Your SlideShare is downloading. ×
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

AWS (Hadoop) Meetup 30.04.09

1,203

Published on

Follow on from last months AWS User Group Intro to AWS talk. This talk by Nicola Cardace is looking at some of the offerings in the AWS eco-system.

Follow on from last months AWS User Group Intro to AWS talk. This talk by Nicola Cardace is looking at some of the offerings in the AWS eco-system.

Published in: Technology
0 Comments
5 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,203
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
5
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • 200bytes/transaction
    Milk – assuming each transaction is for 1Gallon
    Who needs another programming Language (PLSQL )
    Gotchas later on (about networking trends)
    Anyone can rent a computer!!!! (UC Berkeley)
  • UC Berkeley EC2 example
  • UC Berkeley EC2 example
  • Point out that now we know how HDFS works – we can run maps close to data
  • Point out that now we know how HDFS works – we can run maps close to data
  • Point out that now we know how HDFS works – we can run maps close to data
  • Nomenclature: Core switch and Top of Rack
  • Simple map-reduce is easy – but it can get complicated very quickly.
  • Multi table inserts and multi group by’s allow us to reduce the number of scans required. Poor man’s alternative to MQO.
  • Transcript

    • 1. LONDON USERGROUP APRIL 30th 2009 Nicola Cardace
    • 2. Topics • Auto-Scaling Using Amazon EC2 and Scalr • Nginx and Memcached on EC2, a 400% boost! • NASDAQ exchange re-play on AWS • Persistent Django on Amazon EC2 and EBS • Taking Massive Distributed Computing to the Common Man - Hadoop on Amazon EC2/S3
    • 3. Auto-Scaling Using Amazon EC2 and Scalr Scalr, a redundant, self-curing, self-scaling hosting solution built on EC2
    • 4. http://developer.amazonwebservices.com/connect/entry.jspa?externalID=1603 http://developer.amazonwebservices.com/connect/entry.jspa?externalID=1357&categ tinyurl.com/4lkr3n tinyurl.com/c7num9 Scalr sourcecode: http://scalr.googlecode.com/svn/trunk/ ***
    • 5. Scalr overview • By using Scalr, you can create a server farm that uses prebuilt AMIs for load balancing, web servers, and databases. You also can customize a generic AMI, which you can use to host your actual application. • Scalr monitors the health of the entire server farm, ensuring that instances stay running and that load averages stay below a configurable threshold. If an instance crashes, another one of the proper type will be launched and added to the load balancer.
    • 6. Scalr (2) • Scalr is an open source, fully redundant, self-curing, and self-scaling hosting environment that uses Amazon EC2. • Scalr allows network administrators to create virtual server farms, using prebuilt components. Scalr uses four Amazon Machine Instances (AMIs) for load balancing, databases, application server, and a generic base image. • Administrators can preconfigure one machine and, when the load warrants, bring online additional machines with the same image, to handle the increased requests.
    • 7. Nginx and Memcached on EC2 400% boost!
    • 8. Nginx and Memcached on EC2 400% boost! (with a five minutes config tweak!)
    • 9. Originally developed by Igor Sysoev for rambler.ru (second largest Russian web-site), it is a high-performance HTTP server / reverse proxy known for its stability, performance, and ease of use. The great track record, a lot of great modules, and an active development community have rightfully earned it a steady uptick of users
    • 10. memcached is a high-performance, distributed memory object caching system, generic in nature, but intended for use in speeding up dynamic web applications by alleviating database load. “Memcached, the darling of every web-developer, is capable of turning almost any application into a speed- demon. Benchmarking one of my own Rails applications resulted in ~850 req/s on commodity, non-optimized hardware - more than enough in the case of this application. However, what if we took Mongrel out of the equation? Nginx, by default, comes prepackaged with the Memcached module, which allows us to bypass the Mongrel (from rubyforge) servers and talk to Memcached directly. Same hardware, and a quick test later: ~3,550 req/s, or almost a 400% improvement!”
    • 11. Nginx and Memcached on EC2 400% boost! http://tinyurl.com/3a7t9y ***
    • 12. NASDAQ exchange re-play on AWS your homework 
    • 13. Persistent Django on Amazon EC2 and EBS
    • 14. Credit: Thomas Brox Røst, Visiting researcher, Decision Systems Group, Harvard Persistent Django on Amazon EC2 and EBS - The easy way thomas.broxrost.com tinyurl.com/6b48g9
    • 15. Now that Amazon’s Elastic Block Store (EBS) is publicly available, running a complete Django installation on Amazon Web Services (AWS) is easier than ever. --- EBS provides persistent storage, which means that the Django database is kept safe even after the Django EC2 instances terminate
