Error while writing data from databricks to Azure synapse Dedicated Pool

Question

I am reading delta tables from my databricks workspace as DF and then I am trying to write this DF into Azure Synapse Dedicated Sql Pool, but I am getting error like:-

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]

Below is my code:-

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())

The data is getting copied to my adls staging area but after that it is throwing an error

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]

The complete error is

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

B. B. Naga Sai Vamsi · Answer 1 · 2022-09-23T11:48:30.443

0

I tried to reproduce the same in my environment and I got same error.

To resolve above error. please follow below steps:

Steps 1:

Connect storage account with Azure Databricks:

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

enter image description here

Step 2 :

Make sure to check JDBC URL connection configuration details and storage account syntax:

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>"

enter image description here

Sample Data frame df12:

enter image description here

Step 3: Writing data from Databricks to 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()

enter image description here

Output:

enter image description here

edited Sep 23 '22 at 11:48

answered Sep 23 '22 at 10:42

B. B. Naga Sai Vamsi

2,386
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hi @Sai Vamsi , One Query here .option("dbTable", "table1") -- what is this dbtable and table 1 defining in your code, do we need to create a schema first in dedicated pool for the same table as we have in our databricks, or it will be created automatically – BigData Lover Nov 29 '22 at 17:08
As per above approach, if you want insert dataframe data into SQL table then, using `option ("dbTable", "")` – B. B. Naga Sai Vamsi Nov 29 '22 at 17:54
do we need to create a schema first in dedicated pool first or not? – BigData Lover Nov 30 '22 at 06:04
Yes, you need to create. – B. B. Naga Sai Vamsi Nov 30 '22 at 06:09
hi @SaiVamsi, as i am working in synapse notebook, what changes i can make in this code df12.write \ .format("com.databricks.spark.sqldw") \ .option("url", jdbc) \ .option("forwardSparkAzureStorageCredentials", "true") \ .option("dbTable", "table1") \ .option("tempDir", blob) \ .mode("overwrite") \ .save() – BigData Lover Nov 30 '22 at 06:24
Hi @BigDataLover, please make it as question, what did you tried and expecting. – B. B. Naga Sai Vamsi Nov 30 '22 at 06:41
hi @Sai https://stackoverflow.com/questions/74617642/how-to-load-a-spark-dataframe-data-into-synapse-dedicated-pool-in-synapse/74617696?noredirect=1#comment131711281_74617696 – BigData Lover Nov 30 '22 at 06:42
this is the question – BigData Lover Nov 30 '22 at 06:42

Error while writing data from databricks to Azure synapse Dedicated Pool

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