Couchbase vs Scylla: What are the differences?

Introduction

In this article, we will discuss the key differences between Couchbase and Scylla, two popular database systems. Both Couchbase and Scylla offer high performance and scalability, but they have distinct features and use cases that set them apart.

1. Data Model: One major difference between Couchbase and Scylla lies in their data models. Couchbase is a document-oriented database that stores data in JSON-like documents. This flexibility allows for dynamic schemas and easy handling of complex data structures. On the other hand, Scylla is a column-oriented database that stores data in tables with rows and columns resembling relational databases. This structure provides efficient storage and retrieval for large quantities of data with a predefined schema.

2. Consistency Model: Couchbase and Scylla also differ in their consistency models. Couchbase offers strong consistency, ensuring that all clients see the same data at the same time. This is important for applications where data integrity is critical. In contrast, Scylla provides eventual consistency, where updates to data may take some time to propagate to all replicas. This makes Scylla suitable for systems that prioritize high availability and low latency over strict consistency.

3. Data Distribution: Another key difference is how Couchbase and Scylla distribute data across their clusters. Couchbase employs a distributed hash table (DHT) approach, commonly known as consistent hashing. It evenly distributes data across multiple nodes in the cluster, providing automatic sharding and load balancing. On the other hand, Scylla uses a partitioned row store (Paxos) algorithm to distribute data. This algorithm ensures fault tolerance and linear scalability while maintaining data locality for optimal performance.

4. Query Language: Couchbase and Scylla also vary in their query languages. Couchbase uses N1QL (pronounced as "nickel"), a SQL-like query language that provides easy ad hoc querying and analytics capabilities. N1QL supports complex joins, filtering, and aggregation for flexible data retrieval. In contrast, Scylla uses CQL (Cassandra Query Language), which is similar to SQL but designed specifically for Cassandra and Scylla. CQL offers a simplified query syntax and supports basic CRUD operations.

5. Secondary Indexing: Couchbase and Scylla handle secondary indexing differently. In Couchbase, secondary indexes are automatically maintained by the database, allowing for efficient querying on non-primary key attributes. This makes it easy to add, update, or remove indexes without impacting the application's performance. Scylla, however, does not natively support secondary indexes. Instead, it encourages denormalization and materialized views to optimize queries for different use cases.

6. Storage Engine: Lastly, Couchbase and Scylla use different storage engines. Couchbase utilizes a memory-centric storage engine called Memory-First Architecture (MFA). This engine prioritizes data in-memory for fast access and leverages disk storage for persistence and durability. Scylla, on the other hand, uses its own storage engine called ScyllaDB. It is built from scratch, utilizing the Seastar framework and leveraging the power of C++ to deliver high performance and low latency.

In Summary, Couchbase and Scylla differ in terms of their data models, consistency models, data distribution methods, query languages, secondary indexing approaches, and storage engines. These differences stem from their design philosophies, making them suitable for different use cases and workloads.