Working with Connector SDK

Fivetran Connector SDK

The Fivetran Connector SDK is available as a GitHub repository that provides examples that we recommend you consider and try out.

Follow the Connector SDK setup guide to start using the SDK. This page covers several topics that will enable you to successfully use the SDK to build your custom data pipelines.

SDK runtime environment

The Connector SDK runtime environment executes your submitted Python script in an isolated environment. It installs the libraries listed in the requirements.txt file, and after installation, it runs the provided Python script.

Python version support

We support the following Python versions:

• 3.12.8 (the default version, used when you don't explicitly specify the version when deploying the connection with the --python or --python-version arguments)
• 3.11.11
• 3.10.16
• 3.9.21

NOTE: The versions listed above are the most up-to-date stable versions of Python as of Jan 22, 2025. While patch versions are usually compatible, a specific patch version can be critical for any Python library your code depends on.

Python version support - upgrades and deprecations

We are constantly expanding the list of supported Python versions. If you need support for a particular version, submit a feature request.

When Python.org deprecates a particular version, we discontinue support for that version of Python as well.

We will not immediately remove support for an existing connection that’s already uploaded and running, even if new connections can’t be configured due to an incompatible patch version. Instead, we will work with you to transition to an updated, supported Python version.

Pre-installed DB drivers

We pre-install the msodbcsql17 and msodbcsql18 drivers for connecting to Microsoft SQL server. We also pre-install libpq5 and libpq-dev, which is required to support psycopg and psycopg2 respectively.

Pre-installed packages

We pre-install the fivetran_connector_sdk:latest and requests:2.32.3 package when running your code.

System resources

The environment running your code has:

• 1 GB of RAM
• Less than 0.5 vCPUs of an 8 Core N2 or equivalent machine

Our production infrastructure allows for some variation in resources available to your connections at any given moment, load balancing across multiple connections.

Working with requirements.txt file

Usually, your connector's code will need to import additional Python libraries. To ensure the correct libraries are installed before we run your code in Fivetran, you must include a standard pip requirements.txt file in the root directory of your connector project. In your requirements.txt file, you must list all the Python libraries your connector uses along with any required versions. The weather example provides an example.

You can use PyPI and Git sources to install packages. For example:

pandas
git+https://github.com/oracle/python-oracledb.git@main#egg=oracledb

You can also quickly install all the needed libraries when setting up your environment using the following command:

pip install -r requirements.txt

NOTE: fivetran_connector_sdk:latest and requests:2.32.3 is available when executing your code. Do not declare them in your requirements.txt to avoid dependency conflicts.

Working with configuration.json file

You can pass configuration values to your connector using a configuration.json file. This file is not uploaded with your code, and you shouldn't attempt to read from it directly. After deployment, the values are provided as dictionary arguments in the Update() and Schema() methods at runtime. See our configuration example to learn how to use configuration values. These values are securely stored and encrypted. For more details on encryption, see our Data Credential Encryption documentation.

Initial deployment steps

1. Create a configuration.json file in your project’s root directory.
2. Use it for deployment.
3. Delete it after deployment (we do not store this file, and it should not be added to your code repository).

NOTE: After deployment, you can edit the configuration values in the dashboard.

Format of configuration.json

The file must contain valid JSON with key-value pairs, where the only allowed type of values is STRING:

{
  "key1": "value1",
  "key2": "value2"
}

Using configuration.json

You can pass configuration values by referencing the configuration file using either absolute or relative path:

--configuration /Users/john.doe/example/config.json
--configuration ../../configuration.json

To avoid storing sensitive information locally in your configuration.json file, you can set values to empty strings before deploying. You can manage the values after deployment in your Fivetran dashboard.

Managing configuration after deployment

Once deployed, you can edit configuration values in the setup form of your connection. The latest values — whether edited in the dashboard or redeployed with configuration.json — will be used in all syncs. Any new value in configuration.json (even an empty string) overwrites existing values.

Important notes

• Maximum 100 key-value pairs allowed.
• Only keys are visible in the Fivetran dashboard.
• Values are obfuscated for security.
• Configuration values are only available at runtime.
• Use this method to securely pass credentials like API keys.
• Please do not hardcode credentials in your Python code to follow best security practices. If you have already uploaded such code, rotate the credentials immediately.

