他

2023年6月20日

分享這篇文章提供的新聞分享這篇文章的最新基準Altoros評估速度、功能、和總擁有成本(TCO)流行的數據庫作為服務(DBaaS)。2023年6月20日,而加州ltoros,從谘詢公司專注於研究和開發全球2000個組織今天宣布其最新的性能基準測試的結果報告,委托他雲數據庫平台的公司。性能的研究提供了一個比較分析四種NoSQL數據庫雲:他CapellaTM,亞馬遜DynamoDB, MongoDB地圖集,複述,企業雲。基準比較這些流行數據庫的吞吐量和延遲四個業務場景和四個不同集群配置。Altoros”是在前幾年,他五車二證明它確實很好地用一個update-heavy和其他數據密集型的用例,尤其是當需要大規模增加,”伊凡Shyrma說Altoros數據工程師。“數據庫超過亞馬遜DynamoDB, MongoDB阿特拉斯,和複述,企業雲性能、速度、功能、和TCO大多數工作負載和集群大小。”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." Download the report here . About Altoros Altoros is an experienced IT services provider that helps enterprises to increase operational efficiency and accelerate the delivery of innovative products by shortening time to market. Relying on the power of cloud automation, microservices, AI/ML, and industry knowledge, its customers are able to get a sustainable competitive advantage. For more, visit www.altoros.com or follow @altoros. Media contact