……

近期又有需求为:导入Oracle的表到hive库中;

关于spark读取Oracle到hive有以下两点需要说明:

 1、数据量较小时,可以直接使用spark.read.jdbc(orclUrl,table_name,orclProperties)读取,效率应该没什么问题,能很快完成;

 2、数据量较大时候,使用spark.read.jdbc(orclUrl,table_name,分区条件,orclProperties)方法,分区读取,该方法可根据分区条件同时多线程读取;原理为在读取Oracle的SQL最后加入where+不同的分区条件;例如oracle 中的id为1~10;分区之后为where id >=1 and id <=5和where id >=6 and id <=10;两个线程同时读取;

源码如下:spark2.2.0;请注意看官方注释

  /**
   * Construct a `DataFrame` representing the database table accessible via JDBC URL
   * url named table using connection properties. The `predicates` parameter gives a list
   * expressions suitable for inclusion in WHERE clauses; each one defines one partition
   * of the `DataFrame`.
   *
   * Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash
   * your external database systems.
   *
   * @param url JDBC database url of the form `jdbc:subprotocol:subname`
   * @param table Name of the table in the external database.
   * @param predicates Condition in the where clause for each partition.
   * @param connectionProperties JDBC database connection arguments, a list of arbitrary string
   *                             tag/value. Normally at least a "user" and "password" property
   *                             should be included. "fetchsize" can be used to control the
   *                             number of rows per fetch.
   * @since 1.4.0
   */
  def jdbc(
      url: String,
      table: String,
      predicates: Array[String],
      connectionProperties: Properties): DataFrame = {
    assertNoSpecifiedSchema("jdbc")
    // connectionProperties should override settings in extraOptions.
    val params = extraOptions.toMap ++ connectionProperties.asScala.toMap
    val options = new JDBCOptions(url, table, params)
    val parts: Array[Partition] = predicates.zipWithIndex.map { case (part, i) =>
      JDBCPartition(part, i) : Partition
    }
    val relation = JDBCRelation(parts, options)(sparkSession)
    sparkSession.baseRelationToDataFrame(relation)
  }

在实际工作中发现。spark读取Oracle时,Oracle中的date类型并不能得到很好的支持,例如:2018-10-10 23:00格式的时间,在去读取到hive表中之后只剩下了2018-10-10,小时和分钟没了;

可行的解决方案如下:重写java的方言,代码如下:

import org.apache.spark.sql.jdbc.JdbcDialect;
import org.apache.spark.sql.jdbc.JdbcDialects;
import org.apache.spark.sql.jdbc.JdbcType;
import org.apache.spark.sql.types.DataType;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.MetadataBuilder;
import scala.Option;
import java.sql.Types;

public class OracleDateTypeInit {
    public static void oracleInit() {
        JdbcDialect dialect = new JdbcDialect() {

            //判断是否为oracle库
            @Override
            public boolean canHandle(String url) {
                return url.startsWith("jdbc:oracle");
            }

            //用于读取Oracle数据库时数据类型的转换
            @Override
            public Option<DataType> getCatalystType(int sqlType, String typeName, int size, MetadataBuilder md) {
                if (sqlType == Types.DATE && typeName.equals("DATE") && size == 0)
                    return Option.apply(DataTypes.TimestampType);
                return Option.empty();
            }

            //用于写Oracle数据库时数据类型的转换
            @Override
            public Option<JdbcType> getJDBCType(DataType dt) {
                if (DataTypes.StringType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("VARCHAR2(255)", Types.VARCHAR));
                } else if (DataTypes.BooleanType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("NUMBER(1)", Types.NUMERIC));
                } else if (DataTypes.IntegerType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("NUMBER(10)", Types.NUMERIC));
                } else if (DataTypes.LongType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("NUMBER(19)", Types.NUMERIC));
                } else if (DataTypes.DoubleType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("NUMBER(19,4)", Types.NUMERIC));
                } else if (DataTypes.FloatType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("NUMBER(19,4)", Types.NUMERIC));
                } else if (DataTypes.ShortType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("NUMBER(5)", Types.NUMERIC));
                } else if (DataTypes.ByteType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("NUMBER(3)", Types.NUMERIC));
                } else if (DataTypes.BinaryType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("BLOB", Types.BLOB));
                } else if (DataTypes.TimestampType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("DATE", Types.DATE));
                } else if (DataTypes.DateType.sameType(dt)) {
                    return Option.apply(
                            new JdbcType("DATE", Types.DATE));
                } else if (DataTypes.createDecimalType()
                        .sameType(dt)) { //unlimited
/*                    return DecimalType.Fixed(precision, scale)
                            =>Some(JdbcType("NUMBER(" + precision + "," + scale + ")",
                            java.sql.Types.NUMERIC))*/
                    return Option.apply(
                            new JdbcType("NUMBER(38,4)", Types.NUMERIC));
                }
                return Option.empty();
            }
        };
        //注册此方言
        JdbcDialects.registerDialect(dialect);
    }
}

使用时调用就可以了

//spark直接读取hive之后date类型的数据只剩年月日了,需要转为TimestampType
OracleDateTypeInit.oracleInit()

 

 posted on 2020-09-08 17:50  大码王  阅读(1709)  评论(0编辑  收藏  举报
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