The Most Important Thing You Need To Know About Flash


Storage for DBAs: There are many things you need to know about flash: it’s performance, it’s behaviour, it’s durability etc. But there’s one single piece of information which tells you more than anything else, because it gives you an insight into the future of not just flash memory, but the primary data storage industry. Let me explain, but first let me contrast against something we all more familiar with: disk drives.

Disk Drive Market History

Almost all disk drives are made by three manufacturers: Western Digital, Seagate and Toshiba. There used to be a lot more than that, but all the others either went out of business or got acquired. These are tough times for disk drive manufacturers, with sales expected to take a double-digit dive in 2013. It was not always this way though; for decades the thirst for HDDs was unquenchable, with large volumes of them being required in desktop PCs (remember them?) as well as enterprise disk arrays. Fast forward to the present day and desktop PCs have been ambushed by tablets and SSDs, while flash is now similarly disrupting the data centre.

For a minute though, let’s remember the golden era of disk. If we rewind around eight years ago, the disk industry was thriving. To quote a TrendFocus report published in Businesswire (emphasis added by me):

The industry’s 25% unit growth in 2005 was based on solid fundamentals in core markets like PCs and servers… Booming notebook PC sales caused a surge in 2.5″ HDD shipments to 77 million, a 45% growth. Enterprise HDD shipments grew 11% to over 26 million units. HDD industry revenue was $28 billion, an increase of 18% from 2004.

An 18% annual increase… that’s impressive! As the quote states, this growth was built on the “solid fundamentals” of PCs and Servers – nobody foresaw the end of the PC disk market,2013 HDD Market by Application because a new and exciting range of “Notebook PCs” seemed ready to drive demand even higher. And on top of that, two new market segments were growing rapidly: Consumer Electronics and Mobile. The graph on the left was created in 2005 and showed HDD market predictions going forward until 2010. The yellow and light blue colours indicate CE and Mobile respectively – you can see that there was a lot of optimism for the future – while the reddish colour indicates the huge returns from desktop PCs (while the Enterprise segment is merely a small purple brick at the bottom of each column). But somewhere at the bottom of a 2005 Q4 report published later that year were the first indications of a changing tide:

Shipments accounted for slightly less than the 20 percent forecast, and the drop-off is attributed to Apple’s decision to move away from 1-inch-based hard drives for its iPod mini business.

What did Apple move to? Take a guess. Very slowly, but very surely, the potential CE and Mobile markets evaporated. At the same time, the Desktop PC business died a lingering death, leaving enterprise storage as the mainstay for hard drives.

That’s one thing which remained constant through this period though: the enterprise HDD market. Enterprise Storage vendors like EMC and NetApp bought up massive volumes of disk drives to put in enterprise class arrays for their customers – and these massive volumes meant two critical outcomes:

  1. The larger Enterprise Storage vendors bought at enormous discounts
  2. HDD vendors selling to Enterprise Storage focussed their R&D efforts on improving the characteristics that these customers desired

The characteristics required for enterprise storage were: density and performance. It’s a simple case of supply and demand. As with all business, the demand shapes the supply.

NAND Flash Market Forces

One thing that flash has in common with disk is the relatively small number of manufacturers. Note that I’m not talking about companies like Violin here, I’m talking about the flash chip manufacturers who own the fabs. In 2012 the NAND flash market consisted of Samsung (38%), Toshiba (28%) – the inventor of flash, Micron (14%) and Hynix (12%). But who was the largest buyer worldwide? Was it EMC? Netapp? IBM, HP or Dell?

2013 NAND Market by ApplicationIn 2011, Apple became the largest worldwide consumer of NAND flash. And as I’m sure you can guess, the reason for this was the iPhone. Today, according to IC Insights, the majority of NAND flash (59%) is used in smartphones, tablets and portable devices, with another 17% used in USB keys and cameras. If you look at the pie chart on the right, that little red portion marked SSD (just 13%) comprises all the flash used in both enterprise storage and consumer solid state drives (e.g. the ones you might get in an ultrabook).

And that trend is only going one way. By the end of 2013 it is forecast that there will be nearly 1.5 billion smartphones in the world – one smartphone for every five people. Meanwhile, tablets are not only the fastest growing segments but also one of the fastest-growing consumer devices of all time.

