In-Memory BI Is Not the Future, It’s the Past
Why the current in-memory BI hype can be misleading.
By: Elad Israeli
Oct. 7, 2010 10:25 AM
In recent times, one of the most popular subjects related to the field of Business Intelligence (BI) has been In-memory BI technology. The subject gained popularity largely due to the success of QlikTech, provider of the in-memory-based QlikView BI product. Following QlikTech’s lead, many other BI vendors have jumped on the in-memory “hype wagon,” including the software giant, Microsoft, which has been aggressively marketing PowerPivot, their own in-memory database engine.
The increasing hype surrounding in-memory BI has caused BI consultants, analysts and even vendors to spew out endless articles, blog posts and white papers on the subject, many of which have also gone the extra mile to describe in-memory technology as the future of business intelligence, the death blow to the data warehouse and the swan song of OLAP technology. I find one of these in my inbox every couple of weeks.
Just so it is clear - the concept of in-memory business intelligence is not new. It has been around for many years. The only reason it became widely known recently is because it wasn’t feasible before 64-bit computing became commonly available. Before 64-bit processors, the maximum amount of RAM a computer could utilize was barely 4GB, which is hardly enough to accommodate even the simplest of multi-user BI solutions. Only when 64-bit systems became cheap enough did it became possible to consider in-memory technology as a practical option for BI.
The success of QlikTech and the relentless activities of Microsoft’s marketing machine have managed to confuse many in terms of what role in-memory technology plays in BI implementations. And that is why many of the articles out there, which are written by marketers or market analysts who are not proficient in the internal workings of database technology (and assume their readers aren’t either), are usually filled with inaccuracies and, in many cases, pure nonsense.
The purpose of this article is to put both in-memory and disk-based BI technologies in perspective, explain the differences between them and finally lay out, in simple terms, why disk-based BI technology isn’t on its way to extinction. Rather, disk-based BI technology is evolving into something that will significantly limit the use of in-memory technology in typical BI implementations.
But before we get to that, for the sake of those who are not very familiar with in-memory BI technology, here’s a brief introduction to the topic.
Disk and RAM
Most modern computers have 15-100 times more available disk storage than they do RAM. My laptop, for example, has 8GB of RAM and 300GB of available disk space. However, reading data from disk is much slower than reading the same data from RAM. This is one of the reasons why 1GB of RAM costs approximately 320 times that of 1GB of disk space.
Another important distinction is what happens to the data when the computer is powered down: data stored on disk is unaffected (which is why your saved documents are still there the next time you turn on your computer), but data residing in RAM is instantly lost. So, while you don’t have to re-create your disk-stored Microsoft Word documents after a reboot, you do have to re-load the operating system, re-launch the word processor and reload your document. This is because applications and their internal data are partly, if not entirely, stored in RAM while they are running.
Disk-based Databases and In-memory Databases
Disk-based databases are engineered to efficiently query data residing on the hard drive. At a very basic level, these databases assume that the entire data cannot fit inside the relatively small amount of RAM available and therefore must have very efficient disk reads in order for queries to be returned within a reasonable time frame. The engineers of such databases have the benefit of unlimited storage, but must face the challenges of relying on relatively slow disk operations.
On the other hand, in-memory databases work under the opposite assumption that the data can, in fact, fit entirely inside the RAM. The engineers of in-memory databases benefit from utilizing the fastest storage system a computer has (RAM), but have much less of it at their disposal.
That is the fundamental trade-off in disk-based and in-memory technologies: faster reads and limited amounts of data versus slower reads and practically unlimited amounts of data. These are two critical considerations for business intelligence applications, as it is important both to have fast query response times and to have access to as much data as possible.
The Data Challenge
The challenges in creating this data store using traditional disk-based technologies is what gave in-memory technology its 15 minutes (ok, maybe 30 minutes) of fame. Having the entire data model stored inside RAM allowed bypassing some of the challenges encountered by their disk-based counterparts, namely the issue of query response times or ‘slow queries.’
