EC2 推出 18TB 與 24TB 的機器...

AWS 又把機器給生出來啦:「EC2 High Memory Update – New 18 TB and 24 TB Instances」。

一樣是限制要買三年 RI 才能用,不過價錢頁面上好像還在更新,在「Amazon EC2 Dedicated Hosts Pricing」只看到了之前就公佈的 12TB 價錢,還沒看到 18TB 與 24TB 的部份...

然後以前會跟同事說,資料小於這台機器記憶體大小的不能叫 big data (當時是 12TB),現在升級到 24TB 啦...

Backblaze 開了歐洲區機房

Backblaze 開了歐洲機房,所以包括了一般性的 Computer BackupB2 Cloud Storage 都可以選擇要放哪邊了...

歐洲的點是放在荷蘭:

Big news: Our first European data center, in Amsterdam, is open and accepting customer data!

價錢也都跟美國的相同:

Whether you choose EU Central or US West, your pricing for our products will be unchanged:

對於在意資料放美國機房的問題應該有緩解一些...

RDBMS 裡的各種 Lock 與 Isolation Level

來推薦其他人寫的文章 (雖然是在 Medium 上...):「複習資料庫的 Isolation Level 與圖解五個常見的 Race Conditions」、「對於 MySQL Repeatable Read Isolation 常見的三個誤解」,另外再推薦英文維基百科上的「Snapshot isolation」條目。

兩篇文章都是中文 (另外一個是英文維基百科條目),就不重複講了,這邊主要是拉條目的內容記錄起來,然後寫一些感想...

SQL-92 定義 Isolation 的時候,技術還沒有這麼成熟,所以當時在訂的時候其實是以當時的技術背景設計 Isolation,所以當技術發展起來後,發生了一些 SQL-92 的定義沒那麼好用的情況:

Unfortunately, the ANSI SQL-92 standard was written with a lock-based database in mind, and hence is rather vague when applied to MVCC systems. Berenson et al. wrote a paper in 1995 critiquing the SQL standard, and cited snapshot isolation as an example of an isolation level that did not exhibit the standard anomalies described in the ANSI SQL-92 standard, yet still had anomalous behaviour when compared with serializable transactions.

其中一個就是 Snapshot Isolation,近代的資料庫系統都用這個概念實做,但實際上又有不少差別...

另外「Jepsen: MariaDB Galera Cluster」這篇裡出現的這張也很有用,裡面描述了不同層級之間會發生的問題:

這算是當系統有一點規模時 (i.e. 不太可能使用 SERIALIZABLE 避免這類問題),開發者需要了解的資料庫限制...

PostgreSQL 的 Bloom index

前幾天才跟人提到 PostgreSQL 的功能與完整性比 MySQL 多不少,剛剛又看到 Percona 的「Bloom Indexes in PostgreSQL」這篇,裡面提到了 PostgreSQL 可以使用 Bloom filter 當作 index。

查了一下資料是從 PostgreSQL 9.6 支援的 (參考「PostgreSQL: Documentation: 9.6: bloom」這邊的說明),不過說明裡面沒看到 DELETE (以及 UPDATE) 會怎麼處理,因為原版的 Bloom filter 資料結構應該沒有能力處理刪除的情況...

另外這幾年比較有名的應該是 Cuckoo filter,不只支援刪除,而且空間與效能都比 Bloom filter 好,不知道為什麼是實做 Bloom filter...

Salesforce 打算買 Tableau

Saleforce 打算要買下 Tableau...

兩邊簽了協議並且都發了新聞稿出來,分別是 Salesforce 的:「Salesforce Signs Definitive Agreement to Acquire Tableau」,以及 Tableau 的:「Salesforce Signs Definitive Agreement to Acquire Tableau」。

這兩家都是掛在 NYSE 的公司,而這次的併購會直接以股票交易 (Tableau 的 1 股換 Salesforce 的 1.103 股,看起來是基於這三天股價計算):

SAN FRANCISCO, Calif. and SEATTLE, Wash. — June 10, 2019 -- Salesforce (NYSE:CRM), the global leader in CRM, and Tableau Software (NYSE: DATA), the leading analytics platform, have entered into a definitive agreement under which Salesforce will acquire Tableau in an all-stock transaction, pursuant to which each share of Tableau Class A and Class B common stock will be exchanged for 1.103 shares of Salesforce common stock, representing an enterprise value of $15.7 billion (net of cash), based on the trailing 3-day volume weighted average price of Salesforce's shares as of June 7, 2019.

Salesforce 想要買 Tableau 是個蠻意外的新聞...

Apple 新的「Find My」帶來的隱私問題

這次 WWDC 推出的新功能,已經有人在討論機制與隱私問題了:「How does Apple (privately) find your offline devices?」。

前一代的「Find my iPhone」需要透過網路與 GPS 資料才能在系統上看到,這一代則是加上 BLE beacon,然後任何一台 iOS device 收到後就回傳回給蘋果:

Every active iPhone will continuously monitor for BLE beacon messages that might be coming from a lost device. When it picks up one of these signals, the participating phone tags the data with its own current GPS location; then it sends the whole package up to Apple’s servers.

幾個隱私問題在於,代傳的 iOS device 也會暴露位置資訊給蘋果,另外收到 BLE beacon 的 iOS device 本身是否可以解讀遺失機器的資訊?而商家看起來也可以利用這個方式主動發送攻擊而得知不少資料 (像是文章裡提到先前蘋果透過 randomize mac address 加強隱私的問題,這邊又多開了一個洞),現在蘋果給的資訊還不夠清楚,需要真的逆向工程確認才知道...

