MongoDB Query Component

MongoDB Query

This component connects to a MongoDB server to retrieve data and load it into a table. This stages the data, so the table is reloaded each time. You may then use transformations to enrich and manage the data in permanent tables.

The component offers both a Basic and Advanced mode (see below) for generating the MongoDB query.

Note:If you are connecting to a MongoDB instance with security enabled, it is required that you specify the Auth-Database parameter in the 'Connection Options' property. In this case, it is advised that users are familiar with the Auth Database Connection Option. If connecting to a replica set, it is advised that users are familiar with the Read Preference Connection Option.

All Connection Options are explained in the Data Model.

Warning: This component is potentially destructive. If the target table undergoes a change in structure, it will be recreated. Otherwise, the target table is truncated. Setting the Load Option 'Recreate Target Table' to 'Off' will prevent both recreation and truncation. Do not modify the target table structure manually.


Property Setting Description
Name Text The descriptive name for the component.
Basic/Advanced Mode Choice Basic: This mode will build a MongoDB Query for you using settings from Data Source, Data Selection and Data Source Filter parameters. In most cases, this will be sufficient.
Advanced: This mode will require you to write an SQL-like query which is translated into one or more MongoDB queries.
Port Text Use to select an alternative port number. The default of 27017 is used if this is blank.
Username Text A valid MongoDB username to use for authentication. (This is not always required.)
Password Text A valid password for the Username above. (This is not always required.) Users have the option to store their password inside the component but we highly recommend using the Password Manager option.
Server Text The server IP or DNS address of the MongoDB server endpoint.
Database Text The Database name on the MongoDB server.
Flatten Objects Choice Yes - Nested document structures are flattened into a set of fields. Determining which fields are available can become expensive in this mode, since more data needs to be scanned in order to determine which fields are available.
No - Nested document structures are returns as JSON strings. They can be further queried/manipulated by JSON functions in a transformation job after being staged.
Flatten Arrays Integer

The maximum number of elements that any array can be flattened to. Flattened arrays have each element placed into its own respective (newly created) column.
Entering 0 for this property will ensure all arrays remain in JSON string format.
Entering -1 for this property will ensure all elements from arrays are flattened.
Requesting to flatten more elements than exist in an array will result in all elements of that array being flattened.

Determining which fields are available can become expensive in this mode, since more data needs to be scanned in order to determine which fields are available.

