Playing The Data Reduction Lottery

Picture courtesy of Capsun Poe

Picture courtesy of Capsun Poe

Storage for DBAs: Do you want to sell your house? Or your car? Let’s go with the car – just indulge me on this one. You have a car, which you weren’t especially planning on selling, but I’m making you an offer you can’t refuse. I’m offering you one million dollars so how can you say no?

The only thing is, when we come to make the trade I turn up not with a suitcase full of cash but a single Mega Millions lottery ticket. How would you feel about that? You may well feel aggrieved that I am offering you something which cost me just $1 but my response is this: it has an effective value of well over $1m. Does that work for you?

Blurred Lines

The thing is, this happens all the time in product marketing and we just put up with it. Oracle’s new Exadata Database Machine X4-2 has 44.8TB of raw flash in a full rack configuration, yet the datasheet states it has an effective flash capacity of 448TB. Excuse me? Let’s read the small print to find out what this means: apparently this is “the size of the data files that can often be stored in Exadata and be accessed at the speed of flash memory“.  No guarantees then, you just might get that, if you’re lucky. I thought datasheets where supposed to be about facts?

Meanwhile, back in storageland, a look at some of the datasheets from various flash array vendors throws up a similar practice. One vendor shows the following flash capacity figures for their array:

  • 2.75 – 11 TBs raw capacity
  • 5 – 50 TBs effective capacity

In my last two posts I covered deduplication and data compression as part of an overall data reduction strategy in storage. To recap, I gave my opinion that dedupe has no place with databases (although it has major benefits in workloads such as VDI) while data compression has benefits but is not necessarily best implemented at the storage level.

Here’s the thing. Your database vendor’s software has options that allow you to perform data reduction. You can also buy host-level software to do this. And of course, you can buy storage products that do this too. So which is best? It probably depends on which vendor you ask (i.e. database, host-level or storage), since each one is chasing revenue for that option – and in some storage vendor cases the data reduction is “always on”, which means you get it whether you want it or not (and whether you want to pay for it or not). But what you should know is this: your friendly flash storage vendor has the most to gain or lose when it comes to data reduction software.

Lies, Damned Lies and Capacities

When you purchase storage, you invariably buy it at a value based on price per usable capacity, most commonly using the unit of dollars per GB. This is simply a convenient way of comparing the price of competing products which may otherwise have different capacities: if a storage array costs $X and gives you GB of usable capacity, then the price in $/GB (dollars per gig) is therefore X/Y.

Now this practice originally developed when buying disk arrays – and there are some arguments to be made that $/GB carries less significance with flash… but everyone does it. Even if you aren’t doing it, chances are somebody in your purchasing department is. And even though it may not be the best way to compare two different products, you can bet that the vendor whose product has the lowest $/GB price will be the one looking most comfortable when it comes to decision day.

But what if there was a way to massage those figures? Each vendor wants to beat the competition, so they start to say things like, “Hey, what about if you use our storage compression features? On average our customers see a 10x reduction in data. This means the usable capacity is actually 10Y!“. Wouldn’t you know it? The price per gig (which is now X/10Y) just came down by 90%!

The First Rule of Compression

You all know this, but I’m going to say it anyway. Different sets of data result in different levels of compression (and deduplication). It’s obvious. Yet in the sterile environment of datasheets and TCO calculations it often gets overlooked. So let me spell it out for once and for all:

The first rule of compression is that the compression ratio is entirely dependant on the data being compressed.

Thus if you are buying or selling a product that uses compression, deduplication and data reduction, you cannot make any guarantees. Sure you can talk about “average compression ratios”, but what does that mean? Is there really such a thing as the average dataset?

Conclusion: Know What You Are Paying For

It’s a very simple message: when you buy a flash array (or indeed any storage array) be sure to understand the capacity values you are buying and paying for. Dollar per GB values are only relevant with usable capacities, not so-called effective or logical capacities. Also, don’t get too hung up on raw capacity values, since they won’t help you when you run out of usable space.

