python-3.6.zip added from Github

README.cosmo contains the necessary links.

third_party/python/Doc/includes/dbpickle.py

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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()