bcongdon
Joined 9 years ago
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143
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22
Member since
Jan 8
Recent submissions
Comment · 6 years ago
Comment · 6 years ago
https://benjamincongdon.me/blog
I like to write about productivity, Go/Rust, and my various web development projects. I also tend to write pretty frequently about programming language ergonomics.
Popular posts:
* https://benjamincongdon.me/blog/2019/11/11/The-Value-in-Gos-...
* https://benjamincongdon.me/blog/2018/03/23/Python-Idioms-in-...
* https://benjamincongdon.me/blog/2018/03/01/Scraping-the-Web-...
Favorite posts:
* https://benjamincongdon.me/blog/2019/03/07/Generative-Doodli...
* https://benjamincongdon.me/blog/2018/10/07/Wordscapes/
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I also have a blogroll of other blogs I think are interesting: https://benjamincongdon.me/blogroll
... and a list of books that I've read: https://benjamincongdon.me/books
Comment · 8 years ago
Pricing depends a lot on how much memory your job requires[1] and how much processing each record requires -- i.e. the pricing is more sensitive to usage.
As a very rough estimate, for a light-to-medium load of 1Tb, the cost would probably be in the ballpark of ~$0.50. AWS's own reference MR framework[2] (which is mostly a tech demo) quotes prices in a similar order of magnitude.
Corral isn't great for processing-heavy MR jobs, as Lambda pricing rises quickly if you need a lot of memory or take a lot of time with each record. But, for small-ish low-overhead jobs, it can pretty easily beat the pricing and hassle of using something like EMR.
[1]: https://aws.amazon.com/lambda/pricing/#Lambda_pricing_detail... [2]: https://github.com/awslabs/lambda-refarch-mapreduce/
Comment · 8 years ago
Yeah, max execution time (and max memory usage) are the main constraints of using Lambda.
Corral deals with this by splitting input data into small enough chunks that each chunk can be processed within the timeout -- I exposed options for setting the amount of data that each Lambda function has to process. However, if each data item requires more than 5 min of processing, then corral won't work for you.
The "driver" that coordinates the Lambda functions runs locally (not in Lambda), so it doesn't have this constraint.
Comment · 8 years ago
Hi HN, author here. Corral is my attempt at a performant, easy-to-deploy MapReduce. Unlike traditional frameworks like Hadoop, it uses AWS Lambda for execution and is “serverless” as a result. It was initially kicked-off by AWS adding Lambda support for Go, but draws on experience I’ve had using Lambda tools like Zappa[1] and Serverless[2] in the past.
I think there’s a lot of interesting applications for using function-as-a-service platforms as executors in data processing frameworks such as this.
If you’re interested more in the development/internals of this project, I wrote a blog post with more details: https://benjamincongdon.me/blog/2018/05/02/Introducing-Corra...
[1]: https://github.com/Miserlou/Zappa [2]: https://serverless.com/
Comment · 8 years ago
The iOS app has been pretty good (if a bit buggy and slow), but the desktop web interface is so bad to the point that I haven't used it in over a year -- and I use myfitnesspal daily.
I suspected something like the `ff` labeling approach would improve the solution, but I had no idea how much of a difference `all_different` made vs. `all_distinct`!