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Difficulty level: ♦♦♦♦♢
❝ FIXME ❞
— FIXME
FIXME
Open the Python Shell and define the following variable:
>>> shell = 1
Keep that window open. Now open another Python Shell and define the following variable:
>>> shell = 2
Throughout this chapter, I will use the shell variable to indicate which Python Shell is being used in each example.
⁂
The concept of serialization is simple. You have a data structure in memory that you want to save, reuse, or send to someone else. How would you do that? Well, that depends on how you want to save it, how you want to reuse it, and to whom you want to send it. Many games allow you to save your progress when you quit the game and pick up where you left off when you relaunch the game. (Actually, many non-gaming applications do this as well.) In this case, a data structure that captures “your progress so far” needs to be stored on disk when you quit, then loaded from disk when you relaunch. The data is only meant to be used by the same program that created it, never sent over a network, and never read by anything other than the program that created it. Therefore, the interoperability issues are limited to ensuring that later versions of the program can read data written by earlier versions.
For cases like this, the pickle module is ideal. It’s part of the Python standard library, so it’s always available. It’s fast; the bulk of it is written in C, like the Python interpreter itself. It can store arbitrarily complex Python data structures.
What can the pickle module store?
bytes objects, byte arrays, and None.
If this isn’t enough for you, the pickle module is also extensible, as you’ll see later in this chapter.
The pickle module works with data structures. Let’s build one.
>>> shell ① 1 >>> entry = {} ② >>> entry['title'] = 'Dive into history, 2009 edition' >>> entry['article_link'] = 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition' >>> entry['comments_link'] = None >>> entry['internal_id'] = b'\xde\xd5\xb4\xf8' >>> entry['tags'] = ('diveintopython', 'docbook', 'html') >>> entry['published'] = True >>> import time >>> entry['published_date'] = time.strptime('Fri Mar 27 22:20:42 2009') ③ >>> entry['published_date'] time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1)
pickle module. Don’t read too much into these values.
time module contains a data structure (time_struct) to represent a point in time (accurate to one millisecond) and functions to manipulate time structs. The strptime() function takes a formatted string an converts it to a time_struct. This string is in the default format, but you can control that with format codes. See the time module for more details.
That’s a handsome-looking Python dictionary. Let’s save it to a file.
>>> shell ① 1 >>> import pickle >>> with open('entry.pickle', 'wb') as f: ② ... pickle.dump(entry, f) ③ ...
open() function to open a file. Set the file mode to 'wb' to open the file for writing in binary mode. Wrap it in a with statement to ensure the file is closed automatically when you’re done with it.
dump() function in the pickle module takes a serializable Python data structure, serializes it into a binary, Python-specific format using the latest version of the pickle protocol, and saves it to an open file.
That last sentence was pretty important.
pickle module takes a Python data structure and saves it to a file.
entry.pickle file you just created and do anything useful with it in Perl, PHP, Java, or any other language.
pickle module. The pickle protocol has changed several times as new data types have been added to the Python language, but there are still limitations.
pickle module will use the latest version of the pickle protocol. This ensures that you have maximum flexibility in the types of data you can serialize, but it also means that the resulting file will not be readable by older versions of Python that do not support the latest version of the pickle protocol.
Now switch to your second Python Shell — i.e. not the one where you created the entry dictionary.
>>> shell ① 2 >>> entry ② Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'entry' is not defined >>> import pickle >>> with open('entry.pickle', 'rb') as f: ③ ... entry = pickle.load(f) ④ ... >>> entry ⑤ {'comments_link': None, 'internal_id': b'\xde\xd5\xb4\xf8', 'title': 'Dive into history, 2009 edition', 'tags': ('diveintopython', 'docbook', 'html'), 'article_link': 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition', 'published_date': time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1), 'published': True}
entry.pickle file you created in Python Shell #1. The pickle module uses a binary data format, so you should always open pickle files in binary mode.
pickle.load() function takes a stream object, reads the serialized data from the stream, creates a new Python object, recreates the serialized data in the new Python object, and returns the new Python object.
The pickle.dump() / pickle.load() cycle results in an identical copy of the original data structure.
>>> shell ① 1 >>> with open('entry.pickle', 'rb') as f: ② ... entry2 = pickle.load(f) ③ ... >>> entry2 == entry ④ True >>> entry2['tags'] ⑤ ('diveintopython', 'docbook', 'html') >>> entry2['internal_id'] b'\xde\xd5\xb4\xf8'
entry.pickle file.
entry.pickle file. Now you’ve read the serialized data from that file and created a perfect replica of the original data structure.
