I have a azure databricks cluster onto which I have mounted a container.
Under Cluster logging section, I have provided my mounting path and I am receiving logs on my blob container but I wanted to create a logging system using log4j logger which would create a single file for a day on my blob container.
Any suggestions/documentation details would help.
Related
I am using logging on my databricks clusters and I am sending my log data to blob container which I have mounted on my cluster (Cluster Configuration -> Advanced Options -> Logging -> Mounted Path).
Earlier, all the logs were getting generated but after some day (maybe because of some change) there are no logs being generated on log4j console of databricks.
I checked the same on blob container as well there also only executor logs are being logged
Blob Log Image 1
I tried recreating the same issue on some other cluster, but there I could find all the logs are getting generated as expected.
Blob log Image 2
This may occur if the SAS of your Blob storage expires after mounting with databricks.
To trouble shoot this, create two clusters and store one cluster's logs in dbfs and another in mounted blob with specified time for expiration of SAS.
After expiration check the logs stored in blob and dbfs. If logs are not storing in only blob this issue because of SAS expiration. If logs not storing in both dbfs and blob, then there is an issue with the workspace for cluster logs.
My suggestion is trying with new databricks workspace and if there is no issue with logs in both dbfs and blob, you can try Diagnostic logging in Azure Databricks - Azure Databricks | Microsoft Docs
We could use some help on how to send Spark Driver and worker logs to a destination outside Azure Databricks, like e.g. Azure Blob storage or Elastic search using Eleastic-beats.
When configuring a new cluster, the only options on get reg log delivery destination is dbfs, see
https://docs.azuredatabricks.net/user-guide/clusters/log-delivery.html.
Any input much appreciated, thanks!
Maybe the following could be helpful :
First you specify a dbfs location for your Spark driver and worker logs.
https://docs.databricks.com/user-guide/clusters/log-delivery.html
Then, you create a mount point that links your dbfs folder to a Blob Storage container.
https://docs.databricks.com/spark/latest/data-sources/azure/azure-storage.html#mount-azure-blob-storage-containers-with-dbfs
Hope this help !
I'm trying to access a wasb(Azure blob storage) file in Spark and need to specify the account key.
How do I specify the account in the spark-env.sh file?
fs.azure.account.key.test.blob.core.windows.net
EC5sNg3qGN20qqyyr2W1xUo5qApbi/zxkmHMo5JjoMBmuNTxGNz+/sF9zPOuYA==
WHen I try this it throws the following error
fs.azure.account.key.test.blob.core.windows.net: command not found
From your description, it is not clear that the Spark you used is either on Azure or on local.
For Spark running on local, refer this blog post which introduces how to access Azure Blob Storage from Spark. The key is that you need to configure Azure Storage account as HDFS-compatible storage in core-site.xml file and add two jars hadoop-azure & azure-storage to your classpath for accessing HDFS via the protocol wasb[s].
For Spark running on Azure, the difference is just only access HDFS with wasb, all configurations have been done by Azure when creating HDInsight cluster with Spark.
How to get list of file from Azure blob storage in Spark and Scala.
I am not getting any idea to approach this.
I don't know the Spark you used is either on Azure or on local. So they are two cases, but similar.
For Spark running on local, there is an offical blog which introduces how to access Azure Blob Storage from Spark. The key is that you need to configure Azure Storage account as HDFS-compatible storage in core-site.xml file and add two jars hadoop-azure & azure-storage to your classpath for accessing HDFS via the protocol wasb[s]. You can refer to the offical tutorial to know HDFS-compatible storage with wasb, and the blog about configuration for HDInsight more details.
For Spark running on Azure, the difference is just only access HDFS with wasb, the other preparations has been done by Azure when creating HDInsight cluster with Spark.
The method for listing files is listFiles or wholeTextFiles of SparkContext.
Hope it helps.
If you are using databricks, try the below
dbutils.fs.ls(“blob_storage_location”)
I have an new HDInisght:Spark cluster that I spun up
I want to upload a file via AMbari portal but I don't see the HDFS option:
What am I missing? How can I get my .csv up to the server so I can start using it in the Python notebook?
HDInsight clusters do not work off local HDFS. They use Azure Blob Storage instead. So upload to the storage account that got attached to the cluster during its creation.
More info:
https://learn.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-use-blob-storage