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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."
There is a time and place for SQL. There is a time and place to avoid SQL.
SQL is great for financial data. SQL is terrible for genetic data.
This is a boring sig
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.
The horrible lag I get when using address completion in Firefox 3 makes me wish more people thought that way!
I've seen strong reactions from various camps with regard to concern over saying no to SQL. I'm not sure why people freak out over it. First, you have to strike out toward new things if you want to progress the world. Second, SQL hasn't caused people to stop using spreadsheets or Access databases. Third, there are groups that get together to dispute that the earth is round; insisting that it is flat. Or that gray aliens are visiting earth regularly and probing our anuses.
Bring on the next fascinating data technology. SQL will continue to have a major place for many years to come, no matter what happens.
It's pretty easy to say "yes" to alternatives without saying "no" to SQL.
Just because a crowbar can pull out a stubborn nail better doesn't mean they should replace all the hammers. Then what would we put nails in with? Different tools for different jobs.
Porquoi?
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.
I'm not seeing anything that offers a real advantage over using advanced features like one finds in postgres combined with memcached. Some of my program likes to think of its data as a structured object while other parts like seeing that data as rows in a table (they even link up to other tables through foreign keys!).
Saying no to SQL and relational databases is just fine if you've got something better to replace it with. However I know of no such thing. The reason they're popular is that they are so powerful for data storage. If something better came along you wouldn't even need to say no to SQL. You'd just say yes to the newer better rival.
These posts express my own personal views, not those of my employer
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.
You didn't learn to backup after the first time?
Qxe4
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.
Epic Fail. You're wrong. It in now way results in a "Cartesion Product". That would be a "Cross Join", not an "inner join". From my experience, people who complain about SQL and relational database, are, for the most part, ignorant. They really don't even understand what they are saying or what they are talking about. I've seen so much abuse and misunderstanding of relational data and SQL in my career, that I just have to laugh at this sort of thing.
Over-the-top Response Guy! Giving "Over-the-Top Responses" since 1970.
It's just that now that we can assume local clusters and WANs worth of co-operating data stores, there are probably better, more performant ways of implementing persistence, replication, distribution of data than traditional RDBMS implementations.
You can also assume magical fairy dust and free energy, but that doesn't make it so. You can ask if there are better ways, but you can't assume it, and in the end you will find there is no magic.
Clusters and replication are NOT NEW. Not even remotely new. There is, in fact, nothing new architecturally at all that would indicate some new capability that hasn't already been repeatedly analyzed and tried. That doesn't mean you can't tweak something for a situation, or that you need a giant Oracle database for everything, but "the web" and "cheap hardware" change the equation by precisely nothing.
What has changed the equation is cheap, unimportant data, which covers the majority of the web. "Real" applications, where data integrity is important (like say, your bank account), and immediate accuracy guaranteed, require the main thing you use a database for: data integrity. Your facebook page, your google search, that blog entry, or some video on youtube: these don't matter. If it's a little slow, or doesn't update immediately, or you get an error, no one is losing money. No one cares.
In essence, if a reliable database isn't important for your app, your app isn't really handling important data. This may be fine; in the mainstream, there's a lot of noncritical stuff. But this doesn't make databases unimportant.
Don't think of it as a flame---it's more like an argument that does 3d6 fire damage
That is one view. It's nice and all, but incomplete. The issue is performance.
Any time you're dealing with a large quantity of data, it's always easiest to process or filter where it's located. Transmitting it, processing it, and transmitting back changes adds an unreasonable amount of overhead. Hence, SQL is a "Query" language. In other words, you have the RDBMS do reasonable data processing and filtering of records for you. Your application should only need to specify the operations performed, and should only process data if your computation is particularly unusual. This makes feasible computations that would otherwise be entirely unreasonable. (note that an application working on the same machine generally has the same issue as one working on a separate system. SQL servers present the application with a stream of data - pipe, socket, etc)
My opinion: SQL is horrendous. It's a pain to use, and many basic data transforms cannot be described in that language (at least without some huge, awful, convoluted command == maintenance nightmare).
I won't join Slashcott. OTOH, If Beta goes live, I just won't be back until it's fixed. Sorry Dice.
I have no idea what rubycodez's experience was but just because I can recover from backup doesn't mean data wasn't lost by the filesystem. More importantly external risk management schemes (i.e. backup) don't relieve the file system of the obligation not to kill the copy of my data I've entrusted it with.
...What democracy is to methods of government.
The worst ever devised excepting everything else that has ever been tried.
Any insufficiently advanced magic is indistinguishable from technology.
Flat files are a perfectly viable option in some circumstances. Not everything requires data uniformity or the ability to run complex ad-hoc queries, nor does everything need information to be controlled by a separate process running on a different machine. Not every system integrates multiple applications through a shared data-store. The NoSQL crowd isn't arguing that SQL is bad, just overused. There are a great many situations where something like flat files or Berkeley DB is more than sufficient, and yet people still use relational technology. In my experience it's generally because that's all they know. In their mind, if one needs to store data one uses SQL. They don't select the right tool for the job because they honestly don't know there are other tools.
Use the appropriate tool. Always. There are tons.
Don't use a relational database to try to represent hierarchical data. Don't try to use LDAP to do analytics. Think of the performance implications before you have more than two users accessing your system. Data storage is a very different animal, you are often (though not always) I/O bound. This is very different from being limited by the amount of instructions you can deal with per unit of time. Don't think otherwise because it will bite you in the ass.
And still I see people making the same stupid mistakes over and over. But it's pretty simple really:
A solution designed to be generic will ALWAYS be slower than a solution that is customized. This shouldn't be surprising. If you have serious performance requirements (ESPECIALLY if they are coupled with huge amounts of data) then a custom solution is definitely something you should look into. At some point you will run into a brick wall and find out that there is stuff you can't do with the solution you have in place. This is natural. Custom solutions to hard problems always lead to restrictions in terms of future features. Always. You will NEVER be able to anticipate all features that you would like to have. (Yes, this is true for Google as well. No they don't have any special kind of magic dust that they sprinkle on their things there, they do the best they can and then they get bitten in the ass too, just like everybody else.)
I've had a wonderful time, but this wasn't it -- Groucho Marx
One of the reasons is because RDBMSs offer a lot of tools, like atomicity, durability, backup/restore, centralization, point-in-time-recovery, etc. Many application developers need these things without actually needing the abstraction of a relational system.
Social scientists are inspired by theories; scientists are humbled by facts.
How many Googles or Yahoos are there? Like, 5. Let them do whatever broken things they want -- it works for them... for now. It's still expensive, probably just as much as "big iron". Not to mention the countless engineer hours and hosting/electricity costs for their "scale out" systems. It's what happens when you let a bunch of ivory tower PhDs solve real engineering problems.
In the end, the rest of us serious enterprise engineers will allow Oracle, Microsoft, and the people who have been doing this for 30 years to optimize their code to run on multicore mainframes ... which is where massive computing belongs. Then we query it with a few lines SQL instead of convoluted algorithms in some "Map Reduce" environment, and you move on with our lives.
If your data isn't complex enough to require a RDBMS, you almost certainly don't need a program.
Really? IM, word processor, spreadsheet, vector graphics, photo editor, ... Google probably uses MapReduce without a "normal" RDBMS behind it - is that data complex enough?
Ok, you owe me a new keyboard. Coffee EVERYWHERE!