spark-applications | This directory contains sample application using Apache

by baghelamit Java Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | spark-applications Summary

spark-applications is a Java library typically used in Big Data, Spark, Hadoop applications. spark-applications has no bugs, it has no vulnerabilities and it has low support. However spark-applications build file is not available. You can download it from GitHub.

This directory contains sample application using Apache Spark.

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Quality

Security

License

Reuse

Support

spark-applications has a low active ecosystem.

It has 7 star(s) with 9 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

spark-applications has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of spark-applications is current.

Quality

spark-applications has 0 bugs and 0 code smells.

Security

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

spark-applications code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

spark-applications does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

spark-applications has no build file. You will be need to create the build yourself to build the component from source.

It has 715 lines of code, 36 functions and 20 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed spark-applications and discovered the below as its top functions. This is intended to give you an instant insight into spark-applications implemented functionality, and help decide if they suit your requirements.

Main entry point
Set the email address
Set the ID
Set the username
Main method to start Spark streaming
Gets the SparkSession instance
Starts Spark application
Demonstrates how to sample a user
Main method for testing
Entry point to the Spark application
Starts the Spark Session
Main launcher for Couchbase application
Entry point for testing
Main method
Main method to start a SparkCSV dataset
Main method for testing

Get all kandi verified functions for this library.

spark-applications Key Features

No Key Features are available at this moment for spark-applications.

spark-applications Examples and Code Snippets

No Code Snippets are available at this moment for spark-applications.

Community Discussions

Trending Discussions on spark-applications

EMR ignores spark submit parameters (memory/cores/etc)

Use Apache Spark efficiently to push data to elasticsearch

QUESTION

EMR ignores spark submit parameters (memory/cores/etc)

Asked 2021-Oct-01 at 21:05

I'm trying to use all resources on my EMR cluster.

The cluster itself is 4 m4.4xlarge machines (1 driver and 3 workers) with 16 vCore, 64 GiB memory, EBS Storage:128 GiB

When launching the cluster through the cli I'm presented with following options (all 3 options were executed within the same data pipeline):

Just use "maximizeResourceAllocation" without any other spark-submit parameter

This only gives me 2 executors presented here

Don't put anything, leave spark-defaults to do their job

Gives following low-quality executors

Use AWS's guide on how to configure cluster in EMR

Following this guide, I deduced following spark-submit parameters:

...

ANSWER

Answered 2021-Sep-22 at 17:16

Have you tried setting --master yarn parameter and replace parameter spark.executor.memoryOverhead by spark.yarn.executor.memoryOverhead ?

Source https://stackoverflow.com/questions/69286530

QUESTION

Use Apache Spark efficiently to push data to elasticsearch

Asked 2020-Nov-03 at 10:13

I have 27 million records in an xml file, that I want to push it into elasticsearch index Below is the code snippet written in spark scala, i'l be creating a spark job jar and going to run on AWS EMR

How can I efficiently use the spark to complete this exercise? Please guide.

I have a gzipped xml of 12.5 gb which I am loading into spark dataframe. I am new to Spark..(Should I split this gzip file? or spark executors will take care of it?)

...

ANSWER

Answered 2020-Aug-28 at 13:31

Not a complete answer but still a bit long for a comment. There are a few tips I would like to suggest.

It's not clear but I assume your worry hear is the execution time. As suggested in the comments you can improve the performance by adding more nodes/executors to the cluster. If the gzip file is loaded without partitioning in spark, then you should split it to a reasonable size. (Not too small - This will make the processing slow. Not too big - executors will run OOM).

parquet is a good file format when working with Spark. If you can convert your XML to parquet. It's super compressed and lightweight.

Reading on your comments, coalesce does not do a full shuffle. The coalesce algorithm changes the number of nodes by moving data from some partitions to existing partitions. This algorithm obviously cannot increase the number of partitions. Use repartition instead. The operation is costly but it can increase the number of partitions. Check this for more facts: https://medium.com/@mrpowers/managing-spark-partitions-with-coalesce-and-repartition-4050c57ad5c4

Source https://stackoverflow.com/questions/63501883

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

Vulnerabilities

No vulnerabilities reported

Install spark-applications

You can download it from GitHub.
You can use spark-applications like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the spark-applications component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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