Lazy prime number generator in Python

Published: 2019-02-27
Tags: python, generators

A while ago I read The Genuine Sieve of Eratosthenes paper by Melissa E. O'Neill where the author explains how the algorithm that is often portrayed in introductory texts on functional programming as the lazy functional implementation of Sieve of Eratosthenes is actually not the sieve and is actually even worse than trial division (testing numbers by checking if any prime already found divides them). She then outlines and implements an algorithm that is actually a lazy and functional sieve and uses that to show how important the choice of a suitable datastructure can be for performance.

After reading the paper I wrote a quick implementation of the algorithm in Clojure and forgot about it. I recently had to do some data processing at work. I had to load some data, do some transformations and save it for later analysis (classic ETL). I wasn't sure how much data there would be and so I decided it's best to write my script in a lazy manner, so it wouldn't have to fetch all the data before starting to process it. I was doing this in Python and so had a chance to explore Python generators a little more in-depth (I've somehow managed to ignore this really neat part of Python up until then) and as I learned more about generator expressions and functions I realised that the lazy algorithm for generating prime numbers can be expressed really easily and clearly using generator functions.

In this post I want to quickly run through the basics of generator functions in Python and explain how they can be used to create lazy sequences. Then I'll explain how the trial division algorithm works and use it to create a generator function for prime numbers. And lastly I'll go through the prime number algorithm described by Prof. O'Neill and show how to implement it in Python.

I also want to thank my friend Bram Geron for his input and help while I was experimenting with some of the different possible implemenetations, working through the differences in complexity and learning about the different tools out there. At least for me this sort of process is way more fun and rewarding when you can share it with others and bounce ideas off each other.

Generator functions

In Python generator functions are one of the ways to create iterators, which is to say objects that have a __next__ method. When you call the Python inbuilt next function and pass it an iterator it in turn calls the __next__ method, which returs the next item in the iterator. This is done implicitly in for loops and many other places in Python, so you don't often have to use next in your own code. Mind you this is a bit of a simplification and if you'd like a full view of the differences between iterables, iterators, and other related interfaces you might want to check out Iterables vs. Iterators vs. Generators by Vincent Driessen.

Generator functions offer a nice and flexible way for creating iterators without the boilerplate of making one by creating a class. Instead, a generator looks exactly like a function, except it has one or more yield statements. When you call this function a generator object is returned (generators are also iterators), but the function is not executed. Instead, once next is called on the generator object the function actually runs. Once the execution reaches a yield statement the argument is returned as the first item of the iterator and execution is paused. The next time next is called the execution resumes from the same point until the next yield statement is reached. When the generator is finished it can either use an empty return statement or the function can simply finish without one.

Let's write a generator that describes the Fibonacci sequence as a a simple example. We first set up two variables to contain the two previous items of the sequence and set them both to 1 as we know those are the first two items. We can emit those immediately after and then enter an infinite loop where we calculate the next item by adding the two previous items, emit the new item and then update our values for the two previous items of the sequence.

def fibonacci():
    a = 1
    b = 1
    yield a
    yield b
    while True:
        c = a + b
        yield c
        a = b
        b = c

We can then use it to create a generator object, get the first 10 items using itertools.islice and print them.

from itertools import islice

fs = fibonacci()
for f in islice(fs, 10):

There is more to say about generator functions in Python, including yield from and sending values back to the generator using the .send() method, but this will be enough to cover the basics required for the rest of the article.

Trial division

Moving on to actually generating some prime numbers - trial division is an algorithm that more or less follows from the definition of prime numbers. You take a candidate number and check how many numbers divide into it. Of course, you can make some optimizations. First of all, if you find a number (other than 1 or itself) that divides into it you already know it's not a prime number, so you don't have to continue and instead move on to the next candidate. Also, if you have already found all the prime numbers smaller than the current candidate then it's much faster to only check the current candidate for prime factors. Lastly, there's no need to check factors larger than the square root of the candidate - for every factor greater than the square root there will be one that is less.

from itertools import count

def primes_trial_division():
    primes = []
    for candidate in count(2):
        factor_found = False
        for p in primes:
            if candidate % p == 0:
                factor_found = True
            elif p * p > candidate:
        if not factor_found:
            yield candidate

Here we start with an empty list of primes and using itertools.count start counting up starting with 2. We could add 2 to the initial list of primes and count up from 3 with a step of 2 and it would create a performance boost. However, it's a constant factor improvement and does not affect the time complexity. If you are interested in making this more performant you should go through the paper linked at the top of this post - the author goes through a generalization of the stepping technique called a wheel that can be used to further improve constant factors.

