├── .gitignore
├── LICENSE
├── README.md
├── demo
└── flask
│ ├── .dockerignore
│ ├── Dockerfile
│ ├── README.md
│ ├── app
│ ├── __init__.py
│ ├── main.py
│ └── templates
│ │ └── public
│ │ └── dynamic.html
│ └── requirements.txt
└── spec
├── __init__.py
├── config
├── README.md
└── micro-config
│ ├── microconfig
│ └── config.py
│ └── setup.py
├── fault-tolerance
└── README.md
├── graphql
├── README.md
└── src
│ ├── __init__.py
│ └── graph.py
├── health
├── README.md
├── microhealth
│ ├── __init__.py
│ ├── api.py
│ ├── healthcheck.py
│ ├── healthcheck_decorators.py
│ ├── healthcheck_response.py
│ └── standard_healthchecks.py
└── setup.py
├── metrics
├── README.md
├── __init__.py
└── src
│ ├── __init__.py
│ ├── counter.py
│ ├── metric.py
│ ├── metric_types.py
│ ├── metric_units.py
│ ├── tag.py
│ └── tests
│ ├── __init__.py
│ ├── test_counter.py
│ ├── test_metric_types.py
│ ├── test_metric_units.py
│ └── test_tag.py
├── open-api
└── README.md
├── open-tracing
└── README.md
└── rest-client
└── README.md
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 | .pyre/
130 |
131 | .idea/
132 |
133 | spec/config/micro-config/microconfig/conf.properties
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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/README.md:
--------------------------------------------------------------------------------
1 | # python-microprofile
2 | Implement microprofile for python language
3 |
4 | ## overview
5 | So what are microframeworks
6 | ------
7 | So what are microframeworks? Usually, they are a bare framework for a rest server, according to Wikipedia: minimalistic web application frameworks. Each one has its own flavor: some have a reactive patterns for defining endpoints, some added integrated metrics.
8 | Microservice Architecture has become very common in most companies. The big question is of course what is a microservice.
9 | From the site microservices.io, this is the summary:
10 | * Microservices - also known as the microservice architecture - is an architectural style that structures an application as a collection of services that are
11 | * Highly maintainable and testable
12 | * Loosely coupled
13 | * Independently deployable
14 | * Organized around business capabilities
15 | * Owned by a small team
16 |
17 | The main issue is that up till now, all the discussions of microservices were about the size and the independence of the service (deployment and development).
18 | What was not covered well is the minimum requirement of the service for monitoring and orchestration. Kubernetis gave to docker a standard way of distributed deployment and scale.
19 | A similar framework for viewing all services via a single lense was missing.
20 |
21 | 
22 |
23 | ## goal
24 | In this project we try to collect into one place the best practices and implementations of microprofiles for python.
25 | You can see in each spec what we have found to be the best packages that come closest to the microprofile standard.
26 | In cases that there is nothing we try to implement them ourselfs.
27 |
28 | ## Installing Python 3
29 |
30 | For mac install python3
31 | `brew install python3`
32 |
33 | Also check out [How to install python3 on OSX](https://gist.github.com/alfasin/bc88d4eb0217f13cbc7ccef53e8eadb3)
34 |
35 | ### setup env
36 |
37 | ```buildoutcfg
38 | python3 -m pip install --user --upgrade pip
39 | python3 -m pip --version # (test install)
40 | python3 -m pip install --user virtualenv
41 | python3 -m venv env
42 | source env/bin/activate
43 | which python # (test env)
44 | ```
45 |
--------------------------------------------------------------------------------
/demo/flask/.dockerignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 | MANIFEST
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 | .pytest_cache/
49 |
50 | # Translations
51 | *.mo
52 | *.pot
53 |
54 | # Django stuff:
55 | *.log
56 | local_settings.py
57 | db.sqlite3
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # Environments
85 | .env
86 | .venv
87 | env/
88 | venv/
89 | ENV/
90 | env.bak/
91 | venv.bak/
92 |
93 | # Spyder project settings
94 | .spyderproject
95 | .spyproject
96 |
97 | # Rope project settings
98 | .ropeproject
99 |
100 | # mkdocs documentation
101 | /site
102 |
103 | # mypy
104 | .mypy_cache/
105 |
106 | .vscode/
107 | settings.json
108 |
--------------------------------------------------------------------------------
/demo/flask/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM tiangolo/uwsgi-nginx-flask:python3.7
2 |
3 | COPY ./requirements.txt /app/requirements.txt
4 | WORKDIR /app
5 | RUN pip install -r requirements.txt
6 |
7 | COPY ./app /app
8 | ENTRYPOINT [ "python" ]
9 | CMD [ "main.py" ]
10 |
--------------------------------------------------------------------------------
/demo/flask/README.md:
--------------------------------------------------------------------------------
1 | # python-microprofile
2 |
3 | ## flask framework
4 |
5 | The module will show an example of how to use the microprofile libraries within a flask context
6 |
7 | to build the docker file run:
8 |
9 | ### build app
10 | * pip install -r requirements.txt
11 |
12 |
13 | ### build docker
14 | docker build -t flask-demo:latest .
