Understanding Flash: Fabrication, Shrinkage and the Next Big Thing

Semiconductor Fabrication Plant (picture courtesy of SemiWiki.com)

Semiconductor Fabrication Plant (picture courtesy of SemiWiki.com)

Before I draw this series on Understanding Flash to a close, I wanted to briefly touch on the subject of manufacturing. Don’t worry, I’ve taken heed of the kind feedback I had after my floating gate transistor blog post (“Please stop talking about electrons!“) and will instead focus on the commercial aspects, because ultimately they affect the price you will be paying for your flash-based storage. If you’re not familiar with the way things are you might be surprised…

The Supplier Landscape

Semiconductor chips, such as the NAND flash packages found in your storage, phones and tablets, are manufactured in fabrication plants known as “fabs” or flash “foundries”. To say that fabs are not cheap to build would be somewhat of an understatement – they are mind-bogglingly, ludicrously expensive.

In 2014 the global semiconductor industry posted record sales totalling $335.8 billion. That’s the entire semiconductor industry, not just the subset that produces NAND flash… and I think you’ll agree that’s quite a lot of money. But to put that figure into perspective, when Samsung decided to build an entirely new fab in late 2014, it had to commit $15 billion for a project that won’t be completed “until mid-2017″.

Clearly a fab is an eye-watering investment – and it is mainly for this reason that there are (at the time of writing) only six key companies worldwide who run flash foundries. What’s more, because of that staggering cost, four of those six are working together in pairs to share the investment burden. The four teams are:

  • Toshiba and SanDisk
  • Samsung
  • Intel and Micron
  • SK Hynix

With only four sets of fabs in operation, the market is hardly awash with an abundance of NAND flash – which of course suits the fab operators just fine as it keeps the price of flash higher. Oversupply would be unsustainable for an industry with such high costs.

Process Shrinking

Talking of costs, the fab operators are never allowed to stand still because of the relentless drive to make things smaller – Moore’s Law doesn’t just apply to processors; NAND flash is a semiconductor too. The act of taking the design of a microchip and reducing it in size is known as a process shrink – and it brings all sorts of benefits. Remember that a NAND flash cell is basically constructed from floating gate transistors? Well if those transistors are reduced in size:

  • Less current is needed per transistor, reducing the overall power consumption of the flash
  • This in turn reduces the heat output of an identical design with the same clock frequency (or alternatively the clock frequency can be increased)
  • Most importantly, more flash dies can be produced from the same silicon wafers (the raw material), resulting in either reduced costs or increased density

It may sound like smaller is better, but as always there is a flip side. Retooling a flash foundry to move to a smaller process geometry takes time and money – and the return on investment for the fab is reduced with each shrink. But we’ll come back to that in a minute.

Process Geometries

The different sizes in which flash is fabricated are known as process geometries. Traditionally, they are defined by measuring the distance (in nanometers) between the source and drain of each transistor (although these days this practice has become much less well-defined). In a strange quirk of algebra, the two digit numbers are written as <digit><letter> so that, for example, the first geometry to be in the range 29-20nm (e.g. 25nm) is called 2X, then a second in that range (e.g. 21nm) is called 2Y. Once into the teens (e.g. 19nm) we go back to 1X. [Although interestingly, Toshiba and SanDisk have a second generation NAND which they call 1Y to distinguish it from first-generation 1X even though both are 19nm]

Here’s the current technology roadmap for NAND flash courtesy of my friends at TechInsights:

Technology Roadmap for NAND Flash (Image courtesy of TechInsights)

Technology Roadmap for NAND Flash (Image courtesy of TechInsights)

So what’s the problem with flash getting smaller? Well, hopefully for the very last time, cast your mind back to the concept of floating gate transistors. On these tiny devices the floating gates effectively capture and store electrons that are passing from one side of the transistor to another, retaining charge – and that charge determines the cell’s value of 0 or 1. But at the tiny geometries that flash is approaching now, the number of electrons involved is desperately small, resulting in large margins for error. Electrons, after all, are notoriously good at hide and seek.

Floating Gate Limitations (SK Hynix Presentation - Aug 2012)

Floating Gate Limitations (SK Hynix Presentation – Aug 2012)

This means that flash cells are less reliable, that even more error correction codes are required… and that ultimately, we are reaching what is known as the scaling limit. It looks like the end is it sight for flash as we know it.

