从源代码编译构建Hive3.1.3
- 编译说明
- 编译Hive3.1.3
- 更改Maven配置
- 下载源码
- 修改项目pom.xml
- 修改hive源码
- 修改说明
- 修改standalone-metastore模块
- 修改ql模块
- 修改spark-client模块
- 修改druid-handler模块
- 修改llap-server模块
- 修改llap-tez模块
- 修改llap-common模块
- 编译打包
- 异常集合
- 异常1
- 异常2
- 异常3
- 异常4
- 编译打包成功
- 总结
编译说明
使用Hive官方提供的预编译安装包是最常见和推荐的方式来使用Hive，适用于大多数用户。这些预编译的安装包经过了测试和验证，在许多不同的环境中都能正常运行。
在某些特定情况下，可能需要从源代码编译Hive，而不是使用预编译的安装包。
编译Hive源代码的场景、原因如下：
1.定制配置：
如果希望对Hive进行一些特定的配置定制或修改，例如更改默认的参数设置、添加新的数据存储后端、集成新的执行引擎等，那么编译源代码将能够修改和定制 Hive 的配置。
2.功能扩展：
如果需要扩展Hive的功能，例如添加自定义的 UDF（用户定义函数）、UDAF（用户定义聚合函数）、UDTF（用户定义表生成函数）等，编译源代码将添加和构建这些自定义功能。
3.调试和修改 Bug：
如果在使用Hive过程中遇到了问题，或者发现了bug，并希望进行调试和修复，那么编译源代码将能够获得运行时的源代码，进而进行调试和修改。
4.最新特性和改进：
如果希望使用Hive的最新特性、改进和优化，但这些特性尚未发布到官方的预编译包中，可以从源代码编译最新的版本，以获得并使用这些功能。
5.参与社区贡献：
如果对Hive有兴趣并希望为其开发做贡献，通过编译源代码，可以获取到完整的开发环境，包括构建工具、测试框架和源代码，以便与Hive社区一起开发和贡献代码。
编译Hive3.1.3
当使用Spark作为Hive的执行引擎时，但是Hive3.1.3本身支持的Spark版本是2.3，故此需要重新编译Hive，让Hive支持较新版本的Spark。计划编译Hive支持Spark3.4.0，Hadoop版本3.1.3
更改Maven配置
更改maven的settings.xml文件，看情况决定是否添加如下仓库地址，仅供参考：
<!-- 阿里云仓库 -->
<mirror>
<id>aliyun-central</id>
<name>阿里云公共仓库</name>
<url>https://maven.aliyun.com/repository/central</url>
<mirrorOf>*</mirrorOf>
</mirror>
<!-- 中央仓库 -->
<mirror>
<id>repo</id>
<mirrorOf>central</mirrorOf>
<name>Human Readable Name for this Mirror.</name>
<url>https://repo.maven.apache.org/maven2</url>
</mirror>
下载源码
下载需要编译的Hive版本源码，这里打算重新编译Hive3.1.3
wget https://archive.apache.org/dist/hive/hive-3.1.3/pache-hive-3.1.3-src.tar.gz
IDEA打开pache-hive-3.1.3-src项目，打开项目后肯定会各种爆红，不用管
修改项目pom.xml
1.修改Hadoop版本
Hive3.1.3支持的Hadoop版本是3.1.10，但是Hive与Hadoop之间记得有个范围支持，故与Hadoop相关的操作看需求是否更改
<hadoop.version>3.1.0</hadoop.version>
<hadoop.version>3.1.3</hadoop.version>
清楚的记得Hadoop3.1.3使用日志版本是1.7.25
<slf4j.version>1.7.10</slf4j.version>
<slf4j.version>1.7.25</slf4j.version>
2.修改guava版本
由于Hive运行时会加载Hadoop依赖，因此需要修改Hive中guava版本为Hadoop中的guava版本。这里即使不更改，实则在使用Hive时也可能会进行更换guava版本操作(版本差异不大可以不用更换)
<guava.version>19.0</guava.version>
<guava.version>27.0-jre</guava.version>
3.修改spark版本
Hive3.1.3默认支持的Spark是2.3.0，这步也是核心，使其支持Spark3.4.0，使用版本较新，看需求适当降低。另外，明确指定Spark3.4.0使用的是Scala2.13版本，一同修改
<spark.version>2.3.0</spark.version>
<scala.binary.version>2.11</scala.binary.version>
<scala.version>2.11.8</scala.version>
# 原计划编译spark3.4.0 特么的太多坑了 后面不得不放弃
<spark.version>3.4.0</spark.version>
<scala.binary.version>2.12</scala.binary.version>
<scala.version>2.12.17</scala.version>
# 掉坑里折腾惨了,降低spark版本
<spark.version>3.2.4</spark.version>
<scala.binary.version>2.12</scala.binary.version>
<scala.version>2.12.17</scala.version>
修改hive源码
修改说明
修改Hive源代码，会对其进行删除、修改、新增操作，下图是Git版本控制对比图，大家应该都能看懂吧。但还是说明一下：
-：删除该行代码+：新增、修改该行代码
修改hive源码这个操作是核心操作，具体修改哪些源代码，参考：
https://github.com/gitlbo/hive/commits/3.1.2
