Andy Balaam’s Blog

Posts in this series: Lexer, Parser, Evaluator

I’m still really enjoying writing my Scheme interpreter Subs, which can now succesfully run all the example code from SICP up to section 2.3.4. I’ve made the changes I mentioned I would in the Lexer article, so now the Lexer returns Tokens that contain information about their basic types, and I’ve gone through a significant refactoring to replace one of the several massive switch statements with a virtual function call (Martin Fowler would be proud).

Last time I explained how the Parser takes the stream of tokens coming from the Lexer and returns a hierarchical tree of Values, each of which represents an operation or thing in the program.

The Evaluator takes in a tree of Values, “evaluates” it, and returns another Value object, which is the answer. The Evaluator class is by far the most complex part of Subs, so in this post we’ll start with an overview of how it works. Future posts will break down the different parts in more detail.

The most interesting parts of the Evaluator class interface look like this:

class Evaluator
{
public:
    std::auto_ptr<Value> EvalInContext( const Value* value,
        boost::shared_ptr<Environment>& environment );
};

The EvalInContext method takes in a Value to evaluate, and an “environment” *in which to evaluate it. Note that the in the real code it takes a couple more arguments, including a mysterious and annoying boolean called is_tail_call which will be explained later.

* More on environments later. All you need to know for now is that they provide a way of keeping hold of all the things we currently know about, identified by name.

A very simplified version of EvalInContext would look like this:

std::auto_ptr<Value> Evaluator::EvalInContext( const Value* value,
    boost::shared_ptr<Environment>& environment )
{
    if( is_symbol( value ) )
    {
        return eval_symbol( value, environment );
    }

    if( !is_combination( value ) )
    {
        return auto_ptr<Value>( value->Clone() );
    }

    const CombinationValue* combo = to_combination( value );
    CombinationValue::const_iterator it = combo->begin();

    auto_ptr<Value> evaldoptr = EvalInContext( *it, environment );

    if( special_symbol( evaldoptr ) )
    {
        return process_special_symbol( evaldoptr, combo );
    }
    else
    {
        ++it;

        CombinationValue argvalues;
        for( ; it != combo->end(); ++it )
        {
            argvalues.push_back( EvalInContext( *it, environment ).release() );
        }

        return run_procedure( evaldoptr.get(), &argvalues, *this, environment );
    }
}

If the Value to be evaluated is just a symbol, we call eval_symbol which basically looks up the symbol’s name in the environment and returns the value it finds.

If the Value is not a combination (i.e. the root of a tree of other values) it must be a basic type such as a string or an integer. In this case we simple copy the Value and return it.

Otherwise, it’s a combination. To evaluate a combination, we follow the “eval-apply” pattern. The principle is to evaluate all the Values in the combination separately, and then “apply” (run) the first value (the “operator”) as a procedure, using the other values as arguments. The first value must evaluate to something that is recognisable as a procedure, or this doesn’t make sense and we will throw an error.

In practice it’s a tiny bit more complicated. We evaluate the first Value in the combination (by calling EvalInContext recursively), then we check whether it’s a special symbol such as if or let and if so, deal with it separately. Otherwise, we evaluate all the other Values (calling EvalInContext recursively again) and put them into a new CombinationValue, and pass the operator and the arguments to run_procedure, which looks something like this:

std::auto_ptr<Value> run_procedure( const Value* operator,
    const CombinationValue* args, Evaluator& ev,
    boost::shared_ptr<Environment>& environment )
{
    if( is_builtin_procedure( operator ) )
    {
        return handle_builtin_procedure( operator, args, environment );
    }
    else
    {
        std::auto_ptr<Value> ret;

        const CompoundProcedureValue* proc = to_compound_procedure( operator );

        boost::shared_ptr<Environment> new_env =
            proc->ExtendEnvironmentWithArgs( args );

        for( CombinationValue::const_iterator it = proc->GetBody()->begin();
            it != proc->GetBody()->end(); ++it )
        {
            ret = ev.EvalInContext( *it, new_env );
        }

        return ret;
    }
}

Running a procedure means either doing something built-in (for example adding up two numbers and returning the result) or evaluating some other code, which comes from the definition of the procedure being run. First we call ExtendEnvironmentWithArgs to create a new Environment, which contains the argument Values that were supplied, and then we loop through all the sections of the body of the procedure, evaluating each one. Note that we throw away the returned Values for all sections except the last one (this is how Scheme works).

