理解Spark SQL(三）—— Spark SQL程式舉例-有解無憂

上一篇說到，在Spark 2.x當中，實際上SQLContext和HiveContext是過時的，相反是采用SparkSession物件的sql函式來操作SQL陳述句的，使用這個函式執行SQL陳述句前需要先呼叫DataFrame的createOrReplaceTempView注冊一個臨時表，所以關鍵是先要將RDD轉換成DataFrame，實際上，在Spark中實際宣告了

type DataFrame = Dataset[Row]

所以，DataFrame是Dataset[Row]的別名，RDD是提供面向低層次的API，而DataFrame/Dataset提供面向高層次的API（適合于SQL等面向結構化資料的場合），

下面提供一些Spark SQL程式的例子，

例子一：SparkSQLExam.scala

 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 }

使用下面的命令進行編譯：

[root@BruceCentOS4 scala]# scalac SparkSQLExam.scala

在編譯之前，需要在CLASSPATH中增加路徑：

export CLASSPATH=$CLASSPATH:$SPARK_HOME/jars/*:$(/opt/hadoop/bin/hadoop classpath)

然后打包成jar檔案：

[root@BruceCentOS4 scala]# jar -cvf spark_exam_scala.jar bruce

然后通過spark-submit提交程式到yarn集群執行，為了方便從客戶端查看結果，這里采用yarn cient模式運行，

[root@BruceCentOS4 scala]# $SPARK_HOME/bin/spark-submit --class bruce.bigdata.spark.example.SparkSQLExam --master yarn --deploy-mode client spark_exam_scala.jar

運行結果截圖：

例子二：SparkSQLExam.scala（需要啟動hive metastore）

 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資料
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         //將hql查詢出的資料保存到另外一張新建的hive表
25         //找出訂單金額超過1萬美元的產品
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         //將HDFS檔案資料匯入到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 }

使用下面的命令進行編譯：

[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

使用下面的命令運行：

[root@BruceCentOS4 scala]# $SPARK_HOME/bin/spark-submit --class bruce.bigdata.spark.example.SparkHiveExam --master yarn --deploy-mode client spark_exam_scala.jar

程式運行結果：

另外上述程式運行后，hive中多了2張表：

例子三：spark_sql_exam.py

 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()

執行命令運行程式：

[root@BruceCentOS4 spark]# $SPARK_HOME/bin/spark-submit --master yarn --deploy-mode client spark_sql_exam.py

程式運行結果：

例子四：JavaSparkSQLExam.java

 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 }

編譯打包后通過如下命令執行：

[root@BruceCentOS4 spark]# $SPARK_HOME/bin/spark-submit --class bruce.bigdata.spark.example.JavaSparkSQLExam --master yarn --deploy-mode client spark_exam_java.jar

運行結果：

上面是一些關于Spark SQL程式的一些例子，分別采用了Scala/Python/Java來撰寫的，另外除了這三種語言，Spark還支持R語言撰寫程式，因為我自己也不熟悉，就不舉例了，不管用什么語言，其實API都是基本一致的，主要是采用DataFrame和Dataset的高層次API來呼叫和執行SQL，使用這些API，可以輕松的將結構化資料轉化成SQL來操作，同時也能夠方便的操作Hive中的資料，

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標籤：大數據

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