# tStandardizeRow 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 tStandardizeRow 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 tStandardizeRow component belongs to the Data Quality family. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | 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.

| | Column to parse | Select the column to be parsed from the received data
flow | | Standardize this field | Select this check box to standardize the rule-compliant
data identified, that is, to replace the duplicates of the identified
data with the corresponding standardized data from a given index.

For further information about this index providing
standardized data, see [tSynonymOutput](https://help.qlik.com/talend/en-US/components/8.0/synonym-index/tsynonymoutput).

Every time you select or clear this check box, the schema
of this component is changed automatically, so in a given Job, you need
to click the activated Sync
columns button to fix the inconsistencies in the
schema. | | Generate analyzer code as routine | Click this button to enable the data parser of Talend Studio to recognize the rules defined in the Conversion
rules table.

In a given Job, when a rule is created, this operation is
required for the execution of this rule, while if it is on an existing
rule that you have modified, this operation is required only when the
modified rule is of type Enumeration, Format or Combination. For further information about all of the
rule types, see [Rule types](https://help.qlik.com/talend/en-US/components/8.0/standardization/tstandardizerow-tstandardizerow-rule-types-standard-component-enterprise-two). | | ![](https://help.qlik.com/talend/en-US/components/8.0/Resources/images/import.png) and ![](https://help.qlik.com/talend/en-US/components/8.0/Resources/images/export.png) | Click the import or export button to exchange a given
standardization rule set with the DQ
Repository.

- When you click the export button, Talend Studio is switched to the Profiling perspective and the Parser rule
Settings view is opened on the workspace with the relative
contents filled automatically. Then if need be, you can edit the exported rule
set and save it to the Libraries >
Rules > Parser folder in the DQ
Repository tree view.

- When you click the import button, a import wizard is
opened to help you import the standardization rule of interest.

For further information, see [Setting and managing parser rules](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/setting-and-managing-parser-rules). | | Conversion rules | Define the rules you need to apply as the following:

- In the Name
column, type in a name of the rule you want to use. This name is used as
the XML tag or the JSON attribute name and the token name to label the
incoming data identified by this rule.

- In the Type
column, select the type of the rule you need to apply. For further
information about available rule types, see [Rule types](https://help.qlik.com/talend/en-US/components/8.0/standardization/tstandardizerow-tstandardizerow-rule-types-standard-component-enterprise-two).

- In the Value
column, type in the syntax of the rule.

- In the Search
mode column, select a search mode from the list. The
search modes can be used only with the Index rule type. For further information about
available search modes, see [Search modes for Index rules](https://help.qlik.com/talend/en-US/components/8.0/standardization/search-modes-for-index-rules).

- When you select the Index rule type, set the
path as follows:

In local mode:
- Apache Spark 3.1 and earlier: prefix://file path or file:///file path.
- Apache Spark 3.2 and later: file:///file path.
In Standalone and Yarn modes: prefix://file path.
If the index is on a cluster: hdfs://hdpnameservice1/file path.

A test view is provided to help you create the parser rules of
interest. For further information, see [Setting and managing parser rules](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/setting-and-managing-parser-rules). | ## Advanced settings | Properties | Description | | --- | --- | | Advanced options for INDEX rules | - Search UNDEFINED
fields: select this check box if you want the component
to search for undefined tokens in the index run results.

- Word distance for partial
match (available for the *Match
partial* mode): set the maximum number of words allowed to come
inside a sequence of words that may be found in the index, default value
is *1*.

- Max edits for fuzzy
match (Based on the Levenshtein algorithm and available
for fuzzy modes): select an edit distance,*1* or *2*, from the list. Any terms within the edit
distance from the input data are matched. With a max edit distance *2*, for example, you can have up to two
insertions, deletions, or substitutions. The score for each match is
based on the edit distance of that term.

Fuzzy match gains much in performance with Max edits for fuzzy match.

Information noteNote: Jobs migrated in Talend Studio from older releases run correctly, but results might be slightly different
because Max edits for fuzzy match is now
used in place of Minimum similarity for fuzzy
match. | | Output format | -XML: this option
is selected by default. It outputs normalized data in XML format.

-JSON: select this
option to output normalized data in JSON format. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming Job.

This component is used as an intermediate step.

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.

This connection is effective on a per-Job basis.

For further information about a
Talend
Spark Streaming Job, see [Getting started with Spark Streaming Jobs](https://help.qlik.com/talend/r/en-US/8.0/studio-getting-started-guide-data-fabric/getting-started-with-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. | | Connections | Outgoing links (from this component to another):

Row: Main; Reject

Incoming links (from one component to this one):

Row: Main; Reject

For further information regarding connections, see [Using connections in a Job](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/using-connections-in-job). | | 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. |