Once we’ve evaluated and applied our procedure, which of course potentially includes numerous recursive calls to EvalInContext, we end up with a Value that is returned, and we’re done.

Simple eh?

But now I must make a confession: almost everything I have written above is lie. Why would I lie to you? Because I missed out something wonderful and strange called “tail-call optimisation”. I’ll explain next time.

Posts in this series: Lexer, Parser, Evaluator

Subs has reached version 1.3.4, which means that it can successfully run all the tests from chapter 1 of SICP. This is very exciting.

Last time I explained a bit about the Lexer, which takes in a stream of characters and emits a stream of tokens: individual elements of code such as a bracket, a keyword or a symbol.

Generally, parsers emit some kind of tree structure – they understand the raw tokens as a hierarchical structure which (conceptually, at least) will be executed from the bottom up, with each branch-point in the tree being an operation of some kind.

Our parser takes in a stream of tokens, and emits a stream of parsed trees.

Parsing Scheme is very easy, because (except for a couple of exceptions I haven’t implemented yet) there is essentially one rule: start with an open bracket, see a list of things, and then find a close bracket. Of course, one of the “things” you see may itself be another bracketted list, so after parsing you get a tree structure of nested lists.

The parser in Subs looks like this:

class Parser
{
public:
    Parser( ILexer& lexer );
    std::auto_ptr<Value> NextValue();
private:
    ILexer& lexer_;
};

We supply a Lexer in the constructor, which we know will provide us with tokens when we need them via its NextToken() method. The Parser’s NextValue() method returns a pointer to a Value, which is the base class for all the “things” in the Subs interpreter.

There are lots of types of things that inherit from the Value class, but the “parse tree” (the output of the parser) will only consist of a very small subset of them:

• CombinationValue
• DecimalValue
• IntegerValue
• StringValue
• SymbolValue

The CombinationValue class forms the tree structure. Its declaration looks like this:

class CombinationValue : public Value, public std::vector<Value*>
{
    // ...
};

It is simply a list of other Values.

Note that it “owns” those Values in the sense that it deletes them when it is deleted. I have recently made the jump to make Subs depend on BOOST, so it’s on my TODO list to make containers like this use the BOOST smart containers to manage this job for me.

DecimalValue, IntegerValue and StringValue are relatively self-explanatory: they contain numbers and strings that were found as literals in the source code.

SymbolValue is essentially everything else – if the code that recognises the type of a token can’t recognise it as a bracket, a number or a string, we assume it is a symbol, and tuck it away in a SymbolValue to be understood later.

The core of the Parser looks like this (with some error-checking removed):

std::auto_ptr<Value> next_value( ILexer& lexer, Token token )
{
    if( token.Name() == "(" )
    {
        auto_ptr<CombinationValue> ret( new CombinationValue );
        while( true )
        {
            token = lexer.NextToken();
            if( token.Name() == ")" )
            {
                break;
            }
            // Recursive call
            ret->push_back( next_value( lexer, token ).release() );
        }
        return auto_ptr<Value>( ret.release() );
    }
    else
    {
        return ValueFactory::CreateValue( token );
    }
}

(Full code here: Parser.cpp) It’s a simple recursive function that creates a CombinationValue whenever it finds a bracket, and otherwise uses a ValueFactory to create an individual value.

