SparkSQL如何访问Hive和MySQL
本篇内容主要讲解“Spark SQL如何访问Hive和MySQL”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“Spark SQL如何访问Hive和MySQL”吧!
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一： 版本
搭建好的Hadoop环境，Hive环境,Spark环境。本文Hadoop版本为 Hadoop-2.6.4，Hive版本为Hive-2.0.0，Spark版本为spark-1.6.1-bin-hadoop2.6。
二： 配置spark-env.sh
在 SPARK_HOME/conf/spark-env.sh 中配置以下内容：
export SCALA_HOME=/mysoftware/scala-2.11.8
export JAVA_HOME=/mysoftware/jdk1.7.0_80
export SPARK_MASTER_IP=master
export SPARK_WORKER_MEMORY=512m
export master=spark://master:7077
另外往上很多资料都添加了如下两行内容，即：
export CLASSPATH=$CLASSPATH:/mysoftware/spark-1.6.1/lib
export SPARK_CLASSPATH=$SPARK_CLASSPATH:/mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar
在这里spark-env.sh中并没有添加如上两行内容，因为Spark1.0+版本已经将这个否决了，所以在此没有添加，可以看到在启动spark-shell出现如下信息，即：
SPARK_CLASSPATH was detected (set to ':/mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar').
This is deprecated in Spark 1.0+.
Please instead use:
- ./spark-submit with --driver-class-path to augment the driver classpath
- spark.executor.extraClassPath to augment the executor classpath
三：配置spark-defaults.sh
首先将SPARK_HOME/conf/spark-defaults.conf.template 拷贝（cp）一份为 spark-defaults.conf ，然后可以看到该文件中已告知众多配置信息都是默认的即default。所以本文并没有修改，如需要修改，请修改成与自己环境相符合的。
另外，网上很多资料在该文件中内容添加了如下内容，即：
spark.executor.extraClassPath /mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar
spark.driver.extraClassPath /mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar
结果启动spark-shell时，出现了WARN，原因是设置了上面两行内容。Setting。
四： 添加mysql的驱动jar包
将mysql-connector-java-5.1.5-bin.jar 添加到 SPARK_HOME/lib/目录下
五： 添加SPARK_HOME/conf目录下文件
将 hive-site.xml , core-site.xml（为安全起见），hdfs-site.xml（为HDFS配置）拷贝一份至 SPARK_HOME/conf目录下。
官网介绍：
Configuration of Hive is done by placing your hive-site.xml, core-site.xml (for security configuration), hdfs-site.xml (for HDFS configuration) file in conf/.Please note when running the query on a YARN cluster (cluster mode), the datanucleus jars under the lib directory and hive-site.xml under conf/ directory need to be available on the driver and all executors launched by the YARN cluster. The convenient way to do this is adding them through the --jars option and --file option of the spark-submit command.
六：Spark SQL 访问Hive
6.1 第一种方式 启动spark-shell：
bin/spark-shell --driver-class-path /mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar
hadoop@master:/mysoftware/spark-1.6.1$ bin/spark-shell --driver-class-path /mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar
log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's repl log4j profile: org/apache/spark/log4j-defaults-repl.properties
To adjust logging level use sc.setLogLevel("INFO")
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 1.6.1
/_/
Using Scala version 2.10.5 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_80)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context available as sc.
16/06/06 18:56:11 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
16/06/06 18:56:12 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
16/06/06 18:56:20 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
16/06/06 18:56:20 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
16/06/06 18:56:28 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
16/06/06 18:56:28 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
16/06/06 18:56:31 ERROR ObjectStore: Version information found in metastore differs 2.0.0 from expected schema version 1.2.0. Schema verififcation is disabled hive.metastore.schema.verification so setting version.
SQL context available as sqlContext.
scala>运行如下命令,即：
scala> sc
res0: org.apache.spark.SparkContext = org.apache.spark.SparkContext@631a8160
scala> val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)
sqlContext: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@3a957b9e
scala> sqlContext.sql("CREATE TABLE IF NOT EXISTS sparkhive (key INT, value STRING)")
res1: org.apache.spark.sql.DataFrame = [result: string]
当运行完上述第三条命令后，创建的表 sparkhive，能够在hive中查询到，即：
hive> show tables; OK hbase_person hivehbase hivehbase_person hivehbase_student multiplehive sparkhive testhive testsparkhive Time taken: 1.154 seconds, Fetched: 8 row(s)
现在往表中添加数据和查看数据，即：
scala> sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE sparkhive");
scala> sqlContext.sql("FROM sparkhive SELECT key, value").collect()
'examples/src/main/resources/kv1.txt'--》该路径在安装包中有。
scala> sqlContext.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE sparkhive");
res2: org.apache.spark.sql.DataFrame = [result: string]
scala> sqlContext.sql("FROM sparkhive SELECT key, value").collect()
res3: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,val_311], [27,val_27], [165,val_165], [409,val_409], [255,val_255], [278,val_278], [98,val_98], [484,val_484], [265,val_265], [193,val_193], [401,val_401], [150,val_150], [273,val_273], [224,val_224], [369,val_369], [66,val_66], [128,val_128], [213,val_213], [146,val_146], [406,val_406], [429,val_429], [374,val_374], [152,val_152], [469,val_469], [145,val_145], [495,val_495], [37,val_37], [327,val_327], [281,val_281], [277,val_277], [209,val_209], [15,val_15], [82,val_82], [403,val_403], [166,val_166], [417,val_417], [430,val_430], [252,val_252], [292,val_292], [219,val_219], [287,val_287], [153,val_153], [193,val_193], [338,val_338], [446,val_446], [459,val_459], [394,val_394], [237,val_237], [482,val_482], ...
scala>也可以在hive中通过 seelct * from sparkhive查看数据。
6.2 第二种方式 启动spark-shell：
SPARK_CLASSPATH=$SPARK_CLASSPATH:/mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jarbin/spark-shell
但是会出现 一些 WARN 信息，如下：（建议第一种方式启动）
hadoop@master:/mysoftware/spark-1.6.1$ SPARK_CLASSPATH=$SPARK_CLASSPATH:/mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar bin/spark-shell
log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's repl log4j profile: org/apache/spark/log4j-defaults-repl.properties
To adjust logging level use sc.setLogLevel("INFO")
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 1.6.1
/_/
Using Scala version 2.10.5 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_80)
Type in expressions to have them evaluated.
