Cassandra Architecture for SQL Server DBAs

Core Architecture Differences

As a SQL Server DBA, you’ll find that Cassandra’s architecture differs fundamentally from the relational model you’re familiar with:

• Cassandra uses a masterless, peer-to-peer distributed architecture rather than SQL Server’s primary/replica model
• Data is automatically distributed across multiple nodes without a central coordinator
• No single point of failure exists in a properly configured cluster
• Horizontal scaling is achieved by adding more nodes, not upgrading existing hardware

Data Distribution Model

Unlike SQL Server’s tables and indexes:

• Data is distributed using a partitioner (typically Murmur3Hash) that determines which node stores which data
• The partition key (similar to a primary key) determines data placement across the cluster
• Replication factor defines how many copies of data exist across different nodes
• Consistency level determines how many replicas must acknowledge a read/write operation

Storage Architecture

Cassandra’s storage differs significantly from SQL Server’s page-based approach:

• Data is stored in SSTables (Sorted String Tables) on disk
• Writes go to an in-memory structure called Memtable first
• Memtables are periodically flushed to SSTables on disk
• Compaction merges multiple SSTables to optimize storage and performance
• Tombstones mark deleted data instead of immediately removing it

Query Language and Data Modeling

While SQL Server uses T-SQL:

• Cassandra Query Language (CQL) looks similar to SQL but has important limitations
• Queries are optimized for the partition key – there’s no query optimizer like in SQL Server
• Denormalization is encouraged to support efficient queries
• JOINs are not supported – data must be modeled to avoid them
• Secondary indexes exist but have performance limitations

Consistency Model

Unlike SQL Server’s ACID transactions:

• Cassandra follows the AP side of the CAP theorem (Availability and Partition tolerance)
• Tunable consistency lets you choose between strong consistency and availability
• Eventual consistency is the default model
• No built-in transaction support like SQL Server’s BEGIN/COMMIT

Scaling and Replication

Scaling differs significantly:

• Add nodes to scale horizontally rather than vertical scaling
• Replication is automatic based on the replication factor
• Virtual nodes (vnodes) distribute data evenly across the cluster
• Multi-datacenter replication is built-in for geographic distribution

Failure Handling

Cassandra’s approach to failures:

• Gossip protocol detects node failures automatically
• Hinted handoff stores data temporarily when a node is down
• Read repair fixes inconsistencies during read operations
• Anti-entropy repair synchronizes data across replicas

Key Operational Differences

For a SQL Server DBA, these operational aspects will be new:

• No built-in backup/restore like SQL Server – typically uses snapshots
• Repair operations are needed to maintain data consistency
• Compaction management is critical for performance
• Garbage collection tuning affects overall system stability

Understanding these architectural differences will help you transition from the structured, transactional world of SQL Server to Cassandra’s distributed, eventually consistent model designed for massive scale and high availability.