3D NAND Flash

Except it isn’t. If you look again at the Technology Roadmap picture you’ll see entries appearing alongside each foundry operator for 3D NAND. This is a significant new fabrication process that looks like it will be the direction taken by the flash industry for the foreseeable future. I would love to attempt a detailed explanation of 3D NAND design here, but a) it’s beyond me, b) I promised to listen to the feedback after the last time I went sub-atomic, and c) Jim Handy (AKA The Memory Guy) already has a fantastic set of articles all about it.

The point is, 3D NAND maps out a future for NAND flash beyond the scaling limits of 2D planar NAND – and the big fab operators have already invested. That puts 3D NAND in pole position as we increasingly look to non-volatile memory to store our data.

But there are other technologies trying to compete…

The Next Big Thing

The holy grail of memory technology is universal memory. This is a hypothetical technology which brings all the benefits, but none of the drawbacks, of the multitude of memory technologies in use today: SRAM, DRAM, NAND Flash and so on. SRAM (often used on-chip as a CPU cache) is fast but expensive, DRAM is cheaper but must be constantly refreshed (using considerably more power) while NAND flash is comparatively slower and wears out with use. If only there were some new technology that met all our requirements?

Well, first of all, it’s worth mentioning that if universal memory did come about it would have far-reaching consequences on the way we design and build computers… but all the same, there are various technologies in R&D right now which make claims to be the NVM solution of the future: PCM, MRAM, RRAM, FeRAM, Memristor and so on. But these – and any other – technologies all face the same challenge: that massive initial investment required to go from prototype to production. In other words, the cost and time associated with building a fabrication plant.

R&D Centre for SHRAM

R&D Centre for SHRAM

Say you converted your shed into a clean room and invented a wonderful new memory technology called Shed-RAM (SHRAM). SHRAM is fast, cheap, dense and only uses a tiny amount of power. You’ve still got to convince somebody to splurge billions of dollars on a foundry before it can be productised. And, as I’m sure you’ll agree, that’s a pretty big bet on an untested technology – especially if it takes years to build. What happens if, one year in, your neighbour (who recently converted his garage into a clean room) invents Garage-RAM (GaRAM) which makes your SHRAM worthless? Nobody is going to cope well with the loss of billions of dollars on a bad bet.

Conclusion

The investment hurdle required to turn a new NVM technology into a product is both a challenge and a stabiliser to the NVM industry. In theory it could stifle potential new technologies, but at the same time (at least for those of us in enterprise storage) it means there is plenty of warning when the tide turns: you are likely to see any successful new tech in your phone before it appears in your data centre.

Now, who can lend me $15 billion to build a new fab? I can’t afford anything more than 0% interest, but I can offer a lifetime’s supply of USB sticks to sweeten the deal…

Understanding Flash: Floating Gates and Wear

OLYMPUS DIGITAL CAMERA

One of the important characteristics of flash memory is wear. We know from previous articles in this series that flash packages consist of dies, which contain planes, which contain blocks, which in turn contain pages. We also know that these pages contain individual cells which store the bits of data… but to understand what wear is we need to look a little bit closer at those cells, where we will find something called a floating gate transistor.

Now don’t run away. Electronics might not be your thing, but I’m won’t be getting too deep. [After all, I once attempted degree-level education in Electronic Engineering but had to move to another course because I kept burning my fingers on the soldering irons during practical laboratory sessions…] If you really don’t want to think about transistors you can just skip to the section called Wear, or ignore this blog post entirely and spend a few minutes looking at picture of cats instead.

Field Effect Transistors

If you are reading this blog on a computer or a phone (and how else could you be reading it?) you owe a debt of gratitude to a humble device called the metal oxide semiconductor field effect transistor, or MOSFET. These tiny devices revolutionised the world and are considered by some to be the most important invention of the 20th century.

Students being stimulated to go to a bar and drink alcohol

Students being stimulated to go to a bar and drink alcohol

They therefore deserve to be explained in a serious and respectful manner, but sadly I don’t have time for that so I’m going to resort to one of my silly analogies. Again.

