Windows下 IDEA編譯調(diào)試 hive2.3.9的過程解析
更新時間：2023年07月12日 10:03:26 作者：顧棟
這篇文章主要介紹了Windows下 IDEA編譯調(diào)試 hive2.3.9的過程,本文給大家介紹的非常詳細，對大家的學習或工作具有一定的參考借鑒價值，需要的朋友可以參考下
Windows下 IDEA編譯調(diào)試 hive2.3.9
環(huán)境
- IDEA 2021.2
- JDK1.8（試過用高版本的JDK17編譯，不兼容編譯不過）
- 一個Hadoop集群，涉及配置文件core-site.xml，hdfs-site.xml，yarn-site.xml，mapred-site.xml
- Windows環(huán)境中配置hadoop客戶端。
- git
源碼編譯
在Git Bash中執(zhí)行編譯命令，hive2版本的編譯命令 mvn clean package -DskipTests -Pdist。
編譯結(jié)果圖
編譯問題
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-antrun-plugin:1.8:run (generate-version-annotation) on project hive-standalone-metastore-common: An Ant BuildException has occured: exec returned: 127
[ERROR] around Ant part ...<exec failonerror="true" executable="bash">... @ 4:46 in F:\subsys\mystudy\hive-2\hive\standalone-metastore\metastore-common\target\antrun\build-main.xml
[ERROR] -> [Help 1]
[ERROR]
[ERROR] To see the full stack trace of the errors, re-run Maven with the -e switch.
[ERROR] Re-run Maven using the -X switch to enable full debug logging.
[ERROR]
[ERROR] For more information about the errors and possible solutions, please read the following articles:
[ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException
[ERROR]
[ERROR] After correcting the problems, you can resume the build with the command
[ERROR] mvn <goals> -rf :hive-standalone-metastore-common
編譯命令需要在shell環(huán)境下執(zhí)行，，所以采用git bash環(huán)境編譯。
導入IDEA
用IDEA打開項目
問題
Could not find artifact org.apache.directory.client.ldap:ldap-client-api:pom:0.1-SNAPSHOT
原因是：丟失了依賴 解決方案：查看社區(qū)，發(fā)現(xiàn)存在一個issue。issue號是HIVE-21777。在hive service 模塊下的pom.xml文件排掉這個依賴項
<dependency>
<groupId>org.apache.directory.server</groupId>
<artifactId>apacheds-server-integ</artifactId>
<version>${apache-directory-server.version}</version>
<scope>test</scope>
<!-- 增加的部分-->
<exclusions>
<exclusion>
<groupId>org.apache.directory.client.ldap</groupId>
<artifactId>ldap-client-api</artifactId>
</exclusion>
</exclusions>
</dependency>啟動Cli
1.分別把集群的配置文件copy到hive-cli模塊的resources(需要自己新建和配置)下core-site.xml，hdfs-site.xml，yarn-site.xml，mapred-site.xml，新建hive-log4j2.properties和hive-site.xml文件。
2.變更配置項
mapred-site.xml中新增，
<!-- 支持跨平臺提交任務 -->
<property>
<name>mapreduce.app-submission.cross-platform</name>
<value>true</value>
</property>hive-site.xml
<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl" rel="external nofollow" ?>
<configuration>
<property>
<name>hive.cli.print.current.db</name>
<value>true</value>
</property>
<property>
<name>hive.cli.print.header</name>
<value>true</value>
</property>
<!-- hive 元數(shù)據(jù)鏈接地址 HA-->
<property>
<name>hive.metastore.uris</name>
<value>thrift://192.168.1.1:9083,thrift://192.168.1.2:9083</value>
<final>true</final>
</property>
<property>
<name>hive.cluster.delegation.token.store.class</name>
<value>org.apache.hadoop.hive.thrift.MemoryTokenStore</value>
<description>Hive defaults to MemoryTokenStore, or ZooKeeperTokenStore</description>
</property>
<!-- 測試環(huán)境的 hive客戶端本地模式-->
<property>
<name>hive.exec.mode.local.auto</name>
<value>true</value>
</property>
<!--輸入最大數(shù)據(jù)量，默認128MB-->
<property>
<name>hive.exec.mode.local.auto.inputbytes.max</name>
<value>134217728</value>
</property>
<!-- 最大任務數(shù)-->
<property>
<name>hive.exec.mode.local.auto.input.files.max</name>
<value>10</value>
</property>
</configuration>hive-log4j2.properties變更
property.hive.log.dir = /opt/hive/logs
property.hive.log.file = hive-${sys:user.name}.log3.修改hive-cli模塊中的pom文件，將commons-io，和disruptor范圍取消test限制。
4.配置CliDriver啟動，JVM參數(shù)-Djline.WindowsTerminal.directConsole=false,環(huán)境配置,HADOOP_COMMON_LIB_NATIVE_DIR=${HADOOP_HOME}/lib/native;HADOOP_OPTS=-Djava.library.path=$HADOOP_HOME/lib
問題：import org.apache.hive.tmpl.QueryProfileTmpl 缺失
解決 在git bash執(zhí)行mvn generate-sources ,然后拷貝hive service模塊目錄target/generated-jamon/org/apache/hive/tmpl生成的源文件至指定目錄（需要手動創(chuàng)建）,QueryProfileTmpl和QueryProfileTmplImpl。
