cpython/Python/hamt.c at main · https-github-com-nzysoft/cpython · GitHub

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#include "Python.h"

#include "pycore_bitutils.h" // _Py_popcount32()

#include "pycore_hamt.h"

#include "pycore_initconfig.h" // _PyStatus_OK()

#include "pycore_long.h" // _PyLong_Format()

#include "pycore_object.h" // _PyObject_GC_TRACK()

#include <stddef.h> // offsetof()

This file provides an implementation of an immutable mapping using the

Hash Array Mapped Trie (or HAMT) datastructure.

This design allows to have:

1. Efficient copy: immutable mappings can be copied by reference,

making it an O(1) operation.

2. Efficient mutations: due to structural sharing, only a portion of

the trie needs to be copied when the collection is mutated. The

cost of set/delete operations is O(log N).

3. Efficient lookups: O(log N).

(where N is number of key/value items in the immutable mapping.)

HAMT

====

The core idea of HAMT is that the shape of the trie is encoded into the

hashes of keys.

Say we want to store a K/V pair in our mapping. First, we calculate the

hash of K, let's say it's 19830128, or in binary:

0b1001011101001010101110000 = 19830128

Now let's partition this bit representation of the hash into blocks of

5 bits each:

0b00_00000_10010_11101_00101_01011_10000 = 19830128

(6) (5) (4) (3) (2) (1)

Each block of 5 bits represents a number between 0 and 31. So if we have

a tree that consists of nodes, each of which is an array of 32 pointers,

those 5-bit blocks will encode a position on a single tree level.

For example, storing the key K with hash 19830128, results in the following

tree structure:

(array of 32 pointers)

+---+ -- +----+----+----+ -- +----+

root node | 0 | .. | 15 | 16 | 17 | .. | 31 | 0b10000 = 16 (1)

(level 1) +---+ -- +----+----+----+ -- +----+

+---+ -- +----+----+----+ -- +----+

a 2nd level node | 0 | .. | 10 | 11 | 12 | .. | 31 | 0b01011 = 11 (2)

+---+ -- +----+----+----+ -- +----+

a 3rd level node | 0 | .. | 04 | 05 | 06 | .. | 31 | 0b00101 = 5 (3)

+---+ -- +----+----+----+ -- +----+

+---+ -- +----+----+----+----+

a 4th level node | 0 | .. | 04 | 29 | 30 | 31 | 0b11101 = 29 (4)

+---+ -- +----+----+----+----+

+---+ -- +----+----+----+ -- +----+

a 5th level node | 0 | .. | 17 | 18 | 19 | .. | 31 | 0b10010 = 18 (5)

+---+ -- +----+----+----+ -- +----+

+--------------+

+---+ -- +----+----+----+ -- +----+

a 6th level node | 0 | .. | 15 | 16 | 17 | .. | 31 | 0b00000 = 0 (6)

+---+ -- +----+----+----+ -- +----+

V -- our value (or collision)

To rehash: for a K/V pair, the hash of K encodes where in the tree V will

be stored.

To optimize memory footprint and handle hash collisions, our implementation

uses three different types of nodes:

* A Bitmap node;

* An Array node;

* A Collision node.

Because we implement an immutable dictionary, our nodes are also

immutable. Therefore, when we need to modify a node, we copy it, and

do that modification to the copy.

Array Nodes

-----------

These nodes are very simple. Essentially they are arrays of 32 pointers

we used to illustrate the high-level idea in the previous section.

We use Array nodes only when we need to store more than 16 pointers

in a single node.

Array nodes do not store key objects or value objects. They are used

only as an indirection level - their pointers point to other nodes in

the tree.

Bitmap Node

-----------

Allocating a new 32-pointers array for every node of our tree would be

very expensive. Unless we store millions of keys, most of tree nodes would

be very sparse.

When we have less than 16 elements in a node, we don't want to use the

Array node, that would mean that we waste a lot of memory. Instead,

we can use bitmap compression and can have just as many pointers

as we need!

Bitmap nodes consist of two fields:

1. An array of pointers. If a Bitmap node holds N elements, the

array will be of N pointers.

2. A 32bit integer -- a bitmap field. If an N-th bit is set in the

bitmap, it means that the node has an N-th element.

