override getResultType方法，返回Types.SQL_TIMESTAMP.
这样应该可以绕过。
1.10会修复这个问题。
Best,
Jingsong Lee
------------------------------------------------------------------
From:守护 <346531...@qq.com>
Send Time:2019年9月5日(星期四) 12:11
To:user-zh@flink.apache.org JingsongLee <lzljs3620...@aliyun.com>; user-zh
<user-zh@flink.apache.org>
Subject:回复： Re: flink1.9中blinkSQL对定义udf的TIMESTAMP类型报错
在哪声明DataType，这个要引入什么包吗，求指点，我的udf代码如下：
import org.apache.flink.table.functions.ScalarFunction;
import java.sql.Timestamp;
public class UTC2Local extends ScalarFunction {
public Timestamp eval(Timestamp s) {
long timestamp = s.getTime() + 28800000;
return new Timestamp(timestamp);
}
}
------------------ 原始邮件 ------------------
发件人: "JingsongLee"<lzljs3620...@aliyun.com.INVALID>;
发送时间: 2019年9月5日(星期四) 中午11:55
收件人: "user-zh"<user-zh@flink.apache.org>;
主题: Re: flink1.9中blinkSQL对定义udf的TIMESTAMP类型报错
你声明了DataType吗？代码怎么写的？
由于目前只支持精度<=3，所以你得用DataTypes.TIMESTAMP(3)来表示。
Best,
Jingsong Lee
------------------------------------------------------------------
From:守护 <346531...@qq.com>
Send Time:2019年9月5日(星期四) 11:48
To:user-zh <user-zh@flink.apache.org>
Subject:flink1.9中blinkSQL对定义udf的TIMESTAMP类型报错
社区的各位大佬好：
使用场景：flink1.9版本使用flinkSQL创建udf函数使用没有问，当切换到blinkSQL使用这个udf就会报错TIMESTAMP类型错误，udf实现的功能也很简单，就是将时间+8小时，报错信息如下
org.apache.flink.client.program.ProgramInvocationException: The main method
caused an error: Not support dataType: TIMESTAMP(9)
at
org.apache.flink.client.program.PackagedProgram.callMainMethod(PackagedProgram.java:593)
at
org.apache.flink.client.program.PackagedProgram.invokeInteractiveModeForExecution(PackagedProgram.java:438)
at
org.apache.flink.client.program.OptimizerPlanEnvironment.getOptimizedPlan(OptimizerPlanEnvironment.java:83)
at
org.apache.flink.client.program.PackagedProgramUtils.createJobGraph(PackagedProgramUtils.java:80)
at
org.apache.flink.client.program.PackagedProgramUtils.createJobGraph(PackagedProgramUtils.java:122)
at
org.apache.flink.client.cli.CliFrontend.runProgram(CliFrontend.java:227)
at
org.apache.flink.client.cli.CliFrontend.run(CliFrontend.java:205)
at
org.apache.flink.client.cli.CliFrontend.parseParameters(CliFrontend.java:1010)
at
org.apache.flink.client.cli.CliFrontend.lambda$main$10(CliFrontend.java:1083)
at
java.security.AccessController.doPrivileged(Native Method)
at
javax.security.auth.Subject.doAs(Subject.java:422)
at
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1754)
at
org.apache.flink.runtime.security.HadoopSecurityContext.runSecured(HadoopSecurityContext.java:41)
at
org.apache.flink.client.cli.CliFrontend.main(CliFrontend.java:1083)
Caused by: java.lang.RuntimeException: Not support dataType: TIMESTAMP(9)
at
org.apache.flink.table.dataformat.DataFormatConverters.getConverterForDataType(DataFormatConverters.java:248)
at
org.apache.flink.table.planner.codegen.CodeGenUtils$.isConverterIdentity(CodeGenUtils.scala:661)
at
org.apache.flink.table.planner.codegen.CodeGenUtils$.genToInternal(CodeGenUtils.scala:669)
at
org.apache.flink.table.planner.codegen.CodeGenUtils$.genToInternal(CodeGenUtils.scala:665)
at
org.apache.flink.table.planner.codegen.CodeGenUtils$.genToInternalIfNeeded(CodeGenUtils.scala:687)
at
org.apache.flink.table.planner.codegen.calls.ScalarFunctionCallGen.generate(ScalarFunctionCallGen.scala:79)
at
org.apache.flink.table.planner.codegen.ExprCodeGenerator.generateCallExpression(ExprCodeGenerator.scala:737)
at
org.apache.flink.table.planner.codegen.ExprCodeGenerator.visitCall(ExprCodeGenerator.scala:451)
at
org.apache.flink.table.planner.codegen.ExprCodeGenerator.visitCall(ExprCodeGenerator.scala:51)
at
org.apache.calcite.rex.RexCall.accept(RexCall.java:191)
at
org.apache.flink.table.planner.codegen.ExprCodeGenerator.generateExpression(ExprCodeGenerator.scala:131)
at
org.apache.flink.table.planner.codegen.CalcCodeGenerator$$anonfun$5.apply(CalcCodeGenerator.scala:150)
at
org.apache.flink.table.planner.codegen.CalcCodeGenerator$$anonfun$5.apply(CalcCodeGenerator.scala:150)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at
scala.collection.AbstractTraversable.map(Traversable.scala:104)
