tMongoDBLookupInput properties for Apache Spark Streaming - 7.1

MongoDB

author
Talend Documentation Team
EnrichVersion
Cloud
7.1
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 > MongoDB components
Data Quality and Preparation > Third-party systems > Database components > MongoDB components
Design and Development > Third-party systems > Database components > MongoDB components
EnrichPlatform
Talend Studio

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

The Spark Streaming tMongoDBLookupInput 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

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 read preference

Select this check box and from the Read preference drop-down list that is displayed, select the member to which you need to direct the read operations.

If you leave this check box clear, the Job uses the default Read preference, that is to say, uses the primary member in a replica set.

For further information, see MongoDB's documentation about Replication and its Read preferences.

Query

Specify the query statement to select documents from the collection specified in the Collection field.

For example
"{'customer_id':" + row1.customer_id +"}"

In this code, row1 is not the label of the link to tMongoDBLookupInput, but represents the main row entering into tMap.

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.

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"

Limit

Enter the maximum number of records to be retrieved.

Advanced settings

No query timeout

Select this check box to prevent MongoDB servers from stopping idle cursors at the end of 10-minute inactivity of these cursors. In this situation, an idle cursor will stay open until either the results of this cursor are exhausted or you manually close it using the cursor.close() method.

A cursor for MongoDB is a pointer to the result set of a query. By default, that is to say, with this check box being clear, a MongoDB server automatically stops idle cursors after a given inactivity period to avoid excess memory use. For further information about MongoDB cursors, see https://docs.mongodb.org/manual/core/cursors/.

Usage

Usage rule

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