tMongoDBInput properties for Apache Spark Batch

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 drop-down list, select the desired 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.

Number of partitions = Collection size in MB / mongo.input.split_size

mongo.input.split_size   1

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, type in "{'id':'4'}" to retrieve the record whose id is 4 from the collection.

The default query, {} within double quotation marks provided with this component, means to select all of the files. You can also apply a regular expression by putting {'filename':{'$regex':'REGEX_PATTERN'}} to define the file names to be used.

Different from the query statements required in the MongoDB client software, the query here refers to the contents inside find(), such as the query {'filename':{'$regex':'REGEX_PATTERN'}} here is the equivalent of db.blog.find({filename:{$regex:REGEX_PATTERN}}) in the MongoDB client query.

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.

{
               _id: ObjectId("5099803df3f4948bd2f98391"),
               person: { first: "Joe", last: "Walker" }
            }

Column     Parent node path
_id
first       "person"
last        "person"

Limit

Enter the maximum number of records to be retrieved.

Advanced Hadoop MongoDB properties

Add properties to define extra operations you need tMongoDBInput to perform when reading data.

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

Number of partitions = Collection size in MB / mongo.input.split_size

mongo.input.split_size   1

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 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

This connection is effective on a per-Job basis.