RCS serves as a prefetch and caching tool for RAMP layers. All regsitration and lookup calls are handled via a REST API with JSON payloads. Data storage is handled by a JSON document store.
RCS uses CouchDB as a backend for document storage. At its core RCS avoids keeping any state within the application and instead tries to favour the document store for maintaining state. CouchDB was chosen for its master-master replication which allows for easy scaling of multiple nodes and easy partition recovery. pycouchdb is the Python driver used for interacting with the data store.
Documents stored in CouchDB should be stored in a structure which closely follows the RAMP configuration format. Since the value of the cache is on the read performance preprocessing during writes is preferred.
The full API for RCS is described in RCS Service Contract, this section merely addresses a few design decisions.
RCS leverages Flask and the Flask-RESTful extension for the majority of its web service functionality.
RAMP prefers to serve layers in a unilingual manner, prefering to allow bilingual access via multiple services. RCS accomodates this by having layer lookups to be unilingual but requiring layer registration to be bilingual (i.e. layer registration for all languages should be done in a single request). This avoids the case of missing entries for a particular language.
All endpoints encode a key in the URL. This key may be any unique identifier, but ideally it should be short and avoid using characters which need special URL encoding.
The API is split into two write operations register and delete, and two read operations doc and docs. The two read operations fetch either a single document or multiple documents respectively. Write operations should be authorized (they are not at the moment).
Validation of register requests is done via a JSON schema. This allows for more complex validation and avoids having a lot of code for the validation task. The jsonschema library is used for validation.
RCS currently accepts ESRI feature layers and WMS layers in its cache.
ESRI feature layers trigger a lot of prefetching, the payload from the catalog can be fairly minor (just a URL is a valid request). Prefetching is performed for the symbology and feature attributes, images used for symbology are encoded in data URLs and cached in RCS to avoid RAMP having to make multiple image requests later. The requests library is used for performing web requests.
WMS layers are much simpler from a processing point of view. The registration payload encodes most of the data and RCS merely translates it into a RAMP readable configuration fragment.