I have inputs that are text logs and I wrote a Cassandra OutputFormat, the reducers read the old values from their respective column families, increment the counts and write back the new values. Since all of the writes are done by the hadoop jobs and we're not running multiple jobs concurrently, the writes aren't clobbering any values except the ones from the prior hadoop run. If/when atomic increments are available, we'd be able to run concurrent log processing jobs for but for now, this seems to work. I think the biggest risk is that a reduce task fails, hadoop restarts it and the replacement task re-increments the values. We're going to wait and see to determine how much of a problem this is in practice.
I have a situation where I need to accumulate values in Cassandra on an ongoing basis. Atomic increments are still in the works apparently (see https://issues.apache.org/jira/browse/CASSANDRA-721) so for the time being I'll be using Hadoop, and attempting to feed in both the existing values and the new values to a M/R process where they can be combined together and written back out to Cassandra.The approach I'm taking is to use Hadoop's CombineFileInputFormat to blend the existing data (using Cassandra's ColumnFamilyInputFormat) with the newly incoming data (using something like Hadoop's SequenceFileInputFormat).I was just wondering, has anyone here tried this, and were there issues? I'm worried because the CombineFileInputFormat has restrictions around splits being from different pools so I don't know how this will play out with data from both Cassandra and HDFS. The other option, I suppose, is to use a separate M/R process to replicate the data onto HDFS first, but I'd rather avoid the extra step and duplication of storage.Also, if you've tackled a similar situation in the past using a different approach, I'd be keen to hear about it...ThanksMark