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HPCache: memory-efficient OLAP through proportional caching revisited.

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HPCache improves in-memory data caching for faster analytics on high-bandwidth storage. By prioritizing data with high speedup potential over simple frequency, it optimizes memory usage and query performance.

Keywords:
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Area of Science:

  • Database Systems
  • Data Management
  • Computer Architecture

Background:

  • Analytical engines use in-memory caching to speed up data retrieval.
  • Traditional frequency-based caching is less effective with fast storage, as it doesn't account for query processing time.

Purpose of the Study:

  • To propose HPCache, a novel buffer management policy for efficient in-memory caching.
  • To enable fast analytics on high-bandwidth storage by optimizing memory utilization.

Main Methods:

  • Developed HPCache, a policy that caches data based on speedup potential rather than frequency.
  • Quantified caching decision efficiency and formulated an optimization problem.
  • Implemented HPCache in Proteus and used runtime statistics to infer speedup potential.

Main Results:

  • Estimating speedup potential significantly improves memory space utilization.
  • Simple runtime statistics are sufficient for inferring speedup potential.
  • HPCache achieved up to 1.75x speed-up compared to frequency-based caching.

Conclusions:

  • HPCache effectively utilizes in-memory space for input caching with fast storage.
  • The policy enhances analytics performance without requiring workload predictions.