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。
Add a new garbage collection (GC) algorithm named Shenandoah which reduces GC pause times by doing evacuation work concurrently with the running Java threads. Pause times with Shenandoah are independent of heap size, meaning you will have the same consistent pause times whether your heap is 200 MB or 200 GB.
15 rows in set (1 min 53.36 sec)
15 rows in set (1 hour 25 min 51.89 sec)
打開後大約是原來的 1/45 時間，提昇超多...
不過還是有些限制，我最在意的就是目前只支援相容於 MySQL 5.6 的版本 (居然不是先支援 5.7)：
Engine Support – We are launching with support for MySQL 5.6, and are working on support for MySQL 5.7 and PostgreSQL.
然後沒有多餘費用，只是 i/o cost 可能會增加：
Cost – You can make use of Parallel Query at no extra charge. However, because it makes direct access to storage, there is a possibility that your IO cost will increase.
Amazon Aurora Parallel Query improves the performance of large analytic queries by pushing processing down to the Aurora storage layer, spreading processing across hundreds of nodes.
The preview is available for the MySQL-compatible edition of Amazon Aurora, and is currently available in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Ireland) Regions. Sign up to get access.
Parallel workers can now partially aggregate the data and pass the transition values back to the leader, which can combine the partial results to produce the final answer.
We performed some tests on a 4 CPU 64 core server with 256GB of RAM using TPC-H @ 100 GB scale on query 1. This query performs some complex aggregation on just over 600 million records and produces 4 output rows.
由於是 64 cores，所以作者測 1 到 64 workers 的效能，這是測試出來的結果：
要注意 Y 軸是對數比例，紅色是理論值，藍色是實際值，可以看出來平行化的效能頗不錯，雖然開到 64 workers 時效率已經不到一半了。
rhaas=# \timing
Timing is on.
rhaas=# select * from pgbench_accounts where filler like '%a%';
aid | bid | abalance | filler
-----+-----+----------+--------
(0 rows)
Time: 743.061 ms
rhaas=# set max_parallel_degree = 4;
SET
Time: 0.270 ms
rhaas=# select * from pgbench_accounts where filler like '%a%';
aid | bid | abalance | filler
-----+-----+----------+--------
(0 rows)
Time: 213.412 ms
