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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:

  • 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.

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.


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

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

  • min: selects the minimum value

  • max: selects the maximum value

  • avg: calculates the average

  • sum: calculates the sum

  • population standard deviation: calculates the spread of a data distribution. Use this function if the data to be calculated is considered a population on its own. This calculation supports 39 decimal places.
  • sample standard deviation: calculates the spread of a data distribution. Use this function if the data to be calculated is considered a sample from a larger population. This calculation supports 39 decimal places.

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 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:
  • Yarn mode (Yarn client or Yarn cluster):
    • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

    • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

    • When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
    • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis.

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