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Caching in Research and Industry

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Fast and Scalable Cloud Data Management

Abstract

Caching technology can be categorized by several dimensions as illustrated in Fig. 5.1. The first dimension is the location of the cache. In this book, we focus on caches relevant for cloud and database applications, particularly server-side and database caching, reverse and forward proxy caching (mid-tier), and client caching. The second dimension is the granularity of cached data. Examples are files, database records and pages, query results, and page fragments. The third dimension is the update strategy that determines the provided level of consistency.

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Notes

  1. 1.

    Proactive filling of the cache is also referred to as materialization [LR00].

  2. 2.

    Baqend: https://www.baqend.com/.

  3. 3.

    Speed Kit: https://speed-kit.com.

  4. 4.

    This is termed external consistency by the Spanner authors and known in the literature as strict serializability or commit order-preserving conflict serializable (COCSR) [WV02].

  5. 5.

    Hybid logical clocks combine the benefits of logical clocks [Lam78] for simple tracking of causality with physical clocks that are within a defined drift from real time by merging both.

  6. 6.

    In particular for individual operations (transactions with a single read or write), the lack of strict serializability implies that linearizability is not guaranteed in CockroachDB.

  7. 7.

    The idea of building distributed transactions on a shared log is also found in Calvin [Tho+12] (cf. p. 141).

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Gessert, F., Wingerath, W., Ritter, N. (2020). Caching in Research and Industry. In: Fast and Scalable Cloud Data Management. Springer, Cham. https://doi.org/10.1007/978-3-030-43506-6_5

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