FazBrowse GitHub Viewer
|
Trending
|
URL:
|
Home
Tools:
[Download Repo ZIP]
[View Raw Code]
[Original HTTPS Page]
feast/sdk/python/feast/type_map.py at master · elshize/feast · GitHub
elshize
/
feast
Public
forked from
feast-dev/feast
Notifications
You must be signed in to change notification settings
Fork
0
Star
1
Code
Pull requests
0
Actions
Projects
Security and quality
0
Insights
Additional navigation options
Code
Pull requests
Actions
Projects
Security and quality
Insights
Expand file tree
Breadcrumbs
feast
/
sdk
/
python
/
feast
/
type_map.py
Copy path
More file actions
More file actions
Latest commit
History
History
History
1056 lines (930 loc) · 37.4 KB
Breadcrumbs
feast
/
sdk
/
python
/
feast
/
type_map.py
Copy path
File metadata and controls
1056 lines (930 loc) · 37.4 KB
Raw
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
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
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
# Copyright 2019 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
json
import
logging
from
collections
import
defaultdict
from
datetime
import
datetime
,
timezone
from
typing
import
(
TYPE_CHECKING
,
Any
,
Dict
,
Iterator
,
List
,
Optional
,
Sequence
,
Set
,
Sized
,
Tuple
,
Type
,
Union
,
cast
,
)
import
numpy
as
np
import
pandas
as
pd
from
google
.
protobuf
.
timestamp_pb2
import
Timestamp
from
feast
.
protos
.
feast
.
types
.
Value_pb2
import
(
BoolList
,
BytesList
,
DoubleList
,
FloatList
,
Int32List
,
Int64List
,
StringList
,
)
from
feast
.
protos
.
feast
.
types
.
Value_pb2
import
Value
as
ProtoValue
from
feast
.
value_type
import
ListType
,
ValueType
if
TYPE_CHECKING
:
import
pyarrow
# null timestamps get converted to -9223372036854775808
NULL_TIMESTAMP_INT_VALUE
:
int
=
np
.
datetime64
(
"NaT"
).
astype
(
int
)
logger
=
logging
.
getLogger
(
__name__
)
def
feast_value_type_to_python_type
(
field_value_proto
:
ProtoValue
)
->
Any
:
"""
Converts field value Proto to Dict and returns each field's Feast Value Type value
in their respective Python value.
Args:
field_value_proto: Field value Proto
Returns:
Python native type representation/version of the given field_value_proto
"""
val_attr
=
field_value_proto
.
WhichOneof
(
"val"
)
if
val_attr
is
None
:
return
None
val
=
getattr
(
field_value_proto
,
val_attr
)
# If it's a _LIST type extract the list.
if
hasattr
(
val
,
"val"
):
val
=
list
(
val
.
val
)
# Convert UNIX_TIMESTAMP values to `datetime`
if
val_attr
==
"unix_timestamp_list_val"
:
val
=
[
(
datetime
.
fromtimestamp
(
v
,
tz
=
timezone
.
utc
)
if
v
!=
NULL_TIMESTAMP_INT_VALUE
else
None
)
for
v
in
val
]
elif
val_attr
==
"unix_timestamp_val"
:
val
=
(
datetime
.
fromtimestamp
(
val
,
tz
=
timezone
.
utc
)
if
val
!=
NULL_TIMESTAMP_INT_VALUE
else
None
)
return
val
def
feast_value_type_to_pandas_type
(
value_type
:
ValueType
)
->
Any
:
value_type_to_pandas_type
:
Dict
[
ValueType
,
str
]
=
{
ValueType
.
FLOAT
:
"float"
,
ValueType
.
INT32
:
"int"
,
ValueType
.
INT64
:
"int"
,
ValueType
.
STRING
:
"str"
,
ValueType
.
DOUBLE
:
"float"
,
ValueType
.
BYTES
:
"bytes"
,
ValueType
.
BOOL
:
"bool"
,
ValueType
.
UNIX_TIMESTAMP
:
"datetime64[ns]"
,
}
if
value_type
.
name
.
endswith
(
"_LIST"
):
return
"object"
if
value_type
in
value_type_to_pandas_type
:
return
value_type_to_pandas_type
[
value_type
]
raise
TypeError
(
f"Casting to pandas type for type
{
value_type
}
failed. "
f"Type
{
value_type
}
not found"
)
def
python_type_to_feast_value_type
(
name
:
str
,
value
:
Optional
[
Any
]
=
None
,
recurse
:
bool
=
True
,
type_name
:
Optional
[
str
]
=
None
,
)
->
ValueType
:
"""
Finds the equivalent Feast Value Type for a Python value. Both native
and Pandas types are supported. This function will recursively look
for nested types when arrays are detected. All types must be homogenous.
Args:
name: Name of the value or field
value: Value that will be inspected
recurse: Whether to recursively look for nested types in arrays
Returns:
Feast Value Type
"""
type_name
=
(
type_name
or
type
(
value
).
__name__
).
lower
()
type_map
=
{
"int"
:
ValueType
.
INT64
,
"str"
:
ValueType
.
STRING
,
"string"
:
ValueType
.
STRING
,
# pandas.StringDtype
"float"
:
ValueType
.
DOUBLE
,
"bytes"
:
ValueType
.
BYTES
,
"float64"
:
ValueType
.
DOUBLE
,
"float32"
:
ValueType
.
FLOAT
,
"int64"
:
ValueType
.
INT64
,
"uint64"
:
ValueType
.
INT64
,
"int32"
:
ValueType
.
INT32
,
"uint32"
:
ValueType
.
INT32
,
"int16"
:
ValueType
.
