spark-perf | Performance tests for Apache Spark

by databricks Scala Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | spark-perf Summary

spark-perf is a Scala library typically used in Big Data, Kafka, Spark applications. spark-perf has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This is a performance testing framework for Apache Spark 1.0+.

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Quality

Security

License

Reuse

Support

spark-perf has a low active ecosystem.

It has 366 star(s) with 206 fork(s). There are 50 watchers for this library.

It had no major release in the last 6 months.

There are 38 open issues and 17 have been closed. On average issues are closed in 26 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of spark-perf is current.

Quality

spark-perf has 0 bugs and 0 code smells.

Security

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

spark-perf code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

spark-perf is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Installation instructions are not available. Examples and code snippets are available.

It has 6127 lines of code, 446 functions and 62 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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spark-perf Key Features

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spark-perf Examples and Code Snippets

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Community Discussions

Trending Discussions on spark-perf

When does Spark dataframe pull the data from source

QUESTION

When does Spark dataframe pull the data from source

Asked 2021-Sep-17 at 04:15

I am new to spark dataframes and a little bit confused on its working. I had 2 similar piece of codes which is taking different amount of time to complete. Any explanation for these would be really helpful:

...

ANSWER

Answered 2021-Aug-23 at 08:15

When you create a dataframe, you can apply two kind of methods : Transformations and Actions.

Transformations are lazy, it means they are not executed right away. Transformations are for example : withColumn, where, select ... A transformation always return a dataframe. When executed, Spark just checks if they are valid: for exemple, withColumn is a transformation, and when you apply it to your dataframe, the validity of your request is checked directly but not executed :

Source https://stackoverflow.com/questions/68888879

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

Vulnerabilities

No vulnerabilities reported

Install spark-perf

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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