Tag Archives: javascript

Kids’ coding languages

Post Syndicated from Marc Scott original https://www.raspberrypi.org/blog/kids-coding-languages/

Programming is becoming an increasingly useful skill in today’s society. As we continue to rely more and more on software and digital technology, knowing how to code is also more and more valuable. That’s why many parents are looking for ways to introduce their children to programming. You might find it difficult to know where to begin, with so many different kids’ coding languages and platforms available. In this blog post, we explore how children can progress through different programming languages to realise their potential as proficient coders and creators of digital technology.

Two kids share their Scratch coding project on a laptop.

ScratchJr

Everyone needs to start somewhere, and one great option for children aged 5–7 is ScratchJr (Scratch Junior), a visual programming language with drag-and-drop blocks for creating simple programs. ScratchJr is available for free on Android and iOS mobile devices. It’s great for introducing young children to the basics of programming, and they can use it to create interactive stories and games.

Scratch

Moving on from ScratchJr, there’s its web-based sibling Scratch. Scratch offers drag-and-drop blocks for creating programs and comes with an assortment of graphics, sounds, and music for your child to bring their programs to life. This visual programming language is designed specifically for children to learn programming fundamentals. Scratch is available in multiple spoken languages and is perfect for beginners. It allows kids to create interactive stories, animations, and games with ease.

The Raspberry Pi Foundation has a wealth of free Scratch resources we have created specifically for young people who are beginners, such as the ‘Introduction to Scratch’ project path. And if your child is interested in physical computing to interact with the real world using code, they can also learn how to use electronic components, such as buzzers and LEDs, with Scratch and a Raspberry Pi computer.  

Young person using a laptop to code in Scratch, our favourite of all kids' coding languages.

MakeCode

Another fun option for children who want to explore coding and physical computing is the micro:bit. This is a small programmable device with an LED display, buttons, and sensors, and it can be used to create games, animations, interactive projects, and lots more. To control a micro:bit, a visual programming language called MakeCode can be used. The micro:bit can also be programmed using Scratch or text-based languages such as Python, offering an easy transition for children as their coding skills progress. Have a look at our free collection of micro:bit resources to learn more.

HTML

Everyone is familiar with websites, but fewer people know how they are coded. HTML is a markup language that is used to create the webpages we use every day. It’s a great language for children to learn because they can see the results of their code in real time, in their web browser. They can use HTML and CSS to create simple webpages that include links, videos, pictures, and interactive elements, all the while learning how websites are structured and designed. We have many free web design resources for your child, including a basic ‘Introduction to web development’ project path.

Three kids coding at laptops.

Python 

If your child is becoming confident with Scratch and HTML, then using Python is the recommended next stage in their learning. Python is a high-level text-based programming language that is easy to read and learn. It is a popular choice for beginners as it has a simple syntax that often reads like plain English. Many free Python projects for young people are available on our website, including the ‘Introduction to Python’ path.

A kid coding in Python on a laptop.

The Python community is also really welcoming and has produced a myriad of online tutorials and videos to help learners explore this language. Python can be used to do some very powerful things with ease, which is why it is so popular. For example, it is relatively simple to create Python programs to engage in machine learning and data analysis. If you wanted to explore large language models such as GPT, on which the ChatGPT chatbot is based, then Python would be the language of choice.

JavaScript 

JavaScript is the language of the web, and if your child has become proficient in HTML, then this is the next language for them. JavaScript is used to create interactive websites and web applications. As young people become more comfortable with programming, JavaScript is a useful language to progress to, given how ubiquitous the web is today. It can be tricky to learn, but like Python, it has a vast number of libraries of functions that people have already created for it to achieve things more quickly. These libraries make JavaScript a very powerful language to use.

Try out kids’ coding languages

There are many different programming languages, and each one has its own strengths and weaknesses. Some are easy to learn and use, some are really fast, and some are very secure.

Two kids coding together on Code Club World.

Starting with visual languages such as Scratch or MakeCode allows your child to begin to understand the basic concepts of programming without needing any developed reading and keyboard skills. Once their understanding and skills have improved, they can try out text-based languages, find the one that they are comfortable with, and then continue to learn. It’s fairly common for people who are proficient in one programming language to learn other languages quite quickly, so don’t worry about which programming language your child starts with.

Whether your child is interested in working in software development or just wants to learn a valuable — and creative — skill, helping them learn to code and try out different kids’ coding languages is a great way for you to open up new opportunities for them.

The post Kids’ coding languages appeared first on Raspberry Pi Foundation.

Serverless ICYMI Q1 2023

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/serverless-icymi-q1-2023/

Welcome to the 21st edition of the AWS Serverless ICYMI (in case you missed it) quarterly recap. Every quarter, we share all the most recent product launches, feature enhancements, blog posts, webinars, live streams, and other interesting things that you might have missed!

ICYMI2023Q1

In case you missed our last ICYMI, check out what happened last quarter here.

Artificial intelligence (AI) technologies, ChatGPT, and DALL-E are creating significant interest in the industry at the moment. Find out how to integrate serverless services with ChatGPT and DALL-E to generate unique bedtime stories for children.

Example notification of a story hosted with Next.js and App Runner

Example notification of a story hosted with Next.js and App Runner

Serverless Land is a website maintained by the Serverless Developer Advocate team to help you build serverless applications and includes workshops, code examples, blogs, and videos. There is now enhanced search functionality so you can search across resources, patterns, and video content.

SLand-search

ServerlessLand search

AWS Lambda

AWS Lambda has improved how concurrency works with Amazon SQS. You can now control the maximum number of concurrent Lambda functions invoked.

The launch blog post explains the scaling behavior of Lambda using this architectural pattern, challenges this feature helps address, and a demo of maximum concurrency in action.

Maximum concurrency is set to 10 for the SQS queue.

Maximum concurrency is set to 10 for the SQS queue.

AWS Lambda Powertools is an open-source library to help you discover and incorporate serverless best practices more easily. Lambda Powertools for .NET is now generally available and currently focused on three observability features: distributed tracing (Tracer), structured logging (Logger), and asynchronous business and application metrics (Metrics). Powertools is also available for Python, Java, and Typescript/Node.js programming languages.

