A Fine Slice Of SQL Server
Xuan Nguyen Truong writes some tests:
API testing is an essential aspect of software development, ensuring that your application’s endpoints are functioning correctly and reliably. In this guide, we’ll introduce you to implement API testing in Python with Pytest and the Requests library.
I’m a big fan of pytest, as it makes testing in Python so much easier. There’s not a lot of ceremony involved in writing tests and it’s easy to see what’s failing during tests.
Leave a CommentLauren Harold reminds us that time has value and opportunity cost exists:
Open source frameworks like Selenium, Cypress, and Playwright are the first stop for most startups when they automate their end-to-end tests. This process generally happens early, according to our research, with 69% of software teams with just 6-10 developers automating their testing.
But make no mistake. We recently surveyed developers and engineering leaders from around the globe to understand how current practices impact software velocity. One thing that immediately became apparent was that open source testing frameworks are “free,” but their costs snowball once you start adding testing infrastructure, plugins, and test management software.
The proposed solution in the article is no-code end-to-end testing, though that often ends up being brittle like other end-to-end testing solutions, and a huge challenge for complicated flows.
Leave a CommentJared Westover goes on a quest:
Given modern hardware, you might hear that the default setting of 5 for the Cost Threshold for Parallelism (CTFP) is far too low. However, people are left with a decision: Should they change it or leave it alone? If I change it and the performance gets worse, I’ll be left with egg on my face. What exactly is the benefit of increasing it, especially for smaller-cost queries?
Read on to learn more about what Cost Threshold for Parallelism is, how you can set it, and a simple example of how the setting can affect you. Jared also has some links to great resources that I highly recommend you check out.
Comments closedOlivier Van Steenlant runs a test:
In the previous data recipe, Create a Test Class & First Unit Test with tSQLt, we created our very first T-SQL Unit Test to test the database collation. In this data recipe, we will test the execution of a Stored Procedure. Specifically, we will validate what happens when a new User is added to the user dimension.
Click through to see how it all works.
Comments closedAnton Belyaev shows off an open-source utility:
Let’s take a Telegram bot that forwards requests to the OpenAI API and returns the result to the user as an example. If the request to OpenAI violates the system’s security rules, the client will be notified. Additionally, a message will be sent to Kafka for the behavioral control system so that the manager can contact the user, explain that their request was too sensitive even for our bot, and ask them to review their preferences.
The interaction contracts with services are described in a simplified manner to emphasize the core logic. Below is a sequence diagram demonstrating the application’s architecture. I understand that the design may raise questions from a system architecture perspective, but please approach it with understanding — the main goal here is to demonstrate the approach to writing tests.
Read on to see how it all works, as well as links to Anton’s GitHub repo for testing in Kafka.
Comments closedMika Sutinen builds some data:
I recently had a project where I needed quickly to generate some realistic looking test data to PostgreSQL database. While I often like to go for ready-made solutions, this felt like a good opportunity to stretch my coding muscles and develop it myself. Moreover, this seemed like a fun puzzle to solve, and I could probably use the same solution later on elsewhere.
Click through for a description of the generator, as well as a link to Mika’s GitHub repo. Taking a quick peek at it, it does appear that you could probably use this for other data platforms like SQL Server with very limited modification.
Comments closedTesting code using automated tools is common throughout the software development industry. This technique can improve the quality of the code you write as a data scientist. Testing helps refine your code, supports redesign, prevents errors, and makes it harder to write single-use code.
Here, we introduce the pytest framework and show how it can be used to test Python functions. If you don’t use a testing framework as part of your daily workflow, try experimenting with the techniques presented here the next time you write or extend a function.
I am a big fan of pytest because it strikes what I consider to be a great balance between convention and customization. There’s very little administrative overhead to creating test classes and test cases, so tests are easy to build and can it’s trivial to run a test suite or a specific part of one.
Comments closedOlivier Van Steenlandt continues a series on database testing:
We have set up our tSQLt Database Project in the previous data recipe, Create a SSDT Project Template based on your Database Project. Now it’s time to dive into the wonderful world of tSQLt Unit Testing. In the meantime, I have added my data warehouse to my SSDT Solution and added this project as a Database Reference to my Unit Testing Database Project. If you are unsure how to do this, you can find all the information you need in my previous data cookbook which you can access via the following link: Getting Started With Database Projects & Azure DevOps.
Read on for a walkthrough of how to do this.
Comments closedOlivier Van Steenlandt provides an overview of adding tSQLt to a Visual Studio database project:
As a first step in the process, we’re going to create a new Database Project, in my case, I will be calling my Database Project AdventureWorksDW_UnitTesting and my solution AdventureWorks.
If you are not sure how to set up a Database Project in Visual Studio from scratch, don’t worry, you can follow the step-by-step data recipe I released a while ago, Getting Started with Database Projects and Version Control
Read on to learn more about how to add the tSQLt objects and eliminate cross-database reference issues.
Comments closedThuy covers why exploratory testing is important:
Exploratory Testing is a software testing method that testers use to explore, find, and test features, bugs, or issues in an application freely and without the need for a prior testing plan. In exploratory testing, the tester will focus on freely working with the app as a real user and trying to find bugs and issues without following a specific test scenario.
Exploratory testing is a type of testing in which test cases are not created beforehand, but testers can test the system quickly. They can jot down ideas about what needs to be checked before performing the test. The focus of exploratory testing focuses more on testing as a “thinking” activity that explores new cases that do not follow the mainstream activity.
It’s amazing (and dismaying) how many bugs you can find simply by clicking around. The tricky part about exploratory testing is not actually finding bugs, but keeping track of your actions so that a developer knows how to fix the bugs you’ll inevitably find.
Comments closed