知鸦日报2021-08-15

为什么要双减？

第一部分：资本第一杀，我如果是私立中学，如何对公立中学进行绞杀？

第二部分：资本第二杀，横碎中学，开到哪里，卷到哪里；

第三部分：资本第三杀，校外培训机构，如何收益最大化；

第四部分：如果放任资本，未来的英美，就是我们的榜样；

第五部分：教育资本化，恶魔的凝视。

在常规检索的基础上，用户期望返回基于特定字段的聚合结果，且用户期望分页查看检索和聚合结果。

如下图所示：以2020东京奥运会热点新闻亚洲飞人"苏炳添”为例，用户期望查看有关“苏炳添”的新闻，同时期望查看相同标题的相似文章列表。

集团线上业务中⼤量的载体为⼩说，会有⼀些⻓⽂合规词条检测的需求，进⾏⼀些业务的⽂本⻛控处理。需要对站内的中⽂⽂本进⾏分词后，挑选出有⻛险的词条，根据业务场景的不同，再做不同的处理。

With Uber’s business growth and the fast adoption of big data and AI, Big Data scaled to become our most costly infrastructure platform. To reduce operational expenses, we developed a holistic framework with 3 pillars: platform efficiency, supply, and demand (using supply to describe the hardware resources that are made available to run big data storage and compute workload, and demand to describe those workloads). In this post, we will share our work on managing supply and demand. For more details about the context of the larger initiative and improvements in platform efficiency, please refer to our earlier posts: Challenges and Opportunities to Dramatically Reduce the Cost of Uber’s Big Data, and Cost-Efficient Open Source Big Data Platform at Uber.

We describe a novel approach we took to improving S3 read throughput and how we used it to improve the efficiency of our production jobs. The results have been very encouraging. A standalone benchmark showed a 12x improvement in S3 read throughput (from 21 MB/s to 269 MB/s). Increased throughput allowed our production jobs to finish sooner. As a result, we saw 22% reduction in vcore-hours, 23% reduction in memory-hours, and similar reduction in run time of a typical production job. Although we are happy with the results, we are exploring additional enhancements in the future. They are briefly described at the end of this blog.

不要让级别给信息加权，也不要阻碍信息流通。