dictobject.c

python s60 1.4.5版本的源代码

/* Portions Copyright (c) 2005 Nokia Corporation */

/* Dictionary object implementation using a hash table */

#include "Python.h"

typedef PyDictEntry dictentry;
typedef PyDictObject dictobject;

/* Define this out if you don't want conversion statistics on exit. */
#undef SHOW_CONVERSION_COUNTS

/* See large comment block below.  This must be >= 1. */
#define PERTURB_SHIFT 5

/*
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.
*/

/* Object used as dummy key to fill deleted entries */
#ifndef SYMBIAN
static PyObject *dummy; /* Initialized by first call to newdictobject() */
#else
#define dummy (pyglobals->dummy)
#endif

/* forward declarations */
static dictentry *
lookdict_string(dictobject *mp, PyObject *key, long hash);

#ifdef SHOW_CONVERSION_COUNTS
const static long created = 0L;
const static long converted = 0L;

static void
show_counts(void)
{
	fprintf(stderr, "created %ld string dicts\n", created);
	fprintf(stderr, "converted %ld to normal dicts\n", converted);
	fprintf(stderr, "%.2f%% conversion rate\n", (100.0*converted)/created);
}
#endif

/* Initialization macros.
   There are two ways to create a dict:  PyDict_New() is the main C API
   function, and the tp_new slot maps to dict_new().  In the latter case we
   can save a little time over what PyDict_New does because it's guaranteed
   that the PyDictObject struct is already zeroed out.
   Everyone except dict_new() should use EMPTY_TO_MINSIZE (unless they have
   an excellent reason not to).
*/

#define INIT_NONZERO_DICT_SLOTS(mp) do {				\
	(mp)->ma_table = (mp)->ma_smalltable;				\
	(mp)->ma_mask = PyDict_MINSIZE - 1;				\
    } while(0)

#define EMPTY_TO_MINSIZE(mp) do {					\
	memset((mp)->ma_smalltable, 0, sizeof((mp)->ma_smalltable));	\
	(mp)->ma_used = (mp)->ma_fill = 0;				\
	INIT_NONZERO_DICT_SLOTS(mp);					\
    } while(0)

DL_EXPORT(PyObject *)
PyDict_New(void)
{
	register dictobject *mp;
#ifdef SYMBIAN
	SPy_Python_globals* pyglobals = PYTHON_GLOBALS; // avoid TLS reads
#endif
	if (dummy == NULL) { /* Auto-initialize dummy */
		dummy = PyString_FromString("<dummy key>");
		if (dummy == NULL)
			return NULL;
#ifdef SHOW_CONVERSION_COUNTS
		Py_AtExit(show_counts);
#endif
	}
	mp = PyObject_GC_New(dictobject, &PyDict_Type);
	if (mp == NULL)
		return NULL;
	EMPTY_TO_MINSIZE(mp);
	mp->ma_lookup = lookdict_string;
#ifdef SHOW_CONVERSION_COUNTS
	++created;
#endif
	_PyObject_GC_TRACK(mp);
	return (PyObject *)mp;
}

/*
The basic lookup function used by all operations.
This is based on Algorithm D from Knuth Vol. 3, Sec. 6.4.
Open addressing is preferred over chaining since the link overhead for
chaining would be substantial (100% with typical malloc overhead).

The initial probe index is computed as hash mod the table size. Subsequent
probe indices are computed as explained earlier.

All arithmetic on hash should ignore overflow.

(The details in this version are due to Tim Peters, building on many past
contributions by Reimer Behrends, Jyrki Alakuijala, Vladimir Marangozov and
Christian Tismer).

This function must never return NULL; failures are indicated by returning
a dictentry* for which the me_value field is NULL.  Exceptions are never
reported by this function, and outstanding exceptions are maintained.
*/

static dictentry *
lookdict(dictobject *mp, PyObject *key, register long hash)
{
	register int i;
	register unsigned int perturb;
	register dictentry *freeslot;
	register unsigned int mask = mp->ma_mask;
	dictentry *ep0 = mp->ma_table;
	register dictentry *ep;
	register int restore_error;
	register int checked_error;
	register int cmp;
	PyObject *err_type, *err_value, *err_tb;
	PyObject *startkey;

