tMapRDBLookupInput properties for Apache Spark Streaming - 7.0

MapRDB

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Open Studio for Big Data
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Database components > MapRDB components
Data Quality and Preparation > Third-party systems > Database components > MapRDB components
Design and Development > Third-party systems > Database components > MapRDB components
EnrichPlatform
Talend Studio

These properties are used to configure tMapRDBLookupInput running in the Spark Streaming Job framework.

The Spark Streaming tMapRDBLookupInput component belongs to the Databases family.

The component in this framework is available in Talend Real Time Big Data Platform and in Talend Data Fabric.

Basic settings

Storage configuration

Select the tMapRDBConfiguration component from which the Spark system to be used reads the configuration information to connect to MapRDB.

Property type

Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the properties are stored.

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.

Table name

Type in the name of the table from which you need to extract columns.

Table Namespace mappings

Enter the string to be used to construct the mapping between an Apache HBase table and a MapR table.

For the valid syntax you can use, see http://doc.mapr.com/display/MapR40x/Mapping+Table+Namespace+Between+Apache+HBase+Tables+and+MapR+Tables.

Define a row selection

Select this check box and then in the Start row and the End row fields, enter the corresponding row keys to specify the range of the rows you want the current component to extract.

Different from the filters you can set using Is by filter requiring the loading of all records before filtering the ones to be used, this feature allows you to directly select only the rows to be used.

Mapping

Complete this table to map the columns of the table to be used with the schema columns you have defined for the data flow to be processed.

Is by filter

Select this check box to use filters to perform fine-grained data selection from your database, such as selection of keys, or values, based on regular expressions.

Once selecting it, the Filter table that is used to define filtering conditions becomes available.

This feature leverages filters provided by HBase and subject to constraints explained in Apache HBase documentation. Therefore, advanced knowledge of HBase is required to make full use of these filters.

Logical operation
Select the operator you need to use to define the logical relation between filters. This available operators are:
  • And: every defined filtering conditions must be satisfied. It represents the relationship FilterList.Operator.MUST_PASS_ALL

  • Or: at least one of the defined filtering conditions must be satisfied. It represents the relationship: FilterList.Operator.MUST_PASS_ONE

Filter
Click the button under this table to add as many rows as required, each row representing a filter. The parameters you may need to set for a filter are:
  • Filter type: the drop-down list presents pre-existing filter types that are already defined by HBase. Select the type of the filter you need to use.

  • Filter column: enter the column qualifier on which you need to apply the active filter. This parameter becomes mandatory depending on the type of the filter and of the comparator you are using. For example, it is not used by the Row Filter type but is required by the Single Column Value Filter type.

  • Filter family: enter the column family on which you need to apply the active filter. This parameter becomes mandatory depending on the type of the filter and of the comparator you are using. For example, it is not used by the Row Filter type but is required by the Single Column Value Filter type.

  • Filter operation: select from the drop-down list the operation to be used for the active filter.

  • Filter Value: enter the value on which you want to use the operator selected from the Filter operation drop-down list.

  • Filter comparator type: select the type of the comparator to be combined with the filter you are using.

Depending on the Filter type you are using, some or each of the parameters become mandatory. For further information, see HBase filters

Usage

Usage rule

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

This component uses a tMapRDBConfiguration component present in the same Job to connect to MapR-DB.

However, if you need to use tMapRDBLookupInput with Kerberos keytab, configure keytab in the Spark configuration tab instead of in a tMapRDBConfiguration component.

You must drop tMapRDBConfiguration along with the MapRDB-related Subjob to be run in the same Job so that the configuration is used by the whole Job at runtime.

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 store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other 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 or tS3Configuration.

This connection is effective on a per-Job basis.