![A Tour Through Serengeti
serengeti> cluster create --name myElephant
serengeti> cluster list -–name myElephant
name: myElephant, distro: cdh, status:RUNNING
NAME ROLES INSTANCE CPU MEM(MB) TYPE
---------------------------------------------------------------------------
master [hadoop_NameNode, hadoop_jobtracker] 1 2 7500 LOCAL 50
name: myElephant, distro: cdh, status:RUNNING
NAME ROLES INSTANCE CPU MEM(MB) TYPE
---------------------------------------------------------------------------
master [hive, hadoop_client, pig] 1 1 3700 LOCAL 50
NAME HOST IP
-----------------------------------------------------------------
myElephant-client0 rmc-elephant-009.eng.vmware.com 10.0.20.184
15](https://image.slidesharecdn.com/appcap2956v2121001194956phpapp011-13500033964649-phpapp01-121011195908-phpapp01/85/App-Cap2956v2-121001194956-Phpapp01-1-15-320.jpg)
![Serengeti Spec File
[
"distro":"apache", Choice of Distro
{
"name": "master",
"roles": [
"hadoop_NameNode",
"hadoop_jobtracker"
],
"instanceNum": 1,
"instanceType": "MEDIUM",
“ha”:true, HA Option
},
{
"name": "worker",
"roles": [
"hadoop_datanode", "hadoop_tasktracker"
],
"instanceNum": 5,
"instanceType": "SMALL",
"storage": { Choice of Shared Storage or Local Disk
"type": "LOCAL",
"sizeGB": 10
}
},
]
17](https://image.slidesharecdn.com/appcap2956v2121001194956phpapp011-13500033964649-phpapp01-121011195908-phpapp01/85/App-Cap2956v2-121001194956-Phpapp01-1-17-320.jpg)
![Configuring Distro’s
{
"name" : "cdh",
"version" : "3u3",
"packages" : [
{
"roles" : ["hadoop_NameNode", "hadoop_jobtracker",
"hadoop_tasktracker", "hadoop_datanode",
"hadoop_client"],
"tarball" : "cdh/3u3/hadoop-0.20.2-cdh3u3.tar.gz"
},
{
"roles" : ["hive"],
"tarball" : "cdh/3u3/hive-0.7.1-cdh3u3.tar.gz"
},
{
"roles" : ["pig"],
"tarball" : "cdh/3u3/pig-0.8.1-cdh3u3.tar.gz"
}
]
},
18](https://image.slidesharecdn.com/appcap2956v2121001194956phpapp011-13500033964649-phpapp01-121011195908-phpapp01/85/App-Cap2956v2-121001194956-Phpapp01-1-18-320.jpg)
App Cap2956v2 121001194956 Phpapp01 (1)
- 1. APP-CAP2956 Inside the Hadoop Machine Jeff Buell, VMware, Inc. Richard McDougall, VMware, Inc. Sanjay Radia, Hortonworks #vmworldapps
- 2. Disclaimer ! This session may contain product features that are currently under development. ! This session/overview of the new technology represents no commitment from VMware to deliver these features in any generally available product. ! Features are subject to change, and must not be included in contracts, purchase orders, or sales agreements of any kind. ! Technical feasibility and market demand will affect final delivery. ! Pricing and packaging for any new technologies or features discussed or presented have not been determined. 2
- 3. Broad Application of Hadoop technology Horizontal Use Cases Vertical Use Cases Log Processing / Click Financial Services Stream Analytics Machine Learning / Internet Retailer sophisticated data mining Web crawling / text Pharmaceutical / Drug processing Discovery Extract Transform Load Mobile / Telecom (ETL) replacement Image / XML message Scientific Research processing General archiving / Social Media compliance Hadoop’s ability to handle large unstructured data affordably and efficiently makes it a valuable tool kit for enterprises across a number of applications and fields. 3
- 4. How does Hadoop enable parallel processing? ! A framework for distributed processing of large data sets across clusters of computers using a simple programming model. Source: http://architects.dzone.com/articles/how-hadoop-mapreduce-works 4
- 5. Hadoop System Architecture ! MapReduce: Programming framework for highly parallel data processing ! Hadoop Distributed File System (HDFS): Distributed data storage 5