Imagine you have a house full of students, which we’ll call the source. Two doors down the road you have a nightclub full of free alcohol, which we’ll call the drain. However, it’s freezing cold outside and the students won’t venture out of the door, meaning there is no flow of students from source to drain. Now let’s set up a banging sound system behind the wall of the property in between. If we play music loud enough through the wall then we can excite the students into action, causing them to run down the road and enter the nightclub, which creates the flow. The loud music (which we’re going to call the gate) is the stimulus which causes this flow, essentially acting as a switch, while the volume of the music which first drives the students into action is called the threshold. The beauty of this design is that we can control the flow of students from behind the wall, therefore avoiding having to come into direct contact with them. Phew.

I really cannot tell you how bad that analogy is, but I’m afraid it’s only going to get worse later on. If you don’t know how a transistor works I implore you to watch this short video from the excellent folk at Veritasium, which describes it far better than I ever intend to.

Metal Oxide Semiconductor Field Effect Transistor (MOSFET)

Metal Oxide Semiconductor Field Effect Transistor (MOSFET)

In a MOSFET, electrons (students!) can be allowed to flow from the source terminal to the drain terminal by the application of charge to the gate terminal. This charge creates an electric field which alters the behaviour of the silicon layers (the pinkish parts of the diagram) and thus controls the flow. The important bit here is the yellow rectangle which represents an insulating layer (commonly known as the oxide layer). This means the gate is never physically connected to either the source or drain terminals – and you’ll see why this is important as we turn our attention now to something called the floating grate transistor, or FGMOS.

There are many things I haven’t explained in that clumsy analogy, but this is not an electronics blog. Of specific interest is the way that the two types of silicon (n and p) are doped in order to create free electrons and electron holes. Seriously, watch the video. If you haven’t ever learnt how a semiconductor works and my student party analogy is the nearest you get, it would be a crime. Although that’s not going to stop me from using it again in the next section…

Floating Gate Transistors

Floating Gate MOSFET (FGMOS)

Floating Gate MOSFET (FGMOS)

The diagram on the right (labelled “FGMOS”) is of a Floating Gate MOSFET, which is essentially what you will find in a flash memory cell. If you play spot the difference with the previous diagram above (the one labelled “MOSFET”) you’ll see that there are now two gates, one above the yellow oxide layer as before but the other entirely surrounded by it. This second gate is known as the floating gate because it is completely electrically isolated.

Notice also that the oxide layer beneath the floating gate is deliberately thinner than that above it in the diagram. Now, back to my analogy…

strip-doorsThe students are still there, as is the nightclub. The property in between is still there, but this time we have replaced the brick wall at the front with one of those sets of PVC strip curtains that you sometimes find covering the doors of factories, or butchers shops to keep insects out (or even lining the edge of the cold aisle in a data centre). Inside, we’ll put some comfy chairs and maybe a beer fridge. This is now our floating gate party room and the PVC strips are our thinner section of oxide layer.

Excited students going to a nightclub are stimulated so that some fall into the "Floating Gate" room

Excited students going to a nightclub are stimulated so that some fall into the “Floating Gate” room

As we now play the music loud enough to exceed the threshold, the excited students will run up the road towards the bar as before, but this time – with enough excitement – some of them will pass through the divider into our floating gate room and remain there, thus trapping electrons (sorry, I mean students) on the floating gate. Meanwhile, as the floating gate room fills up with people, the sound of the music becomes more muffled which slows down the flow of students in the road outside. To put it another way, the volume threshold at which stimulation will occur rises if there are students (sorry, I mean electrons) on the floating gate.

In a FGMOS, if a high charge is applied to the control gate in the same manner as with a MOSFET, electrons flowing from source to drain can get excited and “jump” through the oxide layer into the floating gate, increasing its retained charge. This is the program operation we have talked about so many times before: the floating gate is the “bucket of electrons” from my previous posts (a classic case of mixed metaphors). To erase the charge stored on the floating gate, a high voltage is applied across the source and drain while a negative voltage is applied to the control gate, causing the retained electrons to “jump” back off the floating gate (through the oxide layer). I’m putting the word “jump” in inverted commas there because it’s slightly more complicated and usually involves a process called Fowler–Nordheim tunnelling. I’ll explain everything I understand about that in the next paragraph.

[This paragraph intentionally left blank]

Yeah, it’s complicated, it involves quantum mechanics and it’s way over my head. I’m just taking it for granted that it works.

Read Operations

Now that we have methods for programming and erasing we just need a way of testing the value stored: a read operation. When we were talking about the MOSFET in the previous section we could control the flow of charge (which I had better start calling current) between source and drain by varying the voltage applied to the gate.