To learn more:

Lambda announced a new feature, runtime management controls, which provide more visibility and control over when Lambda applies runtime updates to your functions. The runtime controls are optional capabilities for advanced customers that require more control over their runtime changes. You can now specify a runtime management configuration for each function with three settings, Automatic (default), Function update, or manual.

There are three new Amazon CloudWatch metrics for asynchronous Lambda function invocations: AsyncEventsReceived, AsyncEventAge, and AsyncEventsDropped. You can track the asynchronous invocation requests sent to Lambda functions to monitor any delays in processing and take corrective actions if required. The launch blog post explains the new metrics and how to use them to troubleshoot issues.

Lambda now supports Amazon DocumentDB change streams as an event source. You can use Lambda functions to process new documents, track updates to existing documents, or log deleted documents. You can use any programming language that is supported by Lambda to write your functions.

There is a helpful blog post suggesting best practices for developing portable Lambda functions that allow you to port your code to containers if you later choose to.

AWS Step Functions

AWS Step Functions has expanded its AWS SDK integrations with support for 35 additional AWS services including Amazon EMR Serverless, AWS Clean Rooms, AWS IoT FleetWise, AWS IoT RoboRunner and 31 other AWS services. In addition, Step Functions also added support for 1000+ new API actions from new and existing AWS services such as Amazon DynamoDB and Amazon Athena. For the full list of added services, visit AWS SDK service integrations.

Amazon EventBridge

Amazon EventBridge has launched the AWS Controllers for Kubernetes (ACK) for EventBridge and Pipes . This allows you to manage EventBridge resources, such as event buses, rules, and pipes, using the Kubernetes API and resource model (custom resource definitions).

EventBridge event buses now also support enhanced integration with Service Quotas. Your quota increase requests for limits such as PutEvents transactions-per-second, number of rules, and invocations per second among others will be processed within one business day or faster, enabling you to respond quickly to changes in usage.

AWS SAM

The AWS Serverless Application Model (SAM) Command Line Interface (CLI) has added the sam list command. You can now show resources defined in your application, including the endpoints, methods, and stack outputs required to test your deployed application.

AWS SAM has a preview of sam build support for building and packaging serverless applications developed in Rust. You can use cargo-lambda in the AWS SAM CLI build workflow and AWS SAM Accelerate to iterate on your code changes rapidly in the cloud.

You can now use AWS SAM connectors as a source resource parameter. Previously, you could only define AWS SAM connectors as a AWS::Serverless::Connector resource. Now you can add the resource attribute on a connector’s source resource, which makes templates more readable and easier to update over time.

AWS SAM connectors now also support multiple destinations to simplify your permissions. You can now use a single connector between a single source resource and multiple destination resources.

In October 2022, AWS released OpenID Connect (OIDC) support for AWS SAM Pipelines. This improves your security posture by creating integrations that use short-lived credentials from your CI/CD provider. There is a new blog post on how to implement it.

Find out how best to build serverless Java applications with the AWS SAM CLI.

AWS App Runner

AWS App Runner now supports retrieving secrets and configuration data stored in AWS Secrets Manager and AWS Systems Manager (SSM) Parameter Store in an App Runner service as runtime environment variables.

AppRunner also now supports incoming requests based on HTTP 1.0 protocol, and has added service level concurrency, CPU and Memory utilization metrics.

Amazon S3

Amazon S3 now automatically applies default encryption to all new objects added to S3, at no additional cost and with no impact on performance.

You can now use an S3 Object Lambda Access Point alias as an origin for your Amazon CloudFront distribution to tailor or customize data to end users. For example, you can resize an image depending on the device that an end user is visiting from.

S3 has introduced Mountpoint for S3, a high performance open source file client that translates local file system API calls to S3 object API calls like GET and LIST.

S3 Multi-Region Access Points now support datasets that are replicated across multiple AWS accounts. They provide a single global endpoint for your multi-region applications, and dynamically route S3 requests based on policies that you define. This helps you to more easily implement multi-Region resilience, latency-based routing, and active-passive failover, even when data is stored in multiple accounts.

Amazon Kinesis

Amazon Kinesis Data Firehose now supports streaming data delivery to Elastic. This is an easier way to ingest streaming data to Elastic and consume the Elastic Stack (ELK Stack) solutions for enterprise search, observability, and security without having to manage applications or write code.

Amazon DynamoDB

Amazon DynamoDB now supports table deletion protection to protect your tables from accidental deletion when performing regular table management operations. You can set the deletion protection property for each table, which is set to disabled by default.

Amazon SNS

Amazon SNS now supports AWS X-Ray active tracing to visualize, analyze, and debug application performance. You can now view traces that flow through Amazon SNS topics to destination services, such as Amazon Simple Queue Service, Lambda, and Kinesis Data Firehose, in addition to traversing the application topology in Amazon CloudWatch ServiceLens.

SNS also now supports setting content-type request headers for HTTPS notifications so applications can receive their notifications in a more predictable format. Topic subscribers can create a DeliveryPolicy that specifies the content-type value that SNS assigns to their HTTPS notifications, such as application/json, application/xml, or text/plain.

EDA Visuals collection added to Serverless Land

The Serverless Developer Advocate team has extended Serverless Land and introduced EDA visuals. These are small bite sized visuals to help you understand concept and patterns about event-driven architectures. Find out about batch processing vs. event streaming, commands vs. events, message queues vs. event brokers, and point-to-point messaging. Discover bounded contexts, migrations, idempotency, claims, enrichment and more!

EDA-visuals

EDA Visuals

To learn more:

Serverless Repos Collection on Serverless Land

There is also a new section on Serverless Land containing helpful code repositories. You can search for code repos to use for examples, learning or building serverless applications. You can also filter by use-case, runtime, and level.