修改standalone-metastore模块
具体修改参考：https://github.com/gitlbo/hive/commit/c073e71ef43699b7aa68cad7c69a2e8f487089fd
创建ColumnsStatsUtils类
代码如下：
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.hive.metastore.columnstats;
import org.apache.hadoop.hive.metastore.api.ColumnStatisticsObj;
import org.apache.hadoop.hive.metastore.columnstats.cache.DateColumnStatsDataInspector;
import org.apache.hadoop.hive.metastore.columnstats.cache.DecimalColumnStatsDataInspector;
import org.apache.hadoop.hive.metastore.columnstats.cache.DoubleColumnStatsDataInspector;
import org.apache.hadoop.hive.metastore.columnstats.cache.LongColumnStatsDataInspector;
import org.apache.hadoop.hive.metastore.columnstats.cache.StringColumnStatsDataInspector;
/**
* Utils class for columnstats package.
*/
public final class ColumnsStatsUtils {
private ColumnsStatsUtils(){}
/**
* Convertes to DateColumnStatsDataInspector if it's a DateColumnStatsData.
* @param cso ColumnStatisticsObj
* @return DateColumnStatsDataInspector
*/
public static DateColumnStatsDataInspector dateInspectorFromStats(ColumnStatisticsObj cso) {
DateColumnStatsDataInspector dateColumnStats;
if (cso.getStatsData().getDateStats() instanceof DateColumnStatsDataInspector) {
dateColumnStats =
(DateColumnStatsDataInspector)(cso.getStatsData().getDateStats());
} else {
dateColumnStats = new DateColumnStatsDataInspector(cso.getStatsData().getDateStats());
}
return dateColumnStats;
}
/**
* Convertes to StringColumnStatsDataInspector
* if it's a StringColumnStatsData.
* @param cso ColumnStatisticsObj
* @return StringColumnStatsDataInspector
*/
public static StringColumnStatsDataInspector stringInspectorFromStats(ColumnStatisticsObj cso) {
StringColumnStatsDataInspector columnStats;
if (cso.getStatsData().getStringStats() instanceof StringColumnStatsDataInspector) {
columnStats =
(StringColumnStatsDataInspector)(cso.getStatsData().getStringStats());
} else {
columnStats = new StringColumnStatsDataInspector(cso.getStatsData().getStringStats());
}
return columnStats;
}
/**
* Convertes to LongColumnStatsDataInspector if it's a LongColumnStatsData.
* @param cso ColumnStatisticsObj
* @return LongColumnStatsDataInspector
*/
public static LongColumnStatsDataInspector longInspectorFromStats(ColumnStatisticsObj cso) {
LongColumnStatsDataInspector columnStats;
if (cso.getStatsData().getLongStats() instanceof LongColumnStatsDataInspector) {
columnStats =
(LongColumnStatsDataInspector)(cso.getStatsData().getLongStats());
} else {
columnStats = new LongColumnStatsDataInspector(cso.getStatsData().getLongStats());
}
return columnStats;
}
/**
* Convertes to DoubleColumnStatsDataInspector
* if it's a DoubleColumnStatsData.
