Viewing file:  dbpickle.py (2.8 KB) -rw-r--r--
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# Simple example presenting how persistent ID can be used to pickle
# external objects by reference.
import pickle
import sqlite3
from collections import namedtuple
# Simple class representing a record in our database.
MemoRecord = namedtuple("MemoRecord", "key, task")
class DBPickler(pickle.Pickler):
def persistent_id(self, obj):
# Instead of pickling MemoRecord as a regular class instance, we emit a
# persistent ID.
if isinstance(obj, MemoRecord):
# Here, our persistent ID is simply a tuple, containing a tag and a
# key, which refers to a specific record in the database.
return ("MemoRecord", obj.key)
else:
# If obj does not have a persistent ID, return None. This means obj
# needs to be pickled as usual.
return None
class DBUnpickler(pickle.Unpickler):
def __init__(self, file, connection):
super().__init__(file)
self.connection = connection
def persistent_load(self, pid):
# This method is invoked whenever a persistent ID is encountered.
# Here, pid is the tuple returned by DBPickler.
cursor = self.connection.cursor()
type_tag, key_id = pid
if type_tag == "MemoRecord":
# Fetch the referenced record from the database and return it.
cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
key, task = cursor.fetchone()
return MemoRecord(key, task)
else:
# Always raises an error if you cannot return the correct object.
# Otherwise, the unpickler will think None is the object referenced
# by the persistent ID.
raise pickle.UnpicklingError("unsupported persistent object")
def main():
import io
import pprint
# Initialize and populate our database.
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
tasks = (
'give food to fish',
'prepare group meeting',
'fight with a zebra',
)
for task in tasks:
cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))
# Fetch the records to be pickled.
cursor.execute("SELECT * FROM memos")
memos = [MemoRecord(key, task) for key, task in cursor]
# Save the records using our custom DBPickler.
file = io.BytesIO()
DBPickler(file).dump(memos)
print("Pickled records:")
pprint.pprint(memos)
# Update a record, just for good measure.
cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")
# Load the records from the pickle data stream.
file.seek(0)
memos = DBUnpickler(file, conn).load()
print("Unpickled records:")
pprint.pprint(memos)
if __name__ == '__main__':
main()
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