Twitter 對 2x 與 3x 的圖片的研究...

所以發現很多時候用 2x 的圖片就夠了?:「Capping image fidelity on ultra-high resolution devices」。

會這樣討論主要是發現螢幕特性:

The most modern screens are OLED. These screens boast some really great features like pure blacks, and are marketed as 3x scale. However, nearly no "3x scale" OLED actually has perfect 3x3 pixels per dot on their screen.

因為螢幕不是真的到 3x 的要求,丟 2x 的圖片出去就好,省頻寬又省下載時間:

This means that most OLED screens that say they are 3x resolution, are actually 3x in the green color, but only 1.5x in the red and blue colors. Showing a 3x resolution image in the app vs a 2x resolution image will be visually the same, though the 3x image takes significantly more data. Even true 3x resolution screens are wasteful as the human eye cannot see that level of detail without something like a magnifying glass.

省下 38% 的資料量,32% 的時間:

There's no difference that the human eye can see, but will save 38% on data and 32% on latency on the capped image load for this particular example which is reflective of most images that load on Twitter.

這也另外帶出了其他的想法,如果沒有太多時間研究的話,可以考慮先提供 2x 的就好,不需要特地做 3x 的版本...

GrabFood 用定位資料修正餐廳的資訊

Grab 的「How we harnessed the wisdom of crowds to improve restaurant location accuracy」這篇是他們的 data team 整理出來,如何使用既有的資料快速的修正餐廳資訊。裡面提到的方法不需要用到 machine learning,光是一些簡單的統計算法就可以快速修正現有的架構。

這些資訊其實是透過司機用的 driver app 蒐集來的,在 driver app 上有大量的資訊傳回伺服器 (像是定時回報的 GPS 位置,以及取餐狀態),而這些司機因為地緣關係,腦袋裡的資訊比地圖會準不少:

One of the biggest advantages we have is the huge driver-partner fleet we have on the ground in cities across Southeast Asia. They know the roads and cities like the back of their hand, and they are resourceful. As a result, they are often able to find the restaurants and complete orders even if the location was registered incorrectly.

所以透過這些資訊他們就可以反過來改善地圖資料,像是透過司機按下「取餐」的按鈕的地點與待的時間,就可以估算餐聽可能的位置,然後拿這個資訊比對地圖上的資料,就很容易發現搬家但是地圖上沒更新的情況:

Fraction of the orders where the pick-up location was not “at” the restaurant: This fraction indicates the number of orders with a pick-up location not near the registered restaurant location (with near being defined both spatially and temporally as above). A higher value indicates a higher likelihood of the restaurant not being in the registered location subject to order volume

Median distance between registered and estimated locations: This factor is used to rank restaurants by a notion of “importance”. A restaurant which is just outside the fixed radius from above can be addressed after another restaurant which is a kilometer away.

另外也有不少其他的改善 (像是必須在離餐聽某個距離內才能點「取餐」,這個「距離」會因為餐聽可能在室內商場而需要的調整),整個成果就會反應在訂單的取消率大幅下降:

整體看起來是系統產生清單後讓人工後續處理 (像是打電話去店家問?),但這個方式所提供的清單準確度應該很高 (因為司機不會沒事跟自己時間過不去,跑到奇怪地方按下取餐),用這些資料跑簡單的演算法就能夠快速修正不少問題...

PostgreSQL 裡的 B-tree 結構

在「Indexes in PostgreSQL — 4 (Btree)」這邊看到講 PostgreSQLB-tree 結構以及常見的查詢會怎麼使用 B-tree。

裡面講了三種查詢,第一種是等號的查詢 (Search by equality),第二種是不等號的查詢 (Search by inequality),第三種是範圍的查詢 (Search by range)。再後面講到排序與 index 的用法。

雖然是分析 PostgreSQL,但裡面是一般性的概念,其他使用 B-tree 結構的資料庫也是類似作法...

找數列的平均值

2016 年的文章,不過算是經典的題目,所以最近又冒出來了。要怎麼找數列的平均值:「Calculating the mean of a list of numbers」。

You have a list of floating point numbers. No nasty tricks - these aren’t NaN or Infinity, just normal “simple” floating point numbers.

Now: Calculate the mean (average). Can you do it?

你有一串浮點數 (沒有 NaN 與 Infinity),要怎麼找出平均值。要考慮的包括:

  • 第一個要處理的就是設計演算法時各種會 overflow 的情況。
  • 降低誤差。
  • 合理的計算量。

好像很適合拿來 data team 面試時互相討論的題目?因為「平均值」是個商業上本來就有意義的指標,而且從 time-series events 灌進來的資料量有機會產生各種 overflow 情境,或是精確度問題,所以這個問題其實是個在真實世界上會遇到的情境。

想了一下,如果是 integer 的確是簡單很多 (可以算出正確的值),但如果是 float 類型真的難很多:

It also demonstrates a problem: Floating point mathematics is very hard, and this makes it somewhat unsuitable for testing with Hypothesis.

馬上想到的地雷是在 IEEE 754 的 float 世界裡,2^24 + 1 還是 2^24

#include <math.h>
#include <stdio.h>

int main(void)
{
    int i;
    float a;

    for (i = 0; i < 32; i++) {
        a = pow(2, i);
        printf("2^%d     = %f\n", i, a);

        a += 1;
        printf("2^%d + 1 = %f\n", i, a);
    }
}

然後在這邊可以看出差異:

2^23     = 8388608.000000
2^23 + 1 = 8388609.000000
2^24     = 16777216.000000
2^24 + 1 = 16777216.000000