Andy Mikhailenko  committed 683b82b

Update documentation: add comparison to MongoKit

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File docs/faq.rst

     The API is still evolving but the core is considered stable since v.0.7.
     Even serious changes under the hood barely affect the public interface.
+What are the alternatives?
+See :doc:`similar`.

File docs/index.rst

+   similar
 Indices and tables

File docs/similar.rst

+Similar Projects
+Below is a list of projects that share one or more major goals with Monk.
+The descriptions may be a bit too critical, but that's only because of the
+inevitable competition between Monk and these projects; Monk aims to be better
+and the list shows how it is better.  I mean, what's the point of creating
+a project if not to make a solution that would incorporate the strong points
+and address the weaknesses of its predecessors?  So, here we go.
+Schema Definition
+  Simple and pythonic, very similar to Monk (actually, Monk's "natural" DSL was
+  inspired by that of MongoKit).  However, everything is tightly bound to
+  MongoDB (not to mention the lacking possibility to work with plain data
+  without ODMs); the required flag, default values and custom validators are
+  defined on the root level, duplicating the structure in each case. Example::
+      # Monk
+      spec = {
+          'foo': 5,
+          'bar': optional(str),
+          'baz': {
+              'quux': 'flux'
+          }
+      }
+      # MongoKit
+      class Spec(Document):
+          structure = {
+              'foo': int,
+              'bar': str,
+              'baz': {
+                  'quux': str
+              }
+          }
+          default_values = {
+              'foo': 5,
+              'quux': 'flux'
+          }
+          required = ['foo', 'baz.quux']
+  Very similar support (and notation) for nested lists and dicts; also supports
+  nested tuples.
+  Type validation (extensible with custom types).  All validators beyond types
+  belong in a separate dictionary which mostly duplicates the schema dictionary.
+  The list of required fields (with names in a MongoDB-ish dot notation, i.e.
+  ``foo.$``) must be defined in yet another place.
+  This approach implies noticeable redundancy for relatively complex documents.
+  The Document class also has an overloadable ``validate()`` method which makes
+  sense for simultaneous multi-field validation.  In Monk you would simply call
+  the normal and a custom validation functions one after another (or overload
+  the method in a similar way if using modeling).
+  Data manipulation mostly embraces conversion between Python types and MongoDB
+  internal representation (via PyMongo).  This can be tuned with "Custom Types"
+  that handle both manipulation and validation.
+  It is unknown whether the list of default values supports callables.
+  The Document class is bound to a MongoDB collection.  Supports dot-expanded
+  dictionary behaviour (like Monk).  Supports polymorphism (Monk doesn't).
+  The underlying functions are not intended to be used separately (in Monk this
+  was one of the main design goals).
+MongoDB extension
+  Tightly bound to MongoDB on all levels.  The document class is bound to
+  a collection (which I found problematic in the past but generally this may be
+  good design).  Very good integration.  PyMongo is accessible when needed
+  (like in Monk).  Keeps the data clean from tool-specific metadata (like Monk).
+  In general, MongoDB support is superior compared to that of Monk but both use
+  PyMongo so the basic functionality is exactly the same.  The choice depends
+  on given project's use cases.
+.. _MongoKit: