The Search for the Source of Data - How SimpleDB Differs from a RDBMS
Update: Top 10 Reasons to Avoid the SimpleDB Hype by Ryan Park provides a well written counter take. Am I really that fawning? If so, doesn't that make me a dear?
All your life you've used a relational database. At the tender age of five you banged out your first SQL query to track your allowance. Your RDBMS allegiance was just assumed, like your politics or religion would have been assumed 100 years ago. They now say--you know them--that relations won't scale and we have to do things differently. New databases like SimpleDB and BigTable are what's different. As a long time RDBMS user what can you expect of SimpleDB? That's what Alex Tolley of MyMeemz.com set out to discover. Like many brave explorers before him, Alex gave a report of his adventures to the Royal Society of the AWS Meetup. Alex told a wild almost unbelievable tale of cultures and practices so different from our own you almost could not believe him. But Alex brought back proof.
Using a relational database is a no-brainer when you have a big organization behind you. Someone else worries about the scaling, the indexing, backups, and so on. When you are out on your own there's no one to hear you scream when your site goes down. In these circumstances you just want a database that works and that you never have to worry about again. That's what attracted Alex to SimpleDB. It's trivial to setup and use, no schema required, insert data on the fly with no upfront preparation, and it will scale with no work on your part. You become free from DIAS (Database Induced Anxiety Syndrome). You don't have to think about or babysit your database anymore. It will just work. And from a business perspective your database becomes a variable cost rather than a high fixed cost, which is excellent for the angel food funding. Those are very nice features in a database. But for those with a relational database background there are some major differences that take getting used to.
No schema. You don't have to define a schema before you use the database. SimpleDB is an attribute-value store and you can use any you like any time you like. It doesn't care. Very different from Victorian world of the RDBMS.
No joins. In relational theory the goal is to minimize update and deletion anomolies by normaling your data into seperate tables related by keys. You then join those tables together when you need the data back. In SimpleDB there are no joins. For many-to-1 relationships this works out great. In SimpleDB attribute values can have multiple values so there's no need to do a join to recover all the values. They are stored together. For many-to-many to relationships life is not so simple. You must code them by hand in your program. This is a common theme in SimpleDB. What the RDBMS does for you automatically must generally be coded by hand with SimpleDB. The wages of scale are more work for the programmer. What a surprise.
Two step query process. In a RDBMS you can select which columns are returned in a query. Not so in SimpleDB. In a query SimpleDB just returns back a record ID, not the values of the record. You need to make another trip to the database to get the record contents. So to minimize your latency you would need to spawn off multiple threads. See, more work for the programmer.
No sorting. Records are not returned in a sorted order. Values for multi-value attribute fields are not returned in sorted order. That means if you want sorted results you must do the sorting. And it also means you must get all the results back before you can do the sorting. More work for the programmer.
Broken cursor. SimpleDB only returns back 250 results at a time. When there are more results you cursor through the result set using a token mechanism. The kicker is you must iterate through the result set sequentially. So iterating through a large result set will take a while. And you can't use your secret EC2 weapon of massive cheap CPU to parallelize the process. More work for the programmer because you have to move logic to the write part of the process instead of the read part because you'll never be able to read fast enough to perform your calculations in a low latency environment.
The promise of scaling is fulfilled. Alex tested retrieving 10 record ids from 3 different database sizes. Using a 1K record database it took an average of 141 msecs to retrieve the 10 record ids. For a 100K record database it took 266 msecs on average. For a 1000K record database it took an average of 433 msecs to retrieve the 10 record ids. It's not fast, but it is relatively consistent. That seems to be a theme with these databases. BigTable isn't exactly a speed demon either. One could conclude that for certain needs at least, SimpleDB scales sufficiently well that you can feel comfortable that your database won't bottleneck your system or cause it to crash under load.
If you have a complex OLAP style database SimpleDB is not for you. But, if you have a simple structure, you want ease of use, and you want it to scale without your ever lifting a finger ever again, then SimpleDB makes sense. The cost is everything you currently know about using databases is useless and all the cool things we take for granted that a database does, SimpleDB does not do.
SimpleDB shifts work out of the database and onto programmers which is why the SimpleDB programming model sucks: it requires a lot more programming to do simple things. I'll argue however that this is the kind of suckiness programmers like. Programmers like problems they can solve with more programming. We don't even care how twisted and inelegant the code is because we can make it work. And as long as we can make it work we are happy. What programmers can't do is make the database scalable through more programming. Making a database scalable is not a solvable problem through more programming. So for programmers the right trade off was made. A scalable database you don't have to worry about for more programming work you already know how to do. How does that sound?
