How can I enable REST API on Spark Master. I am having a Spark cluster with 3 nodes running on google cloud environment. REST API can be enabled in the spark-defaults.conf file with spark.master.rest.enabled to true. Is there any other method to do this?
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I'm using maven and scala to create a spark application that needs to connect to a cluster on azure databricks.
How can i point my sparksession to connect to the databricks cluster?
I saw databricks-connect, but it loads some jar files using sbt.
I don't understand how it achives that connectivity exactly.
My use case requires to run a spark job programmatically on the db cluster upon request, so i need to be able to connect it there.
we are planning to deploy all spark batch and streaming jobs on Kubernetes (as cluster services). I want to know if we can deploy all spark jobs using spark operator manifest file in PROD Kubernetes Server.
Kubernetes cluster
You can take a look at https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke wherein you can run a Dataproc cluster on GKE. It is a managed service by Google Cloud.
I have created a small application that submits a spark job at certain intervals and creates some analytical reports. These jobs can read data from a local filesystem or a distributed filesystem (fs could be HDFS, ADLS or WASB). Can I run this application on Azure databricks cluster?
The application works fine on HDInsights cluster as I was able to access the nodes. I kept my deployable jar at one location, started it using the start-script similarly I could also stop it using the stop-script that I prepared.
One thing I found is that Azure Databricks has its own File System: ADFS, I can also add support for this file system but then will I be able to deploy and run my application as I was able to do it on the HDInsight cluster? If not, is there a way I can submit jobs from an edge node, my HDInsight cluster or any other OnPrem Cluster to Azure Databricks cluster.
Have you looked at Jobs? https://docs.databricks.com/user-guide/jobs.html. You can submit jars to spark-submit just like on HDInsight.
Databricks file system is DBFS - ABFS is used for Azure Data Lake. You should not need to modify your application for these - the file paths will be handled by databricks.
Has anyone tried using Azure data bricks as the spark cluster for CDAP job processing. CDAP documentation details how to add it to Azure HDInsight, but just wondering is there a way to configure CDAP to point to data bricks spark cluster, is it even possible? OR this kind of integration needs a specific data bricks client connector jar? If anyone has any insights that would be helpful.
There is no out of box support for Databricks spark on Azure. But, that said you can develop a new Cloud Runtime that is capable of submitting the jobs to Databricks spark cluster. Here is example of how to write a runtime extension for Cloud Dataproc and EMR.
I am new to AWS EC2 and need to know how I can submit my Spark job to AWS EC2 spark cluster. Like in azure we can directly submit the job through IntelliJ idea with azure plugin.
You can submit a spark job easily through spark-submit command. Refer http://spark.apache.org/docs/latest/submitting-applications.html
Options:
1) login to master or other driver gateway node and use spark-submit to submit the job through YARN/media/etc
2) Use spark submit cluster deploy mode from any machine with sufficient ports and firewall access (may require configuration, such as client config files from Cloudera manager for CDH cluster)
3) Use a server setup like Livy (open source through Cloudera, and MS Azure HDinsights uses and contributes to it) or maybe thrift server. Livy (Livy.io) is a nice simple REST service that also has language APIs for Scala/Java to make it extra easy to submit jobs (and run interactive persisted sessions!)