incubator-cassandra-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Pradeep Kumar Mantha <pradeep...@gmail.com>
Subject Re: Pycassa vs YCSB results.
Date Tue, 05 Feb 2013 18:13:34 GMT
Thanks, I will use the multiprocessing package, since I need to scale it to
multiple nodes.

I will also try to optimize the function calls and use global variables.

Thank you very much for your help.



On Tue, Feb 5, 2013 at 9:12 AM, aaron morton <aaron@thelastpickle.com>wrote:

> The simple thing to do would be use the multiprocessing package and
> eliminate all shared state.
>
> On a multicore box python threads can run on different cores and battle
> over obtaining the GIL.
>
> Cheers
>
>    -----------------
> Aaron Morton
> Freelance Cassandra Developer
> New Zealand
>
> @aaronmorton
> http://www.thelastpickle.com
>
> On 5/02/2013, at 11:34 PM, Tim Wintle <timwintle@gmail.com> wrote:
>
> On Tue, 2013-02-05 at 21:38 +1300, aaron morton wrote:
>
> The first thing I noticed is your script uses python threading library,
> which is hampered by the Global Interpreter Lock
> http://docs.python.org/2/library/threading.html
>
> You don't really have multiple threads running in parallel, try using the
> multiprocessor library.
>
>
> Python _should_ release the GIL around IO-bound work, so this is a
> situation where the GIL shouldn't be an issue (It's actually a very good
> use for python's threads as there's no serialization overhead for
> message passing between processes as there would be in most
> multi-process examples)
>
>
> A constant factor 2 slowdown really doesn't seem that significant for
> two different implementations, and I would not worry about this unless
> you're talking about thousands of machines..
>
> If you are talking about enough machines that this is real $$$, then I
> do think the python code can be optimised a lot.
>
> I'm talking about language/VM specific optimisations - so I'm assuming
> cpython (the standard /usr/bin/python as in the shebang).
>
> I don't know how much of a difference this will make, but I'd be
> interested in hearing your results:
>
>
> I would start by trying rewriting this:
>
>  def start_cassandra_client(Threadname):
>    f=open(Threadname,"w")
>    for key in lines:
>      key=key.strip()
>      st=time.time()
>      f.write(str(cf.get(key))+"\n")
>      et=time.time()
>      f.write("Time taken for a single query is " +
> str(round(1000*(et-st),2))+" milli secs\n")
>      f.close()
>
> As something like this:
>
>  def start_cassandra_client(Threadname):
>    # Avoid variable names outside this scope
>    time_fn = time.time
>    colfam = cf
>    f=open(Threadname,"w")
>    for key in lines:
>      key=key.strip()
>      st=time_fn()
>      f.write(str(colfam.get(key))+"\n")
>      et=time_fn()
>      f.write("Time taken for a single query is " +
> str(round(1000*(et-st),2))+" milli secs\n")
>      f.close()
>
>
> If you don't consider it cheating compared to the java version, I would
> also move the "key.strip()" call to the module initiation instead of
> doing it once per thread, as there's a lot of function dispatch overhead
> in python.
>
>
> I'd also closely compare the IO going on in both versions (the .write
> calls). For example this may be significantly faster:
>
>      et=time_fn()
>      f.write(str(colfam.get(key))+"\nTime taken for a single query is "
> + str(round(1000*(et-st),2))+" milli secs\n")
>
>
> .. I haven't read your java code and I don't know Java IO semantics well
> enough to compare the behaviour of both.
>
> Tim
>
>
>
>
>
> Cheers
>
> -----------------
> Aaron Morton
> Freelance Cassandra Developer
> New Zealand
>
> @aaronmorton
> http://www.thelastpickle.com
>
> On 5/02/2013, at 7:15 AM, Pradeep Kumar Mantha <pradeepm66@gmail.com>
> wrote:
>
> Hi,
>
> Could some one please let me know any hints, why the pycassa
> client(attached) is much slower than the YCSB?
> is it something to attribute to performance difference between python and
> Java? or the pycassa api has some performance limitations?
>
> I don't see any client statements affecting the pycassa performance.
> Please have a look at the simple python script attached and let me know
> your suggestions.
>
> thanks
> pradeep
>
> On Thu, Jan 31, 2013 at 4:53 PM, Pradeep Kumar Mantha <
> pradeepm66@gmail.com> wrote:
>
>
> On Thu, Jan 31, 2013 at 4:49 PM, Pradeep Kumar Mantha <
> pradeepm66@gmail.com> wrote:
> Thanks.. Please find the script as attachment.
>
> Just re-iterating.
> Its just a simple python script which submit 4 threads.
> This script has been scheduled on 8 cores using taskset unix command ,
> thus running 32 threads/node.
> and then scaling to 16 nodes
>
> thanks
> pradeep
>
>
> On Thu, Jan 31, 2013 at 4:38 PM, Tyler Hobbs <tyler@datastax.com> wrote:
> Can you provide the python script that you're using?
>
> (I'm moving this thread to the pycassa mailing list (
> pycassa-discuss@googlegroups.com), which is a better place for this
> discussion.)
>
>
> On Thu, Jan 31, 2013 at 6:25 PM, Pradeep Kumar Mantha <
> pradeepm66@gmail.com> wrote:
> Hi,
>
> I am trying to benchmark cassandra on a 12 Data Node cluster using 16
> clients ( each client uses 32 threads) using custom pycassa client and YCSB.
>
> I found the maximum number of operations/seconds achieved using pycassa
> client is nearly 70k+ reads/second.
> Whereas with YCSB it is ~ 120k reads/second.
>
> Any thoughts, why I see this huge difference in performance?
>
>
> Here is the description of setup.
>
> Pycassa client (a simple python script).
> 1. Each pycassa client starts 4 threads - where each thread queries 76896
> queries.
> 2. a shell script is used to submit 4threads/each core using taskset unix
> command on a 8 core single node. ( 8 * 4 * 76896 queries)
> 3. Another shell script is used to scale the single node shell script to
> 16 nodes  ( total queries now - 16 * 8 * 4 * 76896 queries )
>
> I tried to keep YCSB configuration as much as similar to my custom pycassa
> benchmarking setup.
>
> YCSB -
>
> Launched 16 YCSB clients on 16 nodes where each client uses 32 threads for
> execution and need to query ( 32 * 76896 keys ), i.e 100% reads
>
> The dataset is different in each case, but has
>
> 1. same number of total records.
> 2. same number of fields.
> 3. field length is almost same.
>
> Could you please let me know, why I see this huge performance difference
> and is there any way I can improve the operations/second using pycassa
> client.
>
> thanks
> pradeep
>
>
>
>
> --
> Tyler Hobbs
> DataStax
>
>
>
> <pycassa_client.py>
>
>
>
>
>
>

Mime
View raw message