> ## Documentation Index
> Fetch the complete documentation index at: https://docs.maia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# MongoDB Load

export const designer = "Designer";

export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => {
  const allWarehouses = [...warehouses.map(w => ({
    name: w,
    supported: true
  })), ...unsupportedWarehouses.map(w => ({
    name: w,
    supported: false
  }))];
  return <div style={{
    background: 'var(--colors-background-light, #f9fafb)',
    border: '1px solid var(--colors-border-default, #e5e7eb)',
    borderRadius: '12px',
    padding: '20px 28px',
    marginBottom: '28px',
    boxShadow: '0 1px 4px rgba(0,0,0,0.10)'
  }}>
      <table style={{
    width: '100%',
    borderCollapse: 'collapse'
  }}>
        <tbody>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle',
    width: '180px'
  }}>Project Availability</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>
              <div style={{
    display: 'flex',
    flexWrap: 'wrap',
    gap: '8px'
  }}>
                {allWarehouses.map((w, i) => <span key={i} style={{
    background: w.supported ? '#dcfce7' : '#fee2e2',
    color: w.supported ? '#15803d' : '#b91c1c',
    border: `1px solid ${w.supported ? '#bbf7d0' : '#fca5a5'}`,
    borderRadius: '9999px',
    padding: '3px 12px',
    fontSize: '0.85rem',
    fontWeight: '500',
    whiteSpace: 'nowrap'
  }}>
                    {w.name} {w.supported ? '✅' : '❌'}
                  </span>)}
              </div>
            </td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Component Type</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{componentType}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Inputs</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{connectionInputs}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Outputs</td>
            <td style={{
    verticalAlign: 'middle'
  }}>{connectionOutputs}</td>
          </tr>
        </tbody>
      </table>
    </div>;
};

{/* Intro snippets */}

{/* Configure snippets */}

{/* Advanced settings snippets */}

<ComponentMetadata warehouses={["Snowflake", "BigQuery"]} unsupportedWarehouses={["Amazon Redshift", "Databricks"]} componentType="Orchestration" connectionInputs="One" connectionOutputs="Unlimited" />

<Note>
  For Snowflake projects, the MongoDB Load connector supersedes the [MongoDB Query](/docs/components/mongodb-query) connector, which is no longer available for new pipelines. Existing pipelines that use the MongoDB Query connector will continue to work as expected.

  Databricks and Amazon Redshift projects should continue to use the [MongoDB Query](/docs/components/mongodb-query) connector.
</Note>

The MongoDB Load orchestration component uses the Connect and Configure parameters to extract data from a MongoDB database and load that data into a table, which is then stored in your preferred storage location—this can be your connected cloud data warehouse (Snowflake or Google BigQuery) or a cloud storage location (Amazon S3, Azure Blob Storage, or Google Cloud Storage).

Executing this component performs one of the following actions in your specified destination: creates a new table or replaces an existing table.

When connecting to a MongoDB instance with security enabled, you must specify the `AuthDatabase` parameter in the Connection Options. When connecting to a replica set, familiarize yourself with the `ReadPreference` connection option.

* [AuthDatabase](https://cdn.cdata.com/help/DGM/jdbc/RSBMongodb_p_AuthDatabase.htm)
* [ReadPreference](https://cdn.cdata.com/help/DGM/jdbc/RSBMongodb_p_ReadPreference.htm)

If the component requires access to a cloud provider (AWS, Azure, or Google Cloud), it will use the [cloud credentials](/docs/guides/cloud-credentials) associated with your environment to access resources.

To stage data to Azure Blob Storage, the Azure credentials associated with your environment must be assigned the `Storage Blob Data Contributor` role. For more information, read [User assigned with the Storage Blob Data Contributor role](https://learn.microsoft.com/en-us/answers/questions/1155139/user-assigned-with-storage-blob-data-contributor-r).

<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.
</Warning>

***

## Properties

Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.