What does this mean? It means that NAND flash development is driven by the consumer market, by smartphones and portable devices. In enterprise storage, when we talk about flash we always talk about performance and endurance – but the consumer market isn’t interested in either of these. The consumer market is interested in density, i.e. how much data you can fit on a chip, as well as power consumption and cost. If a NAND flash manufacturer could produce larger flash chips at the cost of 20% slower performance, for example, this would be considered a great result. There’s a fundamental difference in requirements between the consumer and enterprise markets: only the enterprise cares about performance.

The Balance Of Power

In the heyday of disk, the enterprise storage industry had serious influence on what came out of the factories. But with NAND flash, the power of the enterprise storage industry to influence the direction of development is clearly far weaker. Sure there are relationships between flash storage vendors and the manufacturers – in fact, one of the strongest is between Violin and Toshiba – but market forces dictate that NAND flash development will be mostly influenced by the consumer market: the phone in your pocket and the tablet on your desk. (Don’t be confused by claims of “enterprise-class” NAND flash either – the key is to follow where the billions of dollars of R&D money are going, i.e. the consumer market. Enterprise-class flash is merely the least-consumer-like consumer flash…)

cautionWhat does this mean for enterprise storage? It’s simple – it means that each enterprise vendor will have to take “consumer” flash and come up with innovative ways to make it perform like an enterprise product. Each flash vendor needs to do this to deliver the performance you need. Anybody can take a bunch of flash cards or SSDs and put them in a box, but that’s not innovation. The flash vendors who survive the great flash market consolidation will be the ones with intellectual property and patents around making consumer NAND flash perform for the enterprise.

Understand this and you will know the most important thing to ask a potential flash vendor about their product is not “How fast is it?”, or “How long does it last?”, but “Where’s your innovation?”. After all, if your vendor isn’t adding anything to the equation, you might as well be doing it yourself…

Does My Database Need Flash?


Storage for DBAs: Here’s a question I get asked a lot: “Does my database need flash?”. In fact it’s the most common question customers have, followed by the alternative version, “Does my database need SSD?”. In fact, often customers already have some SSDs in their disk arrays but still see poor performance, so really I ought to wind it back a level and call this article, “Does my database need low latency storage?”. This would in fact be a much better headline from a technical perspective, but until I change the name of this site to LowLatencyDBA I’m sticking with the current title.

Flash is no longer a cutting edge new technology, it’s a mainstream product sold by almost every storage vendor. This means that you or your organisation will probably already have some flash sales person beating down your door to flog you some sort of flash product, whether it’s an all-flash array, a hybrid flash/disk system or a set of PCIe flash cards. While these products are diverse in nature, they all share two main characteristics: low latency and large numbers of IOPS. But how do you know whether you really need them?

In a later post I’ll be running through the questions which I think need to be asked in order to whittle down the massive list of flash vendors to the select few capable of servicing your needs. This, of course, will be difficult to achieve without being biased towards my own employer – but that’s a problem for another day. For now, here’s the first (and potentially most important) step: working out whether you actually need low latency flash storage in the first place.

Who Needs Flash?

For the world of databases, there are three main reasons why you might want to switch to low latency flash:

acceleration-consolidation-virtualizationAcceleration – perhaps the most obvious reason is to go faster. There are many reasons why people desire better performance, but they generally boil down to one of two scenarios: Not Good Enough Now and Not Good Enough For The Future. In the former, bad performance is holding back an application, denying potential revenue or incurring penalties in some way (either SLA-based financial penalties or simply the loss of customers due to poor service levels). In the latter, existing infrastructure is incapable of allowing increased agility, i.e. the ability to do more (offering new services for example, or adding more concurrent users).

Consolidation – always on the mind of CIOs and CTOs is the benefit of consolidating database and server estates. Consolidation brings agility and risk benefits as well as the new and important benefit of cost savings. By consolidating (and standardising) multiple databases onto a smaller pool of servers, organisations save money on hardware, on maintenance and administration, and on the holy grail of all cost savings: software license fees. If you think that sounds like an exaggeration, take a look at this article on Wikibon which demonstrates that Oracle license costs account for 82% of the total cost of a traditional database deployment. Consolidation allows for reduced CPU cores, which means a reduction in the number of licenses, but it also increases I/O as workloads are “stacked” on the same infrastructure. The Wikibon article argues that by moving to flash storage and consolidating, the total cost drops significantly – by around 26% in fact.

