View Source

NOTE: The listed in this page reflect the raw data type of the converted column. Depending on the contents of the column, the Transformer Page may re-infer a different data type, when a dataset using this type of source is loaded.

Access/Read

When a Hive data type is imported, its JDBC data type is remapped according to the following table.

Tip: Data precision may be lost during conversion. You may want to generate min and max values and compute significant digits for values in your Hive tables and then compute the same in the .

Source Data Type

Supported?

Notes

array

Y

Array

bigint

Y

Integer

NOTE: The may infer bigint columns containing very large or very small values as String data type.

binary

Y

String

boolean

Y

Bool

char

Y

String

date

Y

Datetime

decimal

Y

Decimal

double

Y

Decimal

float

Y

Decimal

NOTE: On import, some float columns may be interpreted as Integer data type in the . To fix, you can explicitly set the column's data type to Decimal in the Transformer page.

int

Y

Integer

map

Y

Object

smallint

Y

Integer

string

Y

String

struct

Y

Object

timestamp

Y

Datetime

tinyint

Y

Integer

uniontype

N

varchar

Y

String

Notes:

After a dataset has been imported using custom SQL from Hive, disabling type inference may not revert to the source data types for some columns. The workaround is to create a new imported dataset using the same custom SQL with type inference disabled before import. After the dataset is created, use it as a replacement for the corrupted instances of the previous Hive dataset.

Write/Publish

Create new table

Hive Data Type

Notes

String

string

Integer

bigint

NOTE: The may infer Integer columns containing very large or very small values as String data type. Before you publish, you should verify that your columns containing extreme values are interpreted as Integer type. You can import a target schema to assist in lining up your columns with the expected target. For more information, see Overview of RapidTarget.

Decimal

double

Bool

boolean

Datetime

Timestamp/string (see Notes on Datetime columns below)

Target data type is based on the underlying data. Time zone information is retained.

Object

string

Array

string

Append to existing table

If you are publishing to a pre-existing table, the following data type conversions apply:

Columns:
Rows: Target table data types

In any table cell, a Y indicates that the append operation for that data type mapping is supported.

NOTE: You cannot append to Hive map and array column types from of Map and Array type, even if you imported data from this source.

	String	Integer	Datetime	Bool	Decimal	Map	Array	Out of Range error
CHAR	Y	Y	Y	Y	Y	Y	Y
VARCHAR	Y	Y	Y	Y	Y	Y	Y
STRING	Y	Y	Y	Y	Y	Y	Y
INT		Y						NULL
BIGINT		Y						n/a
TINYINT								NULL
SMALLINT								NULL
DECIMAL		Y			Y			NULL
DOUBLE		Y			Y			n/a
FLOAT					Y			NULL
TIMESTAMP			Y
BOOLEAN				Y

Notes on Datetime columns

Run Job

Columns in new tables created for output of Datetime columns are written with the Hive timestamp data type. These columns can be appended.

Before release 4.2.1, Datetime columns were written to Hive as type String. Jobs that were created in these releases and that write to pre-existing tables continue to behave this way.
A single job cannot write Datetime values to one table as String type and to another table as Timestamp type. This type of job should be split into multiple types. The table schemas may require modification.
- The above issue may appear as the following error when executing the job:
  Unable to publish due to datetime data type conflict in column XXXX

Ad-Hoc Publishing

When you export pre-generated results to Hive, all new tables created for Datetime column values continue to store String data type in Hive for Release 4.2.1. These columns can be appended with new String data.
When you publish results from a job through the Publishing dialog to Hive, all Datetime column values are written as String type.
If you are appending to a Timestamp column, the exported Datetime column must be in the following format: yyyy-MM-dd HH:mm:ss.xxxx