Decorators

The method decorators detailed in this section describe request properties that are relevant to all invocations of a consumer method.

headers

class uplink.headers(arg=None, **kwargs)[source]

A decorator that adds static headers for API calls.

@headers({"User-Agent": "Uplink-Sample-App"})
@get("/user")
def get_user(self):
    """Get the current user"""

When used as a class decorator, headers applies to all consumer methods bound to the class:

@headers({"Accept": "application/vnd.github.v3.full+json"})
class GitHub(Consumer):
    ...

headers takes the same arguments as dict.

Parameters:
  • arg – A dict containing header values.
  • **kwargs – More header values.

params

class uplink.params(arg=None, **kwargs)[source]

A decorator that adds static query parameters for API calls.

@params({"sort": "created"})
@get("/user")
def get_user(self):
    """Get the current user"""

When used as a class decorator, params applies to all consumer methods bound to the class:

@params({"client_id": "my-app-client-id"})
class GitHub(Consumer):
    ...

params takes the same arguments as dict.

Parameters:
  • arg – A dict containing query parameters.
  • **kwargs – More query parameters.

json

class uplink.json[source]

Use as a decorator to make JSON requests.

You can annotate a method argument with uplink.Body, which indicates that the argument’s value should become the request’s body. uplink.Body has to be either a dict or a subclass of py:class:collections.Mapping.

Example

@json
@patch(/user")
def update_user(self, **info: Body):
    """Update the current user."""

You can alternatively use the uplink.Field annotation to specify JSON fields separately, across multiple arguments:

Example

@json
@patch(/user")
def update_user(self, name: Field, email: Field("e-mail")):
    """Update the current user."""

Further, to set a nested field, you can specify the path of the target field with a tuple of strings as the first argument of uplink.Field.

Example

Consider a consumer method that sends a PATCH request with a JSON body of the following format:

{
    user: {
        name: "<User's Name>"
    },
}

The tuple ("user", "name") specifies the path to the highlighted inner field:

@json
@patch(/user")
def update_user(
                self,
                new_name: Field(("user", "name"))
):
    """Update the current user."""

form_url_encoded

class uplink.form_url_encoded[source]

URL-encodes the request body.

Used on POST/PUT/PATCH request. It url-encodes the body of the message and sets the appropriate Content-Type header. Further, each field argument should be annotated with uplink.Field.

Example

@form_url_encoded
@post("/users/edit")
def update_user(self, first_name: Field, last_name: Field):
    """Update the current user."""

multipart

class uplink.multipart[source]

Sends multipart form data.

Multipart requests are commonly used to upload files to a server. Further, annotate each part argument with Part.

Example

@multipart
@put(/user/photo")
def update_user(self, photo: Part, description: Part):
    """Upload a user profile photo."""

timeout

class uplink.timeout(seconds)[source]

Time to wait for a server response before giving up.

When used on other decorators it specifies how long (in secs) a decorator should wait before giving up.

Example

@timeout(60)
@get("/user/posts")
def get_posts(self):
    """Fetch all posts for the current users."""

When used as a class decorator, timeout applies to all consumer methods bound to the class.

Parameters:seconds (int) – An integer used to indicate how long should the request wait.

args

class uplink.args(*annotations, **more_annotations)[source]

Annotate method arguments for Python 2.7 compatibility.

Arrange annotations in the same order as their corresponding function arguments.

Example

@args(Path, Query)
@get("/users/{username})
def get_user(self, username, visibility):
    """Get a specific user."""

Use keyword args to target specific method parameters.

Example

@args(visibility=Query)
@get("/users/{username})
def get_user(self, username, visibility):
    """Get a specific user."""
Parameters:
  • *annotations – Any number of annotations.
  • **more_annotations – More annotations, targeting specific method arguments.

response_handler

class uplink.response_handler(handler, requires_consumer=False)[source]

A decorator for creating custom response handlers.

To register a function as a custom response handler, decorate the function with this class. The decorated function should accept a single positional argument, an HTTP response object:

Example

@response_handler
def raise_for_status(response):
    response.raise_for_status()
    return response

Then, to apply custom response handling to a request method, simply decorate the method with the registered response handler:

Example

@raise_for_status
@get("/user/posts")
def get_posts(self):
    """Fetch all posts for the current users."""

To apply custom response handling on all request methods of a uplink.Consumer subclass, simply decorate the class with the registered response handler:

Example

@raise_for_status
class GitHub(Consumer):
   ...

Lastly, the decorator supports the optional argument requires_consumer. When this option is set to True, the registered callback should accept a reference to the Consumer instance as its leading argument:

Example

@response_handler(requires_consumer=True)
def raise_for_status(consumer, response):
    ...

New in version 0.4.0.

error_handler

class uplink.error_handler(exception_handler, requires_consumer=False)[source]

A decorator for creating custom error handlers.

