Introduction In the previous parts of this series, we have shown how to write functions as both combinations of dplyr verbs, SQL query generators that can be executed by Spark and how to use the lower-level API to invoke methods on Java object references from R. In this fifth part, we will look into more details around sparklyr’s invoke() API, investigate...
Introduction In the previous parts of this series, we have shown how to write functions as both combinations of dplyr verbs and SQL query generators that can be executed by Spark, how to execute them with DBI and how to achieve lazy SQL statements that only get executed when needed. In this fourth part, we will look at how to write...
Introduction In the previous part of this series, we looked at writing R functions that can be executed directly by Spark without serialization overhead with a focus on writing functions as combinations of dplyr verbs and investigated how the SQL is generated and Spark plans created. In this third part, we will look at how to write R functions that generate...
Introduction In the first part of this series, we looked at how the sparklyr interface communicates with the Spark instance and what this means for performance with regards to arbitrarily defined R functions. We also examined how Apache Arrow can increase the performance of data transfers between the R session and the Spark instance. In this second part, we will look...
Introduction Apache Spark is a popular open-source analytics engine for big data processing and thanks to the sparklyr and SparkR packages, the power of Spark is also available to R users. This series of articles will attempt to provide practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R...
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Introduction In the previous post, we focused on setting up declarative Jenkins pipelines with emphasis on parametrizing builds and using environment variables across pipeline stages. In this post, we look at various tips that can be useful when automating R application testing and continuous integration, with regards to orchestrating parallelization, combining sources from multiple git repositories and ensuring proper access right...
Introduction Jenkins is a popular open-source tool that helps teams with automation and implementation of continuous integration and deployment pipelines, comparable to for example Atlassian’s Bamboo, GitLab CI or to some extent Travis. In this post, we share some practical lessons learned when integrating R applications via Jenkins for the purpose of continuous integration and regression testing on runner nodes configured...
Introduction Recently I was involved in a task that included reading and writing quite large amounts of data, totaling more than 1 TB worth of csvs without the standard big data infrastructure. After trying multiple approaches, the one that made this possible was using data.table’s reading and writing facilities - fread() and fwrite(). This motivated me to look at benchmarking data.table’s...
Introduction As pointed out by a recent read the R source post on the R hub’s website, reading the actual code, not just the documentation is a great way to learn more about programming and implementation details. But there is one more activity to get even more hands-on experience and understanding of the code in practice. In this post, we provide...
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