tFileInputRegex properties for Apache Spark Streaming

Define a storage configuration component

Select the configuration component to be used to provide the configuration information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local system.

The configuration component to be used must be present in the same Job. For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write the result in a given HDFS system.

Property type

Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the properties are stored.

Folder/File

Browse to, or enter the path pointing to the data to be used in the file system.

If you want to specify more than one files or directories in this field, separate each path using a comma (,).

If the file to be read is a compressed one, enter the file name with its extension; then ttFileInputRegex automatically decompresses it at runtime. The supported compression formats and their corresponding extensions are:

• DEFLATE: *.deflate

• gzip: *.gz

• bzip2: *.bz2

• LZO: *.lzo

The button for browsing does not work with the Spark Local mode; if you are using the other Spark Yarn modes that Talend Studio supports with your distribution, ensure that you have properly configured the connection in a configuration component in the same Job. Use the configuration component depending on the filesystem to be used.

Row separator

The separator used to identify the end of a row.

Regex

This field can contain multiple lines. Type in your regular expressions including the subpattern matching the fields to be extracted.

Note: Antislashes need to be doubled in regexp

Header

Enter the number of rows to be skipped in the beginning of file.

Schema and Edit Schema

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.

Skip empty rows

Select this check box to skip the empty rows.

Die on error

Select the check box to stop the execution of the Job when an error occurs.

Set minimum partitions

Select this check box to control the number of partitions to be created from the input data over the default partitioning behavior of Spark.

In the displayed field, enter, without quotation marks, the minimum number of partitions you want to obtain.

When you want to control the partition number, you can generally set at least as many partitions as the number of executors for parallelism, while bearing in mind the available memory and the data transfer pressure on your network.

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.

Usage rule

This component is used as a start component and requires an output link.

This component is only used to provide the lookup flow (the right side of a join operation) to the main flow of a tMap component. In this situation, the lookup model used by this tMap must be Load once.

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

This connection is effective on a per-Job basis.