Cassandra vs Riak: What are the differences?
- Data Model: Cassandra uses a wide-column store data model, while Riak follows the key-value store data model. This means that Cassandra organizes data into columns within rows, similar to a table, while Riak stores data as key-value pairs without a fixed schema.
- Consistency Model: Cassandra employs eventual consistency by default, allowing data to be inconsistent for a period and then reconciled. In contrast, Riak offers tunable consistency, allowing users to choose between strong consistency or eventual consistency based on their requirements.
- Partitioning: Cassandra uses consistent hashing for partitioning data across multiple nodes but relies on a coordinator node to handle requests. Riak, on the other hand, uses a decentralized partitioning strategy where each node is responsible for a subset of the keyspace, enabling high availability and scalability without a single point of failure.
- Concurrency Control: Cassandra uses the Last-Write-Wins (LWW) conflict resolution strategy, where the most recent write takes precedence in case of conflicts. Riak, however, uses vector clocks to track causal relationships between updates, allowing for more sophisticated conflict resolution and the ability to handle divergent replicas.
- Secondary Indexes: Cassandra supports secondary indexes that enable querying on non-primary key fields, but they come with performance trade-offs. Riak, on the other hand, offers search capabilities through full-text search integration with tools like Riak Search or external indexing solutions.
- Deployment Flexibility: Cassandra is designed for horizontal scalability and is typically deployed in clusters spanning multiple data centers for high availability and fault tolerance. Riak, while also scalable, is often chosen for its deployment simplicity, flexibility, and ease of use in premises with less complex setup requirements.
In Summary, Cassandra and Riak differ in their data models, consistency models, partitioning strategies, concurrency control mechanisms, support for secondary indexes, and deployment flexibility.