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Re: [dm-devel] Announcement: STEC EnhanceIO SSD caching software for Linux kernel



> 
> On Thu, Jan 17, 2013 at 05:52:00PM +0800, Amit Kale wrote:
> > Hi Joe, Kent,
> >
> > [Adding Kent as well since bcache is mentioned below as one of the
> > contenders for being integrated into mainline kernel.]
> >
> > My understanding is that these three caching solutions all have three
> principle blocks.
> 
> Let me try and explain how dm-cache works.
> 
> > 1. A cache block lookup - This refers to finding out whether a block
> was cached or not and the location on SSD, if it was.
> 
> Of course we have this, but it's part of the policy plug-in.  I've done
> this because the policy nearly always needs to do some book keeping
> (eg, update a hit count when accessed).
> 
> > 2. Block replacement policy - This refers to the algorithm for
> replacing a block when a new free block can't be found.
> 
> I think there's more than just this.  These are the tasks that I hand
> over to the policy:
> 
>   a) _Which_ blocks should be promoted to the cache.  This seems to be
>      the key decision in terms of performance.  Blindly trying to
>      promote every io or even just every write will lead to some very
>      bad performance in certain situations.
> 
>      The mq policy uses a multiqueue (effectively a partially sorted
>      lru list) to keep track of candidate block hit counts.  When
>      candidates get enough hits they're promoted.  The promotion
>      threshold his periodically recalculated by looking at the hit
>      counts for the blocks already in the cache.

Multi-queue algorithm typically results in a significant metadata overhead. How much percentage overhead does that imply?

> 
>      The hit counts should degrade over time (for some definition of
>      time; eg. io volume).  I've experimented with this, but not yet
>      come up with a satisfactory method.
> 
>      I read through EnhanceIO yesterday, and think this is where
>      you're lacking.

We have an LRU policy at a cache set level. Effectiveness of the LRU policy depends on the average duration of a block in a working dataset. If the average duration is small enough so a block is most of the times "hit" before it's chucked out, LRU works better than any other policies.

> 
>   b) When should a block be promoted.  If you're swamped with io, then
>      adding copy io is probably not a good idea.  Current dm-cache
>      just has a configurable threshold for the promotion/demotion io
>      volume.  If you or Kent have some ideas for how to approximate
>      the bandwidth of the devices I'd really like to hear about it.
> 
>   c) Which blocks should be demoted?
> 
>      This is the bit that people commonly think of when they say
>      'caching algorithm'.  Examples are lru, arc, etc.  Such
>      descriptions are fine when describing a cache where elements
>      _have_ to be promoted before they can be accessed, for example a
>      cpu memory cache.  But we should be aware that 'lru' for example
>      really doesn't tell us much in the context of our policies.
> 
>      The mq policy uses a blend of lru and lfu for eviction, it seems
>      to work well.
> 
> A couple of other things I should mention; dm-cache uses a large block
> size compared to eio.  eg, 64k - 1m.  This is a mixed blessing;

Yes. We had a lot of debate internally on the block size. For now we have restricted to 2k, 4k and 8k. We found that larger block sizes result in too much of internal fragmentation, in-spite of a significant reduction in metadata size. 8k is adequate for Oracle and mysql.

> 
>  - our copy io is more efficient (we don't have to worry about
>    batching migrations together so much.  Something eio is careful to
>    do).
> 
>  - we have fewer blocks to hold stats about, so can keep more info per
>    block in the same amount of memory.
> 
>  - We trigger more copying.  For example if an incoming write triggers
>    a promotion from the origin to the cache, and the io covers a block
>    we can avoid any copy from the origin to cache.  With a bigger
>    block size this optmisation happens less frequently.
> 
>  - We waste SSD space.  eg, a 4k hotspot could trigger a whole block
>    to be moved to the cache.
> 
> 
> We do not keep the dirty state of cache blocks up to date on the
> metadata device.  Instead we have a 'mounted' flag that's set in the
> metadata when opened.  When a clean shutdown occurs (eg, dmsetup
> suspend my-cache) the dirty bits are written out and the mounted flag
> cleared.  On a crash the mounted flag will still be set on reopen and
> all dirty flags degrade to 'dirty'.  

