我正在使用 Spark 将 Dataframe 加载到 Netezza 数据库中。
我在 Netezza JDBC 连接上遇到间歇性错误，但我不知道为什么。
我已经跟踪这个错误一个月了，在数千次执行中，它们已经发生了 18 次，而且所有这些都是在查询执行超过 140 秒之后发生的。
我尝试在 Spark 应用程序中添加 spark.network.timeout 但没有任何反应。
有人遇到过这种类型的错误吗？我可以在 JDBC 连接字符串中添加一些内容吗？
详细日志错误：
-- TypeError: An error occurred while calling o136.save.
: org.netezza.error.NzSQLException: Connection timed out (Connection timed out)
at org.netezza.sql.NzConnection.initSocket(NzConnection.java:2899)
at org.netezza.sql.NzConnection.open(NzConnection.java:293)
at org.netezza.datasource.NzDatasource.getConnection(NzDatasource.java:719)
at org.netezza.datasource.NzDatasource.getConnection(NzDatasource.java:706)
at org.netezza.Driver.connect(Driver.java:155)
at org.apache.spark.sql.execution.datasources.jdbc.connection.BasicConnectionProvider.getConnection(BasicConnectionProvider.scala:49)
at org.apache.spark.sql.execution.datasources.jdbc.connection.ConnectionProviderBase.create(ConnectionProvider.scala:102)
at org.apache.spark.sql.jdbc.JdbcDialect.$anonfun$createConnectionFactory$1(JdbcDialects.scala:122)
at org.apache.spark.sql.jdbc.JdbcDialect.$anonfun$createConnectionFactory$1$adapted(JdbcDialects.scala:118)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.getQueryOutputSchema(JDBCRDD.scala:63)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:58)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation$.getSchema(JDBCRelation.scala:242)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:37)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:88)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:75)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:73)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:84)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:98)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:109)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:98)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:94)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:584)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:176)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:584)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
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:560)
at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:94)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:81)
at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:79)
at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:116)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:860)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:390)
at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:363)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:247)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.base/java.lang.Thread.run(Thread.java:829)
'''
如果您使用的是 Netezza 的 Spark SQL 数据连接器，您还可以在创建 DataFrame 时在选项映射中设置这些属性：
val df = spark.read
.format("netezza")
.options(Map(
"loginTimeout" -> "30",
"socketTimeout" -> "60"
))
.load("mydatabase")