INT32
,
"uint16"
:
ValueType
.
INT32
,
"uint8"
:
ValueType
.
INT32
,
"int8"
:
ValueType
.
INT32
,
"bool_"
:
ValueType
.
BOOL
,
# np.bool_
"bool"
:
ValueType
.
BOOL
,
"boolean"
:
ValueType
.
BOOL
,
"timedelta"
:
ValueType
.
UNIX_TIMESTAMP
,
"timestamp"
:
ValueType
.
UNIX_TIMESTAMP
,
"datetime"
:
ValueType
.
UNIX_TIMESTAMP
,
"datetime64[ns]"
:
ValueType
.
UNIX_TIMESTAMP
,
"datetime64[ns, tz]"
:
ValueType
.
UNIX_TIMESTAMP
,
# special dtype of pandas
"datetime64[ns, utc]"
:
ValueType
.
UNIX_TIMESTAMP
,
"category"
:
ValueType
.
STRING
,
}
if
type_name
in
type_map
:
return
type_map
[
type_name
]
if
isinstance
(
value
,
np
.
ndarray
)
and
str
(
value
.
dtype
)
in
type_map
:
item_type
=
type_map
[
str
(
value
.
dtype
)]
return
ValueType
[
item_type
.
name
+
"_LIST"
]
if
isinstance
(
value
, (
list
,
np
.
ndarray
)):
# if the value's type is "ndarray" and we couldn't infer from "value.dtype"
# this is most probably array of "object",
# so we need to iterate over objects and try to infer type of each item
if
not
recurse
:
raise
ValueError
(
f"Value type for field
{
name
}
is
{
type
(
value
)
}
but "
f"recursion is not allowed. Array types can only be one level "
f"deep."
)
# This is the final type which we infer from the list
common_item_value_type
=
None
for
item
in
value
:
if
isinstance
(
item
,
ProtoValue
):
current_item_value_type
:
ValueType
=
_proto_value_to_value_type
(
item
)
else
:
# Get the type from the current item, only one level deep
current_item_value_type
=
python_type_to_feast_value_type
(
name
=
name
,
value
=
item
,
recurse
=
False
)
# Validate whether the type stays consistent
if
(
common_item_value_type
and
not
common_item_value_type
==
current_item_value_type
):
raise
ValueError
(
f"List value type for field
{
name
}
is inconsistent. "
f"
{
common_item_value_type
}
different from "
f"
{
current_item_value_type
}
."
)
common_item_value_type
=
current_item_value_type
if
common_item_value_type
is
None
:
return
ValueType
.
UNKNOWN
return
ValueType
[
common_item_value_type
.
name
+
"_LIST"
]
raise
ValueError
(
f"Value with native type
{
type_name
}
"
f"cannot be converted into Feast value type"
)
def
python_values_to_feast_value_type
(
name
:
str
,
values
:
Any
,
recurse
:
bool
=
True
)
->
ValueType
:
inferred_dtype
=
ValueType
.
UNKNOWN
for
row
in
values
:
current_dtype
=
python_type_to_feast_value_type
(
name
,
value
=
row
,
recurse
=
recurse
)
if
inferred_dtype
is
ValueType
.
UNKNOWN
:
inferred_dtype
=
current_dtype
else
:
if
current_dtype
!=
inferred_dtype
and
current_dtype
not
in
(
ValueType
.
UNKNOWN
,
ValueType
.
NULL
,
):
raise
TypeError
(
f"Input entity
{
name
}
has mixed types,
{
current_dtype
}
and
{
inferred_dtype
}
. That is not allowed. "
)
if
inferred_dtype
in
(
ValueType
.
UNKNOWN
,
ValueType
.
NULL
):
raise
ValueError
(
f"field
{
name
}
cannot have all null values for type inference."
)
return
inferred_dtype
def
_convert_value_type_str_to_value_type
(
type_str
:
str
)
->
ValueType
:
type_map
=
{
"UNKNOWN"
:
ValueType
.
UNKNOWN
,
"BYTES"
:
ValueType
.
BYTES
,
"STRING"
:
ValueType
.
STRING
,
"INT32"
:
ValueType
.
INT32
,
"INT64"
:
ValueType
.
INT64
,
"DOUBLE"
:
ValueType
.
DOUBLE
,
"FLOAT"
:
ValueType
.
FLOAT
,
"BOOL"
:
ValueType
.
BOOL
,
"NULL"
:
ValueType
.
NULL
,
"UNIX_TIMESTAMP"
:
ValueType
.
UNIX_TIMESTAMP
,
"BYTES_LIST"
:
ValueType
.
BYTES_LIST
,
"STRING_LIST"
:
ValueType
.
STRING_LIST
,
"INT32_LIST "
:
ValueType
.
INT32_LIST
,
"INT64_LIST"
:
ValueType
.
INT64_LIST
,
"DOUBLE_LIST"
:
ValueType
.
DOUBLE_LIST
,
"FLOAT_LIST"
:
ValueType
.
FLOAT_LIST
,
"BOOL_LIST"
:
ValueType
.
BOOL_LIST
,
"UNIX_TIMESTAMP_LIST"
:
ValueType
.
UNIX_TIMESTAMP_LIST
,
}
return
type_map
[
type_str
]
def
_type_err
(
item
,
dtype
):
raise
TypeError
(
f'Value "
{
item
}
" is of type
{
type
(
item
)
}
not of type
{
dtype
}
'
)
PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE
:
Dict
[
ValueType
,
Tuple
[
ListType
,
str
,
List
[
Type
]]
]
=
{
ValueType
.