Serverless Repos Collection

Serverless Repos Collection

Serverless Blog Posts

January

Jan 12 – Introducing maximum concurrency of AWS Lambda functions when using Amazon SQS as an event source

Jan 20 – Processing geospatial IoT data with AWS IoT Core and the Amazon Location Service

Jan 23 – AWS Lambda: Resilience under-the-hood

Jan 24 – Introducing AWS Lambda runtime management controls

Jan 24 – Best practices for working with the Apache Velocity Template Language in Amazon API Gateway

February

Feb 6 – Previewing environments using containerized AWS Lambda functions

Feb 7 – Building ad-hoc consumers for event-driven architectures

Feb 9 – Implementing architectural patterns with Amazon EventBridge Pipes

Feb 9 – Securing CI/CD pipelines with AWS SAM Pipelines and OIDC

Feb 9 – Introducing new asynchronous invocation metrics for AWS Lambda

Feb 14 – Migrating to token-based authentication for iOS applications with Amazon SNS

Feb 15 – Implementing reactive progress tracking for AWS Step Functions

Feb 23 – Developing portable AWS Lambda functions

Feb 23 – Uploading large objects to Amazon S3 using multipart upload and transfer acceleration

Feb 28 – Introducing AWS Lambda Powertools for .NET

March

Mar 9 – Server-side rendering micro-frontends – UI composer and service discovery

Mar 9 – Building serverless Java applications with the AWS SAM CLI

Mar 10 – Managing sessions of anonymous users in WebSocket API-based applications

Mar 14 –
Implementing an event-driven serverless story generation application with ChatGPT and DALL-E

Videos

Serverless Office Hours – Tues 10AM PT

Weekly office hours live stream. In each session we talk about a specific topic or technology related to serverless and open it up to helping you with your real serverless challenges and issues. Ask us anything you want about serverless technologies and applications.

January

Jan 10 – Building .NET 7 high performance Lambda functions

Jan 17 – Amazon Managed Workflows for Apache Airflow at Scale

Jan 24 – Using Terraform with AWS SAM

Jan 31 – Preparing your serverless architectures for the big day

February

Feb 07- Visually design and build serverless applications

Feb 14 – Multi-tenant serverless SaaS

Feb 21 – Refactoring to Serverless

Feb 28 – EDA visually explained

March

Mar 07 – Lambda cookbook with Python

Mar 14 – Succeeding with serverless

Mar 21 – Lambda Powertools .NET

Mar 28 – Server-side rendering micro-frontends

FooBar Serverless YouTube channel

Marcia Villalba frequently publishes new videos on her popular serverless YouTube channel. You can view all of Marcia’s videos at https://www.youtube.com/c/FooBar_codes.

January

Jan 12 – Serverless Badge – A new certification to validate your Serverless Knowledge

Jan 19 – Step functions Distributed map – Run 10k parallel serverless executions!

Jan 26 – Step Functions Intrinsic Functions – Do simple data processing directly from the state machines!

February

Feb 02 – Unlock the Power of EventBridge Pipes: Integrate Across Platforms with Ease!

Feb 09 – Amazon EventBridge Pipes: Enrichment and filter of events Demo with AWS SAM

Feb 16 – AWS App Runner – Deploy your apps from GitHub to Cloud in Record Time

Feb 23 – AWS App Runner – Demo hosting a Node.js app in the cloud directly from GitHub (AWS CDK)

March

Mar 02 – What is Amazon DynamoDB? What are the most important concepts? What are the indexes?

Mar 09 – Choreography vs Orchestration: Which is Best for Your Distributed Application?

Mar 16 – DynamoDB Single Table Design: Simplify Your Code and Boost Performance with Table Design Strategies

Mar 23 – 8 Reasons You Should Choose DynamoDB for Your Next Project and How to Get Started

Sessions with SAM & Friends

SAMFiends

AWS SAM & Friends

Eric Johnson is exploring how developers are building serverless applications. We spend time talking about AWS SAM as well as others like AWS CDK, Terraform, Wing, and AMPT.

Feb 16 – What’s new with AWS SAM

Feb 23 – AWS SAM with AWS CDK

Mar 02 – AWS SAM and Terraform

Mar 10 – Live from ServerlessDays ANZ

Mar 16 – All about AMPT

Mar 23 – All about Wing

Mar 30 – SAM Accelerate deep dive

Still looking for more?

The Serverless landing page has more information. The Lambda resources page contains case studies, webinars, whitepapers, customer stories, reference architectures, and even more Getting Started tutorials.

You can also follow the Serverless Developer Advocacy team on Twitter to see the latest news, follow conversations, and interact with the team.

Node.js compatibility for Cloudflare Workers – starting with Async Context Tracking, EventEmitter, Buffer, assert, and util

Post Syndicated from James M Snell original https://blog.cloudflare.com/workers-node-js-asynclocalstorage/

Node.js compatibility for Cloudflare Workers – starting with Async Context Tracking, EventEmitter, Buffer, assert, and util

Node.js compatibility for Cloudflare Workers – starting with Async Context Tracking, EventEmitter, Buffer, assert, and util

Over the coming months, Cloudflare Workers will start to roll out built-in compatibility with Node.js core APIs as part of an effort to support increased compatibility across JavaScript runtimes.

We are happy to announce today that the first of these Node.js APIs – AsyncLocalStorage, EventEmitter, Buffer, assert, and parts of util – are now available for use. These APIs are provided directly by the open-source Cloudflare Workers runtime, with no need to bundle polyfill implementations into your own code.

These new APIs are available today — start using them by enabling the nodejs_compat compatibility flag in your Workers.

Async Context Tracking with the AsyncLocalStorage API

The AsyncLocalStorage API provides a way to track context across asynchronous operations. It allows you to pass a value through your program, even across multiple layers of asynchronous code, without having to pass a context value between operations.

