tTeradataLookupInput properties for Apache Spark Streaming
These properties are used to configure tTeradataLookupInput running in the Spark Streaming Job framework.
The Spark Streaming tTeradataLookupInput component belongs to the Databases family.
The streaming version of this component is available in Talend Real-Time Big Data Platform and in Talend Data Fabric.
Basic settings
Property type |
Either Built-in or Repository . |
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Built-in: No property data stored centrally. |
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Repository: Select the repository file in which the properties are stored. The fields that follow are completed automatically using the data retrieved. |
Use an existing configuration |
Select this check box and in the Component List drop-down list, select the desired connection component to reuse the connection details you already defined. |
Click this icon to open a database connection wizard and store the database connection parameters you set in the component Basic settings view. For more information about setting up and storing database connection parameters, see Centralizing database metadata. |
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Host |
Database server IP address |
Database |
Name of the database |
Username and Password |
DB user authentication data. To enter the password, click the [...] button next to the password field, enter the password in double quotes in the pop-up dialog box, and click OK to save the settings. |
Table |
Name of the table to be used. |
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. |
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Built-In: You create and store the schema locally for this component only. |
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Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. When the schema to be reused has default values that are integers or functions, ensure that these default values are not enclosed within quotation marks. If they are, you must remove the quotation marks manually. For more information, see Retrieving table schemas. |
Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:
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Query type and Query |
Enter your DB query paying particularly attention to properly sequence the fields in order to match the schema definition. The result of the query must contain only records that match join key you need to use in tMap. In other words, you must use the schema of the main flow to tMap to construct the SQL statement here in order to load only the matched records into the lookup flow. This approach ensures that no redundant records are loaded into memory and outputted to the component that follows. |
Advanced settings
Additional JDBC parameters |
Specify additional connection properties in the existing DB connection, to allow specific character set support. E.G.: CHARSET=KANJISJIS_OS to get support of Japanese characters. |
Trim all the String/Char columns |
Select this check box to remove leading and trailing whitespace from all the String/Char columns. |
Trim column |
Remove leading and trailing whitespace from defined columns. |
Connection pool |
In this area, you configure, for each Spark executor, the connection pool used to control the number of connections that stay open simultaneously. The default values given to the following connection pool parameters are good enough for most use cases.
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Evict connections |
Select this check box to define criteria to destroy connections in the connection pool. The following fields are displayed once you have selected it.
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Usage
Usage rule |
This component is used as a start component and requires an output link. This component should use a tTeradataConfiguration component present in the same Job to connect to Oracle. You need to select the Use an existing configuration check box and then select the tTeradataConfiguration component to be used. 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:
This connection is effective on a per-Job basis. |
Limitation |
Due to license incompatibility, one or more JARs required to use this component are not provided. You can install the missing JARs for this particular component by clicking the Install button on the Component tab view. You can also find out and add all missing JARs easily on the Modules tab in the Integration perspective of Talend Studio. For details, see Installing external modules. |