Flink1.10定义UDAGG遇到SQL validation failed. null 问题
作者：互联网
按照以下代码测试定义的UDAGG会一直出现org.apache.flink.table.api.ValidationException: SQL validation failed. null 问题
import org.apache.flink.configuration.JobManagerOptions
import org.apache.flink.table.api.scala.BatchTableEnvironment
import org.apache.flink.table.api.{EnvironmentSettings, TableEnvironment}
import org.apache.flink.table.catalog.hive.HiveCatalog
object testsql {
def main(args: Array[String]): Unit = {
val settings = EnvironmentSettings.newInstance()
.useBlinkPlanner()
.inStreamingMode()
.build()
val tEnv = TableEnvironment.create(settings)
tEnv.sqlUpdate("create function replaces as 'com.bigdata.util.udf.Replaces'")
tEnv.sqlUpdate("create function avgprice as \'com.bigdata.util.udf.AvgPriceAgg\'")
tEnv.sqlUpdate(getSourceSql)//创建数据源
tEnv.sqlUpdate(getSinkSql)//创建写入表
tEnv.sqlUpdate(processSql)//处理逻辑
tEnv.execute("SQL Job")
}
def getSourceSql = "CREATE TABLE order_info (...) with(...)"
def processSql = "INSERT INTO datasink select avgprice(a.price,a.total_count) as avg_price from order_info a group by a.item_id"
def getSinkSql = "CREATE TABLE datasink (...) with(...)"
}
原来运行时的异常信息找不见了，以下是在单元测试的异常
org.apache.flink.table.api.ValidationException: SQL validation failed. null
at org.apache.flink.table.calcite.FlinkPlannerImpl.validateInternal(FlinkPlannerImpl.scala:130)
at org.apache.flink.table.calcite.FlinkPlannerImpl.validate(FlinkPlannerImpl.scala:105)
at org.apache.flink.table.sqlexec.SqlToOperationConverter.convert(SqlToOperationConverter.java:124)
at org.apache.flink.table.planner.ParserImpl.parse(ParserImpl.java:66)
at org.apache.flink.table.api.internal.TableEnvironmentImpl.sqlQuery(TableEnvironmentImpl.java:464)
at TestAvgPriceAgg.TestAgg(TestAvgPriceAgg.java:49)
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 org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:59)
at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:56)
at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
at org.junit.runners.ParentRunner$3.evaluate(ParentRunner.java:306)
at org.junit.runners.BlockJUnit4ClassRunner$1.evaluate(BlockJUnit4ClassRunner.java:100)
at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:366)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:103)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:63)
at org.junit.runners.ParentRunner$4.run(ParentRunner.java:331)
at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:79)
at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:329)
at org.junit.runners.ParentRunner.access$100(ParentRunner.java:66)
at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:293)
at org.junit.runners.ParentRunner$3.evaluate(ParentRunner.java:306)
at org.junit.runners.ParentRunner.run(ParentRunner.java:413)
at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
at com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs(JUnit4IdeaTestRunner.java:68)
at com.intellij.rt.execution.junit.IdeaTestRunner$Repeater.startRunnerWithArgs(IdeaTestRunner.java:47)
at com.intellij.rt.execution.junit.JUnitStarter.prepareStreamsAndStart(JUnitStarter.java:242)
at com.intellij.rt.execution.junit.JUnitStarter.main(JUnitStarter.java:70)
Caused by: java.lang.NullPointerException
at org.apache.flink.util.Preconditions.checkNotNull(Preconditions.java:58)
at org.apache.flink.table.functions.AggregateFunctionDefinition.<init>(AggregateFunctionDefinition.java:48)
at org.apache.flink.table.functions.FunctionDefinitionUtil.createFunctionDefinition(FunctionDefinitionUtil.java:57)
at org.apache.flink.table.catalog.FunctionCatalog.resolvePreciseFunctionReference(FunctionCatalog.java:336)
at org.apache.flink.table.catalog.FunctionCatalog.lambda$resolveAmbiguousFunctionReference$2(FunctionCatalog.java:374)
at java.util.Optional.orElseGet(Optional.java:267)
at org.apache.flink.table.catalog.FunctionCatalog.resolveAmbiguousFunctionReference(FunctionCatalog.java:374)
at org.apache.flink.table.catalog.FunctionCatalog.lookupFunction(FunctionCatalog.java:303)
at org.apache.flink.table.catalog.FunctionCatalogOperatorTable.lookupOperatorOverloads(FunctionCatalogOperatorTable.java:74)
at org.apache.calcite.sql.util.ChainedSqlOperatorTable.lookupOperatorOverloads(ChainedSqlOperatorTable.java:73)
at org.apache.calcite.sql.validate.SqlValidatorImpl.performUnconditionalRewrites(SqlValidatorImpl.java:1194)
