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 <aa...@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 <timwin...@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 <pradeep...@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 < > pradeep...@gmail.com> wrote: > > > On Thu, Jan 31, 2013 at 4:49 PM, Pradeep Kumar Mantha < > pradeep...@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 <ty...@datastax.com> wrote: > Can you provide the python script that you're using? > > (I'm moving this thread to the pycassa mailing list ( > pycassa-disc...@googlegroups.com), which is a better place for this > discussion.) > > > On Thu, Jan 31, 2013 at 6:25 PM, Pradeep Kumar Mantha < > pradeep...@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> > > > > > >