FLOAT_LIST
: (
FloatList
,
"float_list_val"
,
[
np
.
float32
,
np
.
float64
,
float
],
),
ValueType
.
DOUBLE_LIST
: (
DoubleList
,
"double_list_val"
,
[
np
.
float64
,
np
.
float32
,
float
],
),
ValueType
.
INT32_LIST
: (
Int32List
,
"int32_list_val"
, [
np
.
int64
,
np
.
int32
,
int
]),
ValueType
.
INT64_LIST
: (
Int64List
,
"int64_list_val"
, [
np
.
int64
,
np
.
int32
,
int
]),
ValueType
.
UNIX_TIMESTAMP_LIST
: (
Int64List
,
"int64_list_val"
,
[
np
.
datetime64
,
np
.
int64
,
np
.
int32
,
int
,
datetime
,
Timestamp
],
),
ValueType
.
STRING_LIST
: (
StringList
,
"string_list_val"
, [
np
.
str_
,
str
]),
ValueType
.
BOOL_LIST
: (
BoolList
,
"bool_list_val"
, [
np
.
bool_
,
bool
]),
ValueType
.
BYTES_LIST
: (
BytesList
,
"bytes_list_val"
, [
np
.
bytes_
,
bytes
]),
}
PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE
:
Dict
[
ValueType
,
Tuple
[
str
,
Any
,
Optional
[
Set
[
Type
]]]
]
=
{
ValueType
.
INT32
: (
"int32_val"
,
lambda
x
:
int
(
x
),
None
),
ValueType
.
INT64
: (
"int64_val"
,
lambda
x
: (
int
(
x
.
timestamp
())
if
isinstance
(
x
,
pd
.
_libs
.
tslibs
.
timestamps
.
Timestamp
)
else
int
(
x
)
),
None
,
),
ValueType
.
FLOAT
: (
"float_val"
,
lambda
x
:
float
(
x
),
None
),
ValueType
.
DOUBLE
: (
"double_val"
,
lambda
x
:
x
, {
float
,
np
.
float64
,
int
,
np
.
int_
}),
ValueType
.
STRING
: (
"string_val"
,
lambda
x
:
str
(
x
),
None
),
ValueType
.
BYTES
: (
"bytes_val"
,
lambda
x
:
x
, {
bytes
}),
ValueType
.
BOOL
: (
"bool_val"
,
lambda
x
:
x
, {
bool
,
np
.
bool_
,
int
,
np
.
int_
}),
}
def
_python_datetime_to_int_timestamp
(
values
:
Sequence
[
Any
],
)
->
Sequence
[
Union
[
int
,
np
.
int_
]]:
# Fast path for Numpy array.
if
isinstance
(
values
,
np
.
ndarray
)
and
isinstance
(
values
.
dtype
,
np
.
datetime64
):
if
values
.
ndim
!=
1
:
raise
ValueError
(
"Only 1 dimensional arrays are supported."
)
return
cast
(
Sequence
[
np
.
int_
],
values
.
astype
(
"datetime64[s]"
).
astype
(
np
.
int_
))
int_timestamps
=
[]
for
value
in
values
:
if
isinstance
(
value
,
datetime
):
int_timestamps
.
append
(
int
(
value
.
timestamp
()))
elif
isinstance
(
value
,
Timestamp
):
int_timestamps
.
append
(
int
(
value
.
ToSeconds
()))
elif
isinstance
(
value
,
np
.
datetime64
):
int_timestamps
.
append
(
value
.
astype
(
"datetime64[s]"
).
astype
(
np
.
int_
))
# type: ignore[attr-defined]
elif
isinstance
(
value
,
type
(
np
.
nan
)):
int_timestamps
.
append
(
NULL_TIMESTAMP_INT_VALUE
)
else
:
int_timestamps
.
append
(
int
(
value
))
return
int_timestamps
def
_python_value_to_proto_value
(
feast_value_type
:
ValueType
,
values
:
List
[
Any
]
)
->
List
[
ProtoValue
]:
"""
Converts a Python (native, pandas) value to a Feast Proto Value based
on a provided value type
Args:
feast_value_type: The target value type
values: List of Values that will be converted
Returns:
List of Feast Value Proto
"""
# ToDo: make a better sample for type checks (more than one element)
sample
=
next
(
filter
(
_non_empty_value
,
values
),
None
)
# first not empty value
# Detect list type and handle separately
if
"list"
in
feast_value_type
.
name
.
lower
():
# Feature can be list but None is still valid
if
feast_value_type
in
PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE
:
proto_type
,
field_name
,
valid_types
=
PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE
[
feast_value_type
]
# Bytes to array type conversion
if
isinstance
(
sample
, (
bytes
,
bytearray
)):
# Bytes of an array containing elements of bytes not supported
if
feast_value_type
==
ValueType
.
BYTES_LIST
:
raise
_type_err
(
sample
,
ValueType
.
BYTES_LIST
)
json_value
=
json
.
loads
(
sample
)
if
isinstance
(
json_value
,
list
):
if
feast_value_type
==
ValueType
.
BOOL_LIST
:
json_value
=
[
bool
(
item
)
for
item
in
json_value
]
return
[
ProtoValue
(
**
{
field_name
:
proto_type
(
val
=
json_value
)})]
# type: ignore
raise
_type_err
(
sample
,
valid_types
[
0
])
if
sample
is
not
None
and
not
all
(
type
(
item
)
in
valid_types
for
item
in
sample
):
# to_numpy() in utils._convert_arrow_to_proto() upcasts values of type Array of INT32 or INT64 with NULL values to Float64 automatically.
for
item
in
sample
:
if
type
(
item
)
not
in
valid_types
:
if
feast_value_type
in
[
ValueType
.
