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Performance Evaluation Analysis of Spark Streaming Backpressure for Data-Intensive Pipelines.

Kassiano J Matteussi1,2, Julio C S Dos Anjos3, Valderi R Q Leithardt4,5

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Spark Streaming backpressure is effective for small to medium data pipelines but struggles with intensive data surges. This study evaluates its performance, highlighting limitations for data-intensive and stateful applications.

Keywords:
backpressurebig dataspark streamingstream processing

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Area of Science:

  • Computer Science
  • Data Engineering

Background:

  • The rise of streaming applications necessitates efficient Big Data processing.
  • Apache Spark Streaming is a popular choice for data-intensive, in-memory processing.

Purpose of the Study:

  • To evaluate the performance of Spark Streaming's backpressure mechanism.
  • To determine its suitability for data-intensive pipelines under varying pressure conditions.

Main Methods:

  • Comprehensive performance evaluation of Spark Streaming backpressure.
  • Investigating its impact on data-intensive pipelines with memory constraints.

Main Results:

  • Backpressure is suitable for small and medium-sized pipelines (both stateless and stateful).
  • Spark Streaming's memory manager faces limitations with intensive data surges, causing performance degradation.
  • Issues include high latency, excessive garbage collection, out-of-memory errors, and data loss.

Conclusions:

  • Spark Streaming backpressure has limited applicability for large-scale, data-intensive, or stateful applications.
  • Identified limitations point towards the need for improved memory management solutions in Spark Streaming.