So many random musings and theories on Google App Engine, I won’t bother musing about it myself, except to mention that Ian Bicking put together instructions for running Pylons on it. These also work fine for using the latest Pylons 0.9.7 beta.
I got Beaker, the session and caching WSGI middleware that Pylons uses, running fine on Google now, using Google Datastore as the backend. Diving into the Datastore docs to get a grip on what’s the best way to implement it shed some light on the transition any developer thinking about writing data-backed apps for GAE (Google App Engine) will need to tackle.
Some notes on terminology, Google has Entities, Kinds, and Properties. These correspond roughly to Rows, Tables, and Columns in RDBMS-speak. Kinds can also be called classes, because in the Python API, you create a class and inherit from the appropriate datastore class. Entities may also be referred to as instances, since performing a query returns a list of objects (instances).
Sessions and Datastore
First, regarding sessions. Beaker will now let a Pylons app use normal sessions on GAE, the real question is, should you?
The Google User API makes it trivial to get currently logged in user, and the datastore comes with a property type for a ‘table’ that is specifically made for a Google user account reference. So with just one short command, you can have an entity from the Datastore that corresponds to a given user, ie:
userpref = UserPrefs.all().filter('user =', users.get_current_user()).get()
The Datastore is blindingly fast for reads and queries, so there’s a compelling reason to ignore sessions altogether and just fetch the appropriate preferences or what-have-you. This leaves people with the normal reason for wanting more, ie, a session, “But wait, I want to stash other little things with the user when they run around my app!”. Not a problem.
Google’s Datastore has an Expando class for entities that lets you dynamically add properties of various types. It’s like having a RDBMS where you can just add columns to each row, on the fly. The dynamic_properties() entity method makes it easy upon pulling an object, to see what dynamic properties were already assigned.
As far as I’m concerned, this pretty much mitigates the need for a session system. If you didn’t want to require user login, you could always make a little session ID yourself, and keep that on the UserPrefs table as a separate property, then query on that.
Rethinking how you store/query/insert data
Going slowly through all the Datastore docs and especially reading some of the performance information people were drumming up on the GAE mail list brought up a number of issues with how people with RDBMS backgrounds approached Datastore. Many of the table layouts I saw pasted on the mail list were clearly written for how an RDBMS works, with sometimes significant work required to adapt it to deal with Datastore.
A little background might help understand this difference. Google Datastore is implemented on top of BigTable, which is described briefly in the paper as a “sparse, distributed, persistent multi-demensional sorted map”. One of the other descriptions I heard in a talk on data storage techniques at FOO Camp from a Google developer was, “think of a BigTable table as a spreadsheet, except with pretty much as many columns as you want”.
This brings about a fairly big shift in thinking for the developer who grew up on an RDBMS. The fairly normalized organization of data written without regard to massively distributed data stores suddenly becomes a rather big problem. Consider a few of the ‘limitations’ of Datastore that will jump right out at you:
- You cannot query across relations
- You cannot retrieve more than 1000 rows in a query
- Writes are much much slower than you’re used to (a developer on the mail list said 50 inserts with 2 fields each almost ate up the 3 seconds allowed for a web request)
- There are zero database functions available
- There is no “GROUP BY…”, which doesn’t matter much if you read the prior bullet point
- Transactions can only be wrapped around entities in the same entity group (ie, the same section of the distributed database)
- Referential integrity only sort of exists
- No triggers, no views, no constraints
- No GIS Polygon types, or anything beyond just a GeoPoint (Odd, considering that Google has so much mapping stuff)
Then of course, a few of the new things that might leave you scratching your head, quite happy, or both:
- Keys for an entity may have ancestors (ancestors aren’t relations, they’re different and have to do with Entity Groups, which determine what you can do in a transaction, wheeee!)
- An Entity Group doesn’t have to all be of the same Kind, its more of an instruction to Datastore to keep these near each other when distributed
- Key’s can be made before the entity, just so you can make descendent entities of the key, then make the ancestor
- The handy ListProperty, when used in a query, will let you use the conditional argument and apply it to every item in the list (sort of like an uber ‘IN (…)’ query, except it can also find all the data where a member in the list was , or = to something else)
- Making more Entity groups is a good idea when you frequently need a batch of “these few things” for a request, especially if you need to alter them all at once in a transaction
- Normalizing is frequently bad since you can’t query across relations, dynamic properties make it easy to heavily denormalize. If you do normalize some data and its for the same batch of ‘things you always need at once’, use Entity groups. Or use a ReferenceProperty if its merely something related you may occasionally hit.
- The ReferenceProperty() does not have to refer to a known kind, you can decide on the fly what datastore classes to reference if not specified when declaring the ReferenceProperty
- Many to Many relations aren’t what you think, now you could have a ListProperty() of ReferenceProperty()’s, which may or may not all refer to instances of the same class
- A query may return entities of different kinds, if querying for entities of a given ancestor
(There’s probably a bunch more as well, these were some of the obvious ones that jumped out at me)
The end result of this, is that the standard way a developer writes out the table schema for a RDBMS should be dumped almost entirely when considering an app using Google Datastore. Storing data and using Google Datastore isn’t difficult, but it is a pretty hefty paradigm shift, especially if you’ve never left RDBMS-land. This is not a trivial change to make in approaching your data.
I rather enjoyed working with these new ways of tackling data, and the possibilities opened by the ways it lets me store and refer to data in many ways goes beyond the traditional RDBMS. In the short term though, I doubt I’ll be making any GAE app’s until there’s an alternative implementation thats production ready… I just can’t handle the lock-in.
And of course, please note any corrections or inaccuracies in the comments.