For example, say we need to store a 3 elements sparse array:

+---+ -- +---+ -- +----+ -- +----+

| 0 | .. | 4 | .. | 11 | .. | 17 |

+---+ -- +---+ -- +----+ -- +----+

| | |

o1 o2 o3

We allocate a three-pointer Bitmap node. Its bitmap field will be

then set to:

0b_00100_00010_00000_10000 == (1 << 17) | (1 << 11) | (1 << 4)

To check if our Bitmap node has an I-th element we can do:

bitmap & (1 << I)

And here's a formula to calculate a position in our pointer array

which would correspond to an I-th element:

popcount(bitmap & ((1 << I) - 1))

Let's break it down:

* `popcount` is a function that returns a number of bits set to 1;

* `((1 << I) - 1)` is a mask to filter the bitmask to contain bits

set to the *right* of our bit.

So for our 17, 11, and 4 indexes:

* bitmap & ((1 << 17) - 1) == 0b100000010000 => 2 bits are set => index is 2.

* bitmap & ((1 << 11) - 1) == 0b10000 => 1 bit is set => index is 1.

* bitmap & ((1 << 4) - 1) == 0b0 => 0 bits are set => index is 0.

To conclude: Bitmap nodes are just like Array nodes -- they can store

a number of pointers, but use bitmap compression to eliminate unused

pointers.

Bitmap nodes have two pointers for each item:

+----+----+----+----+ -- +----+----+

| k1 | v1 | k2 | v2 | .. | kN | vN |

+----+----+----+----+ -- +----+----+

When kI == NULL, vI points to another tree level.

When kI != NULL, the actual key object is stored in kI, and its

value is stored in vI.

Collision Nodes

---------------

Collision nodes are simple arrays of pointers -- two pointers per

key/value. When there's a hash collision, say for k1/v1 and k2/v2

we have `hash(k1)==hash(k2)`. Then our collision node will be:

+----+----+----+----+

| k1 | v1 | k2 | v2 |

+----+----+----+----+

Tree Structure

--------------

All nodes are PyObjects.

The `PyHamtObject` object has a pointer to the root node (h_root),

and has a length field (h_count).

High-level functions accept a PyHamtObject object and dispatch to

lower-level functions depending on what kind of node h_root points to.

Operations

==========

There are three fundamental operations on an immutable dictionary:

1. "o.assoc(k, v)" will return a new immutable dictionary, that will be

a copy of "o", but with the "k/v" item set.

Functions in this file:

hamt_node_assoc, hamt_node_bitmap_assoc,

hamt_node_array_assoc, hamt_node_collision_assoc

`hamt_node_assoc` function accepts a node object, and calls

other functions depending on its actual type.

2. "o.find(k)" will lookup key "k" in "o".

Functions:

hamt_node_find, hamt_node_bitmap_find,

hamt_node_array_find, hamt_node_collision_find

3. "o.without(k)" will return a new immutable dictionary, that will be

a copy of "o", buth without the "k" key.

Functions:

hamt_node_without, hamt_node_bitmap_without,

hamt_node_array_without, hamt_node_collision_without

Further Reading

===============

1. http://blog.higher-order.net/2009/09/08/understanding-clojures-persistenthashmap-deftwice.html

2. http://blog.higher-order.net/2010/08/16/assoc-and-clojures-persistenthashmap-part-ii.html

3. Clojure's PersistentHashMap implementation:

https://github.com/clojure/clojure/blob/master/src/jvm/clojure/lang/PersistentHashMap.java

Debug

=====

The HAMT datatype is accessible for testing purposes under the

`_testcapi` module:

>>> from _testcapi import hamt

>>> h = hamt()

>>> h2 = h.set('a', 2)

>>> h3 = h2.set('b', 3)

>>> list(h3)

['a', 'b']

When CPython is built in debug mode, a '__dump__()' method is available

to introspect the tree:

>>> print(h3.__dump__())

HAMT(len=2):

BitmapNode(size=4 count=2 bitmap=0b110 id=0x10eb9d9e8):

'a': 2

'b': 3

#define IS_ARRAY_NODE(node) Py_IS_TYPE(node, &_PyHamt_ArrayNode_Type)

#define IS_BITMAP_NODE(node) Py_IS_TYPE(node, &_PyHamt_BitmapNode_Type)

#define IS_COLLISION_NODE(node) Py_IS_TYPE(node, &_PyHamt_CollisionNode_Type)

/* Return type for 'find' (lookup a key) functions.

