Google datastore: official web page from Google | 20 minutes study

Sept. 7, 2021

Here is the article. 

Highly scalable NoSQL database

Firestore is the next generation of Datastore. Learn more about upgrading to Firestore.

Datastore is a highly scalable NoSQL database for your applications. Datastore automatically handles sharding and replication, providing you with a highly available and durable database that scales automatically to handle your applications' load. Datastore provides a myriad of capabilities such as ACID transactions, SQL-like queries, indexes, and much more.

Simple and integrated

With Datastore's RESTful interface, data can easily be accessed by any deployment target. You can build solutions that span across App Engine and Compute Engine and rely on Datastore as the integration point.

Fast and highly scalable

Focus on building your applications without worrying about provisioning and load anticipation. Datastore scales seamlessly and automatically with your data, allowing applications to maintain high performance as they receive more traffic.

Easy-to-use query language

Datastore is a schemaless database, which allows you to worry less about making changes to your underlying data structure as your application evolves. Datastore provides a powerful query engine that allows you to search for data across multiple properties and sort as needed.

1. // List Google companies with fewer than 400 employees.

2. var companies = query.filter('name =', 'Google').filter('size <', 400);




Rich admin dashboard

View entity statistics, query your database, view indexes, and back up/restore your data.

Diverse data types

Datastore supports a variety of data types, including integers, floating-point numbers, strings, dates, and binary data, among others.

Multiple access methods

Access your data via our JSON API, open source clients, or community-maintained ORMs (Objectify, NDB).

ACID transactions

Ensure the integrity of your data by executing multiple datastore operations in a single transaction with ACID characteristics, so all the grouped operations succeed or all fail.

Fully managed

Datastore is fully managed, which means Google automatically handles sharding and replication in order to provide you with a highly available and consistent database.




Google I/O 2012 - SQL vs NoSQL: Battle of the Backends | My third view with a lot of stops to take notes | 60+ minutes study

Sept. 6, 2021

Here is the link. 

Ken Ashcraft, Alfred Fuller Google App Engine now offers both SQL and NoSQL data storage -- but which is right for your application? Advocates of each try to settle the issue once and for all, and show some of the tricks for getting the most out of each. For all I/O 2012 sessions, go to https://developers.google.com/io/

Take my notes - I need to work on learning more carefully. 

App engine - Google app engine (GAE) 

• Build apps on Google's infrastructure
• Platform as a Service (PaaS)
• Easy to build
• Easy to scale
• Easy to maintain
• Focus on what makes your app great!

App Engine _ Storage 

App engine works with Cloud SQL or datastore, cloud storage 

App engine - data store 

• Google storage infrastructure
• Same technology we use for our own applications
• Distilled into well documented APIs
• Build for scale (size and traffic)
• 2 trillion operations per month
• Fully managed 'NoSQL' solution

My notes: App engine data store is NoSQL solution fully managed, built for scale (size and traffic) - 2 trillion operations per months

Cloud SQL

Map reduce - Materialized view 

I like this example, city and age. It is so helpful for me to understand the ideas quickly. 

Fan-in and apply 

Datastore - Consistency 

Scalability - Datastore on Megastore on Bigtable on ...

• All the best features of each layer
• Datastore
• Megastore
• Bigtable
• GFS v2

BigTable with load balancing 

BigTable splitting - load balancing 

Megastore

• Works at scale
• 2011 talk "More 9s Please: Under the covers of the high replication datastore" 
• 9's are important at scale 
• Not reliant on a single datacenter
• Handles local issues
• Handles catastrophic failures

Management 

    

Florida Atlantic University - Official Campus Video Tour

Sept. 6, 2021

Here is the link. 

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Google I/O 2009 - Transactions Across Datacenters.. | Distributed systems

Sept. 6, 2021

Here is the link. 

