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Pepper: An Elastic Web Server Farm for Cloud based on Hadoop - - PowerPoint PPT Presentation

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Pepper: An Elastic Web Server Farm for Cloud based on Hadoop Subramaniam Krishnan, Jean Christophe Counio Yahoo! Inc. MAPRED 1 st December 2010 Agenda Motivation Design Features Applications Evaluation Conclusion

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Pepper: An Elastic Web Server Farm for Cloud based on Hadoop

Subramaniam Krishnan, Jean Christophe Counio Yahoo! Inc. MAPRED 1st December 2010

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Agenda

  • Motivation
  • Design
  • Features
  • Applications
  • Evaluation
  • Conclusion
  • Future Work

Yahoo! Inc 1

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Motivation

2 Yahoo! Inc

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What’s in a Name

Wave 2: Content Freshness

Process 100s of feeds/sec, size in KBs in seconds Web feeds like breaking news, tweets, finance quotes Scalable, high throughput & low latency platform Pepper – elastic web server farm on grid

Wave 1: Grid-ification

Crunch 10-100s of GBs of data in hours Large data like wikipedia Hosted, multi- tenant platform Grid workflow management system (PacMan)

3 Yahoo! Inc Motivation

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Requirements

Elastic: handle intra/inter application load variance Multi-tenant: provide process/memory isolation Sub-second platform overhead Simple API Execute user code in platform context Reliability: transparent fault tolerance

4 Yahoo! Inc Motivation

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Design

5 Yahoo! Inc

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Deployment Flow

  • Web application

deployed as WAR onto HDFS – Job Manager

  • Embedded Jetty server

runs in Map task, registers with ZooKeeper

  • 1 Hadoop job = 1 Map

task = 1 Web Server = 1 Web application

6 Yahoo! Inc Design

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Processing Flow

  • Proxy Router receives

incoming requests, looks up ZooKeeper & redirects to appropriate Web Server

7 Yahoo! Inc Design

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ZooKeeper Hierarchy

8 Yahoo! Inc Design

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Features

9 Yahoo! Inc

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Features

  • Scalability: Web application can scale by configuring more

instances (Elasticity), system can scale with addition of Hadoop nodes

  • Performance: High throughput by ensuring that all the

heavy lifting is done during deployment

  • High Availability/Self-healing: Redundant server
  • instances. Health check piggybacked on TaskTracker

heartbeat

  • Isolation: Hadoop map provides process isolation
  • Ease of Development: Standard Servlet API & WAR

packaging

  • Reuse of Grid Infrastructure: The system runs on a Grid

that can be shared across several applications

10 Yahoo! Inc Features

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Applications

11 Yahoo! Inc

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Applications

  • Web Feeds Processing: Configure workflow
  • rchestration engine to run in-memory, 1 workflow =

1 web-application. Benefits:

  • Scalability
  • Isolation
  • Avoids Hadoop job bootstrap latency and HDFS

small files bottleneck.

  • Online Clustering: Extracts features and assigns

incoming feeds to clusters predetermined by offline

  • clustering. Performed online for Yahoo! News to

identify hot news clusters during ingestion of articles.

12 Yahoo! Inc Applications

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Evaluation

13 Yahoo! Inc

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Setup

  • Hardware: Intel Xeon L5420 2.50GHz with 8GB

DDR2 RAM

  • Software: 64-bit SUN JDK 1.6 update 18 on RHEL AS

4 U8, Linux 2.6.9- 89.ELsmp x86_64

  • Configuration: 8 map slots/node with 512MB heap,

25 threads/Jetty server

  • Number of Computing Hadoop nodes: 3

14 Yahoo! Inc Evaluation

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Linear Scaling for Predefined Capacity

  • Throughput: number of requests handled successfully

per second for a specified number of tasks

15 Yahoo! Inc Evaluation

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Elastic Scaling for Dynamic Capacity

  • Rejection: failure to execute within predefined timeout
  • Load is increased and additional map task allocated at

points A and B based on predefined schedule

  • Failure rate of < 0.001% observed in Production

16 Yahoo! Inc Evaluation

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Pepper Performance Numbers

System Burst Rate (request/mi n) Throughput (requests/da y) Platform Latency (Avg.) Response Time (Avg.) Pepper 2,000 3 million 75 ms 4s PacMan 50 10,000 90s 120s

  • Dataset is Yahoo! News feeds with sizes < 1MB
  • Processing is typically computation intensive like processing

and enriching web feeds that involves validation, normalization, geo tagging, persistence in service stores, etc

17 Yahoo! Inc Evaluation

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Conclusion

18 Yahoo! Inc

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Conclusion

  • Pepper marries the benefits of traditional server farms

i.e. low latency and high throughput with those of cloud i.e. elasticity and isolation

  • In production within Yahoo! from December 2009
  • Current Y! properties - Newspaper Consortium,

Finance & News. Sports & Entertainment are in pipeline

  • System scales linearly with addition of more Hadoop

computing nodes

19 Yahoo! Inc Conclusion

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Future Work

20 Yahoo! Inc

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Future Work

  • On demand allocation of servers
  • Experimenting with async NIO between Proxy Router

& Map Web Engine to increase scalability

  • Improving distribution of requests across web servers
  • Integrate into Hadoop (?)

21 Yahoo! Inc Future Work

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References

  • Hadoop, Web Page http://hadoop.apache.org/
  • J. Dean and S. Ghemawat, “MapReduce: Simplified Data

Processing on Large Cluster”, 6th Symposium on Operating Systems Design and Implementation (OSDI’04), San Francisco, CA, December 2004, pp. 137–150

  • P. Hunt, M. Konar, F.P. Junqueira, and B. Reed,

“ZooKeeper: Wait-free coordination for Internet-scale systems”, Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference, Boston, MA, June 2010,

  • pp. 11- 11
  • Oozie (successor to PacMan), Web Page

http://yahoo.github.com/oozie/, http://www.cloudera.com/blog/2010/07/whats-new-in- cdh3-b2- oozie/

22 Yahoo! Inc

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Questions ?

23 Yahoo! Inc