Mercurial > cpython-lru_cache-weakref

annotate Doc/includes/dbpickle.py @ 78069:a8aa77458e12 3.2

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Add c_size_t and c_ssize_t to the ctypes summary table.
author Antoine Pitrou <solipsis@pitrou.net>
date Thu, 12 Jul 2012 20:31:50 +0200
parents 5e93c5cdc378
children
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1 # Simple example presenting how persistent ID can be used to pickle
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2 # external objects by reference.
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3
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4 import pickle
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5 import sqlite3
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6 from collections import namedtuple
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7
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8 # Simple class representing a record in our database.
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9 MemoRecord = namedtuple("MemoRecord", "key, task")
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10
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11 class DBPickler(pickle.Pickler):
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12
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13 def persistent_id(self, obj):
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14 # Instead of pickling MemoRecord as a regular class instance, we emit a
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15 # persistent ID.
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16 if isinstance(obj, MemoRecord):
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17 # Here, our persistent ID is simply a tuple, containing a tag and a
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18 # key, which refers to a specific record in the database.
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19 return ("MemoRecord", obj.key)
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20 else:
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21 # If obj does not have a persistent ID, return None. This means obj
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22 # needs to be pickled as usual.
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23 return None
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24
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25
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26 class DBUnpickler(pickle.Unpickler):
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27
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28 def __init__(self, file, connection):
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29 super().__init__(file)
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30 self.connection = connection
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31
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32 def persistent_load(self, pid):
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33 # This method is invoked whenever a persistent ID is encountered.
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34 # Here, pid is the tuple returned by DBPickler.
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35 cursor = self.connection.cursor()
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36 type_tag, key_id = pid
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37 if type_tag == "MemoRecord":
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38 # Fetch the referenced record from the database and return it.
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39 cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
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40 key, task = cursor.fetchone()
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41 return MemoRecord(key, task)
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42 else:
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43 # Always raises an error if you cannot return the correct object.
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44 # Otherwise, the unpickler will think None is the object referenced
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45 # by the persistent ID.
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46 raise pickle.UnpicklingError("unsupported persistent object")
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47
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48
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49 def main():
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50 import io
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51 import pprint
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52
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53 # Initialize and populate our database.
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54 conn = sqlite3.connect(":memory:")
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55 cursor = conn.cursor()
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56 cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
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57 tasks = (
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58 'give food to fish',
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59 'prepare group meeting',
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60 'fight with a zebra',
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61 )
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62 for task in tasks:
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63 cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))
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64
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65 # Fetch the records to be pickled.
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66 cursor.execute("SELECT * FROM memos")
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67 memos = [MemoRecord(key, task) for key, task in cursor]
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68 # Save the records using our custom DBPickler.
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69 file = io.BytesIO()
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70 DBPickler(file).dump(memos)
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71
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72 print("Pickled records:")
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73 pprint.pprint(memos)
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74
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75 # Update a record, just for good measure.
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76 cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")
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77
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78 # Load the records from the pickle data stream.
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79 file.seek(0)
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80 memos = DBUnpickler(file, conn).load()
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81
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82 print("Unpickled records:")
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83 pprint.pprint(memos)
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84
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85
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86 if __name__ == '__main__':
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87 main()