Configuration Specs

Configuration Specs in dlt are Python dataclasses that define how complex configuration values, particularly credentials, should be handled. They specify the types, defaults, and parsing methods for these values.

Working with credentials (and other complex configuration values)

For example, a spec like GcpServiceAccountCredentials manages Google Cloud Platform service account credentials, while ConnectionStringCredentials handles database connection strings.

Example

As an example, let's use ConnectionStringCredentials which represents a database connection string.

from dlt.sources.credentials import ConnectionStringCredentials

@dlt.source
def query(sql: str, dsn: ConnectionStringCredentials = dlt.secrets.value):
  ...

The source above executes the sql against database defined in dsn. ConnectionStringCredentials makes sure you get the correct values with correct types and understands the relevant native form of the credentials.

Below are examples of how you can set credentials in secrets.toml and config.toml files.

Example 1. Use the dictionary form.

[dsn]
database="dlt_data"
password="loader"
username="loader"
host="localhost"

Example 2. Use the native form.

dsn="postgres://loader:loader@localhost:5432/dlt_data"

Example 3. Use the mixed form: the password is missing in explicit dsn and will be taken from the secrets.toml.

dsn.password="loader"

You can explicitly provide credentials in various forms:

query("SELECT * FROM customers", "postgres://loader@localhost:5432/dlt_data")
# or
query("SELECT * FROM customers", {"database": "dlt_data", "username": "loader"...})

Built in credentials

We have some ready-made credentials you can reuse:

from dlt.sources.credentials import ConnectionStringCredentials
from dlt.sources.credentials import OAuth2Credentials
from dlt.sources.credentials import GcpServiceAccountCredentials, GcpOAuthCredentials
from dlt.sources.credentials import AwsCredentials
from dlt.sources.credentials import AzureCredentials

ConnectionStringCredentials

The ConnectionStringCredentials class handles connection string credentials for SQL database connections. It includes attributes for the driver name, database name, username, password, host, port, and additional query parameters. This class provides methods for parsing and generating connection strings.

Usage

credentials = ConnectionStringCredentials()

# Set the necessary attributes
credentials.drivername = "postgresql"
credentials.database = "my_database"
credentials.username = "my_user"
credentials.password = "my_password"
credentials.host = "localhost"
credentials.port = 5432

# Convert credentials to connection string
connection_string = credentials.to_native_representation()

# Parse a connection string and update credentials
native_value = "postgresql://my_user:my_password@localhost:5432/my_database"
credentials.parse_native_representation(native_value)

# Get a URL representation of the connection
url_representation = credentials.to_url()

Above, you can find an example of how to use this spec with sources and TOML files.

OAuth2Credentials

The OAuth2Credentials class handles OAuth 2.0 credentials, including client ID, client secret, refresh token, and access token. It also allows for the addition of scopes and provides methods for client authentication.

Usage:

credentials = OAuth2Credentials(
    client_id="CLIENT_ID",
    client_secret="CLIENT_SECRET",
    refresh_token="REFRESH_TOKEN",
    scopes=["scope1", "scope2"]
)

# Authorize the client
credentials.auth()

# Add additional scopes
credentials.add_scopes(["scope3", "scope4"])

OAuth2Credentials is a base class to implement actual OAuth, for example, it is a base class for GcpOAuthCredentials.

GCP Credentials

• GcpServiceAccountCredentials.
• GcpOAuthCredentials.

Google Analytics verified source: the example how to use GCP Credentials.

GcpServiceAccountCredentials

The GcpServiceAccountCredentials class manages GCP Service Account credentials. This class provides methods to retrieve native credentials for Google clients.

Usage

• You may just pass the service.json as string or dictionary (in code and via config providers).
• Or default credentials will be used.

credentials = GcpServiceAccountCredentials()
# Parse a native value (ServiceAccountCredentials)
# Accepts a native value, which can be either an instance of ServiceAccountCredentials
# or a serialized services.json.
# Parses the native value and updates the credentials.
native_value = {"private_key": ".."} # or "path/to/services.json"
credentials.parse_native_representation(native_value)

or more preferred use:

import dlt
from dlt.sources.credentials import GcpServiceAccountCredentials

@dlt.source
def google_analytics(
    property_id: str = dlt.config.value,
    credentials: GcpServiceAccountCredentials = dlt.secrets.value,
):
    # Retrieve native credentials for Google clients
    # For example, build the service object for Google Analytics PI.
    client = BetaAnalyticsDataClient(credentials=credentials.to_native_credentials())

