tAvroStreamInput

Listens on a given directory, reads data from Avro files once they are created
and sends this data to the component that follows.

tAvroStreamInput properties for Apache Spark Streaming

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

The Spark Streaming
tAvroStreamInput component belongs to the File family.

The streaming version of this component is available in Talend Real Time Big Data Platform and in
Talend Data Fabric.

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. 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. 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.
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. Depending on the filesystem to be used, properly configure the corresponding configuration component placed in your Job, for example, a tHDFSConfiguration component for HDFS, a tS3Configuration component for S3 and a tAzureFSConfiguration for Azure Storage and Azure Data Lake Storage. 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 other Spark Yarn modes that the Studio supports with your distribution, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration . Use the configuration component depending on the filesystem to be used.
Die on error	Select the 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.

Usage

Usage rule	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	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 Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster. When using on-premise 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. 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.

Usage rule

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

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 Qubole, add a
  tS3Configuration to your Job to write
  your actual business data in the S3 system with Qubole. Without
  tS3Configuration, this business data is
  written in the Qubole HDFS system and destroyed once you shut
  down your cluster.
- When using on-premise
  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.

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.

Related scenarios

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

Document get from Talend https://help.talend.com

Thank you for watching.

Docs 7.x

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tAvroStreamInput – Docs for ESB 7.x

tAvroStreamInput