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Much Ado About Traversal

Note

This chapter was adapted, with permission, from a blog post by Rob Miller, originally published at http://blog.nonsequitarian.org/2010/much-ado-about-traversal/ .

Traversal is an alternative to URL dispatch which allows Pyramid applications to map URLs to code.

Note

Ex-Zope users whom are already familiar with traversal and view lookup conceptually may want to skip directly to the Traversal chapter, which discusses technical details. This chapter is mostly aimed at people who have previous Pylons experience or experience in another framework which does not provide traversal, and need an introduction to the “why” of traversal.

Some folks who have been using Pylons and its Routes-based URL matching for a long time are being exposed for the first time, via Pyramid, to new ideas such as “traversal” and “view lookup” as a way to route incoming HTTP requests to callable code. Some of the same folks believe that traversal is hard to understand. Others question its usefulness; URL matching has worked for them so far, why should they even consider dealing with another approach, one which doesn’t fit their brain and which doesn’t provide any immediately obvious value?

You can be assured that if you don’t want to understand traversal, you don’t have to. You can happily build Pyramid applications with only URL dispatch. However, there are some straightforward, real-world use cases that are much more easily served by a traversal-based approach than by a pattern-matching mechanism. Even if you haven’t yet hit one of these use cases yourself, understanding these new ideas is worth the effort for any web developer so you know when you might want to use them. Traversal is actually a straightforward metaphor easily comprehended by anyone who’s ever used a run-of-the-mill file system with folders and files.

URL Dispatch

Let’s step back and consider the problem we’re trying to solve. An HTTP request for a particular path has been routed to our web application. The requested path will possibly invoke a specific view callable function defined somewhere in our app. We’re trying to determine which callable function, if any, should be invoked for a given requested URL.

Many systems, including Pyramid, offer a simple solution. They offer the concept of “URL matching”. URL matching approaches this problem by parsing the URL path and comparing the results to a set of registered “patterns”, defined by a set of regular expressions, or some other URL path templating syntax. Each pattern is mapped to a callable function somewhere; if the request path matches a specific pattern, the associated function is called. If the request path matches more than one pattern, some conflict resolution scheme is used, usually a simple order precedence so that the first match will take priority over any subsequent matches. If a request path doesn’t match any of the defined patterns, a “404 Not Found” response is returned.

In Pyramid, we offer an implementation of URL matching which we call URL dispatch. Using Pyramid syntax, we might have a match pattern such as /{userid}/photos/{photoid}, mapped to a photo_view() function defined somewhere in our code. Then a request for a path such as /joeschmoe/photos/photo1 would be a match, and the photo_view() function would be invoked to handle the request. Similarly, /{userid}/blog/{year}/{month}/{postid} might map to a blog_post_view() function, so /joeschmoe/blog/2010/12/urlmatching would trigger the function, which presumably would know how to find and render the urlmatching blog post.

Historical Refresher

Now that we’ve refreshed our understanding of URL dispatch, we’ll dig in to the idea of traversal. Before we do, though, let’s take a trip down memory lane. If you’ve been doing web work for a while, you may remember a time when we didn’t have fancy web frameworks like Pylons and Pyramid. Instead, we had general purpose HTTP servers that primarily served files off of a file system. The “root” of a given site mapped to a particular folder somewhere on the file system. Each segment of the request URL path represented a subdirectory. The final path segment would be either a directory or a file, and once the server found the right file it would package it up in an HTTP response and send it back to the client. So serving up a request for /joeschmoe/photos/photo1 literally meant that there was a joeschmoe folder somewhere, which contained a photos folder, which in turn contained a photo1 file. If at any point along the way we find that there is not a folder or file matching the requested path, we return a 404 response.

As the web grew more dynamic, however, a little bit of extra complexity was added. Technologies such as CGI and HTTP server modules were developed. Files were still looked up on the file system, but if the file ended with (for example) .cgi or .php, or if it lived in a special folder, instead of simply sending the file to the client the server would read the file, execute it using an interpreter of some sort, and then send the output from this process to the client as the final result. The server configuration specified which files would trigger some dynamic code, with the default case being to just serve the static file.

