As a number one platform enabling thousands and thousands of individuals to take pleasure in their favourite meals on the contact of a button, Uber Eats has undoubtedly reworked the way in which we entry and devour meals. However have you ever ever puzzled what lies beneath the floor of this groundbreaking service? On this article, we are going to take an in-depth have a look at the subtle expertise that powers Uber Eats and allows it to course of thousands and thousands of orders every day effectively and successfully.
From progressive instruments and frameworks to strong techniques and scalable infrastructure, the Uber Eats tech stack is a marvel of ingenuity and engineering. We’ll discover the varied parts that work in concord to ship a seamless consumer expertise, from the front-end design to the back-end infrastructure.
Frontend applied sciences of Uber Eats Tech Stack
The frontend of Uber Eats tech stack is a mixture of cutting-edge instruments, frameworks, and techniques that guarantee a responsive, intuitive, and visually interesting interface. Let’s dive into the important thing parts of Uber Eats’ frontend tech stack.
React.js: Uber Eats makes use of React.js, a preferred JavaScript library developed by Fb, because the core of its frontend growth. React.js permits for the creation of reusable UI parts and environment friendly rendering of dynamic content material, making it simpler for the event group to keep up and scale the applying.
Redux: To handle the applying’s state, Uber Eats employs Redux, a predictable state container for JavaScript apps. Redux helps in sustaining a constant state throughout your entire app, simplifying debugging and making it simpler to check the applying. It additionally allows higher collaboration between group members engaged on completely different components of the applying.
GraphQL: To effectively fetch knowledge from the backend, Uber Eats makes use of GraphQL, a question language for APIs. GraphQL permits the frontend to request solely the information it wants, decreasing the quantity of information transferred over the community and enhancing the app’s efficiency. It additionally simplifies API interactions by offering a single endpoint for a number of knowledge sources.
Webpack: For bundling and optimizing the applying’s property (equivalent to JavaScript, CSS, and pictures), Uber Eats makes use of Webpack. This highly effective module bundler improves the efficiency of the applying by minimizing the dimensions of the bundled property and enabling options like code splitting and lazy loading.
Jest and Enzyme: For testing the frontend parts, Uber Eats tech stack employs Jest, a JavaScript testing framework, and Enzyme, a JavaScript testing utility for React. These instruments assist make sure the reliability and stability of the applying by permitting builders to put in writing and run exams for particular person parts and their interactions.
Styled-components: To type the consumer interface, Uber Eats leverages styled-components, a preferred CSS-in-JS library. This method permits for dynamic styling based mostly on the applying’s state, improves efficiency by producing solely the mandatory CSS, and promotes higher componentization and maintainability.
Backend applied sciences
Uber Eats has constructed a strong and scalable backend tech stack to help its ever-growing demand for meals supply companies. On this part, we are going to discover the six key applied sciences that represent the Uber Eats tech stack backend, which embody Node.js, Python, Go, PostgreSQL, Redis, and Apache Cassandra.
Node.js: Uber Eats tech stack makes use of Node.js as a core expertise for constructing its server-side functions. Node.js is understood for its skill to deal with a number of, concurrent connections and its non-blocking I/O, which permits Uber Eats to handle excessive volumes of incoming requests effectively, guaranteeing a easy and responsive expertise for customers.
Python: Python is an integral a part of Uber Eats’ backend tech stack, primarily used for knowledge processing and analytics. Python’s versatility and intensive library help make it an excellent alternative for dealing with the advanced knowledge necessities of a meals supply platform.
Go: Uber Eats employs the Go programming language for constructing high-performance, concurrent techniques. Go’s simplicity, sturdy typing, and environment friendly rubbish assortment make it well-suited for growing scalable and maintainable backend companies that may deal with the rigorous calls for of a meals supply community.
PostgreSQL: Uber Eats depends on PostgreSQL, an open-source relational database administration system, for storing and managing its structured knowledge. PostgreSQL is understood for its robustness, extensibility, and help for superior knowledge varieties, which allow Uber Eats to mannequin advanced relationships between eating places, clients, and orders.
Redis: Uber Eats makes use of Redis, an in-memory knowledge retailer, to cache steadily accessed knowledge and enhance the efficiency of its backend companies. By storing knowledge in-memory, Redis reduces latency and permits Uber Eats to serve requests sooner, offering an optimum consumer expertise.
Apache Cassandra: To deal with the large quantities of information generated by its platform, Uber Eats employs Apache Cassandra, a extremely scalable and distributed NoSQL database. Cassandra’s skill to scale horizontally and supply excessive availability makes it an excellent alternative for managing the big volumes of information produced by Uber Eats’ international operations.
Infrastructure applied sciences
Kafka: Uber Eats makes use of this distributed streaming platform for knowledge streaming, enabling real-time processing of information and seamless communication between companies.
Hive, HDFS, Elasticsearch, MapReduce, and file storage: These applied sciences are used for processing and storing massive datasets, permitting Uber Eats to effectively handle the large quantities of information generated by its platform.
Apache Cassandra: This extremely scalable and distributed NoSQL database is used to handle massive quantities of information throughout many servers, offering excessive availability and fault tolerance.
PostgreSQL: This highly effective relational database administration system is utilized in Uber Eats tech stack for storing and managing structured knowledge, guaranteeing knowledge consistency and integrity.
Conclusion
Uber Eats’ spectacular and various expertise stack has performed a vital position in its speedy progress and talent to fulfill the excessive demand for meals supply companies. By leveraging a mixture of programming languages like Node.js, Python, Go, and Java, in addition to highly effective database and knowledge processing instruments equivalent to PostgreSQL, Redis, Apache Cassandra, Kafka, Hive, HDFS, Elasticsearch, and MapReduce, Uber Eats has constructed a strong and scalable infrastructure. Constructing a full-fledged app expertise just like the Uber Eats tech stack is feasible now with Appscrip.
The Uber Eats tech stack permits them to effectively deal with thousands and thousands of transactions, guarantee real-time knowledge processing and analytics, and supply a seamless consumer expertise. Because the meals supply trade continues to evolve, Uber Eats’ dedication to using cutting-edge applied sciences will undoubtedly allow the corporate to keep up its aggressive edge and adapt to the ever-changing wants of its clients and supply companions.
