1) Is the problem real?
Yes. For very large-scale cluster system, the server and networking components are failing frequently rather than rare. It is a need to build a software system that treat failure as normal rather than rare.
2) What is the solution’s main idea (nugget)?
For the data model, Cassandra is based on google's bigtable, but Cassandra introduces super column family, which can be regarded as column family within a column family.
The system mainly has following contributions.
1. Achieve dynamically partition over nodes by using an order preserving hash function.
2. Cassandra incorporates Zookeeper to let the leader arrange data replica among nodes.
3. Using Scuttlebutt to enable cluster membership and disseminate other system control state.
4. Cassandra can dynamically adding more nodes into the system.
3) Why is solution different from previous work?
Previous systems like Bayou, Coda, etc. cannot achieve scalability and availability, whereas Cassandra does. In order to provide strong consistency guarantee, these systems cannot handle network partitions.
Compared with Cassandra, Dynamo cannot enable high write throughput.
4) Does the paper (or do you) identify any fundamental/hard trade-offs?
Cassandra's partition is based on preserving hash function, which is a probability partition algorithm. It is simple to be implemented. However, compared with other system like HBase which has a master node for global dynamic data partition, Cassandra's partition mechanism may not achieve optimal performance.
5) Do you think the paper will be influential in 10 years? Why or why not?
Yes. I think so. It is because Cassandra is simple and can achieve high throughput especially in writing, which is always a bottleneck for other competitive schemes.
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