Virtualisation – an increasingly prevalent option in the database world. The use of server virtualisation technologies is allowing organisations to move to cloud architectures, where environments are automatically provisioned, managed and migrated across hardware. Virtualisation brings massive agility benefits but also carries a risk because, just like with consolidation, I/O workloads accumulate on the same infrastructure. Unlike consolidation though, virtualisation adds an extra layer of latency, making the I/O even more of a potential bottleneck. Flash systems now make this option practical, as hypervisor vendors begin to realise the potential of flash memory.

There is actually a fourth reason, which is Infrastructure Optimisation. If you have data centres stuffed with disk arrays there is every chance that they can be replaced by a small number of flash arrays, thus reducing power, cooling and real estate requirements and saving large amounts of money. But as this article is primarily targeted at databases I thought I’d leave that one out for now. Consider it the icing on the cake… but don’t forget it, because sometimes it turns out that there’s a lot of icing.

So now we know the reasons why, let’s have a look at which sorts of systems are suitable for flash and which aren’t, starting with the Performance requirement…

Databases Love Flash If…

  • tickThey create lots of I/O! I know, it sounds obvious, but more than once I’ve seen customers with CPU-bound applications that generate hardly any I/O. Flash is a fantastic technology, but its not magic.
  • There is lots of random I/O. Now don’t take that the wrong way – sequential I/O is good too. But if you currently have a random I/O workload running on a disk system you will see the most dramatic benefit after switching that to flash. Here’s why.
  • High amounts of parallelism. The simple fact is that a single process cannot drive anywhere near the amount of I/O that a good flash system can support. If you think of flash as being like a highway, not only is it fast, it’s also wide. Use all the lanes.
  • Large IOWAIT times. If you are using an operating system that has a concept of IOWAIT (Linux and most versions of UNIX do, Windows doesn’t) then this can be a great indicator that processes are stuck waiting on I/O. It’s not perfect though, because IOWAIT is actually an idle wait (within the operating system, this is nothing to do with Oracle wait events) so if the system is really busy it may not be present.

Those are all great indicators, but the next two should be considered the golden rules:

  • I/O wait times are high. Essentially we are looking for high latency from the existing storage system. Flash memory systems should deliver I/O with sub-millisecond latency, so if you see an average latency of 8ms on random reads (db file sequential read), for example, you know there is potential for reducing latency to an eighth of its previous average value.
  • I/O forms a significant percentage of Database Time. If I/O is only responsible for 5% of database time, no amount of lightening-fast flash is going to give you a big performance boost… your problems are elsewhere. On the other hand, if I/O is comprising a large portion of database time, you have lots of room for improvement. (I plan to post a guide to reading AWR Reports pretty soon)

If any of this is ticking boxes for you, it’s time to consider what flash could do for the performance of your database. On the other hand…

Performance Won’t Improve If…

  • red-crossThere isn’t any I/O. Any flash vendor in the industry would be happy to sell you their products in this situation – and let’s face it you’ll get great latency! – but be realistic. If you don’t generate I/O, what’s the point? Unless of course you aren’t after performance. If consolidation, virtualisation or infrastructure optimisation is your aim, there could be a benefit. Also, consider the size of your memory components – if your database produces no physical I/O, could you consider reducing the size of the buffer cache? One of the big benefits of flash to consolidation is the ability to reduce SGA sizes and thus fit more databases onto the same DRAM-restricted server.
  • Single threaded workloads. Sure your application will run slightly faster, but will that speed-up be enough to justify the change of infrastructure? I’m not ruling this out – I have customers with single-threaded ETL jobs that bought flash because it was easier (and cheaper) than rewriting legacy code, but the impact of low-latency storage may well be reduced.
  • Application serialisation points. A session waiting on a lock will not wait any faster! Basically, if your application regularly ties itself in a knot with locks and contention issues, putting it on flash may well just increase the speed at which you hit those problems. Sometimes people use flash to overcome bad programming, but it’s by no means guaranteed to work.
  • CPU-bound systems. CPU starvation is a CPU problem, not an I/O problem. If anything, moving to low-latency storage will reduce the amount of time CPUs spent waiting on I/O and thus increase the amount of time they spend working, i.e. in a busy state. If your CPU is close to the limit and you remove the ballast that is a disk system, you might find that you hit the limit very quickly.

If you are unfortunate enough to be struggling with a badly-performing application that fits into one of these areas, flash probably isn’t the magic bullet you’re looking for.