<ResponseField name="Name" type="string" required>
  A human-readable name for the component.
</ResponseField>

### Connect

<ResponseField name="Authentication Type" type="drop-down" required>
  Currently supports **Username & Password**.
</ResponseField>

{/* <!-- param-start:[username] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Username" type="string">
  A valid MongoDB username.
</ResponseField>

{/* <!-- param-start:[password] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Password" type="drop-down">
  Select the secret that represents your MongoDB password.

  If you have not already saved your credentials for this connector as a secret definition, click **Manage** → **Add secret definition** to create a secret definition representing these credentials. Read [Secrets and secret definitions](/docs/guides/secrets-and-secret-definitions) for details about creating secret definitions.
</ResponseField>

{/* <!-- param-start:[serverHost] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Server Host" type="string" required>
  The server IP or DNS address of the MongoDB server endpoint. For example, `{cluster-name}.cluster-xxxxxxxxxxxx.{region}.docdb.amazonaws.com`.
</ResponseField>

{/* <!-- param-start:[serverPort] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Server Port" type="integer">
  Specify a MongoDB connection port number. The default, `27017`, is used if this is left blank.
</ResponseField>

{/* <!-- param-start:[databaseName] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Database Name" type="string" required>
  The name of the MongoDB server's database that you're connecting to.
</ResponseField>

{/* <!-- param-start:[mongodb-input-v1.connectionOptions] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Connection options" type="column editor">
  * **Parameter:** A JDBC parameter supported by the database driver. The available parameters are explained in the data model. Manual setup is not usually required, since sensible defaults are assumed.
  * **Value:** A value for the given parameter.

  Toggle **Text mode** on at the bottom of the dialog to open a multi-line editor that lets you add items in a single block. For more information, read [Text mode](/docs/guides/components-overview#text-mode).
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.connectionOptions] --> */}

### Configure

{/* <!-- param-start:[dataLoading.loadType] | warehouses: [snowflake] --> */}

<ResponseField name="Load Type" type="drop-down" required>
  * **Full Load:** Select this option to load your entire dataset.
  * **Incremental Load:** Select this option to only load new and updated records from your dataset.
</ResponseField>

{/* <!-- param-end:[dataLoading.loadType] --> */}

<ResponseField name="Mode" type="drop-down" required>
  * **Basic:** This mode will build a query for you using settings from the **Schema**, **Data Source**, **Data Selection**, **Data Source Filter**, **Combine Filters**, and **Row Limit** parameters. In most cases, this mode will be sufficient.
  * **Advanced:** This mode will require you to write an SQL-like query to call data from the service you're connecting to. The available fields and their descriptions are documented in the data model.

  There are some special pseudo columns that can form part of a query filter, but are not returned as data. This is fully described in the data model.

  <Note>
    While the query is exposed in an SQL-like language, the exact semantics can be surprising, for example, filtering on a column can return more data than not filtering on it. This is an impossible scenario with regular SQL.
  </Note>
</ResponseField>

{/* <!-- param-start:[mongodb-input-v1.dataSource] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Data Source" type="drop-down" required>
  Select a single data source to be extracted from the source system and loaded into a table in the destination. The source system defines the data sources available. Use multiple components to load multiple data sources.
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.dataSource] --> */}

{/* <!-- param-start:[snowflake-on-snowflake-environment-input-v1.dataSelection] | warehouses: [snowflake] --> */}

<ResponseField name="Data Selection" type="dual listbox" required>
  Choose one or more columns to return from the query. The columns available are dependent upon the data source selected. Move columns left-to-right to include in the query.

  To use grid variables, toggle **Use Grid Variable** on. For more information, read [Grid variables](/docs/guides/grid-variables).
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.dataSelection] --> */}

{/* <!-- param-start:[snowflake-on-snowflake-environment-input-v1.dataSourceFilter] | warehouses: [snowflake] --> */}

<ResponseField name="Data Source Filter" type="column editor">
  Define one or more filter conditions that each row of data must meet to be included in the load.

