type DataFrame = Dataset[Row]
所以，DataFrame是Dataset[Row]的别名。RDD是提供面向低层次的API，而DataFrame/Dataset提供面向高层次的API（适合于SQL等面向结构化数据的场合）。
下面提供一些Spark SQL程序的例子。
例子一：SparkSQLExam.scala
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1 package bruce.bigdata.spark.example
2
3 import org.apache.spark.sql.Row
4 import org.apache.spark.sql.SparkSession
5 import org.apache.spark.sql.types._
6
7 object SparkSQLExam {
8
9 case class offices(office:Int,city:String,region:String,mgr:Int,target:Double,sales:Double)
10
11 def main(args: Array[String]) {
12
13 val spark = SparkSession
14 .builder
15 .appName("SparkSQLExam")
16 .getOrCreate()
17
18 runSparkSQLExam1(spark)
19 runSparkSQLExam2(spark)
20
21 spark.stop()
22
23 }
24
25
26 private def runSparkSQLExam1(spark: SparkSession): Unit = {
27
28 import spark.implicits._
29
30 val rddOffices=spark.sparkContext.textFile("/user/hive/warehouse/orderdb.db/offices/offices.txt").map(_.split("\t")).map(p=>offices(p(0).trim.toInt,p(1),p(2),p(3).trim.toInt,p(4).trim.toDouble,p(5).trim.toDouble))
31 val officesDataFrame = spark.createDataFrame(rddOffices)
32
33 officesDataFrame.createOrReplaceTempView("offices")
34 spark.sql("select city from offices where region='Eastern'").map(t=>"City: " + t(0)).collect.foreach(println)
35
36
37 }
38
39 private def runSparkSQLExam2(spark: SparkSession): Unit = {
40
41 import spark.implicits._
42 import org.apache.spark.sql._
43 import org.apache.spark.sql.types._
44
45 val schema = new StructType(Array(StructField("office", IntegerType, false), StructField("city", StringType, false), StructField("region", StringType, false), StructField("mgr", IntegerType, true), StructField("target", DoubleType, true), StructField("sales", DoubleType, false)))
46 val rowRDD = spark.sparkContext.textFile("/user/hive/warehouse/orderdb.db/offices/offices.txt").map(_.split("\t")).map(p => Row(p(0).trim.toInt,p(1),p(2),p(3).trim.toInt,p(4).trim.toDouble,p(5).trim.toDouble))
47 val dataFrame = spark.createDataFrame(rowRDD, schema)
48
49 dataFrame.createOrReplaceTempView("offices2")
50 spark.sql("select city from offices2 where region='Western'").map(t=>"City: " + t(0)).collect.foreach(println)
51
52 }
53
54 }
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使用下面的命令进行编译：
[root@BruceCentOS4 scala]# scalac SparkSQLExam.scala
在编译之前，需要在CLASSPATH中增加路径：
export CLASSPATH=$CLASSPATH:$SPARK_HOME/jars/*:$(/opt/hadoop/bin/hadoop classpath)
然后打包成jar文件：
[root@BruceCentOS4 scala]# jar -cvf spark_exam_scala.jar bruce
然后通过spark-submit提交程序到yarn集群执行，为了方便从客户端查看结果，这里采用yarn cient模式运行。
[root@BruceCentOS4 scala]# $SPARK_HOME/bin/spark-submit --class bruce.bigdata.spark.example.SparkSQLExam --master yarn --deploy-mode client spark_exam_scala.jar
运行结果截图：
例子二：SparkSQLExam.scala（需要启动hive metastore）
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1 package bruce.bigdata.spark.example
2
3 import org.apache.spark.sql.{SaveMode, SparkSession}
4
5 object SparkHiveExam {
6
7 def main(args: Array[String]) {
8
9 val spark = SparkSession
10 .builder()
11 .appName("Spark Hive Exam")
12 .config("spark.sql.warehouse.dir", "/user/hive/warehouse")
13 .enableHiveSupport()
14 .getOrCreate()
15
16 import spark.implicits._
17
18 //使用hql查看hive数据
19 spark.sql("show databases").collect.foreach(println)
20 spark.sql("use orderdb")
21 spark.sql("show tables").collect.foreach(println)
22 spark.sql("select city from offices where region='Eastern'").map(t=>"City: " + t(0)).collect.foreach(println)
23
24 //将hql查询出的数据保存到另外一张新建的hive表
25 //找出订单金额超过1万美元的产品
26 spark.sql("""create table products_high_sales(mfr_id string,product_id string,description string)
27 ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LINES TERMINATED BY '\n' STORED AS TEXTFILE""")
28 spark.sql("""select mfr_id,product_id,description
29 from products a inner join orders b
30 on a.mfr_id=b.mfr and a.product_id=b.product
31 where b.amount>10000""").write.mode(SaveMode.Overwrite).saveAsTable("products_high_sales")
32
33 //将HDFS文件数据导入到hive表中
34 spark.sql("""CREATE TABLE IF NOT EXISTS offices2 (office int,city string,region string,mgr int,target double,sales double )
35 ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LINES TERMINATED BY '\n' STORED AS TEXTFILE""")
36 spark.sql("LOAD DATA INPATH '/user/hive/warehouse/orderdb.db/offices/offices.txt' INTO TABLE offices2")
37
38 spark.stop()
39 }
40 }
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使用下面的命令进行编译：
[root@BruceCentOS4 scala]# scalac SparkHiveExam.scala
