Every year on November 11, the world's largest online shopping carnival "Double Eleven" sets off an unprecedented consumption storm in China. E-commerce platforms represented by Alibaba and many brand merchants not only provide consumers with hundreds of millions of discounted products, but also make this event a landmark event that perfectly combines technology and business. This article will analyze the operating mechanism behind Double Eleven from a technical perspective and explore the key technologies and innovative solutions used.
Table of contents
Network infrastructure challenges and solutions
How to ensure system stability with billions of user visits
To ensure the stability of the system with hundreds of millions of user visits, it needs to be considered and implemented from multiple levels. First, powerful hardware facilities are needed to support hundreds of millions of user visits.
Including high-performance servers, storage devices and network equipment. In order to ensure the scalability and stability of the system, you can consider using cloud services, such as cloud servers, object storage, and load balancing.
For large user access, the database is key. You should consider using high-performance database systems, such as MySQL, PostgreSQL, etc. At the same time, the performance and stability of the database can be improved through optimized queries, reasonable sharding, and distributed deployment.
Caching technology can effectively reduce the pressure on the database and improve the response speed of the system. You can use cache servers such as Redis, Memcached, etc. to store commonly used data and reduce access to the database.
By using load balancers such as Nginx, HAProxy, etc., you can share the access pressure of the system and improve the throughput of the system. At the same time, fault-tolerant mechanisms should be designed, such as failover, dual-machine hot backup, etc., to ensure that the system can recover quickly when a failure occurs.
For high-concurrency scenarios, you can consider using distributed system design. By splitting the system into multiple microservices, each microservice is responsible for a part of the function, it can better cope with high concurrency and elastic expansion.
For large databases, consider using sharding and partitioning technologies to improve performance and stability. Sharding can divide data into multiple parts and store them on different database instances or servers, while partitioning can divide data into multiple different sections according to certain rules.
A high-availability architecture should be designed to ensure that the system can quickly switch to backup resources when there is a problem with some hardware or services to ensure system availability.
How to optimize network architecture
Optimizing network architecture requires consideration and implementation from multiple aspects such as network topology design, device selection, routing and switching strategies, virtualization and containerization.
- Network topology design: Reasonably design the network topology, such as using star, tree, ring, mesh and other topologies, and select according to actual needs and network scale. At the same time, the scalability and stability of the network must be considered to avoid an overly complex network structure.
- Equipment selection: Select appropriate network devices, such as routers, switches, load balancers, etc., based on network size and needs. Consider factors such as device performance, stability, scalability, and price.
- Routing and switching strategies: Optimize routing and switching strategies to ensure the efficiency of data packet transmission in the network. Performance can be improved by optimizing routing tables, using smarter switching chips, and using QoS policies.
- Virtualization and containerization: Using virtualization or containerization technology can simplify network management and improve resource utilization and flexibility. Virtualization technology can use KVM, XEN, etc., and containerization technology can use Docker, Kubernetes, etc.
Commonly used load balancing and strategies
In computer networking, load balancing is a technique for distributing workloads (such as network traffic, requests, etc.) across multiple servers or resources. Its purpose is to improve the performance, reliability and scalability of the system by reasonably distributing the load.
The following are several commonly used load balancing and strategies:
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Polling strategy: Distribute requests to different servers in turn in a predefined order. This strategy is simple but does not take into account the actual load of the server.
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Weighted polling strategy: Assign a weight value to each server and poll according to the weight ratio. The weight can be set according to the performance and configuration of the server to achieve more reasonable load distribution.
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Least connections strategy: Select the server with the fewest current connections to handle new requests. This strategy can distribute requests more evenly across servers, but may result in unbalanced performance if servers are processed at different speeds.
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IP Hash Strategy: Hash requests to a specific server based on the source IP address of the request. The same source IP address will always be mapped to the same server, thus ensuring that requests with the same source IP are sent to the same server.
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Domain name resolution strategy: resolve domain names to the IP addresses of multiple servers, and select appropriate servers through DNS load balancing technology. This strategy can be dynamically adjusted based on the geographical location of the request, network conditions and other factors.
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Sticky session strategy: Distribute the user's continuous requests to the same server to maintain the consistency of the user session. Sticky sessions are typically implemented using cookies or URL rewriting.
Big data driven personalized recommendations
Conduct in-depth mining and analysis of user behavior
At the technical level, e-commerce platforms can conduct in-depth mining and analysis of user behavior through data collection, data storage, data cleaning and preprocessing, user portrait modeling, behavioral analysis, etc.
Common front-end methods include collecting user behavior data through clicks, browsing, purchases, collections, etc. The collected user behavior data needs to be stored and managed. E-commerce platforms can use big data storage and processing technologies, such as Hadoop, Spark, NoSQL databases, etc., to store massive user behavior data.
Before in-depth mining and analysis, the original data needs to be cleaned and preprocessed to remove invalid data, duplicate data and noise data, and convert the data into a usable format.
E-commerce platforms can use user behavior data to build user profiles. By analyzing the user's purchasing preferences, browsing history, click behavior and other information, the user's preferences, consumption habits and demand characteristics can be derived, and then personalized recommendation services can be provided for the user.
Based on user behavior data, e-commerce platforms can conduct various behavioral analyses. For example, purchase path analysis, conversion rate analysis, user activity analysis, churn rate analysis, etc., to evaluate the effectiveness of user behavior and improve user experience.
Improve purchase conversion rates with personalized recommendations
Improving purchase conversion rates through personalized recommendations is an effective marketing strategy that utilizes big data and artificial intelligence technology to understand users' shopping habits and preferences and provide them with products or services that are most suitable for them.
First, machine learning or artificial intelligence technology (such as collaborative filtering, content recommendation algorithms, etc.) is used to process and analyze user data obtained through various channels to create a personalized recommendation system. These algorithms predict products that users may be interested in based on their purchasing history, browsing behavior, and more.
Embed recommendation algorithms into your website, mobile app, or other sales channels. When users visit your website, relevant products or services are displayed based on their preferences. This can include showing personalized ads, promotions, or providing personalized recommendations on product pages.
Personalized recommendation systems should be able to update and optimize their recommendations in real time. This means the system should be able to adapt its recommendations based on new data and user behavior.
Provide users with a feedback channel so they can tell you which products or services they are interested in and which they are not. This will help the system continuously improve its recommendation algorithm and provide more accurate personalized recommendations.