at a time” CP Li 李俊邦 Enterprise Technologist Enterprise Solutions & Alliances, Greater China Dell
  2. 2. 2 議程 1.  不同的伺服器⾓角⾊色 1.  Manager 2.  Name Nodes 3.  Edge Nodes 4.  Data Nodes 2.  Hadoop Cluster設計 3.  Etu+Dell 4.  Futures / Roadmap 5.  Questions?
  3. 3. 3 Server Roles - Manager •  系統安裝圖形介⾯面/ 主控台 •  ⼤大多安裝在Edge Node •  常⾒見版本 –  Cloudera Manager –  Apache Ambari
  4. 4. 4 Server Roles – Name Nodes •  存放HDFS的metadata •  Job Manager for YARN data-processing framework •  Primary –  Heartbeats from data nodes –  10th heartbeat is a block report from which it generates metadata •  Standby –  Checks in every hour to mirror metadata / block map –  Not a hot-spare – requires manual fail-over •  High Availability (HA) can be added in some distributions –  Results in a dedicated HA node that acts as a witness to the Name Node cluster
  5. 5. 5 Server Roles - Edge Nodes •  資料進出Hadoop叢集的主要端⼝口 •  可擴展 •  Hadoop叢集裡唯⼀一的多網段節點 PowerEdge  R730  –  Name  Node PowerEdge  R730  –  Standby  Name  Node PowerEdge  R730  –  Edge  Node(s) PowerEdge  R730  –  HA  Node Corporate  Network Data  Network Corporate Data  Network Data  Network Data  Network Data  Network PowerEdge  R730XD  –  Data  Nodes Data  Network
  6. 6. 6 Server Roles - Data Node •  HDFS的主要存放處 •  執⾏行YARN資源管理所指定的資料處理 •  主要屬性 –  記憶體 ›  標配64GB ›  更多服務(Impala/Spark) 需要更多記憶體 –  很多的本地硬碟 (JBOD / Non-RAID mode) ›  SFF (2.5”) for performance-based workloads ›  LFF (3.5”)for capacity-centric workloads –  CPUs – legacy recommendation of 1:1 core:spindle ratio ›  SSDs, faster HDD (10K+), and in-memory workloads make this less of an issue ›  10 and 12 core are the best practice default
  7. 7. Hadoop Cluster Design
  8. 8. 8 Hadoop Cluster Design – Hardware Considerations
  9. 9. 9 Hadoop Cluster Deployment – Installation Best Practices •  Use pre-built, assembled & cabled racks from vendor •  ⾃自動佈署⼯工具 (ex: Open Crowbar) •  Purchase nodes in standard size groups for easy capacity growth and ordering, not in single node increments –  Common increments are ½ or full rack for easy deployment and sizing •  For each type of hardware, purchase spare components to keep on site for easy, rapid repair
  10. 10. 10 Core Hadoop Use Cases 歸檔 ⾼高硬碟/CPU⽐比 記憶體使⽤用低 法規需求 ⻑⾧長期歸檔 資料處 理 ⾼高硬碟/CPU⽐比 記憶體使⽤用中等 DW offload ETL offload EDH 質量分析 IT Log分析 分析 ⾼高核⼼心數 記憶體使⽤用⾼高 市場分析 詐欺預防 網路分析
  11. 11. 11 Common Hadoop Use Case to Ecosystem Tool Mapping
  12. 12. 12 Hadoop Use Case to Ratio Mapping 歸檔 1:2:1 資料處理 1:4:1 分析 2:8:1 CPU (Cores) : Memory (GB) : Disk (數量) – Data Node
  13. 13. 13 Node Considerations Dell PowerEdge R730 Dell PowerEdge R730 Dell PowerEdge R730 Dell PowerEdge R730xd
  14. 14. 14 Node Considerations
  15. 15. 15 HDFS Capacity •  HDFS protects information through replication of the data between nodes, the default Replication Factor is 3, but is configurable. •  HDFS Raw Capacity = Number of Compute Nodes x Number of Drives x Capacity of Drives •  HDFS Usable Capacity = HDFS Raw Capacity/Replication Factor
  16. 16. 16 Big Data Networking Best Practices •  Traditional Ethernet is used since it’s affordable and already prevalent. •  1GbE networking was used initially in early drafts of the solution but with the reduction in cost it’s much more efficient to go with 10GbE. •  Multiple ports are teamed both for redundancy and throughput. LACP or software bonding are the most common methods. •  IPv4 is most widely used. IPv6 has limited support at the OS and Hadoop level.
