Cannot initialize Cluster. Please check your configuration for mapreduce.framework .name and the cor

作者：我家自动化 | 2024-05-08 08:53:31

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cannot initialize cluster. please check your configuration for mapreduce.fra

背景

利用ambari搭建的新环境，跑数据出现了不少问题，但如下问题困扰了很长时间，直到今天才得以解决，每次报错。按照网上的各种方式都不行。我知道问题点肯定在spark2.3.1 集成hive3.1.0的版本问题上，因为hive3.1.0新增了很多功能，如事务等，发布时间没有长时间的积累，出问题很容易不受控制。

环境

采用ambari2.7.1 + spark2.3.1 + hadoop3.1.1 + hive3.1.0

scala2.11.8, jdk1.8

代码


// 可以正常打印
    df.show(10, truncate = false)
 
    df.createOrReplaceTempView("tmp_logistics_track")
 
// 可以正常打印
    spark.sql("select * from tmp_logistics_track").show(20, truncate = false)
 
// 以下是报错所在行
    spark.sql(
      s"""insert overwrite table ods_common.ods_common_logistics_track_d PARTITION(p_day='$day')
         |select dc_id, source_order_id, order_id, tracking_number, warehouse_code, user_id, channel_name,
         |creation_time, update_time, sync_time, last_tracking_time, tracking_change_time, next_tracking_time,
         |tms_nti_time, tms_oc_time, tms_as_time, tms_pu_time, tms_it_time, tms_od_time, tms_wpu_time, tms_rt_time,
         |tms_excp_time, tms_fd_time, tms_df_time, tms_un_time, tms_np_time from tmp_logistics_track
         |""".stripMargin)

部署脚本


#!/usr/bin/env bash
################# LOAD DATA TO HIVE ######################
 
#windows 编辑shell 需要修改 编码为Unix
#命令
#set ff=unix
#SPARK_JARS_BASE_PATH=/home/isuhadoop/ark_data_bin/tag_batch/KafkaToHive/external_jar
 
set -x
 
v_proc_date=$1
#v_proc_date=$(date -d '-0 day' '+%Y%m%d')
 
echo "-----1: $1"
echo "-----2: $2"
 
# shell的使用的磁盘根目录
SHELL_ROOT_DIR=/home/ztsauser/limin_work/warehouse
 
 
v_exec_time=`date "+%Y%m%d%H"`
 
##日志目录
v_log_dir=${SHELL_ROOT_DIR}/logs/LogisticsTrackSourceProcess_${v_proc_date}.log
 
#如果没传参数，退出程序
if [[ "$v_proc_date" = "" ]]
then
    echo "没有传入参数，即将退出程序》》》》》》" > ${v_log_dir}
    exit
fi
 
echo "调用脚本开始》》》》》》" > ${v_log_dir}
 
export HADOOP_USER_NAME=hive
 
/usr/hdp/current/spark2-client/bin/spark-submit --class zt.dc.bigdata.bp.process.warehouse.LogisticsTrackSourceProcess \
--name LogisticsTrackSourceProcess_${v_proc_date} \
--master yarn-cluster \
--queue default \
--deploy-mode cluster \
--num-executors 5 \
--executor-cores 2 \
--executor-memory 18g  \
--files ${SHELL_ROOT_DIR}/config/hive-site.xml \
--jars ${SHELL_ROOT_DIR}/jar/hadoop-distcp-3.1.1.3.0.1.0-187.jar \
${SHELL_ROOT_DIR}/jar/dc-bp-1.0-SNAPSHOT-shaded.jar ${v_proc_date} > ${v_log_dir} 2>&1

异常

Caused by: java.io.IOException: Cannot execute DistCp process: java.io.IOException: Cannot initialize Cluster. Please check your configuration for mapreduce.framework
.name and the correspond server addresses.

