1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
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
|
=====================
YAML I/O
=====================
.. contents::
:local:
Introduction to YAML
====================
YAML is a human readable data serialization language. The full YAML language
spec can be read at `yaml.org
<http://www.yaml.org/spec/1.2/spec.html#Introduction>`_. The simplest form of
yaml is just "scalars", "mappings", and "sequences". A scalar is any number
or string. The pound/hash symbol (#) begins a comment line. A mapping is
a set of key-value pairs where the key ends with a colon. For example:
.. code-block:: yaml
# a mapping
name: Tom
hat-size: 7
A sequence is a list of items where each item starts with a leading dash ('-').
For example:
.. code-block:: yaml
# a sequence
- x86
- x86_64
- PowerPC
You can combine mappings and sequences by indenting. For example a sequence
of mappings in which one of the mapping values is itself a sequence:
.. code-block:: yaml
# a sequence of mappings with one key's value being a sequence
- name: Tom
cpus:
- x86
- x86_64
- name: Bob
cpus:
- x86
- name: Dan
cpus:
- PowerPC
- x86
Sometime sequences are known to be short and the one entry per line is too
verbose, so YAML offers an alternate syntax for sequences called a "Flow
Sequence" in which you put comma separated sequence elements into square
brackets. The above example could then be simplified to :
.. code-block:: yaml
# a sequence of mappings with one key's value being a flow sequence
- name: Tom
cpus: [ x86, x86_64 ]
- name: Bob
cpus: [ x86 ]
- name: Dan
cpus: [ PowerPC, x86 ]
Introduction to YAML I/O
========================
The use of indenting makes the YAML easy for a human to read and understand,
but having a program read and write YAML involves a lot of tedious details.
The YAML I/O library structures and simplifies reading and writing YAML
documents.
YAML I/O assumes you have some "native" data structures which you want to be
able to dump as YAML and recreate from YAML. The first step is to try
writing example YAML for your data structures. You may find after looking at
possible YAML representations that a direct mapping of your data structures
to YAML is not very readable. Often the fields are not in the order that
a human would find readable. Or the same information is replicated in multiple
locations, making it hard for a human to write such YAML correctly.
In relational database theory there is a design step called normalization in
which you reorganize fields and tables. The same considerations need to
go into the design of your YAML encoding. But, you may not want to change
your existing native data structures. Therefore, when writing out YAML
there may be a normalization step, and when reading YAML there would be a
corresponding denormalization step.
YAML I/O uses a non-invasive, traits based design. YAML I/O defines some
abstract base templates. You specialize those templates on your data types.
For instance, if you have an enumerated type FooBar you could specialize
ScalarEnumerationTraits on that type and define the enumeration() method:
.. code-block:: c++
using llvm::yaml::ScalarEnumerationTraits;
using llvm::yaml::IO;
template <>
struct ScalarEnumerationTraits<FooBar> {
static void enumeration(IO &io, FooBar &value) {
...
}
};
As with all YAML I/O template specializations, the ScalarEnumerationTraits is used for
both reading and writing YAML. That is, the mapping between in-memory enum
values and the YAML string representation is only in one place.
This assures that the code for writing and parsing of YAML stays in sync.
To specify a YAML mappings, you define a specialization on
llvm::yaml::MappingTraits.
If your native data structure happens to be a struct that is already normalized,
then the specialization is simple. For example:
.. code-block:: c++
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
template <>
struct MappingTraits<Person> {
static void mapping(IO &io, Person &info) {
io.mapRequired("name", info.name);
io.mapOptional("hat-size", info.hatSize);
}
};
A YAML sequence is automatically inferred if you data type has begin()/end()
iterators and a push_back() method. Therefore any of the STL containers
(such as std::vector<>) will automatically translate to YAML sequences.
Once you have defined specializations for your data types, you can
programmatically use YAML I/O to write a YAML document:
.. code-block:: c++
using llvm::yaml::Output;
Person tom;
tom.name = "Tom";
tom.hatSize = 8;
Person dan;
dan.name = "Dan";
dan.hatSize = 7;
std::vector<Person> persons;
persons.push_back(tom);
persons.push_back(dan);
Output yout(llvm::outs());
yout << persons;
This would write the following:
.. code-block:: yaml
- name: Tom
hat-size: 8
- name: Dan
hat-size: 7
And you can also read such YAML documents with the following code:
.. code-block:: c++
using llvm::yaml::Input;
typedef std::vector<Person> PersonList;
std::vector<PersonList> docs;
Input yin(document.getBuffer());
yin >> docs;
if ( yin.error() )
return;
// Process read document
for ( PersonList &pl : docs ) {
for ( Person &person : pl ) {
cout << "name=" << person.name;
}
}
One other feature of YAML is the ability to define multiple documents in a
single file. That is why reading YAML produces a vector of your document type.