- 6. Job Tracker Schedules Tasks Where the Data Resides Job Tracker Job Input%File Host%1 Host%2 Host%3 Split%1%–%64MB Task%% Task%% Task%% Split%2%–%64MB Tracker Tracker Tracker Split%3%–%64MB Task%<%1 Task%<%2 Task%<%3 DataNode DataNode DataNode %Input%File Block%1%–%64MB Block%2%–%64MB Block%3%–%64MB 6
- 7. Hadoop Distributed File System 7
- 8. Hadoop Data Locality and Replication 8
- 10. Why Virtualize Hadoop? Simple to Operate Highly Available Elastic Scaling ! Rapid deployment ! No more single point of ! Shrink and expand failure cluster on demand ! Unified operations across enterprise ! One click to setup ! Resource Guarantee ! Easy Clone of Cluster ! High availability for MR ! Independent scaling of Jobs Compute and data 10
- 11. Enterprise Challenges with Using Hadoop ! Deployment • Slow to provision • Complex to keep running/tune ! Single Points of Failure • Single point of failure with Name Node and Job tracker • No HA for Hadoop Framework Components (Hive, HCatalog, etc.) ! Low Utilization • Dedicated clusters to run Hadoop with low CPU utilization • No easy way to share resource between Hadoop and non-Hadoop workloads • Noisy neighbor, lack resource containment ! Need Multi-tenant Isolation, Resource Management, etc,… • Noisy Neighbor - no performance or security isolation between different tenants/users • Lack of configuration isolation - Can t run multiple versions on the cluster 11
- 12. Virtualization enables a Common Infrastructure for Big Data MPP DB HBase Hadoop Virtualization Platform Virtualization Platform Hadoop HBase Cluster Consolidation MPP DB ! Simplify • Single Hardware Infrastructure Cluster Sprawling • Unified operations Single purpose clusters for various business applications lead to cluster ! Optimize sprawl. • Shared Resources = higher utilization • Elastic resources = faster on-demand access 12
- 13. Deploy a Hadoop Cluster in under 30 Minutes Step 1: Deploy Serengeti virtual appliance on vSphere. Deploy vHelperOVF to vSphere Step 2: A few simple commands to stand up Hadoop Cluster. Select Compute, memory, storage and network Select configuration template Automate deployment Done 13
- 14. A Tour Through Serengeti $ ssh serengeti@serengeti-vm $ serengeti serengeti> 14
- 15. A Tour Through Serengeti serengeti> cluster create --name myElephant serengeti> cluster list -–name myElephant name: myElephant, distro: cdh, status:RUNNING NAME ROLES INSTANCE CPU MEM(MB) TYPE --------------------------------------------------------------------------- master [hadoop_NameNode, hadoop_jobtracker] 1 2 7500 LOCAL 50 name: myElephant, distro: cdh, status:RUNNING NAME ROLES INSTANCE CPU MEM(MB) TYPE --------------------------------------------------------------------------- master [hive, hadoop_client, pig] 1 1 3700 LOCAL 50 NAME HOST IP ----------------------------------------------------------------- myElephant-client0 rmc-elephant-009.eng.vmware.com 10.0.20.184 15
- 16. A Tour Through Serengeti $ ssh [email protected] $ hadoop jar hadoop-examples.jar teragen 1000000000 tera-data … 16
- 17. Serengeti Spec File [ "distro":"apache", Choice of Distro { "name": "master", "roles": [ "hadoop_NameNode", "hadoop_jobtracker" ], "instanceNum": 1, "instanceType": "MEDIUM", “ha”:true, HA Option }, { "name": "worker", "roles": [ "hadoop_datanode", "hadoop_tasktracker" ], "instanceNum": 5, "instanceType": "SMALL", "storage": { Choice of Shared Storage or Local Disk "type": "LOCAL", "sizeGB": 10 } }, ] 17
- 18. Configuring Distro’s { "name" : "cdh", "version" : "3u3", "packages" : [ { "roles" : ["hadoop_NameNode", "hadoop_jobtracker", "hadoop_tasktracker", "hadoop_datanode", "hadoop_client"], "tarball" : "cdh/3u3/hadoop-0.20.2-cdh3u3.tar.gz" }, { "roles" : ["hive"], "tarball" : "cdh/3u3/hive-0.7.1-cdh3u3.tar.gz" }, { "roles" : ["pig"], "tarball" : "cdh/3u3/pig-0.8.1-cdh3u3.tar.gz" } ] }, 18
