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

Xavier Hernandez xhernandez at datalab.es
Tue Sep 10 07:38:42 UTC 2013

Al 09/09/13 17:25, En/na Vijay Bellur ha escrit:
> 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.
I used default volume options. I'll repeat the tests with this option 



> -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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