Complex credential types
Overview
Often, credentials do not consist of just one api_key
, but instead can be quite a complex structure. In this section, you'll learn how dlt
supports different credential types and authentication options.
Learn about the authentication methods supported by the dlt
RestAPI Client in detail in the RESTClient section.
dlt
supports different credential types by providing various Python data classes called Configuration Specs. These classes define how complex configuration values, particularly credentials, should be handled. They specify the types, defaults, and parsing methods for these values.
Example with ConnectionStringCredentials
ConnectionStringCredentials
handles database connection strings:
from dlt.sources.credentials import ConnectionStringCredentials
@dlt.source
def query(sql: str, dsn: ConnectionStringCredentials = dlt.secrets.value):
...
The source above executes the sql
against the database defined in dsn
. ConnectionStringCredentials
ensures you get the correct values with the 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.
Dictionary form
[dsn]
database="dlt_data"
password="loader"
username="loader"
host="localhost"
Native form
dsn="postgres://loader:loader@localhost:5432/dlt_data"
Mixed form
If all credentials, except the password, are provided explicitly in the code, dlt
will look for the password in secrets.toml
.
dsn.password="loader"
You can explicitly provide credentials in various forms:
query("SELECT * FROM customers", "postgres://loader@localhost:5432/dlt_data") # type: ignore[arg-type]
# or
query("SELECT * FROM customers", {"database": "dlt_data", "username": "loader"}) # type: ignore[arg-type]
Built-in credentials
dlt
offers 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" # type: ignore
credentials.host = "localhost"
credentials.port = 5432
# Convert credentials to a 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:
oauth_credentials = OAuth2Credentials(
client_id="CLIENT_ID",
client_secret="CLIENT_SECRET", # type: ignore
refresh_token="REFRESH_TOKEN", # type: ignore
scopes=["scope1", "scope2"]
)
# Authorize the client
oauth_credentials.auth()
# Add additional scopes
oauth_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
Examples
- Google Analytics verified source: an example of how to use GCP Credentials.
- Google Analytics example: how you can get the refresh token using
dlt.secrets.value
.
Types
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 a string or dictionary (in code and via config providers). - Or default credentials will be used.
gcp_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.
gcp_native_value = {"private_key": ".."} # or "path/to/services.json"
gcp_credentials.parse_native_representation(gcp_native_value)
or more preferred use:
import dlt
from dlt.sources.credentials import GcpServiceAccountCredentials
from google.analytics import BetaAnalyticsDataClient
@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 follows:
[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 API.
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 follows:
[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 the auth()
method to succeed:
- You must provide valid
client_id
,client_secret
,refresh_token
, andproject_id
to get a current access token and authenticate with OAuth. Keep in mind that therefresh_token
must contain all the scopes that are required for your access. - If the
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.
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 theproject_id
from default credentials. If the project id is missing, it will look forproject_id
in the secrets. So it is normal practice to pass partial credentials (justproject_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
aws_credentials = AwsCredentials()
# Set the necessary attributes
aws_credentials.aws_access_key_id = "ACCESS_KEY_ID"
aws_credentials.aws_secret_access_key = "SECRET_ACCESS_KEY"
aws_credentials.region_name = "us-east-1"
or
# Imports an external botocore session and sets the credentials properties accordingly.
import botocore.session
aws_credentials = AwsCredentials()
session = botocore.session.get_session()
aws_credentials.parse_native_representation(session)
print(aws_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 follows:
[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 a 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
az_credentials = AzureCredentials()
# Set the necessary attributes
az_credentials.azure_storage_account_name = "ACCOUNT_NAME"
az_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 follows:
[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 into the `credentials` argument. Both classes implement `auth` so you can always call it.
credentials.auth() # type: ignore[union-attr]
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.
Check out the complete example, to learn how to create unions of credentials that derive from the common class, so you can handle it seamlessly in your code.
Writing custom specs
Custom specifications let you take full control over the function arguments. You can:
- Control which values should be injected, the types, default values.
- Specify optional and final fields.
- Form hierarchical configurations (specs in specs).
- Provide your own handlers for
on_partial
(called before failing on missing config key) oron_resolved
. - Provide your own native value parsers.
- Provide your own default credentials logic.
- Utilize Python dataclass functionality.
- Utilize Python
dict
functionality (specs
instances can be created from dicts and serialized from dicts).
In fact, dlt
synthesizes a unique spec for each decorated function. For example, in the case of google_sheets
, the following class is created:
from dlt.sources.config import configspec, with_config
@configspec
class GoogleSheetsConfiguration(BaseConfiguration):
tab_names: List[str] = None # mandatory
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
andto_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.