Monday Sep 27, 2010

And now, page 2

To my team:

After 20 incredible years at Sun/Oracle, I have decided to try something new.

This was a very hard decision, and not one made lightly.  I have always enjoyed my work, and still do -- everything from MTS-2 to Sun Fellow to Oracle VP.  I love the people I work with and the technology we've created together, which is why I've been doing it for so long.  But I have always wanted to try doing a startup, and recently identified an opportunity that I just can't resist.  (We are in stealth mode, so that's all I can say for now.)

This team will always have a special place in my heart.  Being part of the Solaris team means doing the Right Thing, innovating, changing the rules, and being thought leaders -- creating the ideas that everyone else wants to copy. Add to that Oracle's unmatched market reach and ability to execute, and you have a combination that I believe will succeed in ways we couldn't have imagined two years ago.  I hope that Solaris and ZFS Storage are wildly successful, and that you have fun making it happen.

To the ZFS community:

Thank you for being behind us from Day One.  After a decade in the making, ZFS is now an adult.  Of course there's always more to do, and from this point forward, I look forward to watching you all do it.  There is a great quote whose origin I have never found: "Your ideas will go further if you don't insist on going with them."  That has proven correct many times in my life, and I am confident that it will prove true again.

For me, it's time to try the Next Big Thing.  Something I haven't fully fleshed out yet.  Something I don't fully understand yet.  Something way outside my comfort zone.  Something I might fail at.  Everything worth doing begins that way.  I'll let you know how it goes.

My last day at Oracle will be this Thursday, September 30, 2010.  After that you can reach me at my personal e-mail, with the usual first-dot-last construction.

It has truly been a wonderful couple of decades.  To everyone who taught me, worked with me, learned from me, and supported my efforts in countless ways large and small:  Thank you.

Monday Jun 09, 2008

ZFS in MacOS X Snow Leopard

It's official!

Cheers to Noel, Don, Bertrand, and all the great folks at Apple.

Now when can I get this in Time Capsule?  :-)

Monday May 26, 2008


Chocolate on my peanut butter?

No, peanut butter on my chocolate!

All I can say for the moment is... stay tuned.

Thursday Sep 13, 2007

Space Maps

Every filesystem must keep track of two basic things: where your data is, and where the free space is.

In principle, keeping track of free space is not strictly necessary: every block is either allocated or free, so the free space can be computed by assuming everything is free and then subtracting out everything that's allocated; and the allocated space can be found by traversing the entire filesystem from the root.  Any block that cannot be found by traversal from the root is, by definition, free.

In practice, finding free space this way would be insufferable because it would take far too long for any filesystem of non-trivial size.  To make the allocation and freeing of blocks fast, the filesystem needs an efficient way to keep track of free space.  In this post we'll examine the most common methods, why they don't scale well, and the new approach we devised for ZFS.


The most common way to represent free space is by using a bitmap.  A bitmap is simply an array of bits, with the Nth bit indicating whether the Nth block is allocated or free.  The overhead for a bitmap is quite low: 1 bit per block.  For a 4K blocksize, that's 1/(4096\*8) = 0.003%.  (The 8 comes from 8 bits per byte.)

For a 1GB filesystem, the bitmap is 32KB -- something that easily fits in memory, and can be scanned quickly to find free space.  For a 1TB filesystem, the bitmap is 32MB -- still stuffable in memory, but no longer trivial in either size or scan time.  For a 1PB filesystem, the bitmap is 32GB, and that simply won't fit in memory on most machines.  This means that scanning the bitmap requires reading it from disk, which is slower still.

Clearly, this doesn't scale.

One seemingly obvious remedy is to break the bitmap into small chunks, and keep track of the number of bits set in each chunk.  For example, for a 1PB filesystem using 4K blocks, the free space can be divided into a million bitmaps, each 32KB in size.  The summary information (the million integers indicating how much space is in each bitmap) fits in memory, so it's easy to find a bitmap with free space, and it's quick to scan that bitmap.

But there's still a fundamental problem: the bitmap(s) must be updated not only when a new block is allocated, but also when an old block is freed.  The filesystem controls the locality of allocations (it decides which blocks to put new data into), but it has no control over the locality of frees.  Something as simple as 'rm -rf' can cause blocks all over the platter to be freed.  With our 1PB filesystem example, in the worst case, removing 4GB of data (a million 4K blocks) could require each of the million bitmaps to be read, modified, and written out again.  That's two million disk I/Os to free a measly 4GB -- and that's just not reasonable, even as worst-case behavior.

More than any other single factor, this is why bitmaps don't scale: because frees are often random, and bitmaps that don't fit in memory perform pathologically when they are accessed randomly.


Another common way to represent free space is with a B-tree of extents.  An extent is a contiguous region of free space described by two integers: offset and length.  The B-tree sorts the extents by offset so that contiguous space allocation is efficient.  Unfortunately, B-trees of extents suffer the same pathology as bitmaps when confronted with random frees.