    • 16. To setup Django with persistent PostgreSQL database on AWS: Set up an AWS account Download and install the Elasticfox Firefox extension Add your AWS credentials to Firefox Create a new EC2 security group By default, EC2 instances are an introverted lot: They prefer keeping to themselves and don’t expose any of their ports to the outside world. We will be running a web application on port 8000 so therefore port 8000 has to be opened. (Normally we would be opening port 80, but since I will only be using the Django development web server then port 8000 is preferable). SSH access is also essential, so port 22 should be opened as well. To make this happen we must create a new security group where these ports are opened.
    • 17. Set up a key pair Launch an EC2 Instance Connect with your new instance (ssh using putty) - Install subversion - Install, initialize and launch PostgreSQL - Modify PostgreSQL config to avoid username/password problems - Restart PostgreSQL to enable new security policy - Set up a database for Django - Install Django (checkout from SVN) - Install psycopg2 (for database access from Python) Set up a Django project Test the installation Launch the dev server Create a Django app Create and mount an EBS Instance Mount the filesystem Move the database to persistent storage (with server stopped)
    • 18. ***
    • 19. AWS Elastic MapReduce
    • 20. Amazon Elastic MapReduce
    • 21. Data and Computing Trends: Source: Facebook • Explosion of Data – Web Logs, Ad-Server logs, Sensor Networks, Seismic Data, DNA sequences (?) – User generated content/Web 2.0 – Data as BI => Data as product (Search, Ads, Digg, Quantcast, …) • Declining Revenue/GB – Milk @ $3/gallon => $15M / GB – Ads @ 20c / 10^6 impressions => $1/GB – Google Analytics, Facebook Lexicon == Free! • Hardware Trends – Commodity Rocks: $4K 1U box = 8 cores + 16GB mem + 4x1TB – CPU: SMP  NUMA, Storage: $ Shared-Nothing << $ Shared, Networking: Ethernet
    • 22. Hadoop
    • 23. Hadoop • Parallel Computing platform – Distributed FileSystem (HDFS) – Parallel Processing model (Map/Reduce) – Express Computation in any language – Job execution for Map/Reduce jobs (scheduling+localization+retries/speculation) • Open-Source – Most popular Apache project! – Highly Extensible Java Stack (@ expense of Efficiency) – Develop/Test on EC2! • Ride the commodity curve: – Cheap (but reliable) shared nothing storage – Data Local computing (don’t need high speed networks) – Highly Scalable (@expense of Efficiency)
    • 24. Hadoop
    • 25. Map/Reduce DataFLow
    • 26. Hadoop Running MapReduce
    • 27. In Pictures (Source: Facebook)
    • 28. Looks like this .. Disks Node Disks Node Disks Node Disks Node Disks Node Disks Node 1 Gigabit 4-8 Gigabit Node = DataNode + Map-Reduce
    • 29. Why HIVE? • Large installed base of SQL users  – ie. map-reduce is for ultra-geeks – much much easier to write sql query • Analytics SQL queries translate really well to map-reduce • Files as insufficient data management abstraction – Tables, Schemas, Partitions, Indices
    • 30. Hive Query Language • Basic SQL – From clause subquery – ANSI JOIN (equi-join only) – Multi-table Insert – Multi group-by – Sampling – Objects traversal • Extensibility – Pluggable Map-reduce scripts using TRANSFORM
    • 31. Data Warehousing at Facebook (Scribe is a server for aggregating log data streamed in real time from a large number of servers. It is designed to be scalable, extensible without client-side modification, and robust to failure of the network or any specific machine) Web Servers Scribe Servers Filers Hive on Hadoop Cluster Oracle RAC Federated MySQL
    • 32. Hadoop Usage @ Facebook • Data warehouse running Hive • 600 machines, 4800 cores • 3200 jobs per day • 50+ engineers have used Hadoop • Data statistics: – Total Data: ~2.5PB – Net Data added/day: ~15TB • 6TB of uncompressed source logs • 4TB of uncompressed dimension data reloaded daily – Compression Factor ~5x (gzip, more with bzip) • Usage statistics: – 3200 jobs/day with 800K tasks(map-reduce tasks)/day – 55TB of compressed data scanned daily – 15TB of compressed output data written to hdfs – 80 MM compute minutes/day
    • 33. Hadoop Job types @ Facebook • Production jobs: load data, compute statistics, detect spam, etc • Long experiments: machine learning, etc • Small ad-hoc queries: Hive jobs, sampling • GOAL: Provide fast response times for small jobs and guaranteed service levels for production jobs
    • 34. Usage patterns in Yahoo • ETL – Put large data source (eg. Log files) onto the Hadoop File System – Perform aggregations, transformations, normalizations on the data – Load into RDBMS / data mart • Reporting and Analytics – Run canned and ad-hoc queries over large data – Run analytics and data mining operations on large data – Produce reports for end-user consumption or loading into data mart
    • 35. Usage patterns in Yahoo • Data Processing Pipelines – Multi-step pipelines for data processing – Coordination, scheduling, data collection and publishing of feeds – SLA carrying, regularly scheduled jobs • Machine Learning & Graph Algorithms – Traverse large graphs and data sets, building models and classifiers – Implement machine learning algorithms over massive data sets • General Back end processing – Implement significant portions of back-end, batch oriented processing on the grid – General computation framework – Simplify back-end architecture
    • 36. What is Hadoop Pig Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. http://www.cloudera.com/hadoop-training-pig-introduction
    • 37. Thanks to the kind sponsorship to the AWS LONDON USER GROUP from
    • 38. LONDON USERGROUP Thank you ! @n1c0la

    ×