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Enthusiasts Convene To Say No To SQL, Hash Out New DB Breed

ericatcw writes "The inaugural NoSQL meet-up in San Francisco during last month's Yahoo! Apache Hadoop Summit had a whiff of revolution about it, like a latter-day techie version of the American Patriots planning the Boston Tea Party. Like the Patriots, who rebelled against Britain's heavy taxes, NoSQLers came to share how they had overthrown the tyranny of burdensome, expensive relational databases in favor of more efficient and cheaper ways of managing data, reports Computerworld."

7 of 423 comments (clear)

  1. Tilting at windmills by Anonymous Coward · · Score: 5, Insightful

    Seems to be a silly thing to be against. Relational databases and the stuctured query language may not be perfect, but I bet these people could die in their 90's and people will still be using relational dbs and sql.

    If you want to tout open or cheap dbs and more lightweight types of storage/db servers, then they might have some points, but being against sql is just plain dumb.

    1. Re:Tilting at windmills by Qzukk · · Score: 5, Insightful

      SQL isn't the only way possible to query relational databases. It's nice and does a really good job for even mildly complex queries and I would not want to ditch it just yet, but seriously... who hasn't had a business need for multiple levels of aggregates (eg averages of sums across multiple groupings, say "average across all customers' total balances") As it is, you end up splitting the logic between the database and the application, or creating a view of the first level of aggregation, then querying against that and hoping that the performance doesn't suck total ass.

      --
      If I have been able to see further than others, it is because I bought a pair of binoculars.
  2. Yeah, so why are they better? by Anonymous Coward · · Score: 5, Insightful

    If I was to read the article, I bet somewhere someone would be wittering on about Key Value Datastores.

    The brainchild of a generation brought up on high level collections, they learn one (in this case Map) and apply it to everything.

    Sadly SQL, and RDBMS, works for most people. It maps object data well (oh whaaaa, i have to do foreign keys - GROW SOME FUCKING BALLS YOU LAZY GRADUATE!) and it is well understood. And with abstractions like LINQ to query them, even the lazy dumb Windows .NET programmer doesn't have to strain their brain to learn SQL.

    And when you have terabytes of specific unique data, you clearly should go away to work out how best to store it. Even a RDBMS/SQL solution is too generic for all problems.

  3. Re:Flat Earth by MightyMartian · · Score: 5, Insightful

    And yet where the other corporations; the oil companies, the banks, large merchant conglomerates. In IT we seem to have this sort of myopic view that if it isn't an IT company of some kind, it doesn't exist. Google, as compared to the huge companies that use tools like Oracle, is a bit player. I know that's hard for all of us who have sucked at the teat of silicon valley for so long have a hard time dealing with, but a significant amount of data that has nothing to do with social networking and finding pr0n goes on and does use tools like SQL.

    --
    The world's burning. Moped Jesus spotted on I50. Details at 11.
  4. SQL is not a database by j.+andrew+rogers · · Score: 5, Insightful

    SQL is not a database, it is a standard interface to a feature set commonly associated with relational models. Before everyone standardized on SQL, there were other relational query languages. The "No" part of "NoSQL" refers to the fact that some basic elements of relational implementations cannot be usefully expressed using a much simpler distributed hash table model.

    All the "NoSQL" does is eliminate all the parts of traditional relational databases that do no scale -- discarding the bottleneck rather than fixing it. These are things like joins and external indexing. Unfortunately, discarding those things means you discard a lot of very important functionality as a practical matter, notably the ability to do fast, complex analytics. Adopting the NoSQL architecture runs contrary to the trend toward more real-time, contextual analytical processing. There are a great many analytical applications that are not amenable to batch-mode pattern-matching, and the NoSQL model is a lot less applicable than I think some people want to acknowledge. In its domain, it is a great tool but it has many, many prohibitive limits. We are essentially trading power for scale.

    That said, do not take this as an endorsement of traditional SQL relational databases either, as they have a number of serious limitations themselves. As just mentioned, a number of the core analytical operations those models support are based on algorithms that scale poorly. The SQL language itself has mediocre support for many abstract data types (e.g. spatial) and data models (e.g. graph), which in part reflects the inadequacies of the assumed underlying database algorithms (e.g. B-trees) that are implicit in SQL. The inability to efficiently do event-driven/real-time applications is also more a reflection of the access methods used in databases than any intrinsic weakness in SQL; SQL may be clunky for that purpose, but that is not the real limiter.

    A truly revolutionary deviation from SQL would usefully implement a superset of the features SQL supports, not take them away. Of course, we would need access methods more capable than hash tables and B-trees to useful implement those features, which is a lot more work than discarding features that scale poorly. NoSQL is a stopgap technical measure for that small subset of applications where the serious tradeoffs are acceptable.

  5. Data out-lives applications by 4to6Offshore · · Score: 5, Insightful

    First: my mantra: Data belongs to the organization, not the application... if the app fails and data is accessible then we all go on - if the data fails or is locked away - what was the point of the app again?

    In a SQL database then data is understood by the organisation, DBAs and data architects. If left to app developers taking an app-centric approach to data... I get nervous quickly.

    So long as the data is just as definable and accessible as current SQL databases then all good - give me an app with some odd-ball storage then it is bye-bye.

  6. Re:A time and place for everything by TheNarrator · · Score: 5, Insightful
    I think the main problem that the web 2.0 dynamic language crowd has with RDBMs is that:
    • 1. Relational data is strongly typed. You cannot easily add new fields to a table or store arbitrary types in a column and expect acceptable performance.
    • 2. Migrating large amounts of relational data to a new structure takes a very very long time. Constant refactoring of data models is to be avoided. You have to get it right the first time or at least very early in the development cycle to avoid major headaches...
    • 3. Databases are hard to mock in a testing context. Automated tests can be significantly slowed down with even a test database..
    • 4. Error in database architecture are very difficult to correct due to 1 and 2, especially when used with a dynamically typed language..
    • 5. It's difficult to maintain the data integrity that RDBMSs take for granted in highly scalable distributed systems and have acceptable performance.

    The only real show stopper and a real reason to replace RDBMSs is #5. All the others can be worked around by just deeper study of data modeling techniques. Data modelling is not something most developers can figure out intuitively. There is a lot of theory to be learned to do it right and it can very easily be done badly leading to severe performance problems and an unmaintainable application.
    With regards to # 5: I went to a presentation at Javaone where some Ebay engineers explained that they do not use transactions in any of their database operations. They just leave junk rows around in the db if a transaction half completes and as long as they aren't reachable they don't consider it a big deal. They have to very carefully organize the order in which they manipulate data to avoid data corruption ,but that lets them get around # 5,