Persistence and Serialization¶

Overview¶

Persistence and Serialization are closely related.

Serialization means taking a potentially complex data structure and converting it into a single string of bytes.

https://en.wikipedia.org/wiki/Serialization

Persistence is storing data in a way that it will persist beyond the run-time of your program.

They are closely related, because most forms of persistent storage – simple text files, databases, etc., require that it be turned into a simple string of bytes first. After all, at the end of the day, everything done with computers is ultimately a serial string of bytes.

Serialization is also very useful for transmitting information between systems – over the network, etc.

Serialization¶

This module is less about concepts.

More about learning to use a given module.

So less talk, more coding.

This material is focused on methods available in the Python standard library.

There are third party packages with more options as well.

Persistence¶

Persistence is saving your python data structure(s) to disk – so they will persist once the python process is finished.

Any serial form can provide persistence (by dumping/loading it to/from a file), but not all persistence mechanisms are serial (i.e RDBMS, etc.)

http://wiki.python.org/moin/PersistenceTools

Python Specific Formats¶

These are formats specific to python – convenient to use, but not useful for interchange with other systems.

Python Literals¶

Putting plain old python literals in your file

Gives a nice, human-editable form for config files, etc.

Don’t use for untrusted sources!!!

Good for basic python types.

(can work for your own classes, too – if you write a good __repr__ )

In theory, repr() always gives a form that can be re-constructed.

Often the str() form works too.

pprint (pretty print) module can make it easier to read:

https://docs.python.org/3.5/library/pprint.html

Python Literal Example¶

# a list of dicts
data = [{'this':5, 'that':4}, {'spam':7, 'eggs':3.4}]
In [51]: s = repr(data) # save a string version:
In [52]: data2 = eval(s) # re-construct with eval:
In [53]: data2 == data # they are equal
Out[53]: True
In [54]: data is data2 # but not the same object
Out[54]: False

You can save the string to a file and even use import.

In fact, using a python file and importing it is a great way to handle configuration for your app – very powerful and flexible.

NOTE: eval() is DANGEROUS:

Not so bad if you know where your data is coming from, but eval() will run any code it gets, even:

import sys
sys.system('cd /; rm -rf *')

You really don’t want that run on your machine!

The alternative:

ast.literal_eval is safer than eval:

https://docs.python.org/3.5/library/ast.html#ast-helpers

It will only evaluate literals.

pretty print¶

In [68]: data = [{'this': 5, 'that': 4}, {'eggs': 3.4, 'spam': 7},
         {'foo': 86, 'bar': 4.5}, {'fun': 43, 'baz': 6.5}]
In [69]: import pprint
In [71]: repr(data)
Out[71]: "[{'this': 5, 'that': 4}, {'eggs': 3.4, 'spam': 7}, {'foo': 86, 'bar': 4.5}, {'fun': 43, 'baz': 6.5}]"
In [72]: s = pprint.pformat(data)
In [73]: print(s)
[{'that': 4, 'this': 5},
 {'eggs': 3.4, 'spam': 7},
 {'bar': 4.5, 'foo': 86},
 {'baz': 6.5, 'fun': 43}]

This is a nice option if you want the saved form to be human readable / editable.

Pickle¶

Pickle is a custom binary format for python objects.

You can essentially dump any python object to disk (or string, or socket, or…

In [87]: import pickle
In [83]: data
Out[83]:
[{'that': 4, 'this': 5},
 {'eggs': 3.4, 'spam': 7},
 {'bar': 4.5, 'foo': 86},
 {'baz': 6.5, 'fun': 43}]
In [84]: pickle.dump(data, open('data.pkl', 'wb'))
In [85]: data2 = pickle.load(open('data.pkl', 'rb'))
In [86]: data2 == data
Out[86]: True

https://docs.python.org/3.5/library/pickle.html

Warning

The pickle module is not secure against erroneous or maliciously constructed data. Never unpickle data received from an untrusted or unauthenticated source.

pickle is cool because it can serialize virtually ANY object – including your self-defined classes.

