Healthcheck Bot is a standalone, highly configurable and extendable, application for verifying the status of your software products. The user is free to configure which targets to test, which metrics to monitor, and where to store check outcomes. Healthcheck Bot also supports custom assertions written in Python for more sophisticated check scenarios.
Consider using Healthcheck Bot if:
- you need proactive monitoring of your software. This means you have an existing application in place, and you need to query it, regularly capture its state and compare it to the expected one;
- you want to load the outcomes of proactive healthchecks into existing infrastructure (Graylog, Nagios, whatever)
- the validation of your system state requires more complex comparations than the ones defined in DSL\Yaml\JSON, which are easier to express with code.
- you are familiar with Python.
If none of the above describes your case, you might want to consider one of the following alternatives:
- Nagios and its application checks
- Goss if you need just servers pings, ports checks, server configuration etc.
The package provides executable healthcheckbot as the main entry point. You must pass a configuration file containing definitions of
your watchers and other options. In order to start the application, run:
healthcheckbot -c examples/config.yaml run
If you prefer Docker, there is a pre-built image available. You need to create a config file somewhere on host machine and run Docker as follows:
docker run --rm -it -v $PWD/myconfigs:/srv/config logicify/healthcheckbotIn this case, your config must be named config.yaml; it must be located in ./myconfigs directory on your host machine.
If you want to use a different file, you could set it in env variable CONFIG_FILE:
docker run --rm -it -v $PWD/myconfigs:/srv/config -e CONFIG_FILE=/srv/config/advanced.yaml logicify/healthcheckbotFor the cases when you also need to pass your custom modules, e.g. custom assertions, you need to mount data directory as well. Check the following example:
docker-compose.yaml
healthcheckbot:
image: logicify/healthcheckbot
volumes:
- ./myconfig:/srv/config
- ./mydata:/srv/data
environment:
CONFIG_FILE: /srv/config/my_config_name.yamlmy_config_name.yaml
app:
classpath:
- /srv/data
outputs:
console:
provider: healthcheckbot.outputs.ConsoleOutput
triggers:
each_1_minute:
provider: healthcheckbot.triggers.SimpleTimer
interval: 60
watchers:
google_home:
provider: healthcheckbot.watchers.HttpRequest
url: http://google.com
assert_response_time: 2
assert_status: 200
triggers:
- each_1_minute
custom_assertions:
check_something_interesting:
provider: mypackage.assertions.CustomAssert
my_param: 'val1'In this sample, we mount directory /srv/data from the host and declare it as a part of classpath, so all Python modules from this dir are accessible from the application in runtime. Thus, we can implement CustomAssert module and use it in our configuration. See Customization section for details.
Consider the following configuration example:
outputs:
console:
provider: healthcheckbot.outputs.ConsoleOutput
triggers:
each_1_minute:
provider: healthcheckbot.triggers.SimpleTimer
interval: 60
watchers:
google_home:
provider: healthcheckbot.watchers.HttpRequest
url: http://google.com
assert_status: 200
triggers:
- each_1_minuteIn this example, we define a single watcher that will send HTTP request to http://google.com each minute.
Healthcheck will be treated as failed when the response status is not 200.
The result of the watcher evaluation will be printed to STDOUT.
Generally, there are 4 types of entities (module types) Healthcheck Bot works with: Outputs, Triggers, Watchers, WatcherAsserts.
Sections below describe each of them. Please also note that user is able to implement their own module to extend or override default behaviour and connect it without modifying the core code. See Customization section below.
Regardless of the module type you define, there is a mandatory component called provider. It defines fully qualified name
of the class implementing corresponding module. The rest of options are parameters for module instance.
Output defines the way watcher's evaluation result will be delivered to the end user. It might be as simple as just console output or a more real-life and common record in a database, or a centralized metric collection for a system like CloudWatch or Graylog2.
There is a couple of implementations of outputs built in the package.
