[Gluster-devel] readdir() scalability (was Re: [RFC ] dictionary optimizations)
Xavier Hernandez
xhernandez at datalab.es
Mon Sep 9 08:48:21 UTC 2013
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.
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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