The Data Center and Hadoop
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Jacob Rapp, Cisco jarapp@cisco.com
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The Data Center and Hadoop
- 1. The Data Center and Hadoop Jacob Rapp, Cisco jarapp@cisco.com
- 2. Hadoop Considerations • Traffic Types, Job Patterns, Network Considerations, Compute Network Integration • Co-exist with current Data Center infrastructure • Open, Programmable and Application-Aware Networks Multi-tenancy • Remove the “Silo clusters” 2
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- 4. Reduce Ingress vs. Egress Data Set Analyze 1:0.3 The Time the reducers start is dependent on: Reduce Extract Transform Load (ETL) Ingress vs. Egress Data Set mapred.reduce.slowstart.co mpleted.maps It doesn’t change the amount of data sent to Reducers, but may change the timing to send that data 1:1 Reduce Explode Ingress vs. Egress Data Set 1:2 4
- 5. Small Flows/Messaging (Admin Related, Heart-beats, Keep-alive, delay sensitive application messaging) Small – Medium Incast (Hadoop Shuffle) Large Flows (HDFS Ingest) Large Incast (Hadoop Replication) 5
- 6. NameNode JobTracker ZooKeeper Many-to-Many Traffic Pattern Map 1 Map 2 Map 3 Map N Shuffle Reducer 1 Reducer 2 Reducer 3 Reducer N Output Replication HDFS 6
- 7. Job Patterns have varying impact on network utilization Analyze Simulated with Shakespeare Wordcount Extract Transform Load (ETL) Simulated with Yahoo TeraSort Extract Transform Load (ETL) Simulated with Yahoo TeraSort with output replication
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- 9. Integration Considerations Network Attributes Architecture Availability Capacity, Scale & Oversubscription Flexibility Management & Visibility 9
- 10. Generally 1G is being used largely due to the cost/performance trade-offs. Though 10GE can provide benefits depending on workload Single 1GE 100% Utilized Dual 1GE 75% Utilized 10GE 40% Utilized 10
- 11. • No single point of failure from network view point. No impact on job completion time • NIC bonding configured at Linux – with LACP mode of bonding • Effective load-sharing of traffic flow on two NICs. • Recommended to change the hashing to src-dst-ip-port (both network and NIC bonding in Linux) for optimal load-sharing 11
- 12. 1GE vs. 10GE Buffer Usage 1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313 325 337 349 361 373 385 397 409 421 433 445 457 469 481 493 505 517 529 541 553 565 577 589 601 613 625 637 649 661 673 685 697 709 721 733 745 757 769 781 793 Cell Usage Job Completion Moving from 1GE to 10GE actually lowers the buffer requirement at the switching layer. 1G Buffer Used 10G Buffer Used 1G Map % 1G Reduce % 10G Map % 10G Reduce % By moving to 10GE, the data node has a wider pipe to receive data lessening the need for buffers on the network as the total aggregate transfer rate and amount of data does not increase substantially. This is due, in part, to limits of I/O and Compute capabilities 12
- 13. Findings Goals • 10G and/or Dual attached server provides • Extensive Validation of Hadoop Workload • Reference Architecture Make it easy for Enterprise Demystify Network for Hadoop Deployment Integration with Enterprise with efficient choices of network topology/devices More Details From Hadoop Summit 2012 at: consistent job completion time & better buffer utilization • 10G provide reduce burst at the access layer • Dual Attached Sever is recommended design – 1G or 10G. 10G for future proofing • Rack failure has the biggest impact on job completion time • Does not require non-blocking network • Latency does not matter much in Hadoop workloads http://www.slideshare.net/Hadoop_Summit/ref-arch-validated-and-tested-approach-to-define-a-network-design http://youtu.be/YJODsK0T67A 13
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- 15. n3548-001# show interface brief -------------------------------------------------------------------------------Ethernet VLAN Type Mode Status Reason Speed Port Interface Ch # -------------------------------------------------------------------------------Eth1/1 1 eth access up none 10G(D) -Eth1/2 1 eth access up none 10G(D) -Eth1/3 1 eth access up none 10G(D) -Eth1/4 1 eth access up none 10G(D) -Eth1/5 1 eth access up none 10G(D) –. . Eth1/33 1 eth access up none 10G(D) -Eth1/34 1 eth access up none 10G(D) -Eth1/35 1 eth access down SFP not inserted 10G(D) -Eth1/36 1 eth access down SFP not inserted 10G(D) -Eth1/37 1 eth access down Administratively down 10G(D) – . © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 15