Google I/O 2009 - Transactions Across Datacenters (and Other Weekend Projects) Ryan Barrett -- Contents -- 0:55 - Background quotes 2:30 - Introduction: multihoming for read/write structured storage 5:12 - Three types of consistency: weak, eventual, strong 10:00 - Transactions: definition, background 12:22 - Why multihome? Why try do anything across multiple datacenters? 15:30 - Why *not* multihome? 17:45 - Three kinds of multihoming: none, some, full 27:35 - Multihoming techniques and how to evaluate them 28:30 - Technique #1: Backups 31:39 - Technique #2: Master/slave replication 35:42 - Technique #3: Multi-master replication 39:30 - Technique #4: Two phase commit 43:53 - Technique #5: Paxos 49:35 - Conclusion: no silver bullet. Embrace the tradeoffs! 52:15 - Questions -- End -- If you work on distributed systems, you try to design your system to keep running if any single machine fails. If you're ambitious, you might extend this to entire racks, or even more inconvenient sets of machines. However, what if your entire datacenter falls off the face of the earth? This talk will examine how current large scale storage systems handle fault tolerance and consistency, with a particular focus on the App Engine datastore. We'll cover techniques such as replication, sharding, two phase commit, and consensus protocols (e.g. Paxos), then explore how they can be applied across datacenters. For presentation slides and all I/O sessions, please go to: code.google.com/events/io/sessions.html

US stocks: August - Sept. 5, 2021 | 非农数据 | Nasdaq 100 22% gain, FAANMG六大巨头 15.8%

Sept. 5, 2021

Here are highlights:

1. 8月美国非农业部门新增就业人数23.5万，低于市场此前预期的75万
2. 市场解读: 德尔塔毒株蔓延与美国疫情形势恶化使劳动力市场复苏遇阻，非农数据将使美联储不会过早退出刺激措施。既然美联储继续放水，那就接着跳接着舞。
3. 纳指100确实已经涨到了阶段性的阻力位，FAAMG明显数个交易日的冲高回落，也预示着科技巨头确实要进入一个调整阶段。
4. 纳斯达克指数，却并没有看到明显的阻力线。
5. 纳指100和纳斯达克指数出现了明显的背离。
6. TFAANMG七大巨头，除了TSLA相对表现弱势之外，剩下六大巨头的涨幅都相当惊人，当然特斯拉年内也是收涨3.95%
7. 英伟达年内暴涨75%、亚马逊年内涨7%、苹果年内涨17%、谷歌年内涨64%、Facebook年内涨17%、微软年内涨36%
8. 纳指100成分股里面剩下90家公司从年初到现在对股指涨幅的贡献只有2%。
9. 当前的美股牛市建立在美联储极其宽松的货币政策之下，只要政策没有什么变化，美债收益率持续走低，资金就会持续不断的流向股市，只要FAANMG六大巨头的抱团行情不结束，美股就不可能下跌。既然跌不下去，空头的损失一天比一天多，基本上就会自然迎来轧空行情。
10. 当前还有很多公司没跑赢大盘，还有很多公司处在超跌状态，还有很多公司的股价没有回到历史新高，纳指100已经涨到了关键阻力位，纳斯达克指数还有明显的上涨动能，接下来的中长期走势似乎变得相当清晰明朗了。
11. 二线成长股龙头和小盘股开始接力，进入一个疯狂的补涨阶段，史诗级的轧空会促使股指继续往上加速暴涨。最终在三季度财报季到来时，因为业绩完全支撑不住当前的股价涨幅，最终迎来真正盘整回调，但到年底美股会再次创下历史新高。
12. 仅FAANMG六大巨头就给纳指100贡献了15.8%的涨幅，而纳指100今年就涨了22%。

美股即将迎来“史诗级轧空”

美股研究社

 

又到了周末和大家见面的时刻，三大股指再次走出了强势的一周，纳指更是单周收涨1.58%，似乎再也没有什么力量去阻止美股的上涨了。

我们不妨来复盘下纳指本周的走势。周一纳指收涨0.9%，再次形成了一个跳空缺口，在如此高的位置还能跳空上涨，很大程度上可能是因为空头平仓。

周二到周四，三大股指都比较低迷，连续三天高开低走，正好纳指100也涨到了关键阻力位附近，当我们都认为回补缺口的时候可能要来了。

周五的纳指再次上涨，虽然涨的并不多，但还是令人大跌眼镜，空头连续三天的进攻被消化在无形之中，没能让大盘往下再跌哪怕0.1%。

周五纳指的上涨，主要是因为令人大跌眼镜的非农数据，8月美国非农业部门新增就业人数23.5万，低于市场此前预期的75万。按照正常逻辑，重要经济数据如此之差，美股应该会出现明显调整。

