spark插入动态分区代码报错_hive.metastore.dml.events

作者：很楠不爱3 | 2024-04-23 15:24:45

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hive.metastore.dml.events

现象

SparkSession session = SparkSession.builder()
.config(sparkConf)
.config("hive.exec.dynamic.partition.mode", "nonstrict")//动态分区
//.config("hive.metastore.dml.events","false")
.enableHiveSupport() .getOrCreate();

dataset1.write().mode(SaveMode.Overwrite).insertInto(Constant.RENT_TABLE);

dataset第一次插入分区没有问题，但是第二次重新跑的时候

[load-dynamic-partitions-0] ERROR hive.ql.metadata.Hive - Exception when loading partition with parameters partPath=hdfs://s2cluster/user/hive/warehouse/odszjdata.db/ods_zj_building_area_check_list/.hive-staging_hive_2022-11-07_18-12-45_911_2372413257026787436-1/-ext-10000/period_id=202210, table=ods_zj_building_area_check_list, partSpec={period_id=202210}, replace=true, listBucketingEnabled=false, isAcid=false, hasFollowingStatsTask=false
org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.thrift.TApplicationException: Required field 'filesAdded' is unset! Struct:InsertEventRequestData(filesAdded:null)
   at org.apache.hadoop.hive.ql.metadata.Hive.fireInsertEvent(Hive.java:2437)
   at org.apache.hadoop.hive.ql.metadata.Hive.loadPartitionInternal(Hive.java:1632)
   at org.apache.hadoop.hive.ql.metadata.Hive.lambda$loadDynamicPartitions$4(Hive.java:1974)
   at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
   at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
   at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
   at java.base/java.lang.Thread.run(Thread.java:834)
Caused by: org.apache.thrift.TApplicationException: Required field 'filesAdded' is unset! Struct:InsertEventRequestData(filesAdded:null)
   at org.apache.thrift.TApplicationException.read(TApplicationException.java:111)
   at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79)
   at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.recv_fire_listener_event(ThriftHiveMetastore.java:4836)
   at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.fire_listener_event(ThriftHiveMetastore.java:4823)
   at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.fireListenerEvent(HiveMetaStoreClient.java:2531)
   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 org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.invoke(RetryingMetaStoreClient.java:154)
   at com.sun.proxy.$Proxy29.fireListenerEvent(Unknown Source)
   at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
   at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
   at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
   at java.base/java.lang.reflect.Method.invoke(Method.java:566)
   at org.apache.hadoop.hive.metastore.HiveMetaStoreClient$SynchronizedHandler.invoke(HiveMetaStoreClient.java:2562)
   at com.sun.proxy.$Proxy29.fireListenerEvent(Unknown Source)
   at org.apache.hadoop.hive.ql.metadata.Hive.fireInsertEvent(Hive.java:2435)
   ... 6 more
Exception in thread "main" org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: Exception when loading 1 in table ods_zj_building_area_check_list with loadPath=hdfs://s2cluster/user/hive/warehouse/odszjdata.db/ods_zj_building_area_check_list/.hive-staging_hive_2022-11-07_18-12-45_911_2372413257026787436-1/-ext-10000;
   at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:108)
   at org.apache.spark.sql.hive.HiveExternalCatalog.loadDynamicPartitions(HiveExternalCatalog.scala:924)
   at org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.loadDynamicPartitions(ExternalCatalogWithListener.scala:189)
   at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.processInsert(InsertIntoHiveTable.scala:205)
   at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.run(InsertIntoHiveTable.scala:99)
   at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
   at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
   at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
   at org.apache.spark.sql.execution.SparkPlan
$a n o n f u n $ e x e c u t e $ 1. a p p l y (S p a r k P l a n . s c a l a : 131) a t o r g . a p a c h e . s p a r k . s q l . e x e c u t i o n . S p a r k P l a n$ $anonfun$execute$1.apply(SparkPlan.scala:131)    at org.apache.spark.sql.execution.SparkPlan$ anonfun$execute$1.apply(SparkPlan.scala:127)
   at org.apache.spark.sql.execution.SparkPlan $a n o n f u n $ e x e c u t e Q u e r y $ 1. a p p l y (S p a r k P l a n . s c a l a : 155) a t o r g . a p a c h e . s p a r k . r d d . R D D O p e r a t i o n S c o p e $ . w i t h S c o p e (R D D O p e r a t i o n S c o p e . s c a l a : 151) a t o r g . a p a c h e . s p a r k . s q l . e x e c u t i o n . S p a r k P l a n . e x e c u t e Q u e r y (S p a r k P l a n . s c a l a : 152) a t o r g . a p a c h e . s p a r k . s q l . e x e c u t i o n . S p a r k P l a n . e x e c u t e (S p a r k P l a n . s c a l a : 127) a t o r g . a p a c h e . s p a r k . s q l . e x e c u t i o n . Q u e r y E x e c u t i o n . t o R d d $ l z y c o m p u t e (Q u e r y E x e c u t i o n . s c a l a : 80) a t o r g . a p a c h e . s p a r k . s q l . e x e c u t i o n . Q u e r y E x e c u t i o n . t o R d d (Q u e r y E x e c u t i o n . s c a l a : 80) a t o r g . a p a c h e . s p a r k . s q l . D a t a F r a m e W r i t e r$ $anonfun$executeQuery$1.apply(SparkPlan.scala:155)    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)    at org.apache.spark.sql.DataFrameWriter$ anonfun$runCommand$1.apply(DataFrameWriter.scala:668)
   at org.apache.spark.sql.DataFrameWriter $a n o n f u n $ r u n C o m m a n d $ 1. a p p l y (D a t a F r a m e W r i t e r . s c a l a : 668) a t o r g . a p a c h e . s p a r k . s q l . e x e c u t i o n . S Q L E x e c u t i o n$ $anonfun$runCommand$1.apply(DataFrameWriter.scala:668)    at org.apache.spark.sql.execution.SQLExecution$ anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
   at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
   at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
   at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:668)
   at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:325)
   at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:311)
   at com.tencent.s2.HouseRentSparkJob.main(HouseRentSparkJob.java:56)

