我刚刚在。net的自定义Linq提供程序中找到了这个:

//select is a royal pain in the ass where 
//the parameter passed to CreateQuery isn't actually the one that goes in the call
//requiring this workaround.  Not sure how straight Linq to Objects does it.

还有这个

//expressions have to be compiled in order to work with the method call on 
//straight Enumerable somehow, LINQ to objects itself magically does this.  
//Reflector shows a mess, so I (Aaron) invented my own way.  God love unit tests!

我刚刚也找到了这个…一切都会变得更好

  //ok, this is a hairy, dirty, and nasty piece of code
  //the alternatives are substantially worse than this though
  //i.e. when you do your own provider, LINQ assumes that
  //you are going to implement your own expression tree visitor and
  //do it all yourself.  Frankly, I still have xmas shopping to do
  //and I really don't want us to be foobared when we get
  //even more extension methods added to LINQ
  //therefore, we are pulling execute based on taking the calling the 
  //standard execute on enumerable, but using our own class
  //
  //optimization can occur from here on an as needed basis, that is
  //check for the value of mex.Method.Name, and write a handler for
  //that method
  //
  //also, it may not be a bad idea to rather than do this reflection 
  //each and every time somehow cache the reflected methodinfos and do 
  //lookups that way that said, we need a complete red/green/refactor 
  //cycle here before I am touching that one

还有这个

//Compile that mutherf-ker, invoke it, and get the resulting hash

一个名为monitoring.sh的文件的前两行:

#!/usr/bin/perl
# perl script disguised as a bash script

在为一家芬兰移动网络设备制造商工作期间，我深入研究了硬件抽象层，发现芬兰语单词“puukko”出现了100多次。

“puukko”是一种万能刀，每个芬兰人的工具箱里或家里都有。它被用于从剥土豆到电脑维修(我的观察)的所有事情。我相信在这个语境中，它相当于芬兰语中的“Hack”。

我的芬兰同事否认了这一点，并说这更像是“外科手术/干预”……我差点就相信了，直到我看到下面的评论:

/* Perkele ISO Puukko! */ -> Fucking Big Hack!

用于政府目的的企业等级系统中的评论

'RH 5/24/06 burn me if this dosn't work.. :)

好RH.....公司总裁/首席开发人员

有人抱怨说，“最好的”评论会带来最糟糕的评论。恕我直言，他们更有趣，所以“更好”，但这是我读过的最好的评论:

/*
Major subtleties ahead:  Most hash schemes depend on having a "good" hash
function, in the sense of simulating randomness.  Python doesn't:  its most
important hash functions (for strings and ints) are very regular in common
cases:

>>> map(hash, (0, 1, 2, 3))
[0, 1, 2, 3]
>>> map(hash, ("namea", "nameb", "namec", "named"))
[-1658398457, -1658398460, -1658398459, -1658398462]
>>>

This isn't necessarily bad!  To the contrary, in a table of size 2**i, taking
the low-order i bits as the initial table index is extremely fast, and there
are no collisions at all for dicts indexed by a contiguous range of ints.
The same is approximately true when keys are "consecutive" strings.  So this
gives better-than-random behavior in common cases, and that's very desirable.

OTOH, when collisions occur, the tendency to fill contiguous slices of the
hash table makes a good collision resolution strategy crucial.  Taking only
the last i bits of the hash code is also vulnerable:  for example, consider
[i << 16 for i in range(20000)] as a set of keys.  Since ints are their own
hash codes, and this fits in a dict of size 2**15, the last 15 bits of every
hash code are all 0:  they *all* map to the same table index.

But catering to unusual cases should not slow the usual ones, so we just take
the last i bits anyway.  It's up to collision resolution to do the rest.  If
we *usually* find the key we're looking for on the first try (and, it turns
out, we usually do -- the table load factor is kept under 2/3, so the odds
are solidly in our favor), then it makes best sense to keep the initial index
computation dirt cheap.

The first half of collision resolution is to visit table indices via this
recurrence:

    j = ((5*j) + 1) mod 2**i

For any initial j in range(2**i), repeating that 2**i times generates each
int in range(2**i) exactly once (see any text on random-number generation for
proof).  By itself, this doesn't help much:  like linear probing (setting
j += 1, or j -= 1, on each loop trip), it scans the table entries in a fixed
order.  This would be bad, except that's not the only thing we do, and it's
actually *good* in the common cases where hash keys are consecutive.  In an
example that's really too small to make this entirely clear, for a table of
size 2**3 the order of indices is:

    0 -> 1 -> 6 -> 7 -> 4 -> 5 -> 2 -> 3 -> 0 [and here it's repeating]

If two things come in at index 5, the first place we look after is index 2,
not 6, so if another comes in at index 6 the collision at 5 didn't hurt it.
Linear probing is deadly in this case because there the fixed probe order
is the *same* as the order consecutive keys are likely to arrive.  But it's
extremely unlikely hash codes will follow a 5*j+1 recurrence by accident,
and certain that consecutive hash codes do not.

The other half of the strategy is to get the other bits of the hash code
into play.  This is done by initializing a (unsigned) vrbl "perturb" to the
full hash code, and changing the recurrence to:

    j = (5*j) + 1 + perturb;
    perturb >>= PERTURB_SHIFT;
    use j % 2**i as the next table index;

Now the probe sequence depends (eventually) on every bit in the hash code,
and the pseudo-scrambling property of recurring on 5*j+1 is more valuable,
because it quickly magnifies small differences in the bits that didn't affect
the initial index.  Note that because perturb is unsigned, if the recurrence
is executed often enough perturb eventually becomes and remains 0.  At that
point (very rarely reached) the recurrence is on (just) 5*j+1 again, and
that's certain to find an empty slot eventually (since it generates every int
in range(2**i), and we make sure there's always at least one empty slot).

Selecting a good value for PERTURB_SHIFT is a balancing act.  You want it
small so that the high bits of the hash code continue to affect the probe
sequence across iterations; but you want it large so that in really bad cases
the high-order hash bits have an effect on early iterations.  5 was "the
best" in minimizing total collisions across experiments Tim Peters ran (on
both normal and pathological cases), but 4 and 6 weren't significantly worse.

Historical:  Reimer Behrends contributed the idea of using a polynomial-based
approach, using repeated multiplication by x in GF(2**n) where an irreducible
polynomial for each table size was chosen such that x was a primitive root.
Christian Tismer later extended that to use division by x instead, as an
efficient way to get the high bits of the hash code into play.  This scheme
also gave excellent collision statistics, but was more expensive:  two
if-tests were required inside the loop; computing "the next" index took about
the same number of operations but without as much potential parallelism
(e.g., computing 5*j can go on at the same time as computing 1+perturb in the
above, and then shifting perturb can be done while the table index is being
masked); and the dictobject struct required a member to hold the table's
polynomial.  In Tim's experiments the current scheme ran faster, produced
equally good collision statistics, needed less code & used less memory.

Theoretical Python 2.5 headache:  hash codes are only C "long", but
sizeof(Py_ssize_t) > sizeof(long) may be possible.  In that case, and if a
dict is genuinely huge, then only the slots directly reachable via indexing
by a C long can be the first slot in a probe sequence.  The probe sequence
will still eventually reach every slot in the table, but the collision rate
on initial probes may be much higher than this scheme was designed for.
Getting a hash code as fat as Py_ssize_t is the only real cure.  But in
practice, this probably won't make a lick of difference for many years (at
which point everyone will have terabytes of RAM on 64-bit boxes).
*/

唐纳德·克努斯的另一个经典作品:

注意上面代码中的bug; 我只是证明了它是正确的，并没有尝试过。