Don’t use lru_cache. It stores pointers to the values, so if a mutable value is changed downstream it gets changed in the cache.
That’s not proper caching.
That’s not proper caching.
`lru_cache` does not wrap your cached values in container types on its own, so if you’re getting bit by mutable values it’s probably because what you cached was a mutable container (like `list`, `dict`, or `set`). If you use primitive values in the cache, or immutable containers (like `tuple`, `namedtuple`, or `frozenset`), you won’t hit this problem.
If you were to manually cache values yourself using a `dict`, you’d see similar issues with mutability when storing `list` objects as values. It’s not a problem specific to `lru_cache`.
Edit: Can you show me a caching backend other than local memory (Redis, for example) that implements this behavior?
Imagine you `lru_cache` a zero-argument function that sleeps for a long time, then creates a new temp file with random name and returns a writable handle for it. It should be no surprise that a cached version would have to behave differently: Only one random file would ever be made, and repeated calls to the cached function would return the same handle that was opened originally.
If you didn’t expect this, you might complain that writes and seeks suddenly and mysteriously persist across what should be distinct handles, and maybe this `lru_cache` thing isn’t as harmless as advertised. But it’s not the cache’s fault that you cached an object that can mutate state outside the cache, like on a filesystem.
Logically, the behavior remains the same if instead of actual file handles we simulate files in memory, or if we use a mutable list of strings representing lines of data. Generalizing, you could cache any mutable Python collection and see that in-place changes you make to it, much like data written to a file, will still be there when you read the cache the next time.
The reason you don’t see “frameworks” for this is because tracking references to instantiated Python objects outside of the Python process is pointless — objects are garbage-collected and are not guaranteed to stay at the same memory location from one moment to the next. Further, if the lists themselves are small enough to fit in memory, surely there’s no need for out-of-memory scale to cache simple references to those objects.
The only way you can have this confusion is if the actual cached object could be somehow returned. That only happens when everything in the cache is actually just a Python object in your same process.
I disagree- it works wonders for expensive functions that return strings and ints. Maybe "don't use lru_cache with mutable values" would be more accurate.
Thanks for noting that edge case though, I've never thought about that.
If you want to reduce resource consumption, you could zip the results, or deduplicate the values using a separate dict.
There’s nothing unPythonic about a function returning mutable values.
A common use for caching is wrapping a call to a remote servicer. If the response is JSON, it’s likely to be mutable.
Nothing weird or edgy about that.
The dev creates a PR.
The cache is a decorator. It always returns a CachedValue object. The CachedValue object has an API for reading and writing the returned value.
If you write to the CachedValue object it will update Redis with the new, altered value.
It updated Redis not only for the value derived from that key, but all identical values.
This behavior was not included in the ticket. It is not mentioned in the documentation.
It only occurs when the value is mutable.
What do you say to the developer? Job well done? Or what the f*ck is wrong with you?
I’m a nice person so I’d say something nice. I’d ask what they were thinking.
But if their reasons was “this is how caches works” I’d take a serious look at their resume and ask whether they might need some mentoring.
Was the issue memory and storage?
Much better off using the same assignment semantics used throughout Python and let people choose to deepcopy() all the objects they read from the cache if they really really want to modify them later; it would even work as a simple decorator to stack onto the existing one for such cases.
Aside: If we’re missing “deep coping” anything by default in Python, it’s definitely default parameters :) deep copying caches makes much less sense than that.
Is that really what's going on here? I'm in way too deep to be sure what's best for beginner programmers, but I feel like Python must surely optimise...
sheep = 1
goats = sheep
sheep = sheep + 10
Now, if we imagine those numbers are way bigger (say 200 digits), Python still just works, whereas the low level languages I spend most time with will object because 200 digit integers don't fit in a machine word. You could imagine that copying isn't cheaper in that case, but I don't think I buy it. The 200 digit integer is a relatively small data structure, still probably cheaper to copy it than mess about with small objects which need garbage collecting.
The behavior that results from your example follows from this rule. First, evaluate `1` and call it `sheep`. Then evaluate whatever `sheep` is, once, to get `1` (the same object in memory as every other literal `1` in Python) and call it `goats`.
The last line is where the rule matters: The statement `sheep = sheep + 10` can be read as, “Evaluate `sheep + 10` and call the result `sheep`.” The statement reassigns the name `sheep` in the local namespace to point to a different object, one created by evaluating `sheep + 10`. The actual memory location that `sheep` referred to previously (containing the `int` object `1`) is not changed at all — assignment to a local will never change the value of any other local.
This is easy to remember if you recall that a local namespace is effectively just a `dict`. Your example is equivalent to:
d = {}
d["sheep"] = 1
d["goats"] = d["sheep"]
d["sheep"] = d["sheep"] + 10
For these particular numbers, CPython has one optimization I know of: Small integers (from -5 to 256) are pre-initialized and shared.
See https://docs.python.org/3/c-api/long.html#c.PyLong_FromLong
This is generally invisible to the python programmer, but leads to https://wsvincent.com/python-wat-integer-cache/
On my system these "cached" integers seem to each be 32 byte objects, so, over 8kB of RAM is used by CPython to "cache" the integers -5 through 256 in this way.
There's similar craziness over in String town. If I mint the exact same string a dozen times from a constant, those all have the same id, presumably Python has a similar "cache" of such constant strings. But if I assemble the same result string with concatenation, each of the identical strings has a different id (this is in 3.9)
So, my model of what's going on in a Python program was completely wrong. But the simple pedagogic model in the article was also wrong, just not in a way that's going to trip up new Python programmers.
Not really. You're hitting constant folding: https://arpitbhayani.me/blogs/constant-folding-python
This isn't a pre-made list of certain strings that should be cached, this is the compiler noticing that you mentioned the same constant a bunch of times.
Also in general you would see a lot of things with the same id because python uses references all over the place. E.g. assignment never copies.
I don't think it does that. You can try it yourself:
>>> x = 123456
>>> y = x
>>> id(x)
139871830996560
>>> id(y)
139871830996560 @lru_cache(post=deepcopy, ...)
Something something power of function composition.