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[package]
name = "send-requests"
version = "0.1.0"
edition = "2021"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]
chrono = "0.4.38"
clap = { version = "4.5.4", features = ["derive"] }

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Prerequisites

• Docker
• Your own AWS account
• AWS CLI (https://aws.amazon.com/cli/) + configuration
• SAM (https://aws.amazon.com/serverless/sam/)
• Rust toolchain (recommended https://rustup.rs/)
• Zig (https://ziglang.org/)
• Cargo Lambda (https://www.cargo-lambda.info/)

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{
  "Version": "2012-10-17",
  "Statement": {
    "Effect": "Deny",
    "Action": "*",
    "Resource": "*",
    "Condition": {
      "DateLessThan": {"aws:TokenIssueTime": "2014-05-07T23:47:00Z"}
    }
  }

lmammino

if (typeof Promise.withResolvers === 'undefined') {
  Promise.withResolvers = function () {
    let resolve, reject
    const promise = new Promise((res, rej) => {
      resolve = res
      reject = rej
    })
    return { promise, resolve, reject }
  }
}

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Initial steps

1. Launch Cloud9 IDE with SSM option
2. When it launchers, open the Cloud9 IDE in the AWS Console

With SSM

Install:

• AWS CLI

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[package]
name = "learning-nom"
version = "0.1.0"
edition = "2021"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]
nom = "7.1.2"

lmammino

How to get type checking and autocompletion in your IDE without TypeScript for your AWS Lambda code

A quick demo that shows how JSDoc annotations can help you to get autocompletion and type checking without having to write (and transpile!) TypeScript code.

The video version is available here: https://youtube.com/shorts/60zUEc_vs1o

For more AWS tutorials, follow us at @AWSBites

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Request batching benchmark

Request batching is a Node.js pattern that can be used to optimise how a web server handles identical concurrent requests.

It allows to process the request only once for all the clients concurrently requesting the same information. This can save expensive round trips to backend services and can avoid backend services to be overloaded with many concurrent identical requests. In a way it's a more specialised version of micro-caching.

This can lead to significant performance improvement in cases where there are many concurrent users requesting the same page.

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// prints the first 100 even numbers and exits
let x = 0
LABEL1: do {
  console.log(x)
  x = x + 2
  // JUMP TO THE END OF THE DO-WHILE - A FORWARDS GOTO
  if (x > 100) break LABEL1
  // JUMP TO THE START OF THE DO WHILE - A BACKWARDS GOTO...
  if (x <= 100) continue LABEL1
} while (true)

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import { createReadStream, createWriteStream } from 'fs'

const source = createReadStream('./assets/moby-dick.txt')
const dest = createWriteStream('./assets/moby-dick-decompressed.txt')

source
  .pipe(dest)