Serializing Python Objects - Dive Into Python 3

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Serializing Python Objects

❝ Every Saturday since we’ve lived in this apartment, I have awakened at 6:15, poured myself a bowl of cereal, added
a quarter-cup of 2% milk, sat on this end of this couch, turned on BBC America, and watched Doctor Who. ❞
— Sheldon, The Big Bang Theory

Diving In

On the surface, 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?

All the native datatypes that Python supports: booleans, integers, floating point numbers, complex numbers, strings, bytes objects, byte arrays, and None.
Lists, tuples, dictionaries, and sets containing any combination of native datatypes.
Lists, tuples, dictionaries, and sets containing any combination of lists, tuples, dictionaries, and sets containing any combination of native datatypes (and so on, to the maximum nesting level that Python supports).
Functions, classes, and instances of classes (with caveats).

If this isn’t enough for you, the pickle module is also extensible. If you’re interested in extensibility, check out the links in the Further Reading section at the end of the chapter.

A Quick Note About The Examples in This Chapter

This chapter tells a tale with two Python Shells. All of the examples in this chapter are part of a single story arc. You will be asked to switch back and forth between the two Python Shells as I demonstrate the pickle and json modules.

To help keep things straight, 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.

⁂

Saving Data to a Pickle File

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)

Follow along in Python Shell #1.
The idea here is to build a Python dictionary that could represent something useful, like an entry in an Atom feed. But I also want to ensure that it contains several different types of data, to show off the pickle module. Don’t read too much into these values.
The time module contains a data structure (struct_time) 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 struct_time. 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
...

This is still in Python Shell #1.
Use the 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.
The 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.

The pickle module takes a Python data structure and saves it to a file.
To do this, it serializes the data structure using a data format called “the pickle protocol.”
The pickle protocol is Python-specific; there is no guarantee of cross-language compatibility. You probably couldn’t take the entry.pickle file you just created and do anything useful with it in Perl, PHP, Java, or any other language.
Not every Python data structure can be serialized by the 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.
As a result of these changes, there is no guarantee of compatibility between different versions of Python itself. Newer versions of Python support the older serialization formats, but older versions of Python do not support newer formats (since they don’t support the newer data types).
Unless you specify otherwise, the functions in the 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.
The latest version of the pickle protocol is a binary format. Be sure to open your pickle files in binary mode, or the data will get corrupted during writing.

⁂

Loading Data from a Pickle File

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}

This is Python Shell #2.
There is no entry variable defined here. You defined an entry variable in Python Shell #1, but that’s a completely different environment with its own state.
Open the 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.
The 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.
Now the entry variable is a dictionary with familiar-looking keys and values.

The pickle.dump() / pickle.load() cycle results in a new data structure that is equal to 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 is entry                          ⑤
False
>>> entry2['tags']                           ⑥
('diveintopython', 'docbook', 'html')
>>> entry2['internal_id']
b'\xDE\xD5\xB4\xF8'

Switch back to Python Shell #1.
Open the entry.pickle file.
Load the serialized data into a new variable, entry2.
Python confirms that the two dictionaries, entry and entry2, are equal. In this shell, you built entry from the ground up, starting with an empty dictionary and manually assigning values to specific keys. You serialized this dictionary and stored it in the entry.pickle file. Now you’ve read the serialized data from that file and created a perfect replica of the original data structure.
Equality is not the same as identity. I said you’ve created a perfect replica of the original data structure, which is true. But it’s still a copy.
For reasons that will become clear later in this chapter, I want to point out that the value of the 'tags' key is a tuple, and the value of the 'internal_id' key is a bytes object.

⁂

Pickling Without a File

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

The 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.
Since the pickle protocol uses a binary data format, the pickle.dumps() function returns a bytes object.
The 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 end result is the same: a perfect replica of the original dictionary.

⁂

Bytes and Strings Rear Their Ugly Heads Again

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.

