当前位置： 移动技术网 > IT编程>数据库>其他数据库 > Spark 加载数据库mysql表中数据进行分析

Spark 加载数据库mysql表中数据进行分析

2018年08月15日  | 移动技术网IT编程  | 我要评论

1.工程maven依赖包

 1  
 2 <properties>
 3     <spark_version>2.3.1</spark_version>
 4     <!-- elasticsearch-->
 5     <elasticsearch.version>5.5.2</elasticsearch.version>
 6     <fastjson.version>1.2.28</fastjson.version>
 7     <elasticsearch-hadoop.version>6.3.2</elasticsearch-hadoop.version>
 8     <elasticsearch-spark.version>5.5.2</elasticsearch-spark.version>
 9 </properties>
10 <dependencies>
11     <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
12     <dependency>
13         <groupId>org.apache.spark</groupId>
14         <artifactId>spark-core_2.11</artifactId>
15         <version>${spark_version}</version>
16     </dependency>
17     <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
18     <dependency>
19         <groupId>org.apache.spark</groupId>
20         <artifactId>spark-sql_2.11</artifactId>
21         <version>${spark_version}</version>
22     </dependency>
23     <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-yarn -->
24     <dependency>
25         <groupId>org.apache.spark</groupId>
26         <artifactId>spark-yarn_2.11</artifactId>
27         <version>${spark_version}</version>
28     </dependency>
29     <dependency>
30         <groupId>org.elasticsearch</groupId>
31         <artifactId>elasticsearch-spark-20_2.11</artifactId>
32         <version>${elasticsearch-spark.version}</version>
33     </dependency>
34     <dependency>
35         <groupId>mysql</groupId>
36         <artifactId>mysql-connector-java</artifactId>
37         <version>5.1.46</version>
38     </dependency>
39 </dependencies>

2.spark加载数据库中数据

 1 public class GoodsFromMySQL {
 2 
 3     /**
 4      * 加载数据库数据
 5      *
 6      * @param sc           spark context
 7      * @param sparkSession spark session
 8      */
 9     public static void loadGoodsInfo(SparkContext sc, SparkSession sparkSession) {
10         String url = "jdbc:mysql://x.x.x.x:3306/db-test";
11 
12         String sql = "(SELECT item_name as itemName, goods_category as goodsCategory FROM goods where dict_type='100203' and item_name " +
13                 "is not null) as my-goods";
14 
15         SQLContext sqlContext = SQLContext.getOrCreate(sc);
16         DataFrameReader reader = sqlContext.read().format("jdbc").
17                 option("url", url).option("dbtable", sql).
18                 option("driver", "com.mysql.jdbc.Driver").
19                 option("user", "root").
20                 option("password", "xxxxx");
21 
22 
23         Dataset<Row> goodsDataSet = reader.load();
24 
25         // Looks the schema of this DataFrame.
26         goodsDataSet.printSchema();
27 
28         goodsDataSet.write().mode(SaveMode.Overwrite).json("/data/app/source_new.json");
29     }
30 
31 
32     public static void main(String[] args) {
33         SparkConf conf = new SparkConf().setAppName("my-app");
34         SparkContext sc = new SparkContext(conf);
35 
36         SparkSession sparkSession = new SparkSession(sc);
37 
38         loadGoodsInfo(sc, sparkSession);
39     }
40 }

3.spark支持加载多种数据库，仅需要用户依赖不同的数据库驱动包，并且代码进行微调即可

　　根据以上java代码，仅需调整18行，更改驱动加载类即可。

您可能感兴趣的文章:

如对本文有疑问， 点击进行留言回复！！

相关文章:

验证码：
最近更新的文章
大家感兴趣的文章
移动技术网