Relations

Physical data sources (like Hive tables, relational SQL databases, files etc) are specified using so called relations. Data flows can read and write from and to relations via appropriate mappings (Read Mapping) or build targets (Relation Target).

A relation always contains all required information to connect to the data source (for example the table name and a JDBC Connection in case of a JDBC relation).

Some relations (like files, Hive tables and views) can also be directly created from Flowman, thereby providing a single tool for schema management and data ingestion into tables.

Schema Conversion

Most relations support implicit schema conversions, which means that

  1. when reading from a relation with a user specified schema, the actual schema of the physical data source is converted to the desired user defined schema
  2. when writing to a relation, the schema of the data written is automatically converted to the actual schema of the data sink

The details of these conversions can be controlled via some config variables

  • flowman.default.relation.input.columnMismatchPolicy (default is IGNORE)
  • flowman.default.relation.input.typeMismatchPolicy (default is IGNORE)
  • flowman.default.relation.input.charVarcharPolicy (default is PAD_AND_TRUUNCATE)
  • flowman.default.relation.output.columnMismatchPolicy (default is ADD_REMOVE_COLUMNS)
  • flowman.default.relation.output.typeMismatchPolicy (default is CAST_ALWAYS)
  • flowman.default.relation.output.charVarcharPolicy (default is PAD_AND_TRUUNCATE)

The schema conversion is implemented using two aspects. The first is a mismatch between column (names). This can be configured using the columnMismatchPolicy as follows. Basically the idea is that

  • IGNORE will simply pass through the input columns unchanged
  • ERROR will fail the build once a mismatch between actual and requested schema is detected
  • ADD_COLUMNS_OR_IGNORE will add (NULL) columns from the requested schema to the input schema, and will keep columns in the input schema which are not present in the requested schema
  • ADD_COLUMNS_OR_ERROR will add (NULL) columns from the requested schema to the input schema, but will fail the build if the input schema contains columns not present in the requested schema
  • REMOVE_COLUMNS_OR_IGNORE will remove columns from the input schema which are not present in the requested schema
  • REMOVE_COLUMNS_OR_ERROR will remove columns from the input schema which are not present in the requested schema and will fail if the input schema is missing requested columns
  • ADD_REMOVE_COLUMNS will essentially pass through the requested schema as is
Column Mismatch Policy Input Column Requested Column Result
IGNORE present missing present
IGNORE missing present missing
ERROR present missing build error
ERROR missing present build error
ADD_COLUMNS_OR_IGNORE present missing present
ADD_COLUMNS_OR_IGNORE missing present present
ADD_COLUMNS_OR_ERROR present missing build error
ADD_COLUMNS_OR_ERROR missing present missing
REMOVE_COLUMNS_OR_IGNORE present missing missing
REMOVE_COLUMNS_OR_IGNORE missing present missing
REMOVE_COLUMNS_OR_ERROR present missing missing
REMOVE_COLUMNS_OR_ERROR missing present build error
ADD_REMOVE_COLUMNS present missing missing
ADD_REMOVE_COLUMNS missing present present

The second aspect is the conversion of data types. Again this can be configured using the typeMismatchPolicy as follows:

Type Mismatch Policy Input vs Requested Data Type Result
IGNORE Source can be cast to dest Input Data Type
IGNORE Source cannot be safely cast to dest Input Data Type
ERROR Source can be cast to dest build error
ERROR Source cannot be safely cast to dest build error
CAST_COMPATIBLE_OR_ERROR Source can be cast to dest Target Data Type
CAST_COMPATIBLE_OR_ERROR Source cannot be safely cast to dest build error
CAST_COMPATIBLE_OR_IGNORE Source can be safely cast to dest Target Data Type
CAST_COMPATIBLE_OR_IGNORE Source cannot be safely cast to dest Input Data Type
CAST_ALWAYS Source can be safely cast to dest Target Data Type
CAST_ALWAYS Source cannot be safely cast to dest Target Data Type

The two options flowman.default.relation.input.charVarcharPolicy and flowman.default.relation.output.charVarcharPolicy control how Flowman will treat VARCHAR(n) and CHAR(n) data types. The possible values are

  • IGNORE - Do not apply any length restrictions
  • PAD_AND_TRUNCATE - Truncate VARCHAR(n)/CHAR(n) strings which are too long and pad CHAR(n) strings which are too short
  • PAD - Pad CHAR(n) strings which are too short
  • TRUNCATE - Truncate VARCHAR(n)/CHAR(n) strings which are too long

Relation Types

Flowman directly provides support for the most important data sources, which are also supported directly by Spark. Additional data sources can be implemented as plugins if required.