Apache spark 将Spark数据帧写入红移:保存StructField(用户\代理,ArrayType(StringType,true),true)
我有一个数据帧,模式包括一个数组[String]字段:Apache spark 将Spark数据帧写入红移:保存StructField(用户\代理,ArrayType(StringType,true),true),apache-spark,dataframe,amazon-redshift,Apache Spark,Dataframe,Amazon Redshift,我有一个数据帧,模式包括一个数组[String]字段: StructField("user_agent", ArrayType apply (StringType, true)) ... myDataframe.printSchema (an excerpt) |-- user_agent: array (nullable = true) | |-- element: string (containsNull = true) 我正在使用com.databricks.s
StructField("user_agent", ArrayType apply (StringType, true))
...
myDataframe.printSchema
(an excerpt)
|-- user_agent: array (nullable = true)
| |-- element: string (containsNull = true)
我正在使用com.databricks.spark.redshift包写入redshift。我得到一个错误:
java.lang.IllegalArgumentException: Don't know how to save StructField(user_agent,ArrayType(StringType,true),true) to JDBC
at com.databricks.spark.redshift.JDBCWrapper$$anonfun$schemaString$1.apply(RedshiftJDBCWrapper.scala:253)
at com.databricks.spark.redshift.JDBCWrapper$$anonfun$schemaString$1.apply(RedshiftJDBCWrapper.scala:233)
是否可以使用此软件包将此类数据类型写入Redshift?spark Redshift支持以下数据类型:
field.dataType match {
case IntegerType => "INTEGER"
case LongType => "BIGINT"
case DoubleType => "DOUBLE PRECISION"
case FloatType => "REAL"
case ShortType => "INTEGER"
case ByteType => "SMALLINT" // Redshift does not support the BYTE type.
case BooleanType => "BOOLEAN"
case StringType =>
if (field.metadata.contains("maxlength")) {
s"VARCHAR(${field.metadata.getLong("maxlength")})"
} else {
"TEXT"
}
case TimestampType => "TIMESTAMP"
case DateType => "DATE"
case t: DecimalType => s"DECIMAL(${t.precision},${t.scale})"
case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
}
我也遇到了同样的问题,最终将数组转换为字符串。