What to do?

Deferred frees

One way to mitigate the pathology of random frees is to defer the update of the bitmaps or B-trees, and instead keep a list of recently freed blocks.  When this deferred free list reaches a certain size, it can be sorted, in memory, and then freed to the underlying bitmaps or B-trees with somewhat better locality.  Not ideal, but it helps.

But what if we went further?

Space maps:  log-structured free lists

Recall that log-structured filesystems long ago posed this question: what if, instead of periodically folding a transaction log back into the filesystem, we made the transaction log be the filesystem?

Well, the same question could be asked of our deferred free list: what if, instead of folding it into a bitmap or B-tree, we made the deferred free list be the free space representation?

That is precisely what ZFS does.  ZFS divides the space on each virtual device into a few hundred regions called metaslabs.  Each metaslab has an associated space map, which describes that metaslab's free space.  The space map is simply a log of allocations and frees, in time order.  Space maps make random frees just as efficient as sequential frees, because regardless of which extent is being freed, it's represented on disk by appending the extent (a couple of integers) to the space map object -- and appends have perfect locality.  Allocations, similarly, are represented on disk as extents appended to the space map object (with, of course, a bit set indicating that it's an allocation, not a free).

When ZFS decides to allocate blocks from a particular metaslab, it first reads that metaslab's space map from disk and replays the allocations and frees into an in-memory AVL tree of free space, sorted by offset.  This yields a compact in-memory representation of free space that supports efficient allocation of contiguous space.  ZFS also takes this opportunity to condense the space map: if there are many allocation-free pairs that cancel out, ZFS replaces the on-disk space map with the smaller in-memory version.

Space maps have several nice properties:

  • They don't require initialization: a space map with no entries indicates that there have been no allocations and no frees, so all space is free.

  • They scale: because space maps are append-only, only the last block of the space map object needs to be in memory to ensure excellent performance, no matter how much space is being managed.

  • They have no pathologies: space maps are efficient to update regardless of the pattern of allocations and frees.

  • They are equally efficient at finding free space whether the pool is empty or full (unlike bitmaps, which take longer to scan as they fill up).

Finally, note that when a space map is completely full, it is represented by a single extent.  Space maps therefore have the appealing property that as your storage pool approaches 100% full, the space maps start to evaporate, thus making every last drop of disk space available to hold useful information.

Sunday Jun 24, 2007

ZFS License Announcement

My oldest son, Andrew, recently gave me something cool for the back of my car:


That's Andrew(11), David(8), and Galen(6).  If you look closely, you can see the CEO potential...

What, you were looking for yet another CDDL vs. GPL thread? 

Thursday May 03, 2007

Rampant Layering Violation?

Andrew Morton has famously called ZFS a "rampant layering violation" because it combines the functionality of a filesystem, volume manager, and RAID controller.  I suppose it depends what the meaning of the word violate is.  While designing ZFS we observed that the standard layering of the storage stack induces a surprising amount of unnecessary complexity and duplicated logic.  We found that by refactoring the problem a bit -- that is, changing where the boundaries are between layers -- we could make the whole thing much simpler.

An example from mathematics (my actual background) provides a useful prologue.

Suppose you had to compute the sum, from n=1 to infinity, of 1/n(n+1).

Expanding that out term by term, we have:

        1/(1\*2) + 1/(2\*3) + 1/(3\*4) + 1/(4\*5) + ...

That is,

        1/2 + 1/6 + 1/12 + 1/20 + ...

What does that infinite series add up to?  It may seem like a hard problem, but that's only because we're not looking at it right.  If you're clever, you might notice that there's a different way to express each term:

        1/n(n+1) = 1/n - 1/(n+1)

For example,

        1/(1\*2) = 1/1 - 1/2
        1/(2\*3) = 1/2 - 1/3
        1/(3\*4) = 1/3 - 1/4

Thus, our sum can be expressed as:

        (1/1 - 1/2) + (1/2 - 1/3) + (1/3 - 1/4) + (1/4 - 1/5) + ...

Now, notice the pattern: each term that we subtract, we add back.  Only in Congress does that count as work.  So if we just rearrange the parentheses -- that is, if we rampantly violate the layering of the original problem by using associativity to refactor the arithmetic across adjacent terms of the series -- we get this:

        1/1 + (-1/2 + 1/2) + (-1/3 + 1/3) + (-1/4 + 1/4) + ...


        1/1 + 0 + 0 + 0 + ...

In others words,


Isn't that cool?

Mathematicians have a term for this.  When you rearrange the terms of a series so that they cancel out, it's called telescoping -- by analogy with a collapsable hand-held telescope.  In a nutshell, that's what ZFS does: it telescopes the storage stack.  That's what allows us to have a filesystem, volume manager, single- and double-parity RAID, compression, snapshots, clones, and a ton of other useful stuff in just 80,000 lines of code.