'tags' key is a tuple, and the value of the 'internal_id' key is a bytes object.
The examples in the previous section showed how to serialize a Python object directly to a file on disk. But what if you don’t want or need a file? You can also serialize to a bytes object in memory.
>>> shell 1 >>> b = pickle.dumps(entry) ① >>> type(b) ② <class 'bytes'> >>> entry3 = pickle.loads(b) ③ >>> entry3 == entry ④ True
pickle.dumps() function (note the 's' at the end of the function name) performs the same serialization as the pickle.dump() function. Instead of taking a stream object and writing the serialized data to a file on disk, it simply returns the serialized data.
pickle.dumps() function returns a bytes object.
pickle.loads() function (again, note the 's' at the end of the function name) performs the same deserialization as the pickle.load() function. Instead of taking a stream object and reading the serialized data from a file, it takes a bytes object containing serialized data, such as the one returned by the pickle.dumps() function.
The pickle protocol has been around for many years, and it has matured as Python itself has matured. There are now four different versions of the pickle protocol.
bytes objects and byte arrays. It is a binary format.
Oh look, the difference between bytes and strings rears its ugly head again. (If you’re surprised, you haven’t been paying attention.) What this means in practice is that, while Python 3 can read data pickled with protocol version 2, Python 2 can not read data pickled with protocol version 3.
What does the pickle protocol look like? Let’s jump out of the Python Shell for a moment and take a look at that entry.pickle file we created.
you@localhost:~/diveintopython3/examples$ ls -l entry.pickle -rw-r--r-- 1 you you 324 Aug 3 13:34 entry.pickle you@localhost:~/diveintopython3/examples$ cat entry.pickle comments_linkqNXtagsqXdiveintopythonqXdocbookqXhtmlq?qX publishedq? XlinkXJhttp://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition q Xpublished_dateq ctime struct_time ?qRqXtitleqXDive into history, 2009 editionqu.
That wasn’t terribly helpful. You can see the strings, but other datatypes end up as unprintable (or at least unreadable) characters. Fields are not obviously delimited by tabs or spaces. This is not a format you would want to debug by yourself.
>>> shell
1
>>> import pickletools
>>> with open('entry.pickle', 'rb') as f:
... pickletools.dis(f)
0: \x80 PROTO 3
2: } EMPTY_DICT
3: q BINPUT 0
5: ( MARK
6: X BINUNICODE 'published_date'
25: q BINPUT 1
27: c GLOBAL 'time struct_time'
45: q BINPUT 2
47: ( MARK
48: M BININT2 2009
51: K BININT1 3
53: K BININT1 27
55: K BININT1 22
57: K BININT1 20
59: K BININT1 42
61: K BININT1 4
63: K BININT1 86
65: J BININT -1
70: t TUPLE (MARK at 47)
71: q BINPUT 3
73: } EMPTY_DICT
74: q BINPUT 4
76: \x86 TUPLE2
77: q BINPUT 5
79: R REDUCE
80: q BINPUT 6
82: X BINUNICODE 'comments_link'
100: q BINPUT 7
102: N NONE
103: X BINUNICODE 'internal_id'
119: q BINPUT 8
121: C SHORT_BINBYTES 'ÞÕ´ø'
127: q BINPUT 9
129: X BINUNICODE 'tags'
138: q BINPUT 10
140: X BINUNICODE 'diveintopython'
159: q BINPUT 11
161: X BINUNICODE 'docbook'
173: q BINPUT 12
175: X BINUNICODE 'html'
184: q BINPUT 13
186: \x87 TUPLE3
187: q BINPUT 14
189: X BINUNICODE 'title'
199: q BINPUT 15
201: X BINUNICODE 'Dive into history, 2009 edition'
237: q BINPUT 16
239: X BINUNICODE 'article_link'
256: q BINPUT 17
258: X BINUNICODE 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition'
337: q BINPUT 18
339: X BINUNICODE 'published'
353: q BINPUT 19
355: \x88 NEWTRUE
356: u SETITEMS (MARK at 5)
357: . STOP
highest protocol among opcodes = 3
The most interesting piece of information in that disassembly is on the last line, because it includes the version of the pickle protocol with which this file was saved. There is no explicit version marker in the pickle protocol. To determine which protocol version was used to store a pickle file, you need to look at the markers (“opcodes”) within the pickled data and use hard-coded knowledge of which opcodes were introduced with each version of the pickle protocol. The pickle.dis() function does exactly that, and it prints the result in the last line of the disassembly output. Here is a function that returns just the version number, without printing anything:
import pickletools
def protocol_version(file_object):
maxproto = -1
for opcode, arg, pos in pickletools.genops(file_object):
maxproto = max(maxproto, opcode.proto)
return maxprotoAnd here it is in action:
>>> import pickleversion
>>> with open('entry.pickle', 'rb') as f:
... v = pickleversion.protocol_version(f)
>>> v
3
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FIXME - discussion of pickling class instances, stateful objects, __getstate__ and __setstate__, links to http://docs.python.org/3.1/library/pickle.html#pickle-inst and http://docs.python.org/3.1/library/pickle.html#pickle-state
The data format used by the pickle module is Python-specific. It makes no attempt to be compatible with other programming languages. If cross-language compatibility is one of your requirements, you need to look at other serialization formats. One such format is JSON. “JSON” stands for “JavaScript Object Notation,” but don’t let the name fool you — JSON is explicitly designed to be usable across multiple programming languages.
