spark-cli | DEPRECATED : Renamed to particle-cli

by particle-iot JavaScript Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | spark-cli Summary

spark-cli is a JavaScript library. spark-cli has no bugs, it has no vulnerabilities and it has low support. However spark-cli has a Non-SPDX License. You can download it from GitHub.

DEPRECATED: Renamed to particle-cli. See https://github.com/spark/particle-cli

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Security

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Support

spark-cli has a low active ecosystem.

It has 162 star(s) with 40 fork(s). There are 73 watchers for this library.

It had no major release in the last 6 months.

There are 24 open issues and 104 have been closed. On average issues are closed in 165 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of spark-cli is current.

Quality

spark-cli has 0 bugs and 0 code smells.

Security

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

spark-cli code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

spark-cli has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

Top functions reviewed by kandi - BETA

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

show device informations
Signup with the user input .
Read the results .
Status of an account .
Helper function for interactive user
pts the network
Restart the user
Prompts a listener to the user .
Prompt for selected devices .
Confirm the network

Get all kandi verified functions for this library.

spark-cli Key Features

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

spark-cli Examples and Code Snippets

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

Community Discussions

Trending Discussions on spark-cli

Spark ERROR in cluster: ModuleNotFoundError: No module named 'cst_utils'

Spark workers 'KILLED exitStatus 143' when given huge resources to do simple computation

It's possible to configure the Beam portable runner with the spark configurations?

Spark History Server very slow when driver running on master node

java.lang.ClassNotFoundException: com.mysql.jdbc.Driver in Jupyter Notebook on Amazon EMR

My PySpark Jobs Run Fine in Local Mode, But Fail in Cluster Mode - SOLVED

QUESTION

Spark ERROR in cluster: ModuleNotFoundError: No module named 'cst_utils'

Asked 2022-Feb-21 at 13:36

I have a Spark program with python. The structure of the program is like this:

...

ANSWER

Answered 2022-Feb-21 at 13:36

Problem solved.

First, I installed all packages in each node with this command:

Source https://stackoverflow.com/questions/71153472

QUESTION

Spark workers 'KILLED exitStatus 143' when given huge resources to do simple computation

Asked 2021-Nov-16 at 01:47

Running Spark on Kubernetes, with each of 3 Spark workers given 8 cores and 8G ram, results in

...

ANSWER

Answered 2021-Nov-16 at 01:47

Learned a couple things here. The first is that 143 KILLED does not seem to actually be indicative of failure but rather of executors receiving a signal to shutdown once the job is finished. So, seems draconian when found in logs but is not.

What was confusing me was that I wasn't seeing any "Pi is roughly 3.1475357376786883" text on stdout/stderr. This led me to believe the computation never got that far, which was incorrect.

The issue here is what I was using --deploy-mode cluster when --deploy-mode client actually made a lot more sense in this situation. That is because I was running an ad-hoc container through kubectl run which was not part of the existing deployment. This fits the definition of client mode better, since the submission does not come from an existing Spark worker. When running in --deploy-mode=cluster, you'll never actually see stdout since input/output of the application are not attached to the console.

Once I changed --deploy-mode to client, I also needed to add --conf spark.driver.host as documented here and here, for the pods to be able to resolve back to the invoking host.

Source https://stackoverflow.com/questions/69981541

QUESTION

It's possible to configure the Beam portable runner with the spark configurations?

Asked 2021-Mar-04 at 19:36

TLDR;

It's possible to configure the Beam portable runner with the spark configurations? More precisely, it's possible to configure the spark.driver.host in the Portable Runner?

Motivation

Currently, we have airflow implemented in a Kubernetes cluster, and aiming to use TensorFlow Extended we need to use Apache beam. For our use case Spark would be the appropriate runner to be used, and as airflow and TensorFlow are coded in python we would need to use the Apache Beam's Portable Runner (https://beam.apache.org/documentation/runners/spark/#portability).

The problem

The portable runner creates the spark context inside its container and does not leave space for the driver DNS configuration making the executors inside the worker pods non-communicable to the driver (the job server).

Setup

Following the beam documentation, the job serer was implemented in the same pod as the airflow to use the local network between these two containers. Job server config:

...

ANSWER

Answered 2021-Feb-23 at 22:28

I have three solutions to choose from depending on your deployment requirements. In order of difficulty:

Use the Spark "uber jar" job server. This starts an embedded job server inside the Spark master, instead of using a standalone job server in a container. This would simplify your deployment a lot, since you would not need to start the beam_spark_job_server container at all.

Source https://stackoverflow.com/questions/66320831

QUESTION

Spark History Server very slow when driver running on master node

Asked 2020-Jul-06 at 21:21

I'm using Spark 2.4.5 running on AWS EMR 5.30.0 with r5.4xlarge instances (16 vCore, 128 GiB memory, EBS only storage, EBS Storage:256 GiB) : 1 master, 1 core and 30 task.

I launched Spark Thrift Server on the master node and it's the only job that is running on the cluster

...

ANSWER

Answered 2020-Jul-06 at 21:21

The problem was having only 1 core instance as the logs were saved in HDFS so this instance became a bottleneck. I added another core instance and it's going much better now.

Another solution could be to save the logs to S3/S3A instead of HDFS, changing those parameters in spark-defaults.conf (make sure they are changed in the UI config too) but it might require adding some JAR files to work.

Source https://stackoverflow.com/questions/62521705

QUESTION

java.lang.ClassNotFoundException: com.mysql.jdbc.Driver in Jupyter Notebook on Amazon EMR

Asked 2020-Apr-23 at 14:16

While trying to connect to MySql database in RDS from EMR Jupyter Notebook, I have found the following error :

Code Used:

...

ANSWER

Answered 2020-Apr-23 at 14:16

As it's unable to find driver class when you are running it from Jupyter Notebook, to avoid that you can try by copying mysql-connector-java-5.1.47.jar to the $SPARK_HOME/jars folder. It will resolve your driver issue as per my personal experience.

Source https://stackoverflow.com/questions/61387861

QUESTION

My PySpark Jobs Run Fine in Local Mode, But Fail in Cluster Mode - SOLVED

Asked 2020-Feb-27 at 15:05

I have a four node Hadoop/Spark cluster running in AWS. I can submit and run jobs perfectly in local mode:

...

ANSWER

Answered 2020-Feb-26 at 14:09

Two of these things ended up solving this issue:

First, I added the following lines to all nodes in the yarn-site.xml file:

Source https://stackoverflow.com/questions/60396172

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

Vulnerabilities

No vulnerabilities reported

Install spark-cli

You can download it from GitHub.

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