将数据从数据块写入 Azure 突触专用池时出错

Question

我正在从我的 databricks 工作区作为 DF 读取增量表，然后我尝试将此 DF 写入 Azure Synapse Dedicated Sql 池，但我收到如下错误：-

输入不是有效的 Base-64 字符串，因为它包含非 base 64 字符、两个以上的填充字符或填充字符中的非法字符。 [错误代码 = 110813] [SQLState = S0001]

以下是我的代码： -

from pyspark.sql import *
from pyspark.sql.types import *
from pyspark.sql.functions import *

dwDatabase = "sqldbpool"
dwServer = "synapseworkspace.database.windows.net"
dwUser = "user"
dwPass = "pass"
dwJdbcPort =  "1433"
dwJdbcExtraOptions = "encrypt=true;trustServerCertificate=true;hostNameInCertificate=*.database.windows.net;loginTimeout=30;"
sqlDwUrl = "jdbc:sqlserver://" + dwServer + ":" + dwJdbcPort + ";database=" + dwDatabase + ";user=" + dwUser+";password=" + dwPass + ";$dwJdbcExtraOptions"
sqlDwUrlSmall = "jdbc:sqlserver://" + dwServer + ":" + dwJdbcPort + ";database=" + dwDatabase + ";user=" + dwUser+";password=" + dwPass

spark.conf.set(
    "spark.sql.parquet.writeLegacyFormat",
    "true")

df = StarSchemaDict['Product_Dim'] # The value of the dictionary is a DF.

(df
 .write
 .format("com.databricks.spark.sqldw")
 .option("url", sqlDwUrlSmall)
 .option("dbtable", "Product_Dim")
 .option( "forward_spark_azure_storage_credentials","True")
 .option("tempdir", adls)
 .mode("overwrite")
 .save())

数据被复制到我的 adls 暂存区域，但之后它会引发错误

Py4JJavaError: An error occurred while calling o1509.save.
: com.databricks.spark.sqldw.SqlDWSideException: Azure Synapse Analytics failed to execute the JDBC query produced by the connector.
Underlying SQLException(s):
  - com.microsoft.sqlserver.jdbc.SQLServerException: The input is not a valid Base-64 string as it contains a non-base 64 character, more than two padding characters, or an illegal character among the padding characters.  [ErrorCode = 110813] [SQLState = S0001]

完整的错误是

Py4JJavaError: An error occurred while calling o1509.save.
: com.databricks.spark.sqldw.SqlDWSideException: Azure Synapse Analytics failed to execute the JDBC query produced by the connector.
Underlying SQLException(s):
  - com.microsoft.sqlserver.jdbc.SQLServerException: The input is not a valid Base-64 string as it contains a non-base 64 character, more than two padding characters, or an illegal character among the padding characters.  [ErrorCode = 110813] [SQLState = S0001]
         