  * **Input Column:** Select an input column. The available input columns vary depending upon the data source.
  * **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". Not all data sources support all comparators.
  * **Value:** The value to be compared.

  Toggle **Text mode** on at the bottom of the dialog to open a multi-line editor that lets you add items in a single block. For more information, read [Text mode](/docs/guides/components-overview#text-mode).

  To use grid variables, toggle **Use Grid Variable** on. For more information, read [Grid variables](/docs/guides/grid-variables).
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.dataSourceFilter] --> */}

<ResponseField name="SQL Query" type="code editor">
  This is an SQL-like SELECT query, written in the SQL accepted by your cloud data warehouse. Treat collections as table names, and fields as columns. Only available in **Advanced** mode.
</ResponseField>

{/* <!-- param-start:[mongodb-input-v1.combineFilters] | warehouses: [snowflake, bigquery] --> */}

<ResponseField name="Combine Filters" type="drop-down">
  The data source filters you have defined can be combined using either **And** or **Or** logic. If **And**, then all filter conditions must be satisfied to load the data row. If **Or**, then only a single filter condition must be satisfied. The default is **And**.

  If you have only one filter, or no filters, this parameter is essentially ignored.
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.combineFilters] --> */}

{/* <!-- param-start:[snowflake-on-snowflake-environment-input-v1.limit] | warehouses: [snowflake] --> */}

<ResponseField name="Row Limit" type="integer">
  Set a numeric value to limit the number of rows that are loaded. The default is an empty field, which will load all rows.
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.limit] --> */}

<ResponseField name="High-Water Mark Selection" type="drop-down" required>
  When **Incremental Load** is selected, select a datetime field from your dataset that is always updated when your data changes, such as `dateModified`. The connector will record the maximum value of this field each time you run this pipeline. On subsequent runs when **Incremental Load** is selected, only data with a higher value in this field will be loaded.

  <Note>
    If no rows in the source have a higher value than the stored high-water mark, the task will complete with a "task is skipped" message. This is expected behavior, indicating that no new or updated data was found since the last successful run.
  </Note>
</ResponseField>

### Destination

<Tabs>
  <Tab title="Snowflake">
    <ResponseField name="Destination" type="drop-down" required>
      Select the destination for your data. This is either in Snowflake as a table or as files in cloud storage.

      * **Snowflake:** Load your data into a table in Snowflake. The data must first be staged via Snowflake or a cloud storage solution.
      * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location. The format of these files can differ between source systems and will not have a file extension so we suggest inspecting the output to determine the format of the data.
    </ResponseField>

    <Note>
      When **Incremental Load** is selected, **Cloud Storage** is not supported as a destination. Only warehouse destinations (Snowflake or Google BigQuery) are available for incremental loads.
    </Note>

    <Tabs>
      <Tab title="Snowflake">
        <ResponseField name="Warehouse" type="drop-down" required>
          The Snowflake warehouse used to run the queries. The special value `[Environment Default]` uses the warehouse defined in the environment. Read [Overview of Warehouses](https://docs.snowflake.com/en/user-guide/warehouses-overview.html) to learn more.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.database, snowflake-output-connector-v1.database] | warehouses: [snowflake] --> */}

        <ResponseField name="Database" type="drop-down" required>
          The Snowflake database to access. The special value `[Environment Default]` uses the database defined in the environment. Read [Databases, Tables and Views - Overview](https://docs.snowflake.com/en/guides-overview-db) to learn more.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.schema, snowflake-output-connector-v1.schema] | warehouses: [snowflake] --> */}

        <ResponseField name="Schema" type="drop-down" required>
          The Snowflake schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Database, Schema, and Share DDL](https://docs.snowflake.com/en/sql-reference/ddl-database.html) to learn more.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.tableName, snowflake-output-connector-v1.tableName] | warehouses: [snowflake] --> */}

        <ResponseField name="Table Name" type="string" required>
          The name of the table to be created in your Snowflake database. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.primaryKeys, snowflake-output-connector-v1.primaryKeys] | warehouses: [snowflake] --> */}

        <ResponseField name="Primary Keys" type="dual listbox">
          Choose one or more columns to be designated as the table's primary key.

