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By Hector Castro Most Computer Science curriculums offer at least one course that focuses on databases. Most of the time, these courses promote relational database management systems (RDBMS) by emphasizing the relational model, relational algebra, data normalization, and Structured Query Language (SQL). Key/value data stores are increasing in popularity but our mental model for storing data is still primarily relational. In this talk, we'll explore data modeling for key/value stores using the Uber mobile application as an example. Help us caption & translate this video! http://amara.org/v/FG3z/
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In the talk titled "Throw Some Keys on It: Data Modeling for Key/Value Data Stores by Example," Hector Castro discusses the growing importance of key/value data stores in contrast to traditional relational database management systems (RDBMS). The presentation highlights the differences between these two approaches to data storage, particularly in the context of modern application requirements. ### Key Points: - **Transition from RDBMS to Key/Value Stores**: While RDBMS are foundational due to their ability to manage complex relationships, they are not always suitable for every application, especially those needing high performance and scalability. - **Advantages of Key/Value Stores**: Castro emphasizes several benefits of distributed key/value stores: - Schema-less design, which simplifies data management. - Faster single access reads facilitated by primary key access, beneficial for high-rate workloads. - Effective performance in write-heavy scenarios, particularly because many key/value stores are append-only. - Simplified API, avoiding cumbersome details like joins and groupings, making it easier to scale applications. - **Practical Example Using Uber**: To illustrate the concepts, Castro uses the Uber mobile application as a case study, demonstrating how it can effectively leverage key/value stores. - The example begins by conceptualizing the Uber driver’s location on a coordinate grid for simplicity rather than traditional latitude and longitude. - He discusses the process of requesting a ride and how the Uber app manages driver and passenger data using a key/value model. - **Data Structures in Key/Value Stores**: The talk explains a specific function called 'emit_car_location' in Ruby, which captures data about a vehicle's location while maintaining relevant context. Castro discusses: - Usage of GSet (Grow-only Set) data structures, which allow concurrent writes without risking data loss. - GSets avoid removing data, focusing solely on adding data, which enhances availability and mitigates conflicts during concurrent operations. ### Conclusions and Takeaways: - The importance of choosing the right data storage model is emphasized, particularly in applications that require robust handling of high traffic and large data volumes, such as ride-sharing services like Uber. - Key/value stores can offer solutions that outperform traditional RDBMS in specific scenarios, particularly in terms of speed, scalability, and simplicity in handling data. - Castro encourages developers to rethink their data modeling strategies and to be open to using key/value data stores for applications where relational models may hold unnecessary overhead. Ultimately, his presentation invites a deeper understanding of data modeling in contemporary applications and highlights the potential advantages of adopting key/value data storage methods.
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