Error Handling
==============
When parsing a YAML document, if the input does not match your schema (as
expressed in your XxxTraits<> specializations). YAML I/O
will print out an error message and your Input object's error() method will
return true. For instance the following document:
.. code-block:: yaml
- name: Tom
shoe-size: 12
- name: Dan
hat-size: 7
Has a key (shoe-size) that is not defined in the schema. YAML I/O will
automatically generate this error:
.. code-block:: yaml
YAML:2:2: error: unknown key 'shoe-size'
shoe-size: 12
^~~~~~~~~
Similar errors are produced for other input not conforming to the schema.
Scalars
=======
YAML scalars are just strings (i.e. not a sequence or mapping). The YAML I/O
library provides support for translating between YAML scalars and specific
C++ types.
Built-in types
--------------
The following types have built-in support in YAML I/O:
* bool
* float
* double
* StringRef
* std::string
* int64_t
* int32_t
* int16_t
* int8_t
* uint64_t
* uint32_t
* uint16_t
* uint8_t
That is, you can use those types in fields of MappingTraits or as element type
in sequence. When reading, YAML I/O will validate that the string found
is convertible to that type and error out if not.
Unique types
------------
Given that YAML I/O is trait based, the selection of how to convert your data
to YAML is based on the type of your data. But in C++ type matching, typedefs
do not generate unique type names. That means if you have two typedefs of
unsigned int, to YAML I/O both types look exactly like unsigned int. To
facilitate make unique type names, YAML I/O provides a macro which is used
like a typedef on built-in types, but expands to create a class with conversion
operators to and from the base type. For example:
.. code-block:: c++
LLVM_YAML_STRONG_TYPEDEF(uint32_t, MyFooFlags)
LLVM_YAML_STRONG_TYPEDEF(uint32_t, MyBarFlags)
This generates two classes MyFooFlags and MyBarFlags which you can use in your
native data structures instead of uint32_t. They are implicitly
converted to and from uint32_t. The point of creating these unique types
is that you can now specify traits on them to get different YAML conversions.
Hex types
---------
An example use of a unique type is that YAML I/O provides fixed sized unsigned
integers that are written with YAML I/O as hexadecimal instead of the decimal
format used by the built-in integer types:
* Hex64
* Hex32
* Hex16
* Hex8
You can use llvm::yaml::Hex32 instead of uint32_t and the only different will
be that when YAML I/O writes out that type it will be formatted in hexadecimal.
ScalarEnumerationTraits
-----------------------
YAML I/O supports translating between in-memory enumerations and a set of string
values in YAML documents. This is done by specializing ScalarEnumerationTraits<>
on your enumeration type and define a enumeration() method.
For instance, suppose you had an enumeration of CPUs and a struct with it as
a field:
.. code-block:: c++
enum CPUs {
cpu_x86_64 = 5,
cpu_x86 = 7,
cpu_PowerPC = 8
};
struct Info {
CPUs cpu;
uint32_t flags;
};
To support reading and writing of this enumeration, you can define a
ScalarEnumerationTraits specialization on CPUs, which can then be used
as a field type:
.. code-block:: c++
using llvm::yaml::ScalarEnumerationTraits;
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
template <>
struct ScalarEnumerationTraits<CPUs> {
static void enumeration(IO &io, CPUs &value) {
io.enumCase(value, "x86_64", cpu_x86_64);
io.enumCase(value, "x86", cpu_x86);
io.enumCase(value, "PowerPC", cpu_PowerPC);
}
};
template <>
struct MappingTraits<Info> {
static void mapping(IO &io, Info &info) {
io.mapRequired("cpu", info.cpu);
io.mapOptional("flags", info.flags, 0);
}
};
When reading YAML, if the string found does not match any of the the strings
specified by enumCase() methods, an error is automatically generated.
When writing YAML, if the value being written does not match any of the values
specified by the enumCase() methods, a runtime assertion is triggered.