    # Get a string representation of the credentials
    # Returns a string representation of the credentials in the format client_email@project_id.
    credentials_str = str(credentials)
    ...

while secrets.toml looks as following:

[sources.google_analytics.credentials]
client_id = "client_id" # please set me up!
client_secret = "client_secret" # please set me up!
refresh_token = "refresh_token" # please set me up!
project_id = "project_id" # please set me up!

and config.toml:

[sources.google_analytics]
property_id = "213025502"

GcpOAuthCredentials

The GcpOAuthCredentials class is responsible for handling OAuth2 credentials for desktop applications in Google Cloud Platform (GCP). It can parse native values either as GoogleOAuth2Credentials or as serialized OAuth client secrets JSON. This class provides methods for authentication and obtaining access tokens.

Usage

oauth_credentials = GcpOAuthCredentials()

# Accepts a native value, which can be either an instance of GoogleOAuth2Credentials
# or serialized OAuth client secrets JSON.
# Parses the native value and updates the credentials.
native_value_oauth = {"client_secret": ...}
oauth_credentials.parse_native_representation(native_value_oauth)

or more preferred use:

import dlt
from dlt.sources.credentials import GcpOAuthCredentials

@dlt.source
def google_analytics(
    property_id: str = dlt.config.value,
    credentials: GcpOAuthCredentials = dlt.secrets.value,
):
    # Authenticate and get access token
    credentials.auth(scopes=["scope1", "scope2"])

    # Retrieve native credentials for Google clients
    # For example, build the service object for Google Analytics PI.
    client = BetaAnalyticsDataClient(credentials=credentials.to_native_credentials())

    # Get a string representation of the credentials
    # Returns a string representation of the credentials in the format client_id@project_id.
    credentials_str = str(credentials)
    ...

while secrets.toml looks as following:

[sources.google_analytics.credentials]
client_id = "client_id" # please set me up!
client_secret = "client_secret" # please set me up!
refresh_token = "refresh_token" # please set me up!
project_id = "project_id" # please set me up!

and config.toml:

[sources.google_analytics]
property_id = "213025502"

In order for auth() method to succeed:

• You must provide valid client_id and client_secret, refresh_token and project_id in order to get a current access token and authenticate with OAuth. Mind that the refresh_token must contain all the scopes that you require for your access.
• If refresh_token is not provided, and you run the pipeline from a console or a notebook, dlt will use InstalledAppFlow to run the desktop authentication flow.

Google Analytics example: how you can get the refresh token using dlt.secrets.value.

Defaults

If configuration values are missing, dlt will use the default Google credentials (from default()) if available. Read more about Google defaults.

• dlt will try to fetch the project_id from default credentials. If the project id is missing, it will look for project_id in the secrets. So it is normal practice to pass partial credentials (just project_id) and take the rest from defaults.

AwsCredentials

The AwsCredentials class is responsible for handling AWS credentials, including access keys, session tokens, profile names, region names, and endpoint URLs. It inherits the ability to manage default credentials and extends it with methods for handling partial credentials and converting credentials to a botocore session.

Usage

credentials = AwsCredentials()
# Set the necessary attributes
credentials.aws_access_key_id = "ACCESS_KEY_ID"
credentials.aws_secret_access_key = "SECRET_ACCESS_KEY"
credentials.region_name = "us-east-1"

or

# Imports an external boto3 session and sets the credentials properties accordingly.
import botocore.session

credentials = AwsCredentials()
session = botocore.session.get_session()
credentials.parse_native_representation(session)
print(credentials.aws_access_key_id)

or more preferred use:

@dlt.source
def aws_readers(
    bucket_url: str = dlt.config.value,
    credentials: AwsCredentials = dlt.secrets.value,
):
    ...
    # Convert credentials to s3fs format
    s3fs_credentials = credentials.to_s3fs_credentials()
    print(s3fs_credentials["key"])

    # Get AWS credentials from botocore session
    aws_credentials = credentials.to_native_credentials()
    print(aws_credentials.access_key)
    ...

while secrets.toml looks as following:

[sources.aws_readers.credentials]
aws_access_key_id = "key_id"
aws_secret_access_key = "access_key"
region_name = "region"

and config.toml:

[sources.aws_readers]
bucket_url = "bucket_url"

Defaults

If configuration is not provided, dlt uses the default AWS credentials (from .aws/credentials) as present on the machine:

• It works by creating an instance of botocore Session.
• If profile_name is specified, the credentials for that profile are used. If not - the default profile is used.

