Spark：導入數據到oracle

方案一：

 1     //overwrite JdbcDialect fitting for Oracle
 2     val OracleDialect = new JdbcDialect {
 3       override def canHandle(url: String): Boolean = url.startsWith("jdbc:oracle") || url.contains("oracle")
 4 
 5       //getJDBCType is used when writing to a JDBC table
 6       override def getJDBCType(dt: DataType): Option[JdbcType] = dt match {
 7         case StringType => Some(JdbcType("VARCHAR2(255)", java.sql.Types.VARCHAR))
 8         case BooleanType => Some(JdbcType("NUMBER(1)", java.sql.Types.NUMERIC))
 9         case IntegerType => Some(JdbcType("NUMBER(16)", java.sql.Types.NUMERIC))
10         case LongType => Some(JdbcType("NUMBER(16)", java.sql.Types.NUMERIC))
11         case DoubleType => Some(JdbcType("NUMBER(16,4)", java.sql.Types.NUMERIC))
12         case FloatType => Some(JdbcType("NUMBER(16,4)", java.sql.Types.NUMERIC))
13         case ShortType => Some(JdbcType("NUMBER(5)", java.sql.Types.NUMERIC))
14         case ByteType => Some(JdbcType("NUMBER(3)", java.sql.Types.NUMERIC))
15         case BinaryType => Some(JdbcType("BLOB", java.sql.Types.BLOB))
16         case TimestampType => Some(JdbcType("DATE", java.sql.Types.DATE))
17         case DateType => Some(JdbcType("DATE", java.sql.Types.DATE))
18         //        case DecimalType.Fixed(precision, scale) => Some(JdbcType("NUMBER(" + precision + "," + scale + ")", java.sql.Types.NUMERIC))
19         case DecimalType.Unlimited => Some(JdbcType("NUMBER(38,2)", java.sql.Types.NUMERIC))
20         case _ => None
21       }
22     }
23     //Registering the OracleDialect
24     JdbcDialects.registerDialect(OracleDialect)
25 
26     val connectProperties = new java.util.Properties()
27     connectProperties.put("user", username)
28     connectProperties.put("password", password)
29     Class.forName(driver).newInstance()
30 
31     //write back Oracle
32     //Note: When writing the results back orale, be sure that the target table existing
33     JdbcUtils.saveTable(mr_case_df, oracleDriverUrl, "MR", connectProperties)

方案二：

val test_df=hiveContext.sql("select * from test")

test_df.foreachPartition(rows => {
  Class.forName(driver)

  val connection: Connection = DriverManager.getConnection(oracleDriverUrl, username, password)
  val prepareStatement: PreparedStatement = connection.prepareStatement("insert into RES_CELL(City,Latiude,longitude)values(?,?,?);")

  rows.foreach(row => {
    prepareStatement.setString(1, row.getAs[String]("city"))      
    prepareStatement.setString(2, row.getAs[String]("latitude"))
    prepareStatement.setString(3, row.getAs[String]("longitude"))
    prepareStatement.addBatch()
  })
  prepareStatement.executeBatch()

  prepareStatement.close()
  connection.close()
})

方案三：

使用sqlloader從spark任務提交節點讀取文件導入到oracle。

爲何操做，緣由直接從spark中讀取hive中的數據使用網絡IO鏈接到集羣外的oracle服務器是spark集羣不樂意作的事情，對SPARK寶貴的網絡IO來講開銷特別大。