BitValue
--------
Another common data structure in C++ is a field where each bit has a unique
meaning. This is often used in a "flags" field. YAML I/O has support for
converting such fields to a flow sequence. For instance suppose you
had the following bit flags defined:
.. code-block:: c++
enum {
flagsPointy = 1
flagsHollow = 2
flagsFlat = 4
flagsRound = 8
};
LLVM_YAML_STRONG_TYPEDEF(uint32_t, MyFlags)
To support reading and writing of MyFlags, you specialize ScalarBitSetTraits<>
on MyFlags and provide the bit values and their names.
.. code-block:: c++
using llvm::yaml::ScalarBitSetTraits;
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
template <>
struct ScalarBitSetTraits<MyFlags> {
static void bitset(IO &io, MyFlags &value) {
io.bitSetCase(value, "hollow", flagHollow);
io.bitSetCase(value, "flat", flagFlat);
io.bitSetCase(value, "round", flagRound);
io.bitSetCase(value, "pointy", flagPointy);
}
};
struct Info {
StringRef name;
MyFlags flags;
};
template <>
struct MappingTraits<Info> {
static void mapping(IO &io, Info& info) {
io.mapRequired("name", info.name);
io.mapRequired("flags", info.flags);
}
};
With the above, YAML I/O (when writing) will test mask each value in the
bitset trait against the flags field, and each that matches will
cause the corresponding string to be added to the flow sequence. The opposite
is done when reading and any unknown string values will result in a error. With
the above schema, a same valid YAML document is:
.. code-block:: yaml
name: Tom
flags: [ pointy, flat ]
Sometimes a "flags" field might contains an enumeration part
defined by a bit-mask.
.. code-block:: c++
enum {
flagsFeatureA = 1,
flagsFeatureB = 2,
flagsFeatureC = 4,
flagsCPUMask = 24,
flagsCPU1 = 8,
flagsCPU2 = 16
};
To support reading and writing such fields, you need to use the maskedBitSet()
method and provide the bit values, their names and the enumeration mask.
.. code-block:: c++
template <>
struct ScalarBitSetTraits<MyFlags> {
static void bitset(IO &io, MyFlags &value) {
io.bitSetCase(value, "featureA", flagsFeatureA);
io.bitSetCase(value, "featureB", flagsFeatureB);
io.bitSetCase(value, "featureC", flagsFeatureC);
io.maskedBitSetCase(value, "CPU1", flagsCPU1, flagsCPUMask);
io.maskedBitSetCase(value, "CPU2", flagsCPU2, flagsCPUMask);
}
};
YAML I/O (when writing) will apply the enumeration mask to the flags field,
and compare the result and values from the bitset. As in case of a regular
bitset, each that matches will cause the corresponding string to be added
to the flow sequence.
Custom Scalar
-------------
Sometimes for readability a scalar needs to be formatted in a custom way. For
instance your internal data structure may use a integer for time (seconds since
some epoch), but in YAML it would be much nicer to express that integer in
some time format (e.g. 4-May-2012 10:30pm). YAML I/O has a way to support
custom formatting and parsing of scalar types by specializing ScalarTraits<> on
your data type. When writing, YAML I/O will provide the native type and
your specialization must create a temporary llvm::StringRef. When reading,
YAML I/O will provide an llvm::StringRef of scalar and your specialization
must convert that to your native data type. An outline of a custom scalar type
looks like:
.. code-block:: c++
using llvm::yaml::ScalarTraits;
using llvm::yaml::IO;
template <>
struct ScalarTraits<MyCustomType> {
static void output(const T &value, llvm::raw_ostream &out) {
out << value; // do custom formatting here
}
static StringRef input(StringRef scalar, T &value) {
// do custom parsing here. Return the empty string on success,
// or an error message on failure.
return StringRef();
}
// Determine if this scalar needs quotes.
static bool mustQuote(StringRef) { return true; }
};
Mappings
========
To be translated to or from a YAML mapping for your type T you must specialize
llvm::yaml::MappingTraits on T and implement the "void mapping(IO &io, T&)"
method. If your native data structures use pointers to a class everywhere,
you can specialize on the class pointer. Examples:
.. code-block:: c++
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
// Example of struct Foo which is used by value
template <>
struct MappingTraits<Foo> {
static void mapping(IO &io, Foo &foo) {
io.mapOptional("size", foo.size);
...
