tAvroInput - 6.3

Talend Components Reference Guide

EnrichVersion
6.3
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Talend Big Data
Talend Big Data Platform
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Talend Data Integration
Talend Data Management Platform
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task
Data Governance
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Talend Studio

Function

tAvroInput parses Avro format files in a given distributed file system and loads data to a data flow to pass the data to the transformation component that follows.

Purpose

tAvroInput extracts records from any given Avro format files for other components to process the records.

Depending on the Talend solution you are using, this component can be used in one, some or all of the following Job frameworks:

tAvroInput in Map/Reduce component

Warning

The information in this section is only for users that have subscribed to one of the Talend solutions with Big Data and is not applicable to Talend Open Studio for Big Data users.

In a Talend Map/Reduce Job, tAvroInput, as well as the other Map/Reduce components preceding it, generates native Map/Reduce code. This section presents the specific properties of tAvroInput when it is used in that situation. For further information about a Talend Map/Reduce Job, see Talend Big Data Getting Started Guide.

Component family

MapReduce

 

Basic settings

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 properties are stored centrally under the Hadoop Cluster node of the Repository tree.

The fields that come after are pre-filled in using the fetched data.

For further information about the Hadoop Cluster node, see the Getting Started Guide.

 

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. The schema is either Built-In or stored remotely in the Repository.

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. Related topic: see Talend Studio User Guide.

  

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

 

Folder/File

Browse to, or enter the path pointing to the data to be used in the file system.

If the path you set points to a folder, this component will read all of the files stored in that folder, for example, /user/talend/in; if sub-folders exist, the sub-folders are automatically ignored unless you define the property mapreduce.input.fileinputformat.input.dir.recursive to be true in the Hadoop properties table in the Hadoop configuration tab.

If you want to specify more than one files or directories in this field, separate each path using a comma (,).

Note that you need to ensure you have properly configured the connection to the Hadoop distribution to be used in the Hadoop configuration tab in the Run view.

 

Die on error

Select this check box to stop the execution of the Job when an error occurs.

Clear the check box to skip any rows on error and complete the process for error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

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 check box is cleared, if the component has this check box.

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 further information about variables, see Talend Studio User Guide.

Usage in Map/Reduce Jobs

In a Talend Map/Reduce Job, it is used as a start component and requires a transformation component as output link. The other components used along with it must be Map/Reduce components, too. They generate native Map/Reduce code that can be executed directly in Hadoop.

Once a Map/Reduce Job is opened in the workspace, tAvroInput as well as the MapReduce family appears in the Palette of the Studio.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs, and non Map/Reduce Jobs.

Hadoop Connection

You need to use the Hadoop Configuration tab in the Run view to define the connection to a given Hadoop distribution for the whole Job.

This connection is effective on a per-Job basis.

Scenario: Filtering Avro format employee data

This scenario illustrates how to create a Talend Map/Reduce Job to read, transform and write Avro format data by using Map/Reduce components. This Job generates Map/Reduce code and directly runs in Hadoop. In addition, the Map bar in the workspace indicates that only a mapper will be used in this Job and at runtime, it shows the progress of the Map computation.

Note that the Talend Map/Reduce components are available to subscription-based Big Data users only and this scenario can be replicated only with Map/Reduce components.

The sample data to be used in this scenario is employee information of a company with records virtually reading as follows but actually only visible as Avro format files:

1;Lyndon;Fillmore;21-05-2008
2;Ronald;McKinley;15-08-2008
3;Ulysses;Roosevelt;05-10-2008
4;Harry;Harrison;23-11-2007
5;Lyndon;Garfield;19-07-2007
6;James;Quincy;15-07-2008
7;Chester;Jackson;26-02-2008
8;Dwight;McKinley;16-07-2008
9;Jimmy;Johnson;23-12-2007
10;Herbert;Fillmore;03-04-2008
				 

Before starting to replicate this scenario, ensure that you have appropriate rights and permissions to access the Hadoop distribution to be used. Then proceed as follows:

Linking the components

  1. In the Integration perspective of the Studio, create an empty Map/Reduce Job from the Job Designs node in the Repository tree view.