Just prints serialized JSON output to the STDOUT. There are no configuration parameters.
Usage Example:
outputs:
console:
provider: healthcheckbot.outputs.ConsoleOutputThis one is very similar to console output, but the serialized result will be passed to the logger.
Parameters
| Parameter | Description | Default Value | Required |
|---|---|---|---|
| log_level | Log level to be used when outputting result | INFO | No |
| loger_name | Name of the logger to use | OUT | No |
Triggers are responsible for initiation of worker execution. The most common use case is periodic run, but other scenarios are possible as well, e.g. execution after HTTP call.
This implementation of the trigger is pretty self-explanatory - all it does is periodic watchers execution with constant interval specified as a parameter.
| Parameter | Description | Default Value | Required |
|---|---|---|---|
| interval | Time interval in seconds between iterations | 300 | No |
| start_immediately | If set to True, the first iteration will be triggered immediately after application starts; otherwise, in interval seconds |
True | No |
Example
triggers:
each_1_minute:
provider: healthcheckbot.triggers.SimpleTimer
interval: 60
each_5_minutes:
provider: healthcheckbot.triggers.SimpleTimer
interval: 300Watchers are modules that actually read the system state and could optionally run some assertions over a certain state. Their parameters mostly depend on implementation, but there is a couple of options common for all watchers.
triggers- the list of trigger names that will invoke the given watcher. It is important to list at least one trigger, otherwise, the watcher will never be invoked.custom_assertions- the dictionary containing assertions to be applied as a part of state verification after regular module assertions. See section Watcher Asserts for details.
TBD
User's ability to extend the behavior of any module is a key feature of Healthcheck Bot. In order to make it easier to load modules from the outside, user could extend classpath (folders to be scanned for classes) with a simple configuration option. Consider the following example:
app:
classpath:
- /tmp
outputs:
console:
provider: healthcheckbot.outputs.ConsoleOutput
triggers:
each_1_minute:
provider: healthcheckbot.triggers.SimpleTimer
interval: 60
watchers:
system_time:
provider: logicify.watchers.SystemTimeWatcher
triggers:
- each_1_minuteOur /tmp/logicify folder looks as follows:
/tmp/logicify/
├── watchers.py
└── __init__.py
File watchers.py contains class SystemTimeWatcher that implements WatcherModule:
class SystemTimeWatcher(WatcherModule):
def __init__(self, application):
super().__init__(application)
self.error_when_midnight = False
def obtain_state(self, trigger) -> object:
current_time = datetime.now()
return current_time
def serialize_state(self, state: datetime) -> [dict, None]:
return {
"time": state.isoformat()
}
def do_assertions(self, state: datetime, reporter: ValidationReporter):
if self.error_when_midnight:
if state.time() == time(0, 0):
reporter.error('its_midnight', 'Must be any time except of 00:00')
PARAMS = (
ParameterDef('error_when_midnight', validators=(validators.boolean,)),
)This implementation illustrates how you could create your own watchers. While this example shows only a watcher module, many concepts apply to the Triggers, Outputs and Asserts too.
PARAMS tuple gives you a way to configure arguments for your module. During application, bootstrap parameters from yaml will be sanitized, validated and assigned to the module instance according to definition configured with ParameterDef.
Method obtain_state will be invoked by the trigger. You should implement your state gathering logic here. The result could be any object.
do_assertions will be invoked on state verification stage. state parameter here is what was returned from obtain_state, and reporter instance must be used to report assertion errors (if any).
And finally, serialize_state will be called before passing the result to output. It should convert state object to simple types (dictionaries, lists, primitives).
The initial configuration of dev environment:
virtualenv -p python3 venvsource ./venv/bin/activatepip install -r ./requirements.txt
Dmitry Berezovsky, Logicify (http://logicify.com/)
This plug-in is licensed under GPLv3. This means you are free to use it even in commercial projects. Also note there is no warranty for this free software. Please see the included LICENSE file for details.
