When the event reaches the target, any event listeners registered on the EventTarget are triggered. Although all EventListeners on the EventTarget are guaranteed to be triggered by any event which is received by that EventTarget, no specification is made as to the order in which they will receive the event with regards to the other EventListeners on the EventTarget.
Next, the implementation must determine the current target's candidate event listeners. This must be the list of all event listeners that have been registered on the current target in their order of registration. [HTML5] defines the ordering of listeners registered through event handler attributes.
For example gcc’s libgfortran is missing a libquadmath build dependency. It is natural not to encounter it in real world as libquadmath is usually built along with other small runtimes way before g++ or gfortran is ready.
他的基本想法是把 target 的順序打亂掉，也就是在有指定 --shuffle 時，不一定會照 a -> b -> c 的順序往下遞迴，而可能會是 c -> b -> a 或是其他的順序：
all: a b c
這樣對於抓那些在 -j 平行編譯時會出包的套件也很有幫助，不需要在 -j 開很大的情況下才能重製問題，而是平常就有機會在 CI 環境下被抓出來。
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.
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.
Amazon Aurora is a relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. The MySQL-compatible edition of Aurora delivers up to 5X the throughput of standard MySQL running on the same hardware, and is designed to be compatible with MySQL 5.6, enabling existing MySQL applications and tools to run without requiring modification.
Spectre 的精華在於 CPU 支援 branch prediction 與 out-of-order execution，也就是 CPU 遇到 branch 時會學習怎麼跑，這個資訊提供給 out-of-order execution 就可以大幅提昇執行速度。可以參考以前在「CPU Branch Prediction 的成本...」提到的效率問題。
原理的部份可以看這段程式碼：
這類型程式碼常常出現在現代程式的各種安全檢查上：確認 x 沒問題後再實際將資料拉出來處理。而我們可以透過不斷的丟 x 值進去，讓 CPU 學到以為都是 TRUE，而在 CPU 學壞之後，突然丟進超出範圍的 x，產生 branch misprediction，但卻已經因為 out-of-order execution 而讓 CPU 執行過 y = ... 這段指令，進而導致 cache 的內容改變。
Suppose register R1 contains a secret value. If the speculatively executed memory read of array1[R1] is a cache hit, then nothing will go on the memory bus and the read from [R2] will initiate quickly. If the read of array1[R1] is a cache miss, then the second read may take longer, resulting in different timing for the victim thread.
所以相同道理，利用乘法器被佔用的 timing attack 也可以產生攻擊：
if (false but mispredicts as true)
multiply R1, R2
multiply R3, R4
In addition, of the three user-mode serializing instructions listed by Intel, only cpuid can be used in normal code, and it destroys many registers. The mfence and lfence (but not sfence) instructions also appear to work, with the added benefit that they do not destroy register contents. Their behavior with respect to speculative execution is not defined, however, so they may not work in all CPUs or system configurations.
However, we may manipulate its generation to control speculative execution while modifying the visible, on-stack value to direct how the branch is actually retired.
原因是 Linux 與 OS X 上有 direct-physical map 的機制，會把整塊 physical memory 對應到 virtual memory 的固定位置上，這些位置不會再發給 user space 使用，所以是通的：
On Linux and OS X, this is done via a direct-physical map, i.e., the entire physical memory is directly mapped to a pre-defined virtual address (cf. Figure 2).
而在 Windows 上則是比較複雜，但大部分的 physical memory 都有對應到 kernel address space，而每個 process 裡面也都還是有完整的 kernel address space (只是受到權限控制)，所以 Meltdown 的攻擊仍然有效：
Instead of a direct-physical map, Windows maintains a multiple so-called paged pools, non-paged pools, and the system cache. These pools are virtual memory regions in the kernel address space mapping physical pages to virtual addresses which are either required to remain in the memory (non-paged pool) or can be removed from the memory because a copy is already stored on the disk (paged pool). The system cache further contains mappings of all file-backed pages. Combined, these memory pools will typically map a large fraction of the physical memory into the kernel address space of every process.
A San Diego TV station sparked complaints this week – after an on-air report about a girl who ordered a dollhouse via her parents' Amazon Echo caused Echoes in viewers' homes to also attempt to order dollhouses.
An index_col_name specification can end with ASC or DESC. These keywords are permitted for future extensions for specifying ascending or descending index value storage. Currently, they are parsed but ignored; index values are always stored in ascending order.
所以當 8.0 建立了 a_desc_b_asc (a DESC, b ASC) 這樣的 index，可以看到對於不同 ORDER BY 時效能的差異：(一千萬筆資料)
有些變快可以理解，但有些結果不太清楚造成的原因...
Anyway，對於變慢的兩個 query，他提了一個不算解法的解法，就是加上對應的 index XDDD：
If user wants to avoid filesorts for Query 5 and Query 6, he/she can alter the table to add a key (a ASC, b ASC) . Further to this, if the user wants to avoid backward index scans too, he/she can add both ( a ASC, b DESC) and (a DESC, b DESC).
It seems like the memory usage is between 20% and 25% smaller. Great job!
Memory usage, Python 3.5 => Python 3.6 on Linux x86_64:
./python -c 'import sys; print(sys.getsizeof({str(i):i for i in range(10)}))'
* 10 items: 480 B => 384 B (-20%)
* 100 items: 6240 B => 4720 B (-24%)
* 1000 items: 49248 B => 36984 B (-25%)
Note: the size is the the size of the container itself, not of keys nor values.
不過效能上似乎慢了一些：
3% slowdown in microbench is not surprising.
Compact dict introduces one additional indirection.
Instead, I've added freelist for most compact PyDictKeys.
So I think overall performance is almost same to before compact dict.
不過也有人提到應該拿 3.5 + patch 測，而不是直接拿 3.6 測：
There are a lot of other changes in interpreter core between 3.5 and 3.5 (such as new bytecode and optimized function calls). Could you compare the performance between the version just before adding new dict implementation and the version just after this?