Python 1.x had two pickle protocols, a text-based format (“version 0”) and a binary format (“version 1”).
Python 2.3 introduced a new pickle protocol (“version 2”) to handle new functionality in Python class objects. It is a binary format.
Python 3.0 introduced another pickle protocol (“version 3”) with explicit support for 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.

⁂

Debugging Pickle Files

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. To the naked eye, it’s mostly gibberish.

you@localhost:~/diveintopython3/examples$ ls -l entry.pickle
-rw-r--r-- 1 you  you  358 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 pickletools.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:

[download pickleversion.py]

import pickletools

def protocol_version(file_object):
    maxproto = -1
    for opcode, arg, pos in pickletools.genops(file_object):
        maxproto = max(maxproto, opcode.proto)
    return maxproto

And here it is in action:

>>> import pickleversion
>>> with open('entry.pickle', 'rb') as f:
...     v = pickleversion.protocol_version(f)
>>> v
3

⁂

Serializing Python Objects to be Read by Other Languages

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.

⁂

Saving Data to a JSON File

JSON looks remarkably like a data structure you might define manually in JavaScript. This is no accident; you can actually use the JavaScript eval() function to “decode” JSON-serialized data. (The usual caveats about untrusted input apply, but the point is that JSON is valid JavaScript.) As such, JSON may already look familiar to you.

>>> shell
1
>>> basic_entry = {}                                           ①
>>> basic_entry['id'] = 256
>>> basic_entry['title'] = 'Dive into history, 2009 edition'
>>> basic_entry['tags'] = ('diveintopython', 'docbook', 'html')
>>> basic_entry['published'] = True
>>> basic_entry['comments_link'] = None
>>> import json
>>> with open('basic.json', mode='w', encoding='utf-8') as f:  ②
...     json.dump(basic_entry, f)                              ③

We’re going to create a new data structure instead of re-using the existing entry data structure. Later in this chapter, we’ll see what happens when we try to encode the more complex data structure in JSON.
JSON is a text-based format, which means you need to open this file in text mode and specify a character encoding. You can never go wrong with UTF-8.
Like the pickle module, the json module defines a dump() function which takes a Python data structure and a writeable stream object. The dump() function serializes the Python data structure and writes it to the stream object. Doing this inside a with statement will ensure that the file is closed properly when we’re done.

So what does the resulting JSON serialization look like?

you@localhost:~/diveintopython3/examples$ cat basic.json
{"published": true, "tags": ["diveintopython", "docbook", "html"], "comments_link": null,
"id": 256, "title": "Dive into history, 2009 edition"}

That’s certainly more readable than a pickle file. But JSON can contain arbitrary whitespace between values, and the json module provides an easy way to take advantage of this to create even more readable JSON files.

>>> shell
1
>>> with open('basic-pretty.json', mode='w', encoding='utf-8') as f:
...     json.dump(basic_entry, f, indent=2)                            ①

If you pass an indent parameter to the json.dump() function, it will make the resulting JSON file more readable, at the expense of larger file size. The indent parameter is an integer. 0 means “put each value on its own line.” A number greater than 0 means “put each value on its own line, and use this number of spaces to indent nested data structures.”

And this is the result:

you@localhost:~/diveintopython3/examples$ cat basic-pretty.json
{
  "published": true, 
  "tags": [
    "diveintopython", 
    "docbook", 
    "html"
  ], 
  "comments_link": null, 
  "id": 256, 
  "title": "Dive into history, 2009 edition"
}

⁂

Mapping of Python Datatypes to JSON

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 two important Python datatypes that are completely missing. See if you can spot them:

Notes	JSON	Python 3
	object	dictionary
	array	list
	string	string
	integer	integer
	real number	float
*	`true`	`True`
*	`false`	`False`
*	`null`	`None`
* All JSON values are case-sensitive.

Did you notice what was missing? Tuples & bytes! JSON has an array type, which the json module maps to a Python list, but it does not have a separate type for “frozen arrays” (tuples). And while JSON supports strings quite nicely, it has no support for bytes objects or byte arrays.