* F_ERROR - an error occurred;

* F_NOT_FOUND - the key was not found;

* F_FOUND - the key was found.

typedef enum {F_ERROR, F_NOT_FOUND, F_FOUND} hamt_find_t;

/* Return type for 'without' (delete a key) functions.

* W_ERROR - an error occurred;

* W_NOT_FOUND - the key was not found: there's nothing to delete;

* W_EMPTY - the key was found: the node/tree would be empty

if the key is deleted;

* W_NEWNODE - the key was found: a new node/tree is returned

without that key.

typedef enum {W_ERROR, W_NOT_FOUND, W_EMPTY, W_NEWNODE} hamt_without_t;

/* Low-level iterator protocol type.

* I_ITEM - a new item has been yielded;

* I_END - the whole tree was visited (similar to StopIteration).

typedef enum {I_ITEM, I_END} hamt_iter_t;

#define HAMT_ARRAY_NODE_SIZE 32

typedef struct {

PyObject_HEAD

PyHamtNode *a_array[HAMT_ARRAY_NODE_SIZE];

Py_ssize_t a_count;

} PyHamtNode_Array;

typedef struct {

PyObject_VAR_HEAD

int32_t c_hash;

PyObject *c_array[1];

} PyHamtNode_Collision;

static PyHamtObject *

hamt_alloc(void);

static PyHamtNode *

hamt_node_assoc(PyHamtNode *node,

uint32_t shift, int32_t hash,

PyObject *key, PyObject *val, int* added_leaf);

static hamt_without_t

hamt_node_without(PyHamtNode *node,

uint32_t shift, int32_t hash,

PyObject *key,

PyHamtNode **new_node);

static hamt_find_t

hamt_node_find(PyHamtNode *node,

uint32_t shift, int32_t hash,

PyObject *key, PyObject **val);

#ifdef Py_DEBUG

static int

hamt_node_dump(PyHamtNode *node,

_PyUnicodeWriter *writer, int level);

#endif

static PyHamtNode *

hamt_node_array_new(Py_ssize_t);

static PyHamtNode *

hamt_node_collision_new(int32_t hash, Py_ssize_t size);

static inline Py_ssize_t

hamt_node_collision_count(PyHamtNode_Collision *node);

#ifdef Py_DEBUG

static void

_hamt_node_array_validate(void *obj_raw)

{

PyObject *obj = _PyObject_CAST(obj_raw);

assert(IS_ARRAY_NODE(obj));

PyHamtNode_Array *node = (PyHamtNode_Array*)obj;

Py_ssize_t i = 0, count = 0;

for (; i < HAMT_ARRAY_NODE_SIZE; i++) {

if (node->a_array[i] != NULL) {

count++;

}

assert(count == node->a_count);

}

#define VALIDATE_ARRAY_NODE(NODE) \

do { _hamt_node_array_validate(NODE); } while (0);

#else

#define VALIDATE_ARRAY_NODE(NODE)

#endif

/* Returns -1 on error */

static inline int32_t

hamt_hash(PyObject *o)

{

Py_hash_t hash = PyObject_Hash(o);

#if SIZEOF_PY_HASH_T <= 4

return hash;

#else

if (hash == -1) {

/* exception */

return -1;

}

/* While it's somewhat suboptimal to reduce Python's 64 bit hash to

32 bits via XOR, it seems that the resulting hash function

is good enough (this is also how Long type is hashed in Java.)

Storing 10, 100, 1000 Python strings results in a relatively

shallow and uniform tree structure.

Also it's worth noting that it would be possible to adapt the tree

structure to 64 bit hashes, but that would increase memory pressure

and provide little to no performance benefits for collections with

fewer than billions of key/value pairs.

Important: do not change this hash reducing function. There are many

tests that need an exact tree shape to cover all code paths and

we do that by specifying concrete values for test data's `__hash__`.

