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Siying Dong on Muck Rack

Siying Dong

As seen in: Frontiers, Databricks
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Articles

Breaking the Microbatch Barrier: The Architecture of Apache Spark Real-Time Mode

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By Jerry Peng, Siying Dong, Indrajit Roy
| Databricks
With the launch of real-time mode (RTM) in Apache Spark 4.1, Structured Streaming now delivers millisecond-level latency. In a recent blog post, we showed how RTM can outperform Flink for many low latency feature engineering workloads (see below). In this blog, we will discuss the architectural changes that enabled Structured Streaming to support both high-throughput ETL workloads as well as ultra low-latency workloads. Apache Spark RTM is faster than Flink for feature engineering use cases.
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Frontiers | A scale for assessing the health of children aged 3–6 years in China: development and validation of the TCM constitution scale (3–6 years)

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By Wenle Li, Siying Dong, Zhuqing Li, Yuyang Cai, Ji Wang, Shunqi Chen, Ming-hua Bai, Jia-Xu Chen
| Frontiers
1 Introduction The physical and mental development of children stems from parental genetic inheritance and postnatal nurturing. There is a reciprocal interaction between physical health and emotional states/personality traits.
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Introducing Real-Time Mode in Apache Spark™ Structured Streaming

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By Jerry Peng, Siying Dong, Abhay Bothra, Fatih Emekci
| Databricks
Apache Spark™ Structured Streaming has long powered mission-critical pipelines at scale, from streaming ETL to near real-time analytics and machine learning. Now, we’re expanding that capability to an entirely new class of workloads with real-time mode, a new trigger type that processes events as they arrive, with latency in the tens of milliseconds.
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