Example commands

To test your code locally with configuration values specified in configuration.json:

fivetran debug --configuration configuration.json

To deploy your connector with configuration values specified in configuration.json:

fivetran deploy --api-key <FIVETRAN-BASE-64-ENCODED-API-KEY> --destination <DESTINATION-NAME> --connection <CONNECTION-NAME> --configuration configuration.json

NOTE: Providing --configuration replaces any previous configuration. Omitting it keeps existing values.

You can deploy the same connector.py, referencing a different configuration file each time, to create multiple connections.

Configuration in production

Ensure connector.py reads configurations only from dictionaries provided in the Update() and Schema() methods. The configuration.json file will not be available in the production environment - configurations are passed via the specified dictionaries.

Editing configuration values after deployment

Once you have deployed your connector, you can edit the configuration values in the dashboard by doing the following:

1. Go to the Setup tab of your Connection Overview page.
2. Click Edit Connection.
3. Under Configuration(s), click Edit beside the relevant configuration parameter. The Edit modal window appears in the right part of the page.
4. Enter the Configuration Value.
5. Click Save.
6. Repeat steps 4 to 5 for each configuration parameter you want to edit.

Configuration options

This section covers the casting rules for configuration field values to the following data types:

• boolean
• list
• integer
• dict

Cast to boolean

For example, let's say you have a RESYNC_ON field in configuration.json with value "SUNDAY". You can use it as a boolean in your Python code as follows:

if configuration['RESYNC_ON'].lower() == datetime.now().strftime('%A').lower():
    ... do something ...

One more example, let's say you have a include_all field in configuration.json with value "TRUE". You can use it as a boolean in your Python code as follows:

if configuration[‘include_all’] == 'TRUE':
    ... do something ...

Cast to list

For example, let's say you have a COUNTRIES field in configuration.json with value "NAM,APAC,EMEA". You can use it as a list in your Python code as follows:

fruits = configuration['COUNTRIES'].split(",")
... do something...

Cast to integer

For example, let's say you have an API_QUOTA field in configuration.json with value "12345". You can use it as an integer in your Python code as follows:

api_quota = int(configuration['API_QUOTA'])
...do something...

Cast to dict

For example, let's say you have a CURRENCIES field in configuration.json with value "[{\"From\": \"USD\",\"To\": \"EUR\"},{\"From\": \"USD\",\"To\": \"GBP\"}]". You can use it as a dict in your Python code as follows:

parsed_json = json.loads(configuration['CURRENCIES'])
...do something...

Working with state.json file

When running a connector locally that uses the state variable, a state.json is created in <project_directory>/files/state.json. This file can be edited locally and is used as the starting state for the next local run of the connector. You can also manually create this file to start a connector Debug() run from a particular state.

You can manage the state of Connector SDK connections already deployed to Fivetran by using our API endpoints:

• Retrieve Connection State
• Update Connection State

Code upload guidelines

When using fivetran deploy, we support only .py files and a requirements.txt file as part of the code upload. No other code files are supported or uploaded during the deployment process. Ensure that your code is structured accordingly and all dependencies are listed in requirements.txt.

Working with environment variables

When developing a connector, it is much more convenient to use environment variables for parameters you would otherwise have to enter repeatedly into the command line. We recommend using an environment variable for your FIVETRAN-BASE-64-ENCODED-API-KEY to avoid having to find and copy it every time you deploy your connector. To use an environment variable, create an .env file in the root directory of your connector project. Your file should be in the following format:

FIVETRAN-BASE-64-ENCODED-API-KEY=<your 64 bit encoded Fivetran API key>

Load your .env file for use in your terminal by running the following command:

export $(grep -v '^#' .env | xargs)

Check that your environment variables are now available for use by running the following command:

echo $FIVETRAN-BASE-64-ENCODED-API-KEY 

You should use configuration files for any configuration used within your connector.py code so that it will be available when the code is deployed and running in Fivetran and stored securely within Fivetran.

NOTE: .env files are not deployed to Fivetran with the rest of your code.