* @param cso ColumnStatisticsObj
* @return DoubleColumnStatsDataInspector
*/
public static DoubleColumnStatsDataInspector doubleInspectorFromStats(ColumnStatisticsObj cso) {
DoubleColumnStatsDataInspector columnStats;
if (cso.getStatsData().getDoubleStats() instanceof DoubleColumnStatsDataInspector) {
columnStats =
(DoubleColumnStatsDataInspector)(cso.getStatsData().getDoubleStats());
} else {
columnStats = new DoubleColumnStatsDataInspector(cso.getStatsData().getDoubleStats());
}
return columnStats;
}
/**
* Convertes to DecimalColumnStatsDataInspector
* if it's a DecimalColumnStatsData.
* @param cso ColumnStatisticsObj
* @return DecimalColumnStatsDataInspector
*/
public static DecimalColumnStatsDataInspector decimalInspectorFromStats(ColumnStatisticsObj cso) {
DecimalColumnStatsDataInspector columnStats;
if (cso.getStatsData().getDecimalStats() instanceof DecimalColumnStatsDataInspector) {
columnStats =
(DecimalColumnStatsDataInspector)(cso.getStatsData().getDecimalStats());
} else {
columnStats = new DecimalColumnStatsDataInspector(cso.getStatsData().getDecimalStats());
}
return columnStats;
}
}
接着修改以下内容，具体修改参考以下截图说明
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/aggr/DateColumnStatsAggregator.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/aggr/DecimalColumnStatsAggregator.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/aggr/DoubleColumnStatsAggregator.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/aggr/LongColumnStatsAggregator.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/aggr/StringColumnStatsAggregator.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/cache/DateColumnStatsDataInspector.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/cache/DecimalColumnStatsDataInspector.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/cache/DoubleColumnStatsDataInspector.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/cache/LongColumnStatsDataInspector.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/cache/StringColumnStatsDataInspector.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/merge/DateColumnStatsMerger.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/merge/DecimalColumnStatsMerger.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/merge/DoubleColumnStatsMerger.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/merge/LongColumnStatsMerger.java
standalone-metastore/src/main/java/org/apache/hadoop/hive/metastore/columnstats/merge/StringColumnStatsMerger.java
修改ql模块
ql/src/test/org/apache/hadoop/hive/ql/stats/TestStatsUtils.java
ql/src/test/org/apache/hadoop/hive/ql/exec/tez/SampleTezSessionState.java
ql/src/java/org/apache/hadoop/hive/ql/exec/tez/WorkloadManager.java
修改spark-client模块
spark-client/src/main/java/org/apache/hive/spark/client/metrics/ShuffleWriteMetrics.java
spark-client/src/main/java/org/apache/hive/spark/counter/SparkCounter.java
修改druid-handler模块
druid-handler/src/java/org/apache/hadoop/hive/druid/serde/DruidScanQueryRecordReader.java
修改llap-server模块
llap-server/src/java/org/apache/hadoop/hive/llap/daemon/impl/AMReporter.java
llap-server/src/java/org/apache/hadoop/hive/llap/daemon/impl/LlapTaskReporter.java
llap-server/src/java/org/apache/hadoop/hive/llap/daemon/impl/TaskExecutorService.java
修改llap-tez模块
llap-tez/src/java/org/apache/hadoop/hive/llap/tezplugins/LlapTaskSchedulerService.java
修改llap-common模块
llap-common/src/java/org/apache/hadoop/hive/llap/AsyncPbRpcProxy.java
编译打包
对Hive源码修改完成后，执行编译打包命令：
mvn clean package -Pdist -DskipTests -Dmaven.javadoc.skip=true
mvn clean package -Pdist -DskipTests
在执行编译打包命令过程中，肯定会有各种问题的，这些问题是需要解决的，期间遇到的各种异常请参考下方异常集合对比解决。
注意点