Definitions are important. Without them, nothing we talk about is … well, definite. So here are mine:

Lies, Damned Lies and Capacities


Storage Myths: Storage Compression Has No Downside

Image courtesy of marcovdz

Image courtesy of marcovdz

Storage for DBAs: My last post in this blog series was aimed at dispelling the myth that dedupe is a suitable storage technology for databases. To my surprise it became the most popular article I’ve ever published (based on reads per day). Less surprisingly though, it lead to quite a backlash from some of the other flash storage vendors who responded with comments along the lines of “well we don’t need dedupe because we also have compression”. Fair enough. So today let’s take a look at the benefits and drawbacks of storage-level compression as part of an overall data reduction strategy. And by the way, I’m not against either dedupe or storage-level compression. I just think they have drawbacks as well as benefits – something that isn’t always being made clear in the marketing literature. And being in the storage industry, I know why that is…

What Is Compression?

In storageland we tend to talk about the data reduction suite of tools, which comprise of deduplication, compression and thin provisioning. The latter is a way of freeing up capacity which is allocated but not used… but that’s a topic for another day.

bookshelfDedupe and compression have a lot in common: they both fundamentally involve the identification and removal of patterns, which are then replaced with keys. The simplest way to explain the difference would be to consider a book shelf filled with books. If you were going to dedupe the bookshelf you would search through all of the books, removing any duplicate titles and making a note of how many duplicates there were. Easy. Now you want to compress the books, so you need to read each book and look for duplicate patterns of words. If you find the same sentence repeated numerous times, you can replace it with a pointer to a notebook where you can jot down the original. Hmmm…. less easy. You can see that dedupe is much more of a quick win.

Of course there is more to compression than this. Prior to any removal of duplicate patterns data is usually transformed – and it is the method used in this transformation process that differentiates all of the various compression algorithms. I’m not going to delve into the detail in this article, but if you are interested then a great way to get an idea of what’s involved is to read the Wikipedia page on the BZIP2 file compression tool and look at all the processes involved.

Why Compress?

Data compression is essentially a trade-off, where reduced storage footprint is gained at the expense of extra CPU cycles – and therefore as a consequence, extra time. This additional CPU and time must be spent whenever the compressed data is read, thus increasing the read latency. It also needs to be spent during a write, but – as with dedupe – this can take place during the write process (known as inline) or at some later stage (known as post-process). dedupe-inline-or-post-processInline compression will affect the write latency but will also eliminate the need for a staging area where uncompressed data awaits compression.

Traditionally, compression has been used for archive data, i.e. data that must be retained but is seldom accessed. This is a good fit for compression, since the additional cost of decompression will rarely be paid. However, the use of compression with primary data is a different story: does it make sense to repeatedly incur time and CPU penalties on data that is frequently read or written? The answer, of course, is that it’s entirely down to any business requirements. However, I do strongly believe that – as with dedupe – there should be a choice, rather than an “always on” solution where you cannot say no. One vendor I know makes this rather silly claim: “so important it’s always-on”. What a fine example of a design limitation manifesting itself as a marketing claim.

Where to Compress?

As with all applications, data tends to flow down from users through an application layer, into a database. This database sits on top of a host, which is connected to some sort of persistent storage. There are therefore a number of possible places where data can be compressed:

  • where-to-compressDatabase-level compression, such as using basic compression in Oracle (part of the core product), or the Advanced Compression option (extra license required).
  • Host-level compression, such as you might find in products like Symantec’s Veritas Storage Foundation software suite.
  • Storage-level compression, where the storage array compresses data either at a global level, or more ideally, at some configurable level (e.g. by the LUN).

Of course, compressed data doesn’t easily compress again, since all of the repetitive patterns will have been removed. In fact, running compression algorithms on compressed data is, at the very least, a waste of time and CPU – while in the worst case it could actually increase the size of the compressed data. This means it doesn’t really make sense to use multiple levels of compression, such as both database-level and storage-level. Choosing the correct level is therefore important. So which is best?

Benefits and Drawbacks

If you read some of the marketing literature I’ve seen recently you would soon come to the conclusion that compressing your data at the storage level is the only way to go. It certainly has some advantages, such as ease of deployment: just switch it on and sit back, all of your data is now compressed. But there are drawbacks too – and I believe it pays to make an informed decision.


rev-counter-inverseThe most obvious and measurable drawback is the addition of latency to I/O operations. In the case of inline compression this affects both reads and writes, while post-process compression inevitably results in more background I/O operations taking place, increasing wear and potentially impacting other workloads. Don’t take it for granted that this additional latency won’t affect you, especially at peak workload. Everyone in the flash industry knows about a certain flash vendor whose inline dedupe and compression software has to switch into post-process mode under high load, because it simply cannot cope.


