500X 可扩展性实验指标计算与统一动态框架
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Xinyue Cao | Software Engineer Tech Lead; Bojun Lin | Software Engineer
Xinyue Cao | 软件工程师技术负责人; Bojun Lin | 软件工程师
Overview
概述
Experimentation is a cornerstone of data-driven decision making at Pinterest. Every day, thousands of experiments run on our platform, generating insights that drive product decisions and business strategies. By the end of 2024, over 1,500 metrics — created by experiment users — are computed daily on our Experimentation Platform to meet diverse analytical needs.
实验是 Pinterest 数据驱动决策的基石。每天，成千上万的实验在我们的平台上运行，生成推动产品决策和商业战略的洞察。到 2024 年底，超过 1,500 个由实验用户创建的指标将在我们的实验平台上每天计算，以满足多样化的分析需求。
But as the scale of experimentation grew, so did the challenges. Delays in upstream data ingestion, difficulties in backfilling skipped metrics, and frequent scalability issues began to hinder our ability to deliver timely and reliable results.
但是随着实验规模的扩大，挑战也随之增加。上游数据摄取的延迟、跳过指标的回填困难以及频繁的可扩展性问题开始妨碍我们及时可靠地交付结果的能力。
Enter the Unified Dynamic Framework (UDF) — a scalable, resilient solution that has transformed how we compute experiment metrics. With UDF, our pipelines now support 100X metrics and are designed to scale up to 500X in the coming years. This framework has not only ditched all upstream dependencies and accelerated metric delivery but also reduced engineering effort from months to days to build data pipelines, enabling faster experimentation and innovation. Essentially, it achieves standardization of metric processing for Pinterest experimentation. Engineers are able to focus solely on innovation of metric computation because the majority of the infrastructure challenges and pipeline creation complexities are offloaded to the unified dynamic framework.
引入统一动态框架 (UDF) — 一种可扩展、弹性的解决方案，彻底改变了我们计算实验指标的方式。通过UDF，我们的管道现在支持100倍的指标，并设计为在未来几年内扩展到500倍。这个框架不仅消除了所有上游依赖，加速了指标交...