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Data-Trace Types for Distributed Stream Processing Systems.

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Summary
This summary is machine-generated.

This study introduces type-based programming abstractions for distributed stream processing. These abstractions ensure correct and efficient deployment of computations on platforms like Apache Storm, maintaining data semantics.

Keywords:
distributed data stream processingtypes

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Programming Languages

Background:

  • Distributed stream processing is crucial for modern data analysis.
  • Existing systems struggle with semantic preservation during automatic parallelization, especially with ordered data.
  • Need for robust abstractions to handle complex ordering and synchronization in data streams.

Purpose of the Study:

  • Develop type-based programming abstractions for correct and efficient distributed stream processing.
  • Introduce a data-trace transduction model to generalize stream processing computations.
  • Enable automatic mapping of high-level programs to distributed platforms like Apache Storm.

Main Methods:

  • Proposed a model where communication links have types specifying tagged data and dependency relations for partial ordering.
  • Defined data-trace transduction as a function from input to output data traces, generalizing existing models.
  • Developed programming templates for common stream processing tasks and automated mapping to Apache Storm.

Main Results:

  • The data-trace transduction model handles diverse ordering and synchronization characteristics.
  • Automatic compilation preserves semantics, unlike some existing parallelization methods.
  • Achieved throughput comparable to hand-crafted distributed implementations.

Conclusions:

  • Type-based programming abstractions ensure semantic correctness in distributed stream processing.
  • The proposed model and templates facilitate natural specification of complex queries.
  • Automated deployment on platforms like Apache Storm is efficient and semantically sound.