Al 06/09/13 20:43, En/na Anand Avati ha escrit:
On Fri, Sep 6, 2013 at 1:46 AM, Xavier Hernandez
<xhernandez@xxxxxxxxxx <mailto:xhernandez@xxxxxxxxxx>> 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@xxxxxxxxxx <mailto:xhernandez@xxxxxxxxxx>> 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.