This one is less obvious, but in my opinion far more important. Let’s say you compress your database at the storage-level so that as blocks are written to storage they are compressed, then decompressed again when they are read back out into the buffer cache. That’s great, you’ve saved yourself some storage capacity at the overhead of some latency. But what would have happened if you’d used database-level compression instead?

Random Access MemoryWith database-level compression the data inside the data blocks would be compressed. This means not just that data which resides on storage, but also the data in memory – inside the buffer cache. That means you need less physical memory to hold the same amount of data, because it’s compressed in memory as well as on storage. What will you do with the excess physical memory? You could increase the size of the buffer cache, holding more data in memory and possibly improving performance through a reduction in physical I/O. Or you could run more instances on the same server… in fact, database-level compression is very useful if you want to build a consolidation environment, because it allows a greater density of databases per physical host.

There’s more. Full table scans will scan a smaller number of blocks because the data is compressed. Likewise any blocks sent over the network contain compressed data, which might make a difference to standby or Data Guard traffic. When it comes to compression, the higher up in the stack you begin, the more benefits you will see.

Don’t Believe The Hype

The moral of this story is that compression, just like deduplication, is a fantastic option to have available when and if you want to use it. Both of these tools allow you to trade time and CPU resource in favour of a reduced storage footprint. Choices are a good thing.

They are not, however, guaranteed wins – and they should not be sold as such. Take the time to understand the drawbacks before saying yes. If your storage vendor – or your database vendor (“storage savings of up to 204x“!!) – is pushing compression maybe they have a hidden agenda? In fact, they almost definitely will have.

And that will be the subject of the next post…

Storage Myths: Dedupe for Databases


Spot the duplicate duck

Storage for DBAs: Data deduplication – or “dedupe” – is a technology which falls under the umbrella of data reduction, i.e. reducing the amount of capacity required to store data. In very simple terms it involves looking for repeating patterns and replacing them with a marker: as long as the marker requires less space than the pattern it replaces, you have achieved a reduction in capacity. Deduplication can happen anywhere: on storage, in memory, over networks, even in database design – for example, the standard database star or snowflake schema. However, in this article we’re going to stick to talking about dedupe on storage, because this is where I believe there is a myth that needs debunking: databases are not a great use case for dedupe.

Deduplication Basics: Inline or Post-Process

dedupe-inline-or-post-processIf you are using data deduplication either through a storage platform or via software on the host layer, you have two basic choices: you can deduplicate it at the time that it is written (known as inline dedupe) or allow it to arrive and then dedupe it at your leisure in some transparent manner (known as post-process dedupe). Inline dedupe affects the time taken to complete every write, directly affecting I/O performance. The benefit of post-process dedupe therefore appears to be that it does not affect performance – but think again: post-process dedupe first requires data to be written to storage, then read back out into the dedupe algorithm, before being written to storage again in its deduped format – thus magnifying the amount of I/O traffic and indirectly affecting I/O performance. In addition, post-process dedupe requires more available capacity to provide room for staging the inbound data prior to dedupe.

Deduplication Basics: (Block) Size Matters

In most storage systems dedupe takes place at a defined block size, whereby each block is hashed to produce a unique key before being compared with a master lookup table containing all known hash keys. If the newly-generated key already exists in the lookup table, the block is a duplicate and does not need to be stored again. The block size is therefore pretty important, because the smaller the granularity, the higher the chances of finding a duplicate:

dedupe-block-sizeIn the picture you can see that the pattern “1234”repeats twice over a total of 16 digits. With an 8-digit block size (the lower line) this repeat is not picked up, since the second half of the 8-digit pattern does not repeat. However, by reducing the block size to 4 digits (the upper line) we can now get a match on our unique key, meaning that the “1234” pattern only needs to be stored once.

