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A Helpful Checklist for Selecting a New Database

A checklist of what to investigate when selecting a modern database engine

When you look at the database market, it's a virtual jungle out there. Those of us in the industry 15 years ago can look back and remember when we only had the option to use a relational database from Sybase, Oracle, Microsoft or IBM. That was pretty much it if you were planning to build a new complex application with persistent storage.

Nowadays, the number of options is absolutely brilliant, but this also means you need to do your homework. I found a great visualization of the database market landscape by 451 Research. The similarity to the London underground map is striking.

Image from 451 Research, Matt Aslett

If you don't do your research before choosing a database, you could end up disappointed. So exactly what are the main issues you should consider? This article provides a checklist of what to investigate when selecting a modern database engine.

1) The purpose of the application
This should be easy, shouldn't it? I get a lot of questions about selecting a database. The typical questions are, "I want to build a processing queue type of application. Would MongoDB be a perfect fit?" I understand the confusion and how easy it is to think that any modern NoSQL database is suited to solve any problem. Those of us who work all day using NoSQL databases and know how extreme the differences are between different vendors, it is easy to forget how this business looks from a bird's-eye perspective. This is why I suggest starting with identifying your purpose for the application. Is the data in your application used for analytical and Data Mining (OLAP) or transactional (OLTP) information? If the data is live and operational, go with OLTP. Test this yourself by estimating the ratio between read and write transactions, and that could point you in the right direction. Perhaps you want to solve a storage problem where you store documents or something similar and have no need for true consistency? Then you should consider document databases.

2) True consistency
Are you building an application where you need to be absolutely sure that you cannot get anything but secure transactions? A good example is transferring money from one bank account to another simultaneously from several threads. In this example, you need to be sure that you cannot move the same money twice, and that the money is completely transferred from account 1 to account 2 without risking being withdrawn from account 1 and then lost in cyberspace before deposited to account 2. You cannot accept anything but true consistency in this example. Not eventual consistency, not consistency within "X" ms or even local consistency. You need consistency. Period. Select a database that supports ACID and where the definition of the "C" in the abbreviation is Consistency without any prefix or suffix.

3) High performance
If you are aiming to build a modern, interactive application used by many users at the same time, then you need great performance. Think not only about your usage scenario in the near future, but what about five years from now? Building applications tied to a database will marry you to the database vendor for a long time. Also, imagine what great applications you can build when you are not limited by the performance of the persistent layer.

When you look at the performance figures of most databases, you only see the actual time spent in the database. What you should really be interested in is the complete time your application has to spend on a task before it is completed. The actual time experienced by your end users is a combination of:

  • Client-server communication time
  • Application operations
  • Traverse data between application and database
  • Database executions

4) Is your data safe?
When your data is written, will it be secured for the future? Is there a delay between committing a transaction and securing the information? If the application cannot be restored following a power loss to the absolute latest committed transaction, it is not considered to have durability (the "D" in ACID).

5) Consider in-memory
What about in-memory databases - will your data be safe there? Many modern in-memory databases are fully ACID compliant, which guarantees that all transactions will be secured on a persistent media once they are committed. All reads are done only in-memory, but before any modification or insert is marked as successful, everything will be safe in case of a power loss. Just make sure you are using an in-memory database with durability and not just a memory cache.

The major reason to move everything into RAM is of course to get the best performance possible. Then why isn't every vendor of disk-based databases moving their storage into RAM? That's what they do, but the outcome is not the extreme performance expected. If your database is written for disk-oriented storage, a lot of code will need to be executed to optimize for disk-related problems. Even if the problems are no longer an issue in RAM, the code will still be executed and slow down each operation.

The next step for optimization in RAM is to remove the disk optimizations and just execute what is necessary in-memory. By doing this, you can actually gain about 10 times the performance. That's not bad, but since RAM is way more than 10 times faster than disk, it isn't really that impressive.

Traditional setup of applications using databases

I mentioned earlier that what's interesting for your end users is the combination of client-server communication time, application operations, traversing data, and database executions. Why are we only measuring the third step? The answer is simple - the performance figures will look poor if we include application operations, database executions, and the time it takes to traverse information between the application and database. Even if the application and the database reside in the same set of RAM, the number of copies of information involved in updates and reads (application, traverse layer and database) will make a huge impact on the performance. Also, imagine the number of copies of the same information in the different layers. This will be a source for bugs and increase the maintenance costs and time-to-market.

To make the best use of RAM performance, we need to combine the application and database, and let the application and database share the same objects. If we do this, there will be virtually no delay when modifying your application objects and committing the database transaction, since they point to the same objects. We remove the traversing information completely. Now you can get the performance of your dreams. This is not an option in a scaled-out environment, but for applications that use less storage space in RAM than what can be accessed by modern hardware (a couple of terabytes of data). Given that your application is not an OLAP type, this will not be an issue.

Setup of applications using a database optimized for in-memory

6) Transaction conflicts
Will you have a lot of threads working with the same data? If so, make sure that the database can handle transactional conflicts quickly, safely, and preferably at a high isolation level without locking. Check what the "I" (Isolation level) in ACID stands for in your selected database.

7) Most value for your money
Estimating the total cost of ownership (TCO) should include not only database license fees and database maintenance fees, but also costs for hardware, OS licenses, power, physical rack space and labor costs to maintain it all. If you can find a database that is 10 times faster than traditional databases, you can reduce up to 10 times the costs. Imagine what you'll save if you find a database that gives you 100 times the performance.

In your total cost, you must also consider the complexity of the development environment. How many lines of code are involved in each function? What is the time-to-market for your new application?

8) Easy to use API
Last but definitely not least - as developers, we stumble upon amazing databases with nice performance figures. Later, when starting to dig deeper, we find that we need to implement a shipload of code to get data into persistent storage. Code for declaring database schemas, stored procedures, data transfers between the database and the application, and then try to glue everything together with application objects in the end. Programmers, even those who consider themselves to be brilliant (all of us?), tend to make more bugs if there are more lines of code. Getting an API that helps you minimize the lines of code and gives you ease of use should be a higher priority than it is today. Also, fewer lines of code often give you a shorter time-to-market.

Consider this checklist and you'll be well on your way to finding a database that's best suited for your new, exciting and modern applications. Good luck.

More Stories By Niklas Bjorkman

Niklas Bjorkman is the Technical Marketing Engineer of Starcounter. He has more than 17 years of experience developing advanced business systems and working with object-oriented programming related to persistent storage. Prior to joining Starcounter, Niklas served as CTO of Adtoma, an online advertising and media management company, where he led the development of a next-generation adserve application. He also previously served as CTO of Heads, a real-time retail application vendor, where he developed the company’s supply chain management application.

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