Consider an example where we want to add debug logging that works through multiple layers of an application, where each log contains the ID of the current request. Without AsyncLocalStorage, it would be necessary to explicitly pass the request ID down through every function call that might invoke the logging function:

function logWithId(id, state) {
  console.log(`${id} - ${state}`);
}

function doSomething(id) {
  // We don't actually use id for anything in this function!
  // It's only here because logWithId needs it.
  logWithId(id, "doing something");
  setTimeout(() => doSomethingElse(id), 10);
}

function doSomethingElse(id) {
  logWithId(id, "doing something else");
}

let idSeq = 0;

export default {
  async fetch(req) {
    const id = idSeq++;
    doSomething(id);
    logWithId(id, 'complete');
    return new Response("ok");
  }
}

While this approach works, it can be cumbersome to coordinate correctly, especially as the complexity of an application grows. Using AsyncLocalStorage this becomes significantly easier by eliminating the need to explicitly pass the context around. Our application functions (doSomething and doSomethingElse in this case) never need to know about the request ID at all while the logWithId function does exactly what we need it to:

import { AsyncLocalStorage } from 'node:async_hooks';

const requestId = new AsyncLocalStorage();

function logWithId(state) {
  console.log(`${requestId.getStore()} - ${state}`);
}

function doSomething() {
  logWithId("doing something");
  setTimeout(() => doSomethingElse(), 10);
}

function doSomethingElse() {
  logWithId("doing something else");
}

let idSeq = 0;

export default {
  async fetch(req) {
    return requestId.run(idSeq++, () => {
      doSomething();
      logWithId('complete');
      return new Response("ok");
    });
  }
}

With the nodejs_compat compatibility flag enabled, import statements are used to access specific APIs. The Workers implementation of these APIs requires the use of the node: specifier prefix that was introduced recently in Node.js (e.g. node:async_hooks, node:events, etc)

We implement a subset of the AsyncLocalStorage API in order to keep things as simple as possible. Specifically, we’ve chosen not to support the enterWith() and disable() APIs that are found in Node.js implementation simply because they make async context tracking more brittle and error prone.

Conceptually, at any given moment within a worker, there is a current “Asynchronous Context Frame”, which consists of a map of storage cells, each holding a store value for a specific AsyncLocalStorage instance. Calling asyncLocalStorage.run(...) causes a new frame to be created, inheriting the storage cells of the current frame, but using the newly provided store value for the cell associated with asyncLocalStorage.

const als1 = new AsyncLocalStorage();
const als2 = new AsyncLocalStorage();

// Code here runs in the root frame. There are two storage cells,
// one for als1, and one for als2. The store value for each is
// undefined.

als1.run(123, () => {
  // als1.run(...) creates a new frame (1). The store value for als1
  // is set to 123, the store value for als2 is still undefined.
  // This new frame is set to "current".

  als2.run(321, () => {
    // als2.run(...) creates another new frame (2). The store value
    // for als1 is still 123, the store value for als2 is set to 321.
    // This new frame is set to "current".
    console.log(als1.getStore(), als2.getStore());
  });

  // Frame (1) is restored as the current. The store value for als1
  // is still 123, but the store value for als2 is undefined again.
});

// The root frame is restored as the current. The store values for
// both als1 and als2 are both undefined again.

Whenever an asynchronous operation is initiated in JavaScript, for example, creating a new JavaScript promise, scheduling a timer, etc, the current frame is captured and associated with that operation, allowing the store values at the moment the operation was initialized to be propagated and restored as needed.

const als = new AsyncLocalStorage();
const p1 = als.run(123, () => {
  return promise.resolve(1).then(() => console.log(als.getStore());
});

const p2 = promise.resolve(1); 
const p3 = als.run(321, () => {
  return p2.then(() => console.log(als.getStore()); // prints 321
});

als.run('ABC', () => setInterval(() => {
  // prints "ABC" to the console once a second…
  setInterval(() => console.log(als.getStore(), 1000);
});

als.run('XYZ', () => queueMicrotask(() => {
  console.log(als.getStore());  // prints "XYZ"
}));

Note that for unhandled promise rejections, the “unhandledrejection” event will automatically propagate the context that is associated with the promise that was rejected. This behavior is different from other types of events emitted by EventTarget implementations, which will propagate whichever frame is current when the event is emitted.

const asyncLocalStorage = new AsyncLocalStorage();

asyncLocalStorage.run(123, () => Promise.reject('boom'));
asyncLocalStorage.run(321, () => Promise.reject('boom2'));

addEventListener('unhandledrejection', (event) => {
  // prints 123 for the first unhandled rejection ('boom'), and
  // 321 for the second unhandled rejection ('boom2')
  console.log(asyncLocalStorage.getStore());
});

Workers can use the AsyncLocalStorage.snapshot() method to create their own objects that capture and propagate the context:

const asyncLocalStorage = new AsyncLocalStorage();

class MyResource {
  #runInAsyncFrame = AsyncLocalStorage.snapshot();

  doSomething(...args) {
    return this.#runInAsyncFrame((...args) => {
      console.log(asyncLocalStorage.getStore());
    }, ...args);
  }
}

const resource1 = asyncLocalStorage.run(123, () => new MyResource());
const resource2 = asyncLocalStorage.run(321, () => new MyResource());

resource1.doSomething();  // prints 123
resource2.doSomething();  // prints 321

For more, refer to the Node.js documentation about the AsyncLocalStorage API.

There is currently an effort underway to add a new AsyncContext mechanism (inspired by AsyncLocalStorage) to the JavaScript language itself. While it is still early days for the TC-39 proposal, there is good reason to expect it to progress through the committee. Once it does, we look forward to being able to make it available in the Cloudflare Workers platform. We expect our implementation of AsyncLocalStorage to be compatible with this new API.

The proposal for AsyncContext provides an excellent set of examples and description of the motivation of why async context tracking is useful.

Events with EventEmitter

The EventEmitter API is one of the most fundamental Node.js APIs and is critical to supporting many other higher level APIs, including streams, crypto, net, and more. An EventEmitter is an object that emits named events that cause listeners to be called.

import { EventEmitter } from 'node:events';

const emitter = new EventEmitter();
emitter.on('hello', (...args) => {
  console.log(...args);
});

emitter.emit('hello', 1, 2, 3);

The implementation in the Workers runtime fully supports the entire Node.js EventEmitter API including the captureRejections option that allows improved handling of async functions as event handlers:

const emitter = new EventEmitter({ captureRejections: true });
emitter.on('hello', async (...args) => {
  throw new Error('boom');
});
emitter.on('error', (err) => {
  // the async promise rejection is emitted here!
});

Please refer to the Node.js documentation for more details on the use of the EventEmitter API: https://nodejs.org/dist/latest-v19.x/docs/api/events.html#events.

