spark sql 快速体验调试

spark sql提供了更快的查询性能，如何能够更快的体验，开发和调试spark sql呢？按照正规的步骤我们一般会集成hive，然后使用hive的元数据查询hive表进行操作，这样以来我们还需要考虑跟hive相关的东西，如果我们仅仅是学习spark sql查询功能，那么仅仅使用IDEA的IDE环境即可，而且能够在win上快速体验，不需要hive数据仓库，我们直接使用数组造点数据，然后转成DF，最后直接使用spark sql操作即可。

首先，看下pom文件的核心依赖：

<?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.xuele.bigdata</groupId>    <artifactId>kp_diag</artifactId>    <version>1.0.2</version>            <properties>                <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>                <scala.version>2.11.8</scala.version>                <hadoop.version>2.7.3</hadoop.version>                <spark.version>2.0.2</spark.version>                <spark.hive.version>2.0.2</spark.hive.version>                <spark.sql.version>2.0.2</spark.sql.version>                <neo4j-java-driver.version>1.0.5</neo4j-java-driver.version>                <config.version>1.2.1</config.version>                <jedis.version>2.9.0</jedis.version>                <hbase.version>1.2.0</hbase.version>                <kafka.version>0.9.0.0</kafka.version>                <fastjson.version>1.2.15</fastjson.version>                <elasticsearch.version>2.3.4</elasticsearch.version>            </properties>    <dependencies>        <dependency>            <groupId>org.elasticsearch</groupId>            <artifactId>elasticsearch</artifactId>            <version>${elasticsearch.version}</version>        </dependency>        <dependency>            <groupId>com.alibaba</groupId>            <artifactId>fastjson</artifactId>            <version>${fastjson.version}</version>        </dependency>        <dependency>            <groupId>org.apache.kafka</groupId>            <artifactId>kafka-clients</artifactId>            <version>${kafka.version}</version>        </dependency>        <dependency>            <groupId>redis.clients</groupId>            <artifactId>jedis</artifactId>            <version>${jedis.version}</version>        </dependency>        <dependency>            <groupId>net.jpountz.lz4</groupId>            <artifactId>lz4</artifactId>            <version>1.3</version>        </dependency>        <dependency>            <groupId>com.typesafe</groupId>            <artifactId>config</artifactId>            <version>${config.version}</version>        </dependency>        <dependency>            <groupId>org.apache.hbase</groupId>            <artifactId>hbase-client</artifactId>            <version>${hbase.version}</version>        </dependency>        <dependency>            <groupId>org.apache.hbase</groupId>            <artifactId>hbase-server</artifactId>            <version>${hbase.version}</version>        </dependency>        <!--neo4j的java的驱动-->        <dependency>            <groupId>org.neo4j.driver</groupId>            <artifactId>neo4j-java-driver</artifactId>            <version>${neo4j-java-driver.version}</version>        </dependency>        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql_2.10 -->        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-sql_2.11</artifactId>            <version>${spark.sql.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-hive_2.11</artifactId>            <version>${spark.hive.version}</version>        </dependency>        <dependency>            <groupId>org.scala-lang</groupId>            <artifactId>scala-library</artifactId>            <version>${scala.version}</version>            <scope>compile</scope>        </dependency>        <dependency>            <groupId>org.apache.hadoop</groupId>            <artifactId>hadoop-client</artifactId>            <version>${hadoop.version}</version>        </dependency>        <dependency>            <groupId>org.apache.spark</groupId>            <artifactId>spark-core_2.11</artifactId>            <version>${spark.version}</version>        </dependency>    </dependencies>    <build>        <pluginManagement>            <plugins>                <plugin>                    <groupId>net.alchim31.maven</groupId>                    <artifactId>scala-maven-plugin</artifactId>                    <version>3.2.1</version>                </plugin>                <plugin>                    <groupId>org.apache.maven.plugins</groupId>                    <artifactId>maven-compiler-plugin</artifactId>                    <version>2.0.2</version>                </plugin>            </plugins>        </pluginManagement>        <plugins>            <plugin>                <groupId>net.alchim31.maven</groupId>                <artifactId>scala-maven-plugin</artifactId>                <executions>                    <execution>                        <id>scala-compile-first</id>                        <phase>process-resources</phase>                        <goals>                            <goal>add-source</goal>                            <goal>compile</goal>                        </goals>                    </execution>                    <execution>                        <id>scala-test-compile</id>                        <phase>process-test-resources</phase>                        <goals>                            <goal>testCompile</goal>                        </goals>                    </execution>                </executions>            </plugin>            <plugin>                <groupId>org.apache.maven.plugins</groupId>                <artifactId>maven-compiler-plugin</artifactId>                <executions>                    <execution>                        <phase>compile</phase>                        <goals>                            <goal>compile</goal>                        </goals>                    </execution>                </executions>                <configuration>                    <source>1.7</source>                    <target>1.7</target>                    <encoding>UTF-8</encoding>                </configuration>            </plugin>        </plugins>        <filters>            <filter>src/main/filters/xuele-${build.profile.id}.properties</filter>        </filters>        <!--指定下面的目录为资源文件-->        <resources>            <!--设置自动替换-->            <resource>                <directory>src/main/resources</directory>                <includes>                    <include>**/*</include>                </includes>                <!--也可以用排除标签-->                <!--<excludes></excludes>-->                <!--开启过滤-->                <filtering>true</filtering>            </resource>        </resources>    </build>    <profiles>        <!--默认激活开发配置，使用index-dev.properties来替换实际的文件key-->        <profile>            <id>dev</id>            <activation>                <activeByDefault>true</activeByDefault>            </activation>            <properties>                <build.profile.id>dev</build.profile.id>            </properties>        </profile>        <!-- 测试环境配置 -->        <profile>            <id>test</id>            <properties>                <build.profile.id>test</build.profile.id>            </properties>        </profile>        <!-- 生产环境配置 -->        <profile>            <id>product</id>            <properties>                <build.profile.id>product</build.profile.id>            </properties>        </profile>    </profiles></project>

