MongoDB vs Scylla: What are the differences?
Both MongoDB and Scylla are NoSQL databases that offer high performance, scalability, and flexible data models. Let's explore the key differences between them.
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Data Model: MongoDB uses a flexible document data model, where data is stored in JSON-like documents with dynamic schemas. It allows for easy representation of hierarchical relationships and supports complex data structures. On the other hand, Scylla uses a wide-column data model inspired by Apache Cassandra. It organizes data into rows, each containing multiple columns and column families, making it more suitable for write-intensive workloads.
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Consistency and Availability: MongoDB provides a flexible consistency model, allowing developers to choose between strong, eventual, or linearizability with read/write concern options. It also provides flexible fault tolerance options, allowing for automated failover and replica sets. In contrast, Scylla offers tunable consistency levels, allowing developers to make trade-offs between consistency, availability, and latency. It is designed to handle high availability and fault-tolerance with automatic data replication and partitioning.
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Scalability: MongoDB scales horizontally through sharding, distributing data across multiple nodes. It provides automatic data rebalancing and supports adding or removing nodes dynamically. Scylla, on the other hand, scales linearly by adding more nodes to the cluster. It leverages a shared-nothing architecture, where each node operates independently, leading to high throughput and low-latency performance.
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Data Durability: MongoDB guarantees data durability by persisting data to disk and providing the option to use write concern levels. It also supports replication to ensure data availability in case of node failures. Scylla, on the other hand, ensures data durability through multiple mechanisms, including the use of write-ahead log (WAL) and synchronous replication. It also provides fault-tolerance with automatic data replication and cross-datacenter replication.
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Indexing and Querying: MongoDB supports flexible and rich query capabilities, including secondary indexes, full-text search, and geospatial queries. It also provides aggregation and map-reduce frameworks for complex data processing. Scylla, however, supports limited querying capabilities compared to MongoDB. It is optimized for simple key-value lookups and range queries, rather than complex querying or analytics.
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Use Cases: MongoDB is well-suited for a wide range of use cases, including content management systems, real-time analytics, and mobile applications. It provides flexibility to adapt to changing business requirements and supports a diverse set of data models. On the other hand, Scylla is designed for high-throughput and low-latency use cases, such as time series data, IoT applications, and real-time streaming analytics. It excels in write-heavy workloads with large volumes of data.
In summary, MongoDB offers a flexible document data model, while Scylla uses a wide-column data model. MongoDB provides a range of consistency and fault-tolerance options, while Scylla offers tunable consistency levels. MongoDB is highly scalable through sharding, whereas Scylla scales linearly by adding more nodes. MongoDB supports rich querying capabilities, while Scylla is optimized for simple key-value lookups.