INT32_LIST
,
ValueType
.
INT64_LIST
,
]:
if
not
any
(
np
.
isnan
(
item
)
for
item
in
sample
):
logger
.
error
(
"Array of Int32 or Int64 type has NULL values. to_numpy() upcasts to Float64 automatically."
)
raise
_type_err
(
item
,
valid_types
[
0
])
if
feast_value_type
==
ValueType
.
UNIX_TIMESTAMP_LIST
:
return
[
(
# ProtoValue does actually accept `np.int_` but the typing complains.
ProtoValue
(
unix_timestamp_list_val
=
Int64List
(
val
=
_python_datetime_to_int_timestamp
(
value
)
# type: ignore
)
)
if
value
is
not
None
else
ProtoValue
()
)
for
value
in
values
]
if
feast_value_type
==
ValueType
.
BOOL_LIST
:
# ProtoValue does not support conversion of np.bool_ so we need to convert it to support np.bool_.
return
[
(
ProtoValue
(
**
{
field_name
:
proto_type
(
val
=
[
bool
(
e
)
for
e
in
value
])}
# type: ignore
)
if
value
is
not
None
else
ProtoValue
()
)
for
value
in
values
]
return
[
(
ProtoValue
(
**
{
field_name
:
proto_type
(
val
=
value
)})
# type: ignore
if
value
is
not
None
else
ProtoValue
()
)
for
value
in
values
]
# Handle scalar types below
else
:
if
sample
is
None
:
# all input values are None
return
[
ProtoValue
()]
*
len
(
values
)
if
feast_value_type
==
ValueType
.
UNIX_TIMESTAMP
:
int_timestamps
=
_python_datetime_to_int_timestamp
(
values
)
# ProtoValue does actually accept `np.int_` but the typing complains.
return
[
ProtoValue
(
unix_timestamp_val
=
ts
)
for
ts
in
int_timestamps
]
# type: ignore
(
field_name
,
func
,
valid_scalar_types
,
)
=
PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE
[
feast_value_type
]
if
valid_scalar_types
:
if
(
sample
==
0
or
sample
==
0.0
)
and
feast_value_type
!=
ValueType
.
BOOL
:
# Numpy convert 0 to int. However, in the feature view definition, the type of column may be a float.
# So, if value is 0, type validation must pass if scalar_types are either int or float.
allowed_types
=
{
np
.
int64
,
int
,
np
.
float64
,
float
}
assert
(
type
(
sample
)
in
allowed_types
),
f"Type `
{
type
(
sample
)
}
` not in
{
allowed_types
}
"
else
:
assert
(
type
(
sample
)
in
valid_scalar_types
),
f"Type `
{
type
(
sample
)
}
` not in
{
valid_scalar_types
}
"
if
feast_value_type
==
ValueType
.
BOOL
:
# ProtoValue does not support conversion of np.bool_ so we need to convert it to support np.bool_.
return
[
(
ProtoValue
(
**
{
field_name
:
func
(
bool
(
value
)
if
type
(
value
)
is
np
.
bool_
else
value
# type: ignore
)
}
)
if
not
pd
.
isnull
(
value
)
else
ProtoValue
()
)
for
value
in
values
]
if
feast_value_type
in
PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE
:
out
=
[]
for
value
in
values
:
if
isinstance
(
value
,
ProtoValue
):
out
.
append
(
value
)
elif
not
pd
.
isnull
(
value
):
out
.
append
(
ProtoValue
(
**
{
field_name
:
func
(
value
)}))
else
:
out
.
append
(
ProtoValue
())
return
out
raise
Exception
(
f"Unsupported data type: $
{
str
(
type
(
values
[
0
]))
}
"
)
def
python_values_to_proto_values
(
values
:
List
[
Any
],
feature_type
:
ValueType
=
ValueType
.
UNKNOWN
)
->
List
[
ProtoValue
]:
value_type
=
feature_type
sample
=
next
(
filter
(
_non_empty_value
,
values
),
None
)
# first not empty value
if
sample
is
not
None
and
feature_type
==
ValueType
.
UNKNOWN
:
if
isinstance
(
sample
, (
list
,
np
.
ndarray
)):
value_type
=
(
feature_type
if
len
(
sample
)
==
0
else
python_type_to_feast_value_type
(
""
,
sample
)
)
else
:
value_type
=
python_type_to_feast_value_type
(
""
,
sample
)
if
value_type
==
ValueType
.
UNKNOWN
:
raise
TypeError
(
"Couldn't infer value type from empty value"
)
return
_python_value_to_proto_value
(
value_type
,
values
)
def
_proto_value_to_value_type
(
proto_value
:
ProtoValue
)
->
ValueType
:
"""
Returns Feast ValueType given Feast ValueType string.
Args:
proto_str: str
Returns:
A variant of ValueType.
"""
proto_str
=
proto_value
.
WhichOneof
(
"val"
)
type_map
=
{
"int32_val"
:
ValueType
.
INT32
,
"int64_val"
:
ValueType
.
INT64
,
"double_val"
:
ValueType
.
DOUBLE
,
"float_val"
:
ValueType
.
FLOAT
,
"string_val"
:
ValueType
.
STRING
,
"bytes_val"
:
ValueType
.
BYTES
,
"bool_val"
:
ValueType
.
BOOL
,
"int32_list_val"
:
ValueType
.
INT32_LIST
,
"int64_list_val"
:
ValueType
.
INT64_LIST
,
"double_list_val"
:
ValueType
.
DOUBLE_LIST
,
"float_list_val"
:
ValueType
.
