tFileInputDelimited properties for Apache Spark Streaming

Delimited

author
Talend Documentation Team
EnrichVersion
6.5
EnrichProdName
Talend Open Studio for ESB
Talend Data Management Platform
Talend Open Studio for Data Integration
Talend Data Fabric
Talend MDM Platform
Talend Open Studio for Big Data
Talend Big Data Platform
Talend Real-Time Big Data Platform
Talend Data Integration
Talend Data Services Platform
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Talend Big Data
Talend Open Studio for MDM
task
Design and Development > Third-party systems > File components (Integration) > Delimited components
Data Quality and Preparation > Third-party systems > File components (Integration) > Delimited components
Data Governance > Third-party systems > File components (Integration) > Delimited components
EnrichPlatform
Talend Studio

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

The Spark Streaming tFileInputDelimited component belongs to the File family.

The component in this framework is available in Talend Real Time Big Data Platform and Talend Data Fabric.

Basic settings

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.

The properties are stored centrally under the Hadoop Cluster node of the Repository tree.

For further information about the Hadoop Cluster node, see the Getting Started Guide.

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to Repository. When you create a Spark Job, avoid the reserved word line when naming the fields.

Click Edit schema to make changes to the schema. Note that if you make changes, the schema automatically becomes built-in.

 

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.

Folder/File

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

If the path you set points to a folder, this component will read all of the files stored in that folder, for example, /user/talend/in; if sub-folders exist, the sub-folders are automatically ignored unless you define the property spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive to be true in the Advanced properties table in the Spark configuration tab.

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 this component 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 Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

Die on error

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

Row separator

The separator used to identify the end of a row.

Field separator

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

Header

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

CSV options

Select this check box to include CSV specific parameters such as Escape char and Text enclosure.

Skip empty rows

Select this check box to skip the empty rows.

Advanced settings

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.

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.

Then select the encoding to be used from the list or select Custom and define it manually.

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 (.).

Trim all columns

Select this check box to remove the leading and trailing whitespaces from all columns. When this check box is cleared, the Check column to trim table is displayed, which lets you select particular columns to trim.

Check column to trim

This table is filled automatically with the schema being used. Select the check box(es) corresponding to the column(s) to be trimmed.

Check each row structure against schema

Select this check box to check whether the total number of columns in each row is consistent with the schema. If not consistent, an error message will be displayed on the console.

Check date

Select this check box to check the date format strictly against the input schema.

Decode String for long, int, short, byte Types

Select this check box if any of your numeric types (long, integer, short, or byte type), will be parsed from a hexadecimal or octal string.

Usage

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

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, 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: when using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab; when using other distributions, use a tHDFSConfiguration component to specify the directory.

  • Standalone mode: you need to choose the configuration component depending on the file system you are using, such as tHDFSConfiguration or tS3Configuration.

This connection is effective on a per-Job basis.