Consolidation and Virtualisation

This is a different area where it’s no longer valid to only look at individual databases and their workloads. The key factor for both of these areas is density i.e. the number of databases or virtual machines that can fit on a single physical server. The main challenges here are memory usage and I/O generation: databases SGAs tend to be large, but flash allows for the possibility of reducing the buffer cache; while I/O generation is a problem in the disk world because consolidated workloads tend to create more random I/O. Of course, with flash that’s not really a problem. I’ve written a number of articles on consolidation and virtualisation in the past – I’m sure I’ll be writing more about them in the future too.


I work for a flash vendor – we want you to buy our products. We have competitors who want you to buy their products instead. If everyone in the industry is telling you to buy flash, how do you know if it’s relevant to you? Here’s my advice: make them speak your language and then check their claims against what you can see yourself.

Take some time to understand your workload. Look at the amount of I/O generated and the latency experienced; look at how random the workload is and the ratio of reads to writes (I’ll post a guide for this soon). Ask your (potential) flash vendor how much benefit you will see from your existing storage and then get them to explain why. If you’re a database person, make them speak in your language – don’t accept someone talking in the language of storage. Likewise if you’re an application person make them explain the benefits from an application perspective. You’re the customer, after all.

If your flash vendor can’t communicate with you in your language to explain the benefit you will see, there’s only one course of action: Get rid of them in a flash.


Incidentally, if you live outside the UK and you’re wondering about the picture at the top of this article, check out this. If you live inside the UK you will know it’s a Cillit Bang reference… unless you live in a cave and shun the outside world – in which case, how are you reading this?

Understanding I/O: Random vs Sequential

sushiStorage for DBAs: Ever been to one of those sushi restaurants where the food comes round in dishes on a conveyor belt? As each dish travels around the loop you eye it up and, as long as you can make your mind up in time, grab it. However, if you are as indecisive as me, there’s a chance it will be out of range before you come to your senses – in which case you have to wait for it to complete a further full revolution before getting another chance. And that’s assuming someone else doesn’t get to it first.

Let’s assume that it takes a dish exactly 4 minutes to complete a whole lap of the conveyor belt. And just for simplicity’s sake let’s also assume that no two dishes on the belt are identical. As a hungry diner you look in the little menu and see a particular dish which you decide you want. It’s somewhere on the belt, so how long will it take to arrive?

Probability dictates that it could be anywhere on the belt. It could be passing by right now, requiring no wait time – or it could have just passed out of reach, thus requiring 4 minutes of wait time to go all the way round again. As you follow this random method (choose from the menu then look at the belt) it makes sense that the average wait time will tend towards half way between the min and max wait times, i.e. 2 minutes in this case. So every time you pick a dish you wait an average of 2 minutes: if you have eight dishes the odds say that you will spend (8 x 2) = 16 minutes waiting for your food. Welcome to the disk data diet, I hope you weren’t too hungry?

Now let’s consider an alternative option, where you order eight dishes from the chef and he or she places all of them sequentially (i.e. next to each other) somewhere on the conveyor belt. That location is random, so again you might have to wait anywhere between 0 and 4 minutes (an average of 2 minutes) for the first dish to pass… but the next seven will follow one after the other with no wait time. So now, in this scenario, you only had to wait 2 minutes for all eight dishes. Much better.

I’m sure you will have seen through my analogy right from the start. The conveyor belt is a hard disk and the sushi dishes are blocks which are being eaten / read. I haven’t yet worked out how to factor a bottle Asahi Super Dry into this story, but I’ll have one all the same thanks.

Random versus Sequential I/O

I have another article planned for later in this series which describes the inescapable mechanics of disk. For now though, I’ll outline the basics: every time you need to access a block on a disk drive, the disk actuator arm has to move the head to the correct track (the seek time), then the disk platter has to rotate to locate the correct sector (the rotational latency). This mechanical action takes time, just like the sushi travelling around the conveyor belt.

random-or-sequentialObviously the amount of time depends on where the head was previously located and how fortunate you are with the location of the sector on the platter: if it’s directly under the head you do not need to wait, but if it just passed the head you have to wait for a complete revolution. Even on the fastest 15k RPM disk that takes 4 milliseconds (15,000 rotations per minute = 250 rotations per second, which means one rotation is 1/250th of a second or 4ms). Admittedly that’s faster than the sushi in my earlier analogy, but the chances are you will need to read or write a far larger number of blocks than I can eat sushi dishes (and trust me, on a good day I can pack a fair few away).