15 |
16 | ## run docker
17 | docker run -it --name flask-demo -p 8080:8080 flask-demo:latest
--------------------------------------------------------------------------------
/demo/flask/app/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/tikal-fuseday/python-microprofile/3700db5ca0be6a31df7d8aac8a523f9199484e76/demo/flask/app/__init__.py
--------------------------------------------------------------------------------
/demo/flask/app/main.py:
--------------------------------------------------------------------------------
1 | from flask import Flask, jsonify
2 | import sys
3 | sys.path.append("/Users/slavad/dev/python-microprofile")
4 | import spec.health.microhealth.api as health_api
5 |
6 | # sys.path.append("/Users/mottidadison/work/fuse_022020/python-microprofile/spec/graphql/")
7 | # import graph
8 |
9 | app = Flask(__name__)
10 |
11 |
12 | @app.route('/')
13 | def hello_whale():
14 | app.logger.info('hello_whale')
15 | return 'Whale, Hello there!'
16 |
17 |
18 | @app.route('/health')
19 | def get_health():
20 | return health_api.get_health()
21 |
22 |
23 | @app.route('/health/ready')
24 | def get_health_ready():
25 | return health_api.get_health_ready()
26 |
27 |
28 | @app.route('/health/live')
29 | def get_health_live():
30 | return health_api.get_health_live()
31 |
32 |
33 | if __name__ == '__main__':
34 | app.run(debug=True, host='0.0.0.0', port=8080)
35 |
--------------------------------------------------------------------------------
/demo/flask/app/templates/public/dynamic.html:
--------------------------------------------------------------------------------
1 | {% extends "public/templates/public_template.html" %}
2 |
3 | {% block title %}Profile{% endblock %}
4 |
5 | {% block main %}
6 |
7 |
8 |
9 |
10 |
11 |
Profile
12 |
13 |
14 |
15 |
16 |
17 |
18 | {% endblock %}
--------------------------------------------------------------------------------
/demo/flask/requirements.txt:
--------------------------------------------------------------------------------
1 | Flask==1.1.1
2 | opentracing>=2,<3
3 | jaeger-client
4 | opentracing-utils
5 | basictracer
6 | lightstep
7 | flask_graphql==2.0.1
8 | graphene==2.1.1
9 | dataclasses-json==0.3.8
10 | data
--------------------------------------------------------------------------------
/spec/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/tikal-fuseday/python-microprofile/3700db5ca0be6a31df7d8aac8a523f9199484e76/spec/__init__.py
--------------------------------------------------------------------------------
/spec/config/README.md:
--------------------------------------------------------------------------------
1 | # Microprofile - Config
2 |
3 | Configuration is an essential part of Microprofile specification.
4 | ## Specifications
5 | The full spec can be found here: [1.4 Spec](https://download.eclipse.org/microprofile/microprofile-config-1.4/microprofile-config-spec.pdf).
6 |
7 | Out of the full spec, we are implementing the basics:
8 | * **ConfigSource** abstract class which reads configuration from a single source.
9 | * Each source is defining its ordinal.
10 | * Main configuration that serves values from all sources according to the ordinal. Values from higher ordinal overwrites lower ordinal configuration source.
11 | * Out of the box the default sources includes:
12 | * Command line arguments (default ordinal=400)
13 | * Environment variables (default ordinal=300)
14 | * Properties file (default ordinal=100)
15 |
16 | ## Usage
17 | ### Installation
18 | `pip install microconfig-0.0.1.tar.gz`
19 |
20 | ### Usage
21 | In order to use the default config sources:
22 | ```python
23 | from microconfig.config import Configuration
24 |
25 | conf = Configuration()
26 | val1 = conf.get("key1")
27 | val2 = conf["key2"]
28 | ```
29 |
30 | To use properties file config source:
31 |
32 | ```python
33 | from microconfig.config import Configuration, PropertiesConfigSource
34 |
35 | prop_conf = PropertiesConfigSource("conf.properties")
36 | conf = Configuration(prop_conf)
37 | val1 = conf.get("com.tikal.key1") # From properties file
38 | val2 = conf.get("PATH") # From environment variable
39 | ```
--------------------------------------------------------------------------------
/spec/config/micro-config/microconfig/config.py:
--------------------------------------------------------------------------------
1 | from abc import ABC, abstractmethod
2 |
3 |
4 | class ConfigSource(ABC):
5 |
6 | def __init__(self, ordinal):
7 | self.values = {}
8 | self.ordinal = ordinal
9 |
10 | @abstractmethod
11 | def load(self, *args, **kwargs):
12 | pass
13 |
14 | def get(self, key):
15 | return self.values.get(key)
16 |
17 | def get_ordinal(self):
18 | return self.ordinal
19 |
20 |
21 | class EnvConfigSource(ConfigSource):
22 |
23 | def __init__(self):
24 | super().__init__(500)
25 |
26 | def load(self, *args, **kwargs):
27 | import os
28 | self.values = os.environ.copy()
29 |
30 |
31 | class PropertiesConfigSource(ConfigSource):
32 |
33 | separator = '='
34 |
35 | def __init__(self, filename):
36 | super().__init__(400)
37 | self.filename = filename
38 |
39 | def load(self, *args, **kwargs):
40 | with open(self.filename) as f:
41 |
42 | for line in f:
43 | if PropertiesConfigSource.separator in line:
44 | # Find the name and value by splitting the string
45 | name, value = line.split(PropertiesConfigSource.separator, 1)
46 |
47 | # Assign key value pair to dict
48 | # strip() removes white space from the ends of strings
49 | self.values[name.strip()] = value.strip()
50 |
51 |
52 | class Configuration:
53 |
54 | def __init__(self, *args):
55 | self.configs = []
56 | env_config = EnvConfigSource()
57 | local_configs = {
58 | env_config.get_ordinal(): env_config
59 | }
60 |
61 | for _config in args:
62 | if isinstance(_config, ConfigSource):
63 | local_configs[_config.get_ordinal()] = _config
64 |
65 | for key in sorted(local_configs.keys(), reverse=True):
66 | _config = local_configs[key]
67 | _config.load()
68 | self.configs.append(_config)
69 |
70 | def get(self, key):
71 | for _config in self.configs:
72 | value = _config.get(key)
73 | if value is not None:
74 | return value
75 | return None
76 |
77 | def __getitem__(self, item):
78 | return self.get(item)
79 |
80 |
81 | if __name__ == '__main__':
82 | prop_config = PropertiesConfigSource("conf.properties")
83 | config = Configuration(prop_config)
84 | print(config.get('com.tikalk.key1'))
85 | print(config['PATH'])
86 |
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/spec/config/micro-config/setup.py:
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1 | from setuptools import setup
2 |
3 | setup(
4 | name='microconfig',
5 | version='0.0.1',
6 | packages=['microconfig'],
7 | url='',
8 | license='',
9 | author='haimcohen',
10 | author_email='hcloli@gmail.com',
11 | description=''
12 | )
13 |
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/spec/fault-tolerance/README.md:
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1 | # python-microprofile
2 |
3 | ## fault tolerance
4 |
5 | Fault tolerance allows the developer to define strategies in case of execution failures
6 |
7 | ## retry
8 | Allows the developer to define how many retries will take place before a methiod actually failes.
