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Descripción
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| In this paper we discuss how we architected and developed a parallel data loader for LeanXcale database. The loader is characterized for its efficiency and parallelism. LeanXcale can scale up and scale out to very large numbers and loading data in the traditional way it is not exploiting its full potential in terms of the loading rate it can reach. For this reason, we have created a parallel loader that can reach the maximum insertion rate LeanXcale can handle. LeanXcale also exhibits a dual interface, key-value and SQL, that has been exploited by the parallel loader. Basically, the loading leverages the key-value API and results in a highly efficient process that avoids the overhead of SQL processing. Finally, in order to guarantee the parallelism we have developed a data sampler that samples data to generate a histogram of data distribution and use it to pre-split the regions across LeanXcale instances to guarantee that all instances get an even amount of data during loading, thus g uaranteeing the peak processing loading capability of the deployment. | |
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Internacional
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Si |
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Nombre congreso
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8th International Conference on Data Science, Technology and Applications, DATA 2019 |
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Tipo de participación
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960 |
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Lugar del congreso
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Praga |
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Revisores
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Si |
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ISBN o ISSN
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978-989-758-377-3 |
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DOI
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10.5220/0008318904650469 |
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Fecha inicio congreso
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26/07/2019 |
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Fecha fin congreso
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28/07/2019 |
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Desde la página
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465 |
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Hasta la página
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469 |
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Título de las actas
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Proceedings of the 8th International Conference on Data Science, Technology and Applications |