Traversal (aka Resource Location)

Believe it or not, if you understand how serving files from a file system works,you understand traversal. And if you understand that a server might do something different based on what type of file a given request specifies, then you understand view lookup.

The major difference between file system lookup and traversal is that a file system lookup steps through nested directories and files in a file system tree, while traversal steps through nested dictionary-type objects in a resource tree. Let’s take a detailed look at one of our example paths, so we can see what I mean:

The path /joeschmoe/photos/photo1, has four segments: /, joeschmoe, photos and photo1. With file system lookup we might have a root folder (/) containing a nested folder (joeschmoe), which contains another nested folder (photos), which finally contains a JPG file (photo1). With traversal, we instead have a dictionary-like root object. Asking for the joeschmoe key gives us another dictionary-like object. Asking this in turn for the photos key gives us yet another mapping object, which finally (hopefully) contains the resource that we’re looking for within its values, referenced by the photo1 key.

In pure Python terms, then, the traversal or “resource location” portion of satisfying the /joeschmoe/photos/photo1 request will look something like this pseudocode:

get_root()['joeschmoe']['photos']['photo1']

get_root() is some function that returns a root traversal resource. If all of the specified keys exist, then the returned object will be the resource that is being requested, analogous to the JPG file that was retrieved in the file system example. If a KeyError is generated anywhere along the way, Pyramid will return 404. (This isn’t precisely true, as you’ll see when we learn about view lookup below, but the basic idea holds.)

What Is a “Resource”?

“Files on a file system I understand”, you might say. “But what are these nested dictionary things? Where do these objects, these ‘resources’, live? What are they?”

Since Pyramid is not a highly opinionated framework, it makes no restriction on how a resource is implemented; a developer can implement them as he wishes. One common pattern used is to persist all of the resources, including the root, in a database as a graph. The root object is a dictionary-like object. Dictionary-like objects in Python supply a __getitem__ method which is called when key lookup is done. Under the hood, when adict is a dictionary-like object, Python translates adict['a'] to adict.__getitem__('a'). Try doing this in a Python interpreter prompt if you don’t believe us:

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Python 2.4.6 (#2, Apr 29 2010, 00:31:48)
[GCC 4.4.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> adict = {}
>>> adict['a'] = 1
>>> adict['a']
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>>> adict.__getitem__('a')
1

The dictionary-like root object stores the ids of all of its subresources as keys, and provides a __getitem__ implementation that fetches them. So get_root() fetches the unique root object, while get_root()['joeschmoe'] returns a different object, also stored in the database, which in turn has its own subresources and __getitem__ implementation, etc. These resources might be persisted in a relational database, one of the many “NoSQL” solutions that are becoming popular these days, or anywhere else, it doesn’t matter. As long as the returned objects provide the dictionary-like API (i.e. as long as they have an appropriately implemented __getitem__ method) then traversal will work.

In fact, you don’t need a “database” at all. You could use plain dictionaries, with your site’s URL structure hard-coded directly in the Python source. Or you could trivially implement a set of objects with __getitem__ methods that search for files in specific directories, and thus precisely recreate the traditional mechanism of having the URL path mapped directly to a folder structure on the file system. Traversal is in fact a superset of file system lookup.

Note

See the chapter entitled Resources for a more technical overview of resources.

View Lookup

At this point we’re nearly there. We’ve covered traversal, which is the process by which a specific resource is retrieved according to a specific URL path. But what is “view lookup”?

The need for view lookup is simple: there is more than one possible action that you might want to take after finding a resource. With our photo example, for instance, you might want to view the photo in a page, but you might also want to provide a way for the user to edit the photo and any associated metadata. We’ll call the former the view view, and the latter will be the edit view. (Original, I know.) Pyramid has a centralized view application registry where named views can be associated with specific resource types. So in our example, we’ll assume that we’ve registered view and edit views for photo objects, and that we’ve specified the view view as the default, so that /joeschmoe/photos/photo1/view and /joeschmoe/photos/photo1 are equivalent. The edit view would sensibly be provided by a request for /joeschmoe/photos/photo1/edit.