- 16. n3548-001# show mac address-table dynamic Legend: * - primary entry, G - Gateway MAC, (R) - Routed MAC, O - Overlay MAC age - seconds since first seen,+ - primary entry using vPC PeerLink MAC Addresses VLAN MAC Address Type age Secure NTFY Ports of the connected ---------+-----------------+--------+---------+------+----+----------------devices … and * 1 e8b7.484d.a208 dynamic 60570 F Eth1/31 the port they are F * 1 e8b7.484d.a20a dynamic 60560 F F Eth1/31 on… * 1 e8b7.484d.a73e dynamic 60560 F F Eth1/34 * 1 e8b7.484d.a740 dynamic 60560 F F Eth1/34 * 1 e8b7.484d.ad15 dynamic 60560 F F Eth1/28 * 1 e8b7.484d.ad17 dynamic 60560 F F Eth1/28 * 1 e8b7.484d.b3e9 dynamic 60570 F F Eth1/25 * 1 e8b7.484d.b3eb dynamic 60560 F F Eth1/25 . . © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 16
- 17. n3548-001# portServerMap ======================================= Port Server FQDN --------------------------------------Eth1/1 c200-m2-10g2-001.cluster10g.com Eth1/2 c200-m2-10g2-002.cluster10g.com Eth1/3 c200-m2-10g2-003.cluster10g.com Eth1/4 c200-m2-10g2-004.cluster10g.com Eth1/5 c200-m2-10g2-005.cluster10g.com Eth1/6 c200-m2-10g2-006.cluster10g.com Eth1/7 c200-m2-10g2-031.cluster10g.com Eth1/8 c200-m2-10g2-008.cluster10g.com Eth1/9 c200-m2-10g2-009.cluster10g.com Eth1/11 c200-m2-10g2-011.cluster10g.com . . . © 2013 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 17
- 18. n3548-001# trackerList =========================================== Port Server Server Port ------------------------------------------Eth1/2 c200-m2-10g2-002 50544 Eth1/3 c200-m2-10g2-003 41909 Eth1/4 c200-m2-10g2-004 36480 Eth1/5 c200-m2-10g2-005 38179 Eth1/6 c200-m2-10g2-006 51375 Eth1/7 c200-m2-10g2-031 41915 Eth1/8 c200-m2-10g2-008 50983 Eth1/9 c200-m2-10g2-009 37056 Eth1/11 c200-m2-10g2-011 35882 Eth1/12 c200-m2-10g2-012 44551 . . . © 2013 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 18
- 19. n3548-001# bufferServerMap =================================================================== Port Server 1sec 5sec 60sec 5min 1hr ------------------------------------------------------------------Eth1/1 c200-m2-10g2-001 0KB 0KB 0KB 0KB 0KB Eth1/2 c200-m2-10g2-002 384KB 384KB 1536KB 2304KB 2304KB Eth1/3 c200-m2-10g2-003 384KB 384KB 1152KB 1536KB 1536KB Eth1/4 c200-m2-10g2-004 384KB 384KB 2304KB 2304KB 2304KB Eth1/5 c200-m2-10g2-005 384KB 384KB 768KB 1536KB 1536KB Eth1/6 c200-m2-10g2-006 384KB 2304KB 2304KB 2304KB 2304KB Eth1/7 c200-m2-10g2-031 384KB 384KB 3456KB 3840KB 3840KB Eth1/8 c200-m2-10g2-008 768KB 768KB 2688KB 2688KB 2688KB Eth1/9 c200-m2-10g2-009 384KB 384KB 2304KB 2304KB 2304KB Eth1/11 c200-m2-10g2-011 384KB 384KB 1920KB 1920KB 1920KB . . Eth1/1(c200-m2-10g2-001) . has 0 buffer usage because it’s the name node © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 19
- 20. n3548-001# jobsBuffer Hadoop Job Info ... What jobs were running =================================================================== during peak buffer usage 1 jobs currently running JobId RunTime(secs) User Priority … and for how long were job_201306131423_0009 120 hadoop NORMAL they running =================================================================== Buffer Info - Per Port Port Server 1sec 5sec 60sec 5min 1hr ------------------------------------------------------------------Eth1/1 c200-m2-10g2-001 0KB 0KB 0KB 0KB 0KB Eth1/2 c200-m2-10g2-002 384KB 384KB 768KB 768KB 768KB Eth1/3 c200-m2-10g2-003 384KB 384KB 1152KB 1152KB 1152KB Eth1/4 c200-m2-10g2-004 384KB 1536KB 1536KB 1536KB 1536KB Eth1/5 c200-m2-10g2-005 384KB 768KB 1152KB 1152KB 1152KB . . © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 20
