The tests are here https://massimo-zaniboni.github.io/threads-post/ and this is the repo containing the complete source code https://github.com/massimo-zaniboni/threads-post
Sunday, September 3, 2017
Tuesday, July 25, 2017
The Distributed Internet/Distro Dream
My dream is an internet where all static content is distributed using an automatic caching protocol like IPFS, and where (Linux) distributions use Nix-inspired packages.
In my dream Internet Service Providers (ISP) will install IPFS servers on their POPs, and every local computer will be an IPFS node, maybe caching content only locally. Doing so:
In my dreams Linux will have a standard API for managing packages in a Nix-like way, instead of global conflicting packages:
In my dream Internet Service Providers (ISP) will install IPFS servers on their POPs, and every local computer will be an IPFS node, maybe caching content only locally. Doing so:
- files will be downloaded only one time, and then cached locally
- copy of files will be downloaded from the nearest networks
- static content will remain available also if the reference server is down
- etc..
In my dreams Linux will have a standard API for managing packages in a Nix-like way, instead of global conflicting packages:
- every application will be developed and built as a distinct package
- different versions of the same package would cohexist on the same system
- the build system will have a standard way for expressing relationships between the different used packages
- every target of a build will be a package
- every distribution of an application, will be a distribution of its package with related dependencies
- every programming language environment will use the API instead of implementing a custom repository of libraries
- every package can be customized using an object-oriented like approach: i.e. expressing only the differences respect the upstream/reference code and build configuration
- every package can be a set of packages, and so it can represent a distributions or other high-level concepts
- there can be explicit hierarchical chains of maintainers from end-user distro to upstream
- donations, and contract supports can be shared semi-automatically between maintainers
- etc..
My dream is a world where things can be shared and modified efficiently.
Wednesday, July 5, 2017
Understanding Traversable
I’m a poor object-oriented programmer, not versed to category theory and advanced Haskell. I understand
In particular what is the meaning of
We will start with an example using
Studying type definition, the semantic is clear: extract
We will remove
If we pass a
We can rewrite these functions using the do-notation:
So what is the semantic of
For playing with
and then:
All elements of the list are even, and so the same list without modifications is returned.
If we insert a not even element, then
As usual we can rewrite using the
In this case, the
The List applicative behavior is similar to Prolog: it combines all possible combinations of generators, filtering on constraints.
The List functor behavior is the usual
where
Due to specific implementation of
The corresponding function defined using Prolog-like semantic is
In case of
In this case the
But this behavior is very different from the
If we play with
The semantic using Prolog-like rules is not immediate. A first but bad version is:
It isn’t correct because it combines too few things.
The behavior of
In this form the code is clear, so we don’t derive a Prolog-like version.
In this case
The motivations are:
So
Functor
, Applicative
, Foldable
, but what is the sense behind Traversable
?class (Functor t, Foldable t) => Traversable t where
-- | Map each element of a structure to an action, evaluate these actions
-- from left to right, and collect the results. For a version that ignores
-- the results see 'Data.Foldable.traverse_'.
traverse :: Applicative f => (a -> f b) -> t a -> f (t b)
traverse f = sequenceA . fmap f
-- | Evaluate each action in the structure from left to right, and
-- and collect the results. For a version that ignores the results
-- see 'Data.Foldable.sequenceA_'.
sequenceA :: Applicative f => t (f a) -> f (t a)
sequenceA = traverse id
In particular what is the meaning of
traverse f = sequenceA . fmap f
?TL;DR
For beginner Haskell programmers specific class instances are more understandable and useful than abstract class definitions. It is better understanding and using the IO Monad, or the Maybe Monad, than knowing perfectly the theory behind the generic Monad class definition. For the same reasons, it is better understanding different instances ofTraversable
class, than the abstract theory behind it.Traverse a List applying Maybe semantic
Traversable
uses functions with two generic types: f
that is an Applicative
context, t
that is a Foldable
Functor
.We will start with an example using
Maybe
for the applicative part (the f
), and List
for the Functor
part (the t
).Maybe
has a simple and clear Applicative
semantic: stop the computation and return Nothing
when one of intermediate passages returns Nothing
.List
has a simple Functor
semantic: fmap
applies a function to every element of the list.sequenceA
ThesequenceA
function became sequenceA :: [Maybe a] -> Maybe [a]
Just
from the elements of the list, and if there is any Nothing
element, return Nothing
instead of the list.{-# LANGUAGE ScopedTypeVariables #-}
module Main where
import Data.Traversable
import Control.Applicative
import Data.List as L
-- | All the tests of the code assertions.
main = putStrLn $ show $ L.all id [
mtest1
, mtest2
, mtest1M
, mtest2M
, mtest3
, mtest4
, mtest3M
, mtest4M
, mtest4M'
, stest1
, stest2
, stest3
, ltest1
, ltest2
]
Just
from the list:mf1 :: Maybe [Int]
mf1 = sequenceA [Just 1, Just 2]
mtest1 :: Bool
mtest1 = (mf1 == Just [1, 2])
Nothing
value, then the entire result became Nothing
:mf2 :: Maybe [Int]
mf2 = sequenceA [Just 1, Just 2, Nothing]
mtest2 :: Bool
mtest2 = (mf2 == Nothing)
mf1M :: Maybe [Int]
mf1M = do
x <- return 1
y <- return 2
return [x, y]
mtest1M = (mf1 == mf1M)
mf2M :: Maybe [Int]
mf2M = do
x <- return 1
y <- return 2
z <- empty
return [x, y, z]
mtest2M = (mf2 == mf2M)
sequenceA
for this specific instance of Traversable
? The semantic is self-explanatory studying its type sequenceA :: [Maybe a] -> Maybe [a]
. But sadly for us, we can not generalize it, as we will see in next sections.traverse
Thetraverse
function became: traverse :: (a -> Maybe b) -> [a] -> Maybe [b]
traverse
, we need a function returning Maybe
:mf :: Int -> Maybe Int
mf x = if (even x) then Just x else Nothing
and then:
mf3 :: Maybe [Int]
mf3 = traverse mf [2,4]
mtest3 = (mf3 == Just [2,4])
All elements of the list are even, and so the same list without modifications is returned.