What about the next block? Well, if that next block is somewhere else on the disk, you will need to incur the same penalties of seek time and rotational latency. We call this type of operation a random I/O. But if the next block happened to be located directly after the previous one on the same track, the disk head would encounter it immediately afterwards, incurring no wait time (i.e. no latency). This, of course, is a sequential I/O.

Size Matters

In my last post I described the Fundamental Characteristics of Storage: Latency, IOPS and Bandwidth (or Throughput). As a reminder, IOPS stands for I/Os Per Second and indicates the number of distinct Input/Output operations (i.e. reads or writes) that can take place within one second. You might use an IOPS figure to describe the amount of I/O created by a database, or you might use it when defining the maximum performance of a storage system. One is a real-world value and the other a theoretical maximum, but they both use the term

When describing volumes of data, things are slightly different. Bandwidth is usually used to describe the maximum theoretical limit of data transfer, while throughput is used to describe a real-world measurement. You might say that the bandwidth is the maximum possible throughput. Bandwidth and throughput figures are usually given in units of size over units of time, e.g. Mb/sec or GB/sec. It pays to look carefully at whether the unit is using bits (b) or bytes (B), otherwise you are likely to end up looking a bit silly (sadly, I speak from experience).

In the previous post we stated that IOPS and throughput were related by the following relationship:

Throughput   =   IOPS   x   I/O size

It’s time to start thinking about that I/O size now. If we read or write a single random block in one second then the number of IOPS is 1 and the I/O size is also 1 (I’m using a unit of “blocks” to keep things simple). The Throughput can therefore be calculated as (1 x 1) = 1 block / second.

Alternatively, if we wanted to read or write eight contiguous blocks from disk as a sequential operation then this again would only result in the number of IOPS being 1, but this time the I/O size is 8. The throughput is therefore calculated as (1 x 8) = 8 blocks / second.

Hopefully you can see from this example the great benefit of sequential I/O on disk systems: it allows increased throughput. Every time you increase the I/O size you get a corresponding increase in throughput, while the IOPS figure remains resolutely fixed. But what happens if you increase the number of IOPS?

Latency Kills Disk Performance

In the example above I described a single-threaded process reading or writing a single random block on a disk. That I/O results in a certain amount of latency, as described earlier on (the seek time and rotational latency). We know that the average rotational latency of a 15k RPM disk is 4ms, so let’s add another millisecond for the disk head seek time and call the average I/O latency 5ms. snailHow many (single-threaded) random IOPS can we perform if each operation incurs an average of 5ms wait? The answer is 1 second / 5 ms = 200 IOPS. Our process is hitting a physical limit of 200 IOPS on this disk.

What do you do if you need more IOPS? With a disk system you only really have one choice: add more disks. If each spindle can drive 200 IOPS and you require 80,000 IOPS then you need (80,000 / 200) = 400 spindles. Better clear some space in that data centre, eh?

On the other hand, if you can perform the I/O sequentially you may be able to reduce the IOPS requirement and increase the throughput, allowing the disk system to deliver more data. I know of Oracle customers who spend large amounts of time and resources carving up and re-ordering their data in order to allow queries to perform sequential I/O. They figure that the penalty incurred from all of this preparation is worth it in the long run, as subsequent queries perform better. That’s no surprise when the alternative was to add an extra wing to the data centre to house another bunch of disk arrays, plus more power and cooling to run them. This sort of “no pain, no gain” mentality used to be commonplace because there really weren’t any other options. Until now.

Flash Offers Another Way

The idea of sequential I/O doesn’t exist with flash memory, because there is no physical concept of blocks being adjacent or contiguous. Logically, two blocks may have consecutive block addresses, but this has no bearing on where the actual information is electronically stored. You might therefore say that all flash I/O is random, but in truth the principles of random I/O versus sequential I/O are disk concepts so don’t really apply. And since the latency of flash is sub-millisecond, it should be possible to see that, even for a single-threaded process, a much larger number of IOPS is possible. When we start considering concurrent operations things get even more interesting… but that topic is for another day.

sushi-dishBack to the sushi analogy, there is no longer a conveyor belt – the chefs are standing right in front of you. When you order a dish, it is placed in front of you immediately. Order a number of dishes and you might want to enlist the help of a few friends to eat in parallel, because the food will start arriving faster than you can eat it on your own. This is the world of flash memory, where hunger for data can be satisfied and appetites can be fulfilled. Time to break that disk diet, eh?