Python 3 includes a json module in the standard library. Like the pickle module, the json module has functions for serializing data structures, storing the serialized data on disk, loading serialized data from disk, and unserializing the data back into a new Python object. But there are some important differences, too. First of all, the JSON data format is text-based, not binary. RFC 4627 defines the JSON format and how different types of data must be encoded as text. For example, a boolean value is stored as either the five-character string 'false' or the four-character string 'true'. All JSON values are case-sensitive.
Second, as with any text-based format, there is the issue of whitespace. JSON allows arbitrary amounts of whitespace (spaces, tabs, carriage returns, and line feeds) between values. This whitespace is “insignificant,” which means that JSON encoders can add as much or as little whitespace as they like, and JSON decoders are required to ignore the whitespace between values. This allows you to “pretty-print” your JSON data, nicely nesting values within values at different indentation levels so you can read it in a standard browser or text editor. Python’s json module has options for pretty-printing during encoding.
Third, there’s the perennial problem of character encoding. JSON encodes values as plain text, but as you know, there ain’t no such thing as “plain text.” JSON must be stored in a Unicode encoding (UTF-32, UTF-16, or the default, UTF-8), and section 3 of RFC 4627 defines how to tell which encoding is being used.
Since JSON is not Python-specific, there are some mismatches in its coverage of Python datatypes. Some of them are simply naming differences, but there is one important datatype that is completely missing. See if you can spot it:
| Notes | JSON | Python 3 | ||||||
|---|---|---|---|---|---|---|---|---|
| object | dictionary | |||||||
| array | list | |||||||
| string | string | |||||||
| integer | integer | |||||||
| real number | float | |||||||
true
| True
|
Did you notice what was missing? Bytes! JSON has no support for bytes objects or byte arrays.
Even if JSON has no built-in support for bytes, that doesn’t mean you can’t serialize bytes objects. The json module provides extensibility hooks for encoding and decoding unknown datatypes. (By “unknown,” I mean “not defined in JSON.” Obviously the json module knows about byte arrays, but it’s constrained by the limitations of the JSON specification.) If you want to encode bytes or other datatypes that JSON doesn’t support natively, you need to provide custom encoders and decoders for those types.
>>> shell ① 1 >>> entry FIXME >>> import json >>> with open('entry.json', 'w', encoding='utf-8') as f: ② ... json.dump(entry, f) ... Traceback (most recent call last): File "<stdin>", line 5, in <module> File "C:\Python31\lib\json\__init__.py", line 178, in dump for chunk in iterable: File "C:\Python31\lib\json\encoder.py", line 408, in _iterencode for chunk in _iterencode_dict(o, _current_indent_level): File "C:\Python31\lib\json\encoder.py", line 382, in _iterencode_dict for chunk in chunks: File "C:\Python31\lib\json\encoder.py", line 416, in _iterencode o = _default(o) File "C:\Python31\lib\json\encoder.py", line 170, in default raise TypeError(repr(o) + " is not JSON serializable") TypeError: b'\xde\xd5\xb4\xf8' is not JSON serializable
FIXME
# customserializer.py
def to_json(python_object):
if isinstance(python_object, bytes):
return {'__class__': 'bytes',
'__value__': list(python_object)}
raise TypeError(repr(python_object) + ' is not JSON serializable')FIXME
>>> shell
1
>>> import customserializer
>>> with open('entry.json', 'w', encoding='utf-8') as f:
... json.dump(entry, default = customserializer.to_json)
...