          When using **Incremental Load**, if you select a primary key, the loaded data will be merged with your existing data. If you don't select a primary key, the loaded data will be appended to your existing data.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.cleanStagedFiles, snowflake-output-connector-v1.cleanStagedFiles] | warehouses: [snowflake] --> */}

        <ResponseField name="Clean Staged files" type="boolean" required>
          * **Yes:** Staged files will be destroyed after data is loaded. This is the default setting.
          * **No:** Staged files are retained in the staging area after data is loaded.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.stagePlatform, snowflake-output-connector-v1.stagePlatform] | warehouses: [snowflake] --> */}

        <ResponseField name="Stage Platform" type="drop-down" required>
          Use the drop-down menu to choose where the data is staged before being loaded into your Snowflake table.

          * **Amazon S3:** Stage your data on an AWS S3 bucket.
          * **Snowflake:** Stage your data on a Snowflake internal stage.
          * **Azure Storage:** Stage your data in an Azure Blob Storage container.
          * **Google Cloud Storage:** Stage your data in a Google Cloud Storage bucket.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.snowflake#internalStageType, snowflake-output-connector-v1.snowflake#internalStageType] | warehouses: [snowflake] --> */}

        <ResponseField name="Internal Stage Type" type="drop-down" required>
          Select the Snowflake internal stage type. Use the Snowflake links provided to learn more about each type of stage.

          * **User:** Each Snowflake user has a [user stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#user-stages) allocated to them by default for file storage. You may find the user stage convenient if your files will only be accessed by a single user, but need to be copied into multiple tables.
          * **Named:** A [named stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#named-stages) provides high flexibility for data loading. Users with the appropriate privileges on the stage can load data into any table. Furthermore, because the stage is a database object, any security or access rules that apply to all objects will apply to the named stage.

          Named stages can be altered and dropped. User stages cannot.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.snowflake#internalNamedStage, snowflake-output-connector-v1.snowflake#internalNamedStage] | warehouses: [snowflake] --> */}

        <ResponseField name="Named Stage" type="drop-down" required>
          Select your named stage. Read [Creating a named stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#creating-a-named-stage) to learn how to create a new named stage.

          <Warning>
            There is a known issue where named stages that include special characters or spaces are not supported.
          </Warning>
        </ResponseField>
      </Tab>

      <Tab title="Cloud Storage">
        <ResponseField name="Load Strategy" type="drop-down">
          * **Append Files in Folder:** Appends files to storage folder. This is the default setting.
          * **Overwrite Files in Folder:** Overwrite existing files with matching structure.
        </ResponseField>

        {/* <!-- param-start:[storage-only-output-v2.folderPath, storage-only-output-v1.folderPath] | warehouses: [snowflake] --> */}

        <ResponseField name="Folder Path" type="string">
          The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name.
        </ResponseField>

        {/* <!-- param-start:[storage-only-output-v2.filePrefix, storage-only-output-v1.filePrefix] | warehouses: [snowflake] --> */}

        <ResponseField name="File Prefix" type="string">
          A string of characters that precedes the name of the written files. This can be useful for organizing database objects.
        </ResponseField>

        {/* <!-- param-start:[storage-only-output-v2.storage, storage-only-output-v1.storage] | warehouses: [snowflake] --> */}

        <ResponseField name="Storage" type="drop-down" required>
          A cloud storage location to load your data into files for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.
        </ResponseField>
      </Tab>
    </Tabs>
  </Tab>

  <Tab title="Google BigQuery">
    <ResponseField name="Destination" type="drop-down" required>
      Select the destination for your data:

      * **Google BigQuery:** Load your data into a table in Google BigQuery.
      * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location. The format of these files can differ between source systems and will not have a file extension. Check the output to determine the format of the data.
    </ResponseField>