  17. 17. 17 Attributes of a Good Switch for Big Data •  Non-blocking backplane •  Deep per-port packet buffers (shared buffers do not work well). During sort/shuffle phases of map/reduce operations network traffic is so chaotic that it can saturate any and all shared buffers, impacting multiple host’s network performance. •  Good choices: –  1GbE ›  S55 ›  S60 –  10GbE ›  S4810 ›  S5000 –  40GbE ›  Z9000 ›  Z9500 ›  S6000
  18. 18. 18 Dell Hadoop Solution Logical Diagram
  19. 19. 19 Scale-out Aggregation Layer
  20. 20. 20 Dell Points of Integration •  VLT / VRRP is a very affordable way to team switches both at the ToR and the aggregation tiers. This makes the Dell Networking Force10 switches a great choice. •  Active Fabric Manager –  Speeds up the creation and administration of the required VLT / VRRP configuration on the switches. –  Helps with capacity-planning as customer scale
  21. 21. 21 Big Data Networking Futures •  40GbE onboard LOMs will begin to be used for high-volume clusters. Right now the cost:benefit ratio isn’t there yet. •  As HPC and Big Data converge, we’ll start to see the use of IB for node-to-node connectivity. •  In-memory (Spark / Impala) workloads are reducing the bottlenecks that used to exist at the disk and now move to the processor and network. Expect customers to be looking to increase core counts and network speed to overcome this.
  22. 22. @Dell_Enterprise Enterprise Solutions Etu+Dell = complete Hadoop/Big Data solution provider Best of breed Cloudera partners - Etu Analytic software solutions for Big Data Dell Professional Services for Big Data Dell PowerEdge 13G servers Dell Networking solutions Installation and configuration service Complete end-to-end implementation Discover Plan ImplementInvestigate
  23. 23. 2. Store1. Integrate 4. Act 3. Analyze Solution architecture Analytical output Toad Data Point Desktop – integrate, cleanse Dell Boomi Cloud – integrate, correlate Toad Intelligence Central Data aggregation and virtualization Dell STATISTICA Customer data Order data Events Stock market data Advanced Analytics Marketing campaigns Dell Statistica Big Data Desktop – crawl, save Social Media
  24. 24. 24 Futures •  Speed Improvements in Map / Reduce •  More in-memory workloads –  Possible move to Spark to replace Map/Reduce •  Virtualized Hadoop –  VMWare Big Data Extensions –  Openstack Sahara –  Microsoft HDInsights (Hortonworks)
  25. 25. 25 Dell In-Memory Appliance for Cloudera Enterprise Configurations at a glance Mid-Size Configuration 16 Node Cluster PowerEegeR720- 4 Infrastructure Nodes with ProSupport PowerEdgeR720XD- 12 Data Nodes with ProSupport Cloudera Enterprise Force10- S4810P Force10- S55 Dell Rack 42U ~528TB (disk raw space) Starter Configuration 8 Node Cluster PowerEdge R720- 4 Infrastructure Nodes with ProSupport PowerEdgeR720XD- 4 Data Nodes with ProSupport Cloudera Enterprise Force10- S4810P Force10- S55 Dell Rack 42U ~176TB (disk raw space) Small Enterprise Configuration 24 Node Cluster PowerEdgeR720- 4 Infrastructure Nodes with ProSupport PowerEdgeR720XD- 20 Data Nodes with ProSupport Cloudera Enterprise Force10- S4810P Force10- S55 Dell Rack 42U ~880TB (disk raw space) Expansion Unit- PowerEdgeR720XD-4 Data Nodes w ProSupport, Cloudera Enterprise, Scales in Blocks