本地模式下進行基礎操作
建庫
create database testdb;
建表
create table test001(id int,dummy string) stored as orc;
插入數(shù)據(jù)
use testdb; insert into table testdb.test001 values(1,"X");
查詢數(shù)據(jù)
select * from test001;
刪除數(shù)據(jù)，不建議進行單行或者多行的條件式刪除，可以進行分區(qū)或整表的刪除，重新寫入數(shù)據(jù)的方式。
執(zhí)行記錄：
hive (default)> create database testdb; create database testdb; OK Time taken: 0.072 seconds hive (default)> use testdb; use testdb; OK Time taken: 0.045 seconds hive (testdb)> create table test001(id int,dummy string) stored as orc; create table test001(id int,dummy string) stored as orc; OK Time taken: 0.53 seconds hive (testdb)> insert into table test001 values(1,"X"); insert into table test001 values(1,"X"); Automatically selecting local only mode for query WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases. Query ID = 19057578_20230222162806_f6ce7921-08d3-41db-8408-dfd18339e594 Total jobs = 1 Launching Job 1 out of 1 Number of reduce tasks is set to 0 since there's no reduce operator Job running in-process (local Hadoop) 2023-02-22 16:28:13,266 Stage-1 map = 0%, reduce = 0% 2023-02-22 16:28:14,295 Stage-1 map = 100%, reduce = 0% Ended Job = job_local2039626018_0001 Stage-4 is selected by condition resolver. Stage-3 is filtered out by condition resolver. Stage-5 is filtered out by condition resolver. Moving data to directory hdfs://hadoopdemo/user/bigdata/hive/warehouse/testdb.db/test001/.hive-staging_hive_2023-02-22_16-28-06_738_8793114222801018317-1/-ext-10000 Loading data to table testdb.test001 MapReduce Jobs Launched: Stage-Stage-1: HDFS Read: 4 HDFS Write: 348 SUCCESS Total MapReduce CPU Time Spent: 0 msec OK Time taken: 8.293 seconds _col0 _col1 hive (testdb)> select * from test001; select * from test001; OK test001.id test001.dummy 1 X hive (testdb)> Time taken: 0.241 seconds, Fetched: 1 row(s) hive (testdb)> drop table test001; drop table test001; OK Time taken: 0.218 seconds hive (testdb)> show tables; show tables; OK tab_name values__tmp__table__1 hive (testdb)> Time taken: 0.063 seconds, Fetched: 1 row(s)
values__tmp__table__1為客戶端內(nèi)存中的臨時數(shù)據(jù)，會在關閉客戶端后刪除，不會落盤。
遺留問題
提交任務到y(tǒng)arn集群失敗
取消本地模式，在將任務提交到y(tǒng)arn是會有錯誤。判斷是客戶端環(huán)境配置問題，但是沒有在客戶端處解決。
yarn上的日志
************************************************************/
2023-02-22 16:35:04,263 INFO [main] org.apache.hadoop.security.SecurityUtil: Updating Configuration
2023-02-22 16:35:04,263 INFO [main] org.apache.hadoop.mapreduce.v2.app.MRAppMaster: Executing with tokens:
2023-02-22 16:35:04,263 INFO [main] org.apache.hadoop.mapreduce.v2.app.MRAppMaster: Kind: YARN_AM_RM_TOKEN, Service: , Ident: (appAttemptId { application_id { id: 1 cluster_timestamp: 1677047289113 } attemptId: 2 } keyId: 176967782)
2023-02-22 16:35:04,365 INFO [main] org.apache.hadoop.mapreduce.v2.app.MRAppMaster: OutputCommitter set in config org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter
2023-02-22 16:35:04,367 INFO [main] org.apache.hadoop.service.AbstractService: Service org.apache.hadoop.mapreduce.v2.app.MRAppMaster failed in state INITED; cause: java.lang.RuntimeException: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found
java.lang.RuntimeException: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2427)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster$2.call(MRAppMaster.java:545)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster$2.call(MRAppMaster.java:522)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.callWithJobClassLoader(MRAppMaster.java:1764)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.createOutputCommitter(MRAppMaster.java:522)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.serviceInit(MRAppMaster.java:308)
