24 days of Hackage, 2015: day 21: hood, GHood, Hoed: observation oriented debugging in Haskell

Table of contents for the whole series

A table of contents is at the top of the article for day 1.

Day 21

(Reddit discussion)

How do you debug your Haskell code?

I have to confess up front that I don’t have a good answer to the question of how I debug in Haskell.

I’m not really the right person to talk about debuggers, because the last time I used an official debugging tool was when I was developing in C and C++ and used tools such as gdb and higher-level interfaces to that, and since then, my debugging process for most languages has involved looking at stack traces and logs, insertion of “print” statements, writing finer-grained tests, and refactoring code to find out the root cause of a problem. I do not use official debugger applications any more (with breakpoints, stepping, etc.). But should I?

The question becomes even more complicated when working in Haskell, because I think it honest to say that Haskell does not have a great story for debugging.

I’ve taken a look into a family of debugging tools for Haskell, including hood , GHood , and Hoed , which are all based on the same concept: manual annotation of source code in order to generate traces that can later be analyzed in interesting ways.

Hood

Let’s take a look at hood first, standing for “Haskell Object Observation Debugger”. It’s all about observation through a type class Observable a and an instrumenting function observe :

observe :: Observable a => String -> a -> a

Secret side effects!

Warning! Despite its type signature, this function performs effects underneath using unsafePerformIO , so you have to be careful how you write code that uses observe , in order to get the traces you want.

Example of instrumenting a pipeline

Let’s instrument the word counting pipeline from day 8. We import Debug.Hood.Observe , and copy and paste the original code with modifications to insert calls to observe .

{-# LANGUAGE OverloadedStrings #-} module HoodExample where -- | hood import Debug.Hood.Observe ( Observable (..), observe, observeBase, printO) import qualified Data.MultiSet as MultiSet import qualified Data.Text.Lazy as LazyText import qualified Data.Text.Lazy.Builder as LazyBuilder import Data.Text.Lazy.Builder.Int (decimal) import Data.Ord ( Down (..)) import qualified Data.Char as Char import qualified Data.List as List import Control.Arrow ((>>>)) import Data.Monoid ((<>)) -- | Break up text into "words" separated by spaces, while treating -- non-letters as spaces, count them, output a report line for each -- word and its count in descending order of count but ascending order -- of word. wordCount :: LazyText . Text -> LazyText . Text wordCount = observe "(1) wordCount" >>> LazyText .map replaceNonLetterWithSpace >>> observe "(2) map replaceNonLetterWithSpace" >>> LazyText .words >>> observe "(3) LazyText.words" >>> MultiSet .fromList >>> observe "(4) MultiSet.fromList" >>> MultiSet .toOccurList >>> observe "(5) MultiSet.toOccurList" >>> List .sortOn (snd >>> Down ) >>> observe "(6) List.sortOn (snd >>> Down)" >>> map summarizeWordCount >>> observe "(7) map summarizeWordCount" >>> mconcat >>> observe "(8) mconcat" >>> LazyBuilder .toLazyText >>> observe "(9) LazyBuilder.toLazyText" replaceNonLetterWithSpace :: Char -> Char replaceNonLetterWithSpace c | Char .isLetter c = c | otherwise = ' ' summarizeWordCount :: ( LazyText . Text , MultiSet . Occur ) -> LazyBuilder . Builder summarizeWordCount (word, count) = LazyBuilder .fromLazyText word <> " " <> decimal count <> "

"

(I apologize for the mindless boilerplate in the String description of each observation point: one can imagine writing a Template Haskell wrapper, I guess, if desired.)

Here I’ve chosen just to instrument the natural data “stages” in the pipeline. If we wanted to, we could instrument at any other level as well, e.g. the result of calling summarizeWordCount (with (summarizeWordCount >>> observe "summarize word count" ), or many other more powerful levels, such as instrumenting a function, not just the result of a function call, e.g., (observe "summarizeWordCount function" summarizeWordCount) , which results in collecting all the calls to the function.

