Learning to Think In Code

Week One: This week was a good introduction to how computers really work behind the scenes. I’ve used operating systems my whole life without ever thinking about what’s actually going on when I open an app or run a program. The fact that the OS is the connection point between hardware and software made me realize why it is so important to speak about it. It manages memory, controls processes, handles input and output, and basically keeps everything running smoothly. Without an OS, a computer would just be a pile of hardware that doesn’t know what to do. Beside that, we took a look at computer architecture too which described the interaction of CPU, memory, and storage. I had a rough understanding of the functions of these components already, but this week was enlightening for me. It was like a visualization of the data flow between the processor and the memory, and the instruction execution was like a fine step by step movement of the machine. The lesson about binary, decimal, and hexa...

Briefly summarize the what you consider to be the three (3) most important things you learned in this course. One of the most important things I have learned in this course was how databases actually work behind the scenes, from how data is structured with tables and relationships to how SQL brings it all together. I also learned how normalization improves efficiency and prevents redundancy, which made database design make a lot more sense. I learned how to split up work accordingly as a group efficiently and how to trouble shoot databases, as well as help my group trouble shoot. I learned how SQL and Java can be intertwined as well, which was interesting to learn about and will be useful in my career later! Lastly, I gained a stronger understanding of how to use joins and queries to pull meaningful information from data, which I know will be really valuable for future software projects.

Comparing MongoDB and MySQL: Similarities between MongoDB and MySQL: Both MongoDB and MySQL are popular database management systems used to store and manage data efficiently. They support indexing, replication, and querying to enhance performance and reliability. Each allows for scalable data storage and can be integrated with various programming languages. Plus both have powerful community support comprehensive documentation and are widely used in the web development environment. Differences between MongoDB and MySQL: MongoDB is a no sql database that stores data in flexible, JSON like documents, while MySQL is a relational database that uses structured tables with predefined schemas. MongoDB results in the processing of unstructured or rapidly changing data where MySQL confirms the consistency of strict relationships. MongoDB is more easily scale while MySQL is often preferred for structured data and complex connections. When to Choose One over the Other: MongoDB is ideal for applica...

 Summarize what you have learned this week. This week, I learned how to connect a MySQL database to a Java web application, which has really contributed to my understanding of the back end of web development. Lab 17 prepared the groundwork to form and use SQL queries to interact via data, as Lab 19 is built on that concept by implementing everything into a working web app. Setting up the JDBC connection and testing different inputs taught me how databases, applications, and servers work together efficiently. Concepts covered in the quiz also reinforced my understanding of primary and foreign keys, database normalization, and entity relationships, helping me see how database structure directly affects how information is retrieved and managed in a web application.  I am really enjoy these concepts, working on databases are becoming more comfortable and exciting to work with!

The web site   "Use the Index Luke"  has a page on "slow indexes".    https://use-the-index-luke.com/sql/anatomy/slow-indexes Links to an external site.   If indexes are supposed to speed up performance of query,  what does the author mean by a slow index?  In the article "Slow Indexes" from Use the Index, Luke! , the author explains that not every index lookup is automatically fast. A "slow index" happens when the database still has to do extra work after finding the right index entries. An example of this would be if there were multiple matching rows, the system has to follow the leaf node chain, and then access the table data for each match. These table blocks might be scattered, which increases input and output operations with the storage system and slow things down. In other words, the index itself is not corrupt, it just contains such a huge amount of data that it makes the query run slower than would have been expected, still accessing the...