    at com.databricks.spark.sqldw.Utils$.wrapExceptions(Utils.scala:723)
    at com.databricks.spark.sqldw.DefaultSource.createRelation(DefaultSource.scala:89)
    at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:47)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:80)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:78)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:89)
    at org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$1$1.$anonfun$applyOrElse$1(QueryExecution.scala:160)
    at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$8(SQLExecution.scala:239)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:386)
    at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$1(SQLExecution.scala:186)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:968)
    at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:141)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:336)
    at org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$1$1.applyOrElse(QueryExecution.scala:160)
    at org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$1$1.applyOrElse(QueryExecution.scala:156)
    at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:575)
    at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:167)
    at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:575)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:268)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:264)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
    at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:551)
    at org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$1(QueryExecution.scala:156)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:324)
    at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:156)
    at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:141)
    at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:132)
    at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:186)
    at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:959)
    at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:427)
    at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:396)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:258)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
    at py4j.Gateway.invoke(Gateway.java:295)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:251)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.sql.SQLException: Exception thrown in awaitResult: 
    at com.databricks.spark.sqldw.JDBCWrapper.executeInterruptibly(SqlDWJDBCWrapper.scala:137)
    at com.databricks.spark.sqldw.JDBCWrapper.$anonfun$executeInterruptibly$1(SqlDWJDBCWrapper.scala:115)
    at com.databricks.spark.sqldw.JDBCWrapper.$anonfun$executeInterruptibly$1$adapted(SqlDWJDBCWrapper.scala:115)
    at com.databricks.spark.sqldw.JDBCWrapper.withPreparedStatement(SqlDWJDBCWrapper.scala:357)
    at com.databricks.spark.sqldw.JDBCWrapper.executeInterruptibly(SqlDWJDBCWrapper.scala:115)
    at com.databricks.spark.sqldw.SqlDwWriter.$anonfun$saveToSqlDW$6(SqlDwWriter.scala:247)
    at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)
    at com.databricks.backend.daemon.driver.ProgressReporter$.withStatusCode(ProgressReporter.scala:377)
    at com.databricks.backend.daemon.driver.ProgressReporter$.withStatusCode(ProgressReporter.scala:363)
    at com.databricks.spark.util.SparkDatabricksProgressReporter$.withStatusCode(ProgressReporter.scala:34)
    at com.databricks.spark.sqldw.SqlDwWriter.$anonfun$saveToSqlDW$1(SqlDwWriter.scala:204)
    at com.databricks.spark.sqldw.SqlDwWriter.$anonfun$saveToSqlDW$1$adapted(SqlDwWriter.scala:73)
    at com.databricks.spark.sqldw.JDBCWrapper.withConnection(SqlDWJDBCWrapper.scala:335)
    at com.databricks.spark.sqldw.SqlDwWriter.saveToSqlDW(SqlDwWriter.scala:73)
    at com.databricks.spark.sqldw.DefaultSource.$anonfun$createRelation$3(DefaultSource.scala:122)
    at com.databricks.spark.sqldw.Utils$.wrapExceptions(Utils.scala:692)
    ... 44 more
Caused by: com.microsoft.sqlserver.jdbc.SQLServerException: The input is not a valid Base-64 string as it contains a non-base 64 character, more than two padding characters, or an illegal character among the padding characters. 
    at com.microsoft.sqlserver.jdbc.SQLServerException.makeFromDatabaseError(SQLServerException.java:262)
    at com.microsoft.sqlserver.jdbc.SQLServerStatement.getNextResult(SQLServerStatement.java:1632)
    at com.microsoft.sqlserver.jdbc.SQLServerPreparedStatement.doExecutePreparedStatement(SQLServerPreparedStatement.java:602)
    at com.microsoft.sqlserver.jdbc.SQLServerPreparedStatement$PrepStmtExecCmd.doExecute(SQLServerPreparedStatement.java:524)
    at com.microsoft.sqlserver.jdbc.TDSCommand.execute(IOBuffer.java:7418)
    at com.microsoft.sqlserver.jdbc.SQLServerConnection.executeCommand(SQLServerConnection.java:3272)
    at com.microsoft.sqlserver.jdbc.SQLServerStatement.executeCommand(SQLServerStatement.java:247)
    at com.microsoft.sqlserver.jdbc.SQLServerStatement.executeStatement(SQLServerStatement.java:222)
    at com.microsoft.sqlserver.jdbc.SQLServerPreparedStatement.execute(SQLServerPreparedStatement.java:505)
    at com.databricks.spark.sqldw.JDBCWrapper.$anonfun$executeInterruptibly$2(SqlDWJDBCWrapper.scala:115)
    at com.databricks.spark.sqldw.JDBCWrapper.$anonfun$executeInterruptibly$2$adapted(SqlDWJDBCWrapper.scala:115)
    at com.databricks.spark.sqldw.JDBCWrapper.$anonfun$executeInterruptibly$3(SqlDWJDBCWrapper.scala:129)
    at scala.concurrent.Future$.$anonfun$apply$1(Future.scala:659)
    at scala.util.Success.$anonfun$map$1(Try.scala:255)
    at scala.util.Success.map(Try.scala:213)
    at scala.concurrent.Future.$anonfun$map$1(Future.scala:292)
    at scala.concurrent.impl.Promise.liftedTree1$1(Promise.scala:33)
    at scala.concurrent.impl.Promise.$anonfun$transform$1(Promise.scala:33)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:64)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more

Answer 1

我试图在我的环境中重现相同的内容，但我遇到了同样的错误。

解决上述错误。 请按照以下步骤操作：

步骤 1：

将存储帐户与 Azure Databricks 连接：

spark.conf.set(
  "fs.azure.account.key.<storage_account>.blob.core.windows.net","<Access_key>")

第2步：

确保检查 JDBC URL 连接配置详细信息和存储帐户语法：

jdbc = "jdbc:sqlserver://<Your_server_name>.sql.azuresynapse.net:1433;database=<database_name>;user=<user_Id>;password=<pass>;encrypt=true;trustServerCertificate=false;hostNameInCertificate=*.database.windows.net;loginTimeout=30;"

blob = "wasbs://<container>@<storage_account_name>.blob.core.windows.net/<folder_name>"

• 示例数据框df12 ：

第 3 步：将数据从 Databricks 写入 Azure synapse Dedicated Pool

df12.write \
  .format("com.databricks.spark.sqldw") \
  .option("url", jdbc) \
  .option("forwardSparkAzureStorageCredentials", "true") \
  .option("dbTable", "table1") \
  .option("tempDir", blob) \
  .mode("overwrite") \
  .save()

Output：