FLOAT_LIST
,
"string_list_val"
:
ValueType
.
STRING_LIST
,
"bytes_list_val"
:
ValueType
.
BYTES_LIST
,
"bool_list_val"
:
ValueType
.
BOOL_LIST
,
None
:
ValueType
.
NULL
,
}
return
type_map
[
proto_str
]
def
pa_to_feast_value_type
(
pa_type_as_str
:
str
)
->
ValueType
:
is_list
=
False
if
pa_type_as_str
.
startswith
(
"list<item: "
):
is_list
=
True
pa_type_as_str
=
pa_type_as_str
.
replace
(
"list<item: "
,
""
).
replace
(
">"
,
""
)
if
pa_type_as_str
.
startswith
(
"timestamp"
):
value_type
=
ValueType
.
UNIX_TIMESTAMP
else
:
type_map
=
{
"int32"
:
ValueType
.
INT32
,
"int64"
:
ValueType
.
INT64
,
"double"
:
ValueType
.
DOUBLE
,
"float"
:
ValueType
.
FLOAT
,
"string"
:
ValueType
.
STRING
,
"binary"
:
ValueType
.
BYTES
,
"bool"
:
ValueType
.
BOOL
,
"null"
:
ValueType
.
NULL
,
}
value_type
=
type_map
[
pa_type_as_str
]
if
is_list
:
value_type
=
ValueType
[
value_type
.
name
+
"_LIST"
]
return
value_type
def
bq_to_feast_value_type
(
bq_type_as_str
:
str
)
->
ValueType
:
is_list
=
False
if
bq_type_as_str
.
startswith
(
"ARRAY<"
):
is_list
=
True
bq_type_as_str
=
bq_type_as_str
[
6
:
-
1
]
type_map
:
Dict
[
str
,
ValueType
]
=
{
"DATETIME"
:
ValueType
.
UNIX_TIMESTAMP
,
"TIMESTAMP"
:
ValueType
.
UNIX_TIMESTAMP
,
"INTEGER"
:
ValueType
.
INT64
,
"NUMERIC"
:
ValueType
.
INT64
,
"INT64"
:
ValueType
.
INT64
,
"STRING"
:
ValueType
.
STRING
,
"FLOAT"
:
ValueType
.
DOUBLE
,
"FLOAT64"
:
ValueType
.
DOUBLE
,
"BYTES"
:
ValueType
.
BYTES
,
"BOOL"
:
ValueType
.
BOOL
,
"BOOLEAN"
:
ValueType
.
BOOL
,
# legacy sql data type
"NULL"
:
ValueType
.
NULL
,
}
value_type
=
type_map
.
get
(
bq_type_as_str
,
ValueType
.
STRING
)
if
is_list
:
value_type
=
ValueType
[
value_type
.
name
+
"_LIST"
]
return
value_type
def
mssql_to_feast_value_type
(
mssql_type_as_str
:
str
)
->
ValueType
:
type_map
=
{
"bigint"
:
ValueType
.
FLOAT
,
"binary"
:
ValueType
.
BYTES
,
"bit"
:
ValueType
.
BOOL
,
"char"
:
ValueType
.
STRING
,
"date"
:
ValueType
.
UNIX_TIMESTAMP
,
"datetime"
:
ValueType
.
UNIX_TIMESTAMP
,
"datetimeoffset"
:
ValueType
.
UNIX_TIMESTAMP
,
"float"
:
ValueType
.
FLOAT
,
"int"
:
ValueType
.
INT32
,
"nchar"
:
ValueType
.
STRING
,
"nvarchar"
:
ValueType
.
STRING
,
"nvarchar(max)"
:
ValueType
.
STRING
,
"real"
:
ValueType
.
FLOAT
,
"smallint"
:
ValueType
.
INT32
,
"tinyint"
:
ValueType
.
INT32
,
"varbinary"
:
ValueType
.
BYTES
,
"varchar"
:
ValueType
.
STRING
,
"None"
:
ValueType
.
NULL
,
# skip date, geometry, hllsketch, time, timetz
}
if
mssql_type_as_str
.
lower
()
not
in
type_map
:
raise
ValueError
(
f"Mssql type not supported by feast
{
mssql_type_as_str
}
"
)
return
type_map
[
mssql_type_as_str
.
lower
()]
def
pa_to_mssql_type
(
pa_type
:
"pyarrow.DataType"
)
->
str
:
# PyArrow types: https://arrow.apache.org/docs/python/api/datatypes.html
# MS Sql types: https://docs.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql?view=sql-server-ver16
pa_type_as_str
=
str
(
pa_type
).
lower
()
if
pa_type_as_str
.
startswith
(
"timestamp"
):
if
"tz="
in
pa_type_as_str
:
return
"datetime2"
else
:
return
"datetime"
if
pa_type_as_str
.
startswith
(
"date"
):
return
"date"
if
pa_type_as_str
.
startswith
(
"decimal"
):
return
pa_type_as_str
# We have to take into account how arrow types map to parquet types as well.
# For example, null type maps to int32 in parquet, so we have to use int4 in Redshift.
# Other mappings have also been adjusted accordingly.
type_map
=
{
"null"
:
"None"
,
"bool"
:
"bit"
,
"int8"
:
"tinyint"
,
"int16"
:
"smallint"
,
"int32"
:
"int"
,
"int64"
:
"bigint"
,
"uint8"
:
"tinyint"
,
"uint16"
:
"smallint"
,
"uint32"
:
"int"
,
"uint64"
:
"bigint"
,
"float"
:
"float"
,
"double"
:
"real"
,
"binary"
:
"binary"
,
"string"
:
"varchar"
,
}
if
pa_type_as_str
.
lower
()
not
in
type_map
:
raise
ValueError
(
f"MS SQL Server type not supported by feast
{
pa_type_as_str
}
"
)
return
type_map
[
pa_type_as_str
]
def
redshift_to_feast_value_type
(
redshift_type_as_str
:
str
)
->
ValueType
:
# Type names from https://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html
type_map
=
{
"int2"
:
ValueType
.
