Great reference overall, but some of these will diverge in practice: 141 bytes for a 100 char string won’t hold for non-ASCII strings for example, and will change if/when the object header overhead changes.
I doubt list and string concatenation operate in constant time, or else they affect another benchmark. E.g., you can concatenate two lists in the same time, regardless of their size, but at the cost of slower access to the second one (or both).
More contentiously: don't fret too much over performance in Python. It's a slow language (except for some external libraries, but that's not the point of the OP).
I doubt there is much to gain from knowing how much memory an empty string takes. The article or the listed numbers have a weird fixation on memory usage numbers and concrete time measurements. What is way more important to "every programmer" is time and space complexity, in order to avoid designing unnecessarily slow or memory hungry programs. Under the assumption of using Python, what is the use of knowing that your int takes 28 bytes? In the end you will have to determine, whether the program you wrote meats the performance criteria you have and if it does not, then you need a smarter algorithm or way of dealing with data. It helps very little to know that your 2d-array of 1000x1000 bools is so and so big. What helps is knowing, whether it is too much and maybe you should switch to using a large integer and a bitboard approach. Or switch language.
It's important to know that these numbers will vary based on what you're measuring, your hardware architecture, and how your particular Python binary was built.
For example, my M4 Max running Python 3.14.2 from Homebrew (built, not poured) takes 19.73MB of RAM to launch the REPL (running `python3` at a prompt).
The same Python version launched on the same system with a single invocation for `time.sleep()`[1] takes 11.70MB.
My Intel Mac running Python 3.14.2 from Homebrew (poured) takes 37.22MB of RAM to launch the REPL and 9.48MB for `time.sleep`.
My number for "how much memory it's using" comes from running `ps auxw | grep python`, taking the value of the resident set size (RSS column), and dividing by 1,024.
1: python3 -c 'from time import sleep; sleep(100)'
Initially I thought how efficient strings are... but then I understood how inefficient arithmetic is.
Interesting comparison but exact speed and IO depend on a lot of things, and unlikely one uses Mac mini in production so these numbers definitely aren't representative.
Collection Access and Iteration
How fast can you get data out of Python’s built-in collections? Here is a dramatic example of how much faster the correct data structure is. item in set or item in dict is 200x faster than item in list for just 1,000 items!
It seems to suggest an iteration for x in mylist is 200x slower than for x in myset. It’s the membership test that is much slower. Not the iteration. (Also for x in mydict is an iteration over keys not values, and so isn’t what we think of as an iteration on a dict’s ‘data’).
Also the overall title “Python Numbers Every Programmer Should Know” starts with 20 numbers that are merely interesting.
That all said, the formatting is nice and engaging.
int is larger than float, but list of floats is larger than list of ints
Then again, if you're worried about any of the numbers in this article maybe you shouldn't be using Python at all. I joke, but please do at least use Numba or Numpy so you aren't paying huge overheads for making an object of every little datum.
tfa mentions running benchmark on a multi-core platform, but doesn't mention if benchmark results used multithreading.. a brief look at the code suggests not
Every Python programmer should be thinking about far more important things than low level performance minutiae. Great reference but practically irrelevant except in rare cases where optimization is warranted. If your workload grows to the point where this stuff actually matters, great! Until then it’s a distraction.
This is really weird thing to worry about in python. But is also misleading; Python int is arbitrary precision, they can take up much more storage and arithmetic time depending in their value.
A meta-note on the title since it looks like it’s confusing a lot of commenters: The title is a play on Jeff Dean’s famous “Latency Numbers Every Programmer Should Know” from 2012. It isn’t meant to be interpreted literally. There’s a common theme in CS papers and writing to write titles that play upon themes from past papers. Another common example is the “_____ considered harmful” titles.
That's a long list of numbers that seem oddly specific. Apart from learning that f-strings are way faster than the alternatives, and certain other comparisons, I'm not sure what I would use this for day-to-day.