    <Tabs>
      <Tab title="Google BigQuery">
        <ResponseField name="Project" type="drop-down" required>
          Select the Google BigQuery project to load data into. The special value `[Environment Default]` uses the project defined in the environment.
        </ResponseField>

        <ResponseField name="Dataset" type="drop-down" required>
          Select the Google BigQuery dataset to load data into. The special value `[Environment Default]` uses the dataset defined in the environment.
        </ResponseField>

        <ResponseField name="Table Name" type="string" required>
          The name of the table to be created in your Google BigQuery project. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded.
        </ResponseField>

        <ResponseField name="Primary Keys" type="dual listbox">
          Select one or more columns to be designated as the table's primary key.

          When using **Incremental Load**, if you select a primary key, the loaded data will be merged with your existing data. If you don't select a primary key, the loaded data will be appended to your existing data.
        </ResponseField>

        <ResponseField name="Clean Staged Files" type="boolean" required>
          * **Yes:** Staged files will be destroyed after data is loaded. This is the default setting.
          * **No:** Staged files are retained in the staging area after data is loaded.
        </ResponseField>

        <ResponseField name="Partition Type" type="drop-down">
          Select the time period to use to partition the data loaded into your table, for example **Day** or **Month**.
        </ResponseField>

        <ResponseField name="GCS Bucket" type="drop-down" required>
          Select the Google Cloud Storage (GCS) bucket linked to your Google Cloud Platform account.
        </ResponseField>

        <ResponseField name="Overwrite" type="boolean" required>
          Select whether to overwrite files of the same name when this pipeline runs. Default is **Yes**.
        </ResponseField>
      </Tab>

      <Tab title="Cloud Storage">
        <ResponseField name="Load Strategy" type="drop-down">
          * **Append Files in Folder:** Appends files to storage folder. This is the default setting.
          * **Overwrite Files in Folder:** Overwrite existing files with matching structure.
        </ResponseField>

        <ResponseField name="Folder Path" type="string">
          The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name.
        </ResponseField>

        <ResponseField name="File Prefix" type="string">
          A string of characters that precedes the name of the written files. This can be useful for organizing database objects.
        </ResponseField>

        <ResponseField name="Storage" type="drop-down" required>
          A cloud storage location to load your data into files for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.
        </ResponseField>
      </Tab>
    </Tabs>
  </Tab>
</Tabs>

### Advanced Settings

{/* <!-- param-start:[jdbc-input-v2.outputEncoding, snowflake-input-v1.outputEncoding, postgresql-input-v1.outputEncoding, microsoft-sql-input-v1.outputEncoding, azure-sql-input-v0.outputEncoding, oracle-input-v1.outputEncoding, mariadb-input-v1.outputEncoding, ibm-db2-for-i-input-v1.outputEncoding, databricks-input-v0.outputEncoding, netsuite-input-v1.outputEncoding, microsoft-dynamics-365-input-v1.outputEncoding, gmail-input-v0.outputEncoding, google-sheets-input-v0.outputEncoding, oracle-eloqua-input-v1.outputEncoding, oracle-fusion-financials-input-v1.outputEncoding, oracle-fusion-procurement-input-v1.outputEncoding, oracle-fusion-project-management-input-v1.outputEncoding, oracle-fusion-hcm-input-v1.outputEncoding, sage-intacct-input-v1.outputEncoding, salesforce-input-v1.outputEncoding, sap-netweaver-cdata-input-v1.outputEncoding, shopify-input-v1.outputEncoding, sugarcrm-input-v1.outputEncoding, workday-soap-input-v1.outputEncoding, api-extract-input-v2.outputEncoding, hubspot-input-v1.outputEncoding, mailchimp-input-v0.outputEncoding, microsoft-exchange-input-v0.outputEncoding, survey-monkey-input-v0.outputEncoding, x-ads-input-v2.outputEncoding, jira-input-v1.outputEncoding, marketo-input-v1.outputEncoding] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Output Encoding" type="drop-down">
  Set the file format used to stage extracted records from this component before loading. The default is `CSV`. `CSV` is compact and widely compatible, while `JSON Lines` preserves the difference between NULL and empty-string values.
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.outputEncoding] --> */}