at org.apache.calcite.sql.validate.SqlValidatorImpl.performUnconditionalRewrites(SqlValidatorImpl.java:1179)
at org.apache.calcite.sql.validate.SqlValidatorImpl.performUnconditionalRewrites(SqlValidatorImpl.java:1209)
at org.apache.calcite.sql.validate.SqlValidatorImpl.performUnconditionalRewrites(SqlValidatorImpl.java:1179)
at org.apache.calcite.sql.validate.SqlValidatorImpl.validateScopedExpression(SqlValidatorImpl.java:936)
at org.apache.calcite.sql.validate.SqlValidatorImpl.validate(SqlValidatorImpl.java:650)
at org.apache.flink.table.calcite.FlinkPlannerImpl.validateInternal(FlinkPlannerImpl.scala:126)
... 30 more
大概意思就是sql校验没有通过，对照代码行数在执行processSql 这句的时候有问题，然后查看TableEnvironment发现只支持注册ScalarFunction，并且没有重载方法
查看源码发现TableEnvironment是顶级接口
在实现上是 5 个面向用户的接口，在接口底层进行了不同的实现，5 个接口包括一个 TableEnvironment 接口，两个 BatchTableEnvironment 接口，两个 StreamTableEnvironment 接口，5 个接口文件完整路径如下：
org.apache.flink.table.api.TableEnvironment
org.apache.flink.table.api.java.BatchTableEnvironment
org.apache.flink.table.api.java.StreamTableEnvironment
org.apache.flink.table.api.scala.BatchTableEnvironment
org.apache.flink.table.api.scala.StreamTableEnvironment
其中，TableEnvironment 作为统一的接口，其统一性体现在两个方面，一是对于所有基于JVM的语言(即 Scala API 和 Java API 之间没有区别)是统一的；二是对于 unbounded data （无界数据，即流数据） 和 bounded data （有界数据，即批数据）的处理是统一的。TableEnvironment 提供的是一个纯 Table 生态的上下文环境，适用于整个作业都使用 Table API & SQL 编写程序的场景。TableEnvironment 目前只支持Scalar Functions，不支持注册 UDTF 和 UDAF，用户有注册 UDTF 和 UDAF 的需求时，可以选择使用其他 TableEnvironment。
两个 StreamTableEnvironment 分别用于 Java 的流计算和 Scala 的流计算场景，流计算的对象分别是 Java 的 DataStream 和 Scala 的 DataStream。相比 TableEnvironment，StreamTableEnvironment 提供了 DataStream 和 Table 之间相互转换的接口，如果用户的程序除了使用 Table API & SQL 编写外，还需要使用到 DataStream API，则需要使用 StreamTableEnvironment。
两个 BatchTableEnvironment 分别用于 Java 的批处理场景和 Scala 的批处理场景，批处理的对象分别是 Java 的 DataSet 和 Scala 的 DataSet。相比 TableEnvironment，BatchTableEnvironment 提供了 DataSet 和 Table 之间相互转换的接口，如果用户的程序除了使用 Table API & SQL 编写外，还需要使用到 DataSet API，则需要使用 BatchTableEnvironment。
这样就一目了然了，这里使用的TableEnvironment无法支持UDAGG，通过改造使用StreamTableEnvironment就能够完美运行了
import org.apache.flink.streaming.api.CheckpointingMode
import org.apache.flink.streaming.api.environment.CheckpointConfig.ExternalizedCheckpointCleanup
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment
import org.apache.flink.table.api.{EnvironmentSettings}
import org.apache.flink.table.api.java.StreamTableEnvironment
object tests {
def main(args: Array[String]): Unit = {
val settings = EnvironmentSettings.newInstance()
.useBlinkPlanner()
.inStreamingMode()
.build()
val streamExecEnvironment = getStreamEnv
val tEnv: StreamTableEnvironment = StreamTableEnvironment.create(streamExecEnvironment, settings)
tEnv.sqlUpdate("create function replaces as 'com.bigdata.util.udf.Replaces'")
tEnv.registerFunction("avgprice", new AvgPriceAgg())
tEnv.sqlUpdate(getSourceSql)
tEnv.sqlUpdate(getSinkSql)
tEnv.sqlUpdate(processSql)
tEnv.execute("SQL Job")
}
def getStreamEnv(): StreamExecutionEnvironment = {
val env = StreamExecutionEnvironment.getExecutionEnvironment
env.enableCheckpointing(60 * 1000 * 10, CheckpointingMode.EXACTLY_ONCE)
val config = env.getCheckpointConfig
//RETAIN_ON_CANCELLATION在job canceled的时候会保留externalized checkpoint state
config.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
//用于指定checkpoint coordinator上一个checkpoint完成之后最小等多久可以出发另一个checkpoint，当指定这个参数时，maxConcurrentCheckpoints的值为1
config.setMinPauseBetweenCheckpoints(60 * 1000 * 5)
//用于指定运行中的checkpoint最多可以有多少个,如果有设置了minPauseBetweenCheckpoints，则maxConcurrentCheckpoints这个参数就不起作用了(大于1的值不起作用)
config.setMaxConcurrentCheckpoints(1)
//指定checkpoint执行的超时时间(单位milliseconds)，超时没完成就会被abort掉
config.setCheckpointTimeout(60 * 1000 * 15)
//用于指定在checkpoint发生异常的时候，是否应该fail该task，默认为true，如果设置为false，则task会拒绝checkpoint然后继续运行
//https://issues.apache.org/jira/browse/FLINK-11662 1.10改为配置失败次数 配置false的话就默认最大2147483647
config.setFailOnCheckpointingErrors(false)
env
}
def getSourceSql = "CREATE TABLE order_info (...) with(...)"
def processSql = "INSERT INTO datasink select avgprice(a.price,a.total_count) as avg_price from order_info a group by a.item_id"
def getSinkSql = "CREATE TABLE datasink (...) with(...)"
}
参考文档：https://blog.csdn.net/weixin_44904816/article/details/102550056
标签：java,UDAGG,flink,failed,table,apache,org,null,junit 来源： https://www.cnblogs.com/createweb/p/12502208.html