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
Related
I have around 10 Spark jobs where each would do some transformation and load data into Database. The Spark session has to be opened individually for each job and closed and every time initialization consumes time.
Is it possible to create the Spark session only once and re-use the same across multiple jobs ?
Technically if you use a single Spark Session you will end-up having a single Spark application, because you will have to package and run multiple ETL (Extract, Transform, & Load) within a single JAR file.
If you are running those jobs in production cluster, most likely you are using spark-submit to execute your application jar, which will have to go through initializing phase every-time you submits a job through Spark Master -> Workers in client mode.
In general, having a long running spark session is mostly suitable for prototyping, troubleshooting and debugging purposes, for example a single spark session can be leveraged in spark-shell, or any other interactive development environment, like Zeppelin; but, not with spark-submit as far as I know.
All in all, a couple of design/business questions is worth to consider here; does merging multiple ETL jobs together will generate a code that is easy to sustain, manage and debug? Does it provide the required performance gain? Risk/Cost analysis ? etc.
Hope this would help
You can submit your job once, in other words do spark-submit once. Inside the code which is submitted you can have 10 calls each doing some transformation and load data into Database.
val spark : SparkSession = SparkSession.builder
.appName("Multiple-jobs")
.master("<cluster name>")
.getOrCreate()
method1()
method2()
def method1():Unit = {
//it will give the same spark session created outside the method.
val spark = SparkSession.builder.getOrCreate()
//work
}
However if the job is time consuming say it takes 10 minutes then in comparision you wouldn't be spending a lot of time in creating separate spark sessions. I wouldn't worry about 1 spark session per job. However I will be worried if a separate Spark session is created per method or per unit test case, that is where I will save spark sessions.
I have started working on Spark using Python. I'm working on an application that uses SparkML Linear Regression APIs. When I submit my job in YARN cluster mode, during the execution phase, many pyspark-shell apps get created with YARN as the user. I could see them in the YARN UI. They eventually get finished with succeeded status and my main application which I actually submitted then gets finished with succeeded status. Is this an expected behavior? This is kinda interesting to me since I create the singleton sparkSession instance and use it throughout my application so I don't know why pyspark-shell sessions/apps get created.
The immediate solution would be to use sparkContext instead of sparkSession. But it would be interesting to see your configuration lines to see how you're creating your sessions to be able to tell why multiple apps are being created.
We just updated to Spark 2.2 from Spark 1.6, so we have yet to delve seriously into sparkSessions (which are new in 2+).
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've implemented a custom data layer on Spark that has Spark node persisting some data locally and announcing their persistence of data to the Spark master. This works great by running some custom code on each Spark node and master that we've written, but now I'd like to implement a replication protocol across my cluster. What I'd like to build is that once the master gets a message from a node saying it's persisted data, that the master can randomly select two other nodes and have them persist the same data.
I've been digging through the docs but I don't see an obvious way of the SparkContext giving me a list of live nodes. Am I missing something?
There isnt a public API for doing this. However, you could use the Developer API SparkListener (http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.scheduler.SparkListener). You can create a custom SparkListener class and add it to the SparkContext as
sc.addSparkListener(yourListener)
The system will class the onBlockManagerAdded and onBlockManagerRemoved when a BlockManager gets added or removed, and from the BlockManager's ID, I believe you can get the URL of the nodes running the Spark live executors (which run BlockManagers).
I agree that this is a little hacky. :)