Performance Result Filter Microservice

Introduction

This microservice is used to filter performance response and requests and reads the data from performance microservice. When used in the Swiftops it requires root-service on top of it which passes the query as an input to this service. This can also be used as an template to create new microservices. To create new microservice just create a similar service and register the same in the mongo db by using registartion-service.

service apis :

• performance_report_filter

What does performance_report_filter micro-service do?

If release is given in the input then compare the given release with the default release configured in mongo db else compare with the latest release performance report with its previous release and pass the same release to the base microservice which returns the performace reports url. This url is the output of this filter service.

Pre-Requisite

1. python 3.6.0 or above version.
2. docker Refer Install Docker documentation.
3. [mongo-db] (https://www.mongodb.com/)

Installation

Checkout Repository

$git clone https://github.com/swiftops/performance-result-filter-service.git

Configuration

Steps :

1. Open system.properties edit consul ip
2. Open config.ini and edit <auth_token> and <user_id>.

1. Deploy inside Docker

Steps to start microservice

Once done with the pre-requisite exceute below command to start perfservice microservice

docker build -t <image-name>
docker run -p 5005:5005 -v 8086:8086 --name ms-perfservice -d <image-name>

How to use

In order to call above microservices. we just need to hit below URL from the browser

http://<MACHINE-IP>/perfservice/performance_report_filter

2. On Commit Auto-deploy on specific server.

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To autodeploy your docker container based service on server used below steps

• You need to configure Gitlab Runner to execute Gitlab CI/CD Pipeline. See Gitlab Config

As soon as you configure runner auto deployment will start as you commited the code in repository. refer .gitlab-ci.yml file.

3. Deploy on local environment.

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Pre-Requisite

• Open system.properties edit consul ip

3. Create Virtual Environment

Virtualenv is the easiest and recommended way to configure a custom Python environment for your services. To install virtualenv execute below command:

$pip3 install virtualenv

You can check version for virtual environment version by typing below command:

$virtualenv --version

Create a virtual environment for a project:

$ cd <my_project_folder>
$ virtualenv virtenv

virtualenv virtenv will create a folder in the current directory which will contain the Python executable files, and a copy of the pip library which you can use to install other packages. The name of the virtual environment (in this case, it was virtenv) can be anything; omitting the name will place the files in the current directory instead.

This creates a copy of Python in whichever directory you ran the command in, placing it in a folder named virtenv.

You can also use the Python interpreter of your choice (like python3.6).

$virtualenv -p /usr/bin/python3.6 virtenv

To begin using the virtual environment, it needs to be activated:

$ source virtenv/bin/activate

The name of the current virtual environment will now appear on the left of the prompt (e.g. (virtenv)Your-Computer:your_project UserName$) to let you know that it’s active. From now on, any package that you install using pip will be placed in the virtenv folder, isolated from the global Python installation. You can add python packages needed in your microservice decelopment within virtualenv.

Install python module dependanceies

pip install -r requirements.txt

To start microservice

python services.py

To access Microservice

e.g http://<MACHINE-IP>/perfservice/performance_report_filter

Architechture

Flask

Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions that can add application features as if they were implemented in Flask itself. http://flask.pocoo.org/docs/1.0/quickstart/

Gunicorn

The Gunicorn "Green Unicorn" (pronounced gee-unicorn)[2] is a Python Web Server Gateway Interface (WSGI) HTTP server.

Features

• Natively supports [WSGI] (https://wsgi.readthedocs.io/en/latest/what.html) , [web2py] (http://www.web2py.com/) and [Django] (https://www.djangoproject.com/).
• Automatic worker process management
• Simple Python configuration
• Multiple worker configurations
• Various server hooks for extensibility
• Compatible with Python 2.6+ and Python 3.2+[4] http://docs.gunicorn.org/en/stable/configure.html