spark-udf | Update Spark to add hashed UDFs | Hashing library

by MatteoVH Scala Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | spark-udf Summary

spark-udf is a Scala library typically used in Security, Hashing, Kafka, Spark applications. spark-udf has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Update Spark to add hashed UDFs

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Quality

Security

License

Reuse

Support

spark-udf has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of spark-udf is current.

Quality

spark-udf has 0 bugs and 0 code smells.

Security

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

spark-udf code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

Installation instructions, examples and code snippets are available.

It has 162785 lines of code, 13071 functions and 1506 files.

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

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spark-udf Key Features

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

spark-udf Examples and Code Snippets

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

Community Discussions

Trending Discussions on spark-udf

Vectorized pandas udf in pyspark with dict lookup

Task not serializable: java.io.NotSerializableException - JsonSchema

TypeError: udf() missing 1 required positional argument: 'f'

Log from Spark Java application UDF not appearing in console or executor log file

Creating a user defined function in Spark to process a nested structure column

QUESTION

Vectorized pandas udf in pyspark with dict lookup

Asked 2022-Mar-19 at 02:47

I'm trying to learn to use pandas_udf in pyspark (Databricks).

One of the assignments is to write a pandas_udf to sort by day of the week. I know how to do this using spark udf:

...

ANSWER

Answered 2022-Mar-19 at 01:30

what about we return a dataframe using groupeddata and orderby after you do the udf. Pandas sort_values is quite problematic within udfs.

Basically, in the udf I generate the numbers using python and then concatenate them back to the day column.

Source https://stackoverflow.com/questions/71533765

QUESTION

Task not serializable: java.io.NotSerializableException - JsonSchema

Asked 2022-Mar-10 at 16:10

I am trying to use JsonSchema to validate rows in an RDD, in order to filter out invalid rows.

Here is my code:

...

ANSWER

Answered 2022-Mar-10 at 15:05

OK so a coworker helped me find a solution.

Sources:

Code:

Source https://stackoverflow.com/questions/71423608

QUESTION

TypeError: udf() missing 1 required positional argument: 'f'

Asked 2021-Apr-13 at 07:47

I couldn't find any solution or question to my problem.

If I try to define a Spark-UDF Function (pyspark) e.g.:

...

ANSWER

Answered 2021-Apr-13 at 07:47

After trying lot of things, the problem was that my pyspark version didn't match the spark version.

Source https://stackoverflow.com/questions/67059260

QUESTION

Log from Spark Java application UDF not appearing in console or executor log file

Asked 2020-May-13 at 12:48

I have gone through the following questions and pages seeking an answer for my problem, but they did not solve my problem:

log from spark udf to driver

Logger is not working inside spark UDF on cluster

https://www.javacodegeeks.com/2016/03/log-apache-spark.html

We are using Spark in standalone mode, not on Yarn. I have configured the log4j.properties file in both the driver and executors to define a custom logger "myLogger". The log4j.properties file, which I have replicated in both the driver and the executors, is as follows:

...

ANSWER

Answered 2020-May-13 at 12:48

I have resolved the logging issue. I found out that even in local mode, the logs from UDFs were not being written to the spark log files, even though they were being displayed in the console. Thus I narrowed the problem down to that the UDFs were perhaps not being able to access the file system. Then I found the following question:

How to load local file in sc.textFile, instead of HDFS

Here, there was no solution to my problem, but there was the hint that from inside Spark, if we require to refer to files, we have to refer to the root of the file system as “file:///” as seen by the executing JVM. So, I made a change in the log4j.properties file in driver:

Source https://stackoverflow.com/questions/61750433

QUESTION

Creating a user defined function in Spark to process a nested structure column

Asked 2020-Apr-20 at 09:18

In my data frame, I have a complex data structure that I need to process to update another column. The approach I am tryin is by using a UDF. However, if there is an easier way to do this with, feel free answer with that.

The data frame structure in question is

...

ANSWER

Answered 2020-Apr-20 at 09:18

I found a solution by deconstructing the column since it was in an array, double>> format and following Spark UDF for StructType/Row. However, I believe there still may be a more concise way to do this.

Source https://stackoverflow.com/questions/61315826

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

Vulnerabilities

No vulnerabilities reported

Install spark-udf

git is a version control system, helping you track different versions of your code, synchronize them across different machines, and collaborate with others. GitHub is a site which supports this system, hosting it as a service. If you don't know much about git, we strongly recommend you to familiarize yourself with this system; you'll be spending a lot of time with it! There are many guides to using git online - here is a great one to read.
You should first set up a remote private repository (e.g., spark-homework). Github gives private repository to students (but this may take some time). If you don't have a private repository, think TWICE about checking it in public repository, as it will be available for others to checheckout. Clone your personal repository. It should be empty. Enter the cloned repository, track the course repository and clone it. NOTE: Please do not be overwhelmed by the amount of code that is here. Spark is a big project with a lot of features. The code that we will be touching will be contained within one specific directory: sql/core/src/main/scala/org/apache/spark/sql/execution/. The tests will all be contained in sql/core/src/test/scala/org/apache/spark/sql/execution/. Push clone to your personal repository. Every time that you add some code, you can commit the modifications to the remote repository.

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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