A storage system is more complex than this simple analogy, but at a high level the same idea really does apply.  You can think of any storage stack as a series of translations from one naming scheme to another -- ultimately translating a filename to a disk LBA (logical block address).  Typically it looks like this:

        filesystem(upper): filename to object (inode)
        filesystem(lower): object to volume LBA
        volume manager: volume LBA to array LBA
        RAID controller: array LBA to disk LBA

This is the stack we're about to refactor.

First, note that the traditional filesystem layer is too monolithic.  It would be better to separate the filename-to-object part (the upper half) from the object-to-volume-LBA part (the lower half) so that we could reuse the same lower-half code to support other kinds of storage, like objects and iSCSI targets, which don't have filenames.  These storage classes could then speak to the object layer directly.  This is more efficient than going through something like /dev/lofi, which makes a POSIX file look like a device.  But more importantly, it provides a powerful new programming model -- object storage -- without any additional code.

Second, note that the volume LBA is completely useless. Adding a layer of indirection often adds flexibility, but not in this case: in effect we're translating from English to French to German when we could just as easily translate from English to German directly.  The intermediate French has no intrinsic value.  It's not visible to applications, it's not visible to the RAID array, and it doesn't provide any administrative function.  It's just overhead.

So ZFS telescoped that entire layer away.  There are just three distinct layers in ZFS: the ZPL (ZFS POSIX Layer), which provides traditional POSIX filesystem semantics; the DMU (Data Management Unit), which provides a general-purpose transactional object store; and the SPA (Storage Pool Allocator), which provides virtual block allocation and data transformations (replication, compression, and soon encryption).  The overall ZFS translation stack looks like this:

        ZPL: filename to object
        DMU: object to DVA (data virtual address)
        SPA: DVA to disk LBA

The DMU provides both file and block access to a common pool of physical storage.  File access goes through the ZPL, while block access is just a direct mapping to a single DMU object.  We're also developing new data access methods that use the DMU's transactional capabilities in more interesting ways -- more about that another day.

The ZFS architecture eliminates an entire layer of translation -- and along with it, an entire class of metadata (volume LBAs).  It also eliminates the need for hardware RAID controllers.  At the same time, it provides a useful new interface -- object storage -- that was previously inaccessible because it was buried inside a monolithic filesystem.

I certainly don't feel violated.  Do you?

Saturday Nov 04, 2006

ZFS Block Allocation

Block allocation is central to any filesystem.  It affects not only performance, but also the administrative model (e.g. stripe configuration) and even some core capabilities like transactional semantics, compression, and block sharing between snapshots.  So it's important to get it right.

There are three components to the block allocation policy in ZFS:

  1. Device selection (dynamic striping)
  2. Metaslab selection
  3. Block selection

By design, these three policies are independent and pluggable.  They can be changed at will without altering the on-disk format, which gives us lots of flexibility in the years ahead.

So... let's go allocate a block!

1.  Device selection (aka dynamic striping).  Our first task is device selection.  The goal is to spread the load across all devices in the pool so that we get maximum bandwidth without needing any notion of stripe groups.  You add more disks, you get more bandwidth.  We call this dynamic striping -- the point being that it's done on the fly by the filesystem, rather than at configuration time by the administrator.

There are many ways to select a device.  Any policy would work, including just picking one at random.  But there are several practical considerations:

  1. If a device was recently added to the pool, it'll be relatively empty.  To address such imbalances, we bias the allocation slightly in favor of underutilized devices.  This keeps space usage uniform across all devices.

  2. All else being equal, round-robin is a fine policy, but it's critical to get the granularity right.  If the granularity is too coarse (e.g. 1GB), we'll only get one device worth of bandwidth when doing sequential reads and writes.  If the granularity is too fine (e.g. one block), we'll waste any read buffering the device can do for us.  In practice, we've found that switching from one device to the next every 512K works well for the current generation of disk drives.

  3. That said, for intent log blocks, it's better to round-robin between devices each time we write a log block.  That's because they're very short-lived, so we don't expect to ever need to read them; therefore it's better to optimize for maximum IOPs when writing log blocks.  Neil Perrin integrated support for this earlier today.

  4. More generally, we'll probably want different striping policies for different types of data: large/sequential, small/random, transient (like the intent log), and dnodes (clumping for spatial density might be good).  This is fertile ground for investigation.

