learning-spark | Example code from Learning Spark book

by databricks Java Version: Current License: MIT

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

kandi X-RAY | learning-spark Summary

learning-spark is a Java library typically used in Big Data, Spark applications. learning-spark has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

[buildstatus] Examples for Learning Spark.

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learning-spark has a medium active ecosystem.

It has 3837 star(s) with 2442 fork(s). There are 399 watchers for this library.

It had no major release in the last 6 months.

There are 19 open issues and 8 have been closed. On average issues are closed in 31 days. There are 10 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of learning-spark is current.

Quality

learning-spark has 0 bugs and 0 code smells.

Security

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

learning-spark code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

learning-spark is licensed under the MIT License. This license is Permissive.

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

Reuse

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

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

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

An Accumulator example
Creates a request for the specified sign
Read the exchange call log
Loads the call sign table
Starts the analysis
Calculates the responses of a given access logs
Sets the flags
Redirects a Stream of AccessLogs into a Stream
Basic load json format
The main method
A basic join csv
Load json with spark
Simple flatMap
Basic load sequence file
Creates the options
Demonstrates how to load a table
Starts a JavaRDD query
Main entry point
Starts a streaming log input
Main method for testing
A basic loading sequence file
Main method for testing
Main entry point for testing
Main method
Main launcher for Spark
Main method for testing purposes

Get all kandi verified functions for this library.

learning-spark Key Features

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

learning-spark Examples and Code Snippets

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

Community Discussions

Trending Discussions on learning-spark

How can I resolve Python module import problems stemming from the failed import of NumPy C-extensions for running Spark/Python code on a MacBook Pro?

Learning Spark: Example with where doesn't work

QUESTION

How can I resolve Python module import problems stemming from the failed import of NumPy C-extensions for running Spark/Python code on a MacBook Pro?

Asked 2022-Mar-12 at 22:12

When I try to run the (simplified/illustrative) Spark/Python script shown below in the Mac Terminal (Bash), errors occur if imports are used for numpy, pandas, or pyspark.ml. The sample Python code shown here runs well when using the 'Section 1' imports listed below (when they include from pyspark.sql import SparkSession), but fails when any of the 'Section 2' imports are used. The full error message is shown below; part of it reads: '..._multiarray_umath.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'). Apparently, there was a problem importing NumPy 'c-extensions' to some of the computing nodes. Is there a way to resolve the error so a variety of pyspark.ml and other imports will function normally? [Spoiler alert: It turns out there is! See the solution below!]

The problem could stem from one or more potential causes, I believe: (1) improper setting of the environment variables (e.g., PATH), (2) an incorrect SparkSession setting in the code, (3) an omitted but necessary Python module import, (4) improper integration of related downloads (in this case, Spark 3.2.1 (spark-3.2.1-bin-hadoop2.7), Scala (2.12.15), Java (1.8.0_321), sbt (1.6.2), Python 3.10.1, and NumPy 1.22.2) in the local development environment (a 2021 MacBook Pro (Apple M1 Max) running macOS Monterey version 12.2.1), or (5) perhaps a hardware/software incompatibility.

Please note that the existing combination of code (in more complex forms), plus software and hardware runs fine to import and process data and display Spark dataframes, etc., using Terminal--as long as the imports are restricted to basic versions of pyspark.sql. Other imports seem to cause problems, and probably shouldn't.

The sample code (a simple but working program only intended to illustrate the problem):

...

ANSWER

Answered 2022-Mar-12 at 22:10

Solved it. The errors experienced while trying to import numpy c-extensions involved the challenge of ensuring each computing node had the environment it needed to execute the target script (test.py). It turns out this can be accomplished by zipping the necessary modules (in this case, only numpy) into a tarball (.tar.gz) for use in a 'spark-submit' command to execute the Python script. The approach I used involved leveraging conda-forge/miniforge to 'pack' the required dependencies into a file. (It felt like a hack, but it worked.)

The following websites were helpful for developing a solution:

Hyukjin Kwon's blog, "How to Manage Python Dependencies in PySpark" https://databricks.com/blog/2020/12/22/how-to-manage-python-dependencies-in-pyspark.html
"Python Package Management: Using Conda": https://spark.apache.org/docs/latest/api/python/user_guide/python_packaging.html
Alex Ziskind's video "python environment setup on Apple Silicon | M1, M1 Pro/Max with Conda-forge": https://www.youtube.com/watch?v=2Acht_5_HTo
conda-forge/miniforge on GitHub: https://github.com/conda-forge/miniforge (for Apple chips, use the Miniforge3-MacOSX-arm64 download for OS X (arm64, Apple Silicon).

Steps for implementing a solution:

Install conda-forge/miniforge on your computer (in my case, a MacBook Pro with Apple silicon), following Alex's recommendations. You do not yet need to activate any conda environment on your computer. During installation, I recommend these settings:

Source https://stackoverflow.com/questions/71361081

QUESTION

Learning Spark: Example with where doesn't work

Asked 2021-Oct-26 at 10:57

I'm trying to perform example from book Learning Spark.

There is such form of using column in where expression:

...

ANSWER

Answered 2021-Oct-26 at 10:56

You're probably missing an import within scope of the call site. The $ shortcut is typically introduced by calling import sparksession.implicits._. Intellij often removes this import if you have 'optimize imports' enabled as it doesn't recognise that it's in use.

Source https://stackoverflow.com/questions/69720967

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

Vulnerabilities

No vulnerabilities reported

Install learning-spark

You can download it from GitHub.
You can use learning-spark 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 learning-spark 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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