If this function is changed most of the regression tests would

become useless.

int32_t xored = (int32_t)(hash & 0xffffffffl) ^ (int32_t)(hash >> 32);

return xored == -1 ? -2 : xored;

#endif

}

static inline uint32_t

hamt_mask(int32_t hash, uint32_t shift)

{

return (((uint32_t)hash >> shift) & 0x01f);

}

static inline uint32_t

hamt_bitpos(int32_t hash, uint32_t shift)

{

return (uint32_t)1 << hamt_mask(hash, shift);

}

static inline uint32_t

hamt_bitindex(uint32_t bitmap, uint32_t bit)

{

return (uint32_t)_Py_popcount32(bitmap & (bit - 1));

}

/////////////////////////////////// Dump Helpers

#ifdef Py_DEBUG

static int

_hamt_dump_ident(_PyUnicodeWriter *writer, int level)

{

/* Write `' ' * level` to the `writer` */

PyObject *str = NULL;

PyObject *num = NULL;

PyObject *res = NULL;

int ret = -1;

str = PyUnicode_FromString(" ");

if (str == NULL) {

goto error;

}

num = PyLong_FromLong((long)level);

if (num == NULL) {

goto error;

}

res = PyNumber_Multiply(str, num);

if (res == NULL) {

goto error;

}

ret = _PyUnicodeWriter_WriteStr(writer, res);

error:

Py_XDECREF(res);

Py_XDECREF(str);

Py_XDECREF(num);

return ret;

}

static int

_hamt_dump_format(_PyUnicodeWriter *writer, const char *format, ...)

{

/* A convenient helper combining _PyUnicodeWriter_WriteStr and

PyUnicode_FromFormatV.

PyObject* msg;

int ret;

va_list vargs;

va_start(vargs, format);

msg = PyUnicode_FromFormatV(format, vargs);

va_end(vargs);

if (msg == NULL) {

return -1;

}

ret = _PyUnicodeWriter_WriteStr(writer, msg);

Py_DECREF(msg);

return ret;

}

#endif /* Py_DEBUG */

/////////////////////////////////// Bitmap Node

static PyHamtNode *

hamt_node_bitmap_new(Py_ssize_t size)

{

/* Create a new bitmap node of size 'size' */

PyHamtNode_Bitmap *node;

Py_ssize_t i;

if (size == 0) {

/* Since bitmap nodes are immutable, we can cache the instance

for size=0 and reuse it whenever we need an empty bitmap node.

return (PyHamtNode *)&_Py_SINGLETON(hamt_bitmap_node_empty);

}

assert(size >= 0);

assert(size % 2 == 0);

/* No freelist; allocate a new bitmap node */

node = PyObject_GC_NewVar(

PyHamtNode_Bitmap, &_PyHamt_BitmapNode_Type, size);

if (node == NULL) {

return NULL;

}

Py_SET_SIZE(node, size);

for (i = 0; i < size; i++) {

node->b_array[i] = NULL;

}

node->b_bitmap = 0;

_PyObject_GC_TRACK(node);

return (PyHamtNode *)node;

}

static inline Py_ssize_t

hamt_node_bitmap_count(PyHamtNode_Bitmap *node)

{

return Py_SIZE(node) / 2;

}

static PyHamtNode_Bitmap *

hamt_node_bitmap_clone(PyHamtNode_Bitmap *node)

{

/* Clone a bitmap node; return a new one with the same child notes. */

PyHamtNode_Bitmap *clone;

Py_ssize_t i;

clone = (PyHamtNode_Bitmap *)hamt_node_bitmap_new(Py_SIZE(node));

if (clone == NULL) {

return NULL;

}

for (i = 0; i < Py_SIZE(node); i++) {

clone->b_array[i] = Py_XNewRef(node->b_array[i]);

}

clone->b_bitmap = node->b_bitmap;

return clone;

}

static PyHamtNode_Bitmap *

hamt_node_bitmap_clone_without(PyHamtNode_Bitmap *o, uint32_t bit)

{

assert(bit & o->b_bitmap);

assert(hamt_node_bitmap_count(o) > 1);

PyHamtNode_Bitmap *new = (PyHamtNode_Bitmap *)hamt_node_bitmap_new(

Py_SIZE(o) - 2);

if (new == NULL) {

return NULL;

