Table of Contents

  1. Preface
  2. Introduction to the Informatica Connector Toolkit
  3. Installing and Upgrading the Informatica Connector Toolkit
  4. Building an Adapter
  5. Connection Attributes
  6. Type System
  7. Metadata Objects
  8. Partitioning Capability
  9. Run-time Behavior
  10. Adapter Example: Instagram
  11. Adapter Example: MySQL
  12. Adapter Example: YouTube
  13. Metadata Models
  14. ASO Model
  15. Adapter Project Migration
  16. Frequently Asked Questions

Informatica Connector Toolkit Developer Guide

Informatica Connector Toolkit Developer Guide

Native Types and Semantic Categories

Native Types and Semantic Categories

The native types included in the type system are all possible data types in the data source for which the adapter is built.
You must define the semantic category of each data type in the data source. The Informatica Connector Toolkit API defines each semantic category. Check the data source documentation to verify the data types that are available in the data source.
Use the Informatica Connector Toolkit to associate native data types with semantic categories. The data type name must match the character string that corresponds to the type name returned during the metadata import process, such as Integer, Varchar2, or Blob.
When you define the semantic category for a data type, you can modify the precision and scale returned by the import process so that the data type matches the requirements of the type system.
Use the following semantic categories to classify the native types:
Length semantics
Use this category for native types where length is the principal characteristic. This category can include data types such as Char, Varchar2, Binary, Varbinary, Blob, and Clob.
Integer semantics
Use this category for native types that can contain signed integers. The length of the data type is the number of decimal digits specified in the data type precision. This category can include data types such as Integer, Smallint, Bigint, and Tinyint.
Machine integer semantics
Use this category for native types that can contain signed or unsigned integers. The length of this data type is measured in bytes. The precision of a machine integer type is the maximum number of decimal digits that fits within the length of the data type, regardless of whether all possible values can be stored. For example, a 32-bit (4 byte) machine integer can store up to 10 digits but if the value of each digit is 9, then the value of the integer can result in an overflow.
Decimal semantics
Use this category for native types that can contain an exact real number where precision is the total number of digits and scale is the number of digits to the right of the decimal point. The precision for this semantic category must be greater than or equal to the scale. This category can include data types such as Decimal and Numeric.
Scientific decimal semantics
Use this category for native types that can contain an exact real number where precision is the number of digits stored rather than the total number of digits represented by the number. The total number of digits represented by the number can exceed the precision. The scale of the data type can exceed precision and can be positive or negative. A positive scale represents digits to the right of the decimal point. A negative scale represents the rounding position to the left of the decimal point. This category can include data types such as the Number data type in Oracle with precision and scale specified.
Float semantics
Use this category for binary or decimal floating point data types. An example of a binary floating point type is the binary_float type in Oracle. An example of a decimal floating point number is the Number data type in Oracle with precision and scale not specified.
Gregorian date semantics
Use this category for date types that the adapter can expose as Gregorian dates, times, and timestamps.

Updated May 15, 2019


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