If we insert a not even element, then
Nothing
is returned:
mf4 :: Maybe [Int]
mf4 = traverse mf [2,4,5]
mtest4 = (mf4 == Nothing)
As usual we can rewrite using the
do
notation
mf3M :: Maybe [Int]
mf3M = do
x <- mf 2
y <- mf 4
return [x, y]
mtest3M = (mf3M == mf3)
mf4M :: Maybe [Int]
mf4M = do
x <- mf 2
y <- mf 4
z <- mf 5
return [x, y, z]
mtest4M = (mf4M == mf4)
Traversable
defines also mapM
that is simply traverse
. We can rewrite in this way:
mf4M' :: Maybe [Int]
mf4M' = do
r <- mapM mf [2, 4, 5]
return r
mtest4M' = (mf4M' == mf4M)
In this case, the
traverse
function captures the well known concept of mapM
inside the Maybe
Applicative
.Traverse a List applying List semantic
Now we will use an instance ofTraversable
with List
both as container (for t
), and as Applicative
(for f
).The List applicative behavior is similar to Prolog: it combines all possible combinations of generators, filtering on constraints.
The List functor behavior is the usual
map
: it applies a function to every element of the list.sequenceA
In this case, we have:
sequenceA :: [[a]] -> [[a]]
sequenceA = traverse id
where
traverse :: (a -> [b]) -> [a] -> [[b]]
traverse f = List.foldr cons_f (pure [])
where consF x ys = (:) <$> f x <*> ys
Due to specific implementation of
traverse
for List
, sequenceA
became a combinatoric function performing a “transpose-like” operation, combining columns with lines:
stest1 = sequenceA [[1,2,3], [4,5]] == [[1,4],[1,5],[2,4],[2,5],[3,4],[3,5]]
The corresponding function defined using Prolog-like semantic is
transposeAndCombine :: [[a]] -> [[a]]
transposeAndCombine linesAndCols = tc [] linesAndCols
where
tc :: [a] -> [[a]] -> [[a]]
tc r1 [] = return r1
tc r1 (xs:rs) = do
x <- xs
tc (r1 ++ [x]) rs
stest3 = let l = [[1,2,3], [4,5]]
in transposeAndCombine l == sequenceA l
In case of
Maybe
the Nothing
value invalidates all the computations. In case of List
the value invalidating all computations is []
:
stest2 = sequenceA [[1,2,3], [4,5], []] == []
In this case the
sequenceA
function has a rather useful and reusable behaviour: transpose and combine columns with lines. Knowing this behavior in advance, the sequenceA
function can be called directly, without using the do-notation form that is less clear.But this behavior is very different from the
Traversable
instance with Maybe
and Applicative
. So the Traversable
class does not help us in predicting the sequenceA
semantic. We had to study it case by case.traverse
traverse
became:
traverse :: (a -> [b]) -> [a] -> [[b]]
traverse f = List.foldr cons_f (pure [])
where consF x ys = (:) <$> f x <*> ys
If we play with
traverse
, we obtain:
lf :: Int -> [Int]
lf x = [x * 10, x * 100]
lxs :: [Int]
lxs = [1, 2]
lf1 :: [[Int]]
lf1 = traverse lf lxs
lfxs = [[10, 20], [10, 200], [100, 20], [100, 200]]
ltest1 = (lf1 == lfxs)
The semantic using Prolog-like rules is not immediate. A first but bad version is:
lf1M' :: [Int]
lf1M' = do
x <- lxs
y <- lf x
return y
ltest1M' = (lf1M' == [10, 20, 100, 200])
It isn’t correct because it combines too few things.
The behavior of
traverse
is: transposeAndCombine
the results of the function applications with the list of possible arguments. The corresponding code is:
lf1AsTransf :: [[Int]]
lf1AsTransf = transposeAndCombine (map lf lxs)
ltest2 = (lf1AsTransf == lfxs)
In this form the code is clear, so we don’t derive a Prolog-like version.
In this case
traverse
has not a basic and natural semantic. Probably there are not much cases in real-life code, where we want such strange and extreme combinatoric behavior. Probably we are more interested to the behavior of functions like lfm1M'
, expressed with the do-notation.Conclusions
After these examples, we can return to our original question: what is the meaning oftraverse f = sequenceA . fmap f
? My lazy and arrogant answer is: I don’t bother! :-)The motivations are:
- also if I can grasp the concepts behind
Traversable
, it will not help in understanding real-life code usingTraversable
, because every instance oftraverse
andsequenceA
has a very different and specific semantic. - so I must study each instance of
Traversable
in isolation, for understanding its behavior. This is similar to IO Monad, Maybe Monad, Either Monad: knowing the Monad concepts helps, but every instance has its proper semantic and usage case, and it must be mastered apart. - maybe instance by instance, I can someday comprehend the concepts behind
Traversable
, but up to date this can be postponed, because it seems more complex to master respectFunctor
and other base class. - in the end I’m a poor OO programmer, not a mathematician expert of category-theory.
So
Traversable
can represent many different things in Haskell. Also if I don’t understand completely its meaning, because it is too much abstract and tied to category-theory universe, this does not prevent me from studying and comprehending perfectly its specific instances, and using them in end-user code.
Tuesday, January 24, 2017
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