}
};
// Example of struct Bar which is natively always a pointer
template <>
struct MappingTraits<Bar*> {
static void mapping(IO &io, Bar *&bar) {
io.mapOptional("size", bar->size);
...
}
};
No Normalization
----------------
The mapping() method is responsible, if needed, for normalizing and
denormalizing. In a simple case where the native data structure requires no
normalization, the mapping method just uses mapOptional() or mapRequired() to
bind the struct's fields to YAML key names. For example:
.. code-block:: c++
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
template <>
struct MappingTraits<Person> {
static void mapping(IO &io, Person &info) {
io.mapRequired("name", info.name);
io.mapOptional("hat-size", info.hatSize);
}
};
Normalization
----------------
When [de]normalization is required, the mapping() method needs a way to access
normalized values as fields. To help with this, there is
a template MappingNormalization<> which you can then use to automatically
do the normalization and denormalization. The template is used to create
a local variable in your mapping() method which contains the normalized keys.
Suppose you have native data type
Polar which specifies a position in polar coordinates (distance, angle):
.. code-block:: c++
struct Polar {
float distance;
float angle;
};
but you've decided the normalized YAML for should be in x,y coordinates. That
is, you want the yaml to look like:
.. code-block:: yaml
x: 10.3
y: -4.7
You can support this by defining a MappingTraits that normalizes the polar
coordinates to x,y coordinates when writing YAML and denormalizes x,y
coordinates into polar when reading YAML.
.. code-block:: c++
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
template <>
struct MappingTraits<Polar> {
class NormalizedPolar {
public:
NormalizedPolar(IO &io)
: x(0.0), y(0.0) {
}
NormalizedPolar(IO &, Polar &polar)
: x(polar.distance * cos(polar.angle)),
y(polar.distance * sin(polar.angle)) {
}
Polar denormalize(IO &) {
return Polar(sqrt(x*x+y*y), arctan(x,y));
}
float x;
float y;
};
static void mapping(IO &io, Polar &polar) {
MappingNormalization<NormalizedPolar, Polar> keys(io, polar);
io.mapRequired("x", keys->x);
io.mapRequired("y", keys->y);
}
};
When writing YAML, the local variable "keys" will be a stack allocated
instance of NormalizedPolar, constructed from the supplied polar object which
initializes it x and y fields. The mapRequired() methods then write out the x
and y values as key/value pairs.
When reading YAML, the local variable "keys" will be a stack allocated instance
of NormalizedPolar, constructed by the empty constructor. The mapRequired
methods will find the matching key in the YAML document and fill in the x and y
fields of the NormalizedPolar object keys. At the end of the mapping() method
when the local keys variable goes out of scope, the denormalize() method will
automatically be called to convert the read values back to polar coordinates,
and then assigned back to the second parameter to mapping().
In some cases, the normalized class may be a subclass of the native type and
could be returned by the denormalize() method, except that the temporary
normalized instance is stack allocated. In these cases, the utility template
MappingNormalizationHeap<> can be used instead. It just like
MappingNormalization<> except that it heap allocates the normalized object
when reading YAML. It never destroys the normalized object. The denormalize()
method can this return "this".
Default values
--------------
Within a mapping() method, calls to io.mapRequired() mean that that key is
required to exist when parsing YAML documents, otherwise YAML I/O will issue an
error.
On the other hand, keys registered with io.mapOptional() are allowed to not
exist in the YAML document being read. So what value is put in the field
for those optional keys?
There are two steps to how those optional fields are filled in. First, the
second parameter to the mapping() method is a reference to a native class. That
native class must have a default constructor. Whatever value the default
constructor initially sets for an optional field will be that field's value.
Second, the mapOptional() method has an optional third parameter. If provided
it is the value that mapOptional() should set that field to if the YAML document
does not have that key.
There is one important difference between those two ways (default constructor
and third parameter to mapOptional). When YAML I/O generates a YAML document,
if the mapOptional() third parameter is used, if the actual value being written
is the same as (using ==) the default value, then that key/value is not written.
Order of Keys
--------------
When writing out a YAML document, the keys are written in the order that the
calls to mapRequired()/mapOptional() are made in the mapping() method. This
gives you a chance to write the fields in an order that a human reader of
the YAML document would find natural. This may be different that the order
of the fields in the native class.