}

uint32_t idx = hamt_bitindex(o->b_bitmap, bit);

uint32_t key_idx = 2 * idx;

uint32_t val_idx = key_idx + 1;

uint32_t i;

for (i = 0; i < key_idx; i++) {

new->b_array[i] = Py_XNewRef(o->b_array[i]);

}

assert(Py_SIZE(o) >= 0 && Py_SIZE(o) <= 32);

for (i = val_idx + 1; i < (uint32_t)Py_SIZE(o); i++) {

new->b_array[i - 2] = Py_XNewRef(o->b_array[i]);

}

new->b_bitmap = o->b_bitmap & ~bit;

return new;

}

static PyHamtNode *

hamt_node_new_bitmap_or_collision(uint32_t shift,

PyObject *key1, PyObject *val1,

int32_t key2_hash,

PyObject *key2, PyObject *val2)

{

/* Helper method. Creates a new node for key1/val and key2/val2

pairs.

If key1 hash is equal to the hash of key2, a Collision node

will be created. If they are not equal, a Bitmap node is

created.

int32_t key1_hash = hamt_hash(key1);

if (key1_hash == -1) {

return NULL;

}

if (key1_hash == key2_hash) {

PyHamtNode_Collision *n;

n = (PyHamtNode_Collision *)hamt_node_collision_new(key1_hash, 4);

if (n == NULL) {

return NULL;

}

n->c_array[0] = Py_NewRef(key1);

n->c_array[1] = Py_NewRef(val1);

n->c_array[2] = Py_NewRef(key2);

n->c_array[3] = Py_NewRef(val2);

return (PyHamtNode *)n;

}

else {

int added_leaf = 0;

PyHamtNode *n = hamt_node_bitmap_new(0);

if (n == NULL) {

return NULL;

}

PyHamtNode *n2 = hamt_node_assoc(

n, shift, key1_hash, key1, val1, &added_leaf);

Py_DECREF(n);

if (n2 == NULL) {

return NULL;

}

n = hamt_node_assoc(n2, shift, key2_hash, key2, val2, &added_leaf);

Py_DECREF(n2);

if (n == NULL) {

return NULL;

}

return n;

}

static PyHamtNode *

hamt_node_bitmap_assoc(PyHamtNode_Bitmap *self,

uint32_t shift, int32_t hash,

PyObject *key, PyObject *val, int* added_leaf)

{

/* assoc operation for bitmap nodes.

Return: a new node, or self if key/val already is in the

collection.

'added_leaf' is later used in '_PyHamt_Assoc' to determine if

`hamt.set(key, val)` increased the size of the collection.

uint32_t bit = hamt_bitpos(hash, shift);

uint32_t idx = hamt_bitindex(self->b_bitmap, bit);

/* Bitmap node layout:

+------+------+------+------+ --- +------+------+

| key1 | val1 | key2 | val2 | ... | keyN | valN |

+------+------+------+------+ --- +------+------+

where `N < Py_SIZE(node)`.

The `node->b_bitmap` field is a bitmap. For a given

`(shift, hash)` pair we can determine:

- If this node has the corresponding key/val slots.

- The index of key/val slots.

if (self->b_bitmap & bit) {

/* The key is set in this node */

uint32_t key_idx = 2 * idx;

uint32_t val_idx = key_idx + 1;

assert(val_idx < (size_t)Py_SIZE(self));

PyObject *key_or_null = self->b_array[key_idx];

PyObject *val_or_node = self->b_array[val_idx];

if (key_or_null == NULL) {

/* key is NULL. This means that we have a few keys

that have the same (hash, shift) pair. */

assert(val_or_node != NULL);

PyHamtNode *sub_node = hamt_node_assoc(

(PyHamtNode *)val_or_node,

shift + 5, hash, key, val, added_leaf);

if (sub_node == NULL) {

return NULL;

}

if (val_or_node == (PyObject *)sub_node) {

Py_DECREF(sub_node);

return (PyHamtNode *)Py_NewRef(self);

}

PyHamtNode_Bitmap *ret = hamt_node_bitmap_clone(self);

if (ret == NULL) {

return NULL;

