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marcell
Marcell bookmarked on 2009-09-05 amazon distribution compute conversion programming linux java python storage

"I was ready to deploy Hadoop and my code on a cluster of EC2 machines. For deployment, I created a custom AMI (Amazon Machine Image) for EC2 that was based on a Xen image from my desktop machine. Using some simple Python scripts and the boto library, I booted four EC2 instances of my custom AMI. [..] thanks to the swell people at Amazon, I got access to a few more machines and churned through all 11 million articles in just under 24 hours using 100 EC2 instances, and generated another 1.5TB of data to store in S3."

  • I was ready to deploy Hadoop and my code on a cluster of EC2 machines. For deployment, I created a custom AMI (Amazon Machine Image) for EC2 that was based on a Xen image from my desktop machine. Using some simple Python scripts and the boto library, I booted four EC2 instances of my custom AMI. I logged in, started Hadoop and submitted a test job to generate a couple thousands articles — and to my surprise it just worked.


    I then began some rough calculations and determined that if I used only four machines, it could take some time to generate all 11 million article PDFs. But thanks to the swell people at Amazon, I got access to a few more machines and churned through all 11 million articles in just under 24 hours using 100 EC2 instances, and generated another 1.5TB of data to store in S3.

This link has been bookmarked by 1 people . It was first bookmarked on 05 Sep 2009, by marcell mars.

  • 05 Sep 09
    marcell
    marcell mars

    "I was ready to deploy Hadoop and my code on a cluster of EC2 machines. For deployment, I created a custom AMI (Amazon Machine Image) for EC2 that was based on a Xen image from my desktop machine. Using some simple Python scripts and the boto library, I booted four EC2 instances of my custom AMI. [..] thanks to the swell people at Amazon, I got access to a few more machines and churned through all 11 million articles in just under 24 hours using 100 EC2 instances, and generated another 1.5TB of data to store in S3."

    amazon distribution compute conversion programming linux java python storage

    • I was ready to deploy Hadoop and my code on a cluster of EC2 machines. For deployment, I created a custom AMI (Amazon Machine Image) for EC2 that was based on a Xen image from my desktop machine. Using some simple Python scripts and the boto library, I booted four EC2 instances of my custom AMI. I logged in, started Hadoop and submitted a test job to generate a couple thousands articles — and to my surprise it just worked.


      I then began some rough calculations and determined that if I used only four machines, it could take some time to generate all 11 million article PDFs. But thanks to the swell people at Amazon, I got access to a few more machines and churned through all 11 million articles in just under 24 hours using 100 EC2 instances, and generated another 1.5TB of data to store in S3.