FGMOS Read Thresholds: The voltage threshold at which current begins to flow from drain to source is different depending on the charge stored on the floating gate. By testing at an intermediate reference voltage (VtREF) called the "read point" we can determine whether the floating gate contains charge (which we call ZERO) or not (which we call ONE)

FGMOS Read Thresholds: The voltage threshold at which current begins to flow from drain to source is different depending on the charge stored on the floating gate. By testing at an intermediate reference voltage (VtREF) called the “read point” we can determine whether the floating gate contains charge (which we call ZERO) or not (which we call ONE)

In the FGMOS this can be turned around so that by measuring the current we can determine the voltage on the floating gate, because electrons trapped on the floating gate cause the threshold we’ve previously mentioned to move. By applying a certain voltage (VtREF in the diagram on the right) across the source and drain and then testing the current we can determine if the voltage on the gate is above or below a specific point, called the read point.

So, if we play music at a certain threshold volume when the floating gate party room is empty, the sound travels far enough to stimulate a flow of students to the nightclub. But if the room is full (the floating gate contains charge) this specific volume will not stimulate a flow. Instead, it will require a louder threshold volume to get the students out of bed. And I think it’s time to abandon the student analogy now… let us never speak of it again.

Read Points for SLC and MLC

Read Points for SLC and MLC (image courtesy AnandTech)

If you remember, flash comes in different forms: SLC, MLC and TLC. So why are MLC reads slower than SLC, with TLC even slower still? Well, because SLC contains only one bit of data (two possible states: a zero or one), we only need to test one threshold voltage, i.e. SLC only has one read point. But for MLC, where there are two bits (and therefore four possible states), there are three read points… while for TLC there are even more.

Remember the bucket full of electrons analogy? When you test the SLC bucket to see if it’s above or below 50% full, the answer will tell you whether the stored value is a zero or a one. But for the MLC bucket, the answer to that test isn’t enough: based on the first answer you then need to perform a second test to see if the bucket is above or below 25% / 75% [delete as appropriate] full. All this additional testing takes time, which is why SLC reads are faster than MLC reads, which in turn are faster than TLC reads.

But what about the wear?

Flash Wear

broken-blindsFinally I’m getting to the point. Remember the PVC strip curtains, like the ones in the main picture at the top of this post? What do you think happens to them as all those excited students (sorry) hurtle through them? They get damaged. In a FGMOS the oxide layer which isolates the floating gate from the silicon substrate is designed to be thin enough to allow quantum tunnelling of electrons when a high enough charge is applied, but this process gradually damages the layer. Reads are not a problem because only lower voltages are used and no electron tunnelling takes place. But program and erase operations are a different story, which is why wear is measured by the number of program/erase cycles. As the layer gets more damaged, the isolation of the floating gate is increasingly affected and the probability of stored charge leaking out will increase.

For SLC this is less of an issue, because during a read we only need to measure at one read point – so there is a lot of room for error either side of the threshold. But for MLC, with three read points, we need to be much more exact. Thus the wear caused by using SLC, MLC and TLC isn’t really very different, it’s simply that the tolerances for error are much finer with the increased bit counts of MLC and TLC.

At some point, the oxide layer for a FGMOS will become sufficiently degraded so that it can no longer store charge properly on the floating gate. We won’t know about this until it happens, but at some point a read from the cell will no longer be trustworthy. And clearly that’s a problem for data storage, which is why (just like disk) flash stores error correction codes (ECC) alongside user data to ensure that incorrect information is spotted and dealt with while any underlying pages are marked as unusable – all without impacting users.

Comparison of SLC, MLC and EMLC (courtesy of EE Times)

Comparison of SLC, MLC and EMLC (courtesy of EE Times)

A final point to consider is that there is a (more expensive) form of MLC known as eMLC (the e stands for enterprise, with “normal” MLC then sometimes referred to as consumer or cMLC). The only difference between eMLC and the standard cMLC we have discussed in the past is that program and erase operations are “slowed down” for eMLC in order to cause less damage to the oxide layer. This gives slightly reduced performance but also significantly reduces wear, allowing up to 10x more P/E cycles. Opinion is divided on whether this is actually a worthwhile investment (not for me though, I think it’s a waste of money in the majority of cases).