Data Source Choice The name of a MongoDB collection. Collections are analogous to Tables in other databases.
Data Selection Choice Select one or more columns to return from the query. Columns are determined by scanning the first few documents and looking for fields that appear in each document. Nested fields are searched if Flatten Objects is set to Yes, otherwise nested documents become a single larger string.
See the example to understand this.
Data Source Filter Input Column The available input columns vary depending upon the Data Source and are determined automatically by scanning a number of documents.
Qualifier Is: Compares the column to the value using the comparator.
Not: Reverses the effect of the comparison, so "equals" becomes "not equals", "less than" becomes "greater than or equal to", etc.
Comparator Choose a method of comparing the column to the value. Possible comparators include: 'Equal To', 'Greater than', 'Less than', 'Greater than or equal to', 'Less than or equal to', 'Like', 'Null'.
'Equal To' can match exact strings and numeric values while other comparators such as 'Greater than' will work only with numerics. The 'Like' operator allows the wildcard character (%) to be used at the start and end of a string value to match a column. The Null operator matches only Null values, ignoring whatever the value is set to.
Not all data sources support all comparators, thus it is likely only a subset of the above comparators will be available to choose from.
Value The value to be compared.
SQL Query Text This is an SQL-like SELECT query. Treat collections as tables names, and fields as columns. (Property only available in 'Advanced' Mode)
Limit Number Provides an upper limit on the number of rows retrieved from the MongoDB server. Blank means fetch all records.
Connection Options Parameter A JDBC parameter supported by the Database Driver. The available parameters are explained in the Data Model.
They are usually not required as sensible defaults are assumed.
Value A value for the given Parameter.
Storage Account Select (Azure Only) Select a Storage Account with your desired Blob Container to be used for staging the data.
Blob Container Select (Azure Only) Select a Blob Container to be used for staging the data.
Staging Select (AWS Only) Snowflake Managed: Allow Matillion ETL to create and use a temporary internal stage on Snowflake for staging the data. This stage, along with the staged data, will cease to exist after loading is complete.
Existing Amazon S3 Location: Selecting this will avail the user of properties to specify a custom staging area on S3.
S3 Staging Area Text (AWS Only) The name of an S3 bucket for temporary storage. Ensure your access credentials have S3 access and permission to write to the bucket. See this document for details on setting up access. The temporary objects created in this bucket will be removed again after the load completes, they are not kept.
This property is available when using an Existing Amazon S3 Location for Staging.
Warehouse Select Choose a Snowflake warehouse that will run the load.
Database Select Choose a database to create the new table in.
Type Select Choose between using a standard table or an external table.
Standard: The data will be staged on an S3 bucket before being loaded into a table.
External: The data will be put into an S3 Bucket and referenced by an external table.
Schema Select Select the table schema. The special value, [Environment Default] will use the schema defined in the environment. For more information on using multiple schemas, see this article.
Note: An external schema is required if the 'Type' property is set to 'External'.
Target Table Text Provide a new table name.
Warning: This table will be recreated and will drop any existing table of the same name.
Location Text/Select When using an 'External' type table, Provide an S3 Bucket path that will be used to store the data. Once on an S3 bucket, the data can be referenced by the external table.
Distribution Style Select Auto: (Default) Allow Redshift to manage your distribution style.
Even: Distributes rows around the Redshift cluster evenly.
All: Copy rows to all nodes in the Redshift cluster.
Key: Distribute rows around the Redshift cluster according to the value of a key column.
Table distribution is critical to good performance. See the Amazon Redshift documentation for more information.
Table Distribution Key Select This is only displayed if the Table Distribution Style is set to Key. It is the column used to determine which cluster node the row is stored on.
Table Sort Key Select This is optional, and specifies the columns from the input that should be set as the table's sort-key.
Sort-keys are critical to good performance - see the Amazon Redshift documentation for more information.
Sort Key Options Select Decide whether the sort key is of a compound or interleaved variety - see the Amazon Redshift documentation for more information.
Project Text The target BigQuery project to load data into.
Dataset Text The target BigQuery dataset to load data into.
Cloud Storage Staging Area Text The URL and path of the target Google Storage bucket to be used for staging the queried data.
Encryption Select (AWS Only) Decide on how the files are encrypted inside the S3 Bucket.This property is available when using an Existing Amazon S3 Location for Staging.
None: No encryption.
SSE KMS: Encrypt the data according to a key stored on KMS.
SSE S3: Encrypt the data according to a key stored on an S3 bucket
KMS Key ID Select (AWS Only) The ID of the KMS encryption key you have chosen to use in the 'Encryption' property.
Load Options Multiple Selection Comp Update: Apply automatic compression to the target table (if ON). Default is ON.
Stat Update: Automatically update statistics when filling a table (if ON). Default is ON. In this case, it is updating the statistics of the target table.
Clean S3 Objects: Automatically remove UUID-based objects on the S3 Bucket (if ON). Default is ON. Effectively decides whether to keep the staged data in the S3 Bucket or not.
String Null is Null: Converts any strings equal to "null" into a null value. This is case sensitive and only works with entirely lower-case strings. Default is ON.
Recreate Target Table:Choose whether the component recreates its target table before the data load. If OFF, the existing table will be used. Default is ON.
Load Options Multiple Select Clean Cloud Storage Files: (If On) Destroy staged files on Cloud Storage after loading data. Default is On.
Cloud Storage File Prefix: Give staged file names a prefix of your choice. Default is empty (no prefix).
Auto Debug Select Choose whether to automatically log debug information about your load. These logs can be found in the Task History and should be included in support requests concerning the component. Turning this on will override any debugging Connection Options.
Debug Level Select The level of verbosity with which your debug information is logged. Levels above 1 can log huge amounts of data and result in slower execution.
1: Will log the query, the number of rows returned by it, the start of execution and the time taken, and any errors.
2: Will log everything included in Level 1, cache queries, and additional information about the request, if applicable.
3: Will additionally log the body of the request and the response.
4: Will additionally log transport-level communication with the data source. This includes SSL negotiation.
5: Will additionally log communication with the data source and additional details that may be helpful in troubleshooting problems. This includes interface commands.

Variable Exports

This component makes the following values available to export into variables:

Source Description
Time Taken To Stage The amount of time (in seconds) taken to fetch the data from the data source and upload it to storage.
Time Taken To Load The amount of time (in seconds) taken for the COPY statement to load the data into the target table from the staging area.


Connect to the MongoDB Server and issue the one or more queries. Stream the results into objects into a storage area, recreate or truncate the target table as necessary and then use a COPY command to load the stored objects into the table. Finally, clean up the temporary stored objects.

Connecting to Mongo Atlas

It is possible to use the MongoDB component to connect to Mongo Atlas with minor alteration of the Connection Options.

  1. Whitelist the Matillion ETL instance IP in the Atlas Cluster Security console.
  2. Using the Atlas Cluster Security console, grant user read privileges on the collection to be queried by Matillion.
  3. Set the Server property on the Mongo Query component to the primary server's hostname.
  4. Set Connection Option UseSSL to true.
  5. Set Connection Option AuthDatabase to admin.
  6. In text mode, add a ReplicaSet Connection Option containing a comma-separated list of all the hostnames in the replica set including their port number. For example:,,


This example stages the Airports data from MongoDB to a table. Each document has a structure like this exmaple record:

Each document has a structure like this example record:

        "_id" : ObjectId("5707c81dd47427717191c17e"),
        "city" : "Bay Springs",
        "country" : "USA",
        "iata" : "00M",
        "state" : "MS",
        "airport" : "Thigpen",
        "location" : {
                "lat" : 31.95376472,
                "long" : -89.23450472

Stepping through the component setup is straightforward. The collection is "Airports" within a database called "Flights"

Notice that with Flatten Objects set to No, we have a single Location column:

Each values inside the Location column in the table will look similar to this:

{"lat" : 31.95376472, "long" : -89.23450472}

We may also choose to flatten the nested fields into the table with Flatten Objects set to Yes:

This creates fields "location_lat" and "location_long", instead of Location. Furthermore, instead of a JSON String, location_lat and location_long will be numeric.