详细信息如下：


21/01/09 23:06:08 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:09 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:10 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:11 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:12 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:13 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:14 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:15 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:16 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:17 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:18 INFO Client: Application report for application_1610095260612_0149 (state: RUNNING)
21/01/09 23:06:19 INFO Client: Application report for application_1610095260612_0149 (state: FINISHED)
21/01/09 23:06:19 INFO Client:
	 client token: N/A
	 diagnostics: User class threw exception: org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: Unable to move source hdfs
://ztcluster/warehouse/tablespace/managed/hive/ods_common.db/ods_common_logistics_track_d/.hive-staging_hive_2021-01-09_23-05-39_625_275694820341612468-1/-ext-10000 t
o destination hdfs://ztcluster/warehouse/tablespace/managed/hive/ods_common.db/ods_common_logistics_track_d/p_day=20190321;
	at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:106)
	at org.apache.spark.sql.hive.HiveExternalCatalog.loadPartition(HiveExternalCatalog.scala:843)
	at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.processInsert(InsertIntoHiveTable.scala:249)
	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.executeCollect(commands.scala:115)
	at org.apache.spark.sql.Dataset$$anonfun$6.apply(Dataset.scala:190)
	at org.apache.spark.sql.Dataset$$anonfun$6.apply(Dataset.scala:190)
	at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3259)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
	at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3258)
	at org.apache.spark.sql.Dataset.<init>(Dataset.scala:190)
	at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:75)
	at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:642)
	at zt.dc.bigdata.bp.process.warehouse.LogisticsTrackSourceProcess$.main(LogisticsTrackSourceProcess.scala:122)
	at zt.dc.bigdata.bp.process.warehouse.LogisticsTrackSourceProcess.main(LogisticsTrackSourceProcess.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$4.run(ApplicationMaster.scala:721)
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Unable to move source hdfs://ztcluster/warehouse/tablespace/managed/hive/ods_common.db/ods_common_logisti
cs_track_d/.hive-staging_hive_2021-01-09_23-05-39_625_275694820341612468-1/-ext-10000 to destination hdfs://ztcluster/warehouse/tablespace/managed/hive/ods_common.db/
ods_common_logistics_track_d/p_day=20190321
	at org.apache.hadoop.hive.ql.metadata.Hive.getHiveException(Hive.java:4057)
	at org.apache.hadoop.hive.ql.metadata.Hive.getHiveException(Hive.java:4012)
	at org.apache.hadoop.hive.ql.metadata.Hive.moveFile(Hive.java:4007)
	at org.apache.hadoop.hive.ql.metadata.Hive.replaceFiles(Hive.java:4372)
	at org.apache.hadoop.hive.ql.metadata.Hive.loadPartition(Hive.java:1962)
	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.apache.spark.sql.hive.client.Shim_v3_0.loadPartition(HiveShim.scala:1275)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$loadPartition$1.apply$mcV$sp(HiveClientImpl.scala:747)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$loadPartition$1.apply(HiveClientImpl.scala:745)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$loadPartition$1.apply(HiveClientImpl.scala:745)
	at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:278)
	at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:216)
	at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:215)
	at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:261)
	at org.apache.spark.sql.hive.client.HiveClientImpl.loadPartition(HiveClientImpl.scala:745)
	at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$loadPartition$1.apply$mcV$sp(HiveExternalCatalog.scala:855)
	at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$loadPartition$1.apply(HiveExternalCatalog.scala:843)
	at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$loadPartition$1.apply(HiveExternalCatalog.scala:843)
	at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
	... 21 more
Caused by: java.io.IOException: Cannot execute DistCp process: java.io.IOException: Cannot initialize Cluster. Please check your configuration for mapreduce.framework
.name and the correspond server addresses.
	at org.apache.hadoop.hive.shims.Hadoop23Shims.runDistCp(Hadoop23Shims.java:1151)
	at org.apache.hadoop.hive.common.FileUtils.distCp(FileUtils.java:643)
	at org.apache.hadoop.hive.common.FileUtils.copy(FileUtils.java:625)
	at org.apache.hadoop.hive.common.FileUtils.copy(FileUtils.java:600)
	at org.apache.hadoop.hive.ql.metadata.Hive.moveFile(Hive.java:3921)
	... 40 more
Caused by: java.io.IOException: Cannot initialize Cluster. Please check your configuration for mapreduce.framework.name and the correspond server addresses.
	at org.apache.hadoop.mapreduce.Cluster.initialize(Cluster.java:116)
	at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:109)
	at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:102)
	at org.apache.hadoop.tools.DistCp.createMetaFolderPath(DistCp.java:410)
	at org.apache.hadoop.tools.DistCp.<init>(DistCp.java:116)
	at org.apache.hadoop.hive.shims.Hadoop23Shims.runDistCp(Hadoop23Shims.java:1141)
	... 44 more
 
