I greatly enjoyed reading Andrew Kuchling's Beautiful Code article “Python's Dictionary Implementation: Being All Things to All People,” [1] in which Kuchling explores some of the implementation details of what is arguably CPython's most important data structure: the dict. While these implementation details themselves are fascinating, the article offers a deft illumination of the thought process that led to the selection of a given implementation over its alternatives. The lessons here are not just for open source hackers working on the performance-critical portions of a popular language: they offer sound process guidance to anyone needing to make wise choices in the face of conflicting needs and trade-offs when building any kind of software.
Kuchling summarizes his main thesis about halfway through the article:
When trying to be all things to all people — a time- and memory-efficient data type for Python users, an internal data structure used as part of the interpreter's implementation, and a readable and maintainable code base for Python's developers — it's necessary to complicate a pure, theoretically elegant implementation with special-case code for particular cases… but not too much.
The brilliance of the article rests on how Kuchling shows the available trade-offs for a given problem explicitly, always couching them in reference to those user groups and their real-world needs. It's interesting as a postmortem, but also offers a valuable blueprint for structuring design processes such that compromises get debated on their specific, explicitly articulated merits and shortcomings rather than on hidden assumptions, preferences, and biases. Once the options are on the table, it becomes possible to consider their strengths and weaknesses in light of the needs of affected users rather than having to rely solely on theoretical or aesthetic considerations.
With that in mind, here are some specific lessons we can draw from the article about how to structure the process of analyzing potential software solutions.
Lesson 1: Maintainability Matters, Therefore Maintainers Matter
An important but subtle take away from Kuchling's thesis is that developers form one of the user groups with a stake in the outcome, and that therefore code readability and maintainability constitute legitimate, first-order user concerns that deserve weighted consideration alongside the needs of other user groups. Although Kuchling doesn't provide specific examples demonstrating this trade-off in action, this idea forms a theme woven throughout the entire discussion: unless you have a compelling, measured reason not to, prefer simplicity.
This can be challenging conversation to have, especially in commercial contexts with their strong emphasis on feature delivery and satisfying client user demands. A mechanism we use to allow compromise on this front, which we can call the “maintainability gradient,” suggests that changes that have broader reach or usage mandate a higher level of scrutiny with respect to complexity and maintainability. When considering a client request, for each solution under discussion the development team offers an assessment of its maintainability: the solution's inherent complexity, the ease or difficulty of implementing it within the framework and underlying assumptions of the existing platform, especially under time constraints, and the likelihood that we will be required to tinker with it after delivery. This becomes one of the inputs that helps us determine where in the layered architecture a given solution will be allowed:
Figure 1: Our layered architecture. The top of the stack is the most-specific, least-shared code, while the bottom layers are most shared.
In this context, our maintainability gradient provides guidance about where complication is acceptable and where it is not: while we always strive to create simple solutions, if an end user insists on something complicated or messy, it is shunted to client-specific code where its impact on the platform can be minimized and contained. Conversely, proposed additions to the core platform must meet a higher standard of maintainability: does this change fit within the existing context of the platform? Can it be implemented simply and readably?
Note that this is not an excuse to do slipshod work, even in obscure cul-de-sacs of custom code. Quite the opposite: this approach empowers developers to do great work everywhere within the context of a resource-constrained commercial enterprise. If a solution can't be maintainably generalized under current constraints, we can implement a smaller, specific version where the solution is easier to isolate. We don't always get the trade-off right, but this architecture gives us an axis of compromise that allows maintainability to have a voice in the conversation.
Lesson 2: Whom Do You Serve?
Another key design process lesson from Kuchling's article is that it is imperative to understand users and their needs, and to evaluate proposed solutions in light of those needs. Concretely, this means
- listing each alternative explicitly
- describing the characteristics of each solution relative to each user group
- choosing the solution that is the best compromise among the various alternatives
The short section on handling hash collisions in the article illustrates this process nicely. The problem is this: when implementing a hash table, what do we do when two keys inevitably hash to the same slot?
One option is to use chaining: instead of single values, each slot holds a list of values with the same hash. CPython does not do this “because creating linked lists would require allocating memory for each list item, a relatively slow operation.” In other words, CPython does not use chaining because this solution fails to meet the needs of at least two of the important user groups identified earlier: end-users of Python needing an efficient mapping data structure, and the language implementors who need an efficient platform on which to build objects, modules, and functions.
The next option up for consideration is linear probing: if a needed hash slot $i$ is occupied, examine $i+1$, $i+2$, etc. This solution is quite simple to implement, but again CPython does not use it. Why not? “Many programs use consecutive integers as keys,” which, due to how CPython hashes integers, consume contiguous blocks of the hash table that are inefficient for linear probing. Having identified the performance characteristics of this solution explicitly, it is easy to see the trade-offs being made here and the user groups affected by those trade-offs: linear probing is simple to implement — a boon for the maintainers — but that simplicity yields a solution that does not serve the performance needs of the language implementors or end-users very well.
This analysis is predicated on both understanding who the users are and what their usage patterns look like while simultaneously understanding the spectrum of available solutions considered in light of the needs of those various user groups. In the end, CPython's hash collision resolution algorithm is a pragmatic compromise, guided by experimentation, measurement, and feedback, that concedes a little code complexity in exchange for good performance characteristics in the real world.
Lesson 3: … but not too much
My favorite optimization that Kuchling describes is a complexity/performance trade-off. The dilemma is this: how can the dict implementation serve a general audience — by handling non-string keys, rich comparison operators, and possible exceptions — while also offering good performance for the huge swath of dict usages with exclusively string keys, such as those that underlie the implementation of classes, modules, and functions?
One approach would be to implement separate classes: a specialized, string-only dict optimized for high-performance for this use case along with a more general purpose implementation for regular use. In fact this is exactly what Jython, the Python implementation that runs on the JVM, does: it utilizes Java's java.util.HashMap as the basis for the dict implementation for general purpose use but employs a separate, optimized class to handle the performance-sensitive language implementation bits.
CPython instead uses a single implementation but employs two different lookup functions. By default, dicts are assumed to contain only string keys, and since many of the complexities and failure modes that arise when comparing two arbitrary objects are impossible when comparing two strings, the default lookup function can be optimized to eliminate checks for cases or errors that simply cannot happen. The implementation is a classic use of indirection that relies on a simple function pointer: if an arbitrary, non-string object is searched for or inserted into a dict instance, the search function pointer for that instance is updated to reference the slower but more general purpose lookup implementation that can handle the added complexities concomitant with comparing arbitrary objects.
This example beautifully illuminates a pragmatic, “not too much” compromise that serves its various users well. For the language implementors, the solution provides an important performance boost for lookups in classes and modules and passing of keyword arguments to functions, all of which are exclusively string- keyed and are the most frequent operations in a running Python program. It is also fairly simple, easy to understand, and is likely less code than a two-class solution would be. This in turn helps minimize the potential for implementation drift and maintains the “fits in your head” quality valued by the Python community.
The compromise also serves end users very well: string-keyed dicts are very common in many applications, so end users reap a double benefit: attribute lookups and function calls are faster, and, as an added bonus, portions of their systems that rely on string-keyed dicts are also sped up.
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| [1] | I couldn't find the article online except at Safari (free trial available). |
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