有关Databricks统一分析平台的问题
在Microsoft Azure Databricks中,我的列包含XML字符串,我想拔出HREF,如果存在,则在该HREF中ltr __ :: URL。我尝试了: 选择Xpath_string(my_column,'string(// a ...
com.databricks.sql.transaction.tahoe.deltaunsupportedoperationException: [delta_source_table_ignore_changes]检测到数据更新(用于 示例创建或替换表作为select(map(partitionby-> [],,, clusterby-> [],描述 - > null,iSmanaged--> true,属性 - > {“ delta.enabledeletionvectors”:“ true”},statsonload-> false))))) 版本8处的源表。目前不支持这。如果是 会定期发生,您可以跳过更改,设置 选项为“ true”。如果您想要数据 要反映的更新,请重新启动此查询 CheckPoint目录或使用DLT时进行完整刷新。如果你 需要处理这些更改,请切换到MVS。源表 可以在Path gs gs://databricks..
from pyspark.sql import SparkSession from pyspark.sql.functions import * spark = SparkSession.builder.appName("uber_data_analysis").getOrCreate() df = spark.read.csv("/FileStore/tables/uber_data.csv", header = True, inferSchema = True)
databricksworkflowtaskgroup和Spark_jar task的模板任务参数
Task_group= databricksworkflowerator( group_id =“测试”, databricks_conn_id = dbx_conn_id, job_clusters = [ { “ job_cluster_key”:...
IAM使用df.cache()来cachce数据框架,并使用databricks以min实例为1和max实例自动化为8。但是,由于某些执行者在MIDD中死亡...
特别是,我有一个带有HW信息的表,该表可以通过计划的作业定期更新,例如:
I在Azure Dev-env中有一个表,我需要克隆或将其复制到test-subscription中。 可以使用哪种方法来做到这一点? 该桌子有20 000 000行。是导出dat的选项吗?
I我需要在Azure Dev-env中创建一个克隆或在Test-Subscription中的test-env副本。 可以使用哪些方法来执行此操作? 该桌子有20 000 000行,是选择...
无需使用Databricks安装ADLS GEN 2 ABFSS存储帐户:IllegalargumentException:未支撑的Azure方案:ABFSS
当我尝试使用以下代码安装ADLS Gen Storage帐户时,我会收到错误: 非法玛格门张开:不支持的Azure方案:ABFSS container_name =“ mycontainer”
为fs.azure.account.key with com.com.crelytics检测到的invalid配置值:spark-excel
我已经设置了我的数据链条笔记本,以使用服务主体使用以下配置来访问ADL。 service_credential = dbutils.secrets.get(scope =“”,key =“” 我已经设置了我的databricks笔记本,以使用服务主体使用以下配置访问ADL。 service_credential = dbutils.secrets.get(scope="<scope>",key="<service-credential-key>") spark.conf.set("fs.azure.account.auth.type.<storage-account>.dfs.core.windows.net", "OAuth") spark.conf.set("fs.azure.account.oauth.provider.type.<storage-account>.dfs.core.windows.net", "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider") spark.conf.set("fs.azure.account.oauth2.client.id.<storage-account>.dfs.core.windows.net", "<application-id>") spark.conf.set("fs.azure.account.oauth2.client.secret.<storage-account>.dfs.core.windows.net", service_credential) spark.conf.set("fs.azure.account.oauth2.client.endpoint.<storage-account>.dfs.core.windows.net", "https://login.microsoftonline.com/<directory-id>/oauth2/token") 我可以从ADL读取CSV文件,但是使用Excel文件获取Invalid configuration value detected for fs.azure.account.key。以下是读取Excel文件的代码。 #library used com.crealytics:spark-excel_2.12:3.2.2_0.18.0 df = spark.read.format("com.crealytics.spark.excel") \ .option("header", "true") \ .option("dataAddress", "'Sheet1'!A1:BA100000")\ .option("delimiter", ",") \ .option("inferSchema", "true") \ .option("multiline", "true") \ .load(file_path_full) 堆栈跟踪 Failure to initialize configurationInvalid configuration value detected for fs.azure.account.key at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.services.SimpleKeyProvider.getStorageAccountKey(SimpleKeyProvider.java:51) at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.AbfsConfiguration.getStorageAccountKey(AbfsConfiguration.java:577) at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.AzureBlobFileSystemStore.initializeClient(AzureBlobFileSystemStore.java:1832) at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.AzureBlobFileSystemStore.<init>(AzureBlobFileSystemStore.java:224) at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.initialize(AzureBlobFileSystem.java:142) at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:3469) at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:537) at com.crealytics.spark.excel.WorkbookReader$.readFromHadoop$1(WorkbookReader.scala:60) at com.crealytics.spark.excel.WorkbookReader$.