SparkSQL读取hive数据本地idea运行的方法详解
环境准备:
hadoop版本:2.6.5
spark版本:2.3.0
hive版本:1.2.2
master主机:192.168.100.201
slave1主机:192.168.100.201
pom.xml依赖如下:
<?xml version="1.0" encoding="utf-8"?> <project xmlns="http://maven.apache.org/pom/4.0.0" xmlns:xsi="http://www.w3.org/2001/xmlschema-instance" xsi:schemalocation="http://maven.apache.org/pom/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelversion>4.0.0</modelversion> <groupid>com.spark</groupid> <artifactid>spark_practice</artifactid> <version>1.0-snapshot</version> <properties> <project.build.sourceencoding>utf-8</project.build.sourceencoding> <maven.compiler.source>1.8</maven.compiler.source> <maven.compiler.target>1.8</maven.compiler.target> <spark.core.version>2.3.0</spark.core.version> </properties> <dependencies> <dependency> <groupid>junit</groupid> <artifactid>junit</artifactid> <version>4.11</version> <scope>test</scope> </dependency> <dependency> <groupid>org.apache.spark</groupid> <artifactid>spark-core_2.11</artifactid> <version>${spark.core.version}</version> </dependency> <dependency> <groupid>org.apache.spark</groupid> <artifactid>spark-sql_2.11</artifactid> <version>${spark.core.version}</version> </dependency> <dependency> <groupid>mysql</groupid> <artifactid>mysql-connector-java</artifactid> <version>5.1.38</version> </dependency> <dependency> <groupid>org.apache.spark</groupid> <artifactid>spark-hive_2.11</artifactid> <version>2.3.0</version> </dependency> </dependencies> </project>
注意:一定要将hive-site.xml配置文件放到工程resources目录下
hive-site.xml配置如下:
<?xml version="1.0"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl" rel="external nofollow" ?> <configuration> <!-- hive元数据服务url --> <property> <name>hive.metastore.uris</name> <value>thrift://192.168.100.201:9083</value> </property> <property> <name>hive.server2.thrift.port</name> <value>10000</value> </property> <property> <name>javax.jdo.option.connectionurl</name> <value>jdbc:mysql://node01:3306/hive?createdatabaseifnotexist=true</value> </property> <property> <name>javax.jdo.option.connectiondrivername</name> <value>com.mysql.jdbc.driver</value> </property> <property> <name>javax.jdo.option.connectionusername</name> <value>root</value> </property> <property> <name>javax.jdo.option.connectionpassword</name> <value>123456</value> </property> <property> <name>hive.zookeeper.quorum</name> <value>node01,node02,node03</value> </property> <property> <name>hbase.zookeeper.quorum</name> <value>node01,node02,node03</value> </property> <!-- hive在hdfs上的存储路径 --> <property> <name>hive.metastore.warehouse.dir</name> <value>/user/hive/warehouse</value> </property> <!-- 集群hdfs访问url --> <property> <name>fs.defaultfs</name> <value>hdfs://192.168.100.201:9000</value> </property> <property> <name>hive.metastore.schema.verification</name> <value>false</value> </property> <property> <name>datanucleus.autocreateschema</name> <value>true</value> </property> <property> <name>datanucleus.autostartmechanism</name> <value>checked</value> </property> </configuration>
主类代码:
import org.apache.spark.sql.sparksession object sparksqltest2 { def main(args: array[string]): unit = { val spark: sparksession = sparksession .builder .master("local[*]") .appname("java spark hive example") .enablehivesupport .getorcreate spark.sql("show databases").show() spark.sql("show tables").show() spark.sql("select * from person").show() spark.stop() } }
前提:数据库访问的是default,表person中有三条数据。
测试前先确保hadoop集群正常启动,然后需要启动hive的metastore服务。
./bin/hive --service metastore
运行,结果如下:
如果报错:
exception in thread "main" org.apache.spark.sql.analysisexception: java.lang.runtimeexception: java.io.ioexception: (null) entry in command string: null chmod 0700 c:\users\dell\appdata\local\temp\c530fb25-b267-4dd2-b24d-741727a6fbf3_resources;
at org.apache.spark.sql.hive.hiveexternalcatalog.withclient(hiveexternalcatalog.scala:106)
at org.apache.spark.sql.hive.hiveexternalcatalog.databaseexists(hiveexternalcatalog.scala:194)
at org.apache.spark.sql.internal.sharedstate.externalcatalog$lzycompute(sharedstate.scala:114)
at org.apache.spark.sql.internal.sharedstate.externalcatalog(sharedstate.scala:102)
at org.apache.spark.sql.hive.hivesessionstatebuilder.externalcatalog(hivesessionstatebuilder.scala:39)
at org.apache.spark.sql.hive.hivesessionstatebuilder.catalog$lzycompute(hivesessionstatebuilder.scala:54)
at org.apache.spark.sql.hive.hivesessionstatebuilder.catalog(hivesessionstatebuilder.scala:52)
at org.apache.spark.sql.hive.hivesessionstatebuilder$$anon$1.<init>(hivesessionstatebuilder.scala:69)
at org.apache.spark.sql.hive.hivesessionstatebuilder.analyzer(hivesessionstatebuilder.scala:69)
at org.apache.spark.sql.internal.basesessionstatebuilder$$anonfun$build$2.apply(basesessionstatebuilder.scala:293)
at org.apache.spark.sql.internal.basesessionstatebuilder$$anonfun$build$2.apply(basesessionstatebuilder.scala:293)
at org.apache.spark.sql.internal.sessionstate.analyzer$lzycompute(sessionstate.scala:79)
at org.apache.spark.sql.internal.sessionstate.analyzer(sessionstate.scala:79)
at org.apache.spark.sql.execution.queryexecution.analyzed$lzycompute(queryexecution.scala:57)
at org.apache.spark.sql.execution.queryexecution.analyzed(queryexecution.scala:55)
at org.apache.spark.sql.execution.queryexecution.assertanalyzed(queryexecution.scala:47)
at org.apache.spark.sql.dataset$.ofrows(dataset.scala:74)
at org.apache.spark.sql.sparksession.sql(sparksession.scala:638)
at com.tongfang.learn.spark.hive.hivetest.main(hivetest.java:15)
解决:
1.下载hadoop windows binary包,链接:https://github.com/steveloughran/winutils
2.在启动类的运行参数中设置环境变量,hadoop_home=d:\winutils\hadoop-2.6.4,后面是hadoop windows 二进制包的目录。
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