javierron
commented
1 month ago @florianim Thanks for the submission!
Closed florianim closed 1 month ago
javierron
commented
1 month ago @florianim Thanks for the submission!
peremateu
commented
1 month ago Authors: Peiyang Zheng and Florian Jerome Immig Feedback by: Pere Mateu Raventós and Siham Shahoud
Date Received: 2024-10-08 at 15:35. Number of words: 635
We certify that generative AI, incl. ChatGPT, has not been used to write this feedback. Using generative AI without permission is considered academic misconduct.
We have the pleasure of going through the executable tutorial “Database visualisation with Sampler” provided by the authors Zheng and Immig via KillerCoda and Github. And we would like to thank them for giving us the chance to provide our feedback.
Our feedback is aimed at providing constructive, actionable insights on both strengths and areas for improvement, while also suggesting ways to make the tutorial even more engaging and comprehensive.
One of the most significant strengths of this tutorial is its clarity and structure from the beginning to the end. The introduction provides a well defined overview which contains clear intended outcomes. This helps users understand from the beginning what they will achieve by the end of the tutorial, providing clear goals and direction. Moreover, the tutorial’s importance in Devops is clearly motivated, stating that it is sometimes hard to set up monitoring tools. And we think that is an important point, as it addresses a real challenge many Devops developers face.
In addition, the tutorial has appropriate grammar, spelling, and punctuation, making it easy for users to read and understand the content. Overall, the tutorial is easy to follow, providing flexibility and satisfaction for the reader. It does not require users to open complex or paid accounts for the grader, enhancing accessibility. Additionally, the tutorial is illustrated with informative figures, such as when installing PostgreSQL, the authors attached a figure to make sure the user has installed the database correctly. And that actually helps the reader to understand the tutorial in an effective manner.
While the tutorial has many strengths, there are a few areas where improvements could be made to enhance the overall learning experience.
Lack of background: Firstly, the tutorial assumes a certain level of prior knowledge in database management. The target of this tutorial are computer science students in master level, who might not have worked much with data science before. Therefore, we think there is a lack of background about the topic.
Lack of context: Some of the commands used throughout the tutorial are not explained in sufficient detail. For example, when demonstrating how to create a new database and user, there is no explanation of the commands used to give or remove privileges.
Installing the sampler tool: The tutorial provides commands to install the Sampler tool but does not include descriptions of what each command does. This may leave users confused about the purpose of each step.
So, to address the issues we found, we first suggest adding a paragraph or a section in the introduction where you can find some background. To do that you can explain what is the basic process in which you should use the sampler tool.
Additionally, if you introduce data science terms, make sure to explain them. For example, when you talk about monitoring, you should think that not everyone knows what it is like, so in order to get your message clear, you should point out what the practices monitoring are?
The way to improve the lack of context would be to explain a bit at least the commands that might feel a bit confusing and not trivial, such as when using the syntax of a specific tool, like MySQL. In addition, to improve clarity and structure we suggest writing the title of the figure and number.
The material we can give you is related to the background we were talking about. In this post they explain clearly the concept with a bit of history and clear examples. It also gives the key points included in database monitoring in a nice structure. This way you can get ideas on how to explain what database monitoring is, as well as the concepts around it.
Assignment Proposal
Title
Database Visualization with Sampler
Names and KTH ID
Deadline
Category
Description
This tutorial provides a step-by-step guide on using Sampler to visualize data from databases including MySQL, PostgreSQL and MongoDB. The tutorial covers the installation and setup of Sampler, configuration of database, and creating a YAML configuration file to visualize various metrics such as the number of records, data insertion rate, and database size. By using Sampler, users can create real-time terminal-based dashboards that offer insights into their database's performance and status.
The tutorial will demonstrate how to configure different visual components like barcharts, runcharts, sparklines, and textboxes to provide a comprehensive view of database metrics. This executable tutorial is designed to give users practical experience in setting up and using Sampler to monitor and visualize their databases effectively.
Relevance
Sampler is a lightweight, terminal-based visualization tool that can be easily set up without the complexity of traditional monitoring systems. This tutorial will show how to leverage Sampler to create a customizable and interactive dashboard that helps users gain insights into their database's performance, making it easier to identify and troubleshoot issues. This aligns with the DevOps principles of observability and monitoring, enabling teams to maintain high levels of service quality and system reliability.
Killercoda: https://killercoda.com/florianim/scenario/sampler-database GitHub: https://github.com/florianim/sampler-database-tutorial