azure-databricks

来源： https://stackoverflow.com/questions/63580115/how-to-read-parquet-files-under-a-directory-using-pyspark

来源： https://stackoverflow.com/questions/63580115/how-to-read-parquet-files-under-a-directory-using-pyspark

问题 In PySpark it you can define a schema and read data sources with this pre-defined schema, e. g.: Schema = StructType([ StructField("temperature", DoubleType(), True), StructField("temperature_unit", StringType(), True), StructField("humidity", DoubleType(), True), StructField("humidity_unit", StringType(), True), StructField("pressure", DoubleType(), True), StructField("pressure_unit", StringType(), True) ]) For some datasources it is possible to infer the schema from the data-source and get

问题 I'm looking for a way to be able to connect to Databricks deltalake tables from ADF and other Azure Services(like Data Catalog). I don't see databricks data store listed in ADF data sources. On a similar question - Is possible to read an Azure Databricks table from Azure Data Factory? @simon_dmorias seems to have suggested using ODBC connection to connect to databricks tables. I tried to set up the ODBC connection but it requires IR to be setup. There are 2 options I see when creating the IR.

问题 I am using ADF to execute Databricks notebook. At this time, I have 6 pipelines, and they are executed consequently. Specifically, after the former is done, the latter is executed with multiple parameters by the loop box, and this keeps going. For example, after the first pipeline is done, it will trigger 3 instances of the second pipeline with different parameters, and each of these instances will trigger multiple instances of the third pipeline. As a result, the deeper I go, the more

问题 I am using ADF to execute Databricks notebook. At this time, I have 6 pipelines, and they are executed consequently. Specifically, after the former is done, the latter is executed with multiple parameters by the loop box, and this keeps going. For example, after the first pipeline is done, it will trigger 3 instances of the second pipeline with different parameters, and each of these instances will trigger multiple instances of the third pipeline. As a result, the deeper I go, the more

问题 I am running pyspark from an Azure Machine Learning notebook. I am trying to move a file using the dbutil module. from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() def get_dbutils(spark): try: from pyspark.dbutils import DBUtils dbutils = DBUtils(spark) except ImportError: import IPython dbutils = IPython.get_ipython().user_ns["dbutils"] return dbutils dbutils = get_dbutils(spark) dbutils.fs.cp("file:source", "dbfs:destination") I got this error:

问题 I need to create a token in Azure Databricks using ARM template. I am able to create Azure Databricks using ARM template but unable to create token in Azure Databricks using ARM template Following is the template which i have used to create Azure Databricks { "$schema": "https://schema.management.azure.com/schemas/2015-01- 01/deploymentTemplate.json#", "contentVersion": "1.0.0.0", "parameters": { "workspaceName": { "type": "string", "metadata": { "description": "The name of the Azure

问题 I am trying to install an application on Azure databricks from python3. From the databricks notebook: %sh cd /dbfs/my_path/app_files/my_app/ #( there is a makefile here) make Enter soft-link target file or directory for "lib/include/xxx_app_name" (return if not needed): I cannot use shell to access azure databricks, how I can enter the necessary command for the interactive command line ? thanks 来源： https://stackoverflow.com/questions/61702105/auzre-databricks-install-an-application-but-a

问题 I am using a Spark Databricks cluster and want to add a customized Spark configuration. There is a Databricks documentation on this but I am not getting any clue how and what changes I should make. Can someone pls share the example to configure the Databricks cluster. Is there any way to see the default configuration for Spark in the Databricks cluster. 回答1: To fine tune Spark jobs, you can provide custom Spark configuration properties in a cluster configuration. On the cluster configuration