microbootstrap assists you in creating applications with all the necessary instruments already set up.
# settings.py
from microbootstrap import LitestarSettings
class YourSettings(LitestarSettings):
... # Your settings are stored here
settings = YourSettings()
# application.py
import litestar
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
from your_application.settings import settings
# Use the Litestar application!
application: litestar.Litestar = LitestarBootstrapper(settings).bootstrap()
With microbootstrap, you receive an application with lightweight built-in support for:
sentry
prometheus
opentelemetry
logging
cors
swagger
- with additional offline version supporthealth-checks
Those instruments can be bootstrapped for:
fastapi
litestar
Interested? Let's dive right in ⚡
You can install the package using either pip
or poetry
.
Also, you can specify extras during installation for concrete framework:
fastapi
litestar
For poetry:
$ poetry add microbootstrap -E fastapi
For pip:
$ pip install microbootstrap[fastapi]
To configure your application, you can use the settings object.
from microbootstrap import LitestarSettings
class YourSettings(LitestarSettings):
# General settings
service_debug: bool = False
service_name: str = "my-awesome-service"
# Sentry settings
sentry_dsn: str = "your-sentry-dsn"
# Prometheus settings
prometheus_metrics_path: str = "/my-path"
# Opentelemetry settings
opentelemetry_container_name: str = "your-container"
opentelemetry_endpoint: str = "/opentelemetry-endpoint"
settings = YourSettings()
Next, use the Bootstrapper
object to create an application based on your settings.
import litestar
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
application: litestar.Litestar = LitestarBootstrapper(settings).bootstrap()
This approach will provide you with an application that has all the essential instruments already set up for you.
The settings object is the core of microbootstrap.
All framework-related settings inherit from the BaseServiceSettings
object. BaseServiceSettings
defines parameters for the service and various instruments.
However, the number of parameters is not confined to those defined in BaseServiceSettings
. You can add as many as you need.
These parameters can be sourced from your environment. By default, no prefix is added to these parameters.
Example:
class YourSettings(BaseServiceSettings):
service_debug: bool = True
service_name: str = "micro-service"
your_awesome_parameter: str = "really awesome"
... # Other settings here
To source your_awesome_parameter
from the environment, set the environment variable named YOUR_AWESOME_PARAMETER
.
If you prefer to use a prefix when sourcing parameters, set the ENVIRONMENT_PREFIX
environment variable in advance.
Example:
$ export ENVIRONMENT_PREFIX=YOUR_PREFIX_
Then the settings object will attempt to source the variable named YOUR_PREFIX_YOUR_AWESOME_PARAMETER
.
Each settings object for every framework includes service parameters that can be utilized by various instruments.
You can configure them manually, or set the corresponding environment variables and let microbootstrap to source them automatically.
from microbootstrap.settings import BaseServiceSettings
class ServiceSettings(BaseServiceSettings):
service_debug: bool = True
service_environment: str | None = None
service_name: str = "micro-service"
service_description: str = "Micro service description"
service_version: str = "1.0.0"
... # Other settings here
At present, the following instruments are supported for bootstrapping:
sentry
prometheus
opentelemetry
logging
cors
swagger
Let's clarify the process required to bootstrap these instruments.
To bootstrap Sentry, you must provide at least the sentry_dsn
.
Additional parameters can also be supplied through the settings object.
from microbootstrap.settings import BaseServiceSettings
class YourSettings(BaseServiceSettings):
service_environment: str | None = None
sentry_dsn: str | None = None
sentry_traces_sample_rate: float | None = None
sentry_sample_rate: float = pydantic.Field(default=1.0, le=1.0, ge=0.0)
sentry_max_breadcrumbs: int = 15
sentry_max_value_length: int = 16384
sentry_attach_stacktrace: bool = True
sentry_integrations: list[Integration] = []
sentry_additional_params: dict[str, typing.Any] = {}
... # Other settings here
These settings are subsequently passed to the sentry-sdk package, finalizing your Sentry integration.
Prometheus integration presents a challenge because the underlying libraries for FastAPI
and Litestar
differ significantly, making it impossible to unify them under a single interface. As a result, the Prometheus settings for FastAPI
and Litestar
must be configured separately.