9 |
10 | ## Fallback
11 | Allows the developer to define a function that will be called in case of failure in the called method.
12 |
13 |
14 |
15 |
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/spec/graphql/README.md:
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1 |
2 | Microprofile Python GraphQL Specification
3 | ==========================================
4 |
5 |
6 | ## Specification:
7 |
8 | GraphiQL is a powerfull graphical UI that enables a smooth and easy API discovery.
9 |
10 | Just navigate to `/graphql` in your browser and you'll see [GraphiQL](https://github.com/graphql/graphiql/)
11 |
12 | 
13 |
14 | ## About
15 |
16 | GraphQL is an open-source data query and manipulation language for APIs, and a runtime for fulfilling queries with existing data.
17 |
18 | It provides an alternative, though not necessarily a replacement for REST.
19 |
20 | GraphQL was developed internally by Facebook in 2012 before being publicly released in 2015.
21 |
22 | ## What is GraphQL?
23 | GraphQL is a query language for your API, and a server-side runtime for executing queries by using a type system you define for your data. GraphQL isn't tied to any specific database or storage engine and is instead backed by your existing code and data. [From [graphql.org](https://graphql.org/learn/)]
24 |
25 | GraphQL is used by many large and small customers including Atlassian, Coursera, Facebook, GitHub, PayPal, Twitter, and https://graphql.org/users/[many more].
26 |
27 |
28 |
29 | * More info: https://en.wikipedia.org/wiki/GraphQL
30 |
31 | * Home page: https://graphql.org/
32 |
33 | * Specification: https://facebook.github.io/graphql/draft/
34 |
35 |
36 |
37 | ## Why GraphQL
38 |
39 | The main reasons developers might want to use GraphQL are:
40 |
41 |
42 |
43 | * Improved data consumption for customers (IoS, Android, Web). Allowing for example to be able to retrieve several types of data in a single request or limiting the response data to exactly the specific data requested.
44 |
45 | * Better analysis of the exhaustiveness of data calls (allowing to know the use of each node) and better manage the deletion of deprecated fields.
46 |
47 | * Advanced developer experience:
48 |
49 | ** The schema defines how the data can be accessed and serves as the contract between the client and the server. Developer teams on both sides can work without further communication,
50 |
51 | ** Native schema introspection enabling to discover the API and to refine the queries on the client-side.
52 |
53 | ** On the client-side, the query language provides a lot of flexibility and efficiency enabling developers to adapt to the constraints of their technical environments (IoS, Android, Web).
54 |
55 |
56 |
57 | ## Why MicroProfile
58 |
59 |
60 | The official purpose of MicroProfile is to optimize development for a microservices architecture and delivers application portability across multiple MicroProfile runtimes.
61 |
62 | GraphQL is already widely used in Microservices architectures as the API Endpoint.
63 |
64 | GraphQL continues to grow in popularity, and as such there should be a specification for GraphQL development.
65 |
66 | MicroProfile is the optimal place to host that standard as it is open, ideally suited for incubating technologies, and has broad reach both in terms of the user community and vendor support.
67 |
68 |
69 |
70 | ## What GraphQL is not
71 |
72 |
73 | This specification will focus on making it easy for developers to create a GraphQL Service/Endpoint and publish it as an API.
74 |
75 | Where the data comes from (NoSQL, Relational DB, another service, etc.) is not the concern of this Proposed Specification.
76 |
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/spec/graphql/src/__init__.py:
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/spec/graphql/src/graph.py:
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1 | from flask import Flask
2 | from flask_graphql import GraphQLView
3 | import graphene
4 | from main import app
5 |
6 | class Query(graphene.ObjectType):
7 | hello = graphene.String(name=graphene.String(default_value="World"))
8 |
9 | def resolve_hello(self, info, name):
10 | return 'Hello ' + name
11 |
12 | schema = graphene.Schema(query=Query)
13 |
14 | @app.route('/graphql/status')
15 | def graph():
16 | return "running..."
