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Sharding Techniques
Sharding doesn’t have to be performed inside a data engine. In fact, a relational data engine may be sharded externally, just as easily as any other data storage technique. I received an Email from Liran who has written extensively on the use of sharding using relational data storage in MySQL.
Liran writes:
Sharding is very interesting for us. I’d love to chat about it. I wrote a blog post for you guys concerning Sharding – and I plan to write more.
I took some time to review Liran’s blogs and found them quite interesting. As I was reviewing his writing I started directing my attention to the problem being solved, and the issues that are common to sharding techniques.
The primary problem, as Liran states very well, is the ability for persistence to scale. Sharding is just one of many techniques that have been developed addressing this issue.
Since sharding partitions the data and disburses it across multiple storage repositories, it does not maintain the same kinds of ACID constraints built into relational databases. Multi-stage commits may be required to assure data consistency.
Backup and restoration of data in multiple locations become bigger issues. In some cases, sharding techniques prefer online redundancy and failover to backup and restore techniques.
In systems that shard into traditional relational data stores, each new store represents another database that has to be maintained when changes are released.
It is much easier to design a sharding technology than it is to design a system that operates seamlessly as a single infrastructure.
Well, there are some random thoughts. Something for you to consider. I’m sure there is room for many of you to provide comment and insight. Send your thoughts to btaylor@sswug.org.
Cheers,
Ben
$$SWYNK$$
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