To register a function as a custom error handler, decorate the function with this class. The decorated function should accept three positional arguments: (1) the type of the exception, (2) the exception instance raised, and (3) a traceback instance.

Example

@error_handler
def raise_api_error(exc_type, exc_val, exc_tb):
    # wrap client error with custom API error
    ...

Then, to apply custom error handling to a request method, simply decorate the method with the registered error handler:

Example

@raise_api_error
@get("/user/posts")
def get_posts(self):
    """Fetch all posts for the current users."""

To apply custom error handling on all request methods of a uplink.Consumer subclass, simply decorate the class with the registered error handler:

Example

@raise_api_error
class GitHub(Consumer):
   ...

Lastly, the decorator supports the optional argument requires_consumer. When this option is set to True, the registered callback should accept a reference to the Consumer instance as its leading argument:

Example

@error_handler(requires_consumer=True)
def raise_api_error(consumer, exc_type, exc_val, exc_tb):
   ...

New in version 0.4.0.

Note

Error handlers can not completely suppress exceptions. The original exception is thrown if the error handler doesn’t throw anything.

inject

class uplink.inject(*hooks)[source]

A decorator that applies one or more hooks to a request method.

New in version 0.4.0.

returns.*

Converting an HTTP response body into a custom Python object is straightforward with Uplink; the uplink.returns modules exposes optional decorators for defining the expected return type and data serialization format for any consumer method.

class uplink.returns.json(type=None, key=(), model=None, member=())[source]

Specifies that the decorated consumer method should return a JSON object.

# This method will return a JSON object (e.g., a dict or list)
@returns.json
@get("/users/{username}")
def get_user(self, username):
    """Get a specific user."""

Returning a Specific JSON Field:

The key argument accepts a string or tuple that specifies the path of an internal field in the JSON document.

For instance, consider an API that returns JSON responses that, at the root of the document, contains both the server-retrieved data and a list of relevant API errors:

{
    "data": { "user": "prkumar", "id": 140232 },
    "errors": []
}

If returning the list of errors is unnecessary, we can use the key argument to strictly return the inner field data:

@returns.json(key="data")
@get("/users/{username}")
def get_user(self, username):
    """Get a specific user."""

New in version v0.5.0.

uplink.returns.from_json

Specifies that the decorated consumer method should produce instances of a type class using a registered deserialization strategy (see uplink.loads.from_json())

This decorator accepts the same arguments as uplink.returns.json.

Often, a JSON response body represents a schema in your application. If an existing Python object encapsulates this schema, use the type argument to specify it as the return type:

@returns.from_json(type=User)
@get("/users/{username}")
def get_user(self, username):
    """Get a specific user."""

For Python 3 users, you can alternatively provide a return value annotation. Hence, the previous code is equivalent to the following in Python 3:

@returns.from_json
@get("/users/{username}")
def get_user(self, username) -> User:
    """Get a specific user."""

Both usages typically require also registering a converter that knows how to deserialize the JSON into the specified type (see uplink.loads.from_json()). This step is unnecessary if the type is defined using a library for which Uplink has built-in support, such as marshmallow.

New in version v0.6.0.

alias of json

class uplink.returns.schema(type)[source]

Specifies that the function returns a specific type of response.

In Python 3, to provide a consumer method’s return type, you can set it as the method’s return annotation:

@get("/users/{username}")
def get_user(self, username) -> UserSchema:
    """Get a specific user."""

For Python 2.7 compatibility, you can use this decorator instead:

@returns.schema(UserSchema)
@get("/users/{username}")
def get_user(self, username):
    """Get a specific user."""

To have Uplink convert response bodies into the desired type, you will need to define an appropriate converter (e.g., using uplink.loads).

New in version v0.5.1.

retry

class uplink.retry.retry(when=None, max_attempts=None, on_exception=None, stop=None, backoff=None)[source]

A decorator that adds retry support to a consumer method or to an entire consumer.

Unless you specify the when or on_exception argument, all failed requests that raise an exception are retried.

Unless you specify the max_attempts or stop argument, this decorator continues retrying until the server returns a response.

Unless you specify the backoff argument, this decorator uses capped exponential backoff and jitter, which should benefit performance with remote services under high contention.

Note

Response and error handlers (see here) are invoked after the retry condition breaks or all retry attempts are exhausted, whatever comes first. These handlers will receive the first response/exception that triggers the retry’s stop condition or doesn’t match its when filter.

In other words, responses or exceptions that match the retry condition (e.g., retry when status code is 5xx) are not subject to response or error handlers as long as the request doesn’t break the retry’s stop condition (e.g., stop retrying after 5 attempts).

