专栏首页鸿的学习笔记python源码阅读笔记之字典对象

python源码阅读笔记之字典对象

四、字典对象
首先说下list对象等要点:
1.list获取,插入对象是很快的。删除对象根据args,线性查找。排序是快排。对privt的选取很有讲究。

字典是python里的关联对象,实现是hash table。在python的实现里,dictionary的效率极其高。如注释所言。
/*
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 PyDictObject 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).
*/

具体的实现并没有胡里花俏的技巧,如果散列冲突的话,会使用开放定址法,二次探测。
当然既然已经生成了一个探测链,上面的数据就不可能轻易删除了。

具体的entry如下:
typedef struct {
    /* Cached hash code of me_key.  Note that hash codes are C longs.
     * We have to use Py_ssize_t instead because dict_popitem() abuses
     * me_hash to hold a search finger.
     */
    Py_ssize_t me_hash;
    PyObject *me_key;
    PyObject *me_value;
} PyDictEntry;

从注释看出:
There are three kinds of slots in the table:

1. Unused.  me_key == me_value == NULL
   Does not hold an active (key, value) pair now and never did.  Unused can
   transition to Active upon key insertion.  This is the only case in which
   me_key is NULL, and is each slot's initial state.

2. Active.  me_key != NULL and me_key != dummy and me_value != NULL
   Holds an active (key, value) pair.  Active can transition to Dummy upon
   key deletion.  This is the only case in which me_value != NULL.

3. Dummy.  me_key == dummy and me_value == NULL
   Previously held an active (key, value) pair, but that was deleted and an
   active pair has not yet overwritten the slot.  Dummy can transition to
   Active upon key insertion.  Dummy slots cannot be made Unused again
   (cannot have me_key set to NULL), else the probe sequence in case of
   collision would have no way to know they were once active.
这也印证了我之前的叙述,没有真正意义上的删除。

来看看核心的搜索策略:
/*
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).

lookdict() is general-purpose, and may return NULL if (and only if) a
comparison raises an exception (this was new in Python 2.5).
lookdict_string() below is specialized to string keys, comparison of which can
never raise an exception; that function can never return NULL.  For both, when
the key isn't found a PyDictEntry* is returned for which the me_value field is
NULL; this is the slot in the dict at which the key would have been found, and
the caller can (if it wishes) add the <key, value> pair to the returned
PyDictEntry*.
*/
static PyDictEntry *
lookdict(PyDictObject *mp, PyObject *key, register long hash)
{
    register size_t i;
    register size_t perturb;
    register PyDictEntry *freeslot;
    register size_t mask = (size_t)mp->ma_mask;
    PyDictEntry *ep0 = mp->ma_table;
    register PyDictEntry *ep;
    register int cmp;
    PyObject *startkey;

    i = (size_t)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) {
            startkey = ep->me_key;
            Py_INCREF(startkey);
            cmp = PyObject_RichCompareBool(startkey, key, Py_EQ);
            Py_DECREF(startkey);
            if (cmp < 0)
                return NULL;
            if (ep0 == mp->ma_table && ep->me_key == startkey) {
                if (cmp > 0)
                    return ep;
            }
            else {
                /* The compare did major nasty stuff to the
                 * dict:  start over.
                 * XXX A clever adversary could prevent this
                 * XXX from terminating.
                 */
                return lookdict(mp, key, hash);
            }
        }
        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)
            return ep;
        if (ep->me_hash == hash && ep->me_key != dummy) {
            startkey = ep->me_key;
            Py_INCREF(startkey);
            cmp = PyObject_RichCompareBool(startkey, key, Py_EQ);
            Py_DECREF(startkey);
            if (cmp < 0)
                return NULL;
            if (ep0 == mp->ma_table && ep->me_key == startkey) {
                if (cmp > 0)
                    return ep;
            }
            else {
                /* The compare did major nasty stuff to the
                 * dict:  start over.
                 * XXX A clever adversary could prevent this
                 * XXX from terminating.
                 */
                return lookdict(mp, key, hash);
            }
        }
        else if (ep->me_key == dummy && freeslot == NULL)
            freeslot = ep;
    }
    assert(0);          /* NOT REACHED */
    return 0;
}
如注释所言,这是通用的搜索策略

它会一直搜索有着同一hash值的的value,具体的搜索逻辑如下:
1.根据hash获得entry的索引,这是冲突链上的第一个索引
2.当搜索到unused状态时,表示搜索失败,ep->me_key == key时,搜索成功
3.ep->me_key == dummy && freeslot == NULL设置freeslot
4.ep->me_hash == hash && ep->me_key != dummy查找引用(与上述缓冲池概念差不多,
并不会建立一个实际意义的对象,而只是指针)是否相同。

static PyDictEntry *
lookdict(PyDictObject *mp, PyObject *key, register long hash)
{
    register size_t i;
    register size_t perturb;
    register PyDictEntry *freeslot;
    register size_t mask = (size_t)mp->ma_mask;
    PyDictEntry *ep0 = mp->ma_table;
    register PyDictEntry *ep;
    register int cmp;
    PyObject *startkey;

    i = (size_t)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) {
            startkey = ep->me_key;
            Py_INCREF(startkey);
            cmp = PyObject_RichCompareBool(startkey, key, Py_EQ);
            Py_DECREF(startkey);
            if (cmp < 0)
                return NULL;
            if (ep0 == mp->ma_table && ep->me_key == startkey) {
                if (cmp > 0)
                    return ep;
            }
            else {
                /* The compare did major nasty stuff to the
                 * dict:  start over.
                 * XXX A clever adversary could prevent this
                 * XXX from terminating.
                 */
                return lookdict(mp, key, hash);
            }
        }
        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)
            return ep;
        if (ep->me_hash == hash && ep->me_key != dummy) {
            startkey = ep->me_key;
            Py_INCREF(startkey);
            cmp = PyObject_RichCompareBool(startkey, key, Py_EQ);
            Py_DECREF(startkey);
            if (cmp < 0)
                return NULL;
            if (ep0 == mp->ma_table && ep->me_key == startkey) {
                if (cmp > 0)
                    return ep;
            }
            else {
                /* The compare did major nasty stuff to the
                 * dict:  start over.
                 * XXX A clever adversary could prevent this
                 * XXX from terminating.
                 */
                return lookdict(mp, key, hash);
            }
        }
        else if (ep->me_key == dummy && freeslot == NULL)
            freeslot = ep;
    }
    assert(0);          /* NOT REACHED */
    return 0;
}

理解上述,插入和删除就比较好理解了,只需要在必要时,增加内存空间。

好了,对象体系剖析到这里就可以了。

本文分享自微信公众号 - 鸿的学习笔记(shujuxuexizhilu),作者:鸿

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2017-07-22

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