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Sequoia Python Client SDK

Project description

Piksel Palette

Python Sequoia Client SDK

A Python Client SDK for interacting with client services.

The central idea is that Client SDK allows python application code to communicate with the Piksel Palette RESTful RESTful services. Users can also search, filter and select their response collections.


pip install sequoia-client-sdk


Creating a SequoiaClient

The Sequoia RESTful services have an OAuth token-based authorisation model, meaning that the Client SDK must first acquire a time-limited access token before making further requests.

To create the client it is needed to provide credentials and the url for the service registry:

client = Client("",

Authentication types

When creating the client, authentication type can be specified using the parameter auth_type:

client = Client("",

There are three authentication types:


This is the default type. With CLIENT_GRANT mode grant_client_id and grant_client_secret parameters are used to get an access token. The access token is refreshed automatically when expired. Optionally, byo_token parameter can be provided when instantiating the client, and will be used until it is expired. Then the access token is refreshed automatically.


With this method byo_token is required. That access token will be used to authenticate requests. The access token will be used along the client life and won’t be refreshed.

NO_AUTH type

Mode used when no authentication is required.

Creating an endpoint

An endpoint defines the resource on which to perform the operations.

profile_endpoint = client.workflow.profiles
content_endpoint = client.metadata.contents

API methods


Retrieves one resource given its reference and owner and returns the response retrieved., ref)

Retrieves the list of resources that matches with the criteria and returns the response.

endpoint.browse(owner, criteria)

Creates one or more resources and returns the response retrieved., json)

Criteria API for Requesting Data

The SDK supports a fluent criteria API to abstract client code from the details of the Sequoia query syntax:

endpoint.browse("testmock", Criteria().add(

The following filtering criteria are supported:


Will generate the criteria expression equivalent to: field=diesel (withEngine=diesel)

Selecting fields

The SDK allows to specify which fields will be present in the response, discarding the rest of them.

For now it can be used only for Inclusions


Paginating results


Browse responses can be paginated. To paginate results, browse response has to be used as an iterator.

for response in endpoint.browse('testmock'):
    resources = response.resources
Not iterator

If browse function is not used as an iterator, only first page is retrieved. i.e:

response = endpoint.browse('testmock')
resources_in_page_1 = response.resources
With continue

Sequoia services allow to paginate using the parameter continue, which will return the link to get the following page in the meta of the response. The browse can be call repeatedly while there are pages to be read. Optionally, you can set the number of items per page.

for response in endpoint.browse('testmock', query_string='continue=true&perPage=2'):
    resources = response.resources

Paginating linked resources


When doing an inclusion, service returns a list of linked resources. Those resources can be paginated. Let’s assume a browse of contents is performed with assets resource as an inclusion. To perform pagination:

for linked_assets in endpoint.browse('testmock').linked('assets'):
    for linked_asset in linked_assets:
        asset_name = linked_asset['name']

If linked response is not used as an iterator, only first page of linked resources is retrieved:

linked_assets =  endpoint.browse('testmock').linked('assets')
for linked_asset in linked_assets.resources:
    asset_name = linked_asset['name']

Retrying requests

When a request is returning a retrievable status code, a retry strategy can be configured with backoff_strategy. By default backoff_strategy is

{'wait_gen': backoff.constant, 'interval': 0, 'max_tries': 10}

We can set a different backoff strategy.

client = Client("",
                backoff_strategy={'wait_gen': backoff.expo, 'base':2, 'factor': 1, 'max_tries': 5, 'max_time': 300}

Here an exponential strategy will be used, with a base of 2 and factor 1.

For more info about backoff strategies


It has been tested for Python 3.5 and 3.6

You can use the included command line tool make to work with this project

Preparing environment

Create new virtualenv

It’s encouraging to create a new virtual environment and install all the dependencies in it. You can use these commands:

mkdir -p ~/.virtualenvs
virtualenv -p python3.6 ~/.virtualenvs/sequoia-python-client-sdk
workon sequoia-python-client-sdk
pip install -r requirements.txt
pip install -r requirements_test.txt


There are two different ways of running the tests.

Run tests on the current environment

Using pytest option will run all the unit tests over your environment.

make test
Run tests on every compatible python version

While using the option test will set up a virtual environment for the supported version of Python, i.e. 3.5 and 3.6 and will run all the tests on each of them.

make test-all

To make sure the code fulfills the format run

make lint
  • First release.
1.1.0 (2017-10-25)
  • Upgrade to Python 3.6
1.2.0 (2019-03-06)
1.2.1 (2019-03-26)
  • Load yaml config file for testing in a safer way as specified in PyYAML
2.0.0 (2019-06-06)
  • Removing python 2.7 compatibility
  • Adding backoff to http requests. Configurable backoff from client creation
  • Libraries urllib3 and requests upgraded to solve security issues
2.1.0 (2019-09-30)
  • Modifying setup.cfg to allow different version formats (i.e development versions)
  • Paging with continue parameter
  • When token is expired, it is updated automatically with CLIENT_GRANT auth type
2.1.1 (2019-10-02)
  • Token fetching not restarting backoff. Retries continuing its count instead of restarting it when there is a invalid token

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