  5. If one of the devices is slow or degraded for some reason, it should be avoided.  This is work in progress.

2. Metaslab selection.  We divide each device into a few hundred regions, called metaslabs, because the overall scheme was inspired by the slab allocator. Having selected a device, which metaslab should we use?  Intuitively it seems that we'd always want the one with the most free space, but there are other factors to consider:

  1. Modern disks have uniform bit density and constant angular velocity.  Therefore, the outer recording zones are faster (higher bandwidth) than the inner zones by the ratio of outer to inner track diameter, which is typically around 2:1.  We account for this by assigning a higher weight to the free space in lower-LBA metaslabs.  In effect, this means that we'll select the metaslab with the most free bandwidth rather than simply the one with the most free space.

  2. When a pool is relatively empty, we want to keep allocations in the outer (faster) regions of the disk; this improves both bandwidth and latency (by minimizing seek distances).  Therefore, we assign a higher weight to metaslabs that have been used before.

All of these considerations can be seen in the function metaslab_weight().  Having defined a weighting scheme, the selection algorithm is simple:  always select the metaslab with the highest weight.

3. Block selection.  Having selected a metaslab, we must choose a block within that metaslab.  The current allocation policy is a simple variation on first-fit; it  seems likely that we can do better.  In the future I expect that we'll  have not only a better algorithm, but a whole collection of algorithms, each optimized for a specific workload.  Anticipating this, the block allocation code is fully vectorized; see space_map_ops_t for details.

The mechanism (as opposed to policy) for keeping track of free space in a metaslab is a new data structure called a space map, which I'll describe in the next post.

Saturday Sep 16, 2006

ZFS Internals at Storage Developer Conference

On Monday, September 18, Bill Moore and I will host a four-hour deep dive into ZFS internals at the 2006 Storage Developer Conference in San Jose.  The four-hour format is a lot of fun -- both for us and the audience -- because it allows enough time to explore the architectural choices and trade-offs, explain how things really work, and weave in some amusing stories along the way.  Come join the festivities!

Thursday May 25, 2006

ZFS on FUSE/Linux

You've probably heard by now that Apple is planning to port ZFS to MacOS.

And that Matt Dillon is porting ZFS to DragonFly BSD.

Today I'm pleased to report that ZFS is being ported to FUSE/Linux.

Very cool.  This is another step toward the ultimate goal: ubiquity.  Consumer devices need data integrity just as badly as enterprises do.  With ZFS, even the humblest device -- an iPod, a digital camera -- can provide fast, reliable storage using nothing but commodity hardware and free, open-source software.  And it's about time, don't you think?

Wednesday May 03, 2006

You say zeta, I say zetta

On a lighter note, what is the Z in ZFS?

I've seen the (non-)word 'zetabyte' many times in the press. It's also been noted that zeta is the last letter of the Greek alphabet, which will no doubt surprise many Greeks.

So, here's the real story. When I began the (nameless) project, it needed a name. I had no idea what to call it. I knew that the Big Idea was to bring virtual memory concepts to disks, so things like VSS (Virtual Storage System) came to mind. The problem is, everything I came up with sounded like vapor. I don't know about you, but when I hear "Tiered Web Deployment Virtualization Engine" I assume it's either a vast steaming pile of complexity or a big fluffy cloud of nothing -- which is correct north of 98% of the time. So I didn't want a name that screamed "BS!" out of the gate. That actually ruled out a lot of stuff.

At first I was hesitant to pick a name that ended in FS, because that would pigeon-hole the project in a way that's not quite accurate. It's much more than a filesystem, but then again, it is partly a filesystem, and at least people know what that is. It's a real thing. It's not a Tiered Web Deployment Virtualization Engine. I figured it would be better to correct any initial misimpression than to make no impression at all.

So the hunt was on for something-FS. The first thing I did was to Google all 26 three-letter options, from AFS to ZFS. As it turns out, they've all been used by current or previous products, often many times over. But ZFS hadn't been used much, and certainly not by anything mainstream like (for example) AFS, DFS, NFS or UFS.

I should mention that I ruled out BFS or BonwickFS, because it implies either sole authorship or a failure to share credit. Neither would be good.

So in the end, I picked ZFS for the simplest of reasons: it sounds cool. It doesn't come with any baggage, like YFS (Why FS? Ha ha!). And you can even say it in hex (2f5).

The next task was to retrofit the acronym -- that is, I had to come up with something for the Z to stand for. I actually got out my trusty old 1980 Random House Unabridged and looked through the entire Z section, and found nothing that spoke to me. Zero had a certain appeal (as in zero administrative hassle), but then again -- eh.

OK, what does the web have to say? I don't recall exactly how I stumbled on the zetta prefix, but it seemed ideal: ZFS was to be a 128-bit filesystem, and the next unit after exabyte (the 64-bit limit is 16EB) is zettabyte. Perfect: the zettabyte filesystem.