}

Py_SETREF(ret->b_array[val_idx], (PyObject*)sub_node);

return (PyHamtNode *)ret;

}

assert(key != NULL);

/* key is not NULL. This means that we have only one other

key in this collection that matches our hash for this shift. */

int comp_err = PyObject_RichCompareBool(key, key_or_null, Py_EQ);

if (comp_err < 0) { /* exception in __eq__ */

return NULL;

}

if (comp_err == 1) { /* key == key_or_null */

if (val == val_or_node) {

/* we already have the same key/val pair; return self. */

return (PyHamtNode *)Py_NewRef(self);

}

/* We're setting a new value for the key we had before.

Make a new bitmap node with a replaced value, and return it. */

PyHamtNode_Bitmap *ret = hamt_node_bitmap_clone(self);

if (ret == NULL) {

return NULL;

}

Py_SETREF(ret->b_array[val_idx], Py_NewRef(val));

return (PyHamtNode *)ret;

}

/* It's a new key, and it has the same index as *one* another key.

We have a collision. We need to create a new node which will

combine the existing key and the key we're adding.

`hamt_node_new_bitmap_or_collision` will either create a new

Collision node if the keys have identical hashes, or

a new Bitmap node.

PyHamtNode *sub_node = hamt_node_new_bitmap_or_collision(

shift + 5,

key_or_null, val_or_node, /* existing key/val */

hash,

key, val /* new key/val */

);

if (sub_node == NULL) {

return NULL;

}

PyHamtNode_Bitmap *ret = hamt_node_bitmap_clone(self);

if (ret == NULL) {

Py_DECREF(sub_node);

return NULL;

}

Py_SETREF(ret->b_array[key_idx], NULL);

Py_SETREF(ret->b_array[val_idx], (PyObject *)sub_node);

*added_leaf = 1;

return (PyHamtNode *)ret;

}

else {

/* There was no key before with the same (shift,hash). */

uint32_t n = (uint32_t)_Py_popcount32(self->b_bitmap);

if (n >= 16) {

/* When we have a situation where we want to store more

than 16 nodes at one level of the tree, we no longer

want to use the Bitmap node with bitmap encoding.

Instead we start using an Array node, which has

simpler (faster) implementation at the expense of

having preallocated 32 pointers for its keys/values

pairs.

Small hamt objects (<30 keys) usually don't have any

Array nodes at all. Between ~30 and ~400 keys hamt

objects usually have one Array node, and usually it's

a root node.

uint32_t jdx = hamt_mask(hash, shift);

/* 'jdx' is the index of where the new key should be added

in the new Array node we're about to create. */

PyHamtNode *empty = NULL;

PyHamtNode_Array *new_node = NULL;

PyHamtNode *res = NULL;

/* Create a new Array node. */

new_node = (PyHamtNode_Array *)hamt_node_array_new(n + 1);

if (new_node == NULL) {

goto fin;

}

/* Create an empty bitmap node for the next

hamt_node_assoc call. */

empty = hamt_node_bitmap_new(0);

if (empty == NULL) {

goto fin;

}

/* Make a new bitmap node for the key/val we're adding.

Set that bitmap node to new-array-node[jdx]. */

new_node->a_array[jdx] = hamt_node_assoc(

empty, shift + 5, hash, key, val, added_leaf);

if (new_node->a_array[jdx] == NULL) {

goto fin;

}

/* Copy existing key/value pairs from the current Bitmap

node to the new Array node we've just created. */

Py_ssize_t i, j;

for (i = 0, j = 0; i < HAMT_ARRAY_NODE_SIZE; i++) {

if (((self->b_bitmap >> i) & 1) != 0) {

/* Ensure we don't accidentally override `jdx` element

we set few lines above.

assert(new_node->a_array[i] == NULL);

if (self->b_array[j] == NULL) {

new_node->a_array[i] =

(PyHamtNode *)Py_NewRef(self->b_array[j + 1]);

}

else {

int32_t rehash = hamt_hash(self->b_array[j]);

if (rehash == -1) {

goto fin;

}

new_node->a_array[i] = hamt_node_assoc(

empty, shift + 5,

rehash,

self->b_array[j],

self->b_array[j + 1],

added_leaf);

if (new_node->a_array[i] == NULL) {

goto fin;