	 ApplicationMaster host: 192.168.81.58
	 ApplicationMaster RPC port: 0
	 queue: default
	 start time: 1610204535333
	 final status: FAILED
	 tracking URL: http://szch-ztn-dc-bp-pro-192-168-81-57:8088/proxy/application_1610095260612_0149/
	 user: hive
Exception in thread "main" org.apache.spark.SparkException: Application application_1610095260612_0149 finished with failed status
	at org.apache.spark.deploy.yarn.Client.run(Client.scala:1269)
	at org.apache.spark.deploy.yarn.YarnClusterApplication.start(Client.scala:1627)
	at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:904)
	at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:198)
	at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:228)
	at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:137)
	at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

解决方案

根据网上的方案我进行了以下尝试

尝试一(未解决)

尝试了在客户添加如下pom依赖，并未解决


<dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-common</artifactId>
      <version>${hadoop.version}</version>
    </dependency>
 
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
      <version>${hadoop.version}</version>
      <scope>${scopetype}</scope>
    </dependency>
 
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>${hadoop.version}</version>
      <scope>${scopetype}</scope>
    </dependency>
 
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>${hadoop.version}</version>
      <scope>${scopetype}</scope>
    </dependency>
 
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>${hadoop.version}</version>
      <scope>${scopetype}</scope>
    </dependency>

尝试二（未解决）

修改hdfs-site.xml中

fs.hdfs.impl.disable.cache属性为true

尝试三（未解决）

关闭hive的事务功能

关闭hdp 3.0 创建表自动为acid表的参数：

hive.create.as.insert.only=false

metastore.create.as.acid=false

hive.strict.managed.tables=false

hive.txn.manager=org.apache.hadoop.hive.ql.lockmgr.DummyTxnManager

hive.support.concurrency=false

// hive.stats.autogather属性要设置成true, 否则在执行sql时，会报异常

hive.stats.autogather=true

尝试四（未解决）

检查mapred-site.xml文件的mapreduce.framework.name属性是否为yarn

如果不是改成local再尝试

尝试五（解决）

在找遍了网上所有的方法无解后，想到了一个点，之前在使用hive3.0时，如果分区事先创建好了，通过第三方api或spark客户端写数据时有问题，

所以便尝试了，让创建分区的方法显示声明在代码中，再次尝试，问题解决。但为什么会这样，到目前还未知。

附代码：


    // 删除分区
    spark.sql(s"alter table ods_common.ods_common_logistics_track_d drop if exists partition(p_day='$day')")
    // 添加分区
    spark.sql(s"alter table ods_common.ods_common_logistics_track_d add if not exists partition (p_day='$day')")
 
 
    df.show(10, truncate = false)
 
    df.createOrReplaceTempView("tmp_logistics_track")
 
    spark.sql("select * from tmp_logistics_track").show(20, truncate = false)
 
    spark.sql(
      s"""insert overwrite table ods_common.ods_common_logistics_track_d PARTITION(p_day='$day')
         |select dc_id, source_order_id, order_id, tracking_number, warehouse_code, user_id, channel_name,
         |creation_time, update_time, sync_time, last_tracking_time, tracking_change_time, next_tracking_time,
         |tms_nti_time, tms_oc_time, tms_as_time, tms_pu_time, tms_it_time, tms_od_time, tms_wpu_time, tms_rt_time,
         |tms_excp_time, tms_fd_time, tms_df_time, tms_un_time, tms_np_time from tmp_logistics_track
         |""".stripMargin)

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