tAggregateRow properties in Spark Streaming Jobs - 6.1

Talend Components Reference Guide

EnrichVersion
6.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for Data Quality
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
task
Data Governance
Data Quality and Preparation
Design and Development
EnrichPlatform
Talend Studio

Warning

The streaming version of this component is available in the Palette of the studio on the condition that you have subscribed to Talend Real-time Big Data Platform or Talend Data Fabric.

Component family

Processing

 

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. The schema is either Built-In or stored remotely in the Repository.

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. Related topic: see Talend Studio User Guide.

 

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

 

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, min, max, avg, sum, first, last, list, list(objects), count(distinct), standard deviation.

 

 

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.

Warning

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 in Spark Streaming Jobs

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

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

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.

Log4j

If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.