Not sure I understand this. Is there a guarantee that once an IO is reported as "done" to upstream layer (filesystem/database/application), it is persistent. The persistence should be guaranteed even if there is an OS crash immediately after status is reported. Persistence should be guaranteed for the entire IO range. The next time the application tries to read it, it should get updated data, not stale data.

> Correct me if I'm wrong, but I
> think eio is holding io completion until the dirty bits have been
> committed to disk?

That's correct. In addition to this, we try to batch metadata updates if multiple IOs occur in the same cache set.

> 
> I really view dm-cache as a slow moving hotspot optimiser.  Whereas I
> think eio and bcache are much more of a heirarchical storage approach,
> where writes go through the cache if possible?

Generally speaking, yes. EIO contains dirty data limits to avoid the situation where too much of the HDD is used for storing dirty data reducing the effectiveness of the cache for reads.

> 
> > 3. IO handling - This is about issuing IO requests to SSD and HDD.
> 
>   I get most of this for free via dm and kcopyd.  I'm really keen to
>   see how bcache does; it's more invasive of the block layer, so I'm
>   expecting it to show far better performance than dm-cache.
> 
> > 4. Dirty data clean-up algorithm (for write-back only) - The dirty
>   data clean-up algorithm decides when to write a dirty block in an
>   SSD to its original location on HDD and executes the copy.
> 
>   Yep.
> 
> > When comparing the three solutions we need to consider these aspects.
> 
> > 1. User interface - This consists of commands used by users for
>   creating, deleting, editing properties and recovering from error
>   conditions.
> 
>   I was impressed how easy eio was to use yesterday when I was playing
>   with it.  Well done.
> 
>   Driving dm-cache through dm-setup isn't much more of a hassle
>   though.  Though we've decided to pass policy specific params on the
>   target line, and tweak via a dm message (again simple via dmsetup).
>   I don't think this is as simple as exposing them through something
>   like sysfs, but it is more in keeping with the device-mapper way.

You have the benefit of using a well-know dm interface.

> 
> > 2. Software interface - Where it interfaces to Linux kernel and
> applications.
> 
>   See above.
> 
> > 3. Availability - What's the downtime when adding, deleting caches,
>   making changes to cache configuration, conversion between cache
>   modes, recovering after a crash, recovering from an error condition.
> 
>   Normal dm suspend, alter table, resume cycle.  The LVM tools do this
>   all the time.

Cache creation and deletion will require stopping applications, unmounting filesystems and then remounting and starting the applications. A sysad in addition to this will require updating fstab entries. Do fstab entries work automatically in case they use labels instead of full device paths.

Same with changes to cache configuration.

> 
> > 4. Security - Security holes, if any.
> 
>   Well I saw the comment in your code describing the security flaw you
>   think you've got.  I hope we don't have any, I'd like to understand
>   your case more.

Could you elaborate on which comment you are referring to? Since all of the three caching solutions allow only root user an access, my belief is that there are no security holes. I have listed it here as it's an important consideration for enterprise users.

> 
> > 5. Portability - Which HDDs, SSDs, partitions, other block devices it
> works with.
> 
>   I think we all work with any block device.  But eio and bcache can
>   overlay any device node, not just a dm one.  As mentioned in earlier
>   email I really think this is a dm issue, not specific to dm-cache.

DM was never meant to be cascaded. So it's ok for DM.

We recommend our customers to use a RAID for SSD when running writeback. This is because an SSD failure leads to a catastrophic data loss (dirty data). We support using an md device as a SSD. There are some issues with md devices for the code published in github. I'll get back with a code fix next week.

> 
> > 6. Persistence of cache configuration - Once created does the cache
>   configuration stay persistent across reboots. How are changes in
>   device sequence or numbering handled.
> 
>   We've gone for no persistence of policy parameters.  Instead
>   everything is handed into the kernel when the target is setup.  This
>   decision was made by the LVM team who wanted to store this
>   information themselves (we certainly shouldn't store it in two
>   places at once).  I don't feel strongly either way, and could
>   persist the policy params v. easily (eg, 1 days work).