但是我在服务器跑代码反复测试都是可以重复插入分区的。。window实在是难搞

百度得到

.config("hive.metastore.dml.events","false") 就好了。。 windows确实好了。

没时间分析原因。

参考文章

Supporthttps://knowledge.informatica.com/s/article/594919?language=en_US

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回家越想越不对劲，突然意识到可能是我windows的jar包版本太低导致的。

window还是以前的cdh版本

<spark.version>2.4.0-cdh6.3.2</spark.version>

<hadoop.version>3.0.0-cdh6.3.2</hadoop.version>

但是服务现在的cdp已经是

hadoop 3.1.1 hive3.1.1 spark2.4.7了，

那么我先调整pom的版本 hadoop为3.1.1 结果报错

Unrecognized Hadoop major version number: 3.1.1

这个错也很简单，我们根据报错，查到这个地方，发现是hive-exec1.2.1里ShimLoader里只判断了hadoop版本是1和2 如果是3就会直接报错。

因为我cdp版本是 3.1.1所以我pom引入3.1.1也合情合理吧

现在的问题是什么？

pom里spark版本2.4.7中的hive-exec:1.2.1里只判断了hadoop1 和hadoop2的版本

但是我hadoop引入的版本是3.1.1

解决办法：

1 找个spark版本高的版本可能它就有hive-exec2 hive-exec3(因为我服务器本身是hive3，所以往这方面想) 这样就可以支持hive3了。如下图所示

2.改hadoop的低版本，因为我主要是用spark去开发hive的读写。

最终我选择了1。。结果发现走不通。还是说下过程吧。改了pom发现报错


        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>${spark.version}</version>
            <exclusions>
                <exclusion>
                    <groupId>org.spark-project.hive</groupId>
                    <artifactId>hive-exec</artifactId>
                </exclusion>
                <exclusion>
                    <groupId>org.spark-project.hive</groupId>
                    <artifactId>hive-metastore</artifactId>
                </exclusion>
            </exclusions>
            <!--<scope>provided</scope>-->
        </dependency>
        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-exec</artifactId>
            <version>3.1.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-metastore</artifactId>
            <version>3.1.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-jdbc</artifactId>
            <version>3.1.2</version>
        </dependency>

紧接着又遇到这个错，Exception in thread "main" java.lang.NoSuchFieldError: HIVE_STATS_JDBC_TIMEOUT
at org.apache.spark.sql.hive.HiveUtils$.formatTimeVarsForHiveClient(HiveUtils.scala:204)

遇到这个错其实很简单。我们打开HiveUtils类查看formatTimeVarsForHiveClient这个方法