但市场解读成，德尔塔毒株蔓延与美国疫情形势恶化使劳动力市场复苏遇阻，非农数据将使美联储不会过早退出刺激措施。既然美联储继续放水，那就接着跳接着舞。

如何理解当下的行情？大家不妨来对比下纳指100和纳斯达克指数的技术形态，我们可以清晰的看到，纳指100确实已经涨到了阶段性的阻力位，FAAMG明显数个交易日的冲高回落，也预示着科技巨头确实要进入一个调整阶段。但我们如果再看纳斯达克指数，却并没有看到明显的阻力线。

纳指100和纳斯达克指数出现了明显的背离。上周有个新闻，说的是FAAMG五大巨头的市值超过了日本整体股市，这其实都是在说，过去半年股指的上涨，更多是依赖于超级巨头。TFAANMG七大巨头，除了TSLA相对表现弱势之外，剩下六大巨头的涨幅都相当惊人，当然特斯拉年内也是收涨3.95%。

英伟达年内暴涨75%、亚马逊年内涨7%、苹果年内涨17%、谷歌年内涨64%、Facebook年内涨17%、微软年内涨36%，我们结合这几大巨头在纳指100里面的权重，可以简单的算出，仅FAANMG六大巨头就给纳指100贡献了15.8%的涨幅，而纳指100今年就涨了22%。

如果再把ADBE、PYPL、CSCO、NFLX这几家成分股公司加上，那仅这10家公司就给纳指100贡献了接近20%的涨幅，也就是说，纳指100成分股里面剩下90家公司从年初到现在对股指涨幅的贡献只有2%。再考虑到，纳斯达克指数从年初到现在的涨幅只有19%，这是多么可怕的一件事。

熟悉的行情，熟悉的味道，这其实就是A股里面非常熟知的抱团行情。唯一不同的是，美国科技巨头均有强劲的业绩支撑，而A股里面的消费龙头则是一种镜花水月。所以今年A股的消费白马年初就集体跪了，而美股里面的科技巨头还能再涨大半年时间，即使已经涨这么多了，平均市盈率也在相对可控范围之内。

是泡沫就会破，这是所有人都知道的一件事，当前的美股牛市建立在美联储极其宽松的货币政策之下，只要政策没有什么变化，美债收益率持续走低，资金就会持续不断的流向股市，只要FAANMG六大巨头的抱团行情不结束，美股就不可能下跌。既然跌不下去，空头的损失一天比一天多，基本上就会自然迎来轧空行情。

如果排除Top 10的科技巨头，其他成分股公司对股指的贡献几乎为零，如果排除Top 20的科技巨头，其他成分股公司对股指的贡献基本就是负数。这预示着一件事，那就是当前还有很多公司没跑赢大盘，还有很多公司处在超跌状态，还有很多公司的股价没有回到历史新高，纳指100已经涨到了关键阻力位，纳斯达克指数还有明显的上涨动能，接下来的中长期走势似乎变得相当清晰明朗了。

如果我们复盘过去一年的走势，可以发现FAANMG有明显的两个阶段，第一个阶段是去年9月到今年2月，第二个阶段是今年2月到现在。第一阶段，FAANMG均处在盘整阶段，在一个超级箱体里面震荡，股价基本没有什么涨幅。第二个阶段，则是迎来了一波超级上升浪。

大家记住一件事，在去年9月到今年2月，FAANMG虽然没有给股指贡献涨幅，但纳斯达克100却是一直上涨的，纳斯达克指数则极为少见的跑赢了纳指100，因为其他成分股贡献了股指的大部分上涨。历史总是惊人的相似，接下来半年很有可能会去再一次复制这个过程。

相信这种剧情大家应该可以理解，FAANMG六大巨头，微软都2.26万亿美元了，苹果市值更是超过2.5万亿美元了，谷歌和亚马逊则接近2万亿美元。2万亿美元的市值，除非再发生一次去年3月的崩盘，否则后面即使还有增长，估计未来回报也不会太可观了，毕竟FAANMG的规模已经相当庞大。

2.5万亿美元的苹果，就算能涨，那也涨不到哪里去了，毕竟苹果短时间内不可能再开发一个媲美iPhone的业务，微软也不可能再做出一个类似云计算和office办公软件的现金流，FB和GOOG的广告业务也受制于全球经济发展的增速，亚马逊的AWS和电商高增长阶段也差不多了。