17 |
18 | app.add_url_rule('/graphql', view_func=GraphQLView.as_view('graphql', schema=schema, graphiql=True))
19 |
20 |
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/spec/health/README.md:
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1 | # MicroProfile Health
2 |
3 | ## Motivation
4 |
5 | Health checks are used to probe the state of a computing node from another machine (i.e. kubernetes service controller) with the primary target being cloud infrastructure environments where automated processes maintain the state of computing nodes.
6 |
7 | In this scenario, _health checks are used to determine if a computing node needs to be discarded (terminated, shutdown)_ and eventually replaced by another (healthy) instance.
8 |
9 | It’s not intended (although could be used) as a monitoring solution for human operators.
10 |
11 | ## Proposed solution
12 |
13 | The proposed solution breaks down into two parts:
14 |
15 | - A health check protocol and wireformat
16 | - A Python API to implement health check procedures
17 |
18 | ## Detailed design
19 |
20 | #### Protocol
21 |
22 | This project defines a protocol (wireformat, semantics and possible forms of interactions) between system components that need to determine the “liveliness” of computing nodes in a bigger architecture.
23 | A detailed description of the health check protocol can be found in the [companion document](https://github.com/eclipse/microprofile-health/tree/master/spec/src/main/asciidoc/protocol-wireformat.adoc).
24 |
25 | #### API Usage
26 |
27 | The main API to provide health check procedures on the application level is the `HealthCheck` interface:
28 |
29 | ```python
30 | class Healthcheck:
31 |
32 | @abstractmethod
33 | def call(self) -> HealthcheckResponse:
34 | pass
35 | ```
36 |
37 | Applications are expected to provide health check procedures (implementation of a `HealthCheck`), which will be used by the framework or runtime hosting the application to verify the healthiness of the computing node.
38 |
39 | The runtime will `call()` the `HealthCheck` which in turn creates a `HealthCheckResponse` that signals the health status to a consuming end:
40 |
41 | ```python
42 | @dataclass
43 | class HealthcheckResponse:
44 | status: HealthcheckStatus
45 | checks: List[HealthcheckCheck]
46 |
47 | @staticmethod
48 | def up(checks=None):
49 | return HealthcheckResponse(HealthcheckStatus.UP, checks)
50 |
51 | @staticmethod
52 | def down(checks=None):
53 | return HealthcheckResponse(HealthcheckStatus.DOWN, checks)
54 | ```
55 |
56 | #### Constructing `HealthCheckResponse`s
57 |
58 | Application level code is expected to use one of static methods on `HealthCheckResponse` to retrieve a `HealthCheckResponse` object:
59 |
60 | ```python
61 | livelinessResponse = HealthcheckResponse.up('liveliness', {'foo', 'bar'})
62 | ```
63 |
64 | #### Integration with CDI
65 |
66 | Within CDI contexts, beans that implement `HealthCheck` and annotated with `@Health` are discovered automatically and are invoked by the framework or runtime when the outermost protocol entry point (i.e. `http://HOST:PORT/health`) receives an inbound request.
67 |
68 | ```python
69 | @healthcheck
70 | class CheckDiskSpace(Healthcheck) {
71 |
72 | def call() -> HealtchcheckResponse:
73 | [...]
74 |
75 | ```
76 |
77 | #### On the wire
78 |
79 | It's the responsibility of the runtime to gather all `HealthCheckResponse` s for `HealthCheck` s known to the runtime. This means an inbound HTTP request will lead to a series of invocations
80 | on health check procedures and the runtime will provide a composite response, with a single overall status, i.e.:
81 |
82 | ```json
83 | {
84 | "status": "UP",
85 | "checks": [
86 | {
87 | "name": "first-check",
88 | "status": "UP",
89 | "data": {
90 | "key": "foo",
91 | "foo": "bar"
92 | }
93 | },
94 | {
95 | "name": "second-check",
96 | "status": "UP"
97 | }
98 | ]
99 | }
100 | ```
101 |
102 | The link: [companion object](https://github.com/eclipse/microprofile-health/tree/master/spec/src/main/asciidoc/protocol-wireformat.adoc) contains further information on forms of interaction and the wireformat.