When reading in a YAML document, the keys in the document can be in any order,
but they are processed in the order that the calls to mapRequired()/mapOptional()
are made in the mapping() method. That enables some interesting
functionality. For instance, if the first field bound is the cpu and the second
field bound is flags, and the flags are cpu specific, you can programmatically
switch how the flags are converted to and from YAML based on the cpu.
This works for both reading and writing. For example:
.. code-block:: c++
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
struct Info {
CPUs cpu;
uint32_t flags;
};
template <>
struct MappingTraits<Info> {
static void mapping(IO &io, Info &info) {
io.mapRequired("cpu", info.cpu);
// flags must come after cpu for this to work when reading yaml
if ( info.cpu == cpu_x86_64 )
io.mapRequired("flags", *(My86_64Flags*)info.flags);
else
io.mapRequired("flags", *(My86Flags*)info.flags);
}
};
Tags
----
The YAML syntax supports tags as a way to specify the type of a node before
it is parsed. This allows dynamic types of nodes. But the YAML I/O model uses
static typing, so there are limits to how you can use tags with the YAML I/O
model. Recently, we added support to YAML I/O for checking/setting the optional
tag on a map. Using this functionality it is even possbile to support different
mappings, as long as they are convertable.
To check a tag, inside your mapping() method you can use io.mapTag() to specify
what the tag should be. This will also add that tag when writing yaml.
Validation
----------
Sometimes in a yaml map, each key/value pair is valid, but the combination is
not. This is similar to something having no syntax errors, but still having
semantic errors. To support semantic level checking, YAML I/O allows
an optional ``validate()`` method in a MappingTraits template specialization.
When parsing yaml, the ``validate()`` method is call *after* all key/values in
the map have been processed. Any error message returned by the ``validate()``
method during input will be printed just a like a syntax error would be printed.
When writing yaml, the ``validate()`` method is called *before* the yaml
key/values are written. Any error during output will trigger an ``assert()``
because it is a programming error to have invalid struct values.
.. code-block:: c++
using llvm::yaml::MappingTraits;
using llvm::yaml::IO;
struct Stuff {
...
};
template <>
struct MappingTraits<Stuff> {
static void mapping(IO &io, Stuff &stuff) {
...
}
static StringRef validate(IO &io, Stuff &stuff) {
// Look at all fields in 'stuff' and if there
// are any bad values return a string describing
// the error. Otherwise return an empty string.
return StringRef();
}
};
Sequence
========
To be translated to or from a YAML sequence for your type T you must specialize
llvm::yaml::SequenceTraits on T and implement two methods:
``size_t size(IO &io, T&)`` and
``T::value_type& element(IO &io, T&, size_t indx)``. For example:
.. code-block:: c++
template <>
struct SequenceTraits<MySeq> {
static size_t size(IO &io, MySeq &list) { ... }
static MySeqEl &element(IO &io, MySeq &list, size_t index) { ... }
};
The size() method returns how many elements are currently in your sequence.
The element() method returns a reference to the i'th element in the sequence.
When parsing YAML, the element() method may be called with an index one bigger
than the current size. Your element() method should allocate space for one
more element (using default constructor if element is a C++ object) and returns
a reference to that new allocated space.
Flow Sequence
-------------
A YAML "flow sequence" is a sequence that when written to YAML it uses the
inline notation (e.g [ foo, bar ] ). To specify that a sequence type should
be written in YAML as a flow sequence, your SequenceTraits specialization should
add "static const bool flow = true;". For instance:
.. code-block:: c++
template <>
struct SequenceTraits<MyList> {
static size_t size(IO &io, MyList &list) { ... }
static MyListEl &element(IO &io, MyList &list, size_t index) { ... }
// The existence of this member causes YAML I/O to use a flow sequence
static const bool flow = true;
};
With the above, if you used MyList as the data type in your native data
structures, then then when converted to YAML, a flow sequence of integers
will be used (e.g. [ 10, -3, 4 ]).
