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Patrick Linskey on “cloud store”

by Roberto V. Zicari on November 16, 2009

I have asked Patrick Linskey on his opinion on the new wave of “data stores”, such as “document stores”, and “nosql databases”.

You can read the interview below.

Roberto V. Zicari

RVZ:
Patrick, there has been recently a proliferation of “data stores”, such as “document stores”, and “nosql databases”.
Systems such as CouchDB, MongoDB, SimpleDB, Voldemort, Scalaris, etc. provide less functionality than OODBs but a distributed “object” cache over multiple machines.

See for example: wiki/Nosql,
wiki/wiki/Document-oriented_database,
and the article: NoSQL: Distributed and Scalable Non-Relational Database Systems.

What do you think about it?

Patrick Linskey:
I think that the “cloud store” subset of them are pretty fascinating. Of course, as with so much in the software industry, much of what these projects are doing is old hat. But I think that they’re relatively unique in
(a) successfully combining compelling complementary sets of features together,
(b) building solutions for known and needed use cases, rather than the more ivory-tower approach that’s all too typical of commercial products, and
(c) designing and implementing in a manner oriented to cloud-scale deployment from the very start (i.e., lots of data; geographically diverse data centers; high load requirements).

I expect that all the successful cloud store projects will end up with support for declarative queries and declarative secondary keys. I really don’t like the “nosql” term — I think that Geir Magnusson does a good job of pointing out that the cloud store community is more focused on “alongside SQL”. That is, there’s nothing wrong with using a relational database in the situations where it’s the best tool for the job. The new cloud stores are focused on filling the gaps where most RDB alternatives fall flat.

The way they do it, of course, is by getting rid of problematic features. I think that some of the hype has mis-identified these
problematic features, though. Declarative queries (and full metamodel introspection) and secondary key support are really cool and critical features of all the popular relational databases. The cloud store users out there are doing a lot of extra work because of the absence of these features — essentially re-implementing them in their application code. Imagine how horrible it’d be if you told a modern DB team that they needed to change their app to tune their database!

So: what are cloud stores omitting that enable them to scale so well?
There are two answers:
- cloud stores are intentionally designed to scale. No* single points of failure, built-in support for consensus-based decisions, partitioning / replication as basic primitives, etc. Taking a codebase designed for a single server and evolving it to a multi-server solution is difficult, since single-machine assumptions often calcify into the implementation.

- more importantly, cloud stores aren’t fully ACID, in the traditional sense of the term. By re-casting the data storage problem in more amenable terms (eventual consistency, atomic operations (but not atomic sequences of operations), etc.), the different products can make acceptable trade-offs that traditional single-server ACID stores are simply designed to forbid.

I’d love to see a comparison of established products like TeraData and Coherence to the various new cloud store projects. TeraData, in particular, does an interesting job of re-using the familiar SQL/JDBC model while making a lot of the same compromises and architectural decisions as the new set of cloud stores.

(I’m less interested in — and educated about — the single-server nosql projects. These days, I believe that all single-server databases are basically equivalent, since if you are using a single server, your application is sufficiently simple that you should be able to be successful with any of a number of data storage models.)

-Patrick

Patrick Linskey has been involved in object/relational mapping and databases for the last decade. As the founder and CTO of SolarMetric, Patrick drove the technical direction of the company and oversaw the development of Kodo, through its acquisition by BEA. At BEA, Patrick led the EJB team in designing and implementing the WebLogic Server EJB 3.0 solution, and represented BEA on the JDO and EJB3 expert groups. He is a contributor to the Apache OpenJPA project.

Since leaving Oracle, Patrick has worked on a number of projects, ranging from traditional three-tier web and mobile applications to C# peer – to – peer client applications with custom-designed distributed storage solutions.

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One Comment Leave one →
  1. I think you are spot on with much of what you write here.

    One note – in addition to the horizontal scaling capabilities, I've seen a lot of developers really pleased with single-server MongoDB usage as it makes development faster : eliminates some object-relational mapping friction and no need to predefine/fix columns.

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