CloudFront 把本來的 Lambda@Edge 產品線拆細,推出 CloudFront Functions

Amazon CloudFront 本來的 Lambda@Edge 產品線拆細,多出一個 CloudFront Functions:「Introducing CloudFront Functions – Run Your Code at the Edge with Low Latency at Any Scale」。

就產品面的角度就是限制比 Lambda@Edge 多,但價錢變便宜很多。

先看價錢的部份,CloudFront Functions 的價錢只有 request:

Invocation pricing is $0.10 per 1 million invocations ($0.0000001 per request).

而 Lambda@Edge 則是兩筆費用,光是 request 費用就是六倍:

Request pricing is $0.60 per 1 million requests ($0.0000006 per request).

Duration is calculated from the time your code begins executing until it returns or otherwise terminates. You are charged $0.00005001 for every GB-second used.

當然,CloudFront Functions 便宜帶來的限制也不少,最主要的限制可以從最大執行時間只有 1ms,以及記憶體只能用 2MB 就可以看出來:

但這對於輕量的操作來說已經夠用了,主要就是對 HTTP header 的操作...

另外比較表上看到個有趣的點「JavaScript (ECMAScript 5.1 compliant)」,這樣應該就不會是 Node.js (V8 engine),而是其他的 JS engine?

AWS 區域間的連線測試

Hacker News 首頁上看到「AWS Latency Monitoring」這個,看起來是常態性在所有的機房都開機器一直測試蒐集資料,就可以直接拉出來看...

有常見的 p50 與 p99 資訊,對於在規劃架構的時候還蠻有用,在「mda590/cloudping.co」這邊可以看到他是用 LambdaDynamoDB 的 endpoint 測試。

好像沒有 packet loss rate 的資訊,這個也蠻重要的...

AWS Lambda 的消息:計費方式 1ms、上限變高、自訂 Image

這次 AWS re:InventAWS Lambda 也更新了不少東西:

首先是計價方式的改變,從本來 100ms 降到 1ms,對於這點 Cliff 有提出來了,cold start 會是成本中很重的一環 (在 https://www.facebook.com/clifflu666/posts/10214677842380050 這邊):

不過從本來的 100ms 變成現在的 1ms 只會變便宜,如果本來 100ms 的價錢就可以接受的話,現在看起來沒有理由變差 (humm,心情例外)。

會改變的是,以前可能不會想要對 cold start 最佳化 (因為做了還是收 100ms 的錢),現在就可以考慮進去讓成本再降了。

第二個是 lambda 的上限會變成 10GB RAM 與 6vCPU,剛好前幾天翻資料的時候,有翻到改版前的限制:「lambda_cpu_cores.md」,之前的上限是約 3GB 的記憶體與 2vCPU,現在拉起來讓你可以跑更大的東西...

第三個是讓你可以自訂 container image,而不需要用 AWS 提供的標準 image 跑,這點對於降低 cold start 成本也蠻有幫助的,另外一方面,總算是可以對環境客製化了...

裡面看起來最重要的應該還是第一個,計價方式讓整個成本評估大幅改變...

拿 Cloudflare Workers 跑 Geolocation API

Hacker News Daily 上看到拿 Cloudflare Workers 跑 Geolocation API:「How to make simple Geolocation service」。

作者想要做一個很簡單的 Geolocation API,一開始的想法是在 AWS Lambda 上用 MaxMind 的資料,但 latency 偏高:

However, I quickly realized that the response time isn't what I've expected - on average the response took somewhere between from 200ms to 500ms. So I started looking for other options.

所以作者就想到是不是有有機會丟到 Cloudflare Workers 上,但發現 license 看起來是個問題,另外因為把 MaxMind 的資料庫丟進去,會超過 worker 的時間限制:

And for this case MaxMind offers GeoLite2 database, however you'll be in charge of hosting this database on your server and making regular updates of the database. You also need to make sure your project is compliant with MaxMind's License.

However, this solution had one really big caveat - MaxMind GeoLite2 database does not work on Cloudflare Workers due to some runtime limitations.

結果作者後來發現 Cloudflare Workers 上本身就會帶 Geoloation 資訊了,不需要另外拉 MaxMind 的資料查:

And after exploring their documentation, I realized that the Request object in function have an access to cf object, which contains some useful information about the visitor, including visitor's country!

另外我翻了一下價錢,主要是算次數的,看起來 Free Plan 就可以 100K/day (執行時間限制是 10ms),而如果是付費方案的話則是 USD$0.5/M (50ms),這樣對一些小專案來說,Free Plan 似乎是夠用了...

