I would like to get some opinions on how to select an incremental range
of rows efficiently from a Cassandra CF containing time series data.
have a web application that uses a Cassandra CF as logging storage. We
insert a row into the CF for every "event" of each user of the web
application. The row key is timestamp+userid. The column values are
unstructured data. We only insert rows but never update or delete any
rows in the CF.
The CF grows by about 0.5 million rows per day. We
have a 4 node cluster and use the RandomPartitioner to spread the rows
across the nodes.
There is a need to transfer
the Cassandra data to another relational database periodically. Due to
the large size of the CF, instead of truncating the relational table and
reloading all rows into it each time, we plan to run a job to select
the "delta" rows since the last run and insert them into the relational
We would like to have some flexibility in how often the data
transfer job is done. It may be run several times each day, or it may be
not run at all on a day.
- We are using RandomPartitioner, so range scan by row key is not feasible.
- Add a secondary index on the timestamp column, but reading rows via
secondary index still requires an equality condition and does not
support range scan.
- Add a secondary index on a column containing
the date and hour of the timestamp. Iterate each hour between the time
job was last run and now. Fetch all rows of each hour.
I would appreciate any ideas of other design options of the Cassandra CF to enable extracting the rows efficiently.
Besides Java, has anyone used any ETL tools to do this kind of delta extraction from Cassandra?