If you are using CPython (most likely) remember to use the multiprocessing interface rather than multithreading to avoid the global interpreter lock.
Cheers Ben On Thu, Feb 14, 2013 at 4:35 AM, <ka...@comcast.net> wrote: > I'm not using multi-threads/processes. I'll try multi-threading to see if > I get a boost. > > Thanks. > > Ken.... > > > ------------------------------ > *From: *"Tyler Hobbs" <ty...@datastax.com> > *To: *user@cassandra.apache.org > *Sent: *Wednesday, February 13, 2013 11:06:30 AM > *Subject: *Re: Write performance expectations... > > > 2500 inserts per second is about what a single python thread using pycassa > can do against a local node. Are you using multiple threads for the > inserts? Multiple processes? > > > On Wed, Feb 13, 2013 at 8:21 AM, Alain RODRIGUEZ <arodr...@gmail.com>wrote: > >> Is there a particular reason for you to use EBS ? Instance Store >> are recommended because they improve performances by reducing the I/O >> throttling. >> >> An other thing you should be aware of is that replicating the data to all >> node reduce your performance, it is more or less like if you had only one >> node (at performance level I mean). >> >> Also, writing to different datacenters probably induce some network >> latency. >> >> You should give the EC2 instance type (m1.xlarge / m1.large / ...) if you >> want some feedback about the 2500 w/s, and also give the mean size of your >> rows. >> >> Alain >> >> >> 2013/2/13 <ka...@comcast.net> >> >> Hello, >>> New member here, and I have (yet another) question on write >>> performance. >>> >>> I'm using Apache Cassandra version 1.1, Python 2.7 and Pycassa 1.7. >>> >>> I have a cluster of 2 datacenters, each with 3 nodes, on AWS EC2 using >>> EBS and the RandomPartioner. I'm writing to a column family in a keyspace >>> that's replicated to all nodes in both datacenters, with a consistency >>> level of LOCAL_QUORUM. >>> >>> I'm seeing write performance of around 2500 rows per second. >>> >>> Is this in the ballpark for this kind of configuration? >>> >>> Thanks in advance. >>> >>> Ken.... >>> >>> >> > > > -- > Tyler Hobbs > DataStax <http://datastax.com/> >
