Member since 
    
	
		
		
		08-29-2018
	
	
	
	
	
	
	
	
	
	
	
	
	
	
			
      
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        My Accepted Solutions
| Title | Views | Posted | 
|---|---|---|
| 4655 | 11-14-2019 02:54 AM | |
| 14119 | 11-05-2019 07:51 PM | 
			
    
	
		
		
		02-10-2021
	
		
		09:41 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							It looks like you are running Spark shell on a Windows machine, maybe your local laptop.    Is there anywhere in the code you are mentioning the hostname, "dclvmsbigdmd01"? If not, where is your 172.30.294.196 (hive.metastore.uris)? Does this IP resolve the name dclvmsbigdmd01?    Can you review if the host/domain is reachable from your local?
						
					
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		07-14-2020
	
		
		10:56 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							Hey,    Are there any parameters used in the spark-shell command?    Usually, this delay happens for a lot of reasons from a connection time to resource availability. However, we cannot confirm anything with just the driver logs.    In order to narrow this down, could you share the yarn log of the application for this application using the command, "yarn logs -applicationId application_1594337770867_0003"? We will have more clarity on what has been happening during the delay.    Thanks
						
					
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		07-14-2020
	
		
		06:11 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							Hello,    AFAIK, the Stanford CoreNLP wrapper for Apache Spark should not be a bottleneck in terms of parallel processing. Spark would take care of running it parallelly on multiple documents. Regardless of the number of documents, the number of API requests to the CoreNLP server would remain the same.
						
					
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		07-14-2020
	
		
		04:02 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							Hey, could you share the exact trace of output that you receive? If the issue is on the WebUI, could you also share the Screenshot of what you see?
						
					
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		05-08-2020
	
		
		10:55 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 Okay, let me know if changing HiveContext to SparkContext makes any difference. It could give a lead to resolution. 
						
					
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		05-08-2020
	
		
		02:20 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 Hi @clvi, Try adding the --appOwner <username> to the yarn logs command. However,  I think the application states are erased from the RM state store, probably due to an RM State Restore. 
						
					
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		04-12-2020
	
		
		06:36 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 Hey @hicha , what is the version of Spark you are using?     What do you receive as the output, when using the `Spark Session`, instead of `Hive Context`? 
						
					
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		03-23-2020
	
		
		05:29 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 Hello RIshab,      Can you please mention the error you are facing exactly?       
						
					
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		11-14-2019
	
		
		03:06 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							Hey @gnish,    Thanks for asking.    I haven't tried it before, but hope you have come across the Conversion utility from Zeppelin notes to Jupyter notebooks [1].    However, I notice that the JIRA - ZEPPELIN-2616 [2] for documentation for this feature seems yet to be resolved.    [1] https://github.com/rdblue/jupyter-zeppelin    [2] https://issues.apache.org/jira/browse/ZEPPELIN-2616
						
					
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		11-14-2019
	
		
		02:54 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
		
	
				
		
			
					
				
		
	
		
					
							 Hey @avengers,     Just thought, this could add some more value to this question here.     Spark SQL uses a Hive Metastore to manage the metadata of persistent relational entities (e.g. databases, tables, columns, partitions) in a relational database (for fast access) [1].     Also, I don't think there would be a MetaStore crash if we use it along with HiveOnSpark.     [1] https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-hive-metastore.html 
						
					
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