I would consider to use wide rows. If you add timestamp to your column name you have naturally sorted data. You can easily select any time range without any indexes.
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.
We 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 database.
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?