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ShuffleBench: A Benchmark for Large-Scale Data Shuffling Operations with Distributed Stream Processing Frameworks

Core Concepts
ShuffleBench introduces a new benchmark focusing on large-scale data shuffling operations in stream processing frameworks, addressing a critical gap in existing benchmarks.
ShuffleBench is a novel benchmark designed to evaluate the performance of modern stream processing frameworks, emphasizing data shuffling operations. It provides valuable insights into throughput, latency, and scalability across different frameworks like Flink, Hazelcast, Kafka Streams, and Spark. The study highlights the importance of considering various factors such as record size, consumer count, and selectivity in assessing framework performance. Through detailed experiments and analysis, ShuffleBench offers a standardized approach for comparing stream processing implementations.
Flink achieves the highest throughput. Hazelcast processes data streams with the lowest latency. Spark's throughput can be increased at the cost of higher latency.
"We propose ShuffleBench as a new stream processing benchmark focusing on large-scale data shuffling." "Throughput results show Flink leading followed by Kafka Streams." "Hazelcast processes data with very low latency compared to other frameworks."

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by Söre... at 03-08-2024

Deeper Inquiries

How does ShuffleBench address limitations of existing stream processing benchmarks?

ShuffleBench addresses limitations of existing stream processing benchmarks by focusing on a specific use case where stream processing frameworks are mainly employed for shuffling data records. Unlike other benchmarks that define domain-specific processing logic with high-level APIs, ShuffleBench abstracts the actual aggregation logic as black-box software components. This approach allows for a more general evaluation method applicable to various scenarios. Additionally, ShuffleBench provides well-defined metrics, measurement methods, and highly configurable task samples, enabling researchers and practitioners to evaluate stream processing frameworks based on performance attributes like throughput, latency, and scalability in a representative use case.

How can the findings from ShuffleBench experiments guide industry practitioners in selecting suitable stream processing frameworks?

The findings from ShuffleBench experiments can guide industry practitioners in selecting suitable stream processing frameworks by providing valuable insights into the performance characteristics of different frameworks under large-scale data shuffling operations. By comparing metrics such as throughput and latency across platforms like Flink, Hazelcast, Kafka Streams, and Spark Structured Streaming using real-world use cases inspired by cloud observability requirements, practitioners can make informed decisions based on their specific needs. For example: Throughput Comparison: Practitioners can assess which framework achieves the highest throughput for their workload requirements. Latency Analysis: Understanding how each framework handles latency at different percentiles helps in choosing one that meets real-time processing needs. Scalability Evaluation: The scalability metric provided by ShuffleBench allows practitioners to understand how each framework scales with increasing load or resource demand. By leveraging these insights from ShuffleBench experiments along with considerations like ease of deployment, community support, integration capabilities with existing systems/tools, and cost-effectiveness; industry practitioners can effectively select the most suitable stream processing framework for their specific use cases and business objectives.

What are the implications of varying record sizes on framework performance?

Varying record sizes have significant implications on framework performance in terms of both throughput and latency: Throughput Impact: Smaller record sizes generally lead to higher throughput as more records can be processed within a given time frame due to reduced payload size per record. Frameworks may handle smaller records more efficiently resulting in increased overall throughput compared to larger records. Latency Consideration: While smaller record sizes may improve overall system responsiveness due to quicker individual record handling times (lower serialization/deserialization overhead), they might also introduce additional overhead related to managing a larger number of records per unit time leading potentially longer batch-processing times or queuing delays depending on how batching is implemented within the framework. Practitioners need to carefully consider trade-offs between increased throughput versus potential impacts on system resources (e.g., memory usage) when deciding optimal record sizes for their streaming applications based on factors such as network bandwidth constraints or downstream application requirements for timely data delivery without compromising system efficiency or stability during peak loads or spikes in incoming data volume.