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Spark（九）：Spark SQL访问Hive，MySQL

2016-06-07 00:00 916 查看

摘要: Spark SQL 访问Hive ，访问MySQL。

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

[code=plain]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：

[code=plain]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。

[code=plain]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.jar bin/spark-shell

但是会出现一些 WARN 信息，如下：（建议第一种方式启动）

[code=plain]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：

[code=plain]  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。

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