1.有时本地仓库中的缓存可能会引起依赖项解析错误。可以尝试清理该项目依赖的本地仓库中的maven包，这个命令会清理pom.xml中的包，并重新下载，执行以下命令：
mvn dependency:purge-local-repository
2.修改Pom.xml文件版本号，或更改代码、安装Jar到本地仓库后，建议关闭IDEA重新打开进入，防止缓存、或者更新不及时
异常集合
注意：以下异常均是按照编译Hive支持Spark3.4.0过程中产生的异常，后来降低了Spark的版本。
异常1
1.maven会提示无法找到、无法下载某个Jar包、或者下载Jar耗时长(即使开启魔法也是）
例如：maven仓库找不到
hive-upgrade-acid-3.1.3.jar与pentaho-aggdesigner-algorithm-5.1.5-jhyde_2.jar
具体异常如下，仅供参考：
[ERROR] Failed to execute goal on project hive-upgrade-acid: Could not resolve dependencies for project org.apache.hive:hive-upgrade-acid:jar:3.1.3: Failure to find org.pentaho:pentaho-aggdesigner-algorithm:jar:5.1.5-jhyde in https://maven.aliyun.com/repository/central was cached in the local repository, resolution will not be reattempted until the update interval of aliyun-central has elapsed or updates are forced -> [Help 1]
解决方案：
到以下仓库搜索需要的Jar包，手动下载，并安装到本地仓库
仓库地址1：https://mvnrepository.com/
仓库地址2：https://central.sonatype.com/
仓库地址3：https://developer.aliyun.com/mvn/search
将一个JAR安装到本地仓库，示例命令的语法：
mvn install:install-file -Dfile=<path-to-jar> -DgroupId=<group-id> -DartifactId=<artifact-id> -Dversion=<version> -Dpackaging=<packaging>
<path-to-jar>：JAR文件的路径，可以是本地文件系统的绝对路径。
<group-id>：项目组ID，通常采用反向域名格式，例如com.example。
<artifact-id>：项目的唯一标识符，通常是项目名称。
<version>：项目的版本号。
<packaging>：JAR文件的打包类型，例如jar。
mvn install:install-file -Dfile=./hive-upgrade-acid-3.1.3.jar -DgroupId=org.apache.hive -DartifactId=hive-upgrade-acid -Dversion=3.1.3 -Dpackaging=jar
mvn install:install-file -Dfile=./pentaho-aggdesigner-algorithm-5.1.5-jhyde.jar -DgroupId=org.pentaho -DartifactId=pentaho-aggdesigner-algorithm -Dversion=5.1.5-jhyde -Dpackaging=jar
mvn install:install-file -Dfile=./hive-metastore-2.3.3.jar -DgroupId=org.apache.hive -DartifactId=hive-metastore -Dversion=2.3.3 -Dpackaging=jar
mvn install:install-file -Dfile=./hive-exec-3.1.3.jar -DgroupId=org.apache.hive -DartifactId=hive-exec -Dversion=3.1.3 -Dpackaging=jar
异常2
提示bash相关东西，心凉了一大截。由于window下操作，bash不支持。
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-antrun-plugin:1.7:run (generate-version-annotation) on project hive-common: An Ant BuildException has occured: Execute failed: java.io.IOException: Cannot run program "bash" (in directory "C:\Users\JackChen\Desktop\apache-hive-3.1.3-src\common"): CreateProcess error=2, 系统找不到指定的文件。
[ERROR] around Ant part ...<exec failonerror="true" executable="bash">... @ 4:46 in C:\Users\JackChen\Desktop\apache-hive-3.1.3-src\common\target\antrun\build-main.xml
解决方案：
正常来说，作为开发者，肯定有安装Git，Git有bash窗口，即在Git的Bash窗口执行编译打包命令
mvn clean package -Pdist -DskipTests
异常3
当前进度在Hive Llap Server失败
[INFO] Hive Llap Client ................................... SUCCESS [ 4.030 s]
[INFO] Hive Llap Tez ...................................... SUCCESS [ 4.333 s]
[INFO] Hive Spark Remote Client ........................... SUCCESS [ 5.382 s]
[INFO] Hive Query Language ................................ SUCCESS [01:28 min]
[INFO] Hive Llap Server ................................... FAILURE [ 7.180 s]
[INFO] Hive Service ....................................... SKIPPED
[INFO] Hive Accumulo Handler .............................. SKIPPED
[INFO] Hive JDBC .......................................... SKIPPED
[INFO] Hive Beeline ....................................... SKIPPED
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.6.1:compile (default-compile) on project hive-llap-server: Compilation failure
[ERROR] /C:/Users/JackChen/Desktop/apache-hive-3.1.3-src/llap-server/src/java/org/apache/hadoop/hive/llap/daemon/impl/QueryTracker.java:[30,32] org.apache.logging.slf4j.Log4jMarker▒▒org.apache.logging.slf4j▒в▒▒ǹ▒▒▒▒▒; ▒▒▒▒▒ⲿ▒▒▒▒▒▒ж▒▒▒▒▒з▒▒▒
[ERROR]