Looking back at the disk model, all that sitting around waiting for the sushi conveyor belt just takes too long. Sure you can add more conveyor belts or try to get all of your sushi dishes arranged in a line, but at the end of the day the underlying problem remains: it’s disk. And now that there’s an alternative, disk just seems a bit too fishy to me…

The Fundamental Characteristics of Storage

storage-characteristicsStorage for DBAs: As a rule of thumb, pretty much any storage system can be characterised by three fundamental properties:

Latency is a measurement of delay in a system; so in the case of storage it is the time taken to respond to an I/O request. It’s a term which is frequently misused – more on this later – but when found in the context of a storage system’s data sheet it often means the average latency of a single I/O. Latency figures for disk are usually measured in milliseconds; for flash a more common unit of measurement would be microseconds.

IOPS (which stands for I/Os Per Second) represents the number of individual I/O operations taking place in a second. IOPS figures can be very useful, but only when you know a little bit about the nature of the I/O such as its size and randomicity. If you look at the data sheet for a storage product you will usually see a Max IOPS figure somewhere, with a footnote indicating the I/O size and nature.

Bandwidth (also variously known as throughput) is a measure of data volume over time – in other words, the amount of data that can be pushed or pulled through a system per second. Throughput figures are therefore usually given in units of MB/sec or GB/sec.

As the picture suggests, these properties are all related. It’s worth understanding how and why, because you will invariably need all three in the real world. It’s no good buying a storage system which can deliver massive numbers of IOPS, for example, if the latency will be terrible as a result.

The throughput is simply a product of the number of IOPS and the I/O size:

Throughput   =   IOPS   x   I/O size

So 2,048 IOPS with an 8k blocksize is (2,048 x 8k) = 16,384 kbytes/sec which is a throughput of 16MB/sec.

The latency is also related, although not in such a strict mathematical sense. Simply put, the latency of a storage system will rise as it gets busier. We can measure how busy the system is by looking at either the IOPS or Throughput figures, but throughput unnecessarily introduces the variable of block size so let’s stick with IOPS. We can therefore say that the latency is proportional to the IOPS:

Latency   ∝   IOPS

I like the mathematical symbol in that last line because it makes me feel like I’m writing something intelligent, but to be honest it’s not really accurate. The proportional (∝) symbol suggests a direct relationship, but actually the latency of a system usually increases exponentially as it nears saturation point.

SPC Benchmark for HP 3PAR (17 Oct 2011)

SPC Benchmark for HP 3PAR (17 Oct 2011)

We can see this if we plot a graph of latency versus IOPS – a common way of visualising performance characteristics in the storage world. The graph on the right shows the SPC benchmark results for an HP 3PAR disk system (submitted in 2011). See how the response time seems to hit a wall of maximum IOPS? Beyond this point, latency increases rapidly without the number of IOPS increasing. Even though there are only six data points on the graph it’s pretty easy to visualise where the limit of performance for this particular system is.

I said earlier that the term Latency is frequently misused – and just to prove it I misused it myself in the last paragraph. The SPC performance graph is actually plotting response time and not latency. These two terms, along with variations of the phrase I/O wait time, are often used interchangeably when they perhaps should not be.

According to Wikipedia, “Latency is a measure of time delay experienced in a system“. If your database needs, for example, to read a block from disk then that action requires a certain amount of time. The time taken for the action to complete is the response time. If your user session is subsequently waiting for that I/O before it can continue (a blocking wait) then it experiences I/O wait time which Oracle will chalk up to one of the regular wait events such as db file sequential read.

The latency is the amount of time taken until the device is ready to start reading the block, i.e not including the time taken to complete the read. In the disk world this includes things like the seek time (moving the actuator arm to the correct track) and the rotational latency (spinning the platter to the correct sector), both of which are mechanical processes (and therefore slow).

When I first began working for a storage vendor I found the intricacies of the terminology confusing – I suppose it’s no different to people entering the database world for the first time. I began to realise that there is often a language barrier in I.T. as people with different technical specialties use different vocabularies to describe the same underlying phenomena. For example, a storage person might say that the array is experiencing “high latency” while the database admin says that there is “high User I/O wait time“. The OS admin might look at the server statistics and comment on the “high levels of IOWAIT“, yet the poor user trying to use the application is only able to describe it as “slow“.

At the end of the day, it’s the application and its users that matter most, since without them there would be no need for the infrastructure. So with that in mind, let’s finish off this post by attempting to translate the terms above into the language of applications.