Implementing the Observable type class

Annoyingly, because everything’s based on the Observable type class, we ignore GHC warnings and create orphan instances for various types (the alternative, adding a newtype wrapper for everything, is rather onerous in this situation):

-- | Some orphan instances of 'Observable'. instance Observable LazyText . Text where observer = observeBase instance ( Observable a, Show a) => Observable ( MultiSet . MultiSet a) where observer = observeBase instance Observable LazyBuilder . Builder where observer = observeBase

Sample output

For simplicity, let’s use printO :: Show a => a -> IO () to perform a run that outputs a trace. More generally, there’s runO that executes an arbitrary IO action.

exampleRun :: IO () exampleRun = printO $ wordCount "I have all-too-many words; words I don't like much!"

The output (I just run this in GHCi):

-- (1) wordCount "I have all-too-many words; words I don't like much!" -- (2) map replaceNonLetterWithSpace "I have all too many words words I don t like much " -- (3) LazyText.words "I" : "have" : "all" : "too" : "many" : "words" : "words" : "I" : "don" : "t" : "like" : "much" : [] -- (4) MultiSet.fromList fromOccurList [("I",2),("all",1),("don",1),("have",1),("like",1),("many",1),("much",1),("t",1),("too",1),("words",2)] -- (5) MultiSet.toOccurList ("I", 2) : ("all", 1) : ("don", 1) : ("have", 1) : ("like", 1) : ("many", 1) : ("much", 1) : ("t", 1) : ("too", 1) : ("words", 2) : [] -- (6) List.sortOn (snd >>> Down) ("I", 2) : ("words", 2) : ("all", 1) : ("don", 1) : ("have", 1) : ("like", 1) : ("many", 1) : ("much", 1) : ("t", 1) : ("too", 1) : [] -- (7) map summarizeWordCount "I 2

" : "words 2

" : "all 1

" : "don 1

" : "have 1

" : "like 1

" : "many 1

" : "much 1

" : "t 1

" : "too 1

" : [] -- (8) mconcat "I 2

words 2

all 1

don 1

have 1

like 1

many 1

much 1

t 1

too 1

" -- (9) LazyBuilder.toLazyText "I 2

words 2

all 1

don 1

have 1

like 1

many 1

much 1

t 1

too 1

"

This can definitely be a useful way to debug pipelines, or just to generate traces for use in teaching. For example, here we might see immediately that we didn’t get the answer we wanted (classify “don’t” as a word) because at the third stage, we got “don”, which meant that something went wrong in a previous stage, of which there happened to be only one here, the removal of non-word characters.

There’s a lot more you can do with hood , but this gives a taste.

GHood

Next, we briefly move to GHood , which has the same Observable interface as hood , except we import Debug.Observe instead of Debug.Hood.Observe . So I won’t show the example code because it is literally just copy and paste and changing the import. Module copy/paste issues like this (which also arose on day 15 on IOSpec ) really make me wish Haskell had parameterized modules like ML. (And the Observable orphan instance issue as well, where ML-style modules are the natural way to plug in different ways to observe the same data type as desired.)

GHood is a Java-based graphical back end to hood . When you install GHood , it comes with a Java JAR file. Java is executed to read a log file ObserveEvents.log that is generated from running hood -instrumented code. You can animate, step back and forth, and the traces are shown in a tree structure, including showing evaluation of thunks. It’s an interesting proof of concept from 2001 but is kind of primitive. I decided not to try to include a screenshot of a sample run because the Java Swing app’s output scrolls way to the right in its window and is not really customizable.

Hoed

Hoed has an API based on hood but is a much more modern, sophisticated, active project. It enables “algorithmic debugging” by providing an interactive Web app where it uses a tree structure to repeatedly prompts you for whether results were correct, to narrows down and identify the source of the error based on your feedback.

Furthermore, it comes built in with support for hooking up to QuickCheck for property-based debugging. Check out the documentation and screen shots.

Hoed launches a local Web server you can access with a browser at port 10000. The GitHub repo contains a lot of examples.

Unfortunately, I ran out of time to get it working the way I wanted to show here in the context of using the interactive debugging system, so I’ll just have to say, Hoed is very interesting and I plan to get more into it when I have time. I will update this post later.

Conclusion

I haven’t used debugging tools much in recent years, but this could change as I find ways to make use of new tools that are easy to use. hood -based systems seem useful as one way to collect information during execution without radically restructuring code.

All the code

All my code for my article series are at this GitHub repo.