If I got it right Apache YARN receives Application Master and Node Manager as JAR files. They executed as Java process on the nodes of the YARN cluster.
When I write a Spark program using Python, Does it compiled into JAR somehow?
If not how come Spark is able to execute Python logic on YARN cluster nodes?
The PySpark driver program uses Py4J (http://py4j.sourceforge.net/) to launch a JVM and create a Spark Context. Spark RDD operations written in Python are mapped to operations on PythonRDD.
On the remote workers, PythonRDD launches sub-processes which run Python. The data and code is passed from the Remote Worker's JVM to its Python sub-process using pipes.
Therefore, it is necessary for your YARN nodes to have python installed for this to work.
The python code is not compiled to a JAR, but is distributed around the cluster using Spark. In order to make this possible, user functions written in Python are pickled using the following code https://github.com/apache/spark/blob/master/python/pyspark/cloudpickle.py
Source: https://cwiki.apache.org/confluence/display/SPARK/PySpark+Internals
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I am able to pull the data from databricks connect and run spark jobs perfectly. My question is how to run non-spark or native python code on remote cluster. Not sharing the code due to confidentiality.
When you're using databricks connect, then your local machine is a driver of your Spark job, so non-Spark code will be always executed on your local machine. If you want to execute it remotely, then you need to package it as wheel/egg, or upload Python files onto DBFS (for example, via databricks-cli) and execute your code as Databricks job (for example, using the Run Submit command of Jobs REST API, or create a Job with databricks-cli and use databricks jobs run-now to execute it)
I write pyspark code to deal with some spark-sql data.
Last month, it worked perfectly when I ran spark-submit --master local[25]. From top command, I could see 25 python threads.
However, nothing change but today the spark-submit only create one thread. I wonder what kind of things can cause such problem.
This is on a ubuntu server on AWS, which has 16 CPU cores. The Spark version is 2.2.1 and Python is 3.6
Just find the problem: there is another user running his own spark task on the same instance which occupying resources.
I am using python 2.7 with spark standalone cluster.
When I start the master on the same machine running the python script. it works smoothly.
When I start the master on a remote machine, and try to start spark context on the local machine to access the remote spark master. nothing happens and i get a massage saying that the task did not get any resources.
When i access the master's UI. i see the job, but nothing happens with it, it's just there.
How do i access a remote spark master via a local python script?
Thanks.
EDIT:
I read that in order to do this i need to run the cluster in cluster mode (not client mode), and I found that currently standalone mode does not support this for python application.
Ideas?
in order to do this i need to run the cluster in cluster mode (not client mode), and I found here that currently standalone mode does not support this for python application.
I'm using kafka and spark streaming for a project programmed in python. I want to send data from kafka producer to my streaming program. It's working smoothly when i execute the following command with the dependencies specified:
./spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.1.0 ./kafkastreaming.py
Is there any way where i can specify the dependencies and run the streaming code directly(i.e. without using spark-submit or with using spark-submit but not specifying the dependencies.)
I tried specifying the dependencies in the spark-defaults.conf in the conf dir of spark.
The specified dependencies were:
1.org.apache.spark:spark-streaming-kafka-0-8_2.11:2.1.0
2.org.apache.spark:spark-streaming-kafka-0-8-assembly:2.1.1
NOTE - I referred to spark streaming guide using netcat from
https://spark.apache.org/docs/latest/streaming-programming-guide.html
and it worked without using spark-submit command hence i want to know if i can do the same with kafka and spark streaming.
Provide your additional dependencies into the "jars" folder of your spark distribution. stop and start spark again. This way, dependencies wil be resolved at runtime without adding any additional option in your command line
We are using a remote Spark cluster with YARN (in Hortonworks). Developers want to use Spyder to implement Spark application in Windows. To ssh to cluster using ipython notebook or Jupyter works well. Is there any other way to communicate with Spark cluster from Windows.
Question 1: I got a headache with submitting spark job(written in python) from Windows which has no Spark installed. Is there anyone could help me out of this. Specifically, how to phrase the command line to submit the job.
We could ssh to YARN node in the cluster just in case these might relative to some solution. It is also ping-able from cluster to windows client.
Question 2: What do we need to have in the client side e.g. Spark libraries if we want to do the debug with environment like this?