Hi all,
I have a problem like this,
I have stock transaction data, as follows.
Ticker data:
Company name:
timestamp:
closing price (N): (V)
trades (N) : (V)
......
.....
......
In my model : I want to execute range queries on timestamps, (sorted order)
approaches currently have in mind,
1. I can have ticker data : columnfamily, company name : rowkey,
timestamp: super column, and other attributes as columns. In this way
there will be around *100 rowkeys*, around *1M timestamps*, around *10
columns under one super column.*
Problems
- Cassandra best practices are to use the RandomPartitioner - this gives
you 'free' load balancing, as long as your tokens are evenly distributed.
so the load balancing would happen on 100 row keys. is this acceptable
approach?
- and there is a possibility to have duplicates in timestamps. that
will be a problem.
2. I can have ticker data : keyspace, company name : column family,
timestamp: row key, and other attributes as columns. In this way there
will be around *100 column families*, around *1M row keys*, around *10
columns per one row.*
Problems
- In this way, range queries are not in sorted order.
- and I guess there is also duplicate row key problem
Any suggestions how I can overcome this?
Cheers,
Rajith