To bootstrap prometheus you have to provide prometheus_metrics_path
from microbootstrap.settings import FastApiSettings
class YourFastApiSettings(FastApiSettings):
service_name: str
prometheus_metrics_path: str = "/metrics"
prometheus_metrics_include_in_schema: bool = False
prometheus_instrumentator_params: dict[str, typing.Any] = {}
prometheus_instrument_params: dict[str, typing.Any] = {}
prometheus_expose_params: dict[str, typing.Any] = {}
... # Other settings here
Parameters description:
service_name
- will be attached to metric's names, but has to be named in snake_case.prometheus_metrics_path
- path to metrics handler.prometheus_metrics_include_in_schema
- whether to include metrics route in OpenAPI schema.prometheus_instrumentator_params
- will be passed to Instrumentor
during initialization.prometheus_instrument_params
- will be passed to Instrumentor.instrument(...)
.prometheus_expose_params
- will be passed to Instrumentor.expose(...)
.FastApi prometheus bootstrapper uses prometheus-fastapi-instrumentator that's why there are three different dict for parameters.
To bootstrap prometheus you have to provide prometheus_metrics_path
from microbootstrap.settings import LitestarSettings
class YourFastApiSettings(LitestarSettings):
service_name: str
prometheus_metrics_path: str = "/metrics"
prometheus_additional_params: dict[str, typing.Any] = {}
... # Other settings here
Parameters description:
service_name
- will be attached to metric's names, there are no name restrictions.prometheus_metrics_path
- path to metrics handler.prometheus_additional_params
- will be passed to litestar.contrib.prometheus.PrometheusConfig
.To bootstrap Opentelemetry, you must provide several parameters:
service_name
service_version
opentelemetry_endpoint
opentelemetry_namespace
opentelemetry_container_name
However, additional parameters can also be supplied if needed.
from microbootstrap.settings import BaseServiceSettings
from microbootstrap.instruments.opentelemetry_instrument import OpenTelemetryInstrumentor
class YourSettings(BaseServiceSettings):
service_name: str
service_version: str
opentelemetry_container_name: str | None = None
opentelemetry_endpoint: str | None = None
opentelemetry_namespace: str | None = None
opentelemetry_insecure: bool = True
opentelemetry_instrumentors: list[OpenTelemetryInstrumentor] = []
opentelemetry_exclude_urls: list[str] = []
... # Other settings here
Parameters description:
service_name
- will be passed to the Resource
.service_version
- will be passed to the Resource
.opentelemetry_endpoint
- will be passed to OTLPSpanExporter
as endpoint.opentelemetry_namespace
- will be passed to the Resource
.opentelemetry_insecure
- is opentelemetry connection secure.opentelemetry_container_name
- will be passed to the Resource
.opentelemetry_instrumentors
- a list of extra instrumentors.opentelemetry_exclude_urls
- list of ignored urls.These settings are subsequently passed to opentelemetry, finalizing your Opentelemetry integration.
microbootstrap provides in-memory JSON logging through the use of structlog. For more information on in-memory logging, refer to MemoryHandler.
To utilize this feature, your application must be in non-debug mode, meaning service_debug
should be set to False
.
import logging
from microbootstrap.settings import BaseServiceSettings
class YourSettings(BaseServiceSettings):
service_debug: bool = False
logging_log_level: int = logging.INFO
logging_flush_level: int = logging.ERROR
logging_buffer_capacity: int = 10
logging_unset_handlers: list[str] = ["uvicorn", "uvicorn.access"]
logging_extra_processors: list[typing.Any] = []
logging_exclude_endpoints: list[str] = []
Parameters description:
logging_log_level
- The default log level.logging_flush_level
- All messages will be flushed from the buffer when a log with this level appears.logging_buffer_capacity
- The number of messages your buffer will store before being flushed.logging_unset_handlers
- Unset logger handlers.logging_extra_processors
- Set additional structlog processors if needed.logging_exclude_endpoints
- Exclude logging on specific endpoints.from microbootstrap.settings import BaseServiceSettings
class YourSettings(BaseServiceSettings):
cors_allowed_origins: list[str] = pydantic.Field(default_factory=list)
cors_allowed_methods: list[str] = pydantic.Field(default_factory=list)
cors_allowed_headers: list[str] = pydantic.Field(default_factory=list)
cors_exposed_headers: list[str] = pydantic.Field(default_factory=list)
cors_allowed_credentials: bool = False
cors_allowed_origin_regex: str | None = None
cors_max_age: int = 600
Parameter descriptions:
cors_allowed_origins
- A list of origins that are permitted.cors_allowed_methods
- A list of HTTP methods that are allowed.cors_allowed_headers
- A list of headers that are permitted.cors_exposed_headers
- A list of headers that are exposed via the 'Access-Control-Expose-Headers' header.cors_allowed_credentials
