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Does anyone have experience with Scalearc sharding and it's ability to scale ? Does Scalearc needs to have the whole data in memory or does it behave well with disk storage ? I'm thinking to big database application like 10TB and above.

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2 Answers

ScaleArc does not do Data Sharding

I actually wrote an earlier post where I evaluated it for my former employer : Experience using ScaleArc in test or production?

  • It does provide excellent read/write splitting using a single IP address and proprietary caching (ScaleArc's secret sauce).
  • You configure the ScaleArc server to have one or more Masters and One or More Slaves. It works great with Percona XtraDB Cluster as well as with Master with 1+ Slaves using MySQL Replication.
  • Each DB Server must have the complete dataset.

I doubt that you have the budget for multiple servers using 10TB of disk each.

What you might be looking for is ScaleBase not ScaleArc.

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Thanks for your answer. I was aware that Scalearc was good for Master/Slave replication. But I was wondering about its sharding ability if any ? Did you tried this ? Indeed I'm not planning to have multiple servers using 10TB each, on the opposite I want to shard this 10TB dataset as it has a lot of locality.I'm also looking at Scalebase, parelastic dbshards and some others as well as NOSQL options. TIA –  Tom Sep 26 '13 at 8:59
    
Thank you amrith, this is very interesting I will go through that paper. I'm Just curious to understand how this works, How Join requests are performing (better to forget about it with sharding) and how this kind of solution perform versus vertical scaling (meaning adding more horse power, memory etc...) –  Tom Sep 27 '13 at 19:06
    
I think you meant to put your comment on the other answer. –  RolandoMySQLDBA Sep 27 '13 at 19:45
    
True, thank you for pointing it out. As I don't know how to move my comment I will repost it –  Tom Sep 27 '13 at 20:48
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ScaleArc is primarily a read/write splitting solution, sometimes called clustering. To the best of my knowledge they don't do sharding.

Each 'node' has a complete copy of the data and queries are redirected to one or the other 'node'. The challenge with this primarily that you have many nodes, each with a whole data set, and you only get to accelerate 'read' style queries, not 'writes'.

For the kind of volumes you are looking at, a parallel database architecture seems more appropriate. They would scale considerably better with that kind of data volume and give you very high performance access to all of the data irrespective of the location of a given row of data (i.e. which 'shard' it resides on). Parallel database architectures are considerably superior to 'sharding' and you may want to review some literature such as this paper my Dewitt and Gray.

I notice in your comment to another question that you mention that you are evaluating other solutions. Of the solutions you list, ParElastic is the only one that is truly a 'parallel database' architecture. Full disclosure, I work for ParElastic, and I only mention this distinction because you indicated that you were evaluating these products in your earlier comment.

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Thank you amrith, this is very interesting I will go through that paper. I'm Just curious to understand how this works, How Join requests are performing (better to forget about it with sharding) and how this kind of solution perform versus vertical scaling (meaning adding more horse power, memory etc...) –  Tom Sep 27 '13 at 20:49
    
@Tom, I'm happy to answer that question but it won't fall within the four corners of this question (which talks about ScaleArc sharding). If you want to revise the quesiton, ... –  amrith Sep 28 '13 at 15:27
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