Parameters:
  • when (optional) – A predicate that determines when a retry should be attempted.
  • max_attempts (int, optional) – The number of retries to attempt. If not specified, requests are retried continuously until a response is rendered.
  • on_exception (Exception, optional) – The exception type that should prompt a retry attempt.
  • stop (callable, optional) – A function that creates predicates that decide when to stop retrying a request.
  • backoff (callable, optional) – A function that creates an iterator over the ordered sequence of timeouts between retries. If not specified, exponential backoff is used.

retry.when

The default behavior of the retry decorator is to retry on any raised exception. To override the retry criteria, use the retry decorator’s when argument to specify a retry condition exposed through the uplink.retry.when module:

 from uplink.retry.when import raises

 class GitHub(uplink.Consumer):
     # Retry when a client connection timeout occurs
     @uplink.retry(when=raises(retry.CONNECTION_TIMEOUT))
     @uplink.get("/users/{user}")
     def get_user(self, user):
         """Get user by username."""

Use the | operator to logically combine retry conditions:

 from uplink.retry.when import raises, status

 class GitHub(uplink.Consumer):
     # Retry when an exception is raised or the status code is 503
     @uplink.retry(when=raises(Exception) | status(503))
     @uplink.get("/users/{user}")
     def get_user(self, user):
         """Get user by username."""
class uplink.retry.when.raises(expected_exception)[source]

Retry when a specific exception type is raised.

class uplink.retry.when.status(*status_codes)[source]

Retry on specific HTTP response status codes.

class uplink.retry.when.status_5xx[source]

Retry after receiving a 5xx (server error) response.

retry.backoff

Retrying failed requests typically involves backoff: the client can wait some time before the next retry attempt to avoid high contention on the remote service.

To this end, the retry decorator uses capped exponential backoff with jitter by default, To override this, use the decorator’s backoff argument to specify one of the alternative approaches exposed through the uplink.retry.backoff module:

from uplink import retry, Consumer, get
from uplink.retry.backoff import fixed

class GitHub(Consumer):
   # Employ a fixed one second delay between retries.
   @retry(backoff=fixed(1))
   @get("user/{username}")
   def get_user(self, username):
      """Get user by username."""
 from uplink.retry.backoff import exponential

 class GitHub(uplink.Consumer):
     @uplink.retry(backoff=exponential(multiplier=0.5))
     @uplink.get("/users/{user}")
     def get_user(self, user):
         """Get user by username."""
uplink.retry.backoff.jittered(base=2, multiplier=1, minimum=0, maximum=4.611686018427388e+18)[source]

Waits using capped exponential backoff and full jitter.

The implementation is discussed in this AWS Architecture Blog post, which recommends this approach for any remote clients, as it minimizes the total completion time of competing clients in a distributed system experiencing high contention.

uplink.retry.backoff.exponential(base=2, multiplier=1, minimum=0, maximum=4.611686018427388e+18)[source]

Waits using capped exponential backoff, meaning that the delay is multiplied by a constant base after each attempt, up to an optional maximum value.

uplink.retry.backoff.fixed(seconds)[source]

Waits for a fixed number of seconds before each retry.

retry.stop

By default, the retry decorator will repeatedly retry the original request until a response is rendered. To override this behavior, use the retry decorator’s stop argument to specify one of the strategies exposed in the uplink.retry.stop module:

 from uplink.retry.stop import after_attempt

 class GitHub(uplink.Consumer):
     @uplink.retry(stop=after_attempt(3))
     @uplink.get("/users/{user}")
     def get_user(self, user):

Use the | operator to logically combine strategies:

 from uplink.retry.stop import after_attempt, after_delay

 class GitHub(uplink.Consumer):
     # Stop after 3 attempts or after the backoff exceeds 10 seconds.
     @uplink.retry(stop=after_attempt(3) | after_delay(10))
     @uplink.get("/users/{user}")
     def get_user(self, user):
         pass
class uplink.retry.stop.after_attempt(attempt)[source]

Stops retrying after the specified number of attempts.

class uplink.retry.stop.after_delay(delay)[source]

Stops retrying after the backoff exceeds the specified delay in seconds.

uplink.retry.stop.NEVER

Continuously retry until the server returns a successful response

ratelimit

class uplink.ratelimit(calls=15, period=900, raise_on_limit=False, group_by=<function _get_host_and_port>, clock=<built-in function monotonic>)[source]

A decorator that constrains a consumer method or an entire consumer to making a specified maximum number of requests within a defined time period (e.g., 15 calls every 15 minutes).

Note

The rate limit is enforced separately for each host-port combination. Logically, requests are grouped by host and port, and the number of requests within a time period are counted and capped separately for each group.

By default, when the limit is reached, the client will wait until the current period is over before executing any subsequent requests. If you’d prefer the client to raise an exception when the limit is exceeded, set the raise_on_limit argument.

Parameters:
  • calls (int) – The maximum number of allowed calls that the consumer can make within the time period.
  • period (float) – The duration of each time period in seconds.
  • raise_on_limit (Exception or bool, optional) – Either an exception to raise when the client exceeds the rate limit or a bool. If True, a RateLimitExceeded exception is raised.
class uplink.ratelimit.RateLimitExceeded(calls, period)[source]

A request failed because it exceeded the client-side rate limit.