INT32
,
"int4"
:
ValueType
.
INT32
,
"int8"
:
ValueType
.
INT64
,
"numeric"
:
ValueType
.
DOUBLE
,
"float4"
:
ValueType
.
FLOAT
,
"float8"
:
ValueType
.
DOUBLE
,
"bool"
:
ValueType
.
BOOL
,
"character"
:
ValueType
.
STRING
,
"varchar"
:
ValueType
.
STRING
,
"timestamp"
:
ValueType
.
UNIX_TIMESTAMP
,
"timestamptz"
:
ValueType
.
UNIX_TIMESTAMP
,
"super"
:
ValueType
.
BYTES
,
# skip date, geometry, hllsketch, time, timetz
}
return
type_map
[
redshift_type_as_str
.
lower
()]
def
snowflake_type_to_feast_value_type
(
snowflake_type
:
str
)
->
ValueType
:
type_map
=
{
"BINARY"
:
ValueType
.
BYTES
,
"VARCHAR"
:
ValueType
.
STRING
,
"NUMBER32"
:
ValueType
.
INT32
,
"NUMBER64"
:
ValueType
.
INT64
,
"NUMBERwSCALE"
:
ValueType
.
DOUBLE
,
"DOUBLE"
:
ValueType
.
DOUBLE
,
"BOOLEAN"
:
ValueType
.
BOOL
,
"DATE"
:
ValueType
.
UNIX_TIMESTAMP
,
"TIMESTAMP"
:
ValueType
.
UNIX_TIMESTAMP
,
"TIMESTAMP_TZ"
:
ValueType
.
UNIX_TIMESTAMP
,
"TIMESTAMP_LTZ"
:
ValueType
.
UNIX_TIMESTAMP
,
"TIMESTAMP_NTZ"
:
ValueType
.
UNIX_TIMESTAMP
,
}
return
type_map
[
snowflake_type
]
def
_convert_value_name_to_snowflake_udf
(
value_name
:
str
,
project_name
:
str
)
->
str
:
name_map
=
{
"BYTES"
:
f"feast_
{
project_name
}
_snowflake_binary_to_bytes_proto"
,
"STRING"
:
f"feast_
{
project_name
}
_snowflake_varchar_to_string_proto"
,
"INT32"
:
f"feast_
{
project_name
}
_snowflake_number_to_int32_proto"
,
"INT64"
:
f"feast_
{
project_name
}
_snowflake_number_to_int64_proto"
,
"DOUBLE"
:
f"feast_
{
project_name
}
_snowflake_float_to_double_proto"
,
"FLOAT"
:
f"feast_
{
project_name
}
_snowflake_float_to_double_proto"
,
"BOOL"
:
f"feast_
{
project_name
}
_snowflake_boolean_to_bool_proto"
,
"UNIX_TIMESTAMP"
:
f"feast_
{
project_name
}
_snowflake_timestamp_to_unix_timestamp_proto"
,
"BYTES_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_bytes_to_list_bytes_proto"
,
"STRING_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_varchar_to_list_string_proto"
,
"INT32_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_number_to_list_int32_proto"
,
"INT64_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_number_to_list_int64_proto"
,
"DOUBLE_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_float_to_list_double_proto"
,
"FLOAT_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_float_to_list_double_proto"
,
"BOOL_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_boolean_to_list_bool_proto"
,
"UNIX_TIMESTAMP_LIST"
:
f"feast_
{
project_name
}
_snowflake_array_timestamp_to_list_unix_timestamp_proto"
,
}
return
name_map
[
value_name
].
upper
()
def
pa_to_redshift_value_type
(
pa_type
:
"pyarrow.DataType"
)
->
str
:
# PyArrow types: https://arrow.apache.org/docs/python/api/datatypes.html
# Redshift type: https://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html
pa_type_as_str
=
str
(
pa_type
).
lower
()
if
pa_type_as_str
.
startswith
(
"timestamp"
):
if
"tz="
in
pa_type_as_str
:
return
"timestamptz"
else
:
return
"timestamp"
if
pa_type_as_str
.
startswith
(
"date"
):
return
"date"
if
pa_type_as_str
.
startswith
(
"decimal"
):
# PyArrow decimal types (e.g. "decimal(38,37)") luckily directly map to the Redshift type.
return
pa_type_as_str
if
pa_type_as_str
.
startswith
(
"list"
):
return
"super"
# We have to take into account how arrow types map to parquet types as well.
# For example, null type maps to int32 in parquet, so we have to use int4 in Redshift.
# Other mappings have also been adjusted accordingly.
type_map
=
{
"null"
:
"int4"
,
"bool"
:
"bool"
,
"int8"
:
"int4"
,
"int16"
:
"int4"
,
"int32"
:
"int4"
,
"int64"
:
"int8"
,
"uint8"
:
"int4"
,
"uint16"
:
"int4"
,
"uint32"
:
"int8"
,
"uint64"
:
"int8"
,
"float"
:
"float4"
,
"double"
:
"float8"
,
"binary"
:
"varchar"
,
"string"
:
"varchar"
,
}
return
type_map
[
pa_type_as_str
]
def
_non_empty_value
(
value
:
Any
)
->
bool
:
"""
Check that there's enough data we can use for type inference.