A bit of trivia (in case the rest of this post isn't quite trivial enough for you): the prefix 'zetta' is not of Greek or Latin origin -- at least, not directly. The original SI prefixes were as follows:

	milli	kilo	(1000\^1)
	micro	mega	(1000\^2)
	nano	giga	(1000\^3)
	pico	tera	(1000\^4)
	femto	peta	(1000\^5)
	atto	exa	(1000\^6)

The demand for more extreme units actually began at the small end, so the SI people needed names for 1000\^-7 and 1000\^-8. For seven, they took the Latin "sept" and made "zepto" (septo wouldn't work because s for second is ubiquitous); for eight, they took the Greek "okto" and made "yocto" (octo wouldn't work because o looks like 0). For symmetry with zepto and yocto on the small side, they defined zetta and yotta on the big side. (I also suspect they were drinking heavily, and thought yotta was a real hoot.)

But zettabyte wasn't perfect, actually. We (we were a team by now) found that when you call it the zettabyte filesystem, you have to explain what a zettabyte is, and by then the elevator has reached the top floor and all people know is that you're doing large capacity. Which is true, but it's not the main point.

So we finally decided to unpimp the name back to ZFS, which doesn't stand for anything. It's just a pseudo-acronym that vaguely suggests a way to store files that gets you a lot of points in Scrabble.

And it is, of course, "the last word in filesystems".

Monday May 01, 2006

Smokin' Mirrors

Resilvering -- also known as resyncing, rebuilding, or reconstructing -- is the process of repairing a damaged device using the contents of healthy devices. This is what every volume manager or RAID array must do when one of its disks dies, gets replaced, or suffers a transient outage.

For a mirror, resilvering can be as simple as a whole-disk copy. For RAID-5 it's only slightly more complicated: instead of copying one disk to another, all of the other disks in the RAID-5 stripe must be XORed together. But the basic idea is the same.

In a traditional storage system, resilvering happens either in the volume manager or in RAID hardware. Either way, it happens well below the filesystem.

But this is ZFS, so of course we just had to be different.

In a previous post I mentioned that RAID-Z resilvering requires a different approach, because it needs the filesystem metadata to determine the RAID-Z geometry. In effect, ZFS does a 'cp -r' of the storage pool's block tree from one disk to another. It sounds less efficient than a straight whole-disk copy, and traversing a live pool safely is definitely tricky (more on that in a future post). But it turns out that there are so many advantages to metadata-driven resilvering that we've chosen to use it even for simple mirrors.

The most compelling reason is data integrity. With a simple disk copy, there's no way to know whether the source disk is returning good data. End-to-end data integrity requires that each data block be verified against an independent checksum -- it's not enough to know that each block is merely consistent with itself, because that doesn't catch common hardware and firmware bugs like misdirected reads and phantom writes.

By traversing the metadata, ZFS can use its end-to-end checksums to detect and correct silent data corruption, just like it does during normal reads. If a disk returns bad data transiently, ZFS will detect it and retry the read. If it's a 3-way mirror and one of the two presumed-good disks is damaged, ZFS will use the checksum to determine which one is correct, copy the data to the new disk, and repair the damaged disk.

A simple whole-disk copy would bypass all of this data protection. For this reason alone, metadata-driven resilvering would be desirable even it it came at a significant cost in performance.

Fortunately, in most cases, it doesn't. In fact, there are several advantages to metadata-driven resilvering:

Live blocks only. ZFS doesn't waste time and I/O bandwidth copying free disk blocks because they're not part of the storage pool's block tree. If your pool is only 10-20% full, that's a big win.

Transactional pruning. If a disk suffers a transient outage, it's not necessary to resilver the entire disk -- only the parts that have changed. I'll describe this in more detail in a future post, but in short: ZFS uses the birth time of each block to determine whether there's anything lower in the tree that needs resilvering. This allows it to skip over huge branches of the tree and quickly discover the data that has actually changed since the outage began.

What this means in practice is that if a disk has a five-second outage, it will only take about five seconds to resilver it. And you don't pay extra for it -- in either dollars or performance -- like you do with Veritas change objects. Transactional pruning is an intrinsic architectural capability of ZFS.

Top-down resilvering. A storage pool is a tree of blocks. The higher up the tree you go, the more disastrous it is to lose a block there, because you lose access to everything beneath it.

Going through the metadata allows ZFS to do top-down resilvering. That is, the very first thing ZFS resilvers is the uberblock and the disk labels. Then it resilvers the pool-wide metadata; then each filesystem's metadata; and so on down the tree. Throughout the process ZFS obeys this rule: no block is resilvered until all of its ancestors have been resilvered.

It's hard to overstate how important this is. With a whole-disk copy, even when it's 99% done there's a good chance that one of the top 100 blocks in the tree hasn't been copied yet. This means that from an MTTR perspective, you haven't actually made any progress: a second disk failure at this point would still be catastrophic.

With top-down resilvering, every single block copied increases the amount of discoverable data. If you had a second disk failure, everything that had been resilvered up to that point would be available.

