16 Aug, 2009
Infinitely Scalable Infrastructure and RDBMSes
Posted by Bhavin Turakhia
Since the last several months I have been spending part of my time on conceptualizing an abstracted infrastructure layer that is highly scalable and can be leveraged by any application without having to worry too much about it. I have researched and continue to research conventional and unconventional techniques – partitioning, clustering, replication, shared-nothing architectures, grid computing and so on. This article represents a sliver of my thoughts concerning scalability and RDBMSes -
The holy grail of scalability is to be able to scale your data store. And as data stores go, RDBMSes seem to be the predominant choice (though that is changing – refer http://bit.ly/2lnRet). RDBMSes by their very nature, due to the features they provide (ACID compliance, Transaction safety etc) tend to be difficult to easily scale. This has resulted in the recent mushrooming of data storage options that are feature-poor but scalable out of the box (eg Voldemort, HyperTable etc)
I wanted to chronicle the list of features that a standard RDBMS provides, that we take for granted, so that I have a reference of the features that one may have to compromise on w.r.t application development in favor of easier scalability -
- Range based selects and updates – Being able to fire queries on a table specifying a range of values (eg where age >35). Typically RDBMSes use B+ Tree based indexes which support range based row selection. This in turn allows one to fire range based queries.
- Transactions – In an RDBMS one can perform multiple operations within a transaction and ensure that all of them or none of them go through. This ensures data integrity
- B+ tree indexes
- Foreign key relationships and referential integrity
- Joins and nested selects
- Aggregations (sum, avg etc)
- Advanced scripting using non-native languages (java etc)
- Stored procedures (allow encapsulation of business logic in the database layer)
- Triggers
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