Utility Macros
--------------
Since a common source of sequences is std::vector<>, YAML I/O provides macros:
LLVM_YAML_IS_SEQUENCE_VECTOR() and LLVM_YAML_IS_FLOW_SEQUENCE_VECTOR() which
can be used to easily specify SequenceTraits<> on a std::vector type. YAML
I/O does not partial specialize SequenceTraits on std::vector<> because that
would force all vectors to be sequences. An example use of the macros:
.. code-block:: c++
std::vector<MyType1>;
std::vector<MyType2>;
LLVM_YAML_IS_SEQUENCE_VECTOR(MyType1)
LLVM_YAML_IS_FLOW_SEQUENCE_VECTOR(MyType2)
Document List
=============
YAML allows you to define multiple "documents" in a single YAML file. Each
new document starts with a left aligned "---" token. The end of all documents
is denoted with a left aligned "..." token. Many users of YAML will never
have need for multiple documents. The top level node in their YAML schema
will be a mapping or sequence. For those cases, the following is not needed.
But for cases where you do want multiple documents, you can specify a
trait for you document list type. The trait has the same methods as
SequenceTraits but is named DocumentListTraits. For example:
.. code-block:: c++
template <>
struct DocumentListTraits<MyDocList> {
static size_t size(IO &io, MyDocList &list) { ... }
static MyDocType element(IO &io, MyDocList &list, size_t index) { ... }
};
User Context Data
=================
When an llvm::yaml::Input or llvm::yaml::Output object is created their
constructors take an optional "context" parameter. This is a pointer to
whatever state information you might need.
For instance, in a previous example we showed how the conversion type for a
flags field could be determined at runtime based on the value of another field
in the mapping. But what if an inner mapping needs to know some field value
of an outer mapping? That is where the "context" parameter comes in. You
can set values in the context in the outer map's mapping() method and
retrieve those values in the inner map's mapping() method.
The context value is just a void*. All your traits which use the context
and operate on your native data types, need to agree what the context value
actually is. It could be a pointer to an object or struct which your various
traits use to shared context sensitive information.
Output
======
The llvm::yaml::Output class is used to generate a YAML document from your
in-memory data structures, using traits defined on your data types.
To instantiate an Output object you need an llvm::raw_ostream, and optionally
a context pointer:
.. code-block:: c++
class Output : public IO {
public:
Output(llvm::raw_ostream &, void *context=NULL);
Once you have an Output object, you can use the C++ stream operator on it
to write your native data as YAML. One thing to recall is that a YAML file
can contain multiple "documents". If the top level data structure you are
streaming as YAML is a mapping, scalar, or sequence, then Output assumes you
are generating one document and wraps the mapping output
with "``---``" and trailing "``...``".
.. code-block:: c++
using llvm::yaml::Output;
void dumpMyMapDoc(const MyMapType &info) {
Output yout(llvm::outs());
yout << info;
}
The above could produce output like:
.. code-block:: yaml
---
name: Tom
hat-size: 7
...
On the other hand, if the top level data structure you are streaming as YAML
has a DocumentListTraits specialization, then Output walks through each element
of your DocumentList and generates a "---" before the start of each element
and ends with a "...".
.. code-block:: c++
using llvm::yaml::Output;
void dumpMyMapDoc(const MyDocListType &docList) {
Output yout(llvm::outs());
yout << docList;
}
The above could produce output like:
.. code-block:: yaml
---
name: Tom
hat-size: 7
---
name: Tom
shoe-size: 11
...
Input
=====
The llvm::yaml::Input class is used to parse YAML document(s) into your native
data structures. To instantiate an Input
object you need a StringRef to the entire YAML file, and optionally a context
pointer:
.. code-block:: c++
class Input : public IO {
public:
Input(StringRef inputContent, void *context=NULL);
Once you have an Input object, you can use the C++ stream operator to read
the document(s). If you expect there might be multiple YAML documents in
one file, you'll need to specialize DocumentListTraits on a list of your
document type and stream in that document list type. Otherwise you can
just stream in the document type. Also, you can check if there was
any syntax errors in the YAML be calling the error() method on the Input
object. For example:
.. code-block:: c++
// Reading a single document
using llvm::yaml::Input;
Input yin(mb.getBuffer());
// Parse the YAML file
MyDocType theDoc;
yin >> theDoc;
// Check for error
if ( yin.error() )
return;
.. code-block:: c++
// Reading multiple documents in one file
using llvm::yaml::Input;
LLVM_YAML_IS_DOCUMENT_LIST_VECTOR(std::vector<MyDocType>)
Input yin(mb.getBuffer());
// Parse the YAML file
std::vector<MyDocType> theDocList;
yin >> theDocList;
// Check for error
if ( yin.error() )
return;
|