After skimming over all of them, it seems like most "simple" operations take on the order of 20ns. I will leave with that rule of thumb in mind.
Thanks for the feedback everyone. I appreciate your posting it @woodenchair and @aurornis for pointing out the intent of the article.
The idea of the article is NOT to suggest you should shave 0.5ns off by choosing some dramatically different algorithm or that you really need to optimize the heck out of everything.
In fact, I think a lot of what the numbers show is that over thinking the optimizations often isn't worth it (e.g. caching len(coll) into a variable rather than calling it over and over is less useful that it might seem conceptually).
Just write clean Python code. So much of it is way faster than you might have thought.
My goal was only to create a reference to what various operations cost to have a mental model.
Sad that your comment is downvoted. But yes, for those who need clarification:
1) Measurements are faulty. List of 1,000 ints can be 4x smaller. Most time measurements depend on circumstances that are not mentioned, therefore can't be reproduced.
2) Brainrot AI style. Hashmap is not "200x faster than list!", that's not how complexity works.
3) orjson/ujson are faulty, which is one of the reasons they don't replace stdlib implementation. Expect crashes, broken jsons, anything from them
4) What actually will be used in number-crunching applications - numpy or similar libraries - is not even mentioned.
A lot of people here are commenting that if you have to care about specific latency numbers in Python you should just use another language.
I disagree. A lot of important and large codebases were grown and maintained in Python (Instagram, Dropbox, OpenAI) and it's damn useful to know how to reason your way out of a Python performance problem when you inevitably hit one without dropping out into another language, which is going to be far more complex.
Python is a very useful tool, and knowing these numbers just makes you better at using the tool.
The author is a Python Software Foundation Fellow. They're great at using the tool.
In the common case, a performance problem in Python is not the result of hitting the limit of the language but the result of sloppy un-performant code, for example unnecessarily calling a function O(10_000) times in a hot loop.
For some of these, there are alternative modules you can use, so it is important to know this. But if it really matters, I would think you'd know this already?
For me, it will help with selecting what language is best for a task. I think it won't change my view that python is an excellent language to prototype in though.
I do performance optimization for a system written in Python. Most of these numbers are useless to me, because they’re completely irrelevant until they become a problem, then I measure them myself. If you are writing your code trying to save on method calls, you’re not getting any benefit from using the language and probably should pick something else.
I have some questions and requests for clarification/suspicious behavior I noticed after reviewing the results and the benchmark code, specifically:
- If slotted attribute reads and regular attribute reads are the same latency, I suspect that either the regular class may not have enough "bells on" (inheritance/metaprogramming/dunder overriding/etc) to defeat simple optimizations that cache away attribute access, thus making it equivalent in speed to slotted classes. I know that over time slotting will become less of a performance boost, but--and this is just my intuition and I may well be wrong--I don't get the impression that we're there yet.
- Similarly "read from @property" seems suspiciously fast to me. Even with descriptor-protocol awareness in the class lookup cache, the overhead of calling a method seems surprisingly similar to the overhead of accessing a field. That might be explained away by the fact that property descriptors' "get" methods are guaranteed to be the simplest and easiest to optimize of all call forms (bound method, guaranteed to never be any parameters), and so the overhead of setting up the stack/frame/args may be substantially minimized...but that would only be true if the property's method body was "return 1" or something very fast. The properties tested for these benchmarks, though, are looking up other fields on the class, so I'd expect them to be a lot slower than field access, not just a little slower (https://github.com/mikeckennedy/python-numbers-everyone-shou...).
- On the topic of "access fields of objects" (properties/dataclasses/slots/MRO/etc.), benchmarks are really hard to interpret--not just these benchmarks, all of them I've seen. That's because there are fundamentally two operations involved: resolving a field to something that produces data for it, and then accessing the data. For example, a @property is in a class's method cache, so resolving "instance.propname" is done at the speed of the methcache. That might be faster than accessing "instance.attribute" (a field, not a @property or other descriptor), depending on the inheritance geometry in play, slots, __getattr[ibute]__ overrides, and so on. On the other hand, accessing the data at "instance.propname" is going to be a lot more expensive for most @properties (because they need to call a function, use an argument stack, and usually perform other attribute lookups/call other functions/manipulate locals, etc); accessing data at "instance.attribute" is going to be fast and constant-time--one or two pointer-chases away at most.
