I have a pyspark batch job scheduled on YARN. There is now a requirement to put the logic of the spark job into a web service.
I really don't want there to be 2 copies of the same code, and therefore would like to somehow reuse the spark code inside the service, only replacing the IO parts.
The expected size of the workloads per request is small so I don't want to complicate the service by turning it into a distributed application. I would like instead to run the spark code in local mode inside the service. How do I do that? Is that even a good idea? Are there better alternatives?
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A peer of mine has created code that opens a restful api web service within an interactive spark job. The intent of our company is to use his code as a means of extracting data from various datasources. He can get it to work on his machine with a local instance of spark. He insists that this is a good idea and it is my job as DevOps to implement it with Azure Databricks.
As I understand it interactive jobs are for one-time analytics inquiries and for the development of non-interactive jobs to be run solely as ETL/ELT work between data sources. There is of course the added problem of determining the endpoint for the service binding within the spark cluster.
But I'm new to spark and I have scarcely delved into the mountain of documentation that exists for all the implementations of spark. Is what he's trying to do a good idea? Is it even possible?
The web-service would need to act as a Spark Driver. Just like you'd run spark-shell, run some commands , and then use collect() methods to bring all data to be shown in the local environment, that all runs in a singular JVM environment. It would submit executors to a remote Spark cluster, then bring the data back over the network. Apache Livy is one existing implementation for a REST Spark submission server.
It can be done, but depending on the process, it would be very asynchronous, and it is not suggested for large datasets, which Spark is meant for. Depending on the data that you need (e.g. highly using SparkSQL), it'd be better to query a database directly.
When writing an Application in Scala using Spark, when ran, is it a regular Scala application which "delegates the spark jobs to the spark cluster" and gets the desired results back ?
Or does it get completely compiled to something special consumed by a "spark engine" ?
It depends on the "deploy mode"
If you use local mode, then all actions occur locally, and you don't get any benefits from distribution that Spark is meant for. While it can be used to abstract different libraries and provide clean ways to process data via dataframes or ML, it's not really intended to be used like that
Instead, you can use cluster mode, in which your app just defines the steps to take, and then when submitted, everything happens in the remote cluster. In order to process data back in the driver, you need to use methods such as collect(), or otherwise download the results from remote file systems/databases
I have two Django websites that create a Spark Session to a Cluster which is running on Mesos.
The problem is that whatever Django starts first will create a framework and take 100% the resources permanently, it grabs them and doesn't let them go even if idle.
I am lost on how to make the two frameworks use only the neede resources and have them concurrently access the Spark cluster.
Looked into spark schedulres, dynamic resources for spark and mesos but nothing seems to work.
Is it even possible or should I change the approach?
Self solved using dynamic allocation.
I am developing a spark streaming application which basically reads data off kafka and saves it periodically to HDFS.
I am running pyspark on YARN.
My question is more for production purpose. Right now, I run my application like this:
spark-submit stream.py
Imagine you are going to deliver this spark streaming application (in python) to a client, what would you do in order to keep it running forever? You wouldn't just give this file and say "Run this on the terminal". It's too unprofessional.
What I want to do , is to submit the job to the cluster (or processors in local) and never have to see logs on the console, or use a solution like linux screen to run it in the background (because it seems too unprofessional).
What is the most professional and efficient way to permanently submit a spark-streaming job to the cluster ?
I hope I was unambiguous. Thanks!
You could use spark-jobserver which provides rest interface for uploading your jar and running it . You can find the documentation here spark-jobserver .
I am trying to implement a simple Spark SQL Application that takes a query as input and processes the data. But because I need to cache the data and I have to maintain a single SQL Context object. I am not able to understand how I can use same SQL context and keep getting queries from user.
So how does an application work? When an application is submitted to cluster, does it keep running on the cluster or performs a specific task and shuts down immediately after the task?
Spark application has a driver program that starts and configures the Spark Context. Driver program can be inside your application and you can use the same Spark Context throughout the life of your application.
Spark Context is thread safe, so multiple users can use it to run jobs concurrently.
There is an open source project Zeppelin that does just that.