Type :help for more information.
16/06/06 19:14:10 WARN SparkConf:
SPARK_CLASSPATH was detected (set to ':/mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar').
This is deprecated in Spark 1.0+.
Please instead use:
- ./spark-submit with --driver-class-path to augment the driver classpath
- spark.executor.extraClassPath to augment the executor classpath
16/06/06 19:14:10 WARN SparkConf: Setting 'spark.executor.extraClassPath' to ':/mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar' as a work-around.
16/06/06 19:14:10 WARN SparkConf: Setting 'spark.driver.extraClassPath' to ':/mysoftware/spark-1.6.1/lib/mysql-connector-java-5.1.5-bin.jar' as a work-around.
Spark context available as sc.
16/06/06 19:14:26 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
16/06/06 19:14:27 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
16/06/06 19:14:35 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
16/06/06 19:14:35 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
16/06/06 19:14:39 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
16/06/06 19:14:39 WARN Connection: BoneCP specified but not present in CLASSPATH (or one of dependencies)
SQL context available as sqlContext.
scala>七：Spark SQL 访问MySQL
同样以上述第一种方式启动 spark-shell。
注意以下参数的书写：
"url" -> "jdbc:mysql://192.168.226.129:3306/hive?user=hive&password=xujun",
-- (远程端连接mysql的url地址加用户名与密码企图连接hive数据库)
"dbtable" -> "hive.TBLS", (这里用的是 hive数据库中原本存在的一张表 TBLS )
"driver" -> "com.mysql.jdbc.Driver" ( 驱动 )
7.1 第一种方式，通过 sqlContext.read.format("jdbc").options("xxxx") 加载数据， (中途产生了一个DataFrameReader对象，详情可参见API)
val jdbcDF = sqlContext.read.format("jdbc").options( Map("url" -> "jdbc:mysql://192.168.226.129:3306/hive?user=hive&password=xujun", "dbtable" -> "hive.TBLS","driver" -> "com.mysql.jdbc.Driver")).load()
具体信息如下：
scala> val jdbcDF = sqlContext.read.format("jdbc").options( Map("url" -> "jdbc:mysql://192.168.226.129:3306/hive?user=hive&password=xujun", "dbtable" -> "hive.TBLS","driver" -> "com.mysql.jdbc.Driver")).load()
jdbcDF: org.apache.spark.sql.DataFrame = [TBL_ID: bigint, CREATE_TIME: int, DB_ID: bigint, LAST_ACCESS_TIME: int, OWNER: string, RETENTION: int, SD_ID: bigint, TBL_NAME: string, TBL_TYPE: string, VIEW_EXPANDED_TEXT: string, VIEW_ORIGINAL_TEXT: string]
scala> jdbcDF.show()
+------+-----------+-----+----------------+------+---------+-----+-----------------+--------------+------------------+------------------+
|TBL_ID|CREATE_TIME|DB_ID|LAST_ACCESS_TIME| OWNER|RETENTION|SD_ID| TBL_NAME| TBL_TYPE|VIEW_EXPANDED_TEXT|VIEW_ORIGINAL_TEXT|
+------+-----------+-----+----------------+------+---------+-----+-----------------+--------------+------------------+------------------+
| 11| 1464510462| 1| 0| hive| 0| 11| testhive| MANAGED_TABLE| null| null|
| 22| 1464513715| 1| 0|hadoop| 0| 22| hivehbase| MANAGED_TABLE| null| null|
| 23| 1464517000| 1| 0|hadoop| 0| 23| hbase_person|EXTERNAL_TABLE| null| null|
| 24| 1464517563| 1| 0|hadoop| 0| 24|hivehbase_student|EXTERNAL_TABLE| null| null|
| 29| 1464521014| 1| 0|hadoop| 0| 29| multiplehive| MANAGED_TABLE| null| null|
| 36| 1464522011| 1| 0|hadoop| 0| 36| hivehbase_person| MANAGED_TABLE| null| null|
| 41| 1465227955| 1| 0|hadoop| 0| 41| testsparkhive| MANAGED_TABLE| null| null|
| 46| 1465264720| 1| 0|hadoop| 0| 46| sparkhive| MANAGED_TABLE| null| null|
+------+-----------+-----+----------------+------+---------+-----+-----------------+--------------+------------------+------------------+7.2 第二种方式，通过 sqlContext.load("jdbc","xxxx")来加载数据即：
val jdbcDF = sqlContext.load( "jdbc",Map("url" -> "jdbc:mysql://192.168.226.129:3306/hive?user=hive&password=xujun", "dbtable" -> "hive.TBLS","driver" -> "com.mysql.jdbc.Driver"))
显示数据：
jdbcDF.show()
具体信息跟上述第一种方式一样。
到此，相信大家对“Spark SQL如何访问Hive和MySQL”有了更深的了解，不妨来实际操作一番吧！这里是创新互联网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！
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