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 be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

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.

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 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 other Spark Yarn
modes that the Studio supports with your distribution, ensure that you have properly
configured the connection in a configuration component in the same Job, such as

tHDFSConfiguration
. Use the
configuration component depending on the filesystem to be used.

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.

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 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.