Altoros, a consultancy focusing on research and development for Global 2000 organizations, announced the results of its latest performance benchmark report, commissioned by cloud database platform company Couchbase. The study provides a comparative analysis of the performance of four NoSQL cloud databases: Couchbase Capella, Amazon DynamoDB, MongoDB Atlas, and Redis Enterprise Cloud. The benchmark compares the throughput and latency of these popular databases across four business scenarios and four different cluster configurations.
“As in previous years, Couchbase Capella proved it does very well in an update-heavy and other data-intensive use cases, especially as the need for large scale increases,” said Ivan Shyrma, Data Engineer at Altoros. “The database surpassed Amazon DynamoDB, MongoDB Atlas, and Redis Enterprise Cloud in performance, speed, functionality, and TCO across most workloads and cluster sizes.”
For evaluation consistency, the YCSB (Yahoo! Cloud Serving Benchmark) was used as the default tool. It is an open-source standardized framework used for evaluating the performance of cloud-based database systems, comprising a variety of workload tests.
Workload descriptions
The first workload simulates a write-heavy workload where the database system primarily handles read operations with occasional updates, invoking 50% of reads and 50% of updates.
The second workload creates a scenario where the database system primarily performs read operations, providing insights into the system’s performance and scalability specifically for read-intensive workloads.
The third workload represents a pagination-type query. The database system is evaluated on its ability to efficiently fetch a subset of data from a larger dataset, typically through a combination of read and seek operations.
The fourth workload assesses the performance and scalability of a database system under a workload where the majority of operations are reads (95%), with a smaller proportion of updates (5%).
In the report, Altoros establishes the performance of the database based on the speed at which it handles fundamental operations. These operations are carried out by a workload executor, which drives multiple client threads. Each thread sequentially executes a series of operations by utilizing a database interface layer responsible for loading the database and executing the workload.
To maintain control over the load imposed on the database, the threads regulate the rate at which they generate requests. Additionally, the threads measure the latency and throughput of their operations and communicate these metrics to the statistics module.
“The query engine of Couchbase Capella supports aggregation, filtering, and other operations on large data sets,” continued Shyrma. “As clusters and data sets grow in size, Couchbase Capella ensures a high level of scalability across these operations. Capella was good overall and showed that it is capable of performing any type of query with good performance.”