But to do this, it must run essentially arbitrary code – so not safe.

Do not use it for receiving data from an external source.

But you probably won’t want to do that anyway – pickle is python-specific, not very useful for data interchange.

Shelve¶

A “shelf” is a persistent, dictionary-like object.

(It’s also a place you can put a jar of pickles…)

The values (not the keys!) can be essentially arbitrary Python objects (anything picklable)

NOTE: it will not reflect changes in mutable objects without re-writing them to the db. (or use writeback=True)

If less that 100s of MB – just use a dict and pickle it.

shelve presents a dict interface:

import shelve
d = shelve.open(filename)
d[key] = data   # store data at key
data = d[key]   # retrieve a COPY of data at key
del d[key]      # delete data stored at key
flag = d.has_key(key)   # true if the key exists
d.close()       # close it

(it uses pickle under the hood – same security issues)

https://docs.python.org/3.5/library/shelve.html

LAB¶

Here are two datasets embedded in Python:

add_book_data.py and add_book_data_flat.py

They can be loaded with:

from add_book_data import AddressBook

They have address book data – one with a nested dict, one “flat”. Use the nested version for this exercise.

Write a module that saves the data as python literals in a file
- and reads it back in
Write a module that saves the data as a pickle in a file
- and reads it back in
Write a module that saves the data in a shelve
- and accesses it one by one.

Write some tests to make sure its working!

Interchange Formats¶

These are formats suitable for interchanging data with other systems – written in arbitrary other languages.

In other words: standard formats.

INI¶

INI files

(the old Windows config files)

[Section1]
int = 15
bool = true
float = 3.1415
[Section2]
int = 32
...

Good for configuration data, etc.

ConfigParser¶

The configparser module provides tools for working with INI files:

Writing:

import configparser
config = configparser.ConfigParser()
config.add_section('Section1')
config.set('Section1', 'an_integer', '15')
config.set('Section1', 'a_boolean', 'true')
config.set('Section1', 'a_float', '3.1415')
# Writing our configuration file to 'example.cfg'
config.write(open('example.cfg', 'w'))

Note: all keys and values are strings

Reading `ini` files:¶

>>> config = configparser.ConfigParser()
>>> config.read('example.cfg')
>>> config.sections()
['Section1']
>>> config.get('Section1', 'a_float')
'3.1415'
>>> config.items('Section1')
[('an_integer', '15'), ('a_boolean', 'true'), ('a_float', '3.1415')]

https://docs.python.org/3/library/configparser.html

CSV¶

CSV (Comma Separated Values) format is the most common import and export format for spreadsheets and databases.

No real standard – the Python csv package more or less follows MS Excel “standard” (with other “dialects” available)

Can use delimiters other than commas… (I like tabs better)

Most useful for simple tabular data

The CSV module¶

Reading CSV files:

(uses: eggs.csv)

In [14]: import csv
In [17]: spam_reader = csv.reader(open('eggs.csv'),
                                  skipinitialspace=True)
In [19]: for row in spam_reader:
   ....:     print(row)
['Spam', ' Spam', ' Spam', ' Spam', ' Spam', ' Baked Beans']
['Spam', ' Lovely Spam', ' Wonderful Spam']

The csv module takes care of string quoting, etc. for you.

This is a pretty big deal – that can be a real pain!

NOTE: skipinitialspace is False by default, which can mess up interpreting quotes correctly.

Writing CSV files:

>>> import csv
>>> with open('eggs2.csv', 'w') as outfile:
>>>     spam_writer = csv.writer(outfile,
                                 quoting=csv.QUOTE_MINIMAL)
>>>     spam_writer.writerow(['Spam'] * 5 + ['Baked Beans'])
>>>     spam_writer.writerow(['Spam', 'Lovely Spam', 'Wonderful Spam'])
>>>     spam_writer.writerow(['Spam', 'Spam, Wonderful spam..', 'Very-Wonderful Spam'])

The csv module takes care of string quoting, etc. for you.

