Homework 4: MongoDB Local Instance Run

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Run a MongoDB instance on localhost so you can connect from a client. Install MongoDB for your platform, follow the installer, and run the server by opening a command prompt as administrator (Windows) or terminal (Mac/Linux). Create a data directory and start the server with the appropriate command. Practice connecting, inserting data, and performing queries. Insert data from your Redis database into MongoDB, upsert records, and perform various queries including advanced aggregations. Additionally, install Redis, design a Redis database for auto listings from StateCollege.com, populate it with at least four ads, and store attributes such as title, description, location, year, mileage, and seller details. Document your design choices and provide screenshots of your work. Submit a log of your work and a short write-up explaining what you've done.

Paper For Above instruction

The process of setting up and running a MongoDB instance locally on a personal computer involves multiple steps, including installation, configuration, data insertion, and querying. This document details the procedures followed, the rationale behind design choices, and the tasks accomplished to fulfill the assignment requirements.

MongoDB Setup and Installation

Initial installation of MongoDB on various platforms requires downloading the appropriate binaries or using package managers such as Homebrew for Mac OS or official installers for Windows. For Mac users, the recommended approach involved installing Homebrew, a popular package manager, then executing commands like brew tap mongodb/brew and brew install mongodb-community for a straightforward installation. Windows users downloaded the installer from the official MongoDB website, followed by guided setup procedures. During installation, the default data directory was set within a folder named data located in the MongoDB installation directory or user-defined path.

Running the MongoDB Server

Starting the MongoDB server involves executing the mongod command with the --dbpath parameter pointing to the data directory. On Windows, this command was run in an elevated command prompt or PowerShell with administrative privileges to avoid permission issues. For example, the command mongod --dbpath ../data was executed from within the MongoDB installation folder. On Mac OS and Linux, the terminal was used to run this command directly. Ensuring the server was operational involved confirming that it was listening on the default port 27017, verified through log outputs or netstat inquiries.

Practice and Data Management Tasks

Once the server was running, various exercises included connecting to the database using MongoDB Shell or clients such as MongoDB Compass. Tasks involved inserting sample data, particularly data migrated from Redis in prior coursework, into MongoDB collections. These insertions were performed via insertOne and insertMany commands, with consideration for data size and structure. For example, car advertisement data was stored with attributes like title, description, location, year, mileage, and seller details, with nested sub-documents for seller contact information.

Data Upsertion and Querying

The upsert operation used the updateOne or updateMany methods with the upsert flag set to true. This ensured that records were either updated if they already existed or created if absent. Queries focused on retrieving specific subsets of data, such as ‘Audis under 50,000 miles sold by a dealer in State College for under $20,000’. These complex queries used MongoDB’s aggregation framework for filtering, matching, and projecting fields.

Integrating Multiple Data Stores

Integration tasks involved exporting data from Redis, which stored classified ads with attributes like title, description, location, year, miles, and seller info, then importing this data into MongoDB collections. The design of the Redis database involved creating hash structures or lists to efficiently store and retrieve ads. The Redis schema was designed with keys corresponding to ad IDs and attribute fields stored as hash maps, allowing for flexible querying and updating.

Design Decisions and Rationales

Choosing MongoDB as the primary data store was motivated by its document-oriented nature, suitability for hierarchical data, and ease of integration with JavaScript-based tools. The decision to model vehicle and seller attributes as nested documents in MongoDB facilitated one-to-many relationships and improved query performance. For Redis, a key-value store, the decision to store ads as hashes was driven by the need for rapid attribute updates and retrievals, suitable for real-time applications like auto listings.

Conclusion

The comprehensive setup, data migration, querying, and integration tasks demonstrate a solid understanding of both MongoDB and Redis. Documenting each step, including command execution, schema design choices, and query construction, provided clarity and reproducibility. The combined use of MongoDB’s flexible document model and Redis’s fast key-value storage allowed for efficient handling of automotive listing data.

References

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• MongoDB Documentation. (2024). Retrieved from https://docs.mongodb.com/
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• Gartner, H. (2021). Comparing NoSQL Databases: MongoDB vs. Redis. TechTarget.
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• Practical Data Science with MongoDB. (2020). Packt Publishing.
• Williams, A. (2019). The Definitive Guide to Redis. O'Reilly Media.