# tGenKey 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 tGenKey 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 tGenKey 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.

Click Sync columns to retrieve the schema from the previous component connected in the Job.
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.
Click the import icon to import blocking keys from the match rules that are defined and saved in the Talend Studio repository.

When you click the import icon, a Match Rule Selector wizard is opened to help you import blocking keys from the match rules listed in the Talend Studio repository and use them in your Job.

You can import blocking keys only from match rules that are defined with the VSR algorithm and saved in the Talend Studio repository. For further information, see Importing match rules from the Talend Studio repository

| | Column | Select the columns from the main flow on which you want to define certain
algorithms to set the functional key.

Information noteNote: When you select a date column on which to apply an algorithm or a matching
algorithm, you can decide what to compare in the date format.

For example,
if you want to only compare the year in the date, in the component schema set the type
of the date column to Date and then enter "*yyyy*" in the Date Pattern field.
The component then converts the date format to a string according to the pattern defined
in the schema before starting a string comparison. | | Pre-Algorithm | If required, select the relevant matching algorithm from the list:

Remove diacritical marks: removes any diacritical
mark.

Remove diacritical marks and lower case: removes any
diacritical mark and converts to lower case before generating the code of the column.

Remove diacritical marks and upper case: removes any
diacritical mark and converts to upper case before generating the code of the column.

Lower case: converts the field to lower case before
applying the key algorithm.

Upper case: converts the field to upper case before
applying the key algorithm.

Add left position character: enables you to add a
character to the left of the column.

Add right position character: enables you to add a
character to the right of the column. | | Value | Set the algorithm value, where applicable. | | Algorithm | Select the relevant algorithm from the list:

First character of each word: includes in the
functional key the first character of each word in the column. When using Japanese
characters in the input data, the input text should be tokenized. For more information,
see [tJapaneseTokenize](https://help.qlik.com/talend/r/en-US/8.0/components/text-standardization/tjapanesetokenize).

First N characters of each word: includes in the
functional key first N characters of each word in the column. When using Japanese characters
in the input data, the input text should be tokenized. For more information, see [tJapaneseTokenize](https://help.qlik.com/talend/r/en-US/8.0/components/text-standardization/tjapanesetokenize).

First N characters of the string: includes in the
functional key first N characters of the string.

Last N characters of the string: includes in the
functional key last N characters of the string.

First N consonants of the string: includes in the
functional key first N consonants of the string. Japanese and Chinese characters are not
supported.

First N vowels of the string: includes in the functional
key first N vowels of the string. Japanese and Chinese characters are not supported.

Pick characters: includes in the functional key the
characters located at a fixed position (corresponding to the set digital/range).

Exact: includes in the functional key the full
string.

Substring(a,b): includes in the functional key character
according to the set index.

Soundex code: generates a code according to a
standard English phonetic algorithm. This code represents the character string that will
be included in the functional key. Japanese and Chinese characters are not
supported.

Metaphone code: generates a code according to the
character pronunciation. This code represents the character string that will be included
in the functional key. Japanese and Chinese characters are not supported.

Double-metaphone code: generates a code according to
the character pronunciation using a new version of the Metaphone phonetic algorithm,
that produces more accurate results than the original algorithm. This code represents
the character string that will be included in the functional key. Japanese and Chinese
characters are not supported.

Fingerprint key: generates the functional key from a
string value through the following sequential process:

remove leading and trailing whitespace,
change all characters to their lowercase representation,
remove all punctuation and control characters,
split the string into whitespace-separated tokens,
sort the tokens and remove duplicates,
join the tokens back together,

Because the string parts are sorted, the given order of tokens does not matter. So, Cruise, Tom and Tom Cruise both end up with a fingerprint cruise tom and therefore end up in the same cluster.
normalize extended western characters to their ASCII representation, for example gödel to godel.

This reproduce data entry mistakes performed when entering extended characters with an ASCII-only keyboard. However, this procedure can also lead to false positives, for example gödel and godél would both end up with godel as their fingerprint but they are likely to be different names. So this might work less effectively for datasets where extended characters play substantial differentiation role.

nGramkey: this algorithm is similar to the fingerPrintkey method described above. But instead of using
whitespace separated tokens, it uses n-grams, where the *n* can be
specified by the user. This method generates the functional key from a string value through
the following sequential process:

change all characters to their lowercase representation,
remove all punctuation and control characters,
obtain all the string n-grams,
sort the n-grams and remove duplicates,
join the sorted n-grams back together,
normalize extended western characters to their ASCII representation, for example gödel to godel.

For example, the 2-gram fingerprint of Paris is arispari and the 1-gram fingerprint is aiprs.

The delivered implementation of this algorithm is 2-grams.

Information noteNote:

If the column on which you want to use the nGramkey
algorithm can have data, with only *0* or *1*
characters, you must filter this data before generating the functional key. This way you
avoid potentially comparing records to each other that should not be match
candidates.

Cologne phonetics: a Soundex phonetic algorithm
optimized for the German language. It encodes a string into a Cologne phonetic value.
This code represents the character string that will be included in the functional key.
Japanese and Chinese characters are not supported. | | Value | Set the algorithm value, where applicable.

If you do not set a value for the algorithms which need one, the Job runs with a
compilation error. | | Post-Algorithm | If required, select the relevant matching algorithm from the list:

Use default value (string): enables you to choose a
string to replace null or empty data.

Add left position character: enables you to add a
character to the left of the column.

Add right position character: enables you to add a
character to the right of the column. | | Value | Set the option value, where applicable. | | Show help | Select this check box to display instructions on how to set algorithms/options
parameters. | ## Global Variables | Variables | Description | | --- | --- | | Global Variables | ERROR\_MESSAGE: the error message generated by the
component when an error occurs. This is an After variable and it returns a string. This
variable functions only if the Die on error checkbox is
cleared, if the component has this checkbox.

A Flow variable functions during the execution of a component
while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl+Space to access the variable list and choose the variable to use from
it.

For more information about variables, see [Using contexts and variables](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/contexts-and-variables). | ## 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. | | 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. |