Being successful like Pinterest without its DB adventures...

I just came across this: "Scaling Pinterest and adventures in database sharding"  (http://gigaom.com/data/scaling-pinterest-and-adventures-in-database-sharding/)

"Pinterest has learned about scaling the way most popular sites do — the architecture works until one day it doesn’t"

Pinterest found out that "the architecture" is not scalable and they turned to development of a Scale Out mechanism also called Sharding.

I find it amazing that sharding, or in other words, the idea of "scale out by splitting and parallelizing data across shared-nothing commodity-hardware" is not supplied "out of the box" by "the architecture" (such as database, load-balancer, any other IT stuff). I'm wondering who was the one that decided that an IT issue like scale-out should be outsourced from the database to the application developers?...

Amazing!!

When was the last time you heard about a PHP or Ruby developer wrote code to enable Scale Out. NEVER! Scale Out in the application layer is enabled easily by a magical box called a load balancer, and you can get one from F5 or wherever for a low 4 digit USD. Commodity! 

But to scale the database? To enjoy the obvious advantages of "scale out by splitting and parallelizing data across shared-nothing commodity-hardware"? - for this the world still thinks developers need to stop investing effort in innovation, better product, competitive business. Instead they need harness their how-databases-really-work skills to write band-aid code to scale the DB. 

Amazing... As you know I took it personally, and have been solving this paradox every day now, by bringing a complete, automatic, out-of-the-box "scale-out machine", that we like to call ScaleBase. I think Pinterest story is great, with a great outcome, but it's not always the case with this complex matter, and a generic, repeatable, IT-level solution for Scale Out can make it much easier for all other "Pinterests" out there to be as successful and make the right choice and enjoy the great benefits - without the tremendous efforts and labor in home-grown sharding.

Scale Up, Partitioning, Scale Out

On the 8/16 I conducted a webinar titled: "Scale Up vs. Scale Out" (http://www.slideshare.net/ScaleBase/scalebase-webinar-816-scaleup-vs-scaleout):

ScaleBase Webinar 8.16: ScaleUp vs. ScaleOut from ScaleBase

The webinar was successful, we had many attendees and great participation in questions and answers throughout the session and in the end. Only after the webinar it only occurred to me that one specific graphic was missing from the webinar deck. It was occurred to me after answering several audience questions about "the difference between partitioning and sharding" or "why partitioning doesn't qualify as scale-out". 

Having the webinar today, I would definitely include the following picture, describing the core difference between Scale Up, Partitioning, and Scale Out:

In the above (poor) graphics, I used the black server box as the database server machine, the good old cylinder as the disk or storage device, and the colorful square thingy stands for the database engine. Believe it or not, this is a real complete architecture chart of Oracle 10gR2 SGA, miniatured to a small scale. Yes, all databases including Oracle and also MySQL, are complex beasts, a lot of stuff is going on inside the database engine for every command. 

If my DB is like in the "starting point" then I'm either really small, or I'm in a really bad shape by now. 

Partitioning makes wonders as data grows towards being "big data". It optimizes the data placement on separate files or disks, it makes every partition optimized and "thin" and less fragmented as you would expect from a gigantic busy monolithic table. Still, although splitting the data across files, we're still "stuck" with busy monolithic database engine that relies on a single box "compute" or "computing power". 

While we distributed the data, we didn't distribute the "compute". 

When there is a heavy join operation, there is one busy monolithic database engine to collect data from all partitions and process this join. 

When there are 10000 concurrent transactions to handle right here and now, there is one busy monolithic database engine to do all database-engine activities such as buffer management, locking, thread locks/semaphores, and recovery tasks. Buffers, locking queues, transaction queues... are still the same for all partitions. 

This is where Scale-out is different than partitions. It enables distribution and parallelism of the data as well as the so important compute, brings the compute closer to the data, enables several database engine process different sets of data, handling different sets of the overall session concurrency.

You can think of it as one step forward from partitioning, and it comes with great great results. It's not a simple step though, an abstraction layer is required to represent the databases grid as one database to the application, same as what it's used to use. 

In further posts I'll go into more on this "Scale Out Abstraction Layer", and also about ScaleBase which is a provider of such layer