- A boolean value that dictates whether or not to set the 'Access-Control-Allow-Credentials' header.cors_allowed_origin_regex
- A regex used to match against origins.cors_max_age
- The response caching Time-To-Live (TTL) in seconds, defaults to 600.from microbootstrap.settings import BaseServiceSettings
class YourSettings(BaseServiceSettings):
service_name: str = "micro-service"
service_description: str = "Micro service description"
service_version: str = "1.0.0"
service_static_path: str = "/static"
swagger_path: str = "/docs"
swagger_offline_docs: bool = False
swagger_extra_params: dict[str, Any] = {}
Parameter descriptions:
service_name
- The name of the service, which will be displayed in the documentation.service_description
- A brief description of the service, which will also be displayed in the documentation.service_version
- The current version of the service.service_static_path
- The path for static files in the service.swagger_path
- The path where the documentation can be found.swagger_offline_docs
- A boolean value that, when set to True, allows the Swagger JS bundles to be accessed offline. This is because the service starts to host via static.swagger_extra_params
- Additional parameters to pass into the OpenAPI configuration.from microbootstrap.settings import BaseServiceSettings
class YourSettings(BaseServiceSettings):
service_name: str = "micro-service"
service_version: str = "1.0.0"
health_checks_enabled: bool = True
health_checks_path: str = "/health/"
health_checks_include_in_schema: bool = False
Parameter descriptions:
service_name
- Will be displayed in health check response.service_version
- Will be displayed in health check response.health_checks_enabled
- Must be True to enable health checks.health_checks_path
- Path for health check handler.health_checks_include_in_schema
- Must be True to include health_checks_path
(/health/
) in OpenAPI schema.While settings provide a convenient mechanism, it's not always feasible to store everything within them.
There may be cases where you need to configure a tool directly. Here's how it can be done.
To manually configure an instrument, you need to import one of the available configurations from microbootstrap:
SentryConfig
OpentelemetryConfig
PrometheusConfig
LoggingConfig
SwaggerConfig
CorsConfig
These configurations can then be passed into the .configure_instrument
or .configure_instruments
bootstrapper methods.
import litestar
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
from microbootstrap import SentryConfig, OpentelemetryConfig
application: litestar.Litestar = (
LitestarBootstrapper(settings)
.configure_instrument(SentryConfig(sentry_dsn="https://new-dsn"))
.configure_instrument(OpentelemetryConfig(opentelemetry_endpoint="/new-endpoint"))
.bootstrap()
)
Alternatively,
import litestar
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
from microbootstrap import SentryConfig, OpentelemetryConfig
application: litestar.Litestar = (
LitestarBootstrapper(settings)
.configure_instruments(
SentryConfig(sentry_dsn="https://examplePublicKey@o0.ingest.sentry.io/0"),
OpentelemetryConfig(opentelemetry_endpoint="/new-endpoint")
)
.bootstrap()
)
The application can be configured in a similar manner:
import litestar
from microbootstrap.config.litestar import LitestarConfig
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
from microbootstrap import SentryConfig, OpentelemetryConfig
@litestar.get("/my-handler")
async def my_handler() -> str:
return "Ok"
application: litestar.Litestar = (
LitestarBootstrapper(settings)
.configure_application(LitestarConfig(route_handlers=[my_handler]))
.bootstrap()
)
Important
When configuring parameters with simple data types such as:
str
,int
,float
, etc., these variables overwrite previous values.Example:
from microbootstrap import LitestarSettings, SentryConfig class YourSettings(LitestarSettings): sentry_dsn: str = "https://my-sentry-dsn" application: litestar.Litestar = ( LitestarBootstrapper(YourSettings()) .configure_instrument( SentryConfig(sentry_dsn="https://my-new-configured-sentry-dsn") ) .bootstrap() )
In this example, the application will be bootstrapped with the new
https://my-new-configured-sentry-dsn
Sentry DSN, replacing the old one.However, when you configure parameters with complex data types such as:
list
,tuple
,dict
, orset
, they are expanded or merged.Example:
from microbootstrap import LitestarSettings, PrometheusConfig class YourSettings(LitestarSettings): prometheus_additional_params: dict[str, Any] = {"first_value": 1} application: litestar.Litestar = ( LitestarBootstrapper(YourSettings()) .configure_instrument( PrometheusConfig(prometheus_additional_params={"second_value": 2}) ) .bootstrap() )
In this case, Prometheus will receive
{"first_value": 1, "second_value": 2}
insideprometheus_additional_params
. This is also true forlist
,tuple
, andset
.
