tWriteDelimitedFields properties for Apache Spark Streaming - 7.3

Processing (Integration)
Version
7.3
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Content
Data Governance > Third-party systems > Processing components (Integration)
Data Quality and Preparation > Third-party systems > Processing components (Integration)
Design and Development > Third-party systems > Processing components (Integration)
Last publication date
2024-02-21

These properties are used to configure tWriteDelimitedFields running in the Spark Streaming Job framework.

The Spark Streaming tWriteDelimitedFields component belongs to the Processing family.

The streaming version of this component is available in Talend Real Time Big Data Platform and in Talend Data Fabric.

Basic settings

Output type

Select the type of the data to be outputted into the target file. The data is byte arrays if you select byte.

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.

The schema of this component is read-only. You can click Edit schema to view the schema.

When the output type is String, the read-only single column is messageContent. This column is used to provide strings to the output components such as tJMSOutput.

When the output type is byte, the read-only single column is serializedValue. This column is used to provide byte arrays to the output components such as tKafkaOutput.

Include header

Select this check box to include the column header to the file.

Field separator

Enter a character, a string, or a regular expression to separate fields for the transferred data.

Custom encoding

You may encounter encoding issues when you process the stored data. In that situation, select this check box to display the Encoding list.

Select the encoding from the list or select Custom and define it manually. This field is compulsory for database data handling. The supported encodings depend on the JVM that you are using. For more information, see https://docs.oracle.com.

Advanced settings

Advanced separator (for number)

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

CSV options

Select this check box to include CSV specific parameters such as Escape char and Text enclosure.
Important: With Spark version 2.0 and onward, special characters must be escaped, that is "\\" and "\"" instead of "\" and """.

Usage

Usage rule

This component is used as an intermediate step.

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

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.