⁂

Serializing Datatypes Unsupported by JSON

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
>>> entry                                                 ①
{'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}
>>> import json
>>> with open('entry.json', 'w', encoding='utf-8') as f:  ②
...     json.dump(entry, f)                               ③
... 
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

OK, it’s time to revisit the entry data structure. This has it all: a boolean value, a None value, a string, a tuple of strings, a bytes object, and a time structure.
I know I’ve said it before, but it’s worth repeating: JSON is a text-based format. Always open JSON files in text mode with a UTF-8 character encoding.
Well that’s not good. What happened?

Here’s what happened: the json.dump() function tried to serialize the bytes object b'\xDE\xD5\xB4\xF8', but it failed, because JSON has no support for bytes objects. However, if storing bytes is important to you, you can define your own “mini-serialization format.”

[download customserializer.py]


def to_json(python_object):                                             ①
    if isinstance(python_object, bytes):                                ②
        return {'__class__': 'bytes',
                '__value__': list(python_object)}                       ③
    raise TypeError(repr(python_object) + ' is not JSON serializable')  ④

To define your own “mini-serialization format” for a datatype that JSON doesn’t support natively, just define a function that takes a Python object as a parameter. This Python object will be the actual object that the json.dump() function is unable to serialize by itself — in this case, the bytes object b'\xDE\xD5\xB4\xF8'.
Your custom serialization function should check the type of the Python object that the json.dump() function passed to it. This is not strictly necessary if your function only serializes one datatype, but it makes it crystal clear what case your function is covering, and it makes it easier to extend if you need to add serializations for more datatypes later.
In this case, I’ve chosen to convert a bytes object into a dictionary. The __class__ key will hold the original datatype (as a string, 'bytes'), and the __value__ key will hold the actual value. Of course this can’t be a bytes object; the entire point is to convert it into something that can be serialized in JSON! A bytes object is just a sequence of integers; each integer is somewhere in the range 0–255. We can use the list() function to convert the bytes object into a list of integers. So b'\xDE\xD5\xB4\xF8' becomes [222, 213, 180, 248]. (Do the math! It works! The byte \xDE in hexadecimal is 222 in decimal, \xD5 is 213, and so on.)
This line is important. The data structure you’re serializing may contain types that neither the built-in JSON serializer nor your custom serializer can handle. In this case, your custom serializer must raise a TypeError so that the json.dump() function knows that your custom serializer did not recognize the type.

That’s it; you don’t need to do anything else. In particular, this custom serialization function returns a Python dictionary, not a string. You’re not doing the entire serializing-to-JSON yourself; you’re only doing the converting-to-a-supported-datatype part. The json.dump() function will do the rest.

>>> shell
1
>>> import customserializer                                                             ①
>>> with open('entry.json', 'w', encoding='utf-8') as f:                                ②
...     json.dump(entry, f, default=customserializer.to_json)                           ③
... 
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')                     ④
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

The customserializer module is where you just defined the to_json() function in the previous example.
Text mode, UTF-8 encoding, yadda yadda. (You’ll forget! I forget sometimes! And everything will work right up until the moment that it fails, and then it will fail most spectacularly.)
This is the important bit: to hook your custom conversion function into the json.dump() function, pass your function into the json.dump() function in the default parameter. (Hooray, everything in Python is an object!)
OK, so it didn’t actually work. But take a look at the exception. The json.dump() function is no longer complaining about being unable to serialize the bytes object. Now it’s complaining about a completely different object: the time.struct_time object.

While getting a different exception might not seem like progress, it really is! It’ll just take one more tweak to get past this.


import time

def to_json(python_object):
    if isinstance(python_object, time.struct_time):          ①
        return {'__class__': 'time.asctime',
                '__value__': time.asctime(python_object)}    ②
    if isinstance(python_object, bytes):
        return {'__class__': 'bytes',
                '__value__': list(python_object)}
    raise TypeError(repr(python_object) + ' is not JSON serializable')

Adding to our existing customserializer.to_json() function, we need to check whether the Python object (that the json.dump() function is having trouble with) is a time.struct_time.
If so, we’ll do something similar to the conversion we did with the bytes object: convert the time.struct_time object to a dictionary that only contains JSON-serializable values. In this case, the easiest way to convert a datetime into a JSON-serializable value is to convert it to a string with the time.asctime() function. The time.asctime() function will convert that nasty-looking time.struct_time into the string 'Fri Mar 27 22:20:42 2009'.

