A comprehensive benchmark suite for evaluating speed, throughput, and resource utilization of big data frameworks like Hadoop, Spark, and streaming engines.
HiBench is a big data benchmark suite that evaluates the performance of big data frameworks like Hadoop, Spark, and streaming engines. It provides a collection of standardized workloads to measure speed, throughput, and system resource utilization, helping users compare different frameworks and optimize their deployments.
Data engineers, platform architects, and researchers who need to evaluate, compare, or tune the performance of big data processing frameworks in on-premise or cloud environments.
It offers a comprehensive, multi-framework benchmark suite with diverse real-world workloads, enabling objective performance comparisons and helping identify bottlenecks in big data infrastructure.
HiBench is a big data benchmark suite.
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Includes 29 workloads across micro, ML, SQL, graph, web search, and streaming categories, providing a broad range of real-world testing scenarios for thorough evaluations.
Benchmarks Hadoop, Spark, and streaming frameworks like Flink and Storm, enabling fair performance comparisons across different big data ecosystems.
Features common big data operations such as PageRank, K-means, and SQL queries, ensuring relevance to practical use cases and standardized testing.
Measures system performance beyond raw speed, including I/O throughput and cluster efficiency, for holistic infrastructure assessment.
Intel has archived the project with no maintenance, bug fixes, or security updates, and it has known security issues, making it risky for any deployment.
Supported releases are old, e.g., Flink 1.0.3 and Storm 1.0.1, which do not reflect modern framework capabilities and limit current relevance.
Requires building from source and configuring multiple frameworks, as per the docs, which can be time-consuming and error-prone, especially with outdated dependencies.