You can set the quoting attribute on the dialect object to control that.

https://docs.python.org/3/library/csv.html

JSON¶

JSON (JavaScript Object Notation) is a subset of JavaScript syntax used as a lightweight data interchange format.

LOTS of systems can read JSON – notably browsers…

Python module has an interface similar to pickle

Can handle the standard Python data types

Specializable encoding/decoding for other types – but I wouldn’t do that!

Commonly used for configuration files, etc.

Python json module¶

In [93]: import json
In [94]: s = json.dumps(data)
Out[95]: '[{"this": 5, "that": 4}, {"eggs": 3.4, "spam": 7},
           {"foo": 86, "bar": 4.5}, {"fun": 43, "baz": 6.5}]'
In [96]: data2 = json.loads(s)
Out[97]:
[{u'that': 4, u'this': 5},
 {u'eggs': 3.4, u'spam': 7},
...
In [98]: data2 == data
Out[98]: True # they are the same

(also json.dump() and json.load() for files)

NOTE: JSON is less “rich” than python – no tuples, no distinction between integers and floats, no comments! And keys can only be strings.

http://www.json.org/

https://docs.python.org/3/library/json.html

LAB¶

Use the same addressbook data:

# load with:
from add_book_data import AddressBook

Write a module that saves the data as an INI file
- and reads it back in
Write a module that saves the data as a CSV file
- and reads it back in

( you’ll need the “flat” version for this…)

Write a module that saves the data in JSON
- and reads it back in

XML¶

XML is a standardized version of SGML, designed for use as a data storage / interchange format.

NOTE: HTML is also SGML, and modern versions conform to the XML standard.

XML in the python std lib¶

xml.dom

xml.sax

xml.parsers.expat

xml.etree

https://docs.python.org/3/library/xml.html

elementtree¶

elementtree is the simplest tool – maps pretty directly to XML.

The Element type is a flexible container object, designed to store hierarchical data structures in memory.

Essentially an in-memory XML – can be read from/written to XML

an ElementTree is an entire XML doc

an Element is a node in that tree

https://docs.python.org/3/library/xml.etree.elementtree.html

Write a module that saves the data in XML
- and reads it back in
- this gets ugly!

(NEED a good example here!)

DataBases¶

A database is a system for storing and retrieving data – usually in a filesystem.

We usually think RDBMS and SQL – but there are simpler systems.

dbm¶

dbm is a generic interface to variants of the DBM database

Suitable for storing data that fits well into a python dict with strings as both keys and values

Note: dbm will use the dbm system that works on your system – this may be different on different systems – so the db files may NOT be compatible! whichdb will try to figure it out, but it’s not guaranteed

https://docs.python.org/3/library/dbm.html

NOTE: dbm is getting pretty old fashioned – e.g. it doesn’t handle Unicode

It’s here for completeness, but there are probably better options!

the `dbm` module¶

Writing data:

#creating a dbm file:
import dbm
dbm.open(filename, 'n')

flag options are:

‘r’ – Open existing database for reading only (default)
‘w’ – Open existing database for reading and writing
‘c’ – Open database for reading and writing, creating it if it doesn’t exist
‘n’ – Always create a new, empty database, open for reading and writing

caution – these are different than the file open modes!

dbm provides a dict-like interface:

import dbm
db = dbm.open("dbm", "c")
db["first"] = "bruce"
db["second"] = "micheal"
db["third"] = "fred"
db["second"] = "john" #overwrite
db.close()
# read it:
db = dbm.open("dbm", "r")
for key in db.keys():
    print(key, db[key])

(a lot like shelve, though theoretically compatible with other systems)

https://docs.python.org/3/library/dbm.html

sqlite¶

SQLite:

a C library providing a lightweight disk-based single-file database

Nonstandard variant of the SQL query language

Very broadly used as as an embedded databases for storing application-specific data etc.

