jfcai 发布的文章

整车

捷安特：TCR ，PP
闪电：
崔克：
皮娜：
Scott：

车架

车把

闪电 roval

轮组

闪电 roval
禧玛诺
老王
DT Swiss

花鼓

TR01

套件

禧玛诺：105，U 8070，DURA-AC 9070
SRAM：

功率计？

坐管

外胎

马牌 GP5000
固特异 F1
伊诺华笑脸胎
米其林 PowerCup
朝阳眼睛蛇

image.png

image.png

注：图片引自隼影单车的视频号。

真空胎？

内胎

朝阳

坐垫

头盔

KUSK：
PMP：

清洗剂

WD？？
Mac-Off
KOSFREY柯师傅

如何清洗？喷上清洗剂，然后静置一段时间，刷子刷干净（转动轮子，飞轮大小飞以及牙盘），冲洗并擦干上油。

image.png

链条油

干性、湿性，蜡性

WD-40
氟素
Mac-Off
大猩猩

如何上油？每一节上油，一滴或半滴，转轮并变速每个飞轮，然后静置一段时间擦干。

锁鞋

码表

台子

App

行者

声明： 本文来自 Poe 的回答。

In Apache Flink, processing keyed streams can be done with or without windowing, and each approach serves different use cases. Here’s a comparison of the two:

Processing Keyed Streams Without Windowing

Characteristics:

  • Continuous Processing: Data is processed as it arrives, without grouping into windows.
  • Stateful Operations: You can maintain state across events for each key using operations like keyBy(), map(), or reduce().
  • Event-Time Handling: You can handle events in real time, allowing for low-latency processing.
  • Use Cases: Suitable for applications that need to respond immediately to each event, such as real-time analytics, alerting systems, or monitoring applications.

Example:

DataStream<Event> stream = // your source stream    
stream.keyBy(event -> event.getKey())
      .process(new ProcessFunction<Event, Result>() {
          @Override
          public void processElement(Event value, Context ctx, Collector<Result> out) {
              // Processing logic here
          }
      });

Processing Keyed Streams With Windowing

Characteristics:

  • Batch Processing: Data is grouped into finite windows based on time or count before processing.
  • Aggregation: Commonly used for calculating aggregates over a specific time frame (e.g., sum, average).
  • Window Types: Flink supports different window types, such as tumbling, sliding, and session windows.
  • Event-Time and Processing-Time: You can specify whether to use event-time or processing-time semantics for windowing.
  • Use Cases: Ideal for scenarios where you want to analyze data over intervals, such as calculating metrics per minute, hour, or day.

Example:

DataStream<Event> stream = // your source stream
stream.keyBy(event -> event.getKey())
      .window(TumblingEventTimeWindows.of(Time.minutes(1))) // or any other window type
      .aggregate(new SumAggregator());

Summary of Differences

image.png

Conclusion

  • Without Windowing: Use for real-time, low-latency applications that need immediate responses to each event.
  • With Windowing: Use for scenarios requiring aggregation or computation over a fixed period, like summarizing data trends over time.

内心希望是个 deep dive（虽然也不够资格），但是实际可能是个入门的简单介绍。

什么是 Flink - Overview

Stateful Computations over Data Streams

"Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale." - 摘自 Flink 官网首页

数据流之上的有状态计算
“Apache Flink 是一个框架和分布式处理引擎，用于在无界和有界数据流上进行有状态计算。Flink 设计用于在所有常见的集群环境中运行，以内存速度和任何规模执行计算。” - 翻译 DeepL 完成

架构 - 高阶视角

基于 Flink snapshot version，非 stable version。

Flink Cluster - high level architecture

Flink 集群剖析

Flink 程序剖析

关键概念

Event Time 与 Processing Time

Notions of Time: Event Time and Processing Time

Event Time 与 Watermarks

Event Time and Watermarks

Parallelism

深入话题

任务和调度

Job/Task

计算

一些坑（也就是实现要注意的地方）

参考

https://soulteary.com/：LLM/AI 实现编程相关
https://colobu.com/：鸟窝，Golang 和编程相关
https://blog.wildcat.io/blog/：国外来人，有点杂
https://www.huangjiwei.com/blog/：语言文字
https://www.kawabangga.com/：优秀的运维
https://jaapgrolleman.com/：生活在上海的荷兰人
https://www.daemonology.net/hn-daily/: Hacker News Daily top 10 posts
https://www.skyue.com/: 杂
https://justgoidea.com/: 思考者，通过笔记类的文章发现的
https://manateelazycat.github.io/：优秀的思考者，商业人和开源者

均支持 RSS 订阅。

老雷｜投資人

2004年進入金融市場，主要投資領域為美國股市和國際大宗商品期貨。我在互聯網上分享我的經驗和思考，旨在傳播「讓投資更簡單」之觀念。如果你感興趣，可以來看看我怎樣做投資/交易——面對同樣的行情，你的思路和我的邏輯有何差別。

在會員內容中，我分享三個投資帳戶的詳情（合計逾百萬美元），毫無保留地分享我最即時的思考和最真實的交易——我相信，我能做到的事情，你也能做到。

以下链接都是老雷的 themarketmemo.com

投资理念：https://themarketmemo.com/myphilosophy/
交易纪律：https://themarketmemo.com/tradingchecklist/
趋势指标：https://themarketmemo.com/mytradingview/
市场宽度：https://themarketmemo.com/marketbreadth/
投资美股的 20 张图表：https://themarketmemo.com/20charts/
全市场观察：https://docs.google.com/spreadsheets/d/1_xv9pPrxhx9A4OyhrvyTTJuKNXk8rn0m-eAWvnbdXWI/edit?gid=1512017409#gid=1512017409
超级组合：https://themarketmemo.com/riygr10/

有兴趣还可以订阅会员：https://www.patreon.com/TheMarketMemo

更多内容可以访问其优🐰视频集合

TBD - 待进一步总结视频内容，理念、知识和工具。

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