CST370 - Week 6 Reflection

 Week 6 Learning Recap

        Week 6 of CST370 revealed many clear-cut rules and hands-on navigation tools for the handful of data structures covered, including but not limited to: balanced binary search trees (AVL and 2-3 trees), heaps, and hash tables. In the fight to not feel overwhelmed amid the many moving pieces we deal with in CST370, I greatly appreciate how each structure from this week has a strict internal rule set with an accompanying specific algorithmic repair process when those rules are violated. In theory, that's how all algorithmic concepts should be, but I found working through the example problems to be more tactile and logical than I perhaps expected. This week proved to be a bit more like a Lego set and less abstract than some attempts have proven to be in prior modules. It could just be that the nearness of the next exam looms and needing to absorb the material sufficiently now for the exam is not lost on me. The heap and linear probe operations homework enforced that notion as well.

        Tools such as rotations in AVL trees, node splitting in 2-3 trees, the bottom-up heap construction algorithm, linear probing, and load factor all act as guardrails to keep each structure/resolution process within its respective required constraints. Heaps were a satisfying shift in perspective, array representation and the accompanying algorithm in the textbook was immediately clear. Unlike binary search trees, heaps do not enforce left-to-right ordering; instead, they adhere to parental dominance. Hash tables introduced a different kind of discipline--rather than structural rotations or swaps, resolving table problems depends on managing collisions and maintaining an acceptable load factor. Linear probing illustrates how simple arithmetic can preserve correctness, while rehashing demonstrated the importance of anticipating performance degradation before it heads for a crash. Although the prompt initially looked more daunting at first glance, hash table operations proved to be friendlier to me than heaps when it came to the homework assignments.

Comments

Post a Comment

Popular posts from this blog

Week 1 - CST363 First Post: Relational Database Reflections

From the orientation, your reading from week 1, and your own experience answer the following questions: Relational database tables and spreadsheets look similar with both having rows and columns.  What are some important differences between the two? At first glance, a relational database and a spreadsheet in an application such as Excel look fairly commensurate to each other in purpose and functionality--they both are comprised of rows and columns that hold data organized by column names and row numbers. The truth is that their visual similarity is where the commensuration ends and the vast differences begin. Relational databases adhere to strict and organized rules that ensure they can manage large volumes of structured data as well as complex queries to return results from that data whereas spreadsheets can be filled out arbitrarily. Foreign keys and primary keys can tie individual values or sets of values to other tables of data which in turn impact how data can be returned or c...
Read more

Week 6 - Weekly Reflection

          Week 6 of CST363 offered many learning opportunities both in assigned reading and labs. While the reading gave the initial rundown on database connections, query execution, and the use of prepared statements through APIs like JDBC, the Java labs offered hands-on teaching moments that clarified how to use these concepts in practice. Working directly with JDBC to manage connections, execute SQL queries, and handle transactions worked wonders in solidifying the material. Experiencing Java interface with SQL through IntelliJ and MySQL Workbench additionally contextualized using Connections and using prepared statements to aid prevention of SQL injection attacks. The conceptual topics tackled in the reading are an aspect of computer science that I am always compelled to learn about--in this case it is the different programming paradigms when considering SQL in the context of application development. Qualifying the differences between imperative and dec...
Read more

Week 5 - Slow Indexes Reflection

 Referencing  Slow Indexes in RDBMS : if indexes are supposed to speed up performance of query, what does the author mean by a slow index?       In my time working with indexes thus far, I have seen what miracles they can work for improving query performance. This is my first encounter with unpacking how indexes could actually slow a query down. Author Markus Winand illustrates how index lookups can still be slow and expensive even when functioning properly. Winand explains how index performance entails more than just tree traversal in a clear and inviting fashion. First, the lookup conducts the tree traversal by navigating down the index B-tree to reach the intended leaf node. Upon following the leaf node chain, multiple hits can mean the database needs to retrieve all matching index entries. For each matching index entry, the database will then access the corresponding row in the table, which could be located entirely elsewhere in memory or disk blocks. W...
Read more