List:General Discussion« Previous MessageNext Message »
From:Rick James Date:July 26 2013 7:31pm
Subject:RE: hypothetical question about data storage
View as plain text  
"Count the disk hits"

If you have a filesystem directory, consider that it is designed to handle small numbers
of files per directory.  Consider that there is a limited cache for directories, etc. 
Plus there is the inode (vnode, whatever) storage for each file.  I don't know the details
(and it varies wildly with "filesystem" (ext, xfs, zfs, etc)).

Looking at InnoDB...

Let's say you have a billion rows in a single table, and you need to fetch one row by the
PRIMARY KEY, and it is a MD5 (sha-1, UUID, etc).  Such a key is _very_ random.

A billion rows would need about 5 levels of BTree.  The top levels would quickly all be
cached.  (100M blocks * 16KB = 1.6GB.)  If the leaf nodes add up to 200GB, that is
probably bigger than you innodb_buffer_pool_size.  In that case, a _random_ fetch is
likely to be a cache miss.

A cache miss is about 100ms on normal rotating-media; perhaps 10ms on SSDs.  This limits
your reads to 10 (or 100) per second.

If you have big BLOBs in the table, then it gets messier.  InnoDB does not put more than
8K of a row in the actual 16KB block.  The rest is stored in another block(s).  So, it is
likely to take an extra disk hit (200ms/20ms).

If your data size is 100 times as big as your buffer pool, then it becomes likely that the
next level of the BTree won't be fully cacheable.  Now 300ms/30ms.

I think it is likely that the small number of disk hits for InnoDB is better than the many
disk hits for traversing a directory tree (with large directories) in the filesystem.  I
vote for InnoDB over the directory tree.

Yes, you will have seeks.
No, adding more RAM won't help much.  Here's an argument:
Suppose your data is 20 times as big as the buffer pool and you are doing random fetches
(MD5, etc).  Then 1/20 of fetches are cached; 95% cache miss.  Estimated time: 0.95 *
100ms = 95ms.
Now you double your RAM.  1/10 cached -> 90% cache miss -> 90ms average -> Not
much improvement over 95.

> -----Original Message-----
> From: cknipe@stripped [mailto:cknipe@stripped] On Behalf Of
> Chris Knipe
> Sent: Friday, July 26, 2013 12:30 AM
> To: Johan De Meersman
> Cc: mysql
> Subject: Re: hypothetical question about data storage
> Hi All,
> Thanks for the responces, and I do concur.  I was taking a stab in the
> dark so to speak.
> We are working with our hosting providers currently and will be
> introducing a multitude of small iSCSI SANs to split the storage
> structure over a multitude of disks...   This is something that needs
> to be addressed from a systems perspective rather than an architectural
> one.
> SSD (or Fusion and the like) are unfortunately still way to expensive for
> the capacity that we require (good couple of TBs) - so mechanical disks it
> would need to be.  However, with the use of SANs as we hope, we should be
> able to go up from 4 to over 64 spindles whilst still being able to share
> the storage and have redundancy.
> Many thanks for the inputs and feedbacks...
> --
> C
> On Fri, Jul 26, 2013 at 9:23 AM, Johan De Meersman <vegivamp@stripped>
> wrote:
> > Hey Chris,
> >
> > I'm afraid that this is not what databases are for, and the first thing
> you'll likely run into is amount of concurrent connections.
> >
> > This is typically something you should really tackle from a systems
> perspective. Seek times are dramatically improved on SSD or similar
> storage - think FusionIO cards, but there's also a couple of vendors
> (Violin comes to mind) who provide full-blown SSD SANs.
> >
> > If you prefer staying with spinning disks, you could still improve the
> seeks by focusing on the inner cylinders and potentially by using variable
> sector formatting. Again, there's SANs that do this for you.
> >
> > Another minor trick is to turn off access timestamp updates when you
> mount the filesystem (noatime).
> >
> > Also benchmark different filesystems, there's major differences between
> them. I've heard XFS being recommended, but I've never needed to benchmark
> for seek times myself. We're using IBM's commercial GPFS here, which is
> good with enormous amounts of huge files (media farm here), not sure how
> it'd fare with smaller files.
> >
> > Hope that helps,
> > Johan
> >
> > ----- Original Message -----
> >> From: "Chris Knipe" <savage@stripped>
> >> To: mysql@stripped
> >> Sent: Thursday, 25 July, 2013 11:53:53 PM
> >> Subject: hypothetical question about data storage
> >>
> >> Hi all,
> >>
> >> We run an VERY io intensive file application service.  Currently, our
> >> problem is that our disk spindles are being completely killed due to
> >> insufficient SEEK time on the hard drives (NOT physical read/write
> >> speeds).
> >>
> >> We have an directory structure where the files are stored based on
> >> the MD5 checksum of the file name, i.e.
> >> /0/00/000/000044533779fce5cf3497f87de1d060
> >> The majority of these files, are between 256K and 800K with the ODD
> >> exception (say less than 15%) being more than 1M but no more than 5M
> >> in size.  The content of the files are pure text (MIME Encoded).
> >>
> >> We believe that storing these files into an InnoDB table, may
> >> actually give us better performance:
> >> - There is one large file that is being read/written, instead of
> >> BILLIONS of small files
> >> - We can split the structure so that each directory (4096 in total)
> >> sit's on their own database
> >> - We can move the databases as load increases, which means that we
> >> can potentially run 2 physical database servers, each with 2048
> >> databases
> >> each)
> >> - It's easy to move / migrate the data due to mysql and replication -
> >> same can be said for redundancy of the data
> >>
> >> We are more than likely looking at BLOB columns of course, and we
> >> need to read/write from the DB in excess of 100mbit/s
> >>
> >> Would the experts consider something like this as being feasible?  Is
> >> it worth it to go down this avenue, or are we just going to run into
> >> different problems?  If we are facing different problems, what can we
> >> possibly expect to go wrong here?
> >>
> >> Many thanks, and I look forward to any input.
> >>
> >
> > --
> > Unhappiness is discouraged and will be corrected with kitten pictures.
> --
> Regards,
> Chris Knipe
> --
> MySQL General Mailing List
> For list archives:
> To unsubscribe:

hypothetical question about data storageChris Knipe25 Jul
  • Re: hypothetical question about data storageVahric Muhtaryan25 Jul
  • Re: hypothetical question about data storageJohan De Meersman26 Jul
    • Re: hypothetical question about data storageChris Knipe26 Jul
      • RE: hypothetical question about data storageRick James26 Jul
        • RE: hypothetical question about data storageJohan De Meersman26 Jul
          • Re: hypothetical question about data storageChris Knipe26 Jul
            • Re: hypothetical question about data storagehsv27 Jul
            • Re: hypothetical question about data storagewilliam drescher27 Jul
              • RE: hypothetical question about data storageRick James29 Jul
                • RE: hypothetical question about data storageJohan De Meersman29 Jul
                  • RE: hypothetical question about data storageRick James29 Jul
                    • Re: hypothetical question about data storageCarsten Pedersen30 Jul
                    • Re: hypothetical question about data storageManuel Arostegui30 Jul
                    • RE: hypothetical question about data storageJohan De Meersman30 Jul