Lambda 可以掛 EFS 了

AWS Lambda 可以掛 Amazon EFS 了:「New – A Shared File System for Your Lambda Functions」。

這有點像是一開始 Amazon EC2 只能把資料存到 Amazon S3 上,後來支援 EBS 的感覺:這使得很多程式可以直接用內建的 library 操作檔案系統,而不需要掛 AWS 專用的 library 操作 Amazon S3。

有了一個 filesystem 後馬上就可以想到很多惡搞的方法,像是用 lambda 搞 serverless PHP 之類的,之後應該會看到很有「創意」的玩法...

Lambda 被放進 Savings Plans 了

前幾天才在 Ptt 上回了一些對 Lambda 與 Serverless 的想法,結果剛剛看到 Lambda 被納入 Savings Plans 裡面了:「Savings Plan Update: Save Up to 17% On Your Lambda Workloads」。

最多 17% 的折扣,看起來比其他的低不少,應該是因為 Lambda 比起 EC2 或是 Fargate 更動態的關係:

Today I am happy to be able to tell you that Compute Savings Plans now apply to the compute time consumed by your AWS Lambda functions, with savings of up to 17%.

現有的 Savings Plans 如果沒有用完的部份也會自動套用進去。先放著看看...

AWS Lambda 的 Provisioned Concurrency

AWS Lambda 推出了 Provisioned Concurrency,降低冷啟動所需要的時間以確保效率:「New – Provisioned Concurrency for Lambda Functions」。

看 benchmark 的資料就很清楚,可以避免冷啟動所產生的延遲:

這邊也可以看出來就算是冷啟動,大約也是多個一秒多。如果這個延遲是需要被處理的,就可以考慮用 Provisioned Concurrency 了。

價位上是依照保留的量:

You only pay for the amount of concurrency that you configure and for the period of time that you configure it. Pricing in US East (N. Virginia) is $0.015 per GB-hour for Provisioned Concurrency and $0.035 per GB-hour for Duration. The number of requests is charged at the same rate as normal functions. You can find more information in the Lambda pricing page.

不過如果量再更大,而且考慮成本,應該會考慮改回傳統的架構,用多台 EC2 instance 跑...

AWS 推出了 Live 時全自動上字幕的功能

AWS 推出了在直播時就自動上字幕的功能:「Introducing Live Streaming with Automated Multi-Language Subtitling」,其實就是把現有的服務兜出來:「Live Streaming with Automated Multi-Language Subtitling」。

The solution deploys Live Streaming on AWS which includes AWS Elemental MediaLive, MediaPackage, Amazon CloudFront. The solution also deploys AWS Lambda, Amazon Simple Storage Service, Amazon Transcribe, and Amazon Translate.

對於比較沒那麼要求翻譯品質的情況也許可以玩看看...?

DynamoDB Autoscaling 的各種眉眉角角...

AdRollDynamoDB Autoscaling 的踩雷記錄,裡面有些資訊如果不是跳下去玩應該不會注意到 (魔鬼藏在細節裡的感覺):「Managing DynamoDB Autoscaling with Lambda and Cloudwatch」。

第一個提到的問題是 autoscaling 的觀察對象:

Ideally, the table should scale based on the number of requests that we are making , not the number of requests that are successful.

另外一個是 autoscaling 遇到完全不用的情況下不會 scale down,看起來是某種保護機制。但這使得平常只有拿來讀取的表格在跑完 batch job 後得自己處理 write scale down 問題:

Additionally, at the time of implementing this algorithm, the DynamoDB capacity could not be brought down automatically if the consumption was exactly zero, which can happen if you write to your table in batch instead of realtime, for example.

This meant that, when enabling autoscaling, tables that were read in realtime, but written to in batch, still needed manual intervention to bring the write capacity down after our jobs were done writing.

另外一個問題是 scale down 是有次數限制的:

Another interesting point that might bite users is that capacity decreases are an expensive operation for AWS, so they’re limited.

The number of decreases cited in the documentation can be achieved under very special conditions, since you need to have 4 decreases in the first hour of the day plus one for each of the remaining hours, for a total of 4 (first hour) + 23 (1 hourly) = 27.

後面就是自己研究什麼 algorithm 可以調整的更細,然後用 lambda 重寫... 最後省下 30% 的成本:

Here is where we detected our costs for our batch tables dropping to around 30% of the initial cost.

AdRoll 的規模應該是不小,所以為了省 30% 可以花不少力氣在上面...

AWS Lambda 可以直接掛進 ALB 了...

AWS 這次對 Lambda 還發表了不少功能,除了前面提到透過 Layout 支援其他語言以外 (e.g. Ruby),這邊要再提到另外一個重要的功能。

這次是 ALB 可以直接呼叫 lambda function 了:「Lambda functions as targets for Application Load Balancers」。

以前還得靠 API Gateway 整半天 (因為版本設定),現在直接用 ALB 接就可以了?而且 ALB 這邊有規劃對應的 quota:

There is no change to the hourly price of ALB. The load balancer capacity units (LCUs) of ALB now include 0.4 GB per hour of data processing to AWS Lambda targets.

這樣就更接近 serverless 了...