Storing persistence information in a single place makes sense. 
> 
>   One thing I do provide is a 'hint' array for the policy to use and
>   persist.  The policy specifies how much data it would like to store
>   per cache block, and then writes it on clean shutdown (hence 'hint',
>   it has to cope without this, possibly with temporarily degraded
>   performance).  The mq policy uses the hints to store hit counts.
> 
> > 7. Persistence of cached data - Does cached data remain across
>   reboots/crashes/intermittent failures. Is the "sticky"ness of data
>   configurable.
> 
>   Surely this is a given?  A cache would be trivial to write if it
>   didn't need to be crash proof.

There has to be a way to make it either persistent or volatile depending on how users want it. Enterprise users are sometimes paranoid about HDD and SSD going out of sync after a system shutdown and before a bootup. This is typically for large complicated iSCSI based shared HDD setups.

> 
> > 8. SSD life - Projected SSD life. Does the caching solution cause
>   too much of write amplification leading to an early SSD failure.
> 
>   No, I decided years ago that life was too short to start optimising
>   for specific block devices.  By the time you get it right the
>   hardware characteristics will have moved on.  Doesn't the firmware
>   on SSDs try and even out io wear these days?

That's correct. We don't have to worry about wear leveling. All of the competent SSDs around do that.

What I wanted to bring up was how many SSD writes does a cache read/write result. Write back cache mode is specifically taxing on SSDs in this aspect.

>   That said I think we evenly use the SSD.  Except for the superblock
>   on the metadata device.
> 
> > 9. Performance - Throughput is generally most important. Latency is
>   also one more performance comparison point. Performance under
>   different load classes can be measured.
> 
>   I think latency is more important than throughput.  Spindles are
>   pretty good at throughput.  In fact the mq policy tries to spot when
>   we're doing large linear ios and stops hit counting; best leave this
>   stuff on the spindle.

I disagree. Latency is taken care of automatically when the number of application threads rises.

> 
> > 10. ACID properties - Atomicity, Concurrency, Idempotent,
>   Durability. Does the caching solution have these typical
>   transactional database or filesystem properties. This includes
>   avoiding torn-page problem amongst crash and failure scenarios.
> 
>   Could you expand on the torn-page issue please?

Databases run into torn-page error when an IO is found to be only partially written when it was supposed to be fully written. This is particularly important when an IO was reported to be "done". The original flashcache code we started with over an year ago showed torn-page problem in extremely rare crashes with writeback mode. Our present code contains specific design elements to avoid it.

> 
> > 11. Error conditions - Handling power failures, intermittent and
> permanent device failures.
> 
>   I think the area where dm-cache is currently lacking is intermittent
>   failures.  For example if a cache read fails we just pass that error
>   up, whereas eio sees if the block is clean and if so tries to read
>   off the origin.  I'm not sure which behaviour is correct; I like to
>   know about disk failure early.

Our read-only and write-through mode guarantee that no io errors are introduced regardless of the state SSD is in. So not retrying an io error doesn't cause any future problems. The worst case is a performance hit when an SSD shows an io error or goes completely bad.

It's a different story for write-back. We advise our customers to use RAID on SSD when using write-back as explained above.

> 
> > 12. Configuration parameters for tuning according to applications.
> 
>   Discussed above.
> 
> > We'll soon document EnhanceIO behavior in context of these
>   aspects. We'll appreciate if dm-cache and bcache is also documented.
> 
>   I hope the above helps.  Please ask away if you're unsure about
>   something.
> 
> > When comparing performance there are three levels at which it can be
> > measured
> 
> Developing these caches is tedious.  Test runs take time, and really
> slow the dev cycle down.  So I suspect we've all been using
> microbenchmarks that run in a few minutes.
> 
> Let's get our pool of microbenchmarks together, then work on some
> application level ones (we're happy to put some time into developing
> these).

We do run micro-benchmarks all the time. There are free database benchmarks, so we can try these. Running a full-fledged oracle based benchmarks takes hours, so I am not sure whether I can post that kind of a comparison. Will try to do the best possible.

Thanks.
-Amit

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