Python code coverage for Doc/includes/dbpickle.py

#	count	content
1	n/a	# Simple example presenting how persistent ID can be used to pickle
2	n/a	# external objects by reference.
3	n/a
4	n/a	import pickle
5	n/a	import sqlite3
6	n/a	from collections import namedtuple
7	n/a
8	n/a	# Simple class representing a record in our database.
9	n/a	MemoRecord = namedtuple("MemoRecord", "key, task")
10	n/a
11	n/a	class DBPickler(pickle.Pickler):
12	n/a
13	n/a	def persistent_id(self, obj):
14	n/a	# Instead of pickling MemoRecord as a regular class instance, we emit a
15	n/a	# persistent ID.
16	n/a	if isinstance(obj, MemoRecord):
17	n/a	# Here, our persistent ID is simply a tuple, containing a tag and a
18	n/a	# key, which refers to a specific record in the database.
19	n/a	return ("MemoRecord", obj.key)
20	n/a	else:
21	n/a	# If obj does not have a persistent ID, return None. This means obj
22	n/a	# needs to be pickled as usual.
23	n/a	return None
24	n/a
25	n/a
26	n/a	class DBUnpickler(pickle.Unpickler):
27	n/a
28	n/a	def __init__(self, file, connection):
29	n/a	super().__init__(file)
30	n/a	self.connection = connection
31	n/a
32	n/a	def persistent_load(self, pid):
33	n/a	# This method is invoked whenever a persistent ID is encountered.
34	n/a	# Here, pid is the tuple returned by DBPickler.
35	n/a	cursor = self.connection.cursor()
36	n/a	type_tag, key_id = pid
37	n/a	if type_tag == "MemoRecord":
38	n/a	# Fetch the referenced record from the database and return it.
39	n/a	cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
40	n/a	key, task = cursor.fetchone()
41	n/a	return MemoRecord(key, task)
42	n/a	else:
43	n/a	# Always raises an error if you cannot return the correct object.
44	n/a	# Otherwise, the unpickler will think None is the object referenced
45	n/a	# by the persistent ID.
46	n/a	raise pickle.UnpicklingError("unsupported persistent object")
47	n/a
48	n/a
49	n/a	def main():
50	n/a	import io
51	n/a	import pprint
52	n/a
53	n/a	# Initialize and populate our database.
54	n/a	conn = sqlite3.connect(":memory:")
55	n/a	cursor = conn.cursor()
56	n/a	cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
57	n/a	tasks = (
58	n/a	'give food to fish',
59	n/a	'prepare group meeting',
60	n/a	'fight with a zebra',
61	n/a	)
62	n/a	for task in tasks:
63	n/a	cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))
64	n/a
65	n/a	# Fetch the records to be pickled.
66	n/a	cursor.execute("SELECT * FROM memos")
67	n/a	memos = [MemoRecord(key, task) for key, task in cursor]
68	n/a	# Save the records using our custom DBPickler.
69	n/a	file = io.BytesIO()
70	n/a	DBPickler(file).dump(memos)
71	n/a
72	n/a	print("Pickled records:")
73	n/a	pprint.pprint(memos)
74	n/a
75	n/a	# Update a record, just for good measure.
76	n/a	cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")
77	n/a
78	n/a	# Load the records from the pickle data stream.
79	n/a	file.seek(0)
80	n/a	memos = DBUnpickler(file, conn).load()
81	n/a
82	n/a	print("Unpickled records:")
83	n/a	pprint.pprint(memos)
84	n/a
85	n/a
86	n/a	if __name__ == '__main__':
87	n/a	main()