知鸦日报2021-10-07

近期团队开发的新产品的官网上线了，随之而来的问题就是如何让网站更快更好的传播出去。当然SEO就是最常见的手段之一，对官网的SEO工作有利于产品的网络传播和分享。

有的团队会有专人负责SEO的工作，那么作为离网站页面“最近”的前端工程师，可以为SEO做哪些工作呢？

为了更好的沉淀SEO相关内容，为后续web产品提供优化指南，本文就以前端工程师的角度，通过调研沉淀出了前端通用SEO技术优化指南。

Text resizing is the ability for the user to adjust their preferred font size in their device’s operating system preferences. A variety of users make this adjustment to improve their ability to read small text on a small screen. Once this setting is adjusted, the platform and any apps that have built-in support for this feature will resize displayed text. Check out these examples of how to adjust these text and font resizing settings in Android and iOS platforms. Pro tip: If you don’t already use these settings, grab your phone and try it right now. As we all know, it’s good to put yourself in your customer’s shoes. Plus it’ll be good practice because most of us will find the feature helpful eventually.

In this article I’d like to share my experiences on how one can improve the observability of services through the use of the Consul Connect Service Mesh and later I’ll explain my justification for using it compared to other alternatives. Just for a recap, a service mesh allows you to route HTTP traffic through proxies that are deployed as sidecar containers to a service. These proxies can then expose metrics, logs and traces that allow you to gain rich visibility into the system thus making it more efficient to debug applications quickly and easily. Consul Connect, in addition provides service-to-service authorization and encryption using mutual TLS and uses Envoy as the data plane for the data transmission. In terms of observability, I will cover basic Layer 7 metrics, access logs and distributed tracing using Prometheus, Grafana, AWS CloudWatch Logs and AWS X-Ray.

Spark, Hive and HDFS (Hadoop Distributed File Systems) ecosystems are online analytical processing (OLAP)-oriented technologies. They are designed to process huge amounts of data with full scans. From time to time, users want to use the same data for more ad-hoc oriented tasks:

• Multi-row load— explore small sets (typically 1%) of the data by specific IDs (not random).
• Single-row fetch — for example, building a serving layer to fetch a specific row upon a REST-API request.

These kinds of tasks are traditionally solved using dedicated storage and technology stacks (HBase, Cassandra, etc.) which require data duplication and add significant operational costs.

In this post, we describe our journey for solving this challenge by using only Spark and HDFS. We will start by introducing an example use case, generalize and define the requirements, suggest some optional solutions, and finally dive into our final solution.

The Pinterest Ad Business has grown multi-fold in the past couple years, with respect to both advertisers and users. As we scale our revenue, it becomes imperative to:

• Distribute advertiser spend smoothly over the course of the day
• Avoid over-spending beyond the advertiser’s daily / lifetime budget
• Maximize advertiser value

最近团队有同学提议，想探讨如何才能写出一篇优秀的技术文章。所以尽管水平实在有限，还是按照自己的经验和理解写下了这篇文章，亚历山大之余，也很高兴和大家分享一下自己对技术写作的一些思考。