{/* <!-- param-start:[keySanitizationStrategy] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Key Sanitization Strategy" type="drop-down">
  Choose how to change the column names in your data so that they conform to the destination warehouse's identifier rules:

  * `None`: (Default) Do not change the source column names.
  * `Alphanumeric (lower case)`: Replace non-alphanumeric characters with underscores, change the name to lowercase, and add an underscore at the start of the name if it begins with a digit.
  * `Alphanumeric (upper case)`: Replace non-alphanumeric characters with underscores, change the name to uppercase, and add an underscore at the start of the name if it begins with a digit.

  If two source column names sanitize to the same result, the load fails with an error rather than dropping data.
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.keySanitization] --> */}

{/* <!-- param-start:[facebook-ads-input-v1.autoDebug] | warehouses: [snowflake] --> */}

<ResponseField name="Auto Debug" type="boolean" required>
  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. This property is set to **No** by default. Turning this on will override any debugging **Connection Options** you may have set.
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.autoDebug] --> */}

{/* <!-- param-start:[facebook-ads-input-v1.debugLevel] | warehouses: [snowflake] --> */}

<ResponseField name="Debug Level" type="drop-down" required>
  The level of detail you want to include in your debug logs. Select a level between **1** and **4**:

  1. Will log the query, the number of rows returned by it, the start of execution, the time taken, and any errors.
  2. Will log everything included in Level 1, plus cache queries and additional information about the request, if applicable.
  3. Will log everything included in Levels 1 and 2, and additionally log the body of the request and the response. This is the default logging level when debug logging is activated.
  4. Will log everything included in Levels 1, 2, and 3, and additionally log transport-level communication with the data source. This includes SSL negotiation.

  Levels above 1 can log huge amounts of data and result in slower query execution.
</ResponseField>

{/* <!-- param-end:[mongodb-input-v1.debugLevel] --> */}

{/* <!-- param-start:[snowflake-output-connector-v0.stringNullIsNull, snowflake-output-connector-v1.stringNullIsNull, snowflake-output-connector-v2.stringNullIsNull] | warehouses: [snowflake] --> */}

<ResponseField name="Parse 'Null' & Empty Strings as NULL" type="boolean" required>
  Converts common strings that represent null into a null value. This is case-sensitive and works with the following strings: "", "NULL", "NUL", "Null", "null". The default is **No**.

  <Note>This parameter is only applicable when using Snowflake as your destination.</Note>
</ResponseField>

{/* <!-- param-end:[snowflake-output-connector-v0.stringNullIsNull] --> */}

{/* <!-- param-start:[snowflake-output-connector-v0.trimStringColumns, snowflake-output-connector-v1.trimStringColumns, snowflake-output-connector-v2.trimStringColumns] | warehouses: [snowflake] --> */}

<ResponseField name="Trim String Columns" type="boolean" required>
  When **Yes**, remove leading and trailing characters from a string column. The default is **No**.

  <Note>This parameter is only applicable when using Snowflake as your destination.</Note>
</ResponseField>

{/* <!-- param-end:[snowflake-output-connector-v0.trimStringColumns] --> */}

## Data model

The JDBC driver for this component models MongoDB APIs as relational databases and stored procedures, which are documented in the data model. You'll also find API limitations and requirements.

* [Connection options](https://cdn.cdata.com/help/DGM/jdbc/Connection.htm)

This connector also allows you to query system tables in Advanced mode. To see the available system tables in the data model, read the [System Tables](https://cdn.cdata.com/help/DGM/jdbc/pg_allsystables.htm) section of the data model. For more information about using system tables, read our [System tables](/docs/guides/system-tables-example) guide.

***

## Connecting to MongoDB Atlas

The following steps illustrate the recommended method of using the MongoDB Load component to connect to MongoDB Atlas clusters.