Priority-based resilvering. ZFS doesn't do this one yet, but it's in the pipeline. ZFS resilvering follows the logical structure of the data, so it would be pretty easy to tag individual filesystems or files with a specific resilver priority. For example, on a file server you might want to resilver calendars first (they're important yet very small), then /var/mail, then home directories, and so on.

What I hope to convey with each of these posts is not just the mechanics of how a particular feature is implemented, but to illustrate how all the parts of ZFS form an integrated whole. It's not immediately obvious, for example, that transactional semantics would have anything to do with resilvering -- yet transactional pruning makes recovery from transient outages literally orders of magnitude faster. More on how that works in the next post.

Tuesday Dec 13, 2005


The ZFS team is hosting a BoF at FAST in San Francisco at 9PM tonight.

We'll begin with an in-depth look at what ZFS is and how it works. This will be followed by the customary Airing of Grievances. (I'll get you a free ZFS T-shirt if you can get a Festivus pole past security and into our BoF. (Seriously.))

Quite a few of us will be there, so come on up and meet the folks who put the dot in vmcore.0.

Monday Dec 12, 2005

SEEK_HOLE and SEEK_DATA for sparse files

A sparse file is a file that contains much less data than its size would suggest. If you create a new file and then do a 1-byte write to the billionth byte, for example, you've just created a 1GB sparse file. By convention, reads from unwritten parts of a file return zeroes.

File-based backup and archiving programs like tar, cpio, and rsync can detect runs of zeroes and ignore them, so that the archives they produce aren't filled with zeroes. Still, this is terribly inefficient. If you're a backup program, what you really want is a list of the non-zero segments in the file. But historically there's been no way to get this information without looking at filesystem-specific metadata.

Desperately seeking segments

As part of the ZFS project, we introduced two general extensions to lseek(2): SEEK_HOLE and SEEK_DATA. These allow you to quickly discover the non-zero regions of sparse files. Quoting from the new man page:

       o  If whence is SEEK_HOLE, the offset of the start of  the
          next  hole greater than or equal to the supplied offset
          is returned. The definition of a hole is provided  near
          the end of the DESCRIPTION.

       o  If whence is SEEK_DATA, the file pointer is set to  the
          start  of the next non-hole file region greater than or
          equal to the supplied offset.


     A "hole" is defined as a contiguous  range  of  bytes  in  a
     file,  all  having the value of zero, but not all zeros in a
     file are guaranteed to be represented as holes returned with
     SEEK_HOLE. Filesystems are allowed to expose ranges of zeros
     with SEEK_HOLE, but not required to.  Applications  can  use
     SEEK_HOLE  to  optimise  their behavior for ranges of zeros,
     but must not depend on it to find all such ranges in a file.
     The  existence  of  a  hole  at the end of every data region
     allows for easy programming and implies that a virtual  hole
     exists  at  the  end  of  the  file. Applications should use
     fpathconf(_PC_MIN_HOLE_SIZE) or  pathconf(_PC_MIN_HOLE_SIZE)
     to  determine if a filesystem supports SEEK_HOLE. See fpath-

     For filesystems that do not supply information about  holes,
     the file will be represented as one entire data region.

Any filesystem can support SEEK_HOLE / SEEK_DATA. Even a filesystem like UFS, which has no special support for sparseness, can walk its block pointers much faster than it can copy out a bunch of zeroes. Even if the search algorithm is linear-time, at least the constant is thousands of times smaller.

Sparse file navigation in ZFS

A file in ZFS is a tree of blocks. To maximize the performance of SEEK_HOLE and SEEK_DATA, ZFS maintains in each block pointer a fill count indicating the number of data blocks beneath it in the tree (see below). Fill counts reveal where holes and data reside, so that ZFS can find both holes and data in guaranteed logarithmic time.

  L4                           6
  L3          5                0                1
         -----------      -----------      -----------
  L2     3    0    2                       0    0    1
        ---  ---  ---                     ---  ---  ---
  L1    111       101                               001

An indirect block tree for a sparse file, showing the fill count in each block pointer. In this example there are three block pointers per indirect block. The lowest-level (L1) block pointers describe either one block of data or one block-sized hole, indicated by a fill count of 1 or 0, respectively. At L2 and above, each block pointer's fill count is the sum of the fill counts of its children. At any level, a non-zero fill count indicates that there's data below. A fill count less than the maximum (3L-1 in this example) indicates that there are holes below. From any offset in the file, ZFS can find the next hole or next data in logarithmic time by following the fill counts in the indirect block tree.


At this writing, SEEK_HOLE and SEEK_DATA are Solaris-specific. I encourage (implore? beg?) other operating systems to adopt these lseek(2) extensions verbatim (100% tax-free) so that sparse file navigation becomes a ubiquitous feature that every backup and archiving program can rely on. It's long overdue.

