[Gluster-devel] readdir() scalability (was Re: [RFC ] dictionary optimizations)

[Vijay Bellur]

On 09/09/2013 02:18 PM, Xavier Hernandez wrote:
> Al 06/09/13 20:43, En/na Anand Avati ha escrit:
>>
>> On Fri, Sep 6, 2013 at 1:46 AM, Xavier Hernandez
>> <xhernandez at datalab.es <mailto:xhernandez at datalab.es>> wrote:
>>
>>     Al 04/09/13 18:10, En/na Anand Avati ha escrit:
>>>     On Wed, Sep 4, 2013 at 6:37 AM, Xavier Hernandez
>>>     <xhernandez at datalab.es <mailto:xhernandez at datalab.es>> wrote:
>>>
>>>         Al 04/09/13 14:05, En/na Jeff Darcy ha escrit:
>>>
>>>             On 09/04/2013 04:27 AM, Xavier Hernandez wrote:
>>>
>>>                 I would also like to note that each node can store
>>>                 multiple elements.
>>>                 Current implementation creates a node for each byte
>>>                 in the key. In my
>>>                 implementation I only create a node if there is a
>>>                 prefix coincidence between
>>>                 2 or more keys. This reduces the number of nodes and
>>>                 the number of
>>>                 indirections.
>>>
>>>
>>>             Whatever we do, we should try to make sure that the
>>>             changes are profiled
>>>             against real usage.  When I was making my own dict
>>>             optimizations back in March
>>>             of last year, I started by looking at how they're
>>>             actually used. At that time,
>>>             a significant majority of dictionaries contained just one
>>>             item. That's why I
>>>             only implemented a simple mechanism to pre-allocate the
>>>             first data_pair instead
>>>             of doing something more ambitious.  Even then, the
>>>             difference in actual
>>>             performance or CPU usage was barely measurable.  Dict
>>>             usage has certainly
>>>             changed since then, but I think you'd still be hard
>>>             pressed to find a case
>>>             where a single dict contains more than a handful of
>>>             entries, and approaches
>>>             that are optimized for dozens to hundreds might well
>>>             perform worse than simple
>>>             ones (e.g. because of cache aliasing or branch
>>>             misprediction).
>>>
>>>             If you're looking for other optimization opportunities
>>>             that might provide even
>>>             bigger "bang for the buck" then I suggest that
>>>             stack-frame or frame->local
>>>             allocations are a good place to start.  Or string copying
>>>             in places like
>>>             loc_copy.  Or the entire fd_ctx/inode_ctx subsystem.  Let
>>>             me know and I'll come
>>>             up with a few more.  To put a bit of a positive spin on
>>>             things, the GlusterFS
>>>             code offers many opportunities for improvement in terms
>>>             of CPU and memory
>>>             efficiency (though it's surprisingly still way better
>>>             than Ceph in that regard).
>>>
>>>         Yes. The optimizations on dictionary structures are not a big
>>>         improvement in the overall performance of GlusterFS. I tried
>>>         it on a real situation and the benefit was only marginal.
>>>         However I didn't test new features like an atomic lookup and
>>>         remove if found (because I would have had to review all the
>>>         code). I think this kind of functionalities could improve a
>>>         bit more the results I obtained.
>>>
>>>         However this is not the only reason to do these changes.
>>>         While I've been writing code I've found that it's tedious to
>>>         do some things just because there isn't such functions in
>>>         dict_t. Some actions require multiple calls, having to check
>>>         multiple errors and adding complexity and limiting
>>>         readability of the code. Many of these situations could be
>>>         solved using functions similar to what I proposed.
>>>
>>>         On the other side, if dict_t must be truly considered a
>>>         concurrent structure, there are a lot of race conditions that
>>>         might appear when doing some operations. It would require a
>>>         great effort to take care of all these possibilities
>>>         everywhere. It would be better to pack most of these
>>>         situations into functions inside the dict_t itself where it
>>>         is easier to combine some operations.
>>>
>>>         By the way, I've made some tests with multiple bricks and it
>>>         seems that there is a clear speed loss on directory listings
>>>         as the number of bricks increases. Since bricks should be
>>>         independent and they can work in parallel, I didn't expected
>>>         such a big performance degradation.
>>>
>>>
>>>     The likely reason is that, even though bricks are parallel for
>>>     IO, readdir is essentially a sequential operation and DHT has a
>>>     limitation that a readdir reply batch does not cross server
>>>     boundaries. So if you have 10 files and 1 server, all 10 entries
>>>     are returned in one call to the app/libc. If you have 10 files
>>>     and 10 servers evenly distributed, the app/libc has to perform 10
>>>     calls and keeps getting one file at a time. This problem goes
>>>     away when each server has enough files to fill up a readdir
>>>     batch. It's only when you have too few files and too many servers
>>>     that this "dilution" problem shows up. However, this is just a
>>>     theory and your problem may be something else too..
>>>
>>     I didn't know that DHT was doing a sequential brick scan on
>>     readdir(p) (my fault). Why is that ? Why it cannot return entries
>>     crossing a server boundary ? is it due to a technical reason or is
>>     it only due to the current implementation ?
>>
>>     I've made a test using only directories (50 directories with 50
>>     subdirectories each). I started with one brick and I measured the
>>     time to do a recursive 'ls'. Then I sequentially added an
>>     additional brick, up to 6 (all of them physically independent),
>>     and repeated the ls. The time increases linearly as the number of
>>     bricks augments. As more bricks were added, the rebalancing time
>>     was also growing linearly.
>>
>>     I think this is a big problem for scalability. It can be partially
>>     hidden by using some caching or preloading mechanisms, but it will
>>     be there and it will hit sooner or later.
>>
>>
>>>     Note that Brian Foster's readdir-ahead patch should address this
>>>     problem to a large extent. When loaded on top of DHT, the
>>>     prefiller effectively collapses the smaller chunks returned by
>>>     DHT into a larger chunk requested by the app/libc.
>>>
>>     I've seen it, however I think it will only partially mitigate and
>>     hide an existing problem. Imagine you have some hundreds or a
>>     thousand of bricks. I doubt readdir-ahead or anything else can
>>     hide the enormous latency that the sequential DHT scan will
>>     generate in that case.
>>
>>     The main problem I see is that the full directory structure is
>>     read many times sequentially. I think it would be better to do the
>>     readdir(p) calls in parallel and combine them (possibly in
>>     background). This way the time to scan the directory structure
>>     would be almost constant, independently of the number of bricks.
>>
>>
>> The design of the directory entries in DHT makes this essentially a
>> sequential operation because entries from servers are appended, not
>> striped. What I mean is, the logical ordering of
>>
>> All entries in a directory = All files and dirs in 0th server + All
>> files (no dirs) in 1st server + All files (no dirs) in 2nd server + ..
>> + All files (no dirs) in N'th server.
>>
>> in a sequential manner. If we read the entries of 2nd server along
>> with entries of 1st server, we cannot "use" it till we finish reading
>> all entries of 1st server and get EOD from it - which is why
>> readdir-ahead is a more natural solution than reading in parallel for
>> the above design.
>>
> As I understand it, what the read-ahead translator does is to collect
> one or more answers from the DHT translator and combine them to return a
> single answer as big as possible. If that is correct, it will certainly
> reduce the number of readdir calls from application, however I think it
> will still have a considerable latency when used on big clusters. Anyway
> I don't have any measurement or valid argument to support this, so lets
> see how readdir-ahead works in real environments before discussing about it.
>
>> Also, this is a problem only if each server has fewer entries than
>> what can be returned in a single readdir() request by the application.
>> As long as the server has more than this "minimum threshold" of number
>> of files, the number of batched readdir() made by the client is going
>> to be fixed, and those various requests will be spread across various
>> servers (as opposed to, sending them all to the same server).
>>
> I've seen customers with large amounts of empty, or almost empty,
> directories. Don't ask me why, I don't understand it either...
>
>> So yes, as you add servers for a given small set of files the
>> scalability drops, but that is only till you create more files, when
>> the # of servers stop mattering again.
>>
>> Can you share the actual numbers from the tests you ran?
>>
> I've made the tests in 6 physical servers (Quad Atom D525 1.8 GHz. These
> are the only servers I can use regularly to do tests) connected through
> a dedicated 1 Gbit switch. Bricks are stored in 1TB SATA disks with ZFS.
> One of the servers was also used as a client to do the tests.
>
> Initially I created a volume with a single brick. I initialized the
> volume with 50 directories with 50 subdirectories each (a total of 2500
> directories). No files.