Side note: the wisdom of using recursion could certainly be questioned, since it limits the depth of bracketting we can handle to the size of the C++ stack, but the only other way to get the same result would be to keep our own manual stack of unfinished combinations, and it just seems perverse to re-implement language features like that. What might well be more interesting would be to consider whether we can actually evaluate parts of the tree as we go, without parsing it all at once. This might make the whole setup scale rather better, but would most likely be quite complex. The implementation presented here will work fine for almost any imaginable program – remember we would need not just code whose execution is deeply nested, but whose expression in code had thousands of levels of nesting before the parser would fail.

The ValueFactory uses some basic rules such as “starts and ends with a quote” or “consists of only numbers and a decimal point” to recognise what type of Value to create, and if no rules match it defaults to a SymbolValue.

When we have completed a bracketted expression, we return a complete tree of numbers, strings and symbols, and it is ready to be evaluated, which you can think of as simply expanding the tree we already have into the full expression of itself, and then reducing it back down again to an answer.

Next time, the Evaluator and the famous eval-apply loop.

Posts in this series: Lexer, Parser, Evaluator

I have been having a lot of fun recently writing my Scheme interpreter Subs. I have never implemented a full programming language before, so I am learning fast (mostly through mistakes) and wanted to write down some of the stuff I am discovering.

Note: if you want to learn more about what Scheme is I recommend Scheme (Wikipedia) and the book SICP, which is the inspiration for all this.

I am writing everything from scratch, just because it’s fun (certainly not because I think it is in any way better to do it that way…). As we will see, that gives me opportunities to do things in different ways from the normal way such things are done. So far, every time I find out I’ve deviated from the normal way I’ve quickly discovered why I am wrong, and had to learn the true path.

Text-based programming languages, whether interpreted or compiled, need a lexer. A lexer takes in characters and spits out “tokens”, which are groups of characters that represent a single thing, such as a bracket, a variable name or a number. (Those tokens are then passed on to the parser, which I will cover in a different post.)

Scheme (and other Lisp variants) are fairly easy to lex because they don’t have much syntax – you just need to be able to understand round brackets, numbers and strings, and a couple of special cases that I won’t go into because I haven’t actually implemented them yet. (Mind you, I haven’t implemented strings yet either…)

When I started Subs I took my normal approach of doing whatever I wanted without any research or even much thought, and wrote something that I called a lexer, but which was really something else. It took in a stream of characters, read it one “word” at a time (using whitespace as separators), broke up the word if it contained bracket characters, and emitted a tree structure with each branch representing a bracketted list. It just seemed sensible, while I was watching the brackets flow by, to understand them and create a tree structure.

However, for a number of reasons, that approach turned out to be wrong.

First, reading a “word” at a time made things much harder than simply stepping through each character. It made my initial implementation slightly faster, but as soon as I realised I cared about white space (e.g. keeping track of what line we are on) it had to go. When it went it also turned out to be easier to deal with unusual code layout – for example “a(b” should be lexed as 3 tokens, but would be handed to us as a single word.

Second, and more importantly, creating a tree structure at this point was a waste of time. Creating tree structures is normally the job of a parser, and mixing these responsibilities gave me some pointless inefficiency: the lexer emitted a tree of tokens, which the parser then translated into another tree (of fully-understood code objects). It turned out that walking the tree of tokens and copying that structure in the parser was at least as hard as just taking in a flat stream of tokens and constructing the tree just once.

So, I re-wrote Lexer into something that is starting to become worthy of the name. The most interesting parts of its signature look like this:

class Lexer
{
public:
    Lexer( std::istream& instream );
    Token NextToken();
};

It takes in a reference to a stream, which will provide the characters, and when NextToken is called, it reads enough characters to determine what the next token will be, and returns it.