	SPy_Python_globals* pyglobals = PYTHON_GLOBALS; //  avoid TLS reads

	i = hash & mask;
	ep = &ep0[i];
	if (ep->me_key == NULL || ep->me_key == key)
		return ep;

	restore_error = checked_error = 0;
	if (ep->me_key == dummy)
		freeslot = ep;
	else {
		if (ep->me_hash == hash) {
			/* error can't have been checked yet */
			checked_error = 1;
			if (PyErr_Occurred()) {
				restore_error = 1;
				PyErr_Fetch(&err_type, &err_value, &err_tb);
			}
			startkey = ep->me_key;
			cmp = PyObject_RichCompareBool(startkey, key, Py_EQ);
			if (cmp < 0)
				PyErr_Clear();
			if (ep0 == mp->ma_table && ep->me_key == startkey) {
				if (cmp > 0)
					goto Done;
			}
			else {
				/* The compare did major nasty stuff to the
				 * dict:  start over.
				 * XXX A clever adversary could prevent this
				 * XXX from terminating.
 				 */
 				ep = lookdict(mp, key, hash);
 				goto Done;
 			}
		}
		freeslot = NULL;
	}

	/* In the loop, me_key == dummy is by far (factor of 100s) the
	   least likely outcome, so test for that last. */
	for (perturb = hash; ; perturb >>= PERTURB_SHIFT) {
		i = (i << 2) + i + perturb + 1;
		ep = &ep0[i & mask];
		if (ep->me_key == NULL) {
			if (freeslot != NULL)
				ep = freeslot;
			break;
		}
		if (ep->me_key == key)
			break;
		if (ep->me_hash == hash && ep->me_key != dummy) {
			if (!checked_error) {
				checked_error = 1;
				if (PyErr_Occurred()) {
					restore_error = 1;
					PyErr_Fetch(&err_type, &err_value,
						    &err_tb);
				}
			}
			startkey = ep->me_key;
			cmp = PyObject_RichCompareBool(startkey, key, Py_EQ);
			if (cmp < 0)
				PyErr_Clear();
			if (ep0 == mp->ma_table && ep->me_key == startkey) {
				if (cmp > 0)
					break;
			}
			else {
				/* The compare did major nasty stuff to the
				 * dict:  start over.
				 * XXX A clever adversary could prevent this
				 * XXX from terminating.
 				 */
 				ep = lookdict(mp, key, hash);
 				break;
 			}
		}
		else if (ep->me_key == dummy && freeslot == NULL)
			freeslot = ep;
	}

Done:
	if (restore_error)
		PyErr_Restore(err_type, err_value, err_tb);
	return ep;
}

/*
 * Hacked up version of lookdict which can assume keys are always strings;
 * this assumption allows testing for errors during PyObject_Compare() to
 * be dropped; string-string comparisons never raise exceptions.  This also
 * means we don't need to go through PyObject_Compare(); we can always use
 * _PyString_Eq directly.
 *
 * This is valuable because the general-case error handling in lookdict() is
 * expensive, and dicts with pure-string keys are very common.
 */
static dictentry *
lookdict_string(dictobject *mp, PyObject *key, register long hash)
{
	register int i;
	register unsigned int perturb;
	register dictentry *freeslot;
	register unsigned int mask = mp->ma_mask;
	dictentry *ep0 = mp->ma_table;
	register dictentry *ep;

	SPy_Python_globals* pyglobals = PYTHON_GLOBALS; //  avoid TLS reads

	/* Make sure this function doesn't have to handle non-string keys,
	   including subclasses of str; e.g., one reason to subclass
	   strings is to override __eq__, and for speed we don't cater to
	   that here. */
	if (!PyString_CheckExact(key)) {
#ifdef SHOW_CONVERSION_COUNTS
		++converted;
#endif
		mp->ma_lookup = lookdict;
		return lookdict(mp, key, hash);
	}
	i = hash & mask;
	ep = &ep0[i];
	if (ep->me_key == NULL || ep->me_key == key)
		return ep;
	if (ep->me_key == dummy)
		freeslot = ep;
	else {
		if (ep->me_hash == hash
		    && _PyString_Eq(ep->me_key, key)) {
			return ep;
		}
		freeslot = NULL;
	}

	/* In the loop, me_key == dummy is by far (factor of 100s) the
	   least likely outcome, so test for that last. */
	for (perturb = hash; ; perturb >>= PERTURB_SHIFT) {
		i = (i << 2) + i + perturb + 1;
		ep = &ep0[i & mask];
		if (ep->me_key == NULL)
			return freeslot == NULL ? ep : freeslot;
		if (ep->me_key == key
		    || (ep->me_hash == hash
		        && ep->me_key != dummy
			&& _PyString_Eq(ep->me_key, key)))
			return ep;
		if (ep->me_key == dummy && freeslot == NULL)
			freeslot = ep;
	}
}