- 21. n3548-001(config)# jobsBuffer Hadoop Job Info ... Historic look at =================================================================== the 0 jobs currently running buffer usage … JobId RunTime(secs) User Priority =================================================================== Buffer Info - Per Port Port Server 1sec 5sec 60sec 5min 1hr ------------------------------------------------------------------Eth1/1 c200-m2-10g2-001 0KB 0KB 0KB 0KB 0KB Eth1/2 c200-m2-10g2-002 0KB 0KB 0KB 1920KB 1920KB Eth1/3 c200-m2-10g2-003 0KB 0KB 0KB 2304KB 2304KB Eth1/4 c200-m2-10g2-004 0KB 0KB 0KB 2688KB 2688KB Eth1/5 c200-m2-10g2-005 0KB 0KB 0KB 2304KB 2304KB Eth1/6 c200-m2-10g2-006 0KB 0KB 0KB 2304KB 2304KB Eth1/7 c200-m2-10g2-031 0KB 0KB 0KB 1920KB 2688KB . © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 21
- 22. © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 22
- 23. © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 23
- 24. © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 24
- 25. Buffer Usage Shuffle Replication Reduce Map 0 60 120 180 © 2011 Cisco and/or its affiliates. All rights reserved. 240 300 360 420 480 540 600 660 720 780 Cisco Confidential 25
- 26. github.com/datacenter PTP Grandmaster (OPTIONAL) Push Data Push Data Push Data Analyze (Python Socket) © 2011 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 26
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- 28. Various Multitenant Environments Hadoop + HBASE Need to understand Traffic Patterns Job Based Scheduling Dependent Department Based Permissions and Scheduling Dependent 28
- 29. Client Client Update Read Update Map 1 Map 2 Map 3 Read Region Server Map N Shuffle Region Server Read Read Reducer 1 Reducer 2 Reducer 3 Reducer N Major Compaction Major Compaction Output Replication HDFS 29
- 30. Hbase During Major Compaction 9000 8000 ~45% for Read Improvement Latency (us) 7000 6000 Read/Update Latency Comparison of NonQoS vs. QoS Policy 5000 4000 3000 2000 1000 0 Time UPDATE - Average Latency (us) READ - Average Latency (us) QoS - UPDATE - Average Latency (us) QoS - READ - Average Latency (us) Switch Buffer Usage With Network QoS Policy to prioritize Hbase Update/Read Operations 30
- 31. Hbase + Hadoop Map Reduce 40000 35000 Latency (us) 30000 Read/Update Latency Comparison of NonQoS vs. QoS Policy 25000 ~60% for Read Improvement 20000 15000 10000 5000 0 Time READ - Average Latency (us) QoS - UPDATE - Average Latency (us) QoS - READ - Average Latency (us) 1 70 139 208 277 346 415 484 553 622 691 760 829 898 967 1036 1105 1174 1243 1312 1381 1450 1519 1588 1657 1726 1795 1864 1933 2002 2071 2140 2209 2278 2347 2416 2485 2554 2623 2692 2761 2830 2899 2968 3037 3106 3175 3244 3313 3382 3451 3520 3589 3658 3727 3796 3865 3934 4003 4072 4141 4210 4279 4348 4417 4486 4555 4624 4693 4762 4831 4900 4969 5038 5107 5176 5245 5314 5383 5452 5521 5590 5659 5728 5797 5866 5935 Buffer Used UPDATE - Average Latency (us) Timeline Hadoop TeraSort Hbase Switch Buffer Usage With Network QoS Policy to prioritize Hbase Update/Read Operations
- 32. THANK YOU FOR LISTENING Cisco.com Big Data www.cisco.com/go/bigdata Data Center Script Examples from Presentation: github.com/datacenter Cisco Unified Data Center UNIFIED FABRIC UNIFIED COMPUTING Highly Scalable, Secure Network Fabric Modular Stateless Computing Elements www.cisco.com/go/nexus www.cisco.com/go/ucs UNIFIED MANAGEMENT Automated Management Manages Enterprise Workloads http://www.cisco.com/go/wor kloadautomation
Hinweis der Redaktion
- Generally 1G is being used largely due to the cost/performance trade-offs. Though 10GE can provide benefits depending on workloadReduced spike with 10G and smoother job completion timeMultiple 1G or 10G links can be bonded together to not only increase bandwidth, but increase resiliency.
- Talk about intensity of failure with smaller job vs bigger jobThe MAP job are executed parallel so unit time for each MAP tasks/node remains same and more less completes the job roughly at the same time. However during the failure, set of MAP task remains pending (since other nodes in the cluster are still completing their task) till ALL the node finishes the assigned tasks.Once all the node finishes their MAP task, the left over MAP task being reassigned by name node, the unit time it take to finish those sets of MAP task remain the same(linear) as the time it took to finish the other MAPs – its just happened to be NOT done in parallel thus it could double job completion time. This is the worst case scenario with Terasort, other workload may have variable completion time.