Buffer

The Buffer API in Node.js predates the introduction of the standard TypedArray and DataView APIs in JavaScript by many years and has persisted as one of the most commonly used Node.js APIs for manipulating binary data. Today, every Buffer instance extends from the standard Uint8Array class but adds a range of unique capabilities such as built-in base64 and hex encoding/decoding, byte-order manipulation, and encoding-aware substring searching.

import { Buffer } from 'node:buffer';

const buf = Buffer.from('hello world', 'utf8');

console.log(buf.toString('hex'));
// Prints: 68656c6c6f20776f726c64
console.log(buf.toString('base64'));
// Prints: aGVsbG8gd29ybGQ=

Because a Buffer extends from Uint8Array, it can be used in any workers API that currently accepts Uint8Array, such as creating a new Response:

const response = new Response(Buffer.from("hello world"));

Or interacting with streams:

const writable = getWritableStreamSomehow();
const writer = writable.getWriter();
writer.write(Buffer.from("hello world"));

Please refer to the Node.js documentation for more details on the use of the Buffer API: https://nodejs.org/dist/latest-v19.x/docs/api/buffer.html.

Assertions

The assert module in Node.js provides a number of useful assertions that are useful when building tests.

import {
  strictEqual,
  deepStrictEqual,
  ok,
  doesNotReject,
} from 'node:assert';

strictEqual(1, 1); // ok!
strictEqual(1, "1"); // fails! throws AssertionError

deepStrictEqual({ a: { b: 1 }}, { a: { b: 1 }});// ok!
deepStrictEqual({ a: { b: 1 }}, { a: { b: 2 }});// fails! throws AssertionError

ok(true); // ok!
ok(false); // fails! throws AssertionError

await doesNotReject(async () => {}); // ok!
await doesNotReject(async () => { throw new Error('boom') }); // fails! throws AssertionError

In the Workers implementation of assert, all assertions run in what Node.js calls the “strict assertion mode“, which means that non-strict methods behave like their corresponding strict methods. For instance, deepEqual() will behave like deepStrictEqual().

Please refer to the Node.js documentation for more details on the use of the assertion API: https://nodejs.org/dist/latest-v19.x/docs/api/assert.html.

Promisify/Callbackify

The promisify and callbackify APIs in Node.js provide a means of bridging between a Promise-based programming model and a callback-based model.

The promisify method allows taking a Node.js-style callback function and converting it into a Promise-returning async function:

import { promisify } from 'node:util';

function foo(args, callback) {
  try {
    callback(null, 1);
  } catch (err) {
    // Errors are emitted to the callback via the first argument.
    callback(err);
  }
}

const promisifiedFoo = promisify(foo);
await promisifiedFoo(args);

Similarly, callbackify converts a Promise-returning async function into a Node.js-style callback function:

import { callbackify } from 'node:util';

async function foo(args) {
  throw new Error('boom');
}

const callbackifiedFoo = callbackify(foo);

callbackifiedFoo(args, (err, value) => {
  if (err) throw err;
});

Together these utilities make it easy to properly handle all of the generally tricky nuances involved with properly bridging between callbacks and promises.

Please refer to the Node.js documentation for more information on how to use these APIs: https://nodejs.org/dist/latest-v19.x/docs/api/util.html#utilcallbackifyoriginal, https://nodejs.org/dist/latest-v19.x/docs/api/util.html#utilpromisifyoriginal.

Type brand-checking with util.types

The util.types API provides a reliable and generally more efficient way of checking that values are instances of various built-in types.

import { types } from 'node:util';

types.isAnyArrayBuffer(new ArrayBuffer());  // Returns true
types.isAnyArrayBuffer(new SharedArrayBuffer());  // Returns true
types.isArrayBufferView(new Int8Array());  // true
types.isArrayBufferView(Buffer.from('hello world')); // true
types.isArrayBufferView(new DataView(new ArrayBuffer(16)));  // true
types.isArrayBufferView(new ArrayBuffer());  // false
function foo() {
  types.isArgumentsObject(arguments);  // Returns true
}
types.isAsyncFunction(function foo() {});  // Returns false
types.isAsyncFunction(async function foo() {});  // Returns true
// .. and so on

Please refer to the Node.js documentation for more information on how to use the type check APIs: https://nodejs.org/dist/latest-v19.x/docs/api/util.html#utiltypes. The workers implementation currently does not provide implementations of the util.types.isExternal(), util.types.isProxy(), util.types.isKeyObject(), or util.type.isWebAssemblyCompiledModule() APIs.

What’s next

Keep your eyes open for more Node.js core APIs coming to Cloudflare Workers soon! We currently have implementations of the string decoder, streams and crypto APIs in active development. These will be introduced into the workers runtime incrementally over time and any worker using the nodejs_compat compatibility flag will automatically pick up the new modules as they are added.

Locking down your JavaScript: positive blocking with Page Shield policies

Post Syndicated from Michael Tremante original https://blog.cloudflare.com/page-shield-positive-blocking-policies/

Locking down your JavaScript: positive blocking with Page Shield policies

Locking down your JavaScript: positive blocking with Page Shield policies

Web development teams are tasked with delivering feature-rich applications at lightning speeds. To help them, there are thousands of pre-built JavaScript libraries that they can integrate with little effort.

Not always, however, are these libraries backed with hardened security measures to ensure the code they provide is not tampered with by malicious actors. This ultimately leads to an increased risk of an application being compromised.

Starting today, tackling the risk of external JavaScript libraries just got easier. We are adding a new feature to our client side security solution: Page Shield policies. Using policies you can now ensure only allowed and vetted libraries are executed by your application by simply reviewing a checklist.

Client side libraries

There are more than 4,373 libraries available on cdnjs, a popular JavaScript repository, at the time of writing. These libraries provide access to pre-built functionality to build web applications. The screenshot below shows the most popular on the platform such as React, Vue.js and Bootstrap. Bootstrap alone, according to W3Techs, is used on more than 20% of all websites.

Locking down your JavaScript: positive blocking with Page Shield policies

In addition to library repositories like cdnjs, there are thousands of plugins provided directly by SaaS platforms including from names such as Google, Meta, Microsoft, and more.

