大数据进阶之路——Spark SQL基本配置

Posted 2021-10-20 孙中明

文章目录

• • • Spark安装
    • 编译失败
    • 环境搭建
    • Standalone
    • 本地IDE
    • HiveContextAPP
    • SparkSessinon
    • Spark Shell
    • Spark Sql
    • thriftserver/beeline的使用
    • jdbc

MapReduce的局限性：
1）代码繁琐；
2）只能够支持map和reduce方法；
3）执行效率低下；
4）不适合迭代多次、交互式、流式的处理；

框架多样化：
1）批处理（离线）：MapReduce、Hive、Pig
2）流式处理（实时）： Storm、JStorm
3）交互式计算：Impala

学习、运维成本无形中都提高了很多

===> Spark

Spark安装

前置要求：

1）Building Spark using Maven requires Maven 3.3.9 or newer and Java 7+
2）export MAVEN_OPTS="-Xmx2g -XX:ReservedCodeCacheSize=512m"

mvn编译命令：

./build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package

[hadoop@hadoop001 spark-2.1.0]$ cat pom.xml 
[hadoop@hadoop001 spark-2.1.0]$ pwd
/home/hadoop/source/spark-2.1.0

<properties>
    <hadoop.version>2.2.0</hadoop.version>
    <protobuf.version>2.5.0</protobuf.version>
    <yarn.version>${hadoop.version}</yarn.version>
......
</properties>

...............
<profile>
  <id>hadoop-2.6</id>
  <properties>
    <hadoop.version>2.6.4</hadoop.version>
    <jets3t.version>0.9.3</jets3t.version>
    <zookeeper.version>3.4.6</zookeeper.version>
    <curator.version>2.6.0</curator.version>
  </properties>
</profile>

路径下执行

[hadoop@hadoop001 spark-2.1.0]$ pwd
/home/hadoop/source/spark-2.1.0

==> ./build/mvn -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver -Dhadoop.version=2.6.0-cdh5.7.0 -DskipTests clean package

编译可以运行的包

./dev/make-distribution.sh --name 2.6.0-cdh5.7.0 --tgz -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver -Dhadoop.version=2.6.0-cdh5.7.0

make-distribution.sh

spark-$VERSION-bin-$NAME.tgz

—>spark-2.1.0-bin-2.6.0-cdh5.7.0.tgz

编译失败

Failed to execute goal on project ...: Could not resolve dependencies for project ...

在pom.xml中添加

<repository>
      <id>cloudera</id>
      <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>

如果scala是2.10
需要添加./dev/change-scala-version.sh 2.10

环境搭建

local

• tar -zxvf park-2.1.0-bin-2.6.0-cdh5.7.0.tgz -C ~/app/

• 配置环境SPARK_HOME

• source ~./bash_profile

运行
spark-shell --master local[2]

	at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
	at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37)
a)
Caused by: org.datanucleus.exceptions.NucleusException: Attempt to invoke the "BONECP" plugin to create a ConnectionPool gave an error : The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
	at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:259)
	
 java:104)

.............................................

  at org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)
 571)
  at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:624)
  
  at org.datanucleus.plugin.NonManagedPluginRegistry.createExecutableExtension(NonManagedPluginRegistry.java:631)
  at org.datanucleus.plugin.PluginManager.createExecutableExtension(PluginManager.java:325)
  at org.datanucleus.store.AbstractStoreManager.registerConnectionFactory(AbstractStoreManager.java:282)
  at org.datanucleus.store.AbstractStoreManager.<init>(AbstractStoreManager.java:240)

Caused by: org.datanucleus.store.rdbms.connectionpool.DatastoreDriverNotFoundException: The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH. Please check your CLASSPATH specification, and the name of the driver.
  at org.datanucleus.store.rdbms.connectionpool.AbstractConnectionPoolFactory.loadDriver(AbstractConnectionPoolFactory.java:58)
  at org.datanucleus.store.rdbms.connectionpool.BoneCPConnectionPoolFactory.createConnectionPool(BoneCPConnectionPoolFactory.java:54)
  at org.datanucleus.store.rdbms.ConnectionFactoryImpl.generateDataSources(ConnectionFactoryImpl.java:238)
  ... 145 more

