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MongoDB — Location Based Sharding, Global View of data

September 27, 2010 Leave a comment

Map-of-complexity-science

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So, in my previous blog I raised a question as follows:

“How do you save your data to the closest data center while still having a global view i.e international customers should have a view of their data no matter where they or their data are and without affecting performance?”

What I should also have noted was that I’ve written the very complex routing code as I’m sure so many out there have as well.  So, why would I raise the question if I’ve done this kind of thing before…  Well, there may just be another solution (simpler solution) out there that doesn’t involve this complexity that we as developers like to create for ourselves.  Now, over the last couple of weeks I’ve been doing some research on MongoDB and have come to like the simplicity that they bring across in their product.  Which is why I’m busy writing all the articles under the MongoDB menu on this blog.  It’s very much a work in progress, so don’t expect much at this stage, but the idea is that as time goes past I’ll have a complete set of articles with code running under all kinds of circumstances as well as in different languages…

Why am I blogging about some articles that will be coming up?  Simple…  I do it because I see it as a promise to myself that I must do something and that no matter what, I’ve made the commitment to myself…

MongoDB — Location Based Sharding, Global View of data

September 23, 2010 Leave a comment

An operation engineer overseeing a Network Ope...

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So here’s an interesting scenario which must have been solved by someone out there without having to write very complex routing code. How do you save your data to the closest data center while still having a global view i.e international customers should have a view of their data no matter where they or their data are and without affecting performance?

This is something that I’ll be exploring in the next few days and more specifically in a high transactional large data volume scenario… I’ll write-up an article on it when I have solutions. Maybe it can help someone out there 🙂  Maybe someone out there has a solution…  Who knows 🙂

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