Benchmarks for multi-model databases evaluate the system's performance across various data models—such as document, graph, key-value, and relational—within a single environment. These benchmarks typically assess how well a database can handle diverse workloads, measuring factors like query performance, data retrieval speed, and transaction throughput. The goal is to provide a comprehensive picture of how the database performs when interacting with different types of data, reflecting real-world applications where multiple data models might be used together.
The process usually involves designing test scenarios that cover a range of operations in each data model. For instance, a benchmark might simulate a typical web application that uses documents for user profiles, graphs for social connections, and key-value stores for session management. During the benchmarking, metrics such as response times for queries, latency for transactions, and resource utilization are collected. These metrics help developers understand not just the speed of data access, but also how well the database manages its resources when under different types of load.
Developers can use established benchmarks like TPC for transactional workloads or YCSB (Yahoo! Cloud Serving Benchmark) for NoSQL databases as a reference point. They may also create custom benchmarks tailored to their specific needs, such as incorporating hybrid queries that need to access both graph and document data models simultaneously. By analyzing the results, developers can choose a database that best fits their performance requirements across the various data types their applications handle, ensuring optimal functionality in multi-model environments.