[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/MojoFailureException
[ERROR]
[ERROR] After correcting the problems, you can resume the build with the command
[ERROR] mvn <args> -rf :hive-llap-server
public class QueryTracker extends AbstractService {
// private static final Marker QUERY_COMPLETE_MARKER = new Log4jMarker(new Log4jQueryCompleteMarker());
private static final Marker QUERY_COMPLETE_MARKER = MarkerFactory.getMarker("MY_CUSTOM_MARKER");
}
异常4
编译执行到Hive HCatalog Webhcat模块失败
[INFO] Hive HCatalog ...................................... SUCCESS [ 10.947 s]
[INFO] Hive HCatalog Core ................................. SUCCESS [ 7.237 s]
[INFO] Hive HCatalog Pig Adapter .......................... SUCCESS [ 2.652 s]
[INFO] Hive HCatalog Server Extensions .................... SUCCESS [ 9.255 s]
[INFO] Hive HCatalog Webhcat Java Client .................. SUCCESS [ 2.435 s]
[INFO] Hive HCatalog Webhcat .............................. FAILURE [ 7.284 s]
[INFO] Hive HCatalog Streaming ............................ SKIPPED
[INFO] Hive HPL/SQL ....................................... SKIPPED
[INFO] Hive Streaming ..................................... SKIPPED
具体异常：
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.6.1:compile (default-compile) on project hive-webhcat: Compilation failure
[ERROR] /root/apache-hive-3.1.3-src/hcatalog/webhcat/svr/src/main/java/org/apache/hive/hcatalog/templeton/Main.java:[258,31] 对于FilterHolder(java.lang.Class<org.apache.hadoop.hdfs.web.AuthFilter>), 找不到合适的构造器
[ERROR] 构造器 org.eclipse.jetty.servlet.FilterHolder.FilterHolder(org.eclipse.jetty.servlet.BaseHolder.Source)不适用
[ERROR] (参数不匹配; java.lang.Class<org.apache.hadoop.hdfs.web.AuthFilter>无法转换为org.eclipse.jetty.servlet.BaseHolder.Source)
[ERROR] 构造器 org.eclipse.jetty.servlet.FilterHolder.FilterHolder(java.lang.Class<? extends javax.servlet.Filter>)不适用
[ERROR] (参数不匹配; java.lang.Class<org.apache.hadoop.hdfs.web.AuthFilter>无法转换为java.lang.Class<? extends javax.servlet.Filter>)
[ERROR] 构造器 org.eclipse.jetty.servlet.FilterHolder.FilterHolder(javax.servlet.Filter)不适用
[ERROR] (参数不匹配; java.lang.Class<org.apache.hadoop.hdfs.web.AuthFilter>无法转换为javax.servlet.Filter)
[ERROR]
[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/MojoFailureException
[ERROR]
[ERROR] After correcting the problems, you can resume the build with the command
[ERROR] mvn <args> -rf :hive-webhcat
看源码发现AuthFilter是继承AuthenticationFilter，AuthenticationFilter又实现Filter，应该不会出现此异常信息才对，于是手动修改源码进行强制转换试试，发现任然不行。
public FilterHolder makeAuthFilter() throws IOException {
// FilterHolder authFilter = new FilterHolder(AuthFilter.class);
FilterHolder authFilter = new FilterHolder((Class<? extends Filter>) AuthFilter.class);
UserNameHandler.allowAnonymous(authFilter);
解决方案：
在IDEA中单独编译打包此模块，发现是能构建成功的
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 40.755 s
[INFO] Finished at: 2023-08-06T21:39:17+08:00
[INFO] ------------------------------------------------------------------------
于是乎产生了一个想法：
1.因为项目使用Maven进行打包（执行mvn package），再次执行相同的命令将不会重新打包项目
2.所以先针对项目执行clean命令，然后对该Webhcat模块打包，最后在整体编译打包时，不执行clean操作，直接运行 mvn package -Pdist -DskipTests。
注意：后来降低了Spark版本，没有产生该问题
编译打包成功
经过数个小时的解决问题与漫长的编译打包，终于成功，发现这个界面是多么的美好。
[INFO] --- maven-dependency-plugin:2.8:copy (copy) @ hive-packaging ---
[INFO] Configured Artifact: org.apache.hive:hive-jdbc:standalone:3.1.3:jar
[INFO] Copying hive-jdbc-3.1.3-standalone.jar to C:\Users\JackChen\Desktop\apache-hive-3.1.3-src\packaging\target\apache-hive-3.1.3-jdbc.jar
[INFO] ------------------------------------------------------------------------
[INFO] Reactor Summary for Hive 3.1.3:
[INFO]
