# This match takes 10 sec. in Ruby 3.1, and 0.003 sec. in Ruby 3.2
/^a*b?a*$/ =~ "a" * 50000 + "x"
而另外一組 regexp 也可以看出類似的效果：
用一些記憶體空間換取效能，降低被 DoS 的一些機會。另外一方面，引入了 regexp timeout 的 workaround，緩解真的被打的時候的資源消耗上限：
The optimization above cannot be applied to some kind of regular expressions, such as those including advanced features (e.g., back-references or look-around), or with a huge fixed number of repetitions. As a fallback measure, a timeout feature for Regexp matches is also introduced.
傳統的 Busrting Throughput 模式會依照你的使用空間分配對應的速度，基礎是 50MB/sec per TB 計算，但可以 burst 到 100MB/sec per TB：
When burst credits are available, a file system can drive throughput up to 100 MiBps per TiB of storage, up to the Amazon EFS Region's limit, with a minimum of 100 MiBps. If no burst credits are available, a file system can drive up to 50 MiBps per TiB of storage, with a minimum of 1 MiBps.
Elastic Throughput allows you to drive throughput up to a limit of 3 GiB/s for read operations and 1 GiB/s for write operations per file system in all Regions.
Amazon EFS now delivers up to 60% lower read operation latencies when working with frequently-accessed data and metadata.
另外是對小檔寫入有做處理：
In addition, EFS now delivers up to 40% lower write operation latencies when working with small files (<64 KB) and metadata.
不過這些改進只有在新的 EFS 才會有，而且這波只有 us-east-1 上：
These enhancements are available automatically for all new EFS file systems using General Purpose mode in the US East (N. Virginia) Region, and will become available in the remaining AWS commercial regions over the coming weeks.
With SnapStart, the initialization phase (represented by the Init duration that I showed you earlier) happens when I publish a new version of the function. When I invoke a function that has SnapStart enabled, Lambda restores the snapshot (represented by the Restore duration) before invoking the function handler. As a result, the total cold invoke with SnapStart is now Restore duration + Duration.
比較特別的是亂數產生器需要重新 initialize，不然會有機率產生出一樣的 random data，這個是一般開發者會忽略掉的：
When using SnapStart, any unique content that used to be generated during the initialization must now be generated after initialization in order to maintain uniqueness.
We have updated OpenSSL’s RAND_Bytes to ensure randomness when used in conjunction with SnapStart, and we have verified that java.security.SecureRandom is already snap-resilient.
Our original product, which we’re retroactively calling Pyston-full, is a fork of the entire CPython codebase. Having users install a fully-custom version of Python lets us make changes across the Python implementation, leading to the most optimizations and largest speedups.
但這種方式的安裝與維護都需要另外搞，而且因為 ABI 不相容的問題，遇到一些套件可能會需要自己編 (甚至自己改？)，不能直接用編好的 binary：
The flip side is that it is fairly intensive to set up. While we believe Pyston-full is one of the most highly-compatible alternative Python implementations available, it can be difficult to switch Python implementations regardless of the ease of use of either implementation. Compounded on this, we decided to break the ABI which requires users to recompile extension modules. In theory this is not a big deal, but in practice the lack of available binary packages is a significant disincentive to use an alternative implementation.
The sum of all of this was that while we were very happy to achieve a 30% speedup with Pyston-full, it was very difficult to get people to start using it. We decided to try a different form factor: a pip-installable extension module called Pyston-lite.
但效能的提昇就不像 Pyston-full 這麼高，Pyston-lite 只剩下 10% 了：
So while it’s a bit difficult to accept that we are now providing a 10% speedup instead of 30%, we’ve decided that it’s much more important to provide something that people are willing to use.
In the longer-term future we are planning to submit our JIT upstream as well, but we expect retargeting it to 3.11 to be significantly more work than the other versions due to the extensive amount of changes that were made to the interpreter in that version.
Trainium-based EC2 Trn1 instances solve this challenge by delivering faster time-to-train while offering up to 50% cost-to-train savings over comparable GPU-based instances.
For large-scale model training, Trn1 instances integrate with Amazon FSx for Lustre high-performance storage and are deployed in EC2 UltraClusters. EC2 UltraClusters are hyperscale clusters interconnected with a non-blocking petabit-scale network.
但第一波開放的區域有點少，只有萬年美東一區 us-east-1 與美西二區 us-west-2：
You can launch Trn1 instances today in the AWS US East (N. Virginia) and US West (Oregon) Regions as On-Demand, Reserved, and Spot Instances or as part of a Savings Plan.
In this latest release, PostgreSQL improves on its in-memory and on-disk sorting algorithms, with benchmarks showing speedups of 25% - 400% based on which data types are sorted.
PostgreSQL 15 adds support for LZ4 and Zstandard (zstd) compression to write-ahead log (WAL) files, which can have both space and performance benefits for certain workloads.
I remember back in 2000 ish I worked in R&D of a PC manufacturer and had to check the new PSUs and cases with a 'Test Finger' to make sure all the holes were small enough. The test finger was really expensive IIRC.