103 |
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/spec/health/microhealth/__init__.py:
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/spec/health/microhealth/api.py:
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1 | import json
2 | from collections import defaultdict
3 | from typing import Dict, List
4 |
5 | from demo.flask.app.main import app
6 | from spec.health.microhealth.healthcheck import Healthcheck
7 | from spec.health.microhealth.healthcheck_response import (
8 | HealthcheckSummary,
9 | HealthcheckResponse,
10 | HealthcheckStatus,
11 | EMPTY_HEALTHCHECK_SUMMARY_DOWN,
12 | EMPTY_HEALTHCHECK_SUMMARY_UP)
13 | from spec.health.microhealth.standard_healthchecks import LivelinessHealthcheck, DiskSpaceHealthcheck
14 |
15 | healthchecks_registry: Dict = {}
16 | healthchecks_registry_by_type: Dict[str, List[Healthcheck]] = {}
17 | healthchecker = Healthcheck()
18 |
19 | healthchecks_registry['disk'] = DiskSpaceHealthcheck
20 | healthchecks_registry['live'] = LivelinessHealthcheck
21 | healthchecks_registry_by_type['liveliness'] = [LivelinessHealthcheck]
22 |
23 |
24 | def get_health():
25 | # noinspection PyBroadException
26 | try:
27 | app.logger.debug('/health')
28 | healthcheck_responses = []
29 | for check in healthchecks_registry.values():
30 | healthcheck_responses.append(check().call())
31 | healthcheck_summary = HealthcheckSummary.call(healthcheck_responses)
32 | if healthcheck_summary.status == HealthcheckStatus.DOWN:
33 | return healthcheck_summary.to_json(), 503
34 | else:
35 | return healthcheck_summary.to_json(), 200
36 | except:
37 | app.logger.error('Failed healthcheck', exc_info=True)
38 | return EMPTY_HEALTHCHECK_SUMMARY_DOWN.to_json(), 500
39 |
40 |
41 | def get_health_live():
42 | # noinspection PyBroadException
43 | try:
44 | app.logger.debug('/health/live')
45 | healthcheck_responses = []
46 | for check in healthchecks_registry_by_type.get('liveliness', []):
47 | healthcheck_responses.append(check().call())
48 | healthcheck_summary = HealthcheckSummary.call(healthcheck_responses)
49 | if healthcheck_summary.status == HealthcheckStatus.DOWN:
50 | return healthcheck_summary.to_json(), 503
51 | else:
52 | return healthcheck_summary.to_json(), 200
53 | except:
54 | app.logger.error('Failed healthcheck liveliness', exc_info=True)
55 | return EMPTY_HEALTHCHECK_SUMMARY_DOWN.to_json(), 500
56 |
57 |
58 | def get_health_ready():
59 | # noinspection PyBroadException
60 | try:
61 | app.logger.debug('/health/ready')
62 | healthcheck_responses = []
63 | for check in healthchecks_registry_by_type.get('readiness', []):
64 | healthcheck_responses.append(check().call())
65 | healthcheck_summary = HealthcheckSummary.call(healthcheck_responses)
66 | if healthcheck_summary.status == HealthcheckStatus.DOWN:
67 | return healthcheck_summary.to_json(), 503
68 | else:
69 | return healthcheck_summary.to_json(), 200
70 | except:
71 | app.logger.error('Failed healthcheck ready', exc_info=True)
72 | return EMPTY_HEALTHCHECK_SUMMARY_DOWN.to_json(), 500
73 |
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/spec/health/microhealth/healthcheck.py:
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1 | from abc import abstractmethod
2 |
3 | from spec.health.microhealth.healthcheck_response import HealthcheckResponse
4 |
5 |
6 | class Healthcheck:
7 |
8 | @abstractmethod
9 | def call(self) -> HealthcheckResponse:
10 | pass
11 |
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/spec/health/microhealth/healthcheck_decorators.py:
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1 | import functools
2 | import logging
3 |
4 | from spec.health.microhealth.healthcheck_response import HealthcheckResponse, HealthcheckStatus
5 |
6 |
7 | def healthcheck(func):
8 | @functools.wraps(func)
9 | def wrapper_healthcheck_check(*args, **kwargs) -> HealthcheckResponse:
10 | logging.debug('Running healthcheck check')
11 | res = func(*args, **kwargs)
12 | if res.status == HealthcheckStatus.UP:
13 | logging.debug(f'Healthcheck check {res.name} is UP')
14 | else:
15 | logging.warning(f'Healthcheck check {res.name} is DOWN')
16 | return res
17 |
18 | return wrapper_healthcheck_check
19 |
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/spec/health/microhealth/healthcheck_response.py:
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1 | from dataclasses import dataclass
2 | from enum import Enum
3 | from typing import List
4 |
5 | from dataclasses_json import dataclass_json
6 |
7 |
8 | class HealthcheckStatus(Enum):
9 | UP = 'UP'
10 | DOWN = 'DOWN'
11 |
12 |
13 | @dataclass_json
14 | @dataclass
15 | class HealthcheckResponse:
16 | name: str
17 | status: HealthcheckStatus
18 | data: dict
19 |
20 | @staticmethod
21 | def up(name, data=None):
22 | return HealthcheckResponse(name, HealthcheckStatus.UP, data)
23 |
24 | @staticmethod
25 | def down(name, data=None):
26 | return HealthcheckResponse(name, HealthcheckStatus.DOWN, data)
27 |
28 |
29 | @dataclass_json
30 | @dataclass
31 | class HealthcheckSummary:
32 | status: HealthcheckStatus
33 | checks: List[HealthcheckResponse]
34 |
35 | @staticmethod
36 | def call(checks: List[HealthcheckResponse] = None):
37 | down = any(True for check in checks if check.status != HealthcheckStatus.UP)
38 | if checks and down:
39 | return HealthcheckSummary(HealthcheckStatus.DOWN, checks)
40 | else:
41 | return HealthcheckSummary(HealthcheckStatus.UP, checks)
42 |
43 |
44 | EMPTY_HEALTHCHECK_SUMMARY_UP = HealthcheckSummary(HealthcheckStatus.UP, [])
45 | EMPTY_HEALTHCHECK_SUMMARY_DOWN = HealthcheckSummary(HealthcheckStatus.DOWN, [])
46 |
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/spec/health/microhealth/standard_healthchecks.py:
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1 | import shutil
2 |
3 | from spec.health.microhealth.healthcheck import Healthcheck
4 | from spec.health.microhealth.healthcheck_response import HealthcheckResponse
5 |
6 |
7 | class LivelinessHealthcheck(Healthcheck):
8 | def call(self):
9 | return HealthcheckResponse.up('liveliness', None)
10 |
11 |
12 | class DiskSpaceHealthcheck(Healthcheck):
13 | def call(self):
14 | total_space, used_space, free_space = shutil.disk_usage('/')
15 | healthy = (used_space / total_space) < 0.9
16 | data = {'total': total_space, 'used': used_space, 'free': free_space}
17 | if healthy:
18 | return HealthcheckResponse.up('disk_space', data)
19 | else:
20 | return HealthcheckResponse.down('disk_space', data)
21 |
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/spec/health/setup.py:
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1 | from setuptools import setup
2 |
3 | setup(
4 | name='microprofile-health',
5 | version='0.0.1',
6 | packages=['microhealth'],
7 | url='',
8 | license='',
9 | author='slavad',
10 | author_email='slava.demender@tikalk.com',
11 | description=''
12 | )
13 |
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/spec/metrics/README.md:
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1 | # Python-Microprofile
2 |
3 | Inspired by [Eclipse Microprofile Metrics](https://github.com/eclipse/microprofile-metrics) we want to develop the metrics portion of microprofiling for languages other than Java.