### Prerequisites

Configure **Network Access** in the MongoDB Atlas cluster to allow inbound traffic from {maia} instance's IP address or range. Read [Configure IP Access List Entries](https://www.mongodb.com/docs/atlas/security/ip-access-list) for details.

Create or obtain credentials for a user with sufficient permission to read from the MongoDB Atlas cluster. Read [Configure Database Users](https://www.mongodb.com/docs/atlas/security-add-mongodb-users) for details.

Obtain the MongoDB Atlas cluster's hostname and connection parameters from the MongoDB Atlas console as follows:

1. On the **Database Deployments** screen, click the **Connect** button for the desired cluster.

2. Click **Drivers** → **Java**, then select version **4.3** or later.

3. Note the connection string that appears, as it contains details needed in the steps below. For example:

   ```
   mongodb+srv://<username>:<password>@cluster-name.xxxx.mongodb.net/?retryWrites=true&w=majority&appName=app-name
   ```

   You need the cluster hostname portion of the string: `cluster-name.xxxx.mongodb.net`, where `xxxx` is a unique name.

4. Click **Browse Collections** and locate the desired cluster. Note the name of the **Namespace** for the cluster. This will be used for the **Database** property in the MongoDB Load component.

5. Note the name of the desired table under the chosen **Namespace**. This will be used for the **Data Source** property of the MongoDB Load component.

### Connection process

With the prerequisite steps completed as above, follow this process to create the connection.

1. In {designer}, open or create a new orchestration pipeline, and drag a new MongoDB Load component onto the canvas.

2. Configure the component's **Properties** as follows, using the details collected from the MongoDB Atlas console:

   | Property            | Setting                                                                                         |
   | ------------------- | ----------------------------------------------------------------------------------------------- |
   | Basic/Advanced Mode | Basic                                                                                           |
   | Port                | 27017                                                                                           |
   | Username            | Your Atlas username.                                                                            |
   | Password            | The corresponding Atlas password.                                                               |
   | Server              | The cluster hostname obtained above, for example `mongodb+srv://cluster-name.xxxx.mongodb.net`. |
   | Database            | The **Namespace** obtained above.                                                               |
   | Data Source         | Select the desired table.                                                                       |
   | Data Selection      | Select columns as desired.                                                                      |

3. Set the following **Connection Options**:

   | Parameter    | Value   |
   | ------------ | ------- |
   | UseSSL       | true    |
   | DNSServer    | 8.8.8.8 |
   | AuthDatabase | admin   |

4. Complete the remaining properties as appropriate.

***

## Connecting to an Amazon DocumentDB Cluster

With your cluster created, make note of the cluster details (found by clicking on the cluster name on the Clusters page).

The table below describes the mapping from the DocumentDB cluster details to the component properties:

| MongoDB Load component property | Type    | Amazon DocumentDB property (value)                                                                                                             |
| ------------------------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| Port                            | Integer | Specify a port number. The default, `27017`, is used if this is left blank.                                                                    |
| Username                        | String  | Input a valid MongoDB username for authentication.                                                                                             |
| Password                        | String  | The corresponding password. Store the password as a [secret definition](/docs/guides/secrets-and-secret-definitions).                          |
| Server                          | String  | The server IP or DNS address of the MongoDB server endpoint. The format is: `{cluster-name}.cluster-xxxxxxxxxxxx.{region}.docdb.amazonaws.com` |

In addition to the above details, set the following additional connection options (in the **Connection Options** component property) to connect to an Amazon DocumentDB cluster:

| Connection Option | Value             | Description                                                                                                                                                            |
| ----------------- | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| UseSSL            | True              | Amazon DocumentDB Clusters have SSL enabled by default and this can't be changed.                                                                                      |
| SSLServerCert     | \*                | This automatically accepts the certificate presented by the DocumentDB Cluster, which is already hosted within the same region and security group as the EC2 instance. |
| Other             | "UseFindAPI=true" | This is set to ensure compatibility with the DocumentDB version of the MongoDB API.                                                                                    |