According to our Page Shield data, any large enterprise application is loading AND connecting to tens if not hundreds of different destinations for analytics, payments, real user monitoring, conversion tracking, customer relationship management, and many other features that internal teams “must have”.

Script hosts
(JavaScript loaded from…)
Connection hosts
(Data sent to…)
Google Google
Facebook Facebook
Cloudflare Microsoft
Salesforce Hotjar
Prospect One OneTrust
Open JS Foundation Pinterest
Microsoft TikTok
Hotjar PayPal
hCaptcha Snapchat
Fly.io NewRelic

Ultimately, it is hard for most organizations to not rely on external JavaScript libraries.

Yet another vector for attackers

Although there are good reasons to embed external JavaScript in an application, the proliferation of client side libraries, especially from SaaS providers, has increased scrutiny from malicious actors seeking new ways to exploit web applications. A single compromised SaaS provider that offers a client side library can provide direct access to thousands of applications drastically increasing return on “hacker” investment.

Client side security issues are not new. Attacks such as “web skimming”, also referred to as “Magecart-style” when in the context of payment pages, have been around for a long time. Yet, core application security products often focus on protecting the underlying web application rather than the end user data resulting in a large attack surface that most security teams simply have no visibility on. This gap in visibility, caused by “supply chains”, led us to build Page Shield, Cloudflare’s native client-side security solution.

Although the risk of supply chain attacks is becoming widely known, they are still very much an active threat. New research is being published monthly from vendors in this space highlighting ongoing attack campaigns. The Payment Card Industry Security Standards Council has also introduced new requirements in PCI DSS 4.0* that enforce companies to have systems and processes in place to tackle client side security threats.

Page Shield itself has already been effective at warning customers of ongoing attacks on their applications, such as the screenshot below highlighting an active malicious outbound connection from a Magecart-style attack on a customer e-commerce application.

Locking down your JavaScript: positive blocking with Page Shield policies

Locking down your JavaScript: positive blocking with Page Shield policies

* PCI DSS 4.0 requirements 6.4.3 and 11.6.1 are just two examples focusing on client side security.

Reducing the attack surface

Page Shield aims to detect and alert whenever malicious activity is found within the client environment. That’s still a core focus as we improve detection capabilities further.

We are now also looking at expanding capabilities to also reduce the opportunity for an attacker to compromise an application in the first place. In other words, prevent attacks happening by reducing the attack surface available.

Today we are announcing our first major feature in this space: Page Shield policies. Here’s what it looks like:

Locking down your JavaScript: positive blocking with Page Shield policies

Positive blocking policies

By leveraging our position in the network stack as a reverse proxy, and by using Page Shield policies, you can now enforce client browsers to load and execute JavaScript libraries only from your pre-approved list of allowed sources implementing a positive security model.

This ensures that an attacker that is able to inject a script in a page, won’t be successful in compromising users, as browsers will refuse to load it. At the same time, vetted tools will run without issues.

Policies will also soon allow you to specify data destinations (connection endpoints) also enforcing not only where JavaScript files are being loaded from, but also where the browser can send data to drastically reduce the risk of “Magecart-style” attacks.

CSPs as the core mechanism

Page Shield policies are currently implemented with Content Security Policies (CSPs), a feature natively supported by all major browsers.

CSPs are specially formatted HTTP response headers that are added to HTML page loads. These headers may contain one or more directives that instruct the browser how to and what to execute in the context of the given page.

From today Page Shield policies support the script-src directive. This directive lets application owners specify “where” JavaScript files are allowed to be loaded from. Support for the connect-src directive is also being finalized which behaves similarly to script-src, but specifies where the browser is allowed to send data “to”.

Let’s take a look at a one example and assume we were opening the following web page www.example.com/index.html and the browser received a CSP header as below:

Content-Security-Policy: script-src 'self' *.example.com cdnjs.cloudflare.com https://www.google-analytics.com/analytics.js

The header instructs the browser to allow scripts (defined by the use of the script-src directive) to be loaded from the same hostname as the page itself (defined by self) as well as from any subdomain (*.example.com). It is additionally allowing any script under cdnjs and only a specific script for Google Analytics and no other scripts under the Google owned domain.

This ensures that any attacker injected script from different hosts would not be executed, drastically reducing the attack surface available.

If rather than Content-Security-Policy we had received a Content-Security-Policy-Report-Only header, the policy would not be enforced, but browsers would only send violation reports letting you know what is outside of policy.

This is useful when testing and when investigating new scripts that have been added to your application.

Additional statements are also available and supported by Page Shield within the script-src directive to block inline JavaScript (unsafe-inline) or normally unsafe function calls (unsafe-eval). These directives help prevent other attack types such as cross site scripting attacks (XSS).

Making policy management easy

CSPs, the underlying system used by Page Shield policies, are great but hard to manage. The larger the application, the more complex CSPs become while also causing a bottleneck for application development teams. This leads to CSPs becoming ineffective as security teams broaden the list of allowed hosts to the point that their purpose becomes debatable.

Making policy management easy, and ensuring they are effective, was a core goal of our design process. This led us to build a suggestions feature.

When deploying a policy, the first step is deciding “where” will the policy be applied to. Typical examples may include only your checkout flow or admin pages. This is done using wirefilter syntax, the same syntax that powers Cloudflare’s WAF.

Locking down your JavaScript: positive blocking with Page Shield policies

Once the filter is specified, using the data already collected by Page Shield, the interface will provide a list of suggested directive values, making it very easy to build the simplest and most effective policy for your application. No need to worry about syntax, the policy preview will be shown before committing.

Locking down your JavaScript: positive blocking with Page Shield policies

Finally, policies can be deployed both in “report only/log” and “enforce/allow”, letting you control and test as required.

We are currently finishing work on our alerting backend to warn you whenever we notice a spike in violation reports. This lets you easily return to the policy builder and update it with any newly seen script that may have been added by your development team.

Positive blocking policies are not enough

It is important not to forget that CSPs provide no security or malicious activity detection within the list of allowed endpoints. They are meant to reduce the likelihood of an attack happening by reducing the attack surface available. For this reason, Page Shield’s automated malicious activity detection will continue to function in the background regardless of any policy being deployed.

