Configuration and deployment

As your application grows more complex, you may find that you need to have different settings for your development environment and your production environment. You may even have multiple deployments that all need their own custom configuration.

For this purpose, Asphalt provides a command line interface that will read a YAML formatted configuration file and run the application it describes.

Running the Asphalt launcher

Running the launcher is very straightfoward:

asphalt run yourconfig.yaml [your-overrides.yml...]

Or alternatively:

python -m asphalt run yourconfig.yaml [your-overrides.yml…]

What this will do is:

  1. read all the given configuration files, starting from yourconfig.yaml
  2. merge the configuration files’ contents into a single configuration dictionary using
    merge_config()
  3. call run_application() using the configuration dictionary as keyword
    arguments

Writing a configuration file

A production-ready configuration file should contain at least the following options:

  • component: a dictionary containing the class name and keyword arguments for its constructor
  • logging: a dictionary to be passed to logging.config.dictConfig()

Suppose you had the following component class as your root component:

class MyRootComponent(ContainerComponent):
    def __init__(self, components, data_directory: str):
        super().__init__(components)
        self.data_directory = data_directory

    async def start(ctx):
        self.add_component('mailer', backend='smtp')
        self.add_component('sqlalchemy')
        await super().start(ctx)

You could then write a configuration file like this:

---
max_threads: 20
component:
  type: myproject:MyRootComponent
  data_directory: /some/file/somewhere
  components:
    mailer:
      host: smtp.mycompany.com
      ssl: true
    sqlalchemy:
      url: postgresql:///mydatabase

logging:
  version: 1
  disable_existing_loggers: false
  handlers:
    console:
      class: logging.StreamHandler
      formatter: generic
  formatters:
    generic:
        format: "%(asctime)s:%(levelname)s:%(name)s:%(message)s"
  root:
    handlers: [console]
    level: INFO

In the above configuration you have three top level configuration keys: max_threads, component and logging, all of which are directly passed to run_application() as keyword arguments.

The component section defines the type of the root component using the specially processed type option. You can either specify a setuptools entry point name (from the asphalt.components namespace) or a text reference like module:class (see resolve_reference() for details). The rest of the keys in this section are passed directly to the constructor of the MyRootComponent class.

The components section within component is processed in a similar fashion. Each subsection here is a component type alias and its keys and values are the constructor arguments to the relevant component class. The per-component configuration values are merged with those provided in the start() method of MyRootComponent. See the next section for a more elaborate explanation.

With max_threads: 20, the maximum number of threads in the event loop’s default thread pool executor is set to 20.

The logging configuration tree here sets up a root logger that prints all log entries of at least INFO level to the console. You may want to set up more granular logging in your own configuration file. See the Python standard library documentation for details.

Configuration overlays

Component configuration can be specified on several levels:

  • Hard-coded arguments to add_component()
  • First configuration file argument to asphalt run
  • Second configuration file argument to asphalt run
  • …

Any options you specify on each level override or augment any options given on previous levels. To minimize the effort required to build a working configuration file for your application, it is suggested that you pass as many of the options directly in the component initialization code and leave only deployment specific options like API keys, access credentials and such to the configuration file.

With the configuration presented in the earlier paragraphs, the mailer component’s constructor gets passed three keyword arguments:

  • backend='smtp'
  • host='smtp.mycompany.com'
  • ssl=True

The first one is provided in the root component code while the other two options come from the YAML file. You could also override the mailer backend in the configuration file if you wanted. The same effect can be achieved programmatically by supplying the override configuration to the container component via its components constructor argument. This is very useful when writing tests against your application. For example, you might want to use the mock mailer in your test suite configuration to test that the application correctly sends out emails (and to prevent them from actually being sent to recipients!).

There is another neat trick that lets you easily modify a specific key in the configuration. By using dotted notation in a configuration key, you can target a specific key arbitrarily deep in the configuration structure. For example, to override the logging level for the root logger in the configuration above, you could use an override configuration such as:

---
logging.root.level: DEBUG

The keys don’t need to be on the top level either, so the following has the same effect:

---
logging:
    root.level: DEBUG

Defining multiple services

New in version 4.1.0.

Sometimes it may be more convenient to use a single configuration file for launching your application with different configurations or entry points. To this end, the runner supports the notion of “service definitions” in the configuration file. This is done by replacing the component dictionary with a services dictionary at the top level of the configuration file and either setting the ASPHALT_SERVICE environment variable or by passing the --service (or -s) option when launching the runner. This approach provides the additional advantage of allowing the use of YAML references, like so:

---
services:
  server:
    max_threads: 30
    component:
      type: myproject.server.ServerComponent
      components:
        wamp: &wamp
          host: wamp.example.org
          port: 8000
          tls: true
          auth_id: serveruser
          auth_secret: serverpass
        mailer:
          backend: smtp
  client:
    component:
      type: myproject.client.ClientComponent
      components:
        wamp:
          <<: *wamp
          auth_id: clientuser
          auth_secret: clientpass

Each section under services is like its own distinct top level configuration. Additionally, the keys under each service are merged with any top level configuration, so you can, for example, define a logging configuration there.

Now, to run the server service, do:

asphalt run -s server config.yaml

The client service is run in the same fashion:

asphalt run -s client config.yaml

You can also define a service with a special name, default, which is used in case multiple services are present and no service has been explicitly selected.

Note

The -s/--service command line switch overrides the ASPHALT_SERVICE environment variable.

Performance tuning

Asphalt’s core code and many third part components employ a number of potentially expensive validation steps in its code. The performance hit of these checks is not a concern in development and testing, but in a production environment you will probably want to maximize the performance.

To do this, you will want to disable Python’s debugging mode by either setting the environment variable PYTHONOPTIMIZE to 1 or (if applicable) running Python with the -O switch. This has the effect of completely eliminating all assert statements and blocks starting with if __debug__: from the compiled bytecode.

When you want maximum performance, you’ll also want to use the fastest available event loop implementation. This can be done by specifying the event_loop_policy option in the configuration file or by using the -l or --loop switch. The core library has built-in support for the uvloop event loop implementation, which should provide a nice performance boost over the standard library implementation.