This sounds like great news, let’s just choose a really small block size, right? But no, nothing comes without a price – and in this case the price comes in the size of the hashing lookup table. This table, which contains one key for every unique block, must range in size from containing just one entry (the “ideal” scenario where all data is duplicated) to having one entry for each block (the worst case scenario where every block is unique). By making the block size smaller, we are inversely increasing the maximum size of the hashing table: half the block size means double the potential number of hash entries.

Hash Abuse

Why do we care about having more hash entries? There are a few reasons. First there is the additional storage overhead: if your data is relatively free of duplication (or the block size does not allow duplicates to be detected) then not only will you fail to reclaim any space but you may end up using extra space to store all of the unique keys associated with each block. This is clearly not a great outcome when using a technology designed to reduce the footprint of your data. hashSecondly, the more hash entries you have, the more entries you need to scan through when comparing freshly-hashed blocks during writes or locating existing blocks during reads. In other words, the more of a performance overhead you will suffer in order to read your data and (in the case of inline dedupe) write it.

If this is sounding familiar to you, it’s because the hash data is effectively a database in which storage metadata is stored and retrieved. Just like any database the performance will be dictated by the volume of data as well as the compute resource used to manipulate it, which is why many vendors choose to store this metadata in DRAM. Keeping the data in memory brings certain performance benefits, but with the price of volatility: changes in memory will be lost if the power is interrupted, so regular checkpoints are required to persistent storage. Even then, battery backup is often required, because the loss of even one hash key means data corruption. If you are going to replace your data with markers from a lookup table, you absolutely cannot afford to lose that lookup table, or there will be no coming back.

Database Deduplication – Don’t Be Duped

Now that we know what dedupe is all about, let’s attempt to apply it to databases and see what happens. You may be considering the use of dedupe technology with a database system, or you may simply be considering the use of one of a number of recent storage products that have inline dedupe in place as an “always on” option, i.e. you cannot turn it off regardless of whether it helps or hinders. The vendor may make all sorts of claims about the possibilities of dedupe, but how much benefit will you actually see?

Let’s consider the different components of a database environment in the context of duplication:

  • Oracle datafiles contain data blocks which have block headers at the start of the block. These contain numbers which are unique for each datafile, making deduplication impossible at the database block size. In addition, the end of each block contains a tailcheck section which features a number generated using data such as the SCN, so even if the block were divided into two the second half would offer limited opportunity for dedupe while the first half would offer none.
  • Even if you were able to break down Oracle blocks into small enough chunks to make dedupe realistic, any duplication of data is really a massive warning about your database design: normalise your data! Also, consider features like index key compression which are part of the Enterprise Edition license.
  • Most Oracle installations have multiplexed copies of important files like online redo logs and controlfiles. These files are so important that Oracle synchronously maintains multiple copies in order to ensure against data loss. If your storage system is deduplicating these copies, this is a bad thing – particularly if it’s an always on feature that gives you no option.
  • While unallocated space (e.g. in an ASM diskgroup) might appear to offer the potential for dedupe, this is actually a problem which you should solve using another storage technology: thin provisioning.
  • You may have copies of datafiles residing on the same storage as production, which therefore allow large-scale deduplication to take place; perhaps they are used as backups or test/development environments. However, in the latter case, test/dev environments are a use case for space-efficient snapshots rather than dedupe. And if you are keeping your backups on the same storage system as your production data, well… good luck to you. There is nothing more for you here.
  • Maybe we aren’t talking about production data at all. You have a large storage array which contains multiple copies of your database for use with test/dev environments – and thus large portions of the data are duplicated. Bingo! The perfect use case for storage dedupe, right? Wrong. Database-level problems require database-level solutions, not storage-level workarounds. Get yourself some licenses for Delphix and you won’t look back.

cautionTo conclude, while dedupe is great in use cases like VDI, it offers very limited benefit in database environments while potentially making performance worse. That in itself is worrying, but what I really see as a problem is the way that certain storage vendors appear to be selling their capacity based on assumed levels of dedupe, i.e. “Sure we are only giving you X terabytes of storage for Y price, but actually you’ll get 10:1 dedupe which means the price is really ten times lower!”

Sizing should be based on facts, not assumptions. Just like in the real world, nothings comes for free in I.T. – and we’ve all learnt that the hard way at some point. Don’t be duped.