Hot News

That’s the end of this post – and as usual I’ve committed two of my three regular blogging sins: writing too much, using silly analogies and finishing on a terrible pun. Time to complete the trio:

Some flash companies have been experimenting with methods of refreshing the lifetime of flash, with one avenue of exploration focussed on providing a short burst of heat to repair the damage to the oxide layer. It has been claimed that flash treated this way can sustain over 100 million P/E cycles with no noticeable degradation. If that is really the case – and this technology can be put into production – we might finally find that the Talking Heads were correct: in the world of flash memory we are on the road to no-wear…

New Cookbook: Oracle Linux 6 Update 5 within an Oracle VM Template

Oracle-VMI’ve posted a new installation cookbook for using Oracle within a virtual machine running on Oracle VM. Surprisingly, I was unable to come up with a satisfactory method of accessing external storage that did not involve the use of Oracle ASMLib

Oracle Linux 6 Update 5 within an Oracle VM Template

Understanding Flash: Unpredictable Write Performance

fast-page-slow-page

I’ve spent a lot of time in this blog series talking about the challenges involved in using flash, such as the way that pages have to be erased before they are written and the restriction that erase operations take place on a whole block. I also described the problem of erase operations being slow in comparison to reads and writes – and the resulting processes we have to put in place to manage that problem (i.e. garbage collection) . And most recently I covered the way that garbage collection can result in unpredictable performance.

But so far we’ve always worked under the assumption that reads and writes to NAND flash have the same predictably low latency. This post is all about bursting that particular bubble…

Programming NAND Flash: A Quick Recap

You might remember from my post on the subject of SLC, MLC and TLC that I used the analogy of electrons in a bucket to explain the programming of NAND flash cells:

slc-mlc-tlc-buckets

I’d now like to change that analogy slightly, so I’m asking you to consider that you have an empty bucket and a powerful hose pipe. You can turn the hose on and off whenever you want to fill the bucket up, but you cannot empty water out of the bucket unless you completely empty it. Ok, now we’re ready.

For SLC we simply say that an empty bucket denotes a binary value of 1 and a full bucket denotes binary 0. Thus when you want to program an SLC bucket you simply let rip with your hose pipe until it’s full. No need to measure whether the water line is above or below the halfway point (the threshold), just go crazy. Blam! That was quick, wasn’t it?

For MLC however, we have three thresholds – and again we start with the bucket empty (denoting binary 11). Now, if I want to program the binary values of 01 or 10 in the above diagram I need to be careful, because if I overfill I cannot go backwards. bucketI therefore have to fill a little, test, fill some more, test and so on. It’s actually kind of tricky – and it’s one of the reasons that MLC is both slower than SLC and has a lower wear limit. But here’s the thing… if I want to program my MLC to have a value of binary 00 in the above diagram, I have no such problems because (as with SLC) I can just open the hose up on full power and hit it.

What we’ve demonstrated here is that programming a full charge value to an MLC cell is faster than programming any of the other available values. With a little more thought you can probably see that TLC has this problem to an even worse degree – imagine how accurate you need to be with that hose when you have seven thresholds to consider!

One final thought. We read and write (program) to NAND flash at the page level, which means we are accessing a large collection of cells as if they are one single unit. What are the chances that when we write a page we will want every cell to be programmed to full charge? I’d say extremely low. So even if some cells are programmed “the fast way”, just one “slow” program operation to a non-full-charge threshold will slow the whole program operation down. In other words, I can hardly ever take advantage of the faster latency experienced by full charge operations.

Fast Pages and Slow Pages

The majority of flash seen in the data centre today is MLC, which contains two bits per cell. Is there a way to program MLC in order that, at least sometimes, I can program at the faster speeds of a full-charge operation?

mlc-bucket-msb-lsbLet’s take my MLC bucket diagram from above and remap the binary values like the diagram on the left. What have I changed? Well most importantly I’ve reordered the binary values that correspond to each voltage level; empty charge still represents 11 but now full charge represents 10. Why did I do that?

The clue is the dotted line separating the most significant bit (MSB) and the least significant bit (LSB) of each value. Let’s consider two NAND flash pages, each comprising many cells. Now, instead of having both bits from each MLC cell used for a single page, I will put all of the MSB values into one page and call that the slow page. Then I’ll take all of the LSB values and put that into the other page and call that the fast page.