- Nitty: why's pickling under file I/O? Those benchmarks aren't timing pickle functions that perform IO, they're benchmarking the ser/de functionality and thus should be grouped with json/pydantic/friends above.
- Asyncio's no spring chicken, but I think a lot of the benchmarks listed tell a worse story than necessary, because they don't distinguish between coroutines, Tasks, and Futures. Coroutines are cheap to have and call, but Tasks and Futures have a little more overhead when they're used (even fast CFutures) and a lot more overhead to construct since they need a lot more data resources than just a generator function (which is kinda what a raw coroutine desugars to, but that's not as true as most people think it is...another story for another time). Now, "run_until_complete{}" and "gather()" initially take their arguments and coerce them into Tasks/Futures--that detection, coercion, and construction takes time and consumes a lot of overhead. That's good...
63 comments
[ 2.4 ms ] story [ 75.6 ms ] threadMore contentiously: don't fret too much over performance in Python. It's a slow language (except for some external libraries, but that's not the point of the OP).
When this starts to matter, python stops being the right tool for the job.
Some of those number are very important:
- Set membership check is 19.0 ns, list is 3.85 μs. Knowing what data structure to use for the job is paramount.
- Write 1KB file is 35.1 μs but 1MB file is only 207 μs. Knowing the implications of I/O trade off is essential.
- sum() 1,000 integers is only 1,900 ns: Knowing to leverage the stdlib makes all the difference compared to manual loop.
Etc.
A few years ago I did a Python rewrite of a big clients code base. They had a massive calculation process that took 6 servers 2 hours.
We got it down to 1 server, 10 minutes, and it was not even the goal of the mission, just the side effect of using Python correctly.
In the end, quadratic behavior is quadratic behavior.
For example, my M4 Max running Python 3.14.2 from Homebrew (built, not poured) takes 19.73MB of RAM to launch the REPL (running `python3` at a prompt).
The same Python version launched on the same system with a single invocation for `time.sleep()`[1] takes 11.70MB.
My Intel Mac running Python 3.14.2 from Homebrew (poured) takes 37.22MB of RAM to launch the REPL and 9.48MB for `time.sleep`.
My number for "how much memory it's using" comes from running `ps auxw | grep python`, taking the value of the resident set size (RSS column), and dividing by 1,024.
1: python3 -c 'from time import sleep; sleep(100)'
Also the overall title “Python Numbers Every Programmer Should Know” starts with 20 numbers that are merely interesting.
That all said, the formatting is nice and engaging.
Then again, if you're worried about any of the numbers in this article maybe you shouldn't be using Python at all. I joke, but please do at least use Numba or Numpy so you aren't paying huge overheads for making an object of every little datum.
After skimming over all of them, it seems like most "simple" operations take on the order of 20ns. I will leave with that rule of thumb in mind.
Thanks for the feedback everyone. I appreciate your posting it @woodenchair and @aurornis for pointing out the intent of the article.
The idea of the article is NOT to suggest you should shave 0.5ns off by choosing some dramatically different algorithm or that you really need to optimize the heck out of everything.
In fact, I think a lot of what the numbers show is that over thinking the optimizations often isn't worth it (e.g. caching len(coll) into a variable rather than calling it over and over is less useful that it might seem conceptually).
Just write clean Python code. So much of it is way faster than you might have thought.
My goal was only to create a reference to what various operations cost to have a mental model.
1) Measurements are faulty. List of 1,000 ints can be 4x smaller. Most time measurements depend on circumstances that are not mentioned, therefore can't be reproduced.
2) Brainrot AI style. Hashmap is not "200x faster than list!", that's not how complexity works.