Side note: Subs is written using Test-Driven Development. I re-implemented the Lexer and Parser from scratch (naming the new classes NewLexer and NewParser until they were ready), modified the code that used them to use the new interfaces, ran the tests, and immediately knew that the new classes worked as well as the old ones. That level of confidence is incredibly freeing. I can’t imagine how I would ever have convinced myself the new classes were ready had I not had that safety net of 100s of tests that ensure the interpreter correctly responds to each type of input.

Currently the Token class it returns is pretty much just a string, with some information attached about where that string was in the original text. In researching this article I realised that most lexers attach more information than that to their tokens – they understand its basic type such as integer, decimal, string, bracket or symbol. At the moment in Subs, this work is done in the parser (so for the lexer each token is just a string), but I can see why it is helpful to do this work in the lexer, because for most types we have the information anyway. For example, in order to recognise that ‘”foo (bar”‘ (where the double-quotes are in the real code) is a single token we must understand that it is a string. Since we know it at this stage, we might as well record it in the Token object so we don’t have to work it out again later. When Subs supports strings, I will probably add a “type” field to the token and move this work from the parser.

On a more general programming point, following on from comments I made in a previous post, it is worth noting that the structure of the lexer (and, as we will see later, the parser) uses a technique called “streams”. What this means is that we write functions like NextToken that process a small part of the total problem, and return their answer. If we chain together functions like that (for example the parser’s equivalent function calls NextToken whenever it needs a new token) we can process arbitrarily large input without using lots of memory or slowing down. The lexer is able to process any number of tokens using a very small amount of memory, and will only fall over if it encounters a single token that is ridiculously large.

The stream style can be very useful for some problems, not only because it can be efficient with memory, but also because it can help break problems into neat, small pieces that are easier to implement correctly. It is also useful for writing code in a functional style, because it allows us to avoid having any internal state (e.g. we could easily implement NextToken to take in the stream it should read from and avoid any member variables at all in Lexer), by pushing state out into the input and output, instead of having it in our program. In this case that means instead of reading, storing and then processing a program, our lexer can simply process a few characters and emit a token without knowing anything about the surrounding code or wider context. This makes it much easier to test, and (potentially) easier to do clever things with, like prove mathematically that it is correct (!)

Next time, the parser.

Why would you write a Lisp interpreter?

I find that question difficult to answer, but the joy of open source is that I don’t have to answer it.

Subs is a Scheme interpreter written in C++, and growing out of the excitement I have felt while reading Structure and Interpretation of Computer Programs.

Subs is very incomplete, and will probably never be otherwise, but it is exciting how quickly you can write a functional Lisp interpreter. I plan to go through SICP section by section and ensure all the examples work in Subs. So far I am failing on the last example in section 1.1.1 (because I don’t support new-lines inside statements yet!) but in reality most of the work is done to support quite a lot of stuff (NOT including mutable variables…yuck).

One possible explanation, if such a thing were necessary, is that one day I want to write a new programming language that has all the simplicity and metaprogramming capability of Lisp, with the native performance and deployability of C++, and the syntactic elegance of Python. I’ve got a lot to learn first.

If you’re interested in dealing with large directed graphs of dependent objects and want some tips on how to process them in a way that scales in terms of memory usage, you may be interested in the article I wrote for Overload, which is a journal of the ACCU (an organisation promoting “professionalism in programming”).

The article is Scalable Graph Coverage, or, in my original title, “Comparing scalable algorithms for walking all nodes of a dependency graph”.

It contains lots of code, written in C++, using BOOST Graph Library. The code demonstrates some of the algorithms that are available for choosing batches of nodes to be processed together to reduce the number of nodes that are loaded several times, and without running out of memory.

There is a piece of ancient wisdom which states:

Premature optimisation is the root of all evil

This ancient wisdom is, like all ancient wisdom, correct.

However.

It appears to have been reinterpreted as essentially meaning:

Don’t design for performance until it’s too late

which is clearly, and very importantly, very wrong.

Performance is a feature

Before I begin I want us all to agree that performance is a feature.