4 | This POC will focus on microprofile-metrics for webservices.
5 |
6 | The metrics will be stored in a local registry and will be exposed to the monitoring server via /metrics route.
7 | Metrics that are exposed need to be configured in the server. On top of the pure metrics, metadata needs to be provided.
8 |
9 | The following three sets of sub-resource (scopes) are exposed.
10 |
11 | base: metrics that all MicroProfile vendors have to provide, these metrics will be exposed under `/metrics/base`
12 |
13 | vendor: vendor specific metrics (optional), these metrics will be exposed under `/metrics/vendor`
14 |
15 | application: application-specific metrics (optional), these metrics will be exposed under `/metrics/application`
16 |
17 | ## Base Metrics
18 | Microprofile divides base-metrics into two categories: [required](https://github.com/eclipse/microprofile-metrics/blob/master/spec/src/main/asciidoc/required-metrics.adoc#required-metrics) and [optional](https://github.com/eclipse/microprofile-metrics/blob/master/spec/src/main/asciidoc/required-metrics.adoc#thread-pool-stats).
19 | As you can see, many of these metrics are Java specific and deal with the JVM. In this POC we will treat all these metrics as optional and instead, try to come up with a broader, more generic set of base-metrics.
20 |
21 | ### CPU Utilization
22 | * CPU usage in percentage
23 | and breakdown by:
24 | * (optional) processes executing in user mode (percentage)
25 | * (optional) processes executing in kernel mode (percentage)
26 |
27 | ### Memory
28 | Two metrics will be published:
29 | * total memory (bytes)
30 | * used memory (bytes)
31 |
32 | ### Disk
33 | Two metrics will be published:
34 | * disk space total (bytes)
35 | * disk space used (bytes)
36 |
37 | ### Network
38 | * packets transmitted, received (count)
39 | * bytes transmitted, received (bytes)
40 | * (optional) packets dropped by the driver on transmit, receive (count)
41 | * (optional) total number of sockets in kernel socket lists (count);
42 | * (optional) TCP sockets currently in use (count);
43 | * (optional) UDP sockets currently in use (count)
44 |
45 | ## Vendor Metrics (Optional)
46 | // TODO
47 |
48 | ## Application Metrics (Optional)
49 | // TODO
50 |
51 | ## Metrics Tags
52 | We want to be able to standartize metric-names, meaning, for an outbound HTTP call we'll want to publish a metric called:
53 | `outbound_http_latency_seconds`. The way to differentiate between different outbound calls will be by tags (labels).
54 |
55 | Tags are also useful to expose: app-name, instance-id, cluster-name, service-name, region/zone and etc.
56 |
57 | Example: `outbound_http_latency_seconds{region="eu-west-1",path="/foo"}`
58 |
59 | ## Metadata
60 | Immutable static Metric information to be provided by the implementor, and which includes:
61 | * metric name
62 | * metric unit
63 | * metric type (counter, gauge, timer, histogram and etc)
64 | * description (optional)
65 | * displayName (optional)
66 | * reusable (optional): If set to true, then it is allowed to register a metric multiple times under the
67 | same MetricID.
68 |
69 | The MetricID consists of the metric’s name and tags (if supplied). This is used by the MetricRegistry
70 | to uniquely identify a metric and its corresponding metadata.
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/spec/metrics/src/counter.py:
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1 | from spec.metrics.src.metric import Metric
2 |
3 |
4 | class Counter(Metric):
5 | def __init__(self, *args, **kwargs):
6 | super(Counter, self).__init__(*args, **kwargs)
7 | self._counter = 0
8 |
9 | def inc(self, n=1):
10 | self._counter += n
11 |
12 | def get_count(self):
13 | return self._counter
14 |
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/spec/metrics/src/metric.py:
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1 | import abc
2 | import six
3 |
4 |
5 | @six.add_metaclass(abc.ABCMeta)
6 | class Metric:
7 | def __init__(self, name, unit, metric_type, desc='', display_name='', reusable=False):
8 | self.name = name
9 | self.unit = unit
10 | self.type = metric_type
11 | self.desc = desc
12 | self.display_name = display_name
13 | self.reusable = reusable
14 |
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/spec/metrics/src/metric_types.py:
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1 | from enum import Enum
2 |
3 |
4 | class MetricTypes(Enum):
5 | # A concurrent gauge counts the number of parallel invocations of
6 | # a target (method). Upon entering the target the value is increased.
7 | # It is decreased again upon exiting the target.
8 | CONCURRENT_GAUGE = 'concurrent gauge'
9 |
10 | # A Counter monotonically increases its values.
11 | # An example could be the number of Transactions committed.
