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Amazon Aurora (MySQL) 推出的 Asynchronous Key Prefetch

Amazon Aurora (MySQL) 推出新的效能改善,可以改善 JOIN 時的效能:「Amazon Aurora (MySQL) Speeds Join Queries by More than 10x with Asynchronous Key Prefetch」。

看起來像是某個情況的 optimization,將可能的 random access 換成 sequential access 而得到大量的效能:

This feature applies to queries that require use of the Batched Key Access (BKA) join algorithm and Multi-Range Read (MRR) optimization, and improves performance when the underlying data set is not in the main memory buffer pool or query cache.

其實記憶體還是最好用的加速器,能加大硬拼就先硬拼... XD

Amazon Device Farm 支援讓使用者直接連上去 debug 了...

Amazon Device Farm 推出這樣的功能又朝著設備租賃服務更進一步了:「Amazon Device Farm Launches Direct Device Access for Private Devices」。

Now, with direct device access, mobile applications developers can use individual devices in their private test set as if they were directly connected to their local machine via USB. Developers can now test against a wide array of devices just like they would as if the devices were sitting on their desk.

這樣就可以使用更底層的東西了...

nginx 記錄 TLS 連線資訊

想要在 nginx 的 access log 裡面記錄使用者在 HTTPS 連線使用的 TLS protocol 與 cipher。

在「How can I let nginx log the used SSL/TLS protocol and ciphersuite?」這邊有提到方向是 $ssl_protocol$ssl_cipher (出自「Module ngx_http_ssl_module」內的 Embedded Variables 章節)。

他的方式是在前面就插入 protocol,但我希望前面的欄位保持不變,把 protocol & cipher 放到後面,所以我就加了一個 /etc/nginx/conf.d/combined_ssl.conf (這邊我用 ondrej 的 PPA,在設定檔裡會撈 /etc/nginx/conf.d/ 下的設定,不確定其他的情況如何):

#
log_format combined_ssl '$remote_addr - $remote_user [$time_local] "$request" $status $body_bytes_sent "$http_referer" "$http_user_agent" $ssl_protocol/$ssl_cipher';

然後本來用 combined 的 HTTPS 設定就改成 combined_ssl

來放一陣子再來分析,然後想看看要怎麼調整 cipher...

StackOverflow 上離開 Vim 方法的文章...

被拿出來當 PR 宣傳了:「Stack Overflow: Helping One Million Developers Exit Vim」。

由於 Vim 是 Unix-like 系統一定會內建的 editor,所以常常被拿來放在 tutorial 裡面 (考慮到普及性,但完全不熟的初學者就...),或是不小心在輸入 vipw 或是 visudo 之類的指令就中獎了:

可以看到 pageview 破一百萬次了 XDDD 而且流量也都很穩定:

依照地區來拆開的話:(不過沒有照人口數正規化...)

然後做交叉分析,看這些卡在 Vim 的人平常是看什麼其他的文章:

回到資料分析的角度來看,這些東西可以透過有 cookie 的 access log 做到。有 access log 後可以用 Google CloudBigQuery,也可以用 AWS 家的 Amazon Athena 做。

Swap 對 InnoDB 的影響

Percona 的老大拿 5.7 版做實驗,確認 swap 對 InnoDB 的影響:「The Impact of Swapping on MySQL Performance」。

測試的機器是 32GB RAM,作業系統 (以及 swap) 裝在已經有點年紀的 Intel 520 SSD 上,而 MySQL 則是裝在 Intel 750 NVMe 上。透過對 innodb_buffer_pool 的調整來看情況。

可以看到設為 24GB (記憶體 75% 的量) 時很穩定的在 44K QPS 與 3.5ms (95%):

This gives us about 44K QPS. The 95% query response time (reported by sysbench) is about 3.5ms.

而當設成 32GB 的時候開始可以觀察到 swap i/o,掉到 20K QPS 與 9ms (95%):

We can see that performance stabilizes after a bit at around 20K QPS, with some 380MB/sec disk IO and 125MB/sec swap IO. The 95% query response time has grown to around 9ms.

當拉到 48GB 的時候就更掉更多,6K QPS 與 35ms (95%):

Now we have around 6K QPS. Disk IO has dropped to 250MB/sec, and swap IO is up to 190MB/sec. The 95% query response time is around 35ms.

作者發現掉的比率沒有想像中大:

When I started, I expected severe performance drop even with very minor swapping. I surprised myself by getting swap activity to more than 100MB/sec, with performance “only” halved.

這邊測試用的是 SSD,如果是傳統用磁頭的硬碟,對 random access 應該會很敏感而掉更多:

This assumes your swap space is on an SSD, of course! SSDs handle random IO (which is what paging activity usually is) much better than HDDs.

基本上還是要避免碰到 swap 啦,另外 comment 的地方剛好有提到前陣子在猜測的 best practice,測試時的 vm.swappiness 是設成 1,這應該是作者的 best practice:

Swappiness was set to 1 in this case. I was not expecting this to cause significant impact as swapping is caused by genuine (intended) missconfiguration with more memory required than available.

Amazon EBS 推出新磁碟種類

Amazon EBS 推出了新的磁碟種類,都是比現在更經濟 (白話文:更便宜) 的方案:「Amazon EBS Update – New Cold Storage and Throughput Options」。

第一種是 Amazon EBS Throughput Optimized HDD,代號是 st1;第二種是 Amazon EBS Cold HDD,代號是 sc1,兩種都是傳統磁頭硬碟。

第一種 st1 重視 sequential 的 throughput:

Starts at 250 MB/s for a 1 terabyte volume, and grows by 250 MB/s for every additional provisioned terabyte until reaching a maximum burst throughput of 500 MB/s.

第二種 sc1 則是重視堆資料的費用:

Designed for workloads similar to those for Throughput Optimized HDD that are accessed less frequently; $0.025 / gigabyte / month.

要注意的是,IOPS 是可以累計的,而未滿 1MB 的 access 會計算成 1MB,所以只適合大量 sequential access 的應用,像是 Hadoop 這類 big data 類的應用:

For both of the new magnetic volume types, the burst credit bucket can grow until it reaches the size of the volume. In other words, when a volume’s bucket is full, you can scan the entire volume at the burst rate. Each I/O request of 1 megabyte or less counts as 1 megabyte’s worth of credit. Sequential I/O operations are merged into larger ones where possible; this can increase throughput and maximizes the value of the burst credit bucket (to learn more about how the bucket operates, visit the Performance Burst Details section of my New SSD-Backed Elastic Block Storage post).

另外 sc1 也是目前每單位裡面最便宜的價錢,不知道拿來當 root 會底多慢 XDDD

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