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Thursday Dec 08, 2005

ZFS End-to-End Data Integrity

The job of any filesystem boils down to this: when asked to read a block, it should return the same data that was previously written to that block. If it can't do that -- because the disk is offline or the data has been damaged or tampered with -- it should detect this and return an error.

Incredibly, most filesystems fail this test. They depend on the underlying hardware to detect and report errors. If a disk simply returns bad data, the average filesystem won't even detect it.

Even if we could assume that all disks were perfect, the data would still be vulnerable to damage in transit: controller bugs, DMA parity errors, and so on. All you'd really know is that the data was intact when it left the platter. If you think of your data as a package, this would be like UPS saying, "We guarantee that your package wasn't damaged when we picked it up." Not quite the guarantee you were looking for.

In-flight damage is not a mere academic concern: even something as mundane as a bad power supply can cause silent data corruption.

Arbitrarily expensive storage arrays can't solve the problem. The I/O path remains just as vulnerable, but becomes even longer: after leaving the platter, the data has to survive whatever hardware and firmware bugs the array has to offer.

And if you're on a SAN, you're using a network designed by disk firmware writers. God help you.

What to do? One option is to store a checksum with every disk block. Most modern disk drives can be formatted with sectors that are slightly larger than the usual 512 bytes -- typically 520 or 528. These extra bytes can be used to hold a block checksum. But making good use of this checksum is harder than it sounds: the effectiveness of a checksum depends tremendously on where it's stored and when it's evaluated.

In many storage arrays (see the Dell|EMC PowerVault paper for a typical example with an excellent description of the issues), the data is compared to its checksum inside the array. Unfortunately this doesn't help much. It doesn't detect common firmware bugs such as phantom writes (the previous write never made it to disk) because the data and checksum are stored as a unit -- so they're self-consistent even when the disk returns stale data. And the rest of the I/O path from the array to the host remains unprotected. In short, this type of block checksum provides a good way to ensure that an array product is not any less reliable than the disks it contains, but that's about all.

NetApp's block-appended checksum approach appears similar but is in fact much stronger. Like many arrays, NetApp formats its drives with 520-byte sectors. It then groups them into 8-sector blocks: 4K of data (the WAFL filesystem blocksize) and 64 bytes of checksum. When WAFL reads a block it compares the checksum to the data just like an array would, but there's a key difference: it does this comparison after the data has made it through the I/O path, so it validates that the block made the journey from platter to memory without damage in transit.

This is a major improvement, but it's still not enough. A block-level checksum only proves that a block is self-consistent; it doesn't prove that it's the right block. Reprising our UPS analogy, "We guarantee that the package you received is not damaged. We do not guarantee that it's your package."

The fundamental problem with all of these schemes is that they don't provide fault isolation between the data and the checksum that protects it.

ZFS Data Authentication

End-to-end data integrity requires that each data block be verified against an independent checksum, after the data has arrived in the host's memory. It's not enough to know that each block is merely consistent with itself, or that it was correct at some earlier point in the I/O path. Our goal is to detect every possible form of damage, including human mistakes like swapping on a filesystem disk or mistyping the arguments to dd(1). (Have you ever typed "of=" when you meant "if="?)

A ZFS storage pool is really just a tree of blocks. ZFS provides fault isolation between data and checksum by storing the checksum of each block in its parent block pointer -- not in the block itself. Every block in the tree contains the checksums for all its children, so the entire pool is self-validating. [The uberblock (the root of the tree) is a special case because it has no parent; more on how we handle that in another post.]

When the data and checksum disagree, ZFS knows that the checksum can be trusted because the checksum itself is part of some other block that's one level higher in the tree, and that block has already been validated.

ZFS uses its end-to-end checksums to detect and correct silent data corruption. If a disk returns bad data transiently, ZFS will detect it and retry the read. If the disk is part of a mirror or RAID-Z group, ZFS will both detect and correct the error: it will use the checksum to determine which copy is correct, provide good data to the application, and repair the damaged copy.

As always, note that ZFS end-to-end data integrity doesn't require any special hardware. You don't need pricey disks or arrays, you don't need to reformat drives with 520-byte sectors, and you don't have to modify applications to benefit from it. It's entirely automatic, and it works with cheap disks.

But wait, there's more!

The blocks of a ZFS storage pool form a Merkle tree in which each block validates all of its children. Merkle trees have been proven to provide cryptographically-strong authentication for any component of the tree, and for the tree as a whole. ZFS employs 256-bit checksums for every block, and offers checksum functions ranging from the simple-and-fast fletcher2 (the default) to the slower-but-secure SHA-256. When using a cryptographic hash like SHA-256, the uberblock checksum provides a constantly up-to-date digital signature for the entire storage pool.

Which comes in handy if you ask UPS to move it.

