Cloud Connect 2013- Lock Stock and x Smoking EC2's
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This Slide was presented @ Cloud Connect 2013. Lock, Stock and X Smoking EC2's was by inspired by Guy Ritchie movies. It describes how we put Amazon EMR + Spot EC2 instances to use for a customer and achieved cost savings while solving a Big Data problem.
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Cloud Connect 2013- Lock Stock and x Smoking EC2's
- 2. P1) This Presentation is P2) Strongly Inspired by “Guy Ritchie” Movies P3) Disclaimer : All images are downloaded from internet. If you find any of the content / images violating copyright, please let me know and I will act upon it immediately
- 3. AGENDA • Case • Challenge • Solution • Learning's • About us
- 4. Case Cigarette smoking is injurious to health • Mobile Advertising company, USA • Forbes 1000 clientele • TB’s of unstructured data -> Big Data Problem
- 5. Lock • Hourly ~1 TB • CDN Logs • Text Files • XML Files • Geo data files • Server logs • DB records
- 6. STOCK • Reduce the cost leakage • How to Save $$$ ?
- 7. Challenges • Daily (was OK), Monthly (Pain) and Historical analysis ( almost dead ) • How do we Transfer, Store, Analyze and Share ? • How to optimize costs at this scale ?
- 8. Solution Cigarette smoking is injurious to health • Use AWS Cloud for hosting Analytics module • Amazon EMR for unstructured Log Analysis • Automation using Scripts, Java code and other tools
- 9. Social / 3rd Party Feeds/Cloud Logs Stage 1: Data Transfer • Tsunami UDP • ~1TB un compressed logs every hour • High bandwidth EC2’s for Tsunami UDP • Other Popular Options : • Aspera • AWS Import/Export • WAN optimization • AWS Direct Connect
- 10. Amazon S3 Logs Stage 2: Storage • Amazon Web Services Building Block – S3 • Scalable Object Store • Inherently Fault Tolerant • ~2 TB of compressed logs every day • S3 RR option for intermediate outputs • Amazon Glacier for archivalSocial / 3rd Party Feeds/Cloud
- 11. Amazon S3 Elastic MapReduce Logs Stage 3: Analyze • Elastic MapReduce Service of Amazon • Minimal Setup time • Log Analysis • ~2000 mappers / 750 reducers @ peak • ~250 m1.xlarge task nodes (1000 cores, 3750 GB RAM) @ peakSocial / 3rd Party Feeds/Cloud
- 12. • Amazon EMR is great • But adding Spot EC2 is super cool Wait !!!
- 13. What is Amazon Spot ? 13 • Time-flexible, interruption-tolerant tasks • Bid Price & Spot Price • M1.xlarge Price Comparison • $0.480 per Hour – On Demand • $0.052 per Hour - Spot • You will never pay more than your maximum bid price per hour •Spot Instance may be interrupted • If interrupted you will not be charged for any partial hour of usage. (*Free)
- 14. Spot Bidding Strategies 14 •Just above Spot Price •Between Spot Price & On Demand Price •On Demand Price •Above On Demand Price
- 15. Spot Price Variations - AZ
- 16. Amazon EMR with Spot Instance Project Master Instance Group Core Instance Group Task Instance Group Long-running clusters on-demand on-demand Spot Cost-driven workloads spot spot Spot Data-critical workloads on-demand on-demand Spot Application testing spot Spot Spot
- 17. Amazon S3 Elastic MapReduce Social / 3rd Party Feeds Logs Stage 4: Custom EMR Manager • We created a Custom EMR Manager • Choose spot based on: • Past price trend intelligence • Choose AZ based on Current Market Prices • Choose between Large vs Extra Large • Spot Pricing Strategy : • Set Spot Price = On Demand Price • Over board <20% of On Demand Price at times • Dynamic Sizing the Core / Task nodes • Dynamic EMR Cluster creationCustom EMR Manager
- 18. Some Spot Use Cases 18 • Analytics & Big Data • Scientific computing • Web crawling • Financial model and Analysis • Testing • Image & Media Encoding 66 % savings 50 % savings 57 % savings
- 19. Learning • Spot + On demand EC2 is a deadly combination for cost savings • Every millisecond matters in MR – Tune your code • Merge Files – Bigger ones are better for processing
- 20. More Learning … • Custom Job Manager was designed by us • 1 File Per Mapper was better for our case in AWS • Understand the performance constraints of AWS and work with it • Compress data : Both storage and transit(.LZO & Snappy)
- 21. Continues… • Keep configuration data in local memory or Amazon DynamoDB • Reducers split files suitable for next job mappers • Elasticity – Increase/Decrease Task nodes • Elasticity – Create new EMR Clusters matching the Logs (Core + Task)
- 22. Value • ~56% cost savings from pure On-Demand model for Core+ Task Nodes • Automation vastly reduced Labor cost ( initial + on going) • Customer CXO’s were happy
- 23. • AWS Premium Partner • Solution Experts in • Cloud Computing • Big Data • Identity Management About US