    For further information about how to create a Map/Reduce Job, see Talend Big Data Getting Started Guide.

  2. Drop tAvroInput, tMap, tHDFSOutput and tAvroOutput onto the workspace.

  3. Connect tAvroInput to tMap using the Row > Main link.

  4. Do the same to connect tMap to tHDFSOutput and tAvroOutput, respectively. In doing it, you are prompted to name each link. In this example, name the link to tHDFSOutput to out1 and the link to tAvroOutput to reject.

Setting up Hadoop connection

  1. Click Run to open its view and then click the Hadoop Configuration tab to display its view for configuring the Hadoop connection for this Job.

  2. From the Property type list, select Built-in. If you have created the connection to be used in Repository, then select Repository and thus the Studio will reuse that set of connection information for this Job.

    For further information about how to create an Hadoop connection in Repository, see the chapter describing the Hadoop cluster node of Talend Studio User Guide.

  3. In the Version area, select the Hadoop distribution to be used and its version. If you cannot find from the list the distribution corresponding to yours, select Custom so as to connect to a Hadoop distribution not officially supported in the Studio.

    For a step-by-step example about how to use this Custom option, see Connecting to a custom Hadoop distribution.

    Along with the evolution of Hadoop, please note the following changes:

    • If you use Hortonworks Data Platform V2.2, the configuration files of your cluster might be using environment variables such as ${hdp.version}. If this is your situation, you need to set the mapreduce.application.framework.path property in the Hadoop properties table with the path value explicitly pointing to the MapReduce framework archive of your cluster. For example:

      mapreduce.application.framework.path=/hdp/apps/2.2.0.0-2041/mapreduce/mapreduce.tar.gz#mr-framework
    • If you use Hortonworks Data Platform V2.0.0, the type of the operating system for running the distribution and a Talend Job must be the same, such as Windows or Linux. Otherwise, you have to use Talend Jobserver to execute the Job in the same type of operating system in which the Hortonworks Data Platform V2.0.0 distribution you are using is run. For further information about Talend Jobserver, see the Talend Installation Guide.

  4. In the Name node field, enter the location of the master node, the NameNode, of the distribution to be used. For example, hdfs://tal-qa113.talend.lan:8020.

    If you are using a MapR distribution, you can simply leave maprfs:/// as it is in this field; then the MapR client will take care of the rest on the fly for creating the connection. The MapR client must be properly installed. For further information about how to set up a MapR client, see the following link in MapR's documentation: http://doc.mapr.com/display/MapR/Setting+Up+the+Client

  5. In the Job tracker field, enter the location of the JobTracker of your distribution. For example, tal-qa114.talend.lan:8050.

    Note that the notion Job in this term JobTracker designates the MR or the MapReduce jobs described in Apache's documentation on http://hadoop.apache.org/.

    If you use YARN in your Hadoop cluster such as Hortonworks Data Platform V2.0.0 or Cloudera CDH4.3 + (YARN mode), you need to specify the location of the Resource Manager instead of the Jobtracker. Then you can continue to set the following parameters depending on the configuration of the Hadoop cluster to be used (if you leave the check box of a parameter clear, then at runtime, the configuration about this parameter in the Hadoop cluster to be used will be ignored ):

    • Select the Set resourcemanager scheduler address check box and enter the Scheduler address in the field that appears.

    • Select the Set jobhistory address check box and enter the location of the JobHistory server of the Hadoop cluster to be used. This allows the metrics information of the current Job to be stored in that JobHistory server.

    • Select the Set staging directory check box and enter this directory defined in your Hadoop cluster for temporary files created by running programs. Typically, this directory can be found under the yarn.app.mapreduce.am.staging-dir property in the configuration files such as yarn-site.xml or mapred-site.xml of your distribution.