3) orjson/ujson are faulty, which is one of the reasons they don't replace stdlib implementation. Expect crashes, broken jsons, anything from them
4) What actually will be used in number-crunching applications - numpy or similar libraries - is not even mentioned.
Makes me wonder if the cpython devs have ever considered v8-like NaN-boxing or pointer stuffing.
https://rushter.com/blog/python-strings-and-memory/
I disagree. A lot of important and large codebases were grown and maintained in Python (Instagram, Dropbox, OpenAI) and it's damn useful to know how to reason your way out of a Python performance problem when you inevitably hit one without dropping out into another language, which is going to be far more complex.
Python is a very useful tool, and knowing these numbers just makes you better at using the tool. The author is a Python Software Foundation Fellow. They're great at using the tool.
In the common case, a performance problem in Python is not the result of hitting the limit of the language but the result of sloppy un-performant code, for example unnecessarily calling a function O(10_000) times in a hot loop.
I wrote up a more focused "Python latency numbers you should know" as a quiz here https://thundergolfer.com/computers-are-fast
For me, it will help with selecting what language is best for a task. I think it won't change my view that python is an excellent language to prototype in though.
O(10_000) is a really weird notation.
- If slotted attribute reads and regular attribute reads are the same latency, I suspect that either the regular class may not have enough "bells on" (inheritance/metaprogramming/dunder overriding/etc) to defeat simple optimizations that cache away attribute access, thus making it equivalent in speed to slotted classes. I know that over time slotting will become less of a performance boost, but--and this is just my intuition and I may well be wrong--I don't get the impression that we're there yet.
- Similarly "read from @property" seems suspiciously fast to me. Even with descriptor-protocol awareness in the class lookup cache, the overhead of calling a method seems surprisingly similar to the overhead of accessing a field. That might be explained away by the fact that property descriptors' "get" methods are guaranteed to be the simplest and easiest to optimize of all call forms (bound method, guaranteed to never be any parameters), and so the overhead of setting up the stack/frame/args may be substantially minimized...but that would only be true if the property's method body was "return 1" or something very fast. The properties tested for these benchmarks, though, are looking up other fields on the class, so I'd expect them to be a lot slower than field access, not just a little slower (https://github.com/mikeckennedy/python-numbers-everyone-shou...).
- On the topic of "access fields of objects" (properties/dataclasses/slots/MRO/etc.), benchmarks are really hard to interpret--not just these benchmarks, all of them I've seen. That's because there are fundamentally two operations involved: resolving a field to something that produces data for it, and then accessing the data. For example, a @property is in a class's method cache, so resolving "instance.propname" is done at the speed of the methcache. That might be faster than accessing "instance.attribute" (a field, not a @property or other descriptor), depending on the inheritance geometry in play, slots, __getattr[ibute]__ overrides, and so on. On the other hand, accessing the data at "instance.propname" is going to be a lot more expensive for most @properties (because they need to call a function, use an argument stack, and usually perform other attribute lookups/call other functions/manipulate locals, etc); accessing data at "instance.attribute" is going to be fast and constant-time--one or two pointer-chases away at most.
- Nitty: why's pickling under file I/O? Those benchmarks aren't timing pickle functions that perform IO, they're benchmarking the ser/de functionality and thus should be grouped with json/pydantic/friends above.
- Asyncio's no spring chicken, but I think a lot of the benchmarks listed tell a worse story than necessary, because they don't distinguish between coroutines, Tasks, and Futures. Coroutines are cheap to have and call, but Tasks and Futures have a little more overhead when they're used (even fast CFutures) and a lot more overhead to construct since they need a lot more data resources than just a generator function (which is kinda what a raw coroutine desugars to, but that's not as true as most people think it is...another story for another time). Now, "run_until_complete{}" and "gather()" initially take their arguments and coerce them into Tasks/Futures--that detection, coercion, and construction takes time and consumes a lot of overhead. That's good...