# tAggregateRow properties for Apache Spark Batch These properties are used to configure tAggregateRow running in the Spark Batch Job framework. The Spark Batch tAggregateRow component belongs to the Processing family. The component in this framework is available in all Talend products with Big Data and Talend Data Fabric. ## Basic settings | 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:



Information noteTip: By default, when Spark aggregates BigDecimal, the
precision and scale are set at the maximum values of the data type.
You can change the scale and the precision for BigDecimal in the
corresponding columns of the schema. | | --- | --- | | | 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:



Some functions that are available in a traditional ETL Job, such as first or last, are not available in Spark 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. | ## Advanced settings | 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.

Information noteWarning:


*We advise you to use the BigDecimal type for the output in
order to obtain precise results.* | | --- | --- | | 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. | ## Usage | **Usage rule** | This component is used as an intermediate step.

This component, along with the Spark Batch component Palette it belongs to,
appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a
scenario presents only Standard Jobs, that is to
say traditional
Talend
data integration Jobs. | | --- | --- | | Spark Connection | In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:



This connection is effective on a per-Job basis. |