spark-introduction | presentation given at the IBM Data Science | Machine Learning library

 by   4Quant JavaScript Version: Current License: No License

kandi X-RAY | spark-introduction Summary

kandi X-RAY | spark-introduction Summary

spark-introduction is a JavaScript library typically used in Artificial Intelligence, Machine Learning applications. spark-introduction has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

The presentation given at the IBM Data Science Connect Meeting titled Introduction to Apache Spark
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              spark-introduction has a low active ecosystem.
              It has 4 star(s) with 3 fork(s). There are 2 watchers for this library.
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              It had no major release in the last 6 months.
              spark-introduction has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of spark-introduction is current.

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              spark-introduction has no bugs reported.

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              spark-introduction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              spark-introduction does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              spark-introduction releases are not available. You will need to build from source code and install.

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            Community Discussions

            Trending Discussions on spark-introduction

            QUESTION

            Difference between one-pass and multi-pass computations
            Asked 2020-Apr-09 at 22:07

            I'm reading an article on Apache Spark and I came across the following sentence:

            "Hadoop as a big data processing technology has been around for 10 years and has proven to be the solution of choice for processing large data sets. MapReduce is a great solution for one-pass computations, but not very efficient for use cases that require multi-pass computations and algorithms." (Full article)

            Searching the web yields results about the difference between one-pass and multi-pass compilers (For instance, see This SO question)

            However, I'm not really sure if the answer also applies for data processing. Can somebody explain me what one-pass computation and multi-pass computation is, and why the latter is better, and thus is used in Spark?

            ...

            ANSWER

            Answered 2019-Oct-16 at 08:11

            One pass computations is when you are reading the dataset once whereas multipass computations is when a dataset is read once from the disk and multiple computations or operation are done on the same dataset. Apache Spark processing framework allows you to read data once which is then cached into memory and then we can perform multi pass computations on the data. These computations can be done on the dataset very quickly because the data is present into memory of the machine and apache spark does not need to read the data again from the disk which helps us to save lot of input output operations time. As per the definition of apache spark it is an in memory processing framework which means the data and transformation on which the computation is done is present in memory itself.

            Source https://stackoverflow.com/questions/58407978

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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