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Amazon DynamoDB 跨區 Replication 以及備份

Amazon DynamoDB 實做了全球性的 replication,以及備份功能:「Amazon DynamoDB Update – Global Tables and On-Demand Backup」。

跨區 replication 的功能讓每個 region 都可以存取當地機房的 DynamoDB:

Global Tables – You can now create tables that are automatically replicated across two or more AWS Regions, with full support for multi-master writes, with a couple of clicks. This gives you the ability to build fast, massively scaled applications for a global user base without having to manage the replication process.

這有點類似 GoogleCloud Spanner 在前陣子也推出全球性服務,但 DynamoDB 提供的比較偏向 NoSQL 而不是 RDBMS。

另外一個限制是跨區同步是 async,會有 replication lag 的問題:

Updates are propagated to other Regions asynchronously via DynamoDB Streams and are typically complete within one second (you can track this using the new ReplicationLatency and PendingReplicationCount metrics).

不過如果是這樣的機制,conflict 的問題不知道怎麼解決... 文章裡面沒看到。


Global Tables are available in the US East (Ohio), US East (N. Virginia), US West (Oregon), EU (Ireland), and EU (Frankfurt) Regions today, with more Regions in the works for 2018.


On-Demand Backup – You can now create full backups of your DynamoDB tables with a single click, and with zero impact on performance or availability. Your application remains online and runs at full speed. Backups are suitable for long-term retention and archival, and can help you to comply with regulatory requirements.


We are rolling this new feature out on an account-by-account basis as quickly as possible, with initial availability in the US East (Northern Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) Regions.

新的 DNS Resolver:

看到新的 DNS Resolver 服務,也拿到了還不錯的 IP address,「New “Quad9” DNS service blocks malicious domains for everyone」,服務網站是「Quad 9 | Internet Security and Privacy in a Few Easy Steps」,主打宣稱過濾已知的危險站台...

由政府單位、IBM 以及 Packet Clearing House 成立的:

The Global Cyber Alliance (GCA)—an organization founded by law enforcement and research organizations to help reduce cyber-crime—has partnered with IBM and Packet Clearing House to launch a free public Domain Name Service system.

也就是說,後面三家都不是專門做網路服務的廠商... 於是就會發現連 Client Subnet in DNS Queries (RFC 7871) 都沒提供,於是查出來的地區都不對,這對使用 DNS resolver 位置分配 CDN 節點的服務很傷啊... (或是其他類似服務)

這是 GooglePublic DNS ( 查出來的:

i.kfs.io.               576     IN      CNAME   kwc.kkcube.com.country.mp.kkcube.com.
kwc.kkcube.com.country.mp.kkcube.com. 21599 IN CNAME TW.kwc.kkcube.com.
TW.kwc.kkcube.com.      188     IN      CNAME   i.kfs.io.cdn.cloudflare.net.
i.kfs.io.cdn.cloudflare.net. 299 IN     A
i.kfs.io.cdn.cloudflare.net. 299 IN     A

;; Query time: 28 msec
;; WHEN: Sat Nov 18 05:30:23 CST 2017
;; MSG SIZE  rcvd: 181

這是 Quad9 ( 查出來的:

i.kfs.io.               1800    IN      CNAME   kwc.kkcube.com.country.mp.kkcube.com.
kwc.kkcube.com.country.mp.kkcube.com. 42702 IN CNAME US.kwc.kkcube.com.
US.kwc.kkcube.com.      300     IN      CNAME   i.kfs.io.cdn.cloudflare.net.
i.kfs.io.cdn.cloudflare.net. 300 IN     A
i.kfs.io.cdn.cloudflare.net. 300 IN     A

;; Query time: 294 msec
;; WHEN: Sat Nov 18 05:30:27 CST 2017
;; MSG SIZE  rcvd: 181

再來一點是,在科技領域相信政府單位通常都是一件錯誤的事情,我 pass... XD

MySQL 總算要拔掉 mysql_query_cache 了

半官方的 MySQL blog 上宣佈了拔掉 mysql_query_cache 的計畫:「MySQL 8.0: Retiring Support for the Query Cache」。

作者開頭引用了 ProxySQL 的人對 MySQL Query Cache 的說明:

Although MySQL Query Cache was meant to improve performance, it has serious scalability issues and it can easily become a severe bottleneck.

主要問題在於 MySQL Query Cache 在多 CPU 環境下很難 scale,很容易造成一堆 thread 在搶 lock。而且作者也同意 ProxySQL 的說法,將 cache 放到 client 的效能比較好:

We also agree with Rene’s conclusion, that caching provides the greatest benefit when it is moved closer to the client:

可以看到 Query Cache 在複雜的環境下對效能極傷。而之前也提到過類似的事情了:「Percona 對 mysql_query_cache 的測試 (以 Magento 為例)」、「關閉 MySQL 的 Query Cache」。

一般如果要 cache 的話,透過 InnoDB 裡良好的 index 應該還可以撐不少量起來。

Google 弄出來的 Grumpy:把 Python 2.7 的程式碼轉成 Go...