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Thursday Nov 17, 2005


The original promise of RAID (Redundant Arrays of Inexpensive Disks) was that it would provide fast, reliable storage using cheap disks. The key point was cheap; yet somehow we ended up here. Why?

RAID-5 (and other data/parity schemes such as RAID-4, RAID-6, even-odd, and Row Diagonal Parity) never quite delivered on the RAID promise -- and can't -- due to a fatal flaw known as the RAID-5 write hole. Whenever you update the data in a RAID stripe you must also update the parity, so that all disks XOR to zero -- it's that equation that allows you to reconstruct data when a disk fails. The problem is that there's no way to update two or more disks atomically, so RAID stripes can become damaged during a crash or power outage.

To see this, suppose you lose power after writing a data block but before writing the corresponding parity block. Now the data and parity for that stripe are inconsistent, and they'll remain inconsistent forever (unless you happen to overwrite the old data with a full-stripe write at some point). Therefore, if a disk fails, the RAID reconstruction process will generate garbage the next time you read any block on that stripe. What's worse, it will do so silently -- it has no idea that it's giving you corrupt data.

There are software-only workarounds for this, but they're so slow that software RAID has died in the marketplace. Current RAID products all do the RAID logic in hardware, where they can use NVRAM to survive power loss. This works, but it's expensive.

There's also a nasty performance problem with existing RAID schemes. When you do a partial-stripe write -- that is, when you update less data than a single RAID stripe contains -- the RAID system must read the old data and parity in order to compute the new parity. That's a huge performance hit. Where a full-stripe write can simply issue all the writes asynchronously, a partial-stripe write must do synchronous reads before it can even start the writes.

Once again, expensive hardware offers a solution: a RAID array can buffer partial-stripe writes in NVRAM while it's waiting for the disk reads to complete, so the read latency is hidden from the user. Of course, this only works until the NVRAM buffer fills up. No problem, your storage vendor says! Just shell out even more cash for more NVRAM. There's no problem your wallet can't solve.

Partial-stripe writes pose an additional problem for a transactional filesystem like ZFS. A partial-stripe write necessarily modifies live data, which violates one of the rules that ensures transactional semantics. (It doesn't matter if you lose power during a full-stripe write for the same reason that it doesn't matter if you lose power during any other write in ZFS: none of the blocks you're writing to are live yet.)

If only we didn't have to do those evil partial-stripe writes...

Enter RAID-Z.

RAID-Z is a data/parity scheme like RAID-5, but it uses dynamic stripe width. Every block is its own RAID-Z stripe, regardless of blocksize. This means that every RAID-Z write is a full-stripe write. This, when combined with the copy-on-write transactional semantics of ZFS, completely eliminates the RAID write hole. RAID-Z is also faster than traditional RAID because it never has to do read-modify-write.

Whoa, whoa, whoa -- that's it? Variable stripe width? Geez, that seems pretty obvious. If it's such a good idea, why doesn't everybody do it?

Well, the tricky bit here is RAID-Z reconstruction. Because the stripes are all different sizes, there's no simple formula like "all the disks XOR to zero." You have to traverse the filesystem metadata to determine the RAID-Z geometry. Note that this would be impossible if the filesystem and the RAID array were separate products, which is why there's nothing like RAID-Z in the storage market today. You really need an integrated view of the logical and physical structure of the data to pull it off.

But wait, you say: isn't that slow? Isn't it expensive to traverse all the metadata? Actually, it's a trade-off. If your storage pool is very close to full, then yes, it's slower. But if it's not too close to full, then metadata-driven reconstruction is actually faster because it only copies live data; it doesn't waste time copying unallocated disk space.

But far more important, going through the metadata means that ZFS can validate every block against its 256-bit checksum as it goes. Traditional RAID products can't do this; they simply XOR the data together blindly.

Which brings us to the coolest thing about RAID-Z: self-healing data. In addition to handling whole-disk failure, RAID-Z can also detect and correct silent data corruption. Whenever you read a RAID-Z block, ZFS compares it against its checksum. If the data disks didn't return the right answer, ZFS reads the parity and then does combinatorial reconstruction to figure out which disk returned bad data. It then repairs the damaged disk and returns good data to the application. ZFS also reports the incident through Solaris FMA so that the system administrator knows that one of the disks is silently failing.

Finally, note that RAID-Z doesn't require any special hardware. It doesn't need NVRAM for correctness, and it doesn't need write buffering for good performance. With RAID-Z, ZFS makes good on the original RAID promise: it provides fast, reliable storage using cheap, commodity disks.

For a real-world example of RAID-Z detecting and correcting silent data corruption on flaky hardware, check out Eric Lowe's SATA saga.

The current RAID-Z algorithm is single-parity, but the RAID-Z concept works for any RAID flavor. A double-parity version is in the works.

One last thing that fellow programmers will appreciate: the entire RAID-Z implementation is just 599 lines.

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