    • Select the Use datanode hostname check box to allow the Job to access datanodes via their hostnames. This actually sets the dfs.client.use.datanode.hostname property to true. When connecting to a S3N filesystem, you must select this check box.

  6. If you are accessing the Hadoop cluster running with Kerberos security, select this check box, then, enter the Kerberos principal name for the NameNode in the field displayed. This enables you to use your user name to authenticate against the credentials stored in Kerberos.

    • If this cluster is a MapR cluster of the version 4.0.1 or later, you can set the MapR ticket authentication configuration in addition or as an alternative by following the explanation in Connecting to a security-enabled MapR.

      Keep in mind that this configuration generates a new MapR security ticket for the username defined in the Job in each execution. If you need to reuse an existing ticket issued for the same username, leave both the Force MapR ticket authentication check box and the Use Kerberos authentication check box clear, and then MapR should be able to automatically find that ticket on the fly.

    In addition, since this component performs Map/Reduce computations, you also need to authenticate the related services such as the Job history server and the Resource manager or Jobtracker depending on your distribution in the corresponding field. These principals can be found in the configuration files of your distribution. For example, in a CDH4 distribution, the Resource manager principal is set in the yarn-site.xml file and the Job history principal in the mapred-site.xml file.

    If you need to use a Kerberos keytab file to log in, select Use a keytab to authenticate. A keytab file contains pairs of Kerberos principals and encrypted keys. You need to enter the principal to be used in the Principal field and the access path to the keytab file itself in the Keytab field.

    Note that the user that executes a keytab-enabled Job is not necessarily the one a principal designates but must have the right to read the keytab file being used. For example, the user name you are using to execute a Job is user1 and the principal to be used is guest; in this situation, ensure that user1 has the right to read the keytab file to be used.

  7. In the User name field, enter the login user name for your distribution. If you leave it empty, the user name of the machine hosting the Studio will be used.

  8. In the Temp folder field, enter the path in HDFS to the folder where you store the temporary files generated during Map/Reduce computations.

  9. Leave the default value of the Path separator in server as it is, unless you have changed the separator used by your Hadoop distribution's host machine for its PATH variable or in other words, that separator is not a colon (:). In that situation, you must change this value to the one you are using in that host.

  10. Leave the Clear temporary folder check box selected, unless you want to keep those temporary files.

  11. Leave the Compress intermediate map output to reduce network traffic check box selected, so as to spend shorter time to transfer the mapper task partitions to the multiple reducers.

    However, if the data transfer in the Job is negligible, it is recommended to clear this check box to deactivate the compression step, because this compression consumes extra CPU resources.

  12. If you need to use custom Hadoop properties, complete the Hadoop properties table with the property or properties to be customized. Then at runtime, these changes will override the corresponding default properties used by the Studio for its Hadoop engine.

    For further information about the properties required by Hadoop, see Apache's Hadoop documentation on http://hadoop.apache.org, or the documentation of the Hadoop distribution you need to use.

  13. If the HDFS transparent encryption has been enabled in your cluster, select the Setup HDFS encryption configurations check box and in the HDFS encryption key provider field that is displayed, enter the location of the KMS proxy.

    For further information about the HDFS transparent encryption and its KMS proxy, see Transparent Encryption in HDFS.

  14. If the Hadoop distribution to be used is Hortonworks Data Platform V1.2 or Hortonworks Data Platform V1.3, you need to set proper memory allocations for the map and reduce computations to be performed by the Hadoop system.

    In that situation, you need to enter the values you need in the Mapred job map memory mb and the Mapred job reduce memory mb fields, respectively. By default, the values are both 1000 which are normally appropriate for running the computations.

    If the distribution is YARN, then the memory parameters to be set become Map (in Mb), Reduce (in Mb) and ApplicationMaster (in Mb), accordingly. These fields allow you to dynamically allocate memory to the map and the reduce computations and the ApplicationMaster of YARN.

