As a number one platform enabling hundreds of thousands of individuals to get pleasure from their favourite meals on the contact of a button, Uber Eats has undoubtedly remodeled the way in which we entry and eat meals. However have you ever ever questioned what lies beneath the floor of this groundbreaking service? On this article, we’ll take an in-depth have a look at the delicate expertise that powers Uber Eats and permits it to course of hundreds of 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 are going to discover the assorted elements 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 mix of cutting-edge instruments, frameworks, and techniques that guarantee a responsive, intuitive, and visually interesting interface. Let’s dive into the important thing elements of Uber Eats’ frontend tech stack.
React.js: Uber Eats makes use of React.js, a well-liked JavaScript library developed by Fb, because the core of its frontend growth. React.js permits for the creation of reusable UI elements and environment friendly rendering of dynamic content material, making it simpler for the event workforce to keep up and scale the appliance.
Redux: To handle the appliance’s state, Uber Eats employs Redux, a predictable state container for JavaScript apps. Redux helps in sustaining a constant state throughout all the app, simplifying debugging and making it simpler to check the appliance. It additionally permits higher collaboration between workforce members engaged on totally different elements of the appliance.
GraphQL: To effectively fetch information 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, lowering the quantity of knowledge transferred over the community and bettering the app’s efficiency. It additionally simplifies API interactions by offering a single endpoint for a number of information sources.
Webpack: For bundling and optimizing the appliance’s belongings (comparable to JavaScript, CSS, and pictures), Uber Eats makes use of Webpack. This highly effective module bundler improves the efficiency of the appliance by minimizing the scale of the bundled belongings and enabling options like code splitting and lazy loading.
Jest and Enzyme: For testing the frontend elements, 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 appliance by permitting builders to put in writing and run checks for particular person elements and their interactions.
Styled-components: To type the consumer interface, Uber Eats leverages styled-components, a well-liked CSS-in-JS library. This strategy permits for dynamic styling based mostly on the appliance’s state, improves efficiency by producing solely the mandatory CSS, and promotes higher componentization and maintainability.
Backend applied sciences
Uber Eats has constructed a sturdy and scalable backend tech stack to assist its ever-growing demand for meals supply providers. On this part, we’ll 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 potential 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, making certain a clean and responsive expertise for customers.
Python: Python is an integral a part of Uber Eats’ backend tech stack, primarily used for information processing and analytics. Python’s versatility and intensive library assist make it a really perfect selection for dealing with the advanced information necessities of a meals supply platform.
Go: Uber Eats employs the Go programming language for constructing high-performance, concurrent techniques. Go’s simplicity, robust typing, and environment friendly rubbish assortment make it well-suited for growing scalable and maintainable backend providers 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 information. PostgreSQL is understood for its robustness, extensibility, and assist for superior information sorts, which allow Uber Eats to mannequin advanced relationships between eating places, clients, and orders.
Redis: Uber Eats makes use of Redis, an in-memory information retailer, to cache ceaselessly accessed information and enhance the efficiency of its backend providers. By storing information in-memory, Redis reduces latency and permits Uber Eats to serve requests quicker, offering an optimum consumer expertise.
Apache Cassandra: To deal with the large quantities of knowledge generated by its platform, Uber Eats employs Apache Cassandra, a extremely scalable and distributed NoSQL database. Cassandra’s potential to scale horizontally and supply excessive availability makes it a really perfect selection for managing the massive volumes of knowledge produced by Uber Eats’ world operations.
Infrastructure applied sciences
Kafka: Uber Eats makes use of this distributed streaming platform for information streaming, enabling real-time processing of knowledge and seamless communication between providers.
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 knowledge generated by its platform.
Apache Cassandra: This extremely scalable and distributed NoSQL database is used to handle massive quantities of knowledge 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 information, making certain information consistency and integrity.
Conclusion
Uber Eats’ spectacular and various expertise stack has performed a crucial function in its speedy development and skill to fulfill the excessive demand for meals supply providers. By leveraging a mix of programming languages like Node.js, Python, Go, and Java, in addition to highly effective database and information processing instruments comparable to PostgreSQL, Redis, Apache Cassandra, Kafka, Hive, HDFS, Elasticsearch, and MapReduce, Uber Eats has constructed a sturdy 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 hundreds of thousands of transactions, guarantee real-time information processing and analytics, and supply a seamless consumer expertise. Because the meals supply business 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.
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