$anonfun$apply$4(WorkbookReader.scala:79) at com.crealytics.spark.excel.DefaultWorkbookReader.$anonfun$openWorkbook$3(WorkbookReader.scala:102) at scala.Option.fold(Option.scala:251) at com.crealytics.spark.excel.DefaultWorkbookReader.openWorkbook(WorkbookReader.scala:102) at com.crealytics.spark.excel.WorkbookReader.withWorkbook(WorkbookReader.scala:33) at com.crealytics.spark.excel.WorkbookReader.withWorkbook$(WorkbookReader.scala:32) at com.crealytics.spark.excel.DefaultWorkbookReader.withWorkbook(WorkbookReader.scala:87) at com.crealytics.spark.excel.ExcelRelation.excerpt$lzycompute(ExcelRelation.scala:48) at com.crealytics.spark.excel.ExcelRelation.excerpt(ExcelRelation.scala:48) at com.crealytics.spark.excel.ExcelRelation.headerColumns$lzycompute(ExcelRelation.scala:121) at com.crealytics.spark.excel.ExcelRelation.headerColumns(ExcelRelation.scala:120) at com.crealytics.spark.excel.ExcelRelation.$anonfun$inferSchema$1(ExcelRelation.scala:189) at scala.Option.getOrElse(Option.scala:189) at com.crealytics.spark.excel.ExcelRelation.inferSchema(ExcelRelation.scala:188) at com.crealytics.spark.excel.ExcelRelation.<init>(ExcelRelation.scala:52) at com.crealytics.spark.excel.DefaultSource.createRelation(DefaultSource.scala:52) at com.crealytics.spark.excel.DefaultSource.createRelation(DefaultSource.scala:29) at com.crealytics.spark.excel.DefaultSource.createRelation(DefaultSource.scala:24) at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:385) at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:356) at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:323) at scala.Option.getOrElse(Option.scala:189) at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:323) at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:236) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380) at py4j.Gateway.invoke(Gateway.java:295) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:251) at java.lang.Thread.run(Thread.java:748) Caused by: Invalid configuration value detected for fs.azure.account.key at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.diagnostics.ConfigurationBasicValidator.validate(ConfigurationBasicValidator.java:49) at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.diagnostics.Base64StringConfigurationBasicValidator.validate(Base64StringConfigurationBasicValidator.java:40) at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.services.SimpleKeyProvider.validateStorageAccountKey(SimpleKeyProvider.java:70) at shaded.databricks.azurebfs.org.apache.hadoop.fs.azurebfs.services.SimpleKeyProvider.getStorageAccountKey(SimpleKeyProvider.java:49) ... 42 more ,找到了解决方案。 还需要添加以下配置。 spark._jsc.hadoopConfiguration().set("fs.azure.account.auth.type.<storage-account>.dfs.core.windows.net", "OAuth") spark._jsc.hadoopConfiguration().set("fs.azure.account.oauth.provider.type.<storage-account>.dfs.core.windows.net", "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider") spark._jsc.hadoopConfiguration().set("fs.azure.account.oauth2.client.id.<storage-account>.dfs.core.windows.net", "<application-id>") spark._jsc.hadoopConfiguration().set("fs.azure.account.oauth2.client.secret.<storage-account>.dfs.core.windows.net", service_credential) spark._jsc.hadoopConfiguration().set("fs.azure.account.oauth2.client.endpoint.<storage-account>.dfs.core.windows.net", "https://login.microsoftonline.com/<directory-id>/oauth2/token")
创建或替换databricks中的表返回delta_create_table_with_non_empty_location错误
试图在Databricks中创建表格 创建或替换表foo.bar( 评论varchar(255), row_count int, 日期时间戳 ) 返回以下错误 [
这里是指向相关文档的几个链接。 secret Redaction的限制
如果从另一个路径加载文件,则会在数据链球弹中再次加载同一文件? 或者,如果一段时间后再次将文件放在同一目录中,它是否加载相同的文件?
尽管试图运行我的DLT管道,但我反复遇到这个问题 不确定这里是什么原因? 我也不知道应用更改如何合并? 喜欢基于Sequencecol ...