at org.apache.hadoop.service.AbstractService.init(AbstractService.java:164)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster$5.run(MRAppMaster.java:1722)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1889)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.initAndStartAppMaster(MRAppMaster.java:1719)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.main(MRAppMaster.java:1650)
Caused by: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2395)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2419)
... 12 more
Caused by: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found
at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2299)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2393)
... 13 more
2023-02-22 16:35:04,369 FATAL [main] org.apache.hadoop.mapreduce.v2.app.MRAppMaster: Error starting MRAppMaster
java.lang.RuntimeException: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2427)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster$2.call(MRAppMaster.java:545)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster$2.call(MRAppMaster.java:522)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.callWithJobClassLoader(MRAppMaster.java:1764)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.createOutputCommitter(MRAppMaster.java:522)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.serviceInit(MRAppMaster.java:308)
at org.apache.hadoop.service.AbstractService.init(AbstractService.java:164)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster$5.run(MRAppMaster.java:1722)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1889)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.initAndStartAppMaster(MRAppMaster.java:1719)
at org.apache.hadoop.mapreduce.v2.app.MRAppMaster.main(MRAppMaster.java:1650)
Caused by: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2395)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2419)
... 12 more
Caused by: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found
at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:2299)
at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:2393)
... 13 more
2023-02-22 16:35:04,372 INFO [main] org.apache.hadoop.util.ExitUtil: Exiting with status 1: java.lang.RuntimeException: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.hive.ql.io.HiveFileFormatUtils$NullOutputCommitter not found到此這篇關于Windows下 IDEA編譯調(diào)試 hive2.3.9的文章就介紹到這了,更多相關IDEA編譯調(diào)試 hive2.3.9內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關文章希望大家以后多多支持腳本之家！
相關文章
今天小編就為大家分享一篇關于Java解釋器的運行過程介紹，小編覺得內(nèi)容挺不錯的，現(xiàn)在分享給大家，具有很好的參考價值，需要的朋友一起跟隨小編來看看吧2019-04-04
SpringBoot集成JWT實現(xiàn)token驗證的流程
Json web token (JWT), 是為了在網(wǎng)絡應用環(huán)境間傳遞聲明而執(zhí)行的一種基于JSON的開放標準（(RFC 7519).這篇文章主要介紹了SpringBoot集成JWT實現(xiàn)token驗證,需要的朋友可以參考下2020-01-01
@JsonSerialize(using = LongToStringUtil.class)注解的使
這篇文章主要介紹了@JsonSerialize(using = LongToStringUtil.class)注解的使用方式,具有很好的參考價值,希望對大家有所幫助,如有錯誤或未考慮完全的地方,望不吝賜教2024-08-08
正值春節(jié)假期，小伙伴私信我idea導入spring源碼的過程，今天抽空給大家整理一篇教程幫助大家學習Spring源碼完美導入IDEA的詳細過程，感興趣的朋友一起看看吧2022-02-02
考慮到程序的可移植性，創(chuàng)建文件時建議大家選用"/",因為經(jīng)過測試用java創(chuàng)建文件時在windows平臺下用“/”也是可以的，java貌似在后臺作過處理了。2013-03-03
Maven pom.xml文件中build,plugin標簽的使用小結(jié)
本文主要介紹了Maven pom.xml文件中build,plugin標簽的使用小結(jié),文中通過示例代碼介紹的非常詳細,對大家的學習或者工作具有一定的參考學習價值,需要的朋友們下面隨著小編來一起學習學習吧2025-03-03
MapReduce是一種編程模型，用于大規(guī)模數(shù)據(jù)集的并行運算。概念"Map(映射)"和"Reduce(歸約)"和他們的主要思想，都是從函數(shù)式編程語言里借來的，還有從矢量編程語言里借來的特性。他極大地方便了編程人員在不會分布式并行編程的情況下，將自己的程序運行在分布式系統(tǒng)上。2014-08-08
這篇文章主要介紹了Java異常處理最佳實踐及陷阱防范，文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值，需要的朋友們下面隨著小編來一起學習學習吧2019-04-04
從n個不同元素中任取m（m≤n）個元素，按照一定的順序排列起來，叫做從n個不同元素中取出m個元素的一個排列。當m=n時所有的排列情況叫全排列。這篇文章主要介紹了Java回溯法解決全排列問題2022-10-10
詳解springboot和vue前后端分離開發(fā)跨域登陸問題
這篇文章主要介紹了詳解springboot和vue前后端分離開發(fā)跨域登陸問題，文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值，需要的朋友們下面隨著小編來一起學習學習吧2019-09-09