When working on projects that don't use Litestar or FastAPI, you can still take advantage of monitoring and logging capabilities using InstrumentsSetupper
. This class sets up Sentry, OpenTelemetry, and Logging instruments in a way that's easy to integrate with your project.
You can use InstrumentsSetupper
as a context manager, like this:
from microbootstrap.instruments_setupper import InstrumentsSetupper
from microbootstrap import InstrumentsSetupperSettings
class YourSettings(InstrumentsSetupperSettings):
...
with InstrumentsSetupper(YourSettings()):
while True:
print("doing something useful")
time.sleep(1)
Alternatively, you can use the setup()
and teardown()
methods instead of a context manager:
current_setupper = InstrumentsSetupper(YourSettings())
current_setupper.setup()
try:
while True:
print("doing something useful")
time.sleep(1)
finally:
current_setupper.teardown()
Like bootstrappers, you can reconfigure instruments using the configure_instrument()
and configure_instruments()
methods.
If you miss some instrument, you can add your own.
Essentially, Instrument
is just a class with some abstractmethods.
Every instrument uses some config, so that's first thing, you have to define.
from microbootstrap.instruments.base import BaseInstrumentConfig
class MyInstrumentConfig(BaseInstrumentConfig):
your_string_parameter: str
your_list_parameter: list
Next, you can create an instrument class that inherits from Instrument
and accepts your configuration as a generic parameter.
from microbootstrap.instruments.base import Instrument
class MyInstrument(Instrument[MyInstrumentConfig]):
instrument_name: str
ready_condition: str
def is_ready(self) -> bool:
pass
def teardown(self) -> None:
pass
def bootstrap(self) -> None:
pass
@classmethod
def get_config_type(cls) -> type[MyInstrumentConfig]:
return MyInstrumentConfig
Now, you can define the behavior of your instrument.
Attributes:
instrument_name
- This will be displayed in your console during bootstrap.ready_condition
- This will be displayed in your console during bootstrap if the instrument is not ready.Methods:
is_ready
- This defines the readiness of the instrument for bootstrapping, based on its configuration values. This is required.teardown
- This allows for a graceful shutdown of the instrument during application shutdown. This is not required.bootstrap
- This is the main logic of the instrument. This is not required.Once you have the framework of the instrument, you can adapt it for any existing framework. For instance, let's adapt it for litestar.
import litestar
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
@LitestarBootstrapper.use_instrument()
class LitestarMyInstrument(MyInstrument):
def bootstrap_before(self) -> dict[str, typing.Any]:
pass
def bootstrap_after(self, application: litestar.Litestar) -> dict[str, typing.Any]:
pass
To bind the instrument to a bootstrapper, use the .use_instrument
decorator.
To add extra parameters to the application, you can use:
bootstrap_before
- This adds arguments to the application configuration before creation.bootstrap_after
- This adds arguments to the application after creation.Afterwards, you can use your instrument during the bootstrap process.
import litestar
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
from microbootstrap import SentryConfig, OpentelemetryConfig
from your_app import MyInstrumentConfig
application: litestar.Litestar = (
LitestarBootstrapper(settings)
.configure_instrument(
MyInstrumentConfig(
your_string_parameter="very-nice-parameter",
your_list_parameter=["very-special-list"],
)
)
.bootstrap()
)
Alternatively, you can fill these parameters within your main settings object.
from microbootstrap import LitestarSettings
from microbootstrap.bootstrappers.litestar import LitestarBootstrapper
from your_app import MyInstrumentConfig
class YourSettings(LitestarSettings, MyInstrumentConfig):
your_string_parameter: str = "very-nice-parameter"
your_list_parameter: list = ["very-special-list"]
settings = YourSettings()
application: litestar.Litestar = LitestarBootstrapper(settings).bootstrap()