I work on a real-life "enterprise" application. Its features are entirely driven by the need for immediate cash, not by developers following pipe dreams. And yet, for the last 6-12 months the majority of my time has been spent trying to retrofit performance into this application. Believe me, this is not because we have users who are obsessive about wasting valuable seconds – it’s because our performance sucks so hard it’s deeply embarrassing.

What is your favourite program? How well does it perform? What is your least favourite? Why?

For me, and many other people, their answers to those questions demonstrate the importance of performance. Firefox was launched to improve the performance of Mozilla. People love git because of how fast it is. Lotus Notes is hated so much partly because of its performance. My main complaints about programs I use involve performance (e.g. Thunderbird is too slow for IMAP email.)

A fast response to the user is one of those crucial inches on the journey to software that makes people happy. Making people happy gives you the kind of scary fanboyism that surrounds git. Wouldn’t you like that for your product?

What is optimisation?

When my hero said that premature optimisation was the root of all evil, he was talking in the days when you had to hand-optimise your C in assembly language. Or, more likely in his case, you had to hand-optimise your assembly language into faster assembly language. Optimisation like that very often obfuscates your code.

These days, 99% of the time, your compiler does all of this work for you, so you can have relatively comprehensible code in whatever trendy language you like, and still have the fastest possible local implementation of that in machine code.

Meanwhile, Knuth knew that 99% of your code is simply not performance-critical – it only runs a few times, or it’s just so much faster than some other bit that it doesn’t matter. The lesson we all learn eventually is that the slow bit is never quite what you thought, and you have to measure to find out where to concentrate your effort.

So, if optimisation is obfuscation, and 99% of your code isn’t the bit you need to make faster, it becomes clear that premature optimisation is the root of much evil.

But optimisation is not designing for performance.

Design for performance

Fundamentally, to get good performance, you are going to need to measure the time spent in various parts of your code (I suggest Very Sleepy if you’re on Windows) and make the slow bits faster (or happen less often). However, there are still some principles you can follow that will mean you need to spend less time doing this*.

*Which is a pity, because I really love doing it.

If you don’t design for performance you are almost certainly going to need to restructure large parts of your program later, which is very difficult and time-consuming.

There are two aspects to designing for performance: writing good local code, and creating good global structure.

Write good local code

Before you write an algorithm, think for a few minutes about how to make it work efficiently. e.g. if you’re writing C++, consider whether a deque or a list would be better than a vector for how you’re going to use it.

Think about what is idiomatic for your language and why. Think about what the computer really has to do to produce the results you are asking for. Are there going to be a lot of objects about? Maybe you can avoid copying them too many times. Are you creating and deleting a lot of objects? Can you reuse some instead? (Exercise caution with that one, though – if you start obfuscating you come into conflict with the ancient wisdom.)

Often, if you think through what you are doing, and the most efficient way to do it, you will end up with a faster and more memory-efficient algorithm, that expresses your intention better than if you’d written the first thing that came into your head. There is no downside to that.

Try to minimise the number of times you need to ask the operating system for a chunk of memory: this is surprisingly slow. E.g. in C++, prefer creating by-value data members instead of pointers to objects allocated with their own call to new.

By the way, don't worry if this sounds intimidating. The way to learn this stuff is to measure what you have done and then work out why it is slow. Next time you'll jump straight to the fast solution without the detour to the slow one.

Of course, none of this will matter if you don't have good global structure.

Create good global structure

The hardest and most important work you need to do to have good performance is to have good structure in the ways the different parts of your program interact.

This means thinking about how classes and components communicate with and control each other.

It may be helpful to use a streaming style of communication - can you send little chunks of information to be processed one by one instead of a huge great blob?

Try to make sure your components to use a common infrastructure: if different parts use different string classes you are going to spend most of your time copying from one to the other.