原因没有引入mysql驱动

spark-shell --master local[2] --jar /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar

[hadoop@hadoop001 software]$ spark-shell --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar 
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/10/16 20:42:32 WARN SparkContext: Support for Java 7 is deprecated as of Spark 2.0.0
20/10/16 20:42:33 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
20/10/16 20:42:35 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
20/10/16 20:42:50 ERROR ObjectStore: Version information found in metastore differs 1.1.0 from expected schema version 1.2.0. Schema verififcation is disabled hive.metastore.schema.verification so setting version.
20/10/16 20:42:53 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Spark context Web UI available at http://192.168.43.214:4041
Spark context available as 'sc' (master = local[2], app id = local-1602906155852).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\\ \\/ _ \\/ _ `/ __/  '_/
   /___/ .__/\\_,_/_/ /_/\\_\\   version 2.1.0
      /_/
         
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51)
Type in expressions to have them evaluated.
Type :help for more information.

Standalone

Spark Standalone模式的架构和Hadoop HDFS/YARN很类似的
1 master + n worker

spark-env.sh

SPARK_MASTER_HOST=hadoop001
SPARK_WORKER_CORES=2
SPARK_WORKER_MEMORY=2g
SPARK_WORKER_INSTANCES=1

master:

hadoop1 

slaves:

hadoop2
hadoop3
hadoop4
....
hadoop10

==> start-all.sh 会在 hadoop1机器上启动master进程，在slaves文件配置的所有hostname的机器上启动worker进程

Spark WordCount统计
val file = spark.sparkContext.textFile(“file:///home/hadoop/data/wc.txt”)
val wordCounts = file.flatMap(line => line.split(",")).map((word => (word, 1))).reduceByKey(_ + _)
wordCounts.collect

本地IDE

A master URL must be set in your configuration

点击edit configuration，在左侧点击该项目。在右侧VM options中输入“-Dspark.master=local”，指示本程序本地单线程运行，再次运行即可。

package org.example

import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object SQLContextAPP {
  def main(args: Array[String]): Unit = {
    //1创建相应的Spark
    val sparkConf = new SparkConf()
    sparkConf.setAppName("SQLContextAPP")
    val sc = new SparkContext(sparkConf)
    val sqlContext = new SQLContext(sc)

    //2数据处理
    val people = sqlContext.read.format("json").load("people.json")
    people.printSchema()
    people.show()

    //3关闭资源
    sc.stop()


  }

}

root
 |-- age: long (nullable = true)
 |-- name: string (nullable = true)

.........................


| age|   name|
+----+-------+
|null|Michael|
|  30|   Andy|
|  19| Justin|
+----+-------+

配置maven环境变量cmd控制台提示：mvn不是内部或外部命令，也不是可运行的程序或批处理文件

首先maven环境变量：

变量名：MAVEN_HOME

变量值：E:\\apache-maven-3.2.3

变量名：Path

变量值：;%MAVEN_HOME%\\bin

然后到项目的目录下直接执行

C:\\Users\\jacksun\\IdeaProjects\\SqarkSQL\\ mvn clean package -DskipTests

在集群上测试

spark-submit \\
--name SQLContextApp \\
--class org.example.SQLContextApp \\
--master local[2] \\
/home/hadoop/lib/sql-1.0.jar \\
/home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/examples/src/main/resources/people.json

HiveContextAPP

注意：
1）To use a HiveContext, you do not need to have an existing Hive setup
2）hive-site.xml

package org.example

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.hive.HiveContext

object HiveContextAPP {
  def main(args: Array[String]): Unit = {
    //1创建相应的Spark
    val path =args(0)
    val sparkConf = new SparkConf()