所以接下来的投资思路确实要变了，后续应该朝着哪些板块布局，不是今天这篇文章所能说清楚的，我有时间会和大家去分析分析。现在还是回到接下来的大盘走势上。

BTIG首席股票策略师Julian Emanuel表示，「现在的金融市场基本是都是人为创造的！因为在美国经济刺激与宽松货币政策下，大量资金推动了股市、原物料、房地产市场以及通货膨胀，甚至也推动了令人意外的债券市场走势，即将实际收益率推至 1970 年代低点。从历史数据来看，美国股市一般都会随着波动上升，涨势应该会在今年3季度停滞。如果标普500指数能开始带动史诗般的情绪上扬，这可能让空头回补的压力促使大规模轧空！与1999年年底类似，很有可能在一两周内出现5%的涨幅。」

今天，我们进入了一个极其看涨的股市环境。股市在 2021 年一路狂飙。标准普尔 500 指数已经有 238 个交易日没有出现 5% 的回调。在史无前例的放水环境下，市场忽略了一切利空因素，上涨成为了所有人的共识，任何空头最后都成为推升股指上涨的燃料。

有趣的是，2013-2021 年牛市的走势与 1994-2000 年牛市几乎相同。此刻，纳斯达克指数似乎已准备开始最后的极端贪婪阶段。这种反弹被视为y一种“新常态”，投资者正在大量FOMO购买，因为股票“只能上涨”。纳斯达克指数在高估的担忧中，进一步再次加速上涨并非没有可能。

其实大家也看到了2000年科网泡沫时代的最终走势，在当时估值泡沫已经巨大的时候，纳指竟然还能迎来一波加速暴涨，也就是俗称的“赶顶阶段”。现在的美股FAANMG的估值虽然已高达惊人，但毕竟还有很多股在低位，还能给股指贡献进一步上涨的动能。

大家仔细看，纳斯达克指数的走势，阶段性的阻力位差不多在16200附近，正好对应了5%~6%的涨幅。

那么接下来的走势清晰可见了，FAANMG会在高位横盘震荡消化估值，但市场会进入一个极其狂热的阶段，二线成长股龙头和小盘股开始接力，进入一个疯狂的补涨阶段，史诗级的轧空会促使股指继续往上加速暴涨。最终在三季度财报季到来时，因为业绩完全支撑不住当前的股价涨幅，最终迎来真正盘整回调，但到年底美股会再次创下历史新高。

接下来一个月，忽略任何做空的想法，把每一次回调当成是上车抄底的最佳机会，尽量少关注大盘波动，专注个股基本面研究，可以阶段性的放弃巨头，去买入那些还在低位的高质量成长股，只有这样才能让你放弃对美股估值泡沫的担忧。

“天欲使人灭亡，必先使人疯狂”，美股这一波要涨到让人怀疑人生，要涨到让所有空头都开始反手做多，要涨到没有人再敢看空美股，或许才会迎来真正的回调。

Tommy Hilfiger Womens Logo Hoodie | 30 dollar purchase | My 30 minutes study

 Sept. 5, 2021

I purchased a Tommy Hilfiger womens logo hoodie, so I like to work on a short research about the product. 

Amazon.ca | Product spec | My learning

I found the similar style on Amazon.ca, here is the link. 

The price I paid is $30 Canadian dollars. The original price is $70. 

• 60% Cotton, 40% Polyester
• Pull On closure
• Machine Wash
• Hoodie with Colorblock and Embroidery

I like to look into how Amazon.ca is presenting the product. How many product features are listed on the web page?

Product description

Tommy Hilfiger Hoodie with Colorblock and Embroidery

---

Product details

• Date First Available ‏ : ‎ Oct. 12 2020
• Manufacturer ‏ : ‎ Tommy Hilfiger
• ASIN ‏ : ‎ B08GH42XP3
• Item model number ‏ : ‎ TP00223T
• Department ‏ : ‎ Womens

• Best Sellers Rank: #96,538 in Clothing & Accessories (See Top 100 in Clothing & Accessories)
  • #803 in Women's Hoodies

• Customer Reviews: 
  5.0 out of 5 stars    7 ratings

张维迎陈志武对话

Sept. 5, 2021

Here is the link.