Have you tried turning on "cluster.readdir-optimize"? This could help 
improve readdir performance for the directory hierarchy that you describe.

-Vijay


>
> After each test, I added a new brick and started a rebalance. Once the
> rebalance was completed, I umounted and stopped the volume and restarted
> it again.
>
> The test consisted of 4 'time ls -lR /<testdir> | wc -l'. The first
> result was discarded. The result shown below is the mean of the other 3
> results.
>
> 1 brick: 11.8 seconds
> 2 bricks: 19.0 seconds
> 3 bricks: 23.8 seconds
> 4 bricks: 29.8 seconds
> 5 bricks: 34.6 seconds
> 6 bricks: 41.0 seconds
> 12 bricks (2 bricks on each server): 78.5 seconds
>
> The rebalancing time also grew considerably (these times are the result
> of a single rebalance. They might not be very accurate):
>
>  From 1 to 2 bricks: 91 seconds
>  From 2 to 3 bricks: 102 seconds
>  From 3 to 4 bricks: 119 seconds
>  From 4 to 5 bricks: 138 seconds
>  From 5 to 6 bricks: 151 seconds
>  From 6 to 12 bricks: 259 seconds
>
> The number of disk IOPS didn't exceed 40 in any server in any case. The
> network bandwidth didn't go beyond 6 Mbits/s between any pair of servers
> and none of them reached 100% core usage.
>
> Xavi
>
>> Avati
>>
>>
>>
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