Benchmarks are essential for comparing distributed query engines because they provide standardized tests that measure the performance and efficiency of each engine under various workloads. By simulating real-world use cases, benchmarks can illustrate how well different systems handle tasks such as query execution time, resource usage, and scalability. For instance, using a benchmark like TPC-H allows developers to assess how quickly each engine can run complex analytical queries against large datasets, providing a fair basis for comparison.
When evaluating distributed query engines, benchmarks typically focus on several key metrics: throughput, latency, and resource consumption. Throughput refers to the number of queries processed in a given time frame, which helps identify how well an engine can manage high workloads. Latency indicates the amount of time it takes to complete a single query, crucial for applications requiring real-time responses. Additionally, resource consumption looks at how efficiently the engine uses system resources like CPU and memory, ensuring that developers can optimize performance without unnecessarily inflating infrastructure costs.
Different query engines may excel in different scenarios, which is why benchmarks often include a variety of query types and dataset sizes. For example, Apache Spark might perform exceptionally well on large batch queries, while a system like Presto could shine in interactive analytics. Evaluating these engines using standardized benchmarks enables developers to make informed decisions based on their unique project requirements, ultimately leading to better performance and resource management.