- 19. Open Source of Serengeti, Spring Hadoop, Hadoop Extensions Commercial Vendors Community Projects • Support major distribution and multiple projects • Contribute Hadoop Virtualization Extension (HVE) to Open Source Community 19
- 20. Use Local Disk where it’s Needed SAN Storage NAS Filers Local Storage $2 - $10/Gigabyte $1 - $5/Gigabyte $0.05/Gigabyte $1M gets: $1M gets: $1M gets: 0.5Petabytes 1 Petabyte 10 Petabytes 200,000 IOPS 200,000 IOPS 400,000 IOPS 8Gbyte/sec 10Gbyte/sec 250 Gbytes/sec 20
- 21. Extend Virtual Storage Architecture to Include Local Disk ! Shared Storage: SAN or NAS ! Hybrid Storage • Easy to provision • SAN for boot images, VMs, other • Automated cluster rebalancing workloads • Local disk for Hadoop & HDFS • Scalable Bandwidth, Lower Cost/GB Other VM Other VM Other VM Other VM Other VM Other VM Other VM Other VM Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Hadoop Host Host Host Host Host Host 21
- 22. Hadoop has Significant Ephemeral Data Map%Task% Reduce% Map%Task% Job% Map% Reduce% Sort% Map%Task% Output% file.out* Spills% Map%Task% DFS% Spills% &%Logs% % Shuffle% Map_*.out* Input% Data% spill*.out* 75%%of% Combine% DFS% Intermediate.out* Output% % Disk%Bandwidth% % Data% 12%%of% 12%%of% Bandwidth% Bandwidth% HDFS% 22
- 23. Virtualized Hadoop Performance ! Issues of interest • Native vs various virtual configurations • Local disks vs Fibre Channel SAN • Effect of protecting Hadoop master daemons with Fault Tolerance • Public cloud (renting) vs private cloud (buying) Arista 7124SX 10 GbE switch 24x HP DL380 G7 2x X5687, 72 GB 16x SAS 146 GB Broadcom 10 GbE adapter Qlogic 8 Gb/s HBA … EMC VNX7500 23
- 24. Configuration ! Software • vSphere 5.0 U1 (storage tests), 5.1 (Native/Virtual, FT) • RHEL 6.1 x86_64 • Cloudera CDH3u4 • Hadoop applications: TeraGen, TeraSort, TeraValidate (1 TB) ! Hadoop VMs • Processors (16 logical threads), memory (72 GB), disks (12) partitioned among 1, 2, or 4 VMs per host • Separate VMs for NameNode and JobTracker for storage and FT tests ! Hadoop configuration • One map and one reduce task per vCPU (= logical thread) • Machines are highly loaded • 256 MB block size • FT tests: 8 – 256 MB block sizes to vary load on NN and JT 24
- 25. Native versus Virtual Platforms, 24 hosts, 12 disks/host 450 Elapsed time, seconds (lower is better) 400 350 Native 1 VM 300 2 VMs 4 VMs 250 200 150 100 50 0 TeraGen TeraSort TeraValidate 25
- 26. Local vs Various SAN Storage Configurations 4.5 16 x HP DL380G7, EMC VNX 7500, 96 physical disks Elapsed time ratio to Local disks (lower is better) 4 Local disks SAN JBOD 3.5 SAN RAID-0, 16 KB page size SAN RAID-0 SAN RAID-5 3 2.5 2 1.5 1 0.5 0 TeraGen TeraSort TeraValidate 26
- 27. Performance Effect of FT for Master Daemons ! NameNode and JobTracker placed in separate UP VMs ! Small overhead: Enabling FT causes 2-4% slowdown for TeraSort ! 8 MB case places similar load on NN &JT as >200 hosts with 256 MB 1.04 Elapsed time ratio to FT off TeraSort 1.03 1.02 1.01 1 256 64 16 8 HDFS block size, MB 27
- 28. Different Clouds for Different Folks ! Yahoo! Hadoop 2009: Classic benchmark test, 1460 hosts ! Google/MapR: SaaS on Google Compute Engine ! vSphere 5.1: 24 host cluster, 2 VMs/host, 8 or 12 disks/host, CDH3u4 ! Vastly different cluster sizes • Compare throughput (MB sorted per second) normalized with resources ! Cost: rental or estimate of running continuously for 3 years #cores #disks TeraSort, s MB/s/core MB/s/disk cost Yahoo! 11680 5840 62 1.3 2.6 ~$7 Google/MapR 5024 1256 80 2.4 9.5 $16 vSphere 5.1 192 192 442 11.2 11.2 ~$2 vSphere 5.1 192 288 359 13.8 9.2 ~$2 28
