cpython/Python/hamt.c at 3.11 · python/cpython · GitHub

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

674

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

691

692

693

694

695

696

697

698

699

700

701

702

703

704

705

706

707

708

709

710

711

712

713

714

715

716

717

718

719

720

721

722

723

724

725

726

727

728

729

730

731

732

733

734

735

736

737

738

739

740

741

742

743

744

745

746

747

748

749

750

751

752

753

754

755

756

757

758

759

760

761

762

763

764

765

766

767

768

769

770

771

772

773

774

775

776

777

778

779

780

781

782

783

784

785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

800

801

802

803

804

805

806

807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828

829

830

831

832

833

834

835

836

837

838

839

840

841

842

843

844

845

846

847

848

849

850

851

852

853

854

855

856

857

858

859

860

861

862

863

864

865

866

867

868

869

870

871

872

873

874

875

876

877

878

879

880

881

882

883

884

885

886

887

888

889

890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914

915

916

917

918

919

920

921

922

923

924

925

926

927

928

929

930

931

932

933

934

935

936

937

938

939

940

941

942

943

944

945

946

947

948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

969

970

971

972

973

974

975

976

977

978

979

980

981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

997

998

999

1000

#include "Python.h"

#include "pycore_bitutils.h" // _Py_popcount32

#include "pycore_hamt.h"

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

#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

uint32_t b_bitmap;

PyObject *b_array[1];

} PyHamtNode_Bitmap;

typedef struct {

PyObject_VAR_HEAD

int32_t c_hash;

PyObject *c_array[1];

} PyHamtNode_Collision;

static PyHamtNode_Bitmap *_empty_bitmap_node;

static PyHamtObject *_empty_hamt;

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;

#ifdef HAVE_STDARG_PROTOTYPES

va_start(vargs, format);

#else

va_start(vargs);

#endif

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;

assert(size >= 0);

assert(size % 2 == 0);

if (size == 0 && _empty_bitmap_node != NULL) {

Py_INCREF(_empty_bitmap_node);

return (PyHamtNode *)_empty_bitmap_node;

}

/* 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);

if (size == 0 && _empty_bitmap_node == NULL) {

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

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

_empty_bitmap_node = node;

Py_INCREF(_empty_bitmap_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++) {

Py_XINCREF(node->b_array[i]);

clone->b_array[i] = 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++) {

Py_XINCREF(o->b_array[i]);

new->b_array[i] = 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++) {

Py_XINCREF(o->b_array[i]);

new->b_array[i - 2] = 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;

}

Py_INCREF(key1);

n->c_array[0] = key1;

Py_INCREF(val1);

n->c_array[1] = val1;

Py_INCREF(key2);

n->c_array[2] = key2;

Py_INCREF(val2);

n->c_array[3] = 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);

Py_INCREF(self);

return (PyHamtNode *)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. */

Py_INCREF(self);

return (PyHamtNode *)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_INCREF(val);

Py_SETREF(ret->b_array[val_idx], 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 *)self->b_array[j + 1];

Py_INCREF(new_node->a_array[i]);

}

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++) {

Py_XINCREF(self->b_array[i]);

new_node->b_array[i] = self->b_array[i];

}

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

Py_INCREF(key);

new_node->b_array[key_idx] = key;

Py_INCREF(val);

new_node->b_array[val_idx] = 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++) {

Py_XINCREF(self->b_array[i]);

new_node->b_array[i + 2] = 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: {

View remainder of file in raw view

cpython/Python/hamt.c at 3.11 · python/cpython · GitHub

Repository navigation

Expand file tree

Breadcrumbs

hamt.c

Latest commit

History

Breadcrumbs

hamt.c

File metadata and controls