System testing with Pytest, Docker, and Flask

The composability of fixtures in pytest is an improvement over traditional xUnit setup/teardown, reducing the incentive to commit testing crimes such as multi-stage and stepwise tests. This is great out of the box for unit tests, but I’m going to show how to combine the power of pytest fixtures with Docker to build high-level integration tests for microservices or other complex systems with multiple components. I’ll then build on that to show how to embed mock web services written with Flask right into the test code.

With a sample Java application that makes use of some external resources to offer a data processing service I’ll first quick an overview of Pytest, Docker, and Flask. Then I’ll mix some pre-built code with live test coding to demonstrate how to build high-level system tests which spin up the application and its dependencies in Docker. I’ll then mock one of the external dependencies using Flask, allowing the test to control and verify interaction between the system components. Finally I’ll show how to wrap the Flask application in a WSGI middleware that lets the test inspect interaction with the mocked service.

From a learning and development point of view, building your own is better than re-using someone else’s code so I’ll show how the support code for these features is relatively simple and how the audience can build it themselves to exactly meet their own needs. And I’ll do it all with a sense of fun, a joke or two and maybe a little storytelling.

#docker #python #flask #testing

What is GEEK

Buddha Community

System testing with Pytest, Docker, and Flask

The composability of fixtures in pytest is an improvement over traditional xUnit setup/teardown, reducing the incentive to commit testing crimes such as multi-stage and stepwise tests. This is great out of the box for unit tests, but I’m going to show how to combine the power of pytest fixtures with Docker to build high-level integration tests for microservices or other complex systems with multiple components. I’ll then build on that to show how to embed mock web services written with Flask right into the test code.

With a sample Java application that makes use of some external resources to offer a data processing service I’ll first quick an overview of Pytest, Docker, and Flask. Then I’ll mix some pre-built code with live test coding to demonstrate how to build high-level system tests which spin up the application and its dependencies in Docker. I’ll then mock one of the external dependencies using Flask, allowing the test to control and verify interaction between the system components. Finally I’ll show how to wrap the Flask application in a WSGI middleware that lets the test inspect interaction with the mocked service.

From a learning and development point of view, building your own is better than re-using someone else’s code so I’ll show how the support code for these features is relatively simple and how the audience can build it themselves to exactly meet their own needs. And I’ll do it all with a sense of fun, a joke or two and maybe a little storytelling.

#docker #python #flask #testing

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

• Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
• Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
• While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

Ways to Test Microservices

It is easy to fall into the trap of making testing microservices complicated, but there are ways to avoid this problem. Testing microservices doesn’t have to be complicated at all when you have the right strategy in place.

There are several ways to test microservices too, including:

• Unit testing: Which allows developers to test microservices in a granular way. It doesn’t limit testing to individual microservices, but rather allows developers to take a more granular approach such as testing individual features or runtimes.
• Integration testing: Which handles the testing of microservices in an interactive way. Microservices still need to work with each other when they are deployed, and integration testing is a key process in making sure that they do.
• End-to-end testing: Which⁠—as the name suggests⁠—tests microservices as a complete app. This type of testing enables the testing of features, UI, communications, and other components that construct the app.

What’s important to note is the fact that these testing approaches allow for asynchronous testing. After all, asynchronous development is what makes developing microservices very appealing in the first place. By allowing for asynchronous testing, you can also make sure that components or microservices can be updated independently to one another.

#blog #microservices #testing #caylent #contract testing #end-to-end testing #hoverfly #integration testing #microservices #microservices architecture #pact #testing #unit testing #vagrant #vcr

Docker Explained: Docker Architecture | Docker Registries

Following the second video about Docker basics, in this video, I explain Docker architecture and explain the different building blocks of the docker engine; docker client, API, Docker Daemon. I also explain what a docker registry is and I finish the video with a demo explaining and illustrating how to use Docker hub

In this video lesson you will learn:

• What is Docker Host
• What is Docker Engine
• Learn about Docker Architecture
• Learn about Docker client and Docker Daemon
• Docker Hub and Registries
• Simple demo to understand using images from registries

#docker #docker hub #docker host #docker engine #docker architecture #api

Software Testing 101: Regression Tests, Unit Tests, Integration Tests

Automation and segregation can help you build better software
If you write automated tests and deliver them to the customer, he can make sure the software is working properly. And, at the end of the day, he paid for it.

Ok. We can segregate or separate the tests according to some criteria. For example, “white box” tests are used to measure the internal quality of the software, in addition to the expected results. They are very useful to know the percentage of lines of code executed, the cyclomatic complexity and several other software metrics. Unit tests are white box tests.

#testing #software testing #regression tests #unit tests #integration tests

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

• Master
• Model
• MSDB
• Tempdb
• Resource

Master

• This database contains all the System level Information in SQL Server. The Information in form of Meta data.
• Because of this master database, we are able to access the SQL Server (On premise SQL Server)

Model

• This database is used as a template for new databases.
• Whenever a new database is created, initially a copy of model database is what created as new database.

MSDB

• This database is where a service called SQL Server Agent stores its data.
• SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.

TempDB

• The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
• User can create their own version of temporary tables and those are stored in Tempdb.
• But this database is destroyed and recreated every time when we restart the instance of SQL Server.

Resource

• The resource database is a hidden, read only database that holds the definitions of all system objects.
• When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database