Let's talk real-world functional programming. Disclosure: I'm a fan of functional programming in Python. (This:
https://www.packtpub.com/application-development/functional-python-programming)
The usual culprits for functional programming are
map(),
filter(), generator functions, and the various comprehensions. This is very pleasant and can lead to succinct, expressive code.
The reduce operation, however, is sometimes slippery. The obvious reductions are
sum() and
prod(). Some slightly less obvious reductions are these three:
sum0 = lambda s: sum(1 for _ in s)
sum1 = lambda s: sum(s)
sum2 = lambda s: sum(n**2 for n in s)
The first is essentially
len(s), but stated more formally. It shows how we can add in filter or transformations. If we're working with a
collections.Counter object, we can rewrite these three to work with the
values() of a counter. This allows us to have a statistics library that works with a sequence of simple items or a
Counter of binned items.
(I've left it as an exercise for the reader to create the summaries of
Counters.)
The Health Check Question
The context is an RESTful application's /health end-point. When a client does a GET to /health, we want to provide status of the components on which the app depends as well as a summary.
The details are created like this:
components = (component() for component in COMPONENT_LIST)
init_components = [thing.init_app(app) for thing in components]
details = [component.health() for component in init_components]
We have a list of class definitions for each component. We can create instances of each class. We can initialize these by providing the RESTful app. Finally, we can create a list of the various health end-point status codes.
There's a class definition for other RESTful API's. The health check does a transitive GET to a /health end-point. These are all more-or-less identical.
There are also class definitions for the database and the cache and other non-RESTful components. It's all very pretty and very functional.
Note that the three statements aren't adjacent. They're scattered around to fit better with the way Flask works. The component list is in one place. The initialization happens before the first request. The details are computed as requested.
Also. We don't really use a simple list for the details. It's actually a mapping from which we will derive a vector. I've left that detail out because it's a relatively simple complication.
Representation of Health
We represent health with a simple enumeration of values:
from enum import Enum
class Status(Enum):
OK = "OK"
DEGRADED = "DEGRADED"
DOWN = "DOWN"
This provides the essential definition of health for our purposes. We don't drag around details of the degradation; that's something that we have to determine by looking at our consoles and logs and stuff. Degradation is (a) rare, and (b) nuanced. Some degradations are mere annoyances: one of the servers is being restarted. Other degradations are hints that something else might be going on that needs investigation: database primary server is down and we're running on a secondary.
Summarizing Health
A subset of the details vector, then, looks like this:
[Status.OK, Status.OK, Status.DEGRADED].
How can we summarize this?
First, we need some rules. Like these:
class Status(Enum):
OK = "OK"
DEGRADED = "DEGRADED"
DOWN = "DOWN"
def depth(self, other):
if self == self.OK:
return {self.OK: self.OK,
self.DEGRADED: self.DEGRADED,
self.DOWN: self.DEGRADED}[other]
elif self == self.DEGRADED:
return {self.OK: self.DEGRADED,
self.DEGRADED: self.DEGRADED,
self.DOWN: self.DEGRADED}[other]
elif self == self.DOWN:
return {self.OK: self.DEGRADED,
self.DEGRADED: self.DEGRADED,
self.DOWN: self.DOWN}[other]
The
depth() method implements a comparison operator that defines the relationships. This can be visualized as a table.
| depth | OK | DEGRADED | DOWN |
|---|
| OK | OK | DEGRADED | DOWN |
|---|
| DEGRADED | DEGRADED | DEGRADED | DEGRADED |
|---|
| DOWN | DOWN | DEGRADED | DOWN |
|---|
This allows us to define a function that uses reduce to summarize the vector of status values.
from functools import reduce
def summary(sequence):
return reduce(lambda a, b: a.depth(b), sequence)
The
reduce() function applies a binary operator between items in a vector. We've used
lambda a, b: a.depth(b) to turn the the
depth() method into a binary operator so it can be used with reduce.
The
summary() function is a "depth-reduction" of a vector of status objects. It's defined independently of the actual status objects. The relationships among the status levels are embedded in the class definition where they belong. The actual details of status are pleasantly opaque.
And.
We have an example of map-reduce outside the sphere of big data.
The Integer Alternative
The health rules as shown above are kind of complex. Could they be simplified? The answer is no.
Here's an alternative -- which does
not do what we want.
class Status2(IntEnum):
OK = 1
DEGRADED = 2
DOWN = 3
summary2 = lambda sequence: max(sequence)
This works in some cases, but it doesn't work in others. Another alternative is to change the order to be OK=1, DOWN=2, DEGRADED=3. This doesn't work, either. I'll leave it as an exercise to write out some of the various combinations of values and see how these differ.
JSON Representation
The final detail is JSONification of the status vector and the summary.
json.dumps({"status": summary(vector).name, "details": [s.name for s in vector]})
This converts the various Status objects to text items that fit the Swagger specification for our /health end-points. The
.name attribute reference is required to get the string labels from the enum. An alternative is to customize the JSON encoder to recognize the
Enum objects and extract their names.
Conclusion
Map-Reduce is easy. It surfaces in a number of places. The idea helps encapsulate summarization rules.