使用下面的命令打包：
[root@BruceCentOS4 scala]# jar -cvf spark_exam_scala.jar bruce
使用下面的命令运行：
[root@BruceCentOS4 scala]# $SPARK_HOME/bin/spark-submit --class bruce.bigdata.spark.example.SparkHiveExam --master yarn --deploy-mode client spark_exam_scala.jar
程序运行结果：
另外上述程序运行后，hive中多了2张表：
例子三：spark_sql_exam.py
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1 from __future__ import print_function
2
3 from pyspark.sql import SparkSession
4 from pyspark.sql.types import *
5
6
7 if __name__ == "__main__":
8 spark = SparkSession \
9 .builder \
10 .appName("Python Spark SQL exam") \
11 .config("spark.some.config.option", "some-value") \
12 .getOrCreate()
13
14 schema = StructType([StructField("office", IntegerType(), False), StructField("city", StringType(), False),
15 StructField("region", StringType(), False), StructField("mgr", IntegerType(), True),
16 StructField("Target", DoubleType(), True), StructField("sales", DoubleType(), False)])
17
18 rowRDD = spark.sparkContext.textFile("/user/hive/warehouse/orderdb.db/offices/offices.txt").map(lambda p: p.split("\t")) \
19 .map(lambda p: (int(p[0].strip()), p[1], p[2], int(p[3].strip()), float(p[4].strip()), float(p[5].strip())))
20
21 dataFrame = spark.createDataFrame(rowRDD, schema)
22 dataFrame.createOrReplaceTempView("offices")
23 spark.sql("select city from offices where region='Eastern'").show()
24
25 spark.stop()
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执行命令运行程序：
[root@BruceCentOS4 spark]# $SPARK_HOME/bin/spark-submit --master yarn --deploy-mode client spark_sql_exam.py
程序运行结果：
例子四：JavaSparkSQLExam.java
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1 package bruce.bigdata.spark.example;
2
3 import java.util.ArrayList;
4 import java.util.List;
5
6 import org.apache.spark.api.java.JavaRDD;
7 import org.apache.spark.api.java.function.Function;
8 import org.apache.spark.api.java.function.MapFunction;
9 import org.apache.spark.sql.Dataset;
10 import org.apache.spark.sql.Row;
11 import org.apache.spark.sql.RowFactory;
12 import org.apache.spark.sql.SparkSession;
13 import org.apache.spark.sql.types.DataTypes;
14 import org.apache.spark.sql.types.StructField;
15 import org.apache.spark.sql.types.StructType;
16 import org.apache.spark.sql.AnalysisException;
17
18
19 public class JavaSparkSQLExam {
20 public static void main(String[] args) throws AnalysisException {
21 SparkSession spark = SparkSession
22 .builder()
23 .appName("Java Spark SQL exam")
24 .config("spark.some.config.option", "some-value")
25 .getOrCreate();
26
27 List<StructField> fields = new ArrayList<>();
28 fields.add(DataTypes.createStructField("office", DataTypes.IntegerType, false));
29 fields.add(DataTypes.createStructField("city", DataTypes.StringType, false));
30 fields.add(DataTypes.createStructField("region", DataTypes.StringType, false));
31 fields.add(DataTypes.createStructField("mgr", DataTypes.IntegerType, true));
32 fields.add(DataTypes.createStructField("target", DataTypes.DoubleType, true));
33 fields.add(DataTypes.createStructField("sales", DataTypes.DoubleType, false));
34
35 StructType schema = DataTypes.createStructType(fields);
36
37
38 JavaRDD<String> officesRDD = spark.sparkContext()
39 .textFile("/user/hive/warehouse/orderdb.db/offices/offices.txt", 1)
40 .toJavaRDD();
41
42 JavaRDD<Row> rowRDD = officesRDD.map((Function<String, Row>) record -> {
43 String[] attributes = record.split("\t");
44 return RowFactory.create(Integer.valueOf(attributes[0].trim()), attributes[1], attributes[2], Integer.valueOf(attributes[3].trim()), Double.valueOf(attributes[4].trim()), Double.valueOf(attributes[5].trim()));
45 });
46
47 Dataset<Row> dataFrame = spark.createDataFrame(rowRDD, schema);
48
49 dataFrame.createOrReplaceTempView("offices");
50 Dataset<Row> results = spark.sql("select city from offices where region='Eastern'");
51 results.collectAsList().forEach(r -> System.out.println(r));
52
53 spark.stop();
54 }
55 }
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编译打包后通过如下命令执行：
[root@BruceCentOS4 spark]# $SPARK_HOME/bin/spark-submit --class bruce.bigdata.spark.example.JavaSparkSQLExam --master yarn --deploy-mode client spark_exam_java.jar
运行结果：
上面是一些关于Spark SQL程序的一些例子，分别采用了Scala/Python/Java来编写的。另外除了这三种语言，Spark还支持R语言编写程序，因为我自己也不熟悉，就不举例了。不管用什么语言，其实API都是基本一致的，主要是采用DataFrame和Dataset的高层次API来调用和执行SQL。使用这些API，可以轻松的将结构化数据转化成SQL来操作，同时也能够方便的操作Hive中的数据
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