Why did I do this? Well, consider what happens when I want to program my fast page: in the diagram you can see that it’s possible to turn the LSB value from one to zero by programming it to either of the two higher thresholds… including the full charge threshold. In fact, if you forget about the MSB side for a second, the LSB side very similar to an SLC cell – and therefore performs like one.

The slow page, meanwhile, has to be programmed just like we discussed previously and therefore sees no benefit from this configuration. What’s more, if I want to program the fast page in this way I can’t store data in the corresponding slow page (the one with the matching MSBs) because every time I program a full charge to this cell the MSB ends up with a value of one. Also, when I want to program the slow page I have to erase the whole block first and then program both pages together (slowly!).

It’s kind of complicated… but potentially we now have the option to program certain MLC pages using a faster operation, with the trade-off that other pages will be affected as a result.

Getting To The Point

I should point out here that this is pretty low-level stuff which requires direct access to NAND flash (rather than via an SSD for example). It may also require a working relationship with the flash manufacturer. So why am I mentioning it here?

Well first of all I want to show you that NAND flash is actually a difficult and unpredictable medium on which to store data – unless you truly understand how it works and make allowances for its behaviour. NAND-flashThis is one of the reasons why so many flash products exist on the market with completely differing performance characteristics.

When you look at the datasheet for an MLC flash product and you see write / program times shown as, for example, 1.4 milliseconds it’s important to realise that this is the average of its bi-modal behaviour. Fast (LSB) pages may well have program times of 300 microseconds, while slow (MSB) pages might take up to 2.5 milliseconds.

Secondly, I want to point out that direct access to the flash (instead of via an SSD) brings certain benefits. What if, in my all flash array, I send all inbound user writes to fast pages but then, later on during garbage collection, I move data to be stored in slow pages? If I could do that, I’d effectively be hiding much of the slower performance of MLC writes from my users. And that would be a wonderful thing…

…which is why, at Violin, we’ve been doing it for years :-)

 

Understanding Flash: The Write Cliff

cliffs

For the last couple of posts in this series I’ve been banging on about the importance of garbage collection (GC) in a flash system. I attempted to show you what happens if you don’t perform any GC at all (clue: you turn your flash device into a slow ROM), but clearly in the real world every flash array or SSD vendor has GC technology built into their flash translation layer. So why am I going to devote yet another post to it?

Predictable Performance

When you consider the performance of a system, what’s the number one requirement on your list? Is it “fast”? I would argue not. In my opinion, the first and most important requirement when considering performance is predictability. If you know how a system will perform at any time then (even if you would prefer things to happen faster) you can plan accordingly. If the same repeatable action behaves completely differently over random samples, how can you ever consider it reliable?

Cast your mind back to the post about flash blocks and pages. Remember that reads happen at the page level, as do writes (known as program operations when working with NAND flash)queues-likely but only empty pages can be programmed. To make a page empty you must perform an erase operation – and, crucially, these happen at the block level thereby affecting an entire set of pages.

As you fill up your flash device, pages containing data must be relocated in order to free up blocks so that they can be erased. At this point there are two pieces of bad news to consider:

First of all, in general each flash die can only perform one operation at a time (sometimes this is one operation per plane but that really doesn’t detract from the point). That means if you are performing an erase operation on block A, a read operation from a page in block Z on the same die has to be queued. It’s a completely different block – one of thousands on the same die – but the operation is queued nonetheless.

The second bit of news is that erase operations are slow… really slow. For MLC flash we’re talking maybe 3 milliseconds, which is an age when you compare it to the ~50 microseconds it takes to perform a read. Program operations are also slower than reads (but faster than erases) and they also have to be queued.

So based on all this information, a user who simply reads data at a predictable rate may suddenly see their latency spike up to around 60x higher than the 50us they are expecting if their read gets queued behind an erase. That doesn’t sound like fun does it?