Google 放出 Grumpy,可以把 Python 2.7 的程式碼轉成 Go:「Grumpy: Go running Python!」。


This sad to see that Grumpy is mean to be a replacement of CPython 2.7 instead of CPython 3.x . I presume the code from youtube was written in python 2.x hence the reason but I hope we'll see Grumpy supporting python 3.x :)

回到原文,這次的需求主要是出自 YouTube 的需求:

The front-end server that drives youtube.com and YouTube’s APIs is primarily written in Python, and it serves millions of requests per second! YouTube’s front-end runs on CPython 2.7, so we’ve put a ton of work into improving the runtime and adapting our application to work optimally within it.

然後 Python 的 GIL 又被拿出來鞭屍:

除了 C extension 不支援外,還是有些「過於動態」的語法不支援:

exec, eval and compile: These dynamic features of CPython are not supported by Grumpy because Grumpy modules consist of statically compiled Go code. Supporting dynamic execution would require bundling Grumpy programs with the compilation toolchain which would be unwieldy and impractically slow.

這樣可用的範圍少不少,這個專案可以當作 YouTube 這種規模的網站所做的改善,而不是什麼可以拿來用的工具 :o

Dropbox 在全球建機房加速...

Dropbox 為了加速傳輸,在全球到處建機房降低 latency:「Infrastructure Update: Pushing the edges of our global performance」。

就他們測試發現,透過 proxy server 降低 latency 的效果很不錯:

We’ve tested and applied this configuration in various markets in Europe and Asia. In France, for example, median download speeds are 40% faster after introducing proxy servers, while median upload speeds are approximately 90% faster. In Japan, median download speeds have doubled, while median upload speeds are three times as fast.


關閉 MySQL 的 Query Cache

MySQL 的 Query Cache 是目前已知效能不好的主要因素之一 (global mutex lock 的緣故),在正式環境裡的 best practice 一般都是關閉,之前測過也是一開下去效能就會狂掉...

Percona 的人在討論要怎麼樣才能完全關閉 MySQL 的 Query Cache:「Is Your Query Cache Really Disabled?」,而他們發現只有在 query_cache_typequery_cache_size 都設為 0,而且重開 MySQL 才能完全避免 global mutex lock:

[W]e can see it is not possible to fully disable the query cache on the fly by changing query_cache_type or/and query_cache_size to 0. Based on the code and the tests, if you want to make sure the query cache is fully disabled, change query_cache_size and query_cache_type to 0 and restart MySQL.

應該是要再修正 my.cnf 的 template 了...

Netflix 開發的 Delayed Queue

原來這個叫做 Delayed Queue,難怪之前用其他關鍵字都找不到什麼資料... (就不講其他關鍵字了 XD)

Netflix 發表了他們自己所開發的 Delayed Queue:「Distributed delay queues based on Dynomite」。

本來的架構是用 Cassandra + Zookeeper 來做:

Traditionally, we have been using a Cassandra based queue recipe along with Zookeeper for distributed locks, since Cassandra is the de facto storage engine at Netflix.

但可以馬上想到不少問題,就如同 Netflix 提到的:

Using Cassandra for queue like data structure is a known anti-pattern, also using a global lock on queue while polling, limits the amount of concurrency on the consumer side as the lock ensures only one consumer can poll from the queue at a time.

所以就改放到 Netflix 另外開發的 Dynamite 上:

Dynomite, inspired by Dynamo whitepaper, is a thin, distributed dynamo layer for different storage engines and protocols. Currently these include Redis and Memcached. Dynomite supports multi-datacenter replication and is designed for high availability.

後端是 RedisMemcached 的系統,可以對抗整個機房從 internet 上消失的狀態。

在設計上則是「保證會跑一次」,也就是有可能會有多次的情況,用 Dyno Queues 系統的人必需要考慮進去:

4. At-least-once delivery semantics

雖然整篇講的頗輕鬆,但實際看起來還是很厚重... 暫時還是不會用吧 :o

Percona 對 mysql_query_cache 的測試 (以 Magento 為例)

Percona 的人以現在的觀點來看 mysql_query_cache:「The MySQL query cache: Worst enemy or best friend?」。

起因主要也是懷疑 query cache 是 global mutex 在現在的硬體架構 (主要是 CPU 數量成長) 應該是個負面的影響,但不確定影響多少:

The query cache is well known for its contentions: a global mutex has to be acquired for any read or write operation, which means that any access is serialized. This was not an issue 15 years ago, but with today’s multi-core servers, such serialization is the best way to kill performance.

這邊就有點怪了,PK search 應該是個位數 ms 等級才對 (一般 EC 網站的資料量都應該可以 memory fit),不知道他是怎麼測的:

However from a performance point of view, any query cache hit is served in a few tens of microseconds while the fastest access with InnoDB (primary lookup) still requires several hundreds of microseconds. Yes, the query cache is at least an order of magnitude faster than any query that goes to InnoDB.

anyway,他實際測試兩個不同的 configuration,首先是打開 query cache 的:

再來是關閉 query cache 的:

測試的方式在原文有提到,這邊就不抄過來了。測試的結果可以看到關閉 query cache 得到比較好的 thoughput:

Throughput scales well up to somewhere between 10 and 20 threads (for the record the server I was using had 16 cores). But more importantly, even at the higher concurrencies, the overall throughput continued to increase: at 20 concurrent threads, MySQL was able to serve nearly 3x more queries without the query cache.