If primitive type - just checking that it's not None
If iterable - checking that there's some elements (len > 0)
String is special case: "" - empty string is considered non empty
"""
return
value
is
not
None
and
(
not
isinstance
(
value
,
Sized
)
or
len
(
value
)
>
0
or
isinstance
(
value
,
str
)
)
def
spark_to_feast_value_type
(
spark_type_as_str
:
str
)
->
ValueType
:
# TODO not all spark types are convertible
# Current non-convertible types: interval, map, struct, structfield, binary
type_map
:
Dict
[
str
,
ValueType
]
=
{
"null"
:
ValueType
.
UNKNOWN
,
"byte"
:
ValueType
.
BYTES
,
"string"
:
ValueType
.
STRING
,
"int"
:
ValueType
.
INT32
,
"short"
:
ValueType
.
INT32
,
"bigint"
:
ValueType
.
INT64
,
"long"
:
ValueType
.
INT64
,
"double"
:
ValueType
.
DOUBLE
,
"decimal"
:
ValueType
.
DOUBLE
,
"float"
:
ValueType
.
FLOAT
,
"boolean"
:
ValueType
.
BOOL
,
"timestamp"
:
ValueType
.
UNIX_TIMESTAMP
,
"array<byte>"
:
ValueType
.
BYTES_LIST
,
"array<string>"
:
ValueType
.
STRING_LIST
,
"array<int>"
:
ValueType
.
INT32_LIST
,
"array<bigint>"
:
ValueType
.
INT64_LIST
,
"array<double>"
:
ValueType
.
DOUBLE_LIST
,
"array<decimal>"
:
ValueType
.
DOUBLE_LIST
,
"array<float>"
:
ValueType
.
FLOAT_LIST
,
"array<boolean>"
:
ValueType
.
BOOL_LIST
,
"array<timestamp>"
:
ValueType
.
UNIX_TIMESTAMP_LIST
,
}
if
spark_type_as_str
.
startswith
(
"decimal"
):
spark_type_as_str
=
"decimal"
if
spark_type_as_str
.
startswith
(
"array<decimal"
):
spark_type_as_str
=
"array<decimal>"
# TODO: Find better way of doing this.
if
not
isinstance
(
spark_type_as_str
,
str
)
or
spark_type_as_str
not
in
type_map
:
return
ValueType
.
NULL
return
type_map
[
spark_type_as_str
.
lower
()]
def
spark_schema_to_np_dtypes
(
dtypes
:
List
[
Tuple
[
str
,
str
]])
->
Iterator
[
np
.
dtype
]:
# TODO recheck all typing (also tz for timestamp)
# https://spark.apache.org/docs/latest/api/python/user_guide/arrow_pandas.html#timestamp-with-time-zone-semantics
type_map
=
defaultdict
(
lambda
:
np
.
dtype
(
"O"
),
{
"boolean"
:
np
.
dtype
(
"bool"
),
"double"
:
np
.
dtype
(
"float64"
),
"float"
:
np
.
dtype
(
"float64"
),
"int"
:
np
.
dtype
(
"int64"
),
"bigint"
:
np
.
dtype
(
"int64"
),
"smallint"
:
np
.
dtype
(
"int64"
),
"timestamp"
:
np
.
dtype
(
"datetime64[ns]"
),
},
)
return
(
type_map
[
t
]
for
_
,
t
in
dtypes
)
def
arrow_to_pg_type
(
t_str
:
str
)
->
str
:
try
:
if
t_str
.
startswith
(
"timestamp"
)
or
t_str
.
startswith
(
"datetime"
):
return
"timestamptz"
if
"tz="
in
t_str
else
"timestamp"
return
{
"null"
:
"null"
,
"bool"
:
"boolean"
,
"int8"
:
"tinyint"
,
"int16"
:
"smallint"
,
"int32"
:
"int"
,
"int64"
:
"bigint"
,
"list<item: int32>"
:
"int[]"
,
"list<item: int64>"
:
"bigint[]"
,
"list<item: bool>"
:
"boolean[]"
,
"list<item: double>"
:
"double precision[]"
,
"list<item: timestamp[us]>"
:
"timestamp[]"
,
"uint8"
:
"smallint"
,
"uint16"
:
"int"
,
"uint32"
:
"bigint"
,
"uint64"
:
"bigint"
,
"float"
:
"float"
,
"double"
:
"double precision"
,
"binary"
:
"binary"
,
"string"
:
"text"
,
}[
t_str
]
except
KeyError
:
raise
ValueError
(
f"Unsupported type:
{
t_str
}
"
)
def
pg_type_to_feast_value_type
(
type_str
:
str
)
->
ValueType
:
type_map
:
Dict
[
str
,
ValueType
]
=
{
"boolean"
:
ValueType
.
BOOL
,
"bytea"
:
ValueType
.
BYTES
,
"char"
:
ValueType
.
STRING
,
"bigint"
:
ValueType
.
INT64
,
"smallint"
:
ValueType
.
INT32
,
"integer"
:
ValueType
.
INT32
,
"real"
:
ValueType
.
DOUBLE
,
"double precision"
:
ValueType
.
DOUBLE
,
"boolean[]"
:
ValueType
.
BOOL_LIST
,
"bytea[]"
:
ValueType
.
BYTES_LIST
,
"char[]"
:
ValueType
.
STRING_LIST
,
"smallint[]"
:
ValueType
.
INT32_LIST
,
"integer[]"
:
ValueType
.
INT32_LIST
,
"text"
:
ValueType
.
STRING
,
"text[]"
:
ValueType
.
STRING_LIST
,
"character[]"
:
ValueType
.
