What is the purpose of the getOrCreate method from SparkContext class? I don't understand when we should use this method.
If I have 2 spark applications that are run with spark-submit, and in the main method I instantiate the spark context with SparkContext.getOrCreate, both app will have the same context?
Or the purpose is simpler, and the only purpose is when I create a spark app, and I don't want to send the spark context as a parameter to a method, and I will get it as a singleton object?
If I have 2 spark applications that are run with spark-submit, and in the main method I instantiate the spark context with SparkContext.getOrCreate, both app will have the same context?
No, SparkContext is a local object. It is not shared between applications.
when I create a spark app, and I don't want to send the spark context as a parameter to a method, and I will get it as a singleton object?
This is exactly the reason. SparkContext (or SparkSession) are ubiquitous in Spark applications and core Spark's source, and passing them around would a huge burden.
It also useful for multithreaded applications where arbitrary thread can initalize contexts.
About docs:
is function may be used to get or instantiate a SparkContext and register it as a singleton object. Because we can only have one active SparkContext per JVM, this is useful when applications may wish to share a SparkContext.
Driver runs in its own JVM and there is no built-in mechanism to share it between multiple full-fledged Java applications (proper application executing its own main. Check Is there one JVM per Java application? and Why have one JVM per application? for related general questions). Application refers to "logical application" where multiple modules execute its own code - one example is SparkJob on spark-jobserver. This scenario is no different than passing SparkContext to a function.
Related
I am trying to use DI - Dependency Injection in my code but the nature of Spark - Driver vs Executor is making things tough. DI is trying to make sure that the objects are created during the bootstrap, it works best for the driver but not for the executor. so I am planning to bootstrap the objects for executor as part of it's creation or loaded etc., I am looking for a listener that can I listen to spark context events for executor; I tried looking into the different listeners but none is useful for me. If I have missed or overlooked, is there a listener for spark context or for executor?
After deploying a spark structure streaming application, how can I obtain a spark session on the executor for deploying another job with the same session and same configuration settings?
You cannot get spark session on to executor if you are running spark in cluster mode as spark session object cannot be serialised thus cannot send it to executor. Also, it is against spark design principles to do so.
I may be able to help you with this if you can tell me the problem statement.
Technically you can get spark session on the executor doesn't matter which mode you are running it in but not really worth the effort.Spark session is an object of various internal spark settings along with other user defined settings we provide on startup.
The only reason those configuration settings are not available in executor is because most of them are marked as transient which means those objects will be sent as null as it does not make logical sense to send them to the executors, in the same way it does not make sense to send database connection objects from one node to another.
One of the cumbersome ways to do this would be to get all configuration settings from your spark session in your driver, set in some custom object marked as serializable and send it to the executor. Also your executor environment should be same as driver in terms of all spark jars/directories and other spark properties such as SPARK_HOME etc which can be hectic if you run and realize every time you are missing something. It will be a different spark session object but with all the same settings.
The better option would be to run another spark application with the same settings you provide for your other application as one spark session is associated for one spark application.
It is not possible. I also had similar requirement then I have to create two separate main class and one spark launcher class in that I was doing sparksession.conf.set(main class name ) based on which class i wanted to run. If I want to run both then I was using thread.sleep() to complete first before launching another. I also used sparkListener code to get status whether it has completed or not.
I am aware that this is a late response. Just thought this might be useful.
So, you can use something like the below code snippet in you spark structured streaming application:
for spark versions <= 3.2.1
spark_session_for_this_micro_batch = microBatchOutputDF._jdf.sparkSession()
For spark versions >= 3.3.1:
spark_session_for_this_micro_batch = microBatchOutputDF.sparkSession
Your function can use this spark session to create dataframe there.
You can refer this medium post
pyspark doc
I have an Apache Spark data loading and transformation application with pyspark.sql that runs for half an hour before throwing an AttributeError or other run-time exceptions.
I want to test my application end-to-end with a small data sample, something like Apache Pig's ILLUSTRATE. Sampling down the data does not help much. Is there a simple way to do this?
It sounds like an idea that could easily be handled by a SparkListener. It gives you access to all the low-level details that the web UI of any Spark application could ever be able to show you. All the events that are flying between the driver (namely DAGScheduler and TaskScheduler with SchedulerBackend) and executors are posted to registered SparkListeners, too.
A Spark listener is an implementation of the SparkListener developer API (that is an extension of SparkListenerInterface where all the callback methods are no-op/do-nothing).
Spark uses Spark listeners for web UI, event persistence (for Spark History Server), dynamic allocation of executors and other services.
You can develop your own custom Spark listeners and register them using SparkContext.addSparkListener method or spark.extraListeners setting.
Go to a Spark UI of your job and you will find a DAG Visualization there. That's a graph representing your job
To test your job on a sample use sample as an input first of all ;) Also you may run your spark locally, not on a cluster and then debug it in IDE of your choice (like IDEA)
More info:
This great answer explaining DAG
DAG introduction from DataBricks
I am running spark on Amazon EMR with yarn as the cluster manager. I am trying to write a python app which starts and caches data in memory. How can I allow other python programs to access that cached data i.e.
I start an app Pcache -> Cache data and keep that app running.
Another user can access that same cached data running a different instance.
My understanding was that it should be possible to get a handle on the already running sparkContext and access that data? Is that possible? Or do I need to set up an API on top of that Spark App to access that data. Or may be use something like Spark Job Server of Livy.
It is not possible to share the SparkContext between multiple processes. Indeed your options are to build the API yourself, with one server holding the SparkContext and its clients telling it what to do with it, or use the Spark Job Server which is a generic implementation of the same.
I think this can help you. :)
classmethod getOrCreate(conf=None)
Get or instantiate a SparkContext and register it as a singleton object.
Parameters: conf – SparkConf (optional)
http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.SparkContext.getOrCreate
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.