Traceback (most recent call last):
File "<stdin>", line 9, in <module>
json.dump(entry, f, default=customserializer.to_json)
File "C:\Python31\lib\json\__init__.py", line 178, in dump
for chunk in iterable:
File "C:\Python31\lib\json\encoder.py", line 408, in _iterencode
for chunk in _iterencode_dict(o, _current_indent_level):
File "C:\Python31\lib\json\encoder.py", line 382, in _iterencode_dict
for chunk in chunks:
File "C:\Python31\lib\json\encoder.py", line 416, in _iterencode
o = _default(o)
File "/Users/pilgrim/diveintopython3/examples/customserializer.py", line 12, in to_json
raise TypeError(repr(python_object) + ' is not JSON serializable')
TypeError: time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1) is not JSON serializable
FIXME
# customserializer.py
def to_json(python_object):
if isinstance(python_object, time.struct_time):
return {'__class__': 'time.asctime',
'__value__': time.asctime(python_object)}
if isinstance(python_object, bytes):
return {'__class__': 'bytes',
'__value__': list(python_object)}
raise TypeError(repr(python_object) + ' is not JSON serializable')FIXME
>>> shell
1
>>> with open('entry.json', 'w', encoding='utf-8') as f:
... json.dump(entry, default = customserializer.to_json)
...
FIXME
you@localhost:~/diveintopython3/examples$ ls -l example.json
-rw-r--r-- 1 you you 391 Aug 3 13:34 entry.json
you@localhost:~/diveintopython3/examples$ cat example.json
{"published_date": {"__class__": "time.asctime", "__value__": "Fri Mar 27 22:20:42 2009"},
"comments_link": null, "internal_id": {"__class__": "bytes", "__value__": [222, 213, 180, 248]},
"tags": ["diveintopython", "docbook", "html"], "title": "Dive into history, 2009 edition",
"article_link": "http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition",
"published": true}
FIXME
>>> shell
2
>>> del entry
>>> entry
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'entry' is not defined
>>> import json
>>> with open('entry.json', 'r', encoding='utf-8') as f:
... entry = json.load(f)
...
>>> entry
{'comments_link': None,
'internal_id': {'__class__': 'bytes', '__value__': [222, 213, 180, 248]},
'title': 'Dive into history, 2009 edition',
'tags': ['diveintopython', 'docbook', 'html'],
'article_link': 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition',
'published_date': {'__class__': 'time.asctime', '__value__': 'Fri Mar 27 22:20:42 2009'},
'published': True}
FIXME
# customserializer.py
def from_json(json_object):
if '__class__' in json_object:
if json_object['__class__'] == 'time.asctime':
return time.strptime(json_object['__value__'])
if json_object['__class__'] == 'bytes':
return bytes(json_object['__value__'])
return json_object
>>> shell
2
>>> import customserializer
>>> with open('entry.json', 'r', encoding='utf-8') as f:
... entry = json.load(f, object_hook = customserializer.from_json)
...
>>> entry
{'comments_link': None,
'internal_id': b'\xde\xd5\xb4\xf8',
'title': 'Dive into history, 2009 edition',
'tags': ['diveintopython', 'docbook', 'html'],
'article_link': 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition',
'published_date': time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1),
'published': True}
FIXME
>>> shell
1
>>> import customserializer
>>> with open('entry.json', 'r', encoding='utf-8') as f:
... entry2 = json.load(f, object_hook = customserializer.from_json)
...
>>> entry2 == entry
False
>>> entry['tags']
('diveintopython', 'docbook', 'html')
>>> entry2['tags']
['diveintopython', 'docbook', 'html']
FIXME
☞Many articles about the
picklemodule make references tocPickle. In Python 2, there were two implementations of thepicklemodule, one written in pure Python and another written in C (but still callable from Python). In Python 3, these two modules have been consolidated, so you should always justimport pickle. You may find these articles useful, but you should ignore the now-obsolete information aboutcPickle.
pickle module
pickle and cPickle — Python object serialization
pickle
json — JavaScript Object Notation Serializer
© 2001–9 Mark Pilgrim