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I like to ride motorbikes. Currently I ride a BMW K1200S – a sports tourer that is both fast and comfortable on the road. Before that I had a five year affair with a BMW R1150GS which took me to all sorts of off-the-beaten-track destinations before we abruptly parted company with me flying through the air in one direction as my bike was smashed in the other direction by criminals in a getaway car.
Most motorbike enthusiasts have, like me, owned a few in their lifetimes and in most cases they are of differing types. A road bike, no matter how much you are prepared to spend, can barely travel faster than walking pace on a good quality dirt road because, apart from the obvious things like tyres and suspension, the geometry is all wrong. The converse is similar – a good dirt bike is frustrating, dull and downright dangerous to ride on a road.
Bikers understand the issues around suitability for purpose and compromise more than most (such as car drivers). Our lottery winning fantasies have a motorbike garage filled, not simply with classics or expense, but with a bike suitable for every purpose and occasion – track, off-road, touring, commuting, cafe racing and every other obvious niche. Some may even want a Harley Davidson for the odd occasion that one would want to ride a machine that leaks more oil than fuel it uses and one would want to travel in a perfectly straight line for 200 yards before it overheats and the rider suffers from renal damage.
But I digress. Harley Davidson hogs, fanbois (or whatever the collective noun is for Harley Davidson fans) can move on. This post has nothing to do with you.
There is nothing in the motorbike world that is analogous to the broad suitability of the SQL RDBMS. SQL spans the most simple and lightweight up to complex, powerful and expensive – with virtually every variation in between covered. It is not just motorbikes, a lot of products out there would want such broad suitability – cars, aeroplanes and buildings. SQL is in a very exclusive club of products that is solves such a broad range of the same problem, and in the case of SQL, that problem is data storage and retrieval. Also SQL seems to solve this problem in a way that the relationships between load, volume, cost, power and expense is fairly linear.
SQL’s greatest remaining strength and almost industry wide ubiquity is that it is the default choice for storing and retrieving data. If you want to store a handful of records, you might as well use a SQL database, not text files. And if you want to store and process huge amounts of transactional data, in virtually all cases, a SQL database is the best choice. So over time, as the demands and complexity of our requirements has grown, SQL has filled the gaps like sand on a windswept beach, and exclusively filled every nook and cranny.
We use SQL for mobile devices, we use SQL for maintaining state on the web, we use SQL for storing rich media, and use it to replicate data around the world. SQL has, as it has been forced to satisfy all manner of requirements, been used, abused, twisted and turned and generally made to work in all scenarios. SQL solutions have denormalization, overly complex and inefficient data models with thousands of entities, and tens of thousands of lines of unmaintainable database code. But still, surprisingly, it keeps on giving as hardware capabilities improve, vendors keep adding features and people keep learning new tricks.
But we are beginning to doubt the knee jerk implementation of SQL for every data storage problem and, at least at the fringes of its capabilities, SQL is being challenged. Whether it be developers moving away from over-use of database programming languages, cloud architects realising that SQL doesn’t scale out very well, or simply CIO’s getting fed up with buying expensive hardware and more expensive licences, the tide is turning against SQL’s dominance.
But this post is not an epitaph for SQL, or another some-or-other-technology is dead post. It is rather an acknowledgement of the role that SQL plays – a deliberate metronomic applause and standing ovation for a technology that is, finally, showing that it is not suitable for every conceivable data storage problem. It is commendable that SQL has taken us this far, but the rate at which we are creating information is exceeding the rate at which we can cheaply add power (processing, memory and I/O performance) to the single database instance.
SQL’s Achilles heel lies in its greatest strength – SQL is big on locking, serial updates and other techniques that allow it to be a bastion for consistent, reliable and accurate data. But that conservative order and robustness comes at a cost and that cost is the need for SQL to run on a single machine. Spread across multiple machines, the locking, checking, index updating and other behind the scenes steps suffer from latency issues and the end result is poor performance. Of course, we can build even better servers with lots of processors and memory or run some sort of grid computer, but then things start getting expensive – ridiculously expensive, as heavy metal vendors build boutique, custom machines that only solve today’s problem.
The scale-out issues with SQL have been known for a while by a small group of people who build really big systems. But recently the problems have moved into more general consciousness by Twitter’s fail-whale, which is largely due to data problems, and the increased interest in the cloud by developers and architects of smaller systems.
The cloud, by design, tries to make use of smaller commodity (virtualized) machines and therefore does not readily support SQL’s need for fairly heavyweight servers. So people looking at the cloud find that although there are promises that their application will port easily, are obviously asking how they bring their database into the cloud and finding a distinct lack of answers. The major database players seem to quietly ignore the cloud and don’t have cloud solutions – you don’t see DB2, Oracle or MySQL for the cloud and the only vendor giving it a go, to their credit (and possibly winning market share), is Microsoft with SQL Server. Even then, SQL Azure (the version of SQL Server that runs on Azure) has limitations, and size limitations that are indirectly related to the size of the virtual machine on which it runs.
Much is being made of approaches to get around the scale out problems of SQL and with SQL Azure in particular, discussions around a sharding approach for data. Some of my colleagues were actively discussing this and it led me to weigh in and make the following observation:
There are only two ways to solve the scale out problems of SQL Databases
1. To provide a model that adds another level of abstraction for data usage (EF, Astoria)
2. To provide a model that adds another level of abstraction for more complicated physical data storage (Madison)
In both cases you lose the “SQLness” of SQL.
It is the “SQLness” that is important here and is the most difficult thing to find the right compromise for. “SQLness” to an application developer may be easy to use database drivers and SQL syntax; to a database developer it may be the database programming language and environment; to a data modeller it may be foreign keys; to a DBA it may be the reliability and recoverability offered by transaction logs. None of the models that have been presented satisfy the perspectives of all stakeholders so it is essentially impossible to scale out SQL by the definition of what everybody thinks a SQL database is.
So the pursuit of the holy grail of a scaled out SQL database is impossible. Even if some really smart engineers and mathematicians are able to crack the problem (by their technically and academically correct definition of what a SQL database is), some DBA or developer in some IT shop somewhere is going to be pulling their hair out thinking that this new SQL doesn’t work the way it is supposed to.
What is needed is a gradual introduction of the alternatives and the education of architects as to what to use SQL for and what not to – within the same solution. Just like you don’t need to store all of your video clips in database blob fields, there are other scenarios where SQL is not the only option. Thinking about how to architect systems that run on smaller hardware, without the safety net of huge database servers, is quite challenging and is an area that we need to continuously discuss, debate and look at in more detail.
The days are the assumption that SQL will do everything for us is over and, like motorcyclists, we need to choose the right technology or else we will fall off.