Database Workload Theory

equations

In the scientific world, theoretical physicists postulate theories and ideas, for example the Higgs Boson. After this, experimental physicists design and implement experiments, such as the Large Hadron Collider, to prove or disprove these theories. In this post I’m going to try and do the same thing with databases, except on a smaller budget, with less glamour and zero chance of winning a Nobel prize. On the plus side though, my power bills will be a lot lower.

That last paragraph was really just a grandiose way of saying that I have an idea, but haven’t yet thought of a way to prove it. I’m open to suggestions, feedback and data which prove or disprove it… but for now let’s just look at the theory.

Visualising Database Server I/O Workload

If you look at a database server running a real life workload, you will generally see a pattern in the behaviour of the I/O. If you plot a graph of the two extremes of purely sequential I/O and purely random I/O most workloads will fit somewhere along this sliding scale :

IO-scale

Now of course workloads change all the time, so this is an approximation or average, but it makes sense. After all, we do this in the world of storage, because if the workload is highly random the storage requirements will be very different to if the workload is highly sequential.

What I am going to do now is plot a graph with this as the horizontal axis. The vertical axis will be an exponential representation of the storage footprint used by the database server, i.e. the amount of space used. I can then plot different database server workloads on the graph to see where they fall.

But first, two clarifications. I am at pains to say “database server” instead of “database” because in many environments there are multiple database instances generating I/O on the same server. What we are interested in here is how the storage system is being driven, not how each individual database is behaving. Remember this point and I’ll come back to it soon. The other clarification is regarding workload – because many systems have different windows where I/O patterns change. The classic (and very common) example is the OLTP database where users log off at the end of the day and then batch jobs are run. Let’s plot the OLTP and batch workloads as separate points on our graph.

Here’s what I expect to see:

database-io-workload

There are data points in various places but a correlation is visible which I’ve highlighted with the blue line. Unfortunately this line is nothing new or exciting, it’s just a graphical representation of the fact that large databases tend to perform lots of sequential I/O whereas small databases tend to perform lots of random I/O.

Why is that? Well because in most cases large databases tend to be data warehouses, decision support systems, business intelligence or analytics systems… places where data is bulk loaded through ETL jobs and then scanned to create summary information or spot trends and patterns. Full table scans are the order of the day, hence sequential I/O. On the other hand, smaller databases with lots of random I/O tend to be OLTP-based, highly transactional systems running CRM, ERM or e-Commerce platforms, for example.

Still, it’s a start – and we can visualise this by dividing the graph up into quadrants and calling them zones, like this:

database-io-workload-zonesThis is only an approximation, but it does help with visualising the type of I/O workload generated by database servers. However, there are two more quadrants looking conspicuously un-labelled, so let’s now turn our attention to them.

Database Consolidation I/O Workload

The bottom left quadrant is not very exciting, because small database systems which generate highly-sequential workloads are rare. I have worked on one or two, but none that I ever felt should actually have been designed to work that way. (One was an indexing system which got scrapped and replaced with Lucene, the other I am still not sure actually existed or if it was just a bad dream that I once had…)

The top right quadrant is much more interesting, because this is the world of database consolidation. I said I would come back to the idea that we are interested not in the workload of the database but of the database server.  The reason for this is that as more databases are run on the same server and storage infrastructure, the I/O will usually become increasingly random. If you think about multiple sets of disparate users working on completely different applications and databases, you realise that it quickly becomes impossible to predict any pattern in the behaviour of the I/O. We already know this from the world of VDI, where increasing the number of seats results in an increasingly random I/O requirement.

The top right quadrant requires lots of random I/O and yet is large in capacity. Let’s label it the consolidation zone on our graph:

database-io-workload-consolidation-zone

We now have a graphical representation of three broad areas of I/O workload. If we believe in the trend of database consolidation, as described by the likes of Gartner and IDC, then over time the dots in the DW and OLTP zones will migrate to the consolidation zone. I have already blogged my thoughts on the benefits of database consolidation, bringing with it increased agility and massive savings in operational costs (especially Oracle licenses) – and many of the customers I have been speaking to both at Violin and in my previous role are already on this journey, even if some are still in the planning stages. I therefore expect to see this quadrant become increasingly populated with workloads, particularly as flash storage technologies take away the barriers to entry.

I/O Workload Zone Requirements

The final step in this process is to look at the generic requirements of each of our three workload zones.

database-io-workload-requirements

The data warehouse zone is relatively straightforward, because what these systems need more than anything is bandwidth. Also known as throughput, this is the ability of the storage to pump large volumes of data in and out. There is competition here, because whilst flash memory systems can offer excellent throughput, so can disk systems. So can Exadata of course, it’s what it was designed for. Mind you, flash should enable a lower operational cost, but this isn’t a sales pitch so let’s move on to the next zone.

The OLTP zone is all about latency. To run a highly-transactional system and get good performance and end-user experience, you need consistently low latency. This is where flash memory excels – and disk sucks. We all (hopefully) know why – disk simply cannot overcome the seek time and rotational latency inherent in its design.

The consolidation zone however is particularly interesting, because it has a subtly different set of requirements. For consolidation you need two things: the ability to offer sustained high levels of IOPS, plus predictable latency. Obviously when I say that I mean predictably low, because predictably high latency isn’t going to cut it (after all, that’s what disk systems deliver). If you are running multiple, disparate applications and databases on the same infrastructure (as is the case with consolidation) it is crucial that each does not affect the performance of the other. One system cannot be allowed to impact the others if it misbehaves.

Now obviously disk isn’t in with a hope here – highly random I/O driving massive and sustained levels of IOPS is the worst nightmare for a disk system. For flash it’s a different story – but it’s not plain sailing. Not every flash vendor can truly sustain their performance levels or keep their latency spike-free. Additionally, not every flash vendor has the full set of enterprise features which allow their products to become a complete tier of storage in a consolidation environment.

As database consolidation increases – and in fact accelerates with the continued onset of virtualisation – these are going to be the requirements which truly differentiate the winners from the contenders in the flash market.

It’s going to be fun…

Disclaimer

What Do You Think?These are my thoughts and ideas – I’m not claiming them as facts. The data here is not real – it is my attempt at visualising my opinions based on experience and interaction with customers. I’m quite happy to argue my points and concede them in the face of contrary evidence. Of course I’d prefer to substantiate them with proof, but until I (or someone else) can devise a way of doing that, this is all I have. Feel free to add your voice one way or the other… and yes, I am aware that I suck at graphics.

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One Response to Database Workload Theory

  1. Matt Morris says:

    I would suggest this postulate for your exercise:
    Your DW workload is based on the traditional model(s) used today. However what we have seen with Exadata, Neteeza, GreenPlum, Teradata they break the data down into smaller units.
    Especially exadata – The backend MPP Storage cluster increases the selectivity of the data reducing the amount of information that needs to be returned to the “in-memory” database structures.
    What have found especially with Violin – by approaching DW architecture differently – by building in the selectivity up front. Latency becomes much more important and bandwidth is not a major factor anymore.
    Using Bitmaps much more pervasively and driving the filtering with more intelligent query use – ODS and traditional DW/BI architectures become more Random in nature.
    This greatly can reduce the number of Cores needed in your database server.

    Good luck with your testing

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