    //测试和生产中AppName和Master是通过脚本执行的
    //sparkConf.setAppName("HiveContextAPP").setMaster("local[2]")



    val sc = new SparkContext(sparkConf)
    val hiveContext = new HiveContext(sc)

    //2数据处理
    hiveContext.table("emp").show

    //3关闭资源
    sc.stop()


  }
}

spark-submit \\
--name HiveContextApp \\
--class org.example.HiveContextApp \\
--master local[2] \\
/home/hadoop/lib/sql-1.0.jar \\
--jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar 

SparkSessinon

package org.example

import org.apache.spark.sql.SparkSession

object SparkSessionApp {
  def main(args: Array[String]) {

    val spark = SparkSession.builder().appName("SparkSessionApp")
      .master("local[2]").getOrCreate()

    val people = spark.read.json("people.json")
    people.show()

    spark.stop()
  }
}

Spark Shell

• 启动hive

[hadoop@hadoop001 bin]$ pwd
/home/hadoop/app/hive-1.1.0-cdh5.7.0/bin
[hadoop@hadoop001 bin]$ hive
ls: cannot access /home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/lib/spark-assembly-*.jar: No such file or directory
which: no hbase in (/home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/bin:/home/hadoop/app/scala-2.11.8/bin:/home/hadoop/app/hive-1.1.0-cdh5.7.0/bin:/home/hadoop/app/hadoop-2.6.0-cdh5.7.0/bin:/home/hadoop/app/apache-maven-3.3.9/bin:/home/hadoop/app/jdk1.7.0_51/bin:/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/sbin)

Logging initialized using configuration in jar:file:/home/hadoop/app/hive-1.1.0-cdh5.7.0/lib/hive-common-1.1.0-cdh5.7.0.jar!/hive-log4j.properties
WARNING: Hive CLI is deprecated and migration to Beeline is recommended.
hive> 

• 拷贝
  [hadoop@hadoop001 conf]$ cp hive-site.xml ~/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/conf/

• 启动Spark
  spark-shell --master local[2] --jars /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar

scala> spark.sql("show tables").show
+--------+------------+-----------+
|database|   tableName|isTemporary|
+--------+------------+-----------+
| default|        dept|      false|
| default|         emp|      false|
| default|hive_table_1|      false|
| default|hive_table_2|      false|
| default|           t|      false|
+--------+------------+-----------+


hive> show tables;
OK
dept
emp
hive_wordcount

scala> spark.sql("select * from emp e join dept d on e.deptno=d.deptno").show
+-----+------+---------+----+----------+------+------+------+------+----------+--------+
|empno| ename|      job| mgr|  hiredate|   sal|  comm|deptno|deptno|     dname|     loc|
+-----+------+---------+----+----------+------+------+------+------+----------+--------+
| 7369| SMITH|    CLERK|7902|1980-12-17| 800.0|  null|    20|    20|  RESEARCH|  DALLAS|
| 7499| ALLEN| SALESMAN|7698| 1981-2-20|1600.0| 300.0|    30|    30|     SALES| CHICAGO|
| 7521|  WARD| SALESMAN|7698| 1981-2-22|1250.0| 500.0|    30|    30|     SALES| CHICAGO|
| 7566| JONES|  MANAGER|7839|  1981-4-2|2975.0|  null|    20|    20|  RESEARCH|  DALLAS|
| 7654|MARTIN| SALESMAN|7698| 1981-9-28|1250.0|1400.0|    30|    30|     SALES| CHICAGO|
| 7698| BLAKE|  MANAGER|7839|  1981-5-1|2850.0|  null|    30|    30|     SALES| CHICAGO|
| 7782| CLARK|  MANAGER|7839|  1981-6-9|2450.0|  null|    10以上是关于大数据进阶之路——Spark SQL基本配置的主要内容，如果未能解决你的问题，请参考以下文章
 
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