Secure your end user data today

All Cloudflare paid customers have access to a subset of Page Shield features today. Turning on Page Shield is as simple as clicking a button. Head over to Security > Page Shield and give it a go!

If you are an enterprise customer and are interested in Page Shield policies, reach out to your account team to get access to the full feature set.

Unlocking security updates for transitive dependencies with npm

Post Syndicated from Bryan Dragon original https://github.blog/2023-01-19-unlocking-security-updates-for-transitive-dependencies-with-npm/

Dependabot helps developers secure their software with automated security updates: when a security advisory is published that affects a project dependency, Dependabot will try to submit a pull request that updates the vulnerable dependency to a safe version if one is available. Of course, there’s no rule that says a security vulnerability will only affect direct dependencies—dependencies at any level of a project’s dependency graph could become vulnerable.

Until recently, Dependabot did not address vulnerabilities on transitive dependencies, that is, on the dependencies sitting one or more levels below a project’s direct dependencies. Developers would encounter an error message in the GitHub UI and they would have to manually update the chain of ancestor dependencies leading to the vulnerable dependency to bring it to a safe version.

Screenshot of the warning a user sees when they try to update the version of a project when its dependencies sitting one or more levels below a project’s direct dependencies were out of date. The message reads, "Dependabot cannot update minimist to a non-vulnerable version."

Internally, this would show up as a failed background job due to an update-not-possible error—and we would see a lot of these errors.

Understanding the challenge

Dependabot offers two strategies for updating dependencies: scheduled version updates and security updates. With version updates, the explicit goal is to keep project dependencies updated to the latest available version, and Dependabot can be configured to widen or increase a version requirement so that it accommodates the latest version. With security updates, Dependabot tries to make the most conservative update that removes the vulnerability while respecting version requirements. In this post we’ll be looking at security updates.

As an example, let’s say we have a repository with security updates enabled that contains an npm project with a single dependency on react-scripts@^4.0.3.

Not all package managers handle version requirements in the same way, so let’s quickly refresh. A version requirement like ^4.0.3 (a “caret range”) in npm permits updates to versions that don’t change the leftmost nonzero element in the MAJOR.MINOR.PATCH semver version number. The version requirement ^4.0.3, then, can be understood as allowing versions greater than or equal to 4.0.3 and less than 5.0.0.

On March 18, 2022, a high-severity security advisory was published for node-forge, a popular npm package that provides tools for writing cryptographic and network-heavy applications. The advisory impacts versions earlier than 1.3.0, the patched version released the day before the advisory was published.

While we don’t have a direct dependency on node-forge, if we zoom in on our project’s dependency tree we can see that we do indirectly depend on a vulnerable version:

react-scripts@^4.0.3             4.0.3
  - [email protected]    3.11.1
    - selfsigned@^1.10.7         1.10.14
      - node-forge@^0.10.0       0.10.0

In order to resolve the vulnerability, we need to bring node-forge from 0.10.0 to 1.3.0, but a sequence of conflicting ancestor dependencies prevents us from doing so:

  • 4.0.3 is the latest version of react-scripts permitted by our project
  • 3.11.1 is the only version of webpack-dev-server permitted by [email protected]
  • 1.10.14 is the latest version of selfsigned permitted by [email protected]
  • 0.10.0 is the latest version of node-forge permitted by[email protected]

This is the point at which the security update would fail with an update-not-possible error. The challenge is in finding the version of selfsigned that permits [email protected], the version of webpack-dev-server that permits that version of selfsigned, and so on up the chain of ancestor dependencies until we reach react-scripts.

How we chose npm

When we set out to reduce the rate of update-not-possible errors, the first thing we did was pull data from our data warehouse in order to identify the greatest opportunities for impact.

JavaScript is the most popular ecosystem that Dependabot supports, both by Dependabot enablement and by update volume. In fact, more than 80% of the security updates that Dependabot performs are for npm and Yarn projects. Given their popularity, improving security update outcomes for JavaScript projects promised the greatest potential for impact, so we focused our investigation there.

npm and Yarn both include an operation that audits a project’s dependencies for known security vulnerabilities, but currently only npm natively has the ability to additionally make the updates needed to resolve the vulnerabilities that it finds.

After a successful engineering spike to assess the feasibility of integrating with npm’s audit functionality, we set about productionizing the approach.

Tapping into npm audit

When you run the npm audit command, npm collects your project’s dependencies, makes a bulk request to the configured npm registry for all security advisories affecting them, and then prepares an audit report. The report lists each vulnerable dependency, the dependency that requires it, the advisories affecting it, and whether a fix is possible—in other words, almost everything Dependabot should need to resolve a vulnerable transitive dependency.

node-forge  <=1.2.1
Severity: high
Open Redirect in node-forge - https://github.com/advisories/GHSA-8fr3-hfg3-gpgp
Prototype Pollution in node-forge debug API. - https://github.com/advisories/GHSA-5rrq-pxf6-6jx5
Improper Verification of Cryptographic Signature in node-forge - https://github.com/advisories/GHSA-cfm4-qjh2-4765
URL parsing in node-forge could lead to undesired behavior. - https://github.com/advisories/GHSA-gf8q-jrpm-jvxq
fix available via `npm audit fix --force`
Will install [email protected], which is a breaking change
node_modules/node-forge
  selfsigned  1.1.1 - 1.10.14
  Depends on vulnerable versions of node-forge
  node_modules/selfsigned

There were two ways in which we had to supplement npm audit to meet our requirements:

  1. The audit report doesn’t include the chain of dependencies linking a vulnerable transitive dependency, which a developer may not recognize, to a direct dependency, which a developer should recognize. The last step in a security update job is creating a pull request that removes the vulnerability and we wanted to include some context that lets developers know how changes relate to their project’s direct dependencies.
  2. Dependabot performs security updates for one vulnerable dependency at a time. (Updating one dependency at a time keeps diffs to a minimum and reduces the likelihood of introducing breaking changes.) npm audit and npm audit fix, however, operate on all project dependencies, which means Dependabot wouldn’t be able to tell which of the resulting updates were necessary for the dependency it’s concerned with.