Hopefully it’s clear that the first portion of the edit view’s URL path is going to resolve to the same resource as the non-edit version, specifically the resource returned by get_root()['joeschmoe']['photos']['photo1']. But traveral ends there; the photo1 resource doesn’t have an edit key. In fact, it might not even be a dictionary-like object, in which case photo1['edit'] would be meaningless. When the Pyramid resource location has been resolved to a leaf resource, but the entire request path has not yet been expended, the very next path segment is treated as a view name. The registry is then checked to see if a view of the given name has been specified for a resource of the given type. If so, the view callable is invoked, with the resource passed in as the related context object (also available as request.context). If a view callable could not be found, Pyramid will return a “404 Not Found” response.

You might conceptualize a request for /joeschmoe/photos/photo1/edit as ultimately converted into the following piece of Pythonic pseudocode:

context = get_root()['joeschmoe']['photos']['photo1']
view_callable = get_view(context, 'edit')
request.context = context
view_callable(request)

The get_root and get_view functions don’t really exist. Internally, Pyramid does something more complicated. But the example above is a reasonable approximation of the view lookup algorithm in pseudocode.

Use Cases

Why should we care about traversal? URL matching is easier to explain, and it’s good enough, right?

In some cases, yes, but certainly not in all cases. So far we’ve had very structured URLs, where our paths have had a specific, small number of pieces, like this:

/{userid}/{typename}/{objectid}[/{view_name}]

In all of the examples thus far, we’ve hard coded the typename value, assuming that we’d know at development time what names were going to be used (“photos”, “blog”, etc.). But what if we don’t know what these names will be? Or, worse yet, what if we don’t know anything about the structure of the URLs inside a user’s folder? We could be writing a CMS where we want the end user to be able to arbitrarily add content and other folders inside his folder. He might decide to nest folders dozens of layers deep. How will you construct matching patterns that could account for every possible combination of paths that might develop?

It might be possible, but it certainly won’t be easy. The matching patterns are going to become complex quickly as you try to handle all of the edge cases.

With traversal, however, it’s straightforward. Twenty layers of nesting would be no problem. Pyramid will happily call __getitem__ as many times as it needs to, until it runs out of path segments or until a resource raises a KeyError. Each resource only needs to know how to fetch its immediate children, the traversal algorithm takes care of the rest. Also, since the structure of the resource tree can live in the database and not in the code, it’s simple to let users modify the tree at runtime to set up their own personalized “directory” structures.

Another use case in which traversal shines is when there is a need to support a context-dependent security policy. One example might be a document management infrastructure for a large corporation, where members of different departments have varying access levels to the various other departments’ files. Reasonably, even specific files might need to be made available to specific individuals. Traversal does well here if your resources actually represent the data objects related to your documents, because the idea of a resource authorization is baked right into the code resolution and calling process. Resource objects can store ACLs, which can be inherited and/or overridden by the subresources.

If each resource can thus generate a context-based ACL, then whenever view code is attempting to perform a sensitive action, it can check against that ACL to see whether the current user should be allowed to perform the action. In this way you achieve so called “instance based” or “row level” security which is considerably harder to model using a traditional tabular approach. Pyramid actively supports such a scheme, and in fact if you register your views with guard permissions and use an authorization policy, Pyramid can check against a resource’s ACL when deciding whether or not the view itself is available to the current user.

In summary, there are entire classes of problems that are more easily served by traversal and view lookup than by URL dispatch. If your problems don’t require it, great: stick with URL dispatch. But if you’re using Pyramid and you ever find that you do need to support one of these use cases, you’ll be glad you have traversal in your toolkit.

Note

It is even possible to mix and match traversal with URL dispatch in the same Pyramid application. See the Combining Traversal and URL Dispatch chapter for details.

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