How Rockset Turbocharges Real-Time Personalization at Whatnot
公司规模
Startup
地区
- America
国家
- United States
产品
- Rockset
- Elasticsearch
- Apache Kafka
技术栈
- SQL
- Apache Kafka
- Snowflake
- Airflow
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Cost Savings
- Digital Expertise
技术
- 分析与建模 - 实时分析
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- 电子商务
- Software
适用功能
- 产品研发
- 商业运营
用例
- 实时定位系统 (RTLS)
- 远程资产管理
服务
- 软件设计与工程服务
- 系统集成
关于客户
Whatnot is a venture-backed e-commerce startup designed for the streaming age, offering a live video marketplace for collectors, fashion enthusiasts, and superfans. The platform allows sellers to go live and sell various items through a video auction platform, combining elements of eBay and Twitch. Launched in 2020, Whatnot initially focused on coveted collectibles and has since expanded to offer products in over 100 categories, including Pokemon cards, sneakers, and antique coins. The platform's success hinges on connecting communities of buyers and sellers, gathering real-time signals from users' interactions, and analyzing this data to present the most popular and relevant videos on the home screen of its mobile app or website.
挑战
To maintain and increase growth, Whatnot needed to enhance its home feed by ranking show suggestions based on the most interesting and relevant content in real time. This required an increase in the amount and variety of data to be ingested and analyzed in real time. The existing data pipeline, built on AWS-hosted Elasticsearch, was not scalable enough to handle the anticipated growth of 5-10x in the next year. The high operational overhead of Elasticsearch was draining productivity and limiting the ability to improve the recommendation engine. Adding new user signals to the analytics pipeline was a time-consuming process, taking weeks to implement changes. Maintaining existing queries was also labor-intensive, requiring frequent updates to Elasticsearch indexes and manual testing of the data pipeline components.
解决方案
Whatnot evaluated several real-time analytics platforms to replace Elasticsearch, including Rockset, Materialize, Apache Druid, and Apache Pinot. They chose Rockset for its fully-managed, developer-enhancing platform with real-time ingestion and query speeds, high concurrency, and automatic scalability. Rockset's Converged Index™ feature indexes all fields, including nested ones, ensuring optimized queries without the need for manual index maintenance. This significantly improved developer productivity, allowing new user signals or data sources to be added to the ranking engine quickly. Rockset's SQL-first approach was beneficial for data scientists and machine learning engineers, offering a comprehensive set of SQL commands for complex analytics. The platform's ability to decouple storage from compute provided instant, automatic scalability to handle growing traffic. Rockset's mutable architecture and Write API simplified data upserts, inserts, updates, and deletes. The platform delivered true real-time ingestion and queries with sub-50 millisecond latency, matching Elasticsearch's performance at a lower operational cost and effort.
运营影响
数量效益
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