Translating Storage Into Application

Earlier we defined the three fundamental characteristics of storage. Now let’s attempt to translate them into the language of applications:


Latency is about application acceleration. If you are looking to improve user experience, if you want screens on your ERP system to refresh quicker, if you want release notes to come out of the warehouse printer faster… latency is critical. It is extremely important for highly transactional (OLTP) applications which require fast response times. Examples include call centre systems, CRM, trading, e-Business etc where real-time data is critical and the high latency of spinning disk has a direct negative impact on revenue.

IOPS is for application scalability. IOPS are required for scaling applications and increasing the workload, which most commonly means one of three things: in the OLTP space, increasing the number of concurrent users; in the data warehouse space increasing the parallelism of batch processes, or in the consolidation / virtualisation space increasing the number of database instances located on a single physical platform (i.e. the density). This last example is becoming ever more important as more and more enterprises consolidate their database estates to save on operational and licensing costs.

Bandwidth / Throughput is effectively the amount of data you can push or pull through your system. Obviously that makes it a critical requirement for batch jobs or datawarehouse-type workloads where massive amounts of data need to be processed in order to aggregate and report, or identify trends. Increased bandwidth allows for batch processes to complete in reduced amounts of time or for Extract Transform Load (ETL) jobs to run faster. And every DBA that ever lived at some point had to deal with a batch process that was taking longer and longer until it started to overrun the window in which it was designed to fit…

Finally, a warning. As with any language there are subtleties and nuances which get lost in translation. The above “translation” is just a rough guide… the real message is to remember that I/O is driven by applications. Data sheets tell you the maximum performance of a product in ideal conditions, but the reality is that your applications are unique to your organisation so only you will know what they need. If you can understand what your I/O patterns look like using the three terms above, you are halfway to knowing what the best storage solution is for you…

Performance: It’s All About Balance…

balanceStorage For DBAs: Everyone wants their stuff to go faster. Whether it’s your laptop, tablet, phone, database or application… performance is one of the most desirable characteristics of any system. If your system isn’t fast enough, you start dreaming of more. Maybe you try and tune what you already have, or maybe you upgrade to something better: you buy a phone with a faster processor, or stick an SSD in your laptop… or uninstall Windows 🙂

When it comes to databases, I often find people considering the same set of options for boosting performance (usually in this order): half-heartedly tuning the database, adding more DRAM, *properly* tuning the database, adding or upgrading CPUs, then finally tuning the application. It amazes me how much time, money and effort is often spent trying to avoid getting the application developers to write their code properly, but that’s a subject for another blog.

The point of this blog is the following statement: to achieve the best performance on any system it is important that all of its resources are balanced.

Let’s think about the basic resources that comprise a computer system such as a database server:


  • CPU – the processor, i.e. the thing that actually does the work. Every process pretty much exists to take some input, get on CPU, perform some calculations and produce some output. It’s no exaggeration to call this the heart of the system.
  • Network – communications with the outside world, whether it be the users, the application servers or other databases.
  • Memory – Dynamic Random Access Memory (DRAM) provides a store for data.
  • Storage – for example disk or flash; provides a store for data.

You’ll notice I’ve been a bit disingenuous by describing Memory and Storage the same way, but I want to make a point: both Memory and Storage are there to store data. Why have two different resources for what is essentially the same purpose?

The answer, which you obviously already know, is that DRAM is volatile (i.e. continuous power is required to maintain the stored information, otherwise it is lost) while Storage is persistent (i.e. the stored information remains in place until it is actively changed or removed).

When you think about it like that, the Storage resource has a big advantage over the Memory resource, because the data you are storing is safe from unexpected power loss. So why do we have the DRAM? What does it bring to the party? And why do I keep asking you questions you already know the answer to?

Ok I’ll get to the point, which is this: DRAM is used to drive up CPU utilisation.

The Long Walk

walking-long-roadThe CPU is interacting with the Memory and Storage resources by sending or requesting data. Each request takes a certain amount of time – and that time can vary depending on factors such as the amount of data and whether the resource is busy. But let’s ignore all that for now and just consider the minimum possible time taken to send or receive that data: the latency. CPUs have clock cycles, which you can consider a metronome keeping the beat to which everything else must dance. That’s a gross simplification which may make some people wince (read here if you want to know why), but I’m going to stick with it for the sake of clarity.