Background vs Active / Foreground Garbage Collection

We know that garbage collection has to try and take care of erases in order to stop you running out of space as you fill up your flash. But let’s now consider this in terms of the performance problems caused by “user operations” (i.e. active reads and writes from the host) queuing up behind “background operations” (i.e. activity caused by the flash translation layer doing its job). Clearly the latter will affect the former if we are not careful. It therefore makes sense that we should try and perform all of our background operations at times when they will not cause problems to the users. As you know if you’ve read my blog before, I love an analogy… so let’s consider garbage collection a bit like the process of washing dishes at a busy restaurant.

dishesA restaurant only has a finite number of plates, glasses, cutlery etc, so once stuff is used it has to return to the kitchen and be washed ready for reuse. In a well-functioning restaurant this process takes place without disrupting the flow of cooked meals leaving the kitchen and being served to customers. In the same way, if our flash garbage collection is taking place in a manner which does not affect the active I/O operations of our users, this is known as background garbage collection (BGC). You can consider BGC “a good thing”, since it “hides” the impact of erase operations and results in more stable, predictable I/O times from the host. In other words, the kitchen runs smoothly and our customers are happy. Bravo.

On the other hand, if our dishes are not being washed fast enough in the kitchen of our restaurant, at some point there will be a shortage of clean plates etc and the customers will have to wait. Likewise, if more data is being changed than BGC can keep up with, the flash device is now running out of free space in which to program incoming writes. This means we have to switch into a different mode called active garbage collection (AGC), sometimes also described as foreground garbage collection. In AGC, user I/O inevitably ends up queuing behind background operations – and in severe cases we have to throttle user I/O requests because they cannot be serviced in time. Yes, we actually have to tell the waiters not to take any more orders until we can get our act together in the kitchen.

You might remember from my previous post that all flash vendors overprovision their flash to allow an additional working area where new writes can land while stale pages are being erased. In the same way, most restaurants probably have more plates and cutlery than they have table settings out front. It helps – and the more you overprovision, the more breathing space you have – but at some point if you don’t take care of your dirty dishes you will still run out of clean ones.

The Infamous Write Cliff

There has been a lot of talk about the write cliff by various commentators, flash vendors and bloggers over the years. I’ve read articles that say it’s no longer a problem (“in most SSD arrays”), articles that show it causing significant problems and white papers on how to avoid it.

My advice is to keep it simple: your flash device has an over-provisioned “buffer zone” which you may or may not be able to see (on Violin you can actually configure it). If you change more data on your flash device than the background garbage collection algorithm can keep up with, you will eat into this buffer zone until you hit active garbage collection. Keep pushing your device at this point and you will see the latency rise as the number of IOPS falls. It’s a simple as that.

My good buddy Maxim from Violin’s amazing engineering team demonstrated this to me in real life by deliberately limiting the ability of background garbage collection on a test system and then hitting it with lots of writes. Here’s the result:

active-vs-background-garbage-collection

This same pattern can be seen in numerous other places on the internet; for example, in the Preconditioning Curve graphs of reviews of SSDs. In fact, there are only two possible scenarios where a flash device won’t hit the write cliff (assuming you push it hard enough):

  1. Flash devices (mainly SLC) which can perform garbage collection really quickly and have lots of overprovisioned space (e.g. the Violin Memory 6616 array frequently used for setting benchmark records)
  2. Flash devices where the limited ingest capability means they can never accept enough writes to exhaust their overprovisioned space

That last one might seem contentious, but think about it: it’s a simple fact of NAND flash that erases are slower than writes (programs). This means if you are able to perform enough writes then eventually you will always exhaust any finite overprovisioned buffer space. At that point, writes must slow down to the speed of erases. As the theory of constraints says, “a chain is no stronger than its weakest link“…

Postcard from Oracle OpenWorld 2014: The Oracle FS1 Flash Array

OOW14

A couple of weeks ago I wrote a post about my trip to Oracle OpenWorld 2014 and the surprise announcement of the Oracle FS1 Flash Array. I posted it on the Violin Memory corporate website here:

http://www.violin-memory.com/blog/postcard-oracle-openworld-2014-the-oracle-fs1-flash-array/

Follow the link to find out whether I thought it was the most amazing product in the history of storage, or simply a rehashed Pillar Axiom hybrid array with some SSDs and a ton of marketing.

Understanding Flash: Garbage Collection Matters

garbage-collection

In the last post in this series I discussed some of the various tasks that need to be performed by the flash translation layer – the layer of abstraction that sits between us and the raw NAND flash on which we desire to store our data. One of those tasks is the infamous garbage collection process (or “GC”) – and in these next couple of posts I’m going to look into GC a little deeper.

But first, let’s consider what would happen on a flash system if there were no GC at all.

