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arXiv · 2112.06280

In-Memory Indexed Caching for Distributed Data Processing

Alexandru Uta·Bogdan Ghit·Ankur Dave·Jan Rellermeyer·Peter Boncz
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Abstract

Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets analyzed using coarse-grained transformations. In this paper, we introduce the Indexed DataFrame, an in-memory cache that supports a dataframe abstraction which incorporates indexing capabilities to support fast lookup and join operations. Moreover, it supports appends with multi-version concurrency control. We implement the Indexed DataFrame as a lightweight, standalone library which can be integrated with minimum effort in existing Spark programs. We analyze the performance of the Indexed DataFrame in cluster and cloud deployments with real-world datasets and benchmarks using both Apache Spark and Databricks Runtime. In our evaluation, we show that the Indexed DataFrame significantly speeds-up query execution when compared to a non-indexed dataframe, incurring modest memory overhead.

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Alexandru Uta, Bogdan Ghit, Ankur Dave, Jan Rellermeyer, Peter Boncz. 2021-12-12. In-Memory Indexed Caching for Distributed Data Processing. https://arxiv.org/abs/2112.06280

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