Creating Data Extract from MongoDB

The MongoDB connector is an interface used to import MongoDB’s schema-less BSON documents into a table schema that Panopticon Visualization Server can interpret and analyze. It uses many BSON structure types and MongoDB query features.

Steps:

1.     On the New Data Extract page, select MongoDB in the Connector drop-down list.

2.   Enter the following properties:

Property	Description
URL	Enter either: ·         localhost if the database resides on the same computer, or ·         enter the IP address and port of the computer where MongoDB is installed (e.g., 192.168.1.1:27017). If no port is specified, the default is 27017.
User Id	The user Id that will be used to connect to MongoDB.
Password	The password that will be used to connect to MongoDB.
Authentication DB	The database where the user is created.
Database	The database that will be used.
Collection	The collection that will be used.

 

3.   You can also opt to make the Query Document feature of MongoDB to be available in the Panopticon Visualization Server. Select Use Query Document in the Query Options drop-down list.

This also displays the Method drop-down. Select either Find (Default) or Aggregate.

When Aggregate is selected, you can add all the columns generated by aggregation in the schema.

In addition, the MongoDB command line interface displays query operations with a JSON style syntax.

Enter your desired JSON query document. Refer to http://docs.mongodb.org/manual/tutorial/query-documents/  for more information on the Query Documents feature on MongoDB.

For example:

Queries from the documentation look like this: db.inventory.find ( {type: “snacks”} ). The database and collection are already defined in the UI and the Find operation is handled in the code. The user only needs to enter the query document:

{ type : “snacks” }

This query must include surrounding curly braces as well as matching internal braces should the user decide to make a more advanced query.

4.   Instead of using Use Query Document, select the Parameterize query option.

Click  to populate the Parameter drop-down and select a value. Then select what column to filter on in the Filter By drop-down.

5.   Select either the dot (.) or comma (,) as the Decimal Separator.

6.   Click   to the fetch the schema based on the connection details. This populates the list of columns with the data type found from inspecting the first ‘n’ rows of the input data source.

7. You can also opt to load or save a copy of the column definition.

8.   Click . A new row displays in the JSON list box. Enter the necessary information for each column.

Property	Description
Name	The column name of the source schema. NOTE:  It is recommended to name the column the same as its JSON path for clarity and uniformity.
JsonPath	The JsonPath of the source schema.
Type	The data type of the column. Can be a Text, Numeric, or Time
Structure	Used for more advanced features and are covered in the Row-Wise Array Expansion, Column-Wise Array Expansion, and Bson-Wise Array Expansion  sections. Value is the default structure and will always display data regardless of actual structure.
Column Count	Enabled when Column-Expanded Array structure is selected. Enter the number of columns for the plugin to generate as columns for that array.
Date Format	The format when the data type is Time. NOTE: To parse and format times with higher than millisecond precision, the format string needs to end with a period followed by sequence of lower case Fs. There can be no additional characters following them. For example: yyyy-MM-dd HH:mm:ss.ffffff
Enabled	Determines whether the message field should be processed.

 

To delete a column, check its  or all the column entries, check the topmost , then click .

9.   Click  to save and display the details of the data extract.

10.   Click  then  to display the data preview.