Informatica Mappings

Dynamic Mappings

• •Dynamic sources allow changes to the metadata in flat file and relational sources at run time. When the metadata in a flat file or relational source changes, Read and Lookup transformations can get data object columns directly from the dynamic sources at run time.
• •Transformations can include dynamic ports, which receive one or more columns that can change based on the rules that you define. You can define rules to include or exclude columns in a dynamic port.
The following transformations can include dynamic ports:
• - Aggregator
• - Expression
• - Filter
• - Joiner
• - Lookup
• - Rank
• - Router
• - Sequence Generator
• - Sorter
• - Update Strategy
• •You can define a port selector in the Joiner transformation, in the Lookup transformation, and in the Expression transformation. A port selector is an ordered list of ports that you can reference the in transformation logic. Configure a port selector to filter the ports that flow into the transformation and to reference the ports in a join condition, a lookup condition, or a dynamic expression.
• •You can define a dynamic expression in an Expression transformation. A dynamic expression returns results to a dynamic output port. You can reference a port selector or a dynamic port in a dynamic expression. When you reference a dynamic port or a port selector, the dynamic expression runs one time for each port in the dynamic port or the port selector. The Expression transformation generates a separate output port for each expression instance.
• •Dynamic targets allow you to define the columns for flat file and relational targets at run time. Write transformations can generate columns for the targets at run time based on an associated data object or the mapping flow. Write transformations that represent relational targets can also create or replace tables at run time.
• •Transformations can have links between groups that determine which ports to connect at run time based on a policy or a parameter.
• •Sources and targets, rules for ports, and transformation properties can change at run time based on parameters.

Mapping Outputs

Mapping Task Input

Mapping Task Output

Optimization Methods

Global predicate optimization method

The Data Integration Service can apply the global predicate optimization method. When the Data Integration Service applies the global predicate optimization method, it splits, moves, removes, or simplifies the filters in a mapping. The Data Integration Service filters data as close to the source as possible in the pipeline. It also infers the predicate expressions that a mapping generates.

For more information, see the "Mapping Optimization" chapter in the Informatica 10.0 Performance Tuning Guide.

Pushdown optimization method

You must select a pushdown type to push transformation logic to the source database. You can choose to push down none of the transformation logic, partial transformation logic, or full transformation logic to the source database. You can also view the mapping optimization plan for the pushdown type.

If the mapping has an Update Strategy transformation, you must determine pushdown compatibility for the mapping before you configure pushdown optimization.

For more information, see the "Pushdown Optimization" chapter in the Informatica 10.0 Developer Mapping Guide.

Dataship-join optimization method

If a mapping requires data in two different sized tables in different databases to be joined, the Data Integration Service can apply the dataship-join optimization method.

For more information, see the "Mapping Optimization" chapter in the Informatica 10.0 Performance Tuning Guide.

Mapping Optimization Plan

You can view how optimization methods affect mapping performance in a mapping optimization plan.

For more information, see the "Mapping Optimization" chapter in the Informatica 10.0 Performance Tuning Guide.

Parameters

Parameter usage

You can use parameters to represent additional properties such as connections, SQL statements, sort and group-by port lists, expression variables, and run time environment.

Parameter types

You can use the following parameter types for dynamic mappings: expression, input link set, port, port list, resource, and sort list.

Binding parameters between mappings, mapplets, and transformations

You can bind mapping parameters to mapplet parameters or to transformation parameters in the Instance Value column of a Parameters tab. You can also bind mapplet parameters to transformation parameters.

When you bind a parameter to another parameter, the parameter overrides the other parameter at run time. You can create a mapping or a mapplet parameter from an existing parameter and bind the parameters in one step. Click the Expose as Mapping Parameter option or the Expose as Mapplet Parameter option for the parameter you want to override.

You can bind parameters from a mapping to parameters in a Read or Write logical data object mapping.

Parameter sets

You can define a parameter set for a workflow or mapping. A parameter set is an object in the Model repository that contains a set of parameters and parameter values to use at run time. You use a parameter set with a mapping, Mapping task, or workflow. You can add one or more parameter sets to an application when you deploy the application. You can add a parameter set to multiple applications and deploy them.

Run-time environment parameter

You can set the run-time environment with a parameter. Configure a string parameter at the mapping level. Set the default value to Native or Hadoop. When you select the run-time environment for the mapping, click Assign Parameter and select the parameter that you configured.

Partitioned Mappings

Partitioned transformations

Additional transformations support partitioning. When a mapping enabled for partitioning contains the following transformations, the Data Integration Service can use multiple threads to transform the data:

• - Address Validator
• - Case Converter
• - Classifier
• - Comparison
• - Data Masking
• - Data Processor
• - Decision
• - Key Generator
• - Labeler
• - Match, when configured for identity match analysis
• - Merge
• - Normalizer
• - Parser
• - Sequence Generator
• - Sorter
• - Standardizer
• - Weighted Average

Cache partitioning

For an Aggregator, Joiner, or Rank transformation, you can configure multiple cache directories to optimize performance during cache partitioning for the transformation. You can use the default CacheDir system parameter value if an administrator configured multiple cache directories for the Data Integration Service. Or, you can override the default CacheDir system parameter value to configure multiple cache directories specific to the transformation.

For a Sorter transformation, you can configure multiple work directories to optimize performance during cache partitioning for the transformation. You can use the default TempDir system parameter value if an administrator configured multiple temporary directories for the Data Integration Service. Or, you can override the default TempDir system parameter value to configure multiple directories specific to the transformation.

Mappings that order data

The Data Integration Service can create partitions for a mapping that establishes a sort order. You can establish sort order in a mapping with a sorted flat file source, a sorted relational source, or a Sorter transformation. When the Data Integration Service adds a partition point to a mapping, it might redistribute data and lose the order established earlier in the mapping. To maintain order in a partitioned mapping, you must specify that Expression, Java, Sequence Generator, SQL, and Write transformations maintain the row order in the transformation advanced properties.

Partitioned flat file targets

To optimize performance when multiple threads write to a flat file target, you can configure multiple output file directories for a flat file data object. You can use the default TargetDir system parameter value if an administrator has configured multiple target directories for the Data Integration Service. Or, you can override the default TargetDir system parameter value to configure multiple output file directories specific to the flat file data object.

Suggested parallelism value for transformations

If you override the maximum parallelism for a mapping, you can define a suggested parallelism value for a specific transformation. The Data Integration Service uses the suggested parallelism value for the number of threads for that transformation pipeline stage as long as the transformation can be partitioned. You can define a suggested parallelism value that is less than the maximum parallelism value defined for the mapping or the Data Integration Service. You might want to define a suggested parallelism value to optimize performance for a transformation that contains many ports or performs complicated calculations.

Run-time Properties

Stop on Errors

Stops the mapping if a nonfatal error occurs in the reader, writer, or transformation threads. Default is disabled.

Target Commit Interval

The number of rows to use as a basis for a commit. The Data Integration Service commits data based on the number of target rows that it processes and the constraints on the target table.

Target Load Order Constraints