Firefox plug-in:

https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/

python sqlite module¶

sqlite3 Python module wraps C lib – provides standard DB-API interface

Allows (and requires) SQL queries

Can provide high performance, flexible, portable storage for your app

Example:¶

import sqlite3
# open a connection to a db file:
conn = sqlite3.connect('example.db')
# or build one in-memory
conn = sqlite3.connect(':memory:')
# create a cursor
c = conn.cursor()

Execute SQL with the cursor:¶

# Create table
c.execute("'CREATE TABLE stocks (date text, trans text, symbol text, qty real, price real)'")
# Insert a row of data
c.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)")
# Save (commit) the changes
conn.commit()
# Close the cursor if we are done with it
c.close()

SELECT creates a cursor that can be iterated:

>>> for row in c.execute('SELECT * FROM stocks ORDER BY price'):
        print row
('2006-01-05', 'BUY', 'RHAT', 100, 35.14)
('2006-03-28', 'BUY', 'IBM', 1000, 45.0)
...

Or you can get the rows one by one or in a list:

c.fetchone()
c.fetchall()

Good idea to use the DB-API’s parameter substitution:

t = (symbol,)
c.execute('SELECT * FROM stocks WHERE symbol=?', t)
print c.fetchone()
# Larger example that inserts many records at a time
purchases = [('2006-03-28', 'BUY', 'IBM', 1000, 45.00),
             ('2006-04-05', 'BUY', 'MSFT', 1000, 72.00),
             ('2006-04-06', 'SELL', 'IBM', 500, 53.00),
            ]
c.executemany('INSERT INTO stocks VALUES (?,?,?,?,?)', purchases)

https://docs.python.org/3/library/sqlite3.html

http://xkcd.com/327/

DB-API¶

The DB-API spec (PEP 249) is a specification for interaction between Python and Relational Databases.

Support for a large number of third-party Database drivers:

MySQL

PostgreSQL

Oracle

MSSQL (?)

…

http://www.python.org/dev/peps/pep-0249}

LAB Extras:¶

A few more things you could do:

Use pickle to save/reload a custom class of yours (the Circle class from the first quarter?)
Try writing a json writer for a non-standard data type: A custom class, or a more complex built-in?

Other Options¶

There are a lot of other possibilities outside the standard lib.

Object-Relation Mappers¶

Systems for mapping Python objects to tables

Saves you writing that glue code (and the SQL)

Usually deal with mapping to variety of back-ends:

test with SQLite, deploy with PostgreSQL

SQL Alchemy

http://www.sqlalchemy.org/

Django ORM

https://docs.djangoproject.com/en/dev/topics/db/

Object Databases¶

(we’ll be talking more about this in another class: No SQL Databases)

Directly store and retrieve Python Objects.

Kind of like shelve, but more flexible, and give you searching, etc.

ZODB: (http://www.zodb.org/)

NoSQL¶

Map-Reduce, etc.

– Big deal for “Big Data”: Amazon, Google, etc.

Document-Oriented Storage

MongoDB (BSON interface, JSON documents)
CouchDB (Apache):
- JSON documents
- Javascript querying (MapReduce)
- HTTP API

LAB¶

Load data with:

from add_book_data import AddressBook

Write a module that saves the data in a dbm database
- and reads it back in
Write a module that saves the data in an SQLite database
- and reads it back in
- helps to know SQL here…

Optional:

Do the same with a ORM of your choice.

Persistence and Serialization¶

Overview¶

Serialization¶

Persistence¶

Python Specific Formats¶

Python Literals¶

Python Literal Example¶

pretty print¶

Pickle¶

Shelve¶

LAB¶

Interchange Formats¶

INI¶

ConfigParser¶

Reading ini files:¶

CSV¶

The CSV module¶

JSON¶

Python json module¶

LAB¶

XML¶

XML in the python std lib¶

elementtree¶

DataBases¶

dbm¶

the dbm module¶

sqlite¶

python sqlite module¶

Example:¶

Execute SQL with the cursor:¶

DB-API¶

LAB Extras:¶

Other Options¶

Object-Relation Mappers¶

Object Databases¶

NoSQL¶

LAB¶

Reading `ini` files:¶

the `dbm` module¶