AzureCredentials

The AzureCredentials class is responsible for handling Azure Blob Storage credentials, including account name, account key, Shared Access Signature (SAS) token, and SAS token permissions. It inherits the ability to manage default credentials and extends it with methods for handling partial credentials and converting credentials to a format suitable for interacting with Azure Blob Storage using the adlfs library.

Usage

credentials = AzureCredentials()
# Set the necessary attributes
credentials.azure_storage_account_name = "ACCOUNT_NAME"
credentials.azure_storage_account_key = "ACCOUNT_KEY"

or more preferred use:

@dlt.source
def azure_readers(
    bucket_url: str = dlt.config.value,
    credentials: AzureCredentials = dlt.secrets.value,
):
    ...
    # Generate a SAS token
    credentials.create_sas_token()
    print(credentials.azure_storage_sas_token)

    # Convert credentials to adlfs format
    adlfs_credentials = credentials.to_adlfs_credentials()
    print(adlfs_credentials["account_name"])

    # to_native_credentials() is not yet implemented
    ...

while secrets.toml looks as following:

[sources.azure_readers.credentials]
azure_storage_account_name = "account_name"
azure_storage_account_key = "account_key"

and config.toml:

[sources.azure_readers]
bucket_url = "bucket_url"

Defaults

If configuration is not provided, dlt uses the default credentials using DefaultAzureCredential.

Working with alternatives of credentials (Union types)

If your source/resource allows for many authentication methods, you can support those seamlessly for your user. The user just passes the right credentials and dlt will inject the right type into your decorated function.

Example:

@dlt.source
def zen_source(credentials: Union[ZenApiKeyCredentials, ZenEmailCredentials, str] = dlt.secrets.value, some_option: bool = False):
  # depending on what the user provides in config, ZenApiKeyCredentials or ZenEmailCredentials will be injected in `credentials` argument
  # both classes implement `auth` so you can always call it
  credentials.auth()
  return dlt.resource([credentials], name="credentials")

# pass native value
os.environ["CREDENTIALS"] = "email:mx:pwd"
assert list(zen_source())[0].email == "mx"

# pass explicit native value
assert list(zen_source("secret:🔑:secret"))[0].api_secret == "secret"

# pass explicit dict
assert list(zen_source(credentials={"email": "emx", "password": "pass"}))[0].email == "emx"

This applies not only to credentials but to all specs (see next chapter).

Read the whole test, it shows how to create unions of credentials that derive from the common class, so you can handle it seamlessly in your code.

Writing custom specs

specs let you take full control over the function arguments:

• Which values should be injected, the types, default values.
• You can specify optional and final fields.
• Form hierarchical configurations (specs in specs).
• Provide own handlers for on_partial (called before failing on missing config key) or on_resolved.
• Provide own native value parsers.
• Provide own default credentials logic.
• Adds all Python dataclass goodies to it.
• Adds all Python dict goodies to it (specs instances can be created from dicts and serialized from dicts).

This is used a lot in the dlt core and may become useful for complicated sources.

In fact, for each decorated function a spec is synthesized. In case of google_sheets following class is created:

from dlt.sources.config import configspec, with_config

@configspec
class GoogleSheetsConfiguration(BaseConfiguration):
  tab_names: List[str] = None  # manadatory
  credentials: GcpServiceAccountCredentials = None # mandatory secret
  only_strings: Optional[bool] = False

All specs derive from BaseConfiguration

This class serves as a foundation for creating configuration objects with specific characteristics:

• It provides methods to parse and represent the configuration in native form (parse_native_representation and to_native_representation).

• It defines methods for accessing and manipulating configuration fields.

• It implements a dictionary-compatible interface on top of the dataclass. This allows instances of this class to be treated like dictionaries.

• It defines helper functions for checking if a certain attribute is present, if a field is valid, and for calling methods in the method resolution order (MRO).

More information about this class can be found in the class docstrings.

All credentials derive from CredentialsConfiguration

This class is a subclass of BaseConfiguration and is meant to serve as a base class for handling various types of credentials. It defines methods for initializing credentials, converting them to native representations, and generating string representations while ensuring sensitive information is appropriately handled.

More information about this class can be found in the class docstrings.