然后看一个例子spark sql的测试例子：

import org.apache.spark.sql.SparkSession/**  * spark sql本地测试例子  */object TestGroup {  def main(args: Array[String]): Unit = {    val spark = SparkSession      .builder().master("local[1]")//设置loca模式      .appName("Spark SQL basic example")//设置app的名字      .getOrCreate()    import spark.implicits._//导入隐式的转化函数    import spark.sql //导入sql函数    //使用Seq造数据，三列数据    val df = spark.sparkContext.parallelize(Seq((0,"p",30.9),      (0,"u",22.1),      (1,"r",19.6),      (2,"cat40",20.7),      (2,"cat187",27.9),      (2,"cat183",11.3),      (3,"cat8",35.6))).toDF("id", "name", "price")//转化df的三列数据s      df.createTempView("pro")//创建表明为pro      //按照id分组，统计每组数量，统计每组里面最小的价格，然后收集每组里面的数据      val ds=sql("select id, count(*) as c,min(price) as min_price,collect_list(struct(name, price)) as res  from pro   group by id ");    ds.cache() //需要多次查询的数据，可以缓存起来    //获取查询的结果，遍历获取结果集    ds.select("id","c","res","min_price").collect().foreach(line=>{      import org.apache.spark.sql.Row//导入Row对象      val id=line.getAs[Int]("id")//获取id      val count=line.getAs[Long]("c")//获取数量      val min_price=line.getAs[Double]("min_price")//获取最小的价格      val value=line.getAs[Seq[Row]]("res")//获取每组内的数据集合，注意是一个Row实体      println(id+"  "+count+"  "+"  "+min_price)//打印数据      value.foreach(row=>{//遍历组内数据集合，然后打印        println(row.getAs[String]("name")+" "+row.getAs[Double]("price"))      })    })    spark.stop()  }}

至此，一个涵盖spark sql比较全的功能例子的小工程就完成了，上面的代码直接可在win上运行，而且里面的数据随时自己添加删除，以便于可以测试spark sql与预期效果对比，上面的sql中还用到了分组里面的高级用法，分组后，收集组内数据，注意组内数据收集，如果是单个字段，直接用collect_list或者collect_set即可，但是如果是多个字段，这个时候必须用到struct类型了，最终转化后的类型就是row的集合，里面的每个结构体会被转成一个row对象，一个组的数据，就是List<Row>了，最终可以在代码里面遍历取出。spark sql结合scala编程语言之后可以变得非常灵活，sql不擅长的就用编程语言解决的，sql擅长的就用sql方便快速得到数据，用起来非常干净清爽！

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