The hardest and deepest mystery in getting good performance (and in programming generally) is choosing the right fundamental data structures. I'll never forget the lesson I learnt** when a friend of mine had a conversation with me about a toy project I was doing (that was particularly focussed on trying to be fast) and then went away and produced code that was orders of magnitude faster, simply because he had chosen the right data structure.

**The lesson I learnt was that I am not as good as I think I am.

To be honest this section is a little shorter than I'd like because I know I don't have a lot of answers about how to do this well. I do know, though, that if you don't think about it now you will have the pain of restructuring your program later, when it's full of bug fixes that are going to get rebroken by the restructuring.

Of course, if you do think about it now you're still pretty likely to need to change it later...

Ancient wisdom

Ancient wisdom is usually right, but misinterpreting it and using it as a license to write bad code is a bad idea.

Carry on.

I’ve managed to get NNDB, my C++ data storage library which is almost, but not entirely unlike SQL, into a fit state for a release.

You can create tables, set indices on columns, insert data, retrieve data using something like a SELECT, filter it using something like WHERE (which uses indices where available), and order it using something like an ORDER BY.

So far it has been a fantastic way to get my hands dirty with some Template metaprogramming, and some C++ as it should be*, but the reason why I started this was to help me think about how databases work, so I’m really looking forward to getting into how to implement JOINs. At the moment I have only very vague ideas.

NNDB is based heavily on the STL (part of C++’s standard library), BOOST (a playground for things that might one day be in C++’s standard library, and hang-out for some of the cleverest people alive), and Loki (the continuation of Andrei Alexandrescu’s Template metaprogramming (but used for good, not evil?) library written for and explained in Modern C++ Design. This book ranks in the top 5 most exciting books I have read). I continue to be more impressed by all three the more I learn.

I have even been having discussions with the Loki devs about some code I needed for NNDB that I think might be helpful for other people using Loki. It’s called TypelistForEach and it allows you to loop (at runtime) through all the types in a Typelist and do something for each one.

The project is already working in terms of helping me think about databases. For example, I really hadn’t thought before about how expensive ORDER BY is. To implement it I needed to create a temporary std::map covering the entire result set – in a real database this obviously requires reading every single row before we can even begin to return any results. The way to avoid this is to have an index. Which reminds me: the next thing I need to do is make ORDER BY able to use indices (at the moment it’s only WHEREs that take advantage of them).

So next on my list are:

• ORDER By uses indices
• Non-unique indices (presumably implemented with a std::multimap)
• Joins

I am still very excited so you may see more releases over the next few months.

[* NNDB so far contains zero (0) calls to new and zero (0) calls to delete. Obviously the code it uses (e.g. std::vector) calls them, but that code manages all the memory for me, and most of it uses custom allocators to make it very fast. I have no idea how fast NNDB is, but maybe it could be quite fast. I am pretty confident it doesn't contain any memory errors. Famous last words...]

My latest project is called NNDB.

I’ve worked with databases for quite a long time now, and for a while I’ve been thinking about how they work under the hood. I know very little about it, but I thought I could learn a bit by trying to implement something similar myself.

I’m interested in how queries work against joined tables, how to implement indices and so on.

I’ve also been feeling that I want to do some C++ as an open source project. I do it all day at work, and for some problems it feels like the right tool for the job.

NNDB is sort-of like an in-memory database, but it works with C++ types for its columns, instead of a fixed set like varchar, int etc. You can put your own value-typed classes in the columns, and all values are type-checked at compile time.

It’s always struck me as strange that with a traditional code+SQL setup you have to keep your SQL in sync with your code manually. Of course, there are lots of trendy Object-Relational-Mapping thingies that solve that problem, but I felt it could be approached from another direction: instead of generating code to match your data, or generating SQL to match your code, why not specify your data structure in code?

