README.md

Pydal for Nameko

Pydal extension for Nameko.

As a longtime Web2py user I really appreciate the DAL. Which fortunately now is a standalone library. Having just learnt about nameko the match seems perfect.

Not all supported database drivers will work. Nameko uses the eventlet library and many of the drivers probably aren't compatible. Please share your findings in a ticket or comment to help others know what issues you ran into.

Usage

from nameko.rpc import rpc
from pydal_extension import DalProvider
from pydal import Field
from datetime import datetime 


def define_model(db,other_args):
    db.define_table('something',
                    Field('foo'),
                    Field('ts','string',default=datetime.now().isoformat()) # store timestamps as strings
    )

class pydaltest(object):
    name = 'pydal_test'

    db = DalProvider(define_model)

    @rpc
    def add(self, value):
        self.db.something.insert(foo=value)

    @rpc
    def get_cnt_and_newest_record(self):
        db, id = self.db, self.db.something.id
        sub_select = db(id > 0)._select(id.max())
        cnt = db(id > 0).count()
        newest_record = db(id.belongs(sub_select)).select().first().as_dict()
        return cnt, newest_record

Include the pydal pydal.DAL(...) object as a dependency using the DalProvider. It will open the database based on parameters in the config file. See Config file additions below for details.

The define_model callback parameter of DalProvider lets you define you database model using the connection passed to the callback. When a new worker is spawned, it will receive a connection from the pool and the model is applied, just like in web2py. Connection pooling is included in pydal. This will automatically provide the connections needed for the workers.

Config file additions

The DalProvider knows to which service it is bound. It will use the .name of the service to lookup the args and kwargs applied to the DAL constructor from the configuration. Here is an example for a simple sqlite dabase with some keyword parameters set to the default values, purely for illustrative purposes. You see database_uris as the root of this configuration. It is a dictionary with the service name as a key and a dictionary as value. This latter dictionary contains trhe keys: args, kwargs and other_args. The first two are applied to the DAL constructor like DAL(*args,**kwargs). The latter is available to send microservice specific parameters to the define_model function. Thus you have all freedom to setup the connection parameters as required. (Probably this is useful if you use a larger number of workers, otherwise they will be waiting for a new connection from the pool.)

Mind the - in front of sqlite://.
The args holds a list, it isn't just a string. It's a list of strings.

database_uris:
    pydal_test:
      args:
        - sqlite://somedb.sqlite
      kwargs:
        db_codec : UTF-8
        migrate : true
      other_args:
      

A note on datetime fields

It's easy with pydal to use datetime fields. But if you want to return a record from one service to another beware of nameko's default encoding of json. It will not accept datetime types. Either choose a different messaging format , pickle is an option though i haven't tried it yet, or convert the datetimes to a string. You can apply this to your model, but of course you needn't and just convert the values at the right time, just before returning.