databricks

问题 I can see logs using %sh command on databricks driver node. How can I copy them on my windows machine for analysis? %sh cd eventlogs/4246832951093966440 gunzip eventlog-2019-07-22--14-00.gz ls -l head -1 eventlog-2019-07-22--14-00 Version":"2.4.0","Timestamp":1563801898572,"Rollover Number":0,"SparkContext Id":4246832951093966440} Thanks 回答1: There are different ways to copy driver logs to your local machine. Option1: Cluster Driver Logs: Go to Azure Databricks Workspace => Select the cluster

问题 I can see logs using %sh command on databricks driver node. How can I copy them on my windows machine for analysis? %sh cd eventlogs/4246832951093966440 gunzip eventlog-2019-07-22--14-00.gz ls -l head -1 eventlog-2019-07-22--14-00 Version":"2.4.0","Timestamp":1563801898572,"Rollover Number":0,"SparkContext Id":4246832951093966440} Thanks 回答1: There are different ways to copy driver logs to your local machine. Option1: Cluster Driver Logs: Go to Azure Databricks Workspace => Select the cluster

问题 I can see logs using %sh command on databricks driver node. How can I copy them on my windows machine for analysis? %sh cd eventlogs/4246832951093966440 gunzip eventlog-2019-07-22--14-00.gz ls -l head -1 eventlog-2019-07-22--14-00 Version":"2.4.0","Timestamp":1563801898572,"Rollover Number":0,"SparkContext Id":4246832951093966440} Thanks 回答1: There are different ways to copy driver logs to your local machine. Option1: Cluster Driver Logs: Go to Azure Databricks Workspace => Select the cluster

问题 I am new to Python and stuck with building a hierarchy out of a relational dataset. It would be of immense help if someone has an idea on how to proceed with this. I have a relational data-set with data like _currentnode, childnode_ root, child1 child1, leaf2 child1, child3 child1, leaf4 child3, leaf5 child3, leaf6 so-on. I am looking for some python or pyspark code to build a hierarchy dataframe like below _level1, level2, level3, level4_ root, child1, leaf2, null root, child1, child3, leaf5

问题 I am new to Python and stuck with building a hierarchy out of a relational dataset. It would be of immense help if someone has an idea on how to proceed with this. I have a relational data-set with data like _currentnode, childnode_ root, child1 child1, leaf2 child1, child3 child1, leaf4 child3, leaf5 child3, leaf6 so-on. I am looking for some python or pyspark code to build a hierarchy dataframe like below _level1, level2, level3, level4_ root, child1, leaf2, null root, child1, child3, leaf5

问题 Actaully i want to install a library on my Azure databricks cluster but i cannot use the UI method. it is because everytime my cluster would change and in transition i cannot add library to it using UI. Is there any databricks utility command for doing this? 回答1: There are different methods to install packages in Azure Databricks: GUI Method Method1: Using libraries To make third-party or locally-built code available to notebooks and jobs running on your clusters, you can install a library.

问题 Actaully i want to install a library on my Azure databricks cluster but i cannot use the UI method. it is because everytime my cluster would change and in transition i cannot add library to it using UI. Is there any databricks utility command for doing this? 回答1: There are different methods to install packages in Azure Databricks: GUI Method Method1: Using libraries To make third-party or locally-built code available to notebooks and jobs running on your clusters, you can install a library.

问题 Actaully i want to install a library on my Azure databricks cluster but i cannot use the UI method. it is because everytime my cluster would change and in transition i cannot add library to it using UI. Is there any databricks utility command for doing this? 回答1: There are different methods to install packages in Azure Databricks: GUI Method Method1: Using libraries To make third-party or locally-built code available to notebooks and jobs running on your clusters, you can install a library.

问题 I have to add a customized condition, which has many columns in .withColumn. My scenario is somewhat like this. I have to check many columns row wise if they have Null values, and add those column names to a new column. My code looks somewhat like this: df= df.withColumn("MissingColumns",\ array(\ when(col("firstName").isNull(),lit("firstName")),\ when(col("salary").isNull(),lit("salary")))) Problem is I have many columns which I have to add to the condition. So I tried to customize it using

问题 I have to add a customized condition, which has many columns in .withColumn. My scenario is somewhat like this. I have to check many columns row wise if they have Null values, and add those column names to a new column. My code looks somewhat like this: df= df.withColumn("MissingColumns",\ array(\ when(col("firstName").isNull(),lit("firstName")),\ when(col("salary").isNull(),lit("salary")))) Problem is I have many columns which I have to add to the condition. So I tried to customize it using