In NNDB you define a table something like this:

typedef nndb::Values< unsigned long, std::string, std::string, MyDate >
    PersonValues;

class PersonTable : public nndb::Table
{
public:
    enum Columns
    {
        id,
        first_name,
        last_name,
        date_of_birth
    };
};

Actually, defining your own class is unnecessary, but it’s nice to have an enum to name your columns, and making a class gives you a nice place to put it.

To insert a row you do something like this:

PersonTable person_table;
person_table.Insert( PersonValues( 0,
    "Andy", "Balaam", MyDate( 12000000 ) ) );

You can do simple queries with WHERE and ORDER BY clauses, and I’m working on indexes.

After that will come JOINs, and anything else that takes my fancy.

I don’t anticipate NNDB being useful to anyone – it’s really for me to understand why things are as they are in the world of databases. However, you never know – it may turn out to be a fast and convenient way to store data in the C++ world. I think some of the applications that use databases don’t really need the kind of concurrent multi-user network-accessible features they have, but really just want to search, join and store reliably, and NNDB might one day grow into something that can find a niche.

To explore more, check out the complete example.

I often want to search the SGI C++ reference for a keyword. The best way I have found to jump straight to the page I want is to use Google’s “I’m Feeling Lucky” search limited to searching within sgi.com.

You can create a Firefox keyword search to allow you to do this quickly from the location bar. Now I just type Ctrl-L then e.g. “c vector” to jump straight to the page about std::vector.

To do this, make a bookmark (to anything) and then right-click it in the Bookmarks menu and choose Properties. Edit it to look like this:

The Location field is set to http://www.google.com/search?as_q=%s&as_sitesearch=sgi.com&btnI=1, and the Keyword field is just c.

This post was largely for my own benefit to remember this next time I need to set it up, but I thought it might be useful to others as the information about how to set these searches up is not easy to find.

If you want to go to a google search results page, instead of jumping to the “I’m Feeling Lucky” result, remove the &btnI=1 part.

When you first hear about the Read-Eval-Print Loop you might well think “So what?” as I did.

What’s so great about being able to type commands interactively?

But the thing is that it creeps up on you.

Everyone already knows programming is an interactive thing – we need constant feedback to validate our ideas. Programming on paper is incredibly frustrating because you have to plough on with assumptions that are probably wrong.

It’s just so comfortable to be able to try out ideas in an interactive interpreter.

I mean, it’s really not much hassle to create a new directory, make a new file, edit the file to contain the code you want to try, remember the right command to compile it, then run the program and see the results, is it?

Well, no, it isn’t, but it’s enough of a hassle that sometimes you don’t bother and you try it out in the code you are really working on, and if your work is like mine that means a minimum of 5 minutes to compile and link, and there you are playing foosball again when you could be getting something done.

The REPL gives you a place to try throwaway things extremely quickly, and when you’re working with something beautiful like Python it’s easy to get addicted.

So my mind started to wander and it struck me that a pale imitation of the REPL could be made for us poor C++ programmers, and it would generally serve the purposes I’ve described above.

So IGCC was born. Its name means “Interactive GCC” and it’s a read-eval-print loop for C++ (and, for most cases it will work for C too).

It uses the real GCC underneath, so you know you are running the exact code you would be (and it’s somewhat easier to write than a custom C/C++ interpreter) and all it does is take away the hassle of creating a simple program and compiling it with GCC.

It wraps your code in a standard C program, includes some common dependencies, and compiles it, printing the results of running them immediately. Using it looks like this:

$ ./igcc
g++> int a = 5;
g++> a += 2;
g++> cout << a << endl;
7
g++> --a;
g++> cout << a << endl;
6
g++>

Apart from all the sugar that I’d love to add, the main missing features are some kind of equivalent of the Python dir command, and code completion.

It’s not rocket science, but it might make you a little bit more interactive in your C and C++ coding, which might save you valuable foosball time.

Enjoy, improve, etc. IGCC.

Foosball image taken from http://en.wikipedia.org/wiki/File:Baby_foot_artlibre_jnl.jpg