12 | COUNTER = 'counter'
13 |
14 | # A Gauge has values that 'arbitrarily' goes up/down at each
15 | # sampling. An example could be CPU load
16 | GAUGE = 'gauge'
17 |
18 | # A Meter measures the rate at which a set of events occur.
19 | # An example could be amount of Transactions per Hour.
20 | METERED = 'meter'
21 |
22 | # A Histogram calculates the distribution of a value.
23 | HISTOGRAM = 'histogram'
24 |
25 | # A timer aggregates timing durations and provides duration
26 | # statistics, plus throughput statistics
27 | TIMER = 'timer'
28 |
29 | # A simple timer aggregates timing durations
30 | SIMPLE_TIMER = 'simple timer'
31 |
32 | # Invalid - Placeholder
33 | INVALID = 'invalid'
34 |
35 | @staticmethod
36 | def metric_from(string):
37 | for t in MetricTypes:
38 | if t.value == string:
39 | return t
40 | return MetricTypes.INVALID
41 |
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/spec/metrics/src/metric_units.py:
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1 | from enum import Enum
2 |
3 |
4 | class MetricUnits(Enum):
5 | NONE = "none"
6 |
7 | BITS = "bits"
8 |
9 | KILOBITS = "kilobits"
10 |
11 | MEGABITS = "megabits"
12 |
13 | GIGABITS = "gigabits"
14 |
15 | KIBIBITS = "kibibits"
16 |
17 | MEBIBITS = "mebibits"
18 |
19 | GIBIBITS = "gibibits"
20 |
21 | BYTES = "bytes"
22 |
23 | KILOBYTES = "kilobytes"
24 |
25 | MEGABYTES = "megabytes"
26 |
27 | GIGABYTES = "gigabytes"
28 |
29 | NANOSECONDS = "nanoseconds"
30 |
31 | MICROSECONDS = "microseconds"
32 |
33 | MILLISECONDS = "milliseconds"
34 |
35 | SECONDS = "seconds"
36 |
37 | MINUTES = "minutes"
38 |
39 | HOURS = "hours"
40 |
41 | DAYS = "days"
42 |
43 | PERCENT = "percent"
44 |
45 | PER_SECOND = "per_second"
46 |
47 | @staticmethod
48 | def unit_from(string):
49 | for t in MetricUnits:
50 | if t.value == string:
51 | return t
52 | return MetricUnits.NONE
53 |
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/spec/metrics/src/tag.py:
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1 | import re
2 |
3 |
4 | # Name of the Tag. Must match the regex [a-zA-Z_][a-zA-Z0-9_]#
5 | # A required field which holds the name of the tag.
6 | class Tag:
7 | PATTERN = re.compile(r'[a-zA-Z_][a-zA-Z0-9_]')
8 |
9 | def __init__(self, name, value):
10 | # Constructs the Tag object with the given tag name and tag value
11 | #
12 | # tagName The tag name, must match the regex [a-zA-Z_][a-zA-Z0-9_]
13 | # tagValue The tag value
14 | # raises IllegalArgumentException If the tagName does not match [a-zA-Z_][a-zA-Z0-9_]
15 | if not name or not value:
16 | raise Exception('Invalid arguments. Tag name: [{}], Tag value: [{}]'.format(name, value))
17 |
18 | if not Tag.PATTERN.match(name):
19 | raise Exception('Invalid Tag name: [{}]'.format(name))
20 |
21 | self.name = name
22 | self.value = value
23 |
24 | def get_tag_name(self):
25 | return self.name
26 |
27 | def get_tag_value(self):
28 | return self.value
29 |
30 | def __eq__(self, other):
31 | if not isinstance(other, Tag):
32 | return False
33 |
34 | return self.name == other.name and self.value == other.value
35 |
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/spec/metrics/src/tests/test_counter.py:
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1 | import unittest
2 | from spec.metrics.src.counter import Counter
3 | from spec.metrics.src.metric_units import MetricUnits
4 | from spec.metrics.src.metric_types import MetricTypes
5 |
6 |
7 | class TestCounter(unittest.TestCase):
8 | def test_creation(self):
9 | counter = Counter('test_metric', MetricUnits.NONE, MetricTypes.COUNTER)
10 | self.assertEqual(counter.get_count(), 0)
11 |
12 | def test_inc(self):
13 | counter = Counter('test_metric', MetricUnits.NONE, MetricTypes.COUNTER)
14 | counter.inc()
15 | self.assertEqual(counter.get_count(), 1)
16 | counter.inc(10)
17 | self.assertEqual(counter.get_count(), 11)
18 |
19 |
20 | if __name__ == '__main__':
21 | unittest.main()
22 |
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/spec/metrics/src/tests/test_metric_types.py:
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1 | import unittest
2 | from spec.metrics.src.metric_types import MetricTypes
3 |
4 |
5 | class TestMetricTypes(unittest.TestCase):
6 | def test_metric_from(self):
7 | self.assertEqual(MetricTypes.CONCURRENT_GAUGE, MetricTypes.metric_from('concurrent gauge'))
8 |
9 | def test_metric_from_not_exists(self):
10 | self.assertEqual(MetricTypes.INVALID, MetricTypes.metric_from('bla'))
11 |
12 |
13 | if __name__ == '__main__':
14 | unittest.main()
15 |
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/spec/metrics/src/tests/test_metric_units.py:
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1 | import unittest
2 | from spec.metrics.src.metric_units import MetricUnits
3 |
4 |
5 | class TestMetricUnits(unittest.TestCase):
6 | def test_metric_from(self):
7 | self.assertEqual(MetricUnits.GIBIBITS, MetricUnits.unit_from('gibibits'))
8 |
9 | def test_metric_from_not_exists(self):
10 | self.assertEqual(MetricUnits.NONE, MetricUnits.unit_from('bla'))
11 |
12 |
13 | if __name__ == '__main__':
14 | unittest.main()
15 |
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/spec/metrics/src/tests/test_tag.py:
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1 | import unittest
2 | from spec.metrics.src.tag import Tag
3 |
4 |
5 | class TestTag(unittest.TestCase):
6 | def test_create_instance(self):
7 | tag = Tag('name', 'value')
8 | self.assertEqual(tag.get_tag_name(), 'name')
9 | self.assertEqual(tag.get_tag_value(), 'value')
10 |
11 | def test_create_raises_exception(self):
12 | bad_name = lambda: Tag('_%&$#@', 'value')
13 | self.assertRaises(Exception, bad_name)
14 |
15 | def test_equals(self):
16 | tag1 = Tag('name', 'value')
17 | tag2 = Tag('name', 'value')
18 | self.assertEqual(tag1, tag2)
19 |
20 |
21 | if __name__ == '__main__':
22 | unittest.main()
23 |
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/spec/open-api/README.md:
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1 | # python-microprofile
2 |
3 | ## open-api
4 |
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/spec/open-tracing/README.md:
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1 | # python-microprofile
2 |
3 | ## open trace
4 |
5 | ## Introduction (from microprofile-opentracing-spec-1.3.3)
6 | Distributed tracing allows you to trace the flow of a request across service boundaries.