To deploy your connector.py using an .env file to set a local environment variable for your FIVETRAN-BASE-64-ENCODED-API-KEY, use the following command:

fivetran deploy --destination <DESTINATION-NAME> --connection <CONNECTION-NAME>

You can use .env files to set both your destination and connection names, and to allow to quickly switch between different connections.

For example, create another test.env file that contains the parameters in the following format:

FIVETRAN-BASE-64-ENCODED-API-KEY=<your 64-bit encoded Fivetran API key>
FIVETRAN_DESTINATION_NAME=<your destination name>
FIVETRAN_CONNECTION_NAME=<your connection name>

Load it by running the following command:

export $(grep -v '^#' test.env | xargs)

Deploy this connector by running the following command:

fivetran deploy

or, alternatively, if your connector needs configuration, by running the following command:

fivetran deploy --configuration configuration.json

TIP: Make sure to include your .env files in your .gitignore file to ensure you don't accidentally check your FIVETRAN-BASE-64-ENCODED-API-KEY into your code repository.

Working with warehouse.db

Our testers support only basic operations. Functionalities like schema operations are not supported by fivetran debug. In case you see failures while using fivetran debug and your code has schema definition changes, use the fivetran reset command to reset the warehouse.db and state files:

fivetran reset

If the error persists, then most likely there is an error in your code.

Working with yield and child functions

The yield keyword in a generator function is a standard Python code pattern. The Fivetran Connector SDK uses it to efficiently deliver data to our core platform.

There are two ways that yield can be used with a child function. See the examples below.

Let's assume we have a child function:

def child_function(data):
   for item in data:
      yield item

You can call the function from update() in two ways:

• Using yield from:

  def update(data):
     yield from child_function(data)
  

• Using yield: In this case, you need to iterate over the generation returned from child_function:

  def update(data):
     for item in child_function(data):
        yield item
  

Our pagination examples use the first approach.

Connecting to Duck DB

The Fivetran Local Tester saves the data sent to Fivetran by you connector code into a DuckDB warehouse.db file stored at the <project directory>/files/warehouse.db path:

Using Duck DB CLI

1. Install Duck DB CLI.

2. Run Duck DB CLI:

   duckdb files/warehouse.db
   

3. Once running, you can see the tables and their schema as well as query the warehouse with SQL:

   .tables
   .schema
   SELECT * FROM default_schema.hello_world;
   

4. To stop Duck DB, run the following command:

   .exit
   

Using DBeaver

1. Install DBeaver.

2. Connect to a database and select DuckDB.

   

3. Click Open to navigate to and select the warehouse.db file created by running fivetran debug.

   

4. Click Connection details (name, type, ...) if you wish to provide a custom name to the connection.

   

5. Dbeaver may need to download updated drivers, then should then show that it is connected to the local warehouse.db file and enable you to explore it using regular SQL. Your tables will be located in the tester schema of the warehouse catalog.

   

Rename an existing Dbeaver connection

If you want to rename an existing DBeaver connection, do the following:

1. Right-click the existing connection.

2. Click Edit Connection.

   

3. Click General.

4. Enter a new name for the connection under Connection Name.

   

5. Click OK.

fivetran debug issue with open DBeaver connection

If you use fivetran debug while a connection is already open in DBeaver, the command doesn't work until the connection is closed. The command fails with the following error message:

IO Error: Could not set lock on file "/Users/john.smith/connector/files/warehouse.db": Conflicting lock is held in /Applications/DBeaver.app/Contents/MacOS/dbeaver (PID 28552) by user john.smith. 

This occurs because DuckDB does not support concurrent connections to the same database file, as explained in their concurrency documentation. To resolve this issue, close the connection in DBeaver before running fivetran debug.

Connector SDK logs

You can access your Connector SDK connections' log events by:

• Navigating to the Connector SDK logs tab of your connection details page in the Fivetran dashboard.
• Using the Fivetran Platform Connector. Your connections' logs are available in the CONNECTOR_SDK_LOG table within the associated Fivetran destination.
• Using external log services, previously configured for your destination.

Connector SDK logs provide in-depth event data, including timestamps, log levels, and messages. We prefix all Fivetran process logs with the Fivetran-Platform identifier, and logs from the fivetran-connector-sdk library are prefixed with the Fivetran-Connector-sdk identifier.