  15. If you are using Cloudera V5.5+, you can select the Use Cloudera Navigator check box to enable the Cloudera Navigator of your distribution to trace your Job lineage to the component level, including the schema changes between components.

    With this option activated, you need to set the following parameters:

    • Username and Password: this is the credentials you use to connect to your Cloudera Navigator.

    • Cloudera Navigator URL : enter the location of the Cloudera Navigator to be connected to.

    • Cloudera Navigator Metadata URL: enter the location of the Navigator Metadata.

    • Activate the autocommit option: select this check box to make Cloudera Navigator generate the lineage of the current Job at the end of the execution of this Job.

      Since this option actually forces Cloudera Navigator to generate lineages of all its available entities such as HDFS files and directories, Hive queries or Pig scripts, it is not recommended for the production environment because it will slow the Job.

    • Kill the job if Cloudera Navigator fails: select this check box to stop the execution of the Job when the connection to your Cloudera Navigator fails.

      Otherwise, leave it clear to allow your Job to continue to run.

    • Disable SSL validation: select this check box to make your Job to connect to Cloudera Navigator without the SSL validation process.

      This feature is meant to facilitate the test of your Job but is not recommended to be used in a production cluster.

  16. If you are using Hortonworks Data Platform V2.4.0 onwards and you have installed Atlas in your cluster, you can select the Use Atlas check box to enable Job lineage to the component level, including the schema changes between components.

    With this option activated, you need to set the following parameters:

    • Atlas URL : enter the location of the Atlas to be connected to. It is often http://name_of_your_atlas_node:port

    • Die on error: select this check box to stop the Job execution when Atlas-related issues occur, such as connection issues to Atlas.

      Otherwise, leave it clear to allow your Job to continue to run.

    In the Username field and the Password field, enter the authentication information for access to Atlas.

For further information about the Resource Manager, its scheduler and the ApplicationMaster, see YARN's documentation such as http://hortonworks.com/blog/apache-hadoop-yarn-concepts-and-applications/.

For further information about how to determine YARN and MapReduce memory configuration settings, see the documentation of the distribution you are using, such as the following link provided by Hortonworks: http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html.

Reading Avro data

Configuring tAvroInput

  1. Double-click tAvroInput to open its Component view.

  2. Click the button next to Edit schemato open the schema editor.

  3. Click the button four times to add four rows and in the Column column, rename them to Id, FirstName, LastName and Reg_date, respectively.

  4. In the Type column, select Integer for Id and Date for Reg_date. The date pattern used in this scenario is dd-MM-yyyy.

  5. Click OK to validate these changes and accept the propagation prompted by the pop-up dialog box.

  6. In the Folder/File field, enter the path, or browse to the source file you need the Job to read.

Transforming the data

Configuring tMap

  1. Double-click tMap to open the Map Editor.

  2. Drop the four columns of the input schema from the input side (left) into each of the output flows of the output side (right), that is to say, out1 and reject. This way the input flow and the output flows are mapped.

  3. In the table representing the out1 flow, click to display the filter editing area and enter the following expression to select the employee records that were registered before January 1st, 2008 (01-01-2008).

    row1.Reg_date.before( new Date(108,0,1))
  4. In the table representing the reject flow, click to display the property settings panel.

  5. In the Value field of the Catch output reject row, click the and select true in the pop-up dialog box. This allows you to output the records rejected by the out1 flow.

  6. Click OK to validate these changes and accept the propagation prompted by the pop-up dialog box.

Writing data in HDFS

Configuring the selected employee data

  1. Double-click tHDFSOutput to open its Component view.

  2. In the Folder field, enter the path, or browse to the folder you want to write the employee records registered before January 1st, 2008.

  3. From the Type list, select the data format for the records to be written. In this example, select Text file.

  4. From the Action list, select the operation you need to perform on the file in question. If the file already exists, select Overwrite; otherwise, select Create.