STRING_LIST
,
"bigint[]"
:
ValueType
.
INT64_LIST
,
"real[]"
:
ValueType
.
DOUBLE_LIST
,
"double precision[]"
:
ValueType
.
DOUBLE_LIST
,
"character"
:
ValueType
.
STRING
,
"character varying"
:
ValueType
.
STRING
,
"date"
:
ValueType
.
UNIX_TIMESTAMP
,
"time without time zone"
:
ValueType
.
UNIX_TIMESTAMP
,
"timestamp without time zone"
:
ValueType
.
UNIX_TIMESTAMP
,
"timestamp without time zone[]"
:
ValueType
.
UNIX_TIMESTAMP_LIST
,
"date[]"
:
ValueType
.
UNIX_TIMESTAMP_LIST
,
"time without time zone[]"
:
ValueType
.
UNIX_TIMESTAMP_LIST
,
"timestamp with time zone"
:
ValueType
.
UNIX_TIMESTAMP
,
"timestamp with time zone[]"
:
ValueType
.
UNIX_TIMESTAMP_LIST
,
"numeric[]"
:
ValueType
.
DOUBLE_LIST
,
"numeric"
:
ValueType
.
DOUBLE
,
"uuid"
:
ValueType
.
STRING
,
"uuid[]"
:
ValueType
.
STRING_LIST
,
}
value
=
(
type_map
[
type_str
.
lower
()]
if
type_str
.
lower
()
in
type_map
else
ValueType
.
UNKNOWN
)
if
value
==
ValueType
.
UNKNOWN
:
print
(
"unknown type:"
,
type_str
)
return
value
def
feast_value_type_to_pa
(
feast_type
:
ValueType
,
timestamp_unit
:
str
=
"us"
)
->
"pyarrow.DataType"
:
import
pyarrow
type_map
=
{
ValueType
.
INT32
:
pyarrow
.
int32
(),
ValueType
.
INT64
:
pyarrow
.
int64
(),
ValueType
.
DOUBLE
:
pyarrow
.
float64
(),
ValueType
.
FLOAT
:
pyarrow
.
float32
(),
ValueType
.
STRING
:
pyarrow
.
string
(),
ValueType
.
BYTES
:
pyarrow
.
binary
(),
ValueType
.
BOOL
:
pyarrow
.
bool_
(),
ValueType
.
UNIX_TIMESTAMP
:
pyarrow
.
timestamp
(
timestamp_unit
),
ValueType
.
INT32_LIST
:
pyarrow
.
list_
(
pyarrow
.
int32
()),
ValueType
.
INT64_LIST
:
pyarrow
.
list_
(
pyarrow
.
int64
()),
ValueType
.
DOUBLE_LIST
:
pyarrow
.
list_
(
pyarrow
.
float64
()),
ValueType
.
FLOAT_LIST
:
pyarrow
.
list_
(
pyarrow
.
float32
()),
ValueType
.
STRING_LIST
:
pyarrow
.
list_
(
pyarrow
.
string
()),
ValueType
.
BYTES_LIST
:
pyarrow
.
list_
(
pyarrow
.
binary
()),
ValueType
.
BOOL_LIST
:
pyarrow
.
list_
(
pyarrow
.
bool_
()),
ValueType
.
UNIX_TIMESTAMP_LIST
:
pyarrow
.
list_
(
pyarrow
.
timestamp
(
timestamp_unit
)),
ValueType
.
NULL
:
pyarrow
.
null
(),
}
return
type_map
[
feast_type
]
def
pg_type_code_to_pg_type
(
code
:
int
)
->
str
:
"""Map the postgres type code a Feast type string
Rather than raise an exception on an unknown type, we return the
string representation of the type code. This way rather than raising
an exception on unknown types, Feast will just skip the problem columns.
Note that json and jsonb are not supported but this shows up in the
log as a warning. Since postgres allows custom types we return an unknown for those cases.
See: https://jdbc.postgresql.org/documentation/publicapi/index.html?constant-values.html
"""
PG_TYPE_MAP
=
{
16
:
"boolean"
,
17
:
"bytea"
,
20
:
"bigint"
,
21
:
"smallint"
,
23
:
"integer"
,
25
:
"text"
,
114
:
"json"
,
199
:
"json[]"
,
700
:
"real"
,
701
:
"double precision"
,
1000
:
"boolean[]"
,
1001
:
"bytea[]"
,
1005
:
"smallint[]"
,
1007
:
"integer[]"
,
1009
:
"text[]"
,
1014
:
"character[]"
,
1016
:
"bigint[]"
,
1021
:
"real[]"
,
1022
:
"double precision[]"
,
1042
:
"character"
,
1043
:
"character varying"
,
1082
:
"date"
,
1083
:
"time without time zone"
,
1114
:
"timestamp without time zone"
,
1115
:
"timestamp without time zone[]"
,
1182
:
"date[]"
,
1183
:
"time without time zone[]"
,
1184
:
"timestamp with time zone"
,
1185
:
"timestamp with time zone[]"
,
1231
:
"numeric[]"
,
1700
:
"numeric"
,
2950
:
"uuid"
,
2951
:
"uuid[]"
,
3802
:
"jsonb"
,
3807
:
"jsonb[]"
,
}
return
PG_TYPE_MAP
.
get
(
code
,
"unknown"
)
def
pg_type_code_to_arrow
(
code
:
int
)
->
str
:
return
feast_value_type_to_pa
(
pg_type_to_feast_value_type
(
pg_type_code_to_pg_type
(
code
))
)
View remainder of file in raw view
Back
|
FazBrowse Home
|
New Git URL