tAvroInput properties for Apache Spark Batch

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.

The fields that come after are pre-filled in using the fetched data.

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.

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.

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 (,).

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 correctly 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.

Clear the check box to skip any rows on error and complete the process for error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

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.

Use hierarchical mode

Select this check box to map the binary (including hierarchical) Avro schema to the flat schema defined in the schema editor of the current component. If the Avro message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameter(s):

• Mapping: create the map between the schema columns of the current component and the data stored in the hierarchical Avro message to be handled. In the Node column, you need to enter the JSON path pointing to the data to be read from the Avro message.

Usage rule

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

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