Let’s consider a 2GHz processor – by no means the fastest available clock speed out there today. The 2GHz indicates that the clock cycle is oscillating 2 billion times per second. That means one oscillation every half a nanosecond, which is such a tiny amount of time that we can’t really comprehend it, so instead I’m going to translate it into the act of walking, where each single pace is a clock cycle. With each step taken, an instruction can be executed, so:

One CPU Cycle = Walking 1 Pace

The current generation of DRAM is DDR3 DRAM, which has latencies of around 10 nanoseconds. So now, while walking along, if you want to access data in DRAM you need to incur a penalty of 20 paces where you potentially cannot do anything else.

Accessing DRAM = Walking 20 Paces

Now let’s consider storage – and in particular, our old friend the disk drive. I frequently see horrible latency problems with disk arrays (I guess it goes with the job) but I’ll be kind here and choose a latency of 5 milliseconds, which on a relatively busy system wouldn’t be too bad. 5 milliseconds is of course 5 million nanoseconds, which in our analogy is 10 million steps. According to the American College of Sports Medicine there are an average of 2,000 steps in one mile. So now, walking along and making an I/O request to disk incurs a penalty of 10,000,000 steps or 5,000 miles. Or, to put it another way:

Accessing Disk = Walking from London to San Francisco

Take a minute to consider the impact. Previously you were able to execute an instruction every step, but now you need to walk a fifth of the way around the planet before you can continue working. That’s going to impact your ability to get stuff done.

Maybe you think 5 milliseconds is high for disk latency (or maybe you think anyone walking from London to San Francisco might face some ocean-based issues) but you can see that the numbers easily translate: every millisecond of latency is equivalent to walking one thousand miles.

Don’t forget what that means back in the real world: it translates to your processor sitting there not doing anything because it’s waiting on I/O. Increasing the speed of that processor only increases the amount of work it’s unable to do during that wait time. If you didn’t have DRAM as a “temporary” store for data, how would you ever manage to do any work? No wonder In-Memory technologies are so popular these days.

Moore’s Law Isn’t Helping

Moores-LawIt’s often stated or inferred that Moore’s Law is bringing us faster processors every couple of years, when in fact the original statement was on doubling the number of transistors on an integrated circuit. But the underlying point remains that processor performance is increasing all the time. Looking at the four resources we outlined above, you could say that in a similar way DRAM technologies are progressing while network protocols are getting faster (10Gb Ethernet is commonplace, Infiniband is increasingly prevalent and 40Gb or 100Gb Ethernet is not far away).

On the other hand, disk performance has been stationary for years. According to this manual from Seagate the performance of CPUs increased 2,000,000x between 1987 and 2004 yet the performance of hard disk drives only increased 11x. That’s hardly surprising – how many years ago did the 15k RPM disk drive come out? We’re still waiting for something faster but the manufacturers have hit the limits of physics. The idea of helium-filled drives has been floated (sorry, couldn’t resist) and indeed they could be on the shelves soon, but if you ask me the whole concept is so up-in-the-air (sorry, I really can’t help it) that I have serious doubts whether it will actually take off (ok I promise that’s the last one).

The consequence of Moore’s Law is that the imbalance between disk storage and the other resources such as CPU is getting worse all the time. If you have performance issues caused by this imbalance – and then move to a newer, faster server with more processing power… the imbalance will only get worse.

The Silicon Data Centre

light_bulbDisk, as a consequence of its mechanical nature, cannot keep up with silicon as the number of transistors on a processor doubles every two years. Well as the saying goes, if you can’t beat them, join them. So why not put your persistent data store on silicon?

This is the basis of the argument for moving to flash memory: it’s silicon-based. The actual technology most vendors are using is NAND flash but that’s not massively important and technologies will come and go. The important point is to get storage onto the graph of Moore’s Law. Going back to the walking analogy above, an I/O to flash memory takes in the region of 200 microseconds, i.e. 200 thousand nanoseconds. This is a number of orders of magnitude faster than disk but still represents walking 400,000 paces or 200 miles. But unlike disk, the performance is getting better. And by moving storage to silicon we also pick up many other benefits such as reduced power consumption, space and cooling requirements. And most importantly we restore some balance to your server infrastructure.

Think about it. You have to admit that, as an argument, it’s pretty well balanced.

Footnote: Yes I know that by representing CPU clock cycles as instructions I am contributing to the Megahertz Myth. Sorry about that. Also, I strongly advise reading this article in the NoCOUG journal which makes some great points about DRAM and CPU utilisation. My favourite quote is, “Idle processors do not speed up database processing!” which is so obvious and yet so often overlooked.