用的本地模式,pom.xml中添加了mysql驱动包,mysql已经开启,写入的时候发现用format("jdbc").save()的方式发现会有does not allow create table as select的异常,于是去官方文档上发现了使用jdbc()的方式,测试
正常,说明下Properties是java.util.Properties
java
1 public class Demo {
2 private static SparkSession session = SparkSession.builder().appName("demo").master("local").getOrCreate();
3
4 public static void main(String[] args) {
5 Map<String, String> options = new HashMap<>();
6 options.put("url", "jdbc:mysql://127.0.0.1:3306/studentmanage");
7 options.put("driver", "com.mysql.jdbc.Driver");
8 options.put("dbtable", "studentmanage.admin");
9 options.put("user", "root");
10 options.put("password", "root");
11
12 // 读取
13 Dataset<Row> dataset = session.read().format("jdbc").options(options).load();
14 dataset.show();
15
16 // 创建数据
17 List<Row> list = new ArrayList<Row>();
18 Row row1 = RowFactory.create("tele", "123", "male", "China", 1, "admin");
19 Row row2 = RowFactory.create("wyc", "123", "male", "China", 1, "admin");
20 Row row3 = RowFactory.create("xxx", "123", "male", "China", 1, "admin");
21 list.add(row1);
22 list.add(row2);
23 list.add(row3);
24
25 // 写入
26 StructType schema = DataTypes
27 .createStructType(Arrays.asList(DataTypes.createStructField("name", DataTypes.StringType, false),
28 DataTypes.createStructField("pwd", DataTypes.StringType, false),
29 DataTypes.createStructField("sex", DataTypes.StringType, false),
30 DataTypes.createStructField("nation", DataTypes.StringType, false),
31 DataTypes.createStructField("status", DataTypes.IntegerType, false),
32 DataTypes.createStructField("type", DataTypes.StringType, false)));
33
34 Dataset<Row> ds = session.createDataFrame(list, schema);
35
36 Properties connectionProperties = new Properties();
37 connectionProperties.put("user", "root");
38 connectionProperties.put("password", "root");
39
40 // 也可以对dataset进行遍历使用原生的jdbc或者dbutils等进行写入
41 ds.write().mode(SaveMode.Append).jdbc("jdbc:mysql://127.0.0.1:3306/studentmanage", "admin",
42 connectionProperties);
43
44 session.stop();
45 }
46 }
scala
1 object Demo {
2 def main(args: Array[String]): Unit = {
3 val session = SparkSession.builder().appName("demo").master("local").getOrCreate()
4
5 val options = Map[String, String](
6 ("url", "jdbc:mysql://127.0.0.1:3306/studentmanage"),
7 ("driver", "com.mysql.jdbc.Driver"),
8 ("dbtable", "studentmanage.admin"),
9 ("user", "root"),
10 ("password", "root"))
11
12 //读取
13 val df = session.read.options(options).format("jdbc").load()
14
15 df.show()
16
17 //写入
18 val arrBuffer = Array(Row("yeye", "123", "male", "us", 1, "admin")).toBuffer
19
20 val schema = DataTypes.createStructType(Array(
21 StructField("name", DataTypes.StringType, false),
22 StructField("pwd", DataTypes.StringType, false),
23 StructField("sex", DataTypes.StringType, false),
24 StructField("nation", DataTypes.StringType, false),
25 StructField("status", DataTypes.IntegerType, false),
26 StructField("type", DataTypes.StringType, false)))
27
28 val result = session.createDataFrame(arrBuffer, schema)
29
30 val properties = new Properties
31 properties.put("user", "root")
32 properties.put("password", "root")
33
34 result.write.mode(SaveMode.Append).jdbc("jdbc:mysql://127.0.0.1:3306/studentmanage", "admin", properties)
35
36 session.stop
37 }
38 }