With these two custom conversions, the entire entry data structure should serialize to JSON without any further problems.

>>> shell
1
>>> with open('entry.json', 'w', encoding='utf-8') as f:
...     json.dump(entry, f, default=customserializer.to_json)
...

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}

⁂

Loading Data from a JSON File

Like the pickle module, the json module has a load() function which takes a stream object, reads JSON-encoded data from it, and creates a new Python object that mirrors the JSON data structure.

>>> shell
2
>>> del entry                                             ①
>>> 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)                              ②
... 
>>> entry                                                 ③
{'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}

For demonstration purposes, switch to Python Shell #2 and delete the entry data structure that you created earlier in this chapter with the pickle module.
In the simplest case, the json.load() function works the same as the pickle.load() function. You pass in a stream object and it returns a new Python object.
I have good news and bad news. Good news first: the json.load() function successfully read the entry.json file you created in Python Shell #1 and created a new Python object that contained the data. Now the bad news: it didn’t recreate the original entry data structure. The two values 'internal_id' and 'published_date' were recreated as dictionaries — specifically, the dictionaries with JSON-compatible values that you created in the to_json() conversion function.

json.load() doesn’t know anything about any conversion function you may have passed to json.dump(). What you need is the opposite of the to_json() function — a function that will take a custom-converted JSON object and convert it back to the original Python datatype.

# add this to customserializer.py
def from_json(json_object):                                   ①
    if '__class__' in json_object:                            ②
        if json_object['__class__'] == 'time.asctime':
            return time.strptime(json_object['__value__'])    ③
        if json_object['__class__'] == 'bytes':
            return bytes(json_object['__value__'])            ④
    return json_object

This conversion function also takes one parameter and returns one value. But the parameter it takes is not a string, it’s a Python object — the result of deserializing a JSON-encoded string into Python.
All you need to do is check whether this object contains the '__class__' key that the to_json() function created. If so, the value of the '__class__' key will tell you how to decode the value back into the original Python datatype.
To decode the time string returned by the time.asctime() function, you use the time.strptime() function. This function takes a formatted datetime string (in a customizable format, but it defaults to the same format that time.asctime() defaults to) and returns a time.struct_time.
To convert a list of integers back into a bytes object, you can use the bytes() function.

That was it; there were only two datatypes handled in the to_json() function, and now those two datatypes are handled in the from_json() function. This is the result:

>>> shell
2
>>> import customserializer
>>> with open('entry.json', 'r', encoding='utf-8') as f:
...     entry = json.load(f, object_hook=customserializer.from_json)  ①
... 
>>> entry                                                             ②
{'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}

To hook the from_json() function into the deserialization process, pass it as the object_hook parameter to the json.load() function. Functions that take functions; it’s so handy!
The entry data structure now contains an 'internal_id' key whose value is a bytes object. It also contains a 'published_date' key whose value is a time.struct_time object.

There is one final glitch, though.

>>> 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                                                    ①
False
>>> entry['tags']                                                      ②
('diveintopython', 'docbook', 'html')
>>> entry2['tags']                                                     ③
['diveintopython', 'docbook', 'html']

Even after hooking the to_json() function into the serialization, and hooking the from_json() function into the deserialization, we still haven’t recreated a perfect replica of the original data structure. Why not?
In the original entry data structure, the value of the 'tags' key was a tuple of three strings.
But in the round-tripped entry2 data structure, the value of the 'tags' key is a list of three strings. JSON doesn’t distinguish between tuples and lists; it only has a single list-like datatype, the array, and the json module silently converts both tuples and lists into JSON arrays during serialization. For most uses, you can ignore the difference between tuples and lists, but it’s something to keep in mind as you work with the json module.