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Python and Relational/Non-relational Databases
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Andrew Godwin
October 22, 2010
Programming
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120
Python and Relational/Non-relational Databases
A talk I gave at PyCon Ukraine 2010.
Andrew Godwin
October 22, 2010
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Transcript
Relational / Non-relational Databases Python and Andrew Godwin
Introduction Python for 5 years Django core developer Data modelling
/ visualisation
""Andrew speaks English like a machine gun speaks bullets."" Reinout
van Rees
If I speak too fast - tell me!
What is a relational database?
A relational database is a “collection of relations”
It's what a lot of people are used to.
Relational Databases PostgreSQL MySQL SQLite
Let's pick PostgreSQL (it's a good choice)
Usage conn = psycopg2.connect( host="localhost", user="postgres" ) cursor = conn.cursor()
cursor.execute('SELECT * FROM users WHERE username = "andrew";') for row in cursor.fetchall(): print row
You've probably seen all that before.
Now, to introduce some non-relational databases
Document Databases MongoDB CouchDB
Key-Value Stores Redis Cassandra
Message Queues AMQP Celery
Various Others Graph databases Filesystems VCSs
Redis and MongoDB are two good examples here
Redis: Key-value store with strings, lists, sets, channels and atomic
operations.
Redis Example conn = redis.Redis(host="localhost") print conn.get("top_value") conn.set("last_user", "andrew") conn.inc("num_runs")
conn.sadd("users", "andrew") conn.sadd("users", "martin") for item in conn.smembers("users"): print item
MongoDB: Document store with indexing and a wide range of
query filters.
MongoDB Example conn = pymongo.Connection("localhost") db = conn['mongo_example'] coll =
db['users'] coll.insert({ "username": "andrew", "uid": 1000, }) for entry in coll.find({"username": "andrew"}): print entry
These all solve different problems - you can't easily replace
one with the other.
""When all you have is a hammer, everything looks like
a nail"" Abraham Manslow (paraphrased)
JOIN - your best friend, and your worst enemy.
Denormalising your data speeds up reads, and slows down writes.
Schemaless != Denormalised
Atomic operations are nice. conn.incrby("num_users', 2)
But SQL can do some of them. UPDATE foo SET
bar = bar + 1 WHERE baz;
Redis, the datastructures server. SETNX, GETSET, EXPIRES and friends
Locks / Semaphores conn.setnx("lock:foo", time.time() + 3600) val = conn.decr("sem:foo")
if val >= 0: ... else: conn.incr("sem:foo")
Queues conn.lpush("myqueue", "workitem") todo = conn.lpop("myqueue") (or publish/subscribe)
Priority Queues conn.zadd("myqueue", "handle-meltdown", 1) conn.zadd("myqueue", "feed-cats", 5) todo =
conn.zrange("myqueue", 0, 1) conn.zrem(todo)
Lock-free linked lists! new_id = "bgrdsd" old_end = conn.getset(":end", new_id)
conn.set("%s:next" % old_end, new_id)
Performance-wise, the less checks/integrity the faster it goes.
Maturity can sometimes be an issue, but new features can
appear rapidly.
You can also use databases for the wrong thing -
it often only matters ""at scale""
But how does this all relate to Python?
Most databases - even new ones - have good Python
bindings
Postgres: PsycoPG2 Redis: redis-py MongoDB: pymongo (and more - neo4j,
VCSen, relational, etc.)
Some databases have Python available inside (Postgres has it as
an option)
Document databases map really well to Python dicts
You may find non-relational databases a nicer way to store
state - for any app
Remember, you might still need transactions/reliability. (Business logic is probably
better off on mature systems for now)
Overall? Just keep all the options in mind. Don't get
caught by trends, and don't abuse your relational store
Thanks. Andrew Godwin @andrewgodwin http://aeracode.org