}

j += 2;

}

VALIDATE_ARRAY_NODE(new_node)

/* That's it! */

res = (PyHamtNode *)new_node;

fin:

Py_XDECREF(empty);

if (res == NULL) {

Py_XDECREF(new_node);

}

return res;

}

else {

/* We have less than 16 keys at this level; let's just

create a new bitmap node out of this node with the

new key/val pair added. */

uint32_t key_idx = 2 * idx;

uint32_t val_idx = key_idx + 1;

uint32_t i;

*added_leaf = 1;

/* Allocate new Bitmap node which can have one more key/val

pair in addition to what we have already. */

PyHamtNode_Bitmap *new_node =

(PyHamtNode_Bitmap *)hamt_node_bitmap_new(2 * (n + 1));

if (new_node == NULL) {

return NULL;

}

/* Copy all keys/values that will be before the new key/value

we are adding. */

for (i = 0; i < key_idx; i++) {

new_node->b_array[i] = Py_XNewRef(self->b_array[i]);

}

/* Set the new key/value to the new Bitmap node. */

new_node->b_array[key_idx] = Py_NewRef(key);

new_node->b_array[val_idx] = Py_NewRef(val);

/* Copy all keys/values that will be after the new key/value

we are adding. */

assert(Py_SIZE(self) >= 0 && Py_SIZE(self) <= 32);

for (i = key_idx; i < (uint32_t)Py_SIZE(self); i++) {

new_node->b_array[i + 2] = Py_XNewRef(self->b_array[i]);

}

new_node->b_bitmap = self->b_bitmap | bit;

return (PyHamtNode *)new_node;

}

static hamt_without_t

hamt_node_bitmap_without(PyHamtNode_Bitmap *self,

uint32_t shift, int32_t hash,

PyObject *key,

PyHamtNode **new_node)

{

uint32_t bit = hamt_bitpos(hash, shift);

if ((self->b_bitmap & bit) == 0) {

return W_NOT_FOUND;

}

uint32_t idx = hamt_bitindex(self->b_bitmap, bit);

uint32_t key_idx = 2 * idx;

uint32_t val_idx = key_idx + 1;

PyObject *key_or_null = self->b_array[key_idx];

PyObject *val_or_node = self->b_array[val_idx];

if (key_or_null == NULL) {

/* key == NULL means that 'value' is another tree node. */

PyHamtNode *sub_node = NULL;

hamt_without_t res = hamt_node_without(

(PyHamtNode *)val_or_node,

shift + 5, hash, key, &sub_node);

switch (res) {

case W_EMPTY:

/* It's impossible for us to receive a W_EMPTY here:

- Array nodes are converted to Bitmap nodes when

we delete 16th item from them;

- Collision nodes are converted to Bitmap when

there is one item in them;

- Bitmap node's without() inlines single-item

sub-nodes.

So in no situation we can have a single-item

Bitmap child of another Bitmap node.

Py_UNREACHABLE();

case W_NEWNODE: {

assert(sub_node != NULL);

if (IS_BITMAP_NODE(sub_node)) {

PyHamtNode_Bitmap *sub_tree = (PyHamtNode_Bitmap *)sub_node;

if (hamt_node_bitmap_count(sub_tree) == 1 &&

sub_tree->b_array[0] != NULL)

{

/* A bitmap node with one key/value pair. Just

merge it into this node.

Note that we don't inline Bitmap nodes that

have a NULL key -- those nodes point to another

tree level, and we cannot simply move tree levels

up or down.

PyHamtNode_Bitmap *clone = hamt_node_bitmap_clone(self);

if (clone == NULL) {

Py_DECREF(sub_node);

return W_ERROR;

}

PyObject *key = sub_tree->b_array[0];

PyObject *val = sub_tree->b_array[1];

Py_XSETREF(clone->b_array[key_idx], Py_NewRef(key));

Py_SETREF(clone->b_array[val_idx], Py_NewRef(val));

Py_DECREF(sub_tree);

*new_node = (PyHamtNode *)clone;

return W_NEWNODE;

}

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cpython/Python/hamt.c at main · https-github-com-nzysoft/cpython · GitHub

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