tAggregateRow MapReduce properties (deprecated)

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.

Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

• View schema: choose this option to view the schema only.

• Change to built-in property: choose this option to change the schema to Built-in for local changes.

• Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

Built-In: You create and store the schema locally for this component only.

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

Group by

Define the aggregation sets, the values of which will be used for calculations.

Output Column: Select the column label in the list offered based on the schema structure you defined. You can add as many output columns as you wish to make more precise aggregations.

Ex: Select Country to calculate an average of values for each country of a list or select Country and Region if you want to compare one country's regions with another country' regions.

Input Column: Match the input column label with your output columns, in case the output label of the aggregation set needs to be different.

Operations

Select the type of operation along with the value to use for the calculation and the output field.

Output Column: Select the destination field in the list.

Function: Select the operator among:

• count: calculates the number of rows

• min: selects the minimum value

• max: selects the maximum value

• avg: calculates the average

• sum: calculates the sum

• list: lists values of an aggregation by multiple keys.

• list (object): lists Java values of an aggregation by multiple keys

• count (distinct): counts the number of the distinct rows

• standard deviation: calculates the variability of a set of value.

• union (geometry): makes the union of a set of Geometry objects

Some functions that are available in a traditional ETL Job, such as first or last, are not available in MapReduce Jobs because these functions does not make sense in a distributed environment.

Input column: Select the input column from which the values are taken to be aggregated.

Ignore null values: Select the check boxes corresponding to the names of the columns for which you want the NULL value to be ignored.

Delimiter(only for list operation)

Enter the delimiter you want to use to separate the different operations.

Use financial precision, this is the max precision for "sum" and "avg" operations, checked option heaps more memory and slower than unchecked.

Select this check box to use a financial precision. This is a max precision but consumes more memory and slows the processing.

Check type overflow (slower)

Checks the type of data to ensure that the Job doesn't crash.

Check ULP (Unit in the Last Place), ensure that a value will be incremented or decremented correctly, only float and double types. (slower)

Select this check box to ensure the most precise results possible for the Float and Double types.

Global Variables

ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it.

For further information about variables, see Talend Studio User Guide.

Usage rule

In a Talend Map/Reduce Job, this component is used as an intermediate step and other components used along with it must be Map/Reduce components, too. They generate native Map/Reduce code that can be executed directly in Hadoop.

For further information about a Talend Map/Reduce Job, see the sections describing how to create, convert and configure a Talend Map/Reduce Job of the Talend Big Data Getting Started Guide .

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs, and non Map/Reduce Jobs.