Why We Need Garbage Collection

Let’s imagine a very simple flash system comprising of just five blocks, each with just ten pages. In the beginning, all the pages are empty, so we can say that 100% of the capacity is Free:

garbage-collection-fill1

 

There’s no point in wasting all that lovely flash, so I’m going to write some data to it. Remember that a write operation in the world of flash is known as a program operation and it takes place at the page level. So here goes:

garbage-collection-fill2

 

You can see a couple of things happening here. First of all, some of the capacity is now showing as Used. Secondly, you can see that our wear levelling algorithm has kicked in to spread the data over all the blocks fairly evenly. Some blocks have more used pages than others, but the wear levelling doesn’t have to be exact, just approximate. Let’s add some more data:

garbage-collection-fill3

 

Now we’re at 50% Used – and again the data has been spread evenly for the purposes of wear levelling. The flash translation later is also taking care of logical to physical block mapping, so although data has been spread across multiple blocks it has the appearance of being sequential when accessed by the calling application.

Time For Some Updates

So far so good. But at some point we’re probably going to want to update some of the existing data. And as we know, on NAND flash that’s not straightforward because used pages cannot be updated without the entire block first being erased. So for now we simply write the updated data to an empty page and mark the old page as stale. The logical to physical block mapping allows us to simply redirect the logical address of an updated block to the physical address of the new page:

garbage-collection-fill4

See how some of the blocks which were showing as Used (in red) are now showing as Stale (in yellow)? And of course the capacity graph is now showing a percentage of the total capacity as being stale. But have you also noticed that we are approaching the point where we can never free up the stale pages?

Never forget that NAND flash can only be erased at the block level. In the above example, the first (left-most) block has two stale pages and four used pages. In order to free up those stale pages I need to copy the used pages somewhere else and then erase the block. If I don’t do that very soon, I’m going to run out of free pages in the other blocks and then … I’m screwed:

garbage-collection-fill5

This situation is a disaster, because at this point I can never free up the stale blocks, which means I’ve effectively just turned my flash system into a read only device. And yet if you look at the capacity graph, it’s only around 75% full of real data (the stuff in red). So does this mean I can never use 100% of a flash device?

The Return of Over-Provisioning

Earlier in this Storage for DBAs blog series I wrote an article about one of my least favourite practices in the world of hard disk drives: over-provisioning. I now have to own up and confess that in the world of flash we do pretty much the same thing, although for slightly different reasons (and without the massive overhead of power, cooling and real-estate costs that makes me so mad with disk).

To ensure that there is always enough room for garbage collection to continue (as well as to allow for things like bad blocks, wear levelling etc), pretty much every flash device vendor (from SSDs through to All Flash Arrays) uses the same trick:

garbage-collection-overprovision

Yep, there’s more flash in your device than you can actually see. For example, rip off the covers of a 400GB HGST Ultrastar SSD800MM (the SSD that used to be found in XtremIO arrays) and you’ll find 576GB of raw flash. There’s 44% more capacity on the device than it advertises when you access it. This extra area is not pinned by the way, it’s not a dedicated set of blocks or pages. It’s just mandatory headroom that stops situations like the one we described above ever occurring.

The Violin Way

Violin Intelligent Memory Module (VIMM)Since I work for Violin Memory it would be remiss of me not to discuss the concept of format levels at this point. When you buy flash in the form of SSDs, or flash arrays that contain SSDs, the amount of over-provisioning is generally fixed. Almost all SSDs have a predefined over-provision area and that cannot be changed (if there is an SSD on the market that allows this, I am not aware of it).

One of the advantages we have at Violin is that we build our arrays from the ground up using NAND flash chips rather than pre-packaged SSDs – our flash is located on our unique Violin Intelligent Memory Modules (VIMMs). This means we have control down to the NAND flash level – and one of the benefits here is that we can choose how much over-provisioning is required based on workload. At Violin we call this the format level of an array and we allow customers to set it when they take delivery (it can also be changed at a later time but obviously requires a reformat of the array).

I see this as a great benefit because some workloads are very read-intensive and so a large over-provision would essentially be a waste of good flash. Conversely some workloads are so write-heavy that the hard limits found on an SSD device would not be suitable. trashcanIn general I believe that choice (as long as it comes with advice) is a good thing.

In the next post we’ll look at garbage collection some more, in particular the concept of background versus foreground garbage collection, as well as cover the infamous write cliff. But for now, just remember: no matter what form of flash you are using, in order for it to work and perform properly, something somewhere has to take out the trash…

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