# tFileInputJSON properties for Apache Spark Streaming (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. These properties are used to configure tFileInputJSON running in the Spark Streaming Job framework. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. The Spark Streaming tFileInputJSON component belongs to the File family. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | 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 fields that come after are pre-filled in using the fetched data.

For further information about the File Json node, see Centralizing JSON file metadata.

| | 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.

| | Read by | Select a way of extracting the JSON data in the file.

Xpath: Extracts the JSON data based on the XPath query.
JsonPath: Extracts the JSON data based on the JSONPath query. Note that it is recommended to read the data by JSONPath in order to gain better performance.

| | Folder/File | Browse to, or enter the path pointing to the data to be used in the file system.

If the path you entered points to a folder, all files stored in
that folder will be read.

If the file to be read is a compressed one, enter the file name
with its extension; then tFileInputJSON 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. | | Die on error | Select the checkbox to stop the execution of the Job when an error
occurs. | | Loop Jsonpath query | Enter the JSONPath or XPath of the node on which the loop is based.

Note if you have selected Xpath from the Read by drop-down list, the Loop
Xpath query field is displayed instead. | | Mapping | Complete this table to map the columns defined in the schema to the
corresponding JSON nodes.

Column: The Column cells are automatically filled with the defined schema column names.
Json query/JSONPath query: Specify the JSONPath node that holds the desired data. For more information about JSONPath expressions, see http://goessner.net/articles/JsonPath/.

This column is available only when JsonPath is selected from the Read By list.
XPath query: Specify the XPath node that holds the desired data.

This column is available only when Xpath is selected from the Read By list.
Get Nodes: Select this check box to extract the JSON data of all the nodes or select the check box next to a specific node to extract the data of that node.

This column is available only when Xpath is selected from the Read By list.

| ## Advanced settings | Properties | Description | | --- | --- | | 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. | | 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 (*.*). | | 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 | Usage guidance | Description | | --- | --- | | **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 | 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 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. |