High Performance, Scalable MongoDB in a Bare Metal Cloud

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    22-Jun-2015

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<ul><li> 1. High Performance,Scalable MongoDBin a Bare Metal CloudHarold Hannon, Sr. Software Architect</li></ul> <p> 2. 100kservers24kcustomers23million domains 3. 13 data centers16 network POPs20Gb fiber interconnectsGlobal Footprint 4. On the agenda today.. Big Data considerations Some deployment options Performance Testing with JSBenchmarking Harness Review some internal product researchperformed Discuss the impact of those findings onour product development 5. Build me a Big DataSolution 6. Product Use Case MongoDB deployed for customers on purchase Complex configurations including sharding andreplication Configurable via Portal interface Performance tuned to 3 t-shirt sizedeployments 7. Big Data Requirements High Performance Reliable, Predictable Performance Rapidly Scalable Easy to Deploy 8. Requirements ReviewedCloud Provider Bare Metal InstanceHigh PerformanceReliable, PredictablePerformanceRapidly ScalableXEasy to DeployXIve got nothing 9. The Marc-O-MeterIM NOTHAPPY 10. Marc Angry 11. Thinking about Big Data 12. The 3 Vs 13. Physical Deployment 14. Public Cloud 15. Public Cloud Speed of deployment Great for bursting use case Imaging and cloning make POC/Dev work easy Shared I/O Great for POC/DEV Excellent for App level applications Not consistent enough for disk intensive applications Must have application developed for cloud 16. Physical Servers 17. Bare Metal Build to your specs Robust, quickly scaled environment Management of all aspects of environment Image Based No Hypervisor Single Tenant Great for Big Data Solutions 18. The Proof is in the Pudding 19. Beware The Best Case Test Case1 8 5 8 1 7 . 61 9 0 5 2 5 . 41 8 7 8 8 2 . 21 9 1 1 0 1 . 81 8 4 4 0 8 . 81 8 8 1 3 5 . 41 8 7 0 8 0 . 61 8 6 3 4 3 . 41 9 1 8 9 9 . 61 8 7 7 3 6 . 61 8 8 9 7 8 . 81 8 7 4 4 01 8 6 9 5 0 . 41 8 7 6 2 31 8 7 7 8 3 . 81 8 7 7 7 5 . 81 9 2 8 0 6 . 81 8 6 6 4 3 . 2 20. Do It Yourself Data Set Sizing Document/Object Sizes Platform Controlled client or AFAIC Concurrency Local or Remote Client Read/Write Tests 21. JS Benchmarking Harness Data Set Sizing Document/Object Sizes Platform Controlled client or AFAIC Concurrency Local or Remote Client Read/Write Tests 22. db.foo.drop();db.foo.insert( { _id : 1 } )ops = [{op: "findOne", ns: "test.foo", query: {_id: 1}},{op: "update", ns: "test.foo", query: {_id: 1}, update: {$inc: {x: 1}}}]for ( var x = 1; x</p>

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