It’s commonly thought that relational databases are too slow for BI queries over data in (or close to) its raw form due to the fact they are disk-based. The truth is, however, that it’s because of how they use the disk and how often they use it.
Relational databases were designed with transactional processing in mind. But having a database be able to support high-performance insertions and updates of transactions (i.e., rows in a table) as well as properly accommodating the types of queries typically executed in BI solutions (e.g., aggregating, grouping, joining) is impossible. These are two mutually-exclusive engineering goals, that is to say they require completely different architectures at the very core. You simply can’t use the same approach to ideally achieve both.
In addition, the standard query language used to extract transactions from relational databases (SQL) is syntactically designed for the efficient fetching of rows, while rare are the cases in BI where you would need to scan or retrieve an entire row of data. It is nearly impossible to formulate an efficient BI query using SQL syntax.
So while relational databases are great as the backbone of operational applications such as CRM, ERP or Web sites, where transactions are frequently and simultaneously inserted, they are a poor choice for supporting analytic applications which usually involve simultaneous retrieval of partial rows along with heavy calculations.
Regardless of what fancy footwork is used with an in-memory database, storing the entire dataset in RAM has a serious implication: the amount of data you can query with in-memory technology is limited by the amount of free RAM available, and there will always be much less available RAM than available disk space.
The bottom line is that this limited memory space means that the quality and effectiveness of your BI application will be hindered: the more historical data to which you have access and/or the more fields you can query, the better analysis, insight and, well, intelligence you can get.
You could add more and more RAM, but then the hardware you require becomes exponentially more expensive. The fact that 64-bit computers are cheap and can theoretically support unlimited amounts of RAM does not mean they actually do in practice. A standard desktop-class (read: cheap) computer with standard hardware physically supports up to 12GB of RAM today. If you need more, you can move on to a different class of computer which costs about twice as much and will allow you up to 64GB. Beyond 64GB, you can no longer use what is categorized as a personal computer but will require a full-blown server which brings you into very expensive computing territory.
It is also important to understand that the amount of RAM you need is not only affected by the amount of data you have, but also by the number of people simultaneously querying it. Having 5-10 people using the same in-memory BI application could easily double the amount of RAM required for intermediate calculations that need to be performed to generate the query results. A key success factor in most BI solutions is having a large number of users, so you need to tread carefully when considering in-memory technology for real-world BI. Otherwise, your hardware costs may spiral beyond what you are willing or able to spend (today, or in the future as your needs increase).
There are other implications to having your data model stored in memory, such as having to re-load it from disk to RAM every time the computer reboots and not being able to use the computer for anything other than the particular data model you’re using because its RAM is all used up.
A Note about QlikView and PowerPivot In-memory Technologies
PowerPivot uses a similar concept, but is engineered somewhat differently due to the fact it’s meant to be used largely within Excel. In this respect, PowerPivot relies on a columnar approach to storage that is better suited for the types of calculations conducted in Excel 2010, as well as for compression. Quality of compression is a significant differentiator between in-memory technologies as better compression means that you can store more data in the same amount RAM (i.e., more data is available for users to query). In its current version, however, PowerPivot is still very limited in the amounts of data it supports and requires a ridiculous amount of RAM.
The Present and Future Technologies
These types of technologies are not theoretical anymore and are already utilized by businesses worldwide. Some are designed to distribute different portions of complex queries across multiple cheaper computers (this is a good option for cloud-based BI systems) and some are designed to take advantage of 21st-century hardware (multi-core architectures, upgraded CPU cache sizes, etc.) to extract more juice from off-the-shelf computers.
A Final Note: ElastiCube Technology
When we began our research at SiSense, our technological assumption was that it is possible to achieve in-memory-class query response times, even for hundreds of users simultaneously accessing massive data sets, while keeping the data (mostly) stored on disk. The result of our hybrid disk-based/in-memory technology is a BI solution based on what we now call ElastiCube, after which this blog is named. You can read more about this technological approach, which we call Just-in-Time In-memory Processing, at our BI Software Evolved technology page.
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