  5. Select the Merge result to single file check box and enter the path, or browse to the file you need to write the merged output data in.

  6. If the file for the merged data exists, select the Override target file check box to overwrite that file.

Configuring the rejected employee data

  1. Double-click tAvroOutput to open its Component view.

  2. In the Folder field, enter the path, or browse to the folder you want to write the employee records registered after January 1st, 2008.

  3. From the Action list, select the operation you need to perform on the folder in question. If the folder already exists, select Overwrite; otherwise, select Create.

Executing the Job

Then you can press F6 to run this Job and the Map bar in the workspace shows the progress of the Map computation.

Once done, you can check the results in the web console of the Hadoop distribution being used.

The records in the out1 flow is outputted and merged into one text file.

The records in the reject flow is outputted as Avro format files.

If you need to obtain more execution information about this Job, you can check the web console of the Jobtracker of the Hadoop distribution being used.

tAvroInput properties in Spark Batch Jobs

Component family

File / Input

 

Basic settings

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.

Note that 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 properties are stored centrally under the Hadoop Cluster node of the Repository tree.

The fields that come after are pre-filled in using the fetched data.

For further information about the Hadoop Cluster node, see the Getting Started Guide.

 

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. The schema is either Built-In or stored remotely in the Repository.

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. Related topic: see Talend Studio User Guide.

  

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

 

Folder/File

Browse to, or enter the path pointing to the data to be used in the file system.

If the path you set points to a folder, this component will read all of the files stored in that folder, for example, /user/talend/in; if sub-folders exist, the sub-folders are automatically ignored unless you define the property spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive to be true in the Advanced properties table in the Spark configuration tab.

If you want to specify more than one files or directories in this field, separate each path using a comma (,).

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

 

Die on error

Select this check box to stop the execution of the Job when an error occurs.

Clear the check box to skip any rows on error and complete the process for error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

Advanced settings

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.

 

Use hierarchical mode

Select this check box to handle the hierarchical Avro schema. If the Avro message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameters:

  • Local path to the avro schema: browse to the file which defines the schema of the Avro data to be processed.

  • Mapping: create the map between the schema columns of the current component and the data stored in the hierarchical Avro message to be handled. In the Node column, you need to enter the JSON path pointing to the data to be read from the Avro message.

Usage in Spark Batch Jobs

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

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch 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

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. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.

Log4j

If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.

Related scenarios

No scenario is available for the Spark Batch version of this component yet.

tAvroInput properties in Spark Streaming Jobs

Component family

File / Input

 

Basic settings

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.

Note that 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 properties are stored centrally under the Hadoop Cluster node of the Repository tree.

The fields that come after are pre-filled in using the fetched data.

For further information about the Hadoop Cluster node, see the Getting Started Guide.

 

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. The schema is either Built-In or stored remotely in the Repository.

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. Related topic: see Talend Studio User Guide.

  

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

 

Folder/File

Browse to, or enter the path pointing to the data to be used in the file system.

If the path you set points to a folder, this component will read all of the files stored in that folder, for example, /user/talend/in; if sub-folders exist, the sub-folders are automatically ignored unless you define the property spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive to be true in the Advanced properties table in the Spark configuration tab.

If you want to specify more than one files or directories in this field, separate each path using a comma (,).

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

 

Die on error

Select this check box to stop the execution of the Job when an error occurs.

Clear the check box to skip any rows on error and complete the process for error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

Advanced settings

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.

 

Use hierarchical mode

Select this check box to handle the hierarchical Avro schema. If the Avro message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameters:

  • Local path to the avro schema: browse to the file which defines the schema of the Avro data to be processed.

  • Mapping: create the map between the schema columns of the current component and the data stored in the hierarchical Avro message to be handled. In the Node column, you need to enter the JSON path pointing to the data to be read from the Avro message.

Usage in Spark Streaming Jobs

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

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. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.

Log4j

If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.

Related scenarios

No scenario is available for the Spark Streaming version of this component yet.