tMongoDBOutput properties for Apache Spark Batch - 7.1

MongoDB

author
Talend Documentation Team
EnrichVersion
Cloud
7.1
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Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Open Studio for Big Data
Talend Real-Time Big Data Platform
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Data Governance > Third-party systems > Database components > MongoDB components
Data Quality and Preparation > Third-party systems > Database components > MongoDB components
Design and Development > Third-party systems > Database components > MongoDB components
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Talend Studio

These properties are used to configure tMongoDBOutput running in the Spark Batch Job framework.

The Spark Batch tMongoDBOutput component belongs to the Databases family.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

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.

MongoDB configuration

Select this check box and in the Component List click the relevant connection component to reuse the connection details you already defined.

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.

If a column in the database is a JSON document and you need to read the entire document, put an asterisk (*) in the DB column column, without quotation marks around.

Collection

Enter the name of the collection to be used.

A MongoDB collection is the equivalent of an RDBMS table and contains documents.

Set write concern

Select this check box to set the level of acknowledgement requested from for write operations. Then you need to select the level of this operation.

For further information, see the related MongoDB documentation on http://docs.mongodb.org/manual/core/write-concern/.

Action on data

The following operations are available:

  • Insert: insert documents.

  • Set: modifies the existing fields of an existing document and appends a field if it does not exist in this document.

    If you need to apply this action on all the documents in the collection to be used, select the Update all document check box that is displayed; otherwise, only the first document is updated.

    If you need to append a new field to a parent node you specify in the Mapping table, select Append to parent. If you leave this check box clear, this new field is appended to the root of the document being updated.

  • Upsert with set: inserts a document if it does not exist otherwise it applies the same rules as Set

    If you need to apply this action on all the documents in the collection to be used, select the Update all document check box that is displayed; otherwise, only the first document is updated.

    If you need to append a new field to a parent node you specify in the Mapping table, select Append to parent. If you leave this check box clear, this new field is appended to the root of the document being updated.

Mapping

Each column of the schema defined for this component represents a field of the documents to be read. In this table, you need to specify the parent nodes of these fields, if any.

For example, in the document reading as follows
{
               _id: ObjectId("5099803df3f4948bd2f98391"),
               person: { first: "Joe", last: "Walker" }
            }
The first and the last fields have person as their parent node but the _id field does not have any parent node. So once completed, this Mapping table should read as follows:
Column     Parent node path
_id
first       "person"
last        "person"

Advanced settings

Advanced Hadoop MongoDB properties

Add properties to define extra operations you need tMongoDBOutput to perform when writing data.

The available properties are listed and explained in MongoDB Connector for Hadoop.

Usage

Usage rule

This component is used as an end component and requires an input link.

This component should use a tMongoDBConfiguration component present in the same Job to connect to a MongoDB database. You need to drop a tMongoDBConfiguration component alongside this component and configure the Basic settings of this component to use tMongoDBConfiguration.

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

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.