For every candidate we iterate over the list of primes we have already found. If we find that a prime from our list divides into the current candidate we note that we have found a factor and break the loop. In case we find that we've started checking primes that are larger than the square root of the candidate we break as well - if we haven't come across a factor by then there's no point iterating through the rest of the prime list.

Finally, if we see that we haven't found a prime factor of our candidate we know that the candidate is in fact a prime number, so we yield it and append to the list of known primes. Since itertools.count produces an infinite sequence this generator function will produce and infinite sequence of prime numbers as well.

If you're interested in the time complexity there is a great discussion in the paper linked to at the top of the post. For trial division it turns out to be \(O(n \sqrt{n} / (\log n)^2)\), where \(n\) is the number of candidates we want to test.

Lazy sieve

To implement a lazy sieve as described by Prof. O'Neill we use the general idea of the classic sieve of Eratosthenes algorithm, but whereas the classic sieve removes all multiples of all the primes already discovered from a given starting set the lazy sieve tries to do as little work upfront as possible. The algorithm keeps a map where the values are (maybe empty) lists of prime numbers and the key of such list is the number that is the next one divisible by the primes in the list. In other words, it maps from numbers to their prime factors. This map starts out empty as we don't know any prime numbers yet.

from itertools import count

def primes_lazy_sieve():
    multiples = {}
    for candidate in count(2):
        candidate_divisors = multiples.pop(candidate, None)
        if candidate_divisors is None:
            yield candidate
            multiples[candidate * candidate] = [candidate]
            for divisor in candidate_divisors:
                multiples.setdefault(candidate + divisor, []).append(divisor)

We again generate our candidates by counting up from 2 and we look up the candidate in the map. If the map does not contain the candidate (as in there is no such key) it means that it is a prime number (it does not have any prime factors other than itself) - in that case we yield it and then add it to the list of known prime factors for candidate times 2 (in the code above it is instead candidate squared, I will explain this later). If the map contains the candidate it means it's instead a composite number. We remove the record from the map, but the prime divisors get "propagated" up to their next multiple, which is the candidate plus the prime. This way every prime we've found is only ever in one of the lists at any given time, always assigned to the key that is the next multiple of that prime (next with respect to the current candidate).

The reason it is possible to use candidate squared instead of candidate times 2 is because the candidate times 2 case will already be eliminated since 2 is one of its prime factors. The first composite number that would not be eliminated by smaller prime factors is candidate squared, so we instead start there.

In her paper Prof. O'Neill the importance of choosing the right data structure for the job and shows that using a priority queue yields better time complexity. This makes sense - in Haskell the Data.Map datastructure is based on binary trees and so every access incurs an \(O(\log n)\) cost, whereas a priority queue offers \(O(1)\) access to the first item. In this case it's the item with the lowest priority and the only one we need to check to figure out if our candidate has any factors. This change of datastructure creates a significant performance difference.

Before I started writing this post I tried to implement this suggestion and speed up the lazy sieve above using a Python priority queue library called heapq. To my surprise the complexity increased quite dramatically. After a little investigation into why I'm having such different results I looked up Haskell's Data.Map and realized it's based on binary trees while Python dictionaries are hash tables and thus have \(O(1)\) time complexity for getting, setting and deleting items. Since these are the only operations I'm using there's not much that can be done to improve complexity on this front. The hashing function that Python uses for positive integers is simply identity, so there's not much performance to be gained there either.

Looking at the time complexity of the lazy sieve we see that every candidate regardless of whether or not it's a prime number will incur a pop from the map, which means there is an \(O(n)\) component when looking for all primes less than \(n\). If the candidate is a prime we add it to the map and if it's a composite we iterate over all it's prime factors and add them to the map. Instead of thinking about counting the number of factors of composites we can consider that every prime candidate will be added to the map and then moved \(n / p\) times (starting at candidate squared means this number is lower, but that's not significant for the time complexity calculation) and each of those operations is constant in time. This leads to the same complexity calculation as you would have for the classic sieve of Eratosthenes, which means that the complexity of the lazy sieve is \(O(n \log \log n)\). If you're interested in the fine details I refer again to the paper by Prof. O'Neill where she derives this result.

The bigger picture

My intention when writing this post was not only to cover basic syntax of Python generator functions and run through the lazy sieve algorithm, but really to show how generator functions can give us a way to express certain lazy algorithms in a very readable and concise way. I know I personally find them much easier to follow than the same thing expressed as a class implementing the iterable interface. In a lot of ways it's about knowing what variables are in scope and who else has access to them. In the case of a generator expression it's pretty obvious - regular function scoping rules apply, the variables are local and no other code has access to them. In an iterable object the answer is "it depends".

Even if you don't see any use for generator functions in the code you're writing at the moment I'd suggest having a play with them to get a sense of how they work and how to use them. At the very least it will show you another way of looking at problems and provide a new way to structure your code.