I switched my implementation to use a thread pool of 10 threads each
multi-getting 10 keys/rows. This reduces my time from 50s to 5s for
fetching all 1,000 messages.
I started looking through the Cassandra source to find where the
parallel requests are actually made, and I believe it's in
org.apache.cassandra.service.StorageProxy.java fetchRows, is this
correct? I noticed a number of logger.debug calls, what do I need to
set in my log4j.properties file to see these messages as they would
probably help me determine what is taking so long. Currently my
log4j.properties file looks like this and I'm not seeing these
On Thu, Feb 10, 2011 at 12:53 PM, Bill Speirs <firstname.lastname@example.org
> Each message row is well under 1K. So I don't think it is network... plus
> all boxes are on a fast LAN.
> On Feb 10, 2011 11:59 AM, "Utku Can TopÁu" <email@example.com
>> Dear Bill,
>> How about the size of the row in the Messages CF. Is it too big? Might you
>> be having an overhead of the bandwidth?
>> On Thu, Feb 10, 2011 at 5:00 PM, Bill Speirs <firstname.lastname@example.org
>>> I have a 7 node setup with a replication factor of 1 and a read
>>> consistency of 1 I have two column families: Messages which stores
>>> millions of rows with a UUID for the row key, DateIndex which stores
>>> thousands of rows with a String as the row key. I perform 2 look-ups
>>> for my queries:
>>> 1) Fetch the row from DateIndex that includes the date I'm looking
>>> for. This returns 1,000 columns where the column names are the UUID of
>>> the messages
>>> 2) Do a multi-get (Hector client) using those 1,000 row keys I got
>>> from the first query.
>>> Query 1 is taking ~300ms to fetch 1,000 columns from a single row...
>>> respectable. However, query 2 is taking over 50s to perform 1,000 row
>>> look-ups! Also, when I scale down to 100 row look-ups for query 2, the
>>> time scales in a similar fashion, down to 5s.
>>> Am I doing something wrong here? It seems like taking 5s to look-up
>>> 100 rows in a distributed hash table is way too slow.