/*
Internal routine to insert a new item into the table.
Used both by the internal resize routine and by the public insert routine.
Eats a reference to key and one to value.
*/
static void
insertdict(register dictobject *mp, PyObject *key, long hash, PyObject *value)
{
	PyObject *old_value;
	register dictentry *ep;
	typedef PyDictEntry *(*lookupfunc)(PyDictObject *, PyObject *, long);

	assert(mp->ma_lookup != NULL);
	ep = mp->ma_lookup(mp, key, hash);
	if (ep->me_value != NULL) {
		old_value = ep->me_value;
		ep->me_value = value;
		Py_DECREF(old_value); /* which **CAN** re-enter */
		Py_DECREF(key);
	}
	else {
		if (ep->me_key == NULL)
			mp->ma_fill++;
		else
			Py_DECREF(ep->me_key);
		ep->me_key = key;
		ep->me_hash = hash;
		ep->me_value = value;
		mp->ma_used++;
	}
}

/*
Restructure the table by allocating a new table and reinserting all
items again.  When entries have been deleted, the new table may
actually be smaller than the old one.
*/
static int
dictresize(dictobject *mp, int minused)
{
	int newsize;
	dictentry *oldtable, *newtable, *ep;
	int i;
	int is_oldtable_malloced;
	dictentry small_copy[PyDict_MINSIZE];

	SPy_Python_globals* pyglobals = PYTHON_GLOBALS; //  avoid TLS reads

	assert(minused >= 0);

	/* Find the smallest table size > minused. */
	for (newsize = PyDict_MINSIZE;
	     newsize <= minused && newsize > 0;
	     newsize <<= 1)
		;
	if (newsize <= 0) {
		PyErr_NoMemory();
		return -1;
	}

	/* Get space for a new table. */
	oldtable = mp->ma_table;
	assert(oldtable != NULL);
	is_oldtable_malloced = oldtable != mp->ma_smalltable;

	if (newsize == PyDict_MINSIZE) {
		/* A large table is shrinking, or we can't get any smaller. */
		newtable = mp->ma_smalltable;
		if (newtable == oldtable) {
			if (mp->ma_fill == mp->ma_used) {
				/* No dummies, so no point doing anything. */
				return 0;
			}
			/* We're not going to resize it, but rebuild the
			   table anyway to purge old dummy entries.
			   Subtle:  This is *necessary* if fill==size,
			   as lookdict needs at least one virgin slot to
			   terminate failing searches.  If fill < size, it's
			   merely desirable, as dummies slow searches. */
			assert(mp->ma_fill > mp->ma_used);
			memcpy(small_copy, oldtable, sizeof(small_copy));
			oldtable = small_copy;
		}
	}
	else {
		newtable = PyMem_NEW(dictentry, newsize);
		if (newtable == NULL) {
			PyErr_NoMemory();
			return -1;
		}
	}

	/* Make the dict empty, using the new table. */
	assert(newtable != oldtable);
	mp->ma_table = newtable;
	mp->ma_mask = newsize - 1;
	memset(newtable, 0, sizeof(dictentry) * newsize);
	mp->ma_used = 0;
	i = mp->ma_fill;
	mp->ma_fill = 0;

	/* Copy the data over; this is refcount-neutral for active entries;
	   dummy entries aren't copied over, of course */
	for (ep = oldtable; i > 0; ep++) {
		if (ep->me_value != NULL) {	/* active entry */
			--i;
			insertdict(mp, ep->me_key, ep->me_hash, ep->me_value);
		}
		else if (ep->me_key != NULL) {	/* dummy entry */
			--i;
			assert(ep->me_key == dummy);
			Py_DECREF(ep->me_key);
		}
		/* else key == value == NULL:  nothing to do */
	}

	if (is_oldtable_malloced)
		PyMem_DEL(oldtable);
	return 0;
}

DL_EXPORT(PyObject *)
PyDict_GetItem(PyObject *op, PyObject *key)
{
	long hash;
	dictobject *mp = (dictobject *)op;
	if (!PyDict_Check(op)) {
		return NULL;
	}
#ifdef CACHE_HASH
	if (!PyString_CheckExact(key) ||
	    (hash = ((PyStringObject *) key)->ob_shash) == -1)
#endif
	{
		hash = PyObject_Hash(key);
		if (hash == -1) {
			PyErr_Clear();
			return NULL;
		}
	}
	return (mp->ma_lookup)(mp, key, hash)->me_value;
}

/* CAUTION: PyDict_SetItem() must guarantee that it won't resize the
 * dictionary if it is merely replacing the value for an existing key.
 * This is means that it's safe to loop over a dictionary with
 * PyDict_Next() and occasionally replace a value -- but you can't