- 29. Why Virtualize Hadoop? Simple to Operate Highly Available Elastic Scaling ! Rapid deployment ! No more single point of ! Shrink and expand failure cluster on demand ! Unified operations across enterprise ! One click to setup ! Resource Guarantee ! Easy Clone of Cluster ! High availability for MR ! Independent scaling of Jobs Compute and data 29
- 30. VMware-Hortonworks Joint Engineering ! Hortonworks goal • Expand Hadoop ecosystem • Provide first class support of various platforms • Hadoop should run well on VMs • VMs offer several advantages as presented earlier • Take advantage of vSphere for HA ! First class support for VMs • Topology plugins (Hadoop-8468) • 2 VMs can be on same host • Pick closer data • Schedule tasks closer • Don’t put two replicas on same host • MR-tmp on HDFS using block pools • Elastic Compute-VMs will not need local disk • Fast communications within VMs 30
- 31. Hadoop Full-Stack High Availability Slave Nodes of Hadoop Cluster job job job job job Apps Running Outside Failover JT into Safemode NN JT NN N+K Server Server Server failover HA Cluster for Master Daemons 31
- 32. HA is in HDP 1.0 Using Total System Availability Architecture 32
- 33. HA in Hadoop 1 with HDP1 ! Full Stack High Availability • Namenode • Clients pause automatically • JobTracker pauses automatically • Other Hadoop master services (JT, …) coming ! Use industry proven HA framework • VMWare vSphere-HA • Failover, fencing, … • Corner cases are tricky – if not addressed, corruption • Addition benefits: • N-N & N+K failover • Migration for maintenance 33
- 34. Hadoop NN/JT HA with vSphere 34
- 35. Namenode Failover Times ! 60 Nodes, 60K files, 6 million blocks, 300 TB raw storage – 1-3.5 minutes • Failure detection and Failover – 0.5 to 2 minutes • Namenode Startup (exit safemode) – 30 sec ! 180 Nodes, 200K files, 18 million blocks, 900TB raw storage – 2-4.5 minutes • Failure detection and Failover – 0.5 to 2 minutes • Namenode Startup (exit safemode) – 110 sec For vSphere - OS bootup is needed – 10-20 seconds is included above. Cold Failover is good enough for small/medium clusters Failure Detection and Automatic Failover Dominates 35 35
- 36. Summary ! Advantages of Hadoop on VMs • Cluster Management • Cluster consolidation • Greater Elasticity in mixed environment • Alternate multi-tenancy to capacity scheduler’s offerings ! HA for Hadoop Master Daemons • vSphere based HA for NN, JT, … in Hadoop 1 • Total System Availability Architecture 36
- 37. Why Virtualize Hadoop? Simple to Operate Highly Available Elastic Scaling ! Rapid deployment ! No more single point of ! Shrink and expand failure cluster on demand ! Unified operations across enterprise ! One click to setup ! Resource Guarantee ! Easy Clone of Cluster ! High availability for MR ! Independent scaling of Jobs Compute and data 37
- 38. Elastic Scaling and Multi-tenancy of Hadoop on vSphere VM VM VM VM Current% Hadoop:% Compute T1 T2 % Combined% VM VM Storage/ Storage Storage Compute 1.#Hadoop#in#VM# 2.#Separate#Compute#and#Data# 3.#Mul8.#Clusters# < Single%Tenant% < Single%Tenant% < MulQple%Tenants% < Fixed%Resources% < ElasQc%Compute% < ElasQc%Compute% % 38
- 39. Separated Compute and Data Slot Slot Virtual Slot Virtual Hadoop Slot Virtual Slot Virtual Hadoop Slot Hadoop Node Hadoop Node Node Node Task Tracker Other Task Tracker Task Tracker Workload Virtual Hadoop Datanode Node Virtualization Host VMDK VMDK Truly Elastic Hadoop: Scalable through virtual nodes 39
- 40. References www.projectserengeti.org www.hortonworks.com www.cloudera.com Fault Tolerance performance whitepaper: www.vmware.com/resources/techresources/10301 MapR/Google blog: www.mapr.com/blog/google-mapr 40
- 41. FILL OUT A SURVEY EVERY COMPLETE SURVEY IS ENTERED INTO DRAWING FOR A $25 VMWARE COMPANY STORE GIFT CERTIFICATE
- 42. APP-CAP2956 Inside the Hadoop Machine Jeff Buell, VMware, Inc. Richard McDougall, VMware, Inc. Sanjay Radia, Hortonworks #vmworldapps