7 | This specification specifically addresses the problem of making it easy to instrument services with distributed tracing function, given an existing distributed tracing system in the environment.
8 | This specification specifically does not address the problem of defining, implementing, or configuring the underlying distributed tracing system. The proposal assumes an environment where all services use a common OpenTracing implementation (all Zipkin compatible, all Jaeger compatible, ...).
9 |
10 | ## Rationale (from microprofile-opentracing-spec-1.3.3)
11 | In order for a distributed tracing system to be effective and usable, two things are required
12 | 1. The different services in the environment must agree on the mechanism for transferring correlation ids across services.
13 | 2. The different services in the environment should produce their trace records in format that is consumable by the storage service for distributed trace records.
14 | Without the first, some services will not be included in the trace records associated with a request. Without the second, custom code would need to be written to present the information about a full request flow.
15 |
16 |
17 | There are existing distributed tracing systems that provide a server for distributed trace record storage and viewing, and application libraries for instrumenting microservices. The problem is that the different distributed tracing systems use implementation specific mechanisms for propagating correlation IDs and for formatting trace records, so once a microservice chooses a distributed tracing implementation library to use for its instrumentation, all other microservices in the environment are locked into the same choice.
18 |
19 |
20 | https://github.com/eclipse/microprofile-opentracing/releases
21 |
22 | ## The Challenge
23 | Use the opentrace standard (including python library) and integrate in python with the microprofile standard and annotations
24 | Currently microprofile only supports opentracing, what opentracing will be merging with opencensus to add metrics as well.
25 |
26 | https://opentracing.io/specification/
27 |
28 | ## Implementation
29 | The implementation will based on https://github.com/opentracing/opentracing-python
30 |
31 | The following library has been found: opentracing-utils
32 |
33 | * No extrenal dependencies, only .
34 | * No threadlocals. Either pass spans explicitly or fallback to callstack frames inspection!
35 | * Context agnostic, so no external context implementation dependency (no Tornado, Flask, Django etc …).
36 | * Try to be less verbose - just add the @trace decorator.
37 | * Could be more verbose when needed, without complexity - just accept **kwargs and get the span passed to your traced functions via @trace(pass_span=True).
38 | * Support asyncio/async-await coroutines. (drop support for py2.7)
39 | * Support gevent
40 | * Ability to add OpenTracing support to external libs/frameworks/clients:
41 | Django (via OpenTracingHttpMiddleware)
42 | Flask (via trace_flask())
43 | Requests (via trace_requests())
44 | SQLAlchemy (via trace_sqlalchemy())
45 |
46 | ## API Examples
47 | ```python
48 | # Normal traced function
49 | @trace()
50 | def trace_me():
51 | pass
52 |
53 | # Traced function with access to created span in ``kwargs``
54 | @trace(operation_name='user.operation', pass_span=True)
55 | def user_operation(user, op, **kwargs):
56 | current_span = extract_span_from_kwargs(**kwargs)
57 |
58 | current_span.set_tag('user.id', user.id)
59 |
60 | # Then do stuff ...
61 |
62 | # trace_me will have ``current_span`` as its parent.
63 | trace_me()
64 |
65 | # Traced function using ``follows_from`` instead of ``child_of`` reference.
66 | @trace(use_follows_from=True)
67 | def trace_me_later():
68 | pass
69 |
70 |
71 | # Start a fresh trace - any parent spans will be ignored
72 | @trace(operation_name='epoch', ignore_parent_span=True)
73 | def start_fresh():
74 |
75 | user = {'id': 1}
76 |
77 | # trace decorator will handle trace heirarchy
78 | user_operation(user, 'create')
79 |
80 | # trace_me will have ``epoch`` span as its parent.
81 | trace_me()
82 |
83 | ```
84 |
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/spec/rest-client/README.md:
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1 | # python-microprofile
2 |
3 | ## rest config client
4 |
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