[INFO] Hive Upgrade Acid .................................. SUCCESS [ 5.264 s]
[INFO] Hive ............................................... SUCCESS [ 0.609 s]
[INFO] Hive Classifications ............................... SUCCESS [ 1.183 s]
[INFO] Hive Shims Common .................................. SUCCESS [ 2.239 s]
[INFO] Hive Shims 0.23 .................................... SUCCESS [ 2.748 s]
[INFO] Hive Shims Scheduler ............................... SUCCESS [ 2.286 s]
[INFO] Hive Shims ......................................... SUCCESS [ 1.659 s]
[INFO] Hive Common ........................................ SUCCESS [ 9.671 s]
[INFO] Hive Service RPC ................................... SUCCESS [ 6.608 s]
[INFO] Hive Serde ......................................... SUCCESS [ 6.042 s]
[INFO] Hive Standalone Metastore .......................... SUCCESS [ 42.432 s]
[INFO] Hive Metastore ..................................... SUCCESS [ 2.304 s]
[INFO] Hive Vector-Code-Gen Utilities ..................... SUCCESS [ 1.150 s]
[INFO] Hive Llap Common ................................... SUCCESS [ 3.343 s]
[INFO] Hive Llap Client ................................... SUCCESS [ 2.380 s]
[INFO] Hive Llap Tez ...................................... SUCCESS [ 2.476 s]
[INFO] Hive Spark Remote Client ........................... SUCCESS [31:34 min]
[INFO] Hive Query Language ................................ SUCCESS [01:09 min]
[INFO] Hive Llap Server ................................... SUCCESS [ 7.230 s]
[INFO] Hive Service ....................................... SUCCESS [ 28.343 s]
[INFO] Hive Accumulo Handler .............................. SUCCESS [ 6.179 s]
[INFO] Hive JDBC .......................................... SUCCESS [ 19.058 s]
[INFO] Hive Beeline ....................................... SUCCESS [ 4.078 s]
[INFO] Hive CLI ........................................... SUCCESS [ 3.436 s]
[INFO] Hive Contrib ....................................... SUCCESS [ 4.770 s]
[INFO] Hive Druid Handler ................................. SUCCESS [ 17.245 s]
[INFO] Hive HBase Handler ................................. SUCCESS [ 6.759 s]
[INFO] Hive JDBC Handler .................................. SUCCESS [ 4.202 s]
[INFO] Hive HCatalog ...................................... SUCCESS [ 1.757 s]
[INFO] Hive HCatalog Core ................................. SUCCESS [ 5.455 s]
[INFO] Hive HCatalog Pig Adapter .......................... SUCCESS [ 4.662 s]
[INFO] Hive HCatalog Server Extensions .................... SUCCESS [ 4.629 s]
[INFO] Hive HCatalog Webhcat Java Client .................. SUCCESS [ 4.652 s]
[INFO] Hive HCatalog Webhcat .............................. SUCCESS [ 8.899 s]
[INFO] Hive HCatalog Streaming ............................ SUCCESS [ 4.934 s]
[INFO] Hive HPL/SQL ....................................... SUCCESS [ 7.684 s]
[INFO] Hive Streaming ..................................... SUCCESS [ 4.049 s]
[INFO] Hive Llap External Client .......................... SUCCESS [ 3.674 s]
[INFO] Hive Shims Aggregator .............................. SUCCESS [ 0.557 s]
[INFO] Hive Kryo Registrator .............................. SUCCESS [03:17 min]
[INFO] Hive TestUtils ..................................... SUCCESS [ 1.154 s]
[INFO] Hive Packaging ..................................... SUCCESS [01:58 min]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 38:22 min (Wall Clock)
[INFO] Finished at: 2023-08-08T22:50:15+08:00
[INFO] ------------------------------------------------------------------------
总结
在整个编译、打包过程中，有2点非常重要：
1.相关Jar无法下载或者下载缓慢问题，一定要想方设法解决，因为Jar是构建的核心，缺一不可
2.Jar依赖解决了，但是任然存在可能的兼容性问题，编译问题，遇到问题一定要一一解决，解决一步走一步