Fortunately, there’s a JavaScript API for accessing the audit functionality underlying the npm audit and npm audit fix commands via Arborist, the component npm uses to manage dependency trees. Since Dependabot is a Ruby application, we wrote a helper script that uses the Arborist.audit() API and can be invoked in a subprocess from Ruby. The script takes as input a vulnerable dependency and a list of security advisories affecting it and returns as output the updates necessary to remove the vulnerabilities as reported by npm.

To meet our first requirement, the script uses the audit results from Arborist.audit() to perform a depth-first traversal of the project’s dependency tree, starting with direct dependencies. This top-down, recursive approach allows us to maintain the chain of dependencies linking the vulnerable dependency to its top-level ancestor(s) (which we’ll want to mention later when creating a pull request), and its worst-case time complexity is linear in the total number of dependencies.

function buildDependencyChains(auditReport, name) {
  const helper = (node, chain, visited) => {
    if (!node) {
      return []
    }
    if (visited.has(node.name)) {
      // We've already seen this node; end path.
      return []
    }
    if (auditReport.has(node.name)) {
      const vuln = auditReport.get(node.name)
      if (vuln.isVulnerable(node)) {
        return [{ fixAvailable: vuln.fixAvailable, nodes: [node, ...chain.nodes] }]
      } else if (node.name == name) {
        // This is a non-vulnerable version of the advisory dependency; end path.
        return []
      }
    }
    if (!node.edgesOut.size) {
      // This is a leaf node that is unaffected by the vuln; end path.
      return []
    }
    return [...node.edgesOut.values()].reduce((chains, { to }) => {
      // Only prepend current node to chain/visited if it's not the project root.
      const newChain = node.isProjectRoot ? chain : { nodes: [node, ...chain.nodes] }
      const newVisited = node.isProjectRoot ? visited : new Set([node.name, ...visited])
      return chains.concat(helper(to, newChain, newVisited))
    }, [])
  }
  return helper(auditReport.tree, { nodes: [] }, new Set())
}

To meet our second requirement of operating on one vulnerable dependency at a time, the script takes advantage of the fact that the Arborist constructor accepts a custom audit registry URL to be used when requesting bulk advisory data. We initialize a mock audit registry server using nock that returns only the list of advisories (in the expected format) for the dependency that was passed into the script and we tell the Arborist instance to use it.

const arb = new Arborist({
  auditRegistry: 'http://localhost:9999',
  // ...
})

const scope = nock('http://localhost:9999')
  .persist()
  .post('/-/npm/v1/security/advisories/bulk')
  .reply(200, convertAdvisoriesToRegistryBulkFormat(advisories))

We see both of these use cases—linking a vulnerable dependency to its top-level ancestor and conducting an audit for a single package or a particular set of vulnerabilities—as opportunities to extend Arborist and we’re working on integrating them upstream.

Back in the Ruby code, we parse and verify the audit results emitted by the helper script, accounting for scenarios such as a dependency being downgraded or removed in order to fix a vulnerability, and we incorporate the updates recommended by npm into the remainder of the security update job.

With a viable update path in hand, Dependabot is able to make the necessary updates to remove the vulnerability and submit a pull request that tells the developer about the transitive dependency and its top-level ancestor.

Screenshot of an open pull request that tells the developer about the transitive dependency and its top-level ancestor. The pull request is titled "Bump node-forge and react-scripts" and has a message from Dependabot that reads, "Merging this pull request will resolve 6 Dependabot alerts on node-forge including a high severity alert."

Caveats

When npm audit decides that a vulnerability can only be fixed by changing major versions, it requires use of the force option with npm audit fix. When the force option is used, npm will update to the latest version of a package, even if it means jumping several major versions. This breaks with Dependabot’s previous security update behavior. It also achieves our goal: to unlock conflicting dependencies in order to bring the vulnerable dependency to an unaffected version. Of course, you should still always review the changelog for breaking changes when jumping minor or major versions of a package.

Impact

We rolled out support for transitive security updates with npm in September 2022. Now, having a full quarter of data with the changes in place, we’re able to measure the impact: between Q1Y22 and Q4Y22 we saw a 42% reduction in update-not-possible errors for security updates on JavaScript projects. 🎉

If you have Dependabot security updates enabled on your npm projects, there’s nothing extra for you to do—you’re already benefiting from this improvement.

Looking ahead

I hope this post illustrates some of the considerations and trade-offs that are necessary when making improvements to an established system like Dependabot. We prefer to leverage the native functionality provided by package managers whenever possible, but as package managers come in all shapes and sizes, the approach may vary substantially from one ecosystem to the next.

We hope other package managers will introduce functionality similar to npm audit and npm audit fix that Dependabot can integrate with and we look forward to extending support for transitive security updates to those ecosystems as they do.

The most programmable Supercloud with Cloudflare Snippets

Post Syndicated from Sam Marsh original https://blog.cloudflare.com/snippets-announcement/

The most programmable Supercloud with Cloudflare Snippets

Your traffic, how you like it

The most programmable Supercloud with Cloudflare Snippets

Cloudflare is used by a highly diverse customer base. We offer simple-to-use products for everything from setting HTTP headers to rewriting the URI path and performing URL redirects. Sometimes customers need more than the out-of-the-box functionality, not just adding an HTTP header – but performing some advanced calculation to create the output. Today they would need to create a feature request and wait for it to be shipped, write a Cloudflare Worker, or keep this modification ‘on origin’ – on their own infrastructure.

To simplify this, we are delighted to announce Cloudflare Snippets. Snippets are a new way to perform traffic modifications that users either cannot do via our productised offerings, or want to do programmatically. The best part? The vast majority of customers will pay nothing extra for using Snippets.

Users now have a choice. Perform the action via a rule. Or, if more functionality is needed, write a Snippet.  Neither will mean waiting. Neither will incur additional cost (although a high fair usage cap will apply). Snippets unblocks users to do what they want, when they want. All on Cloudflare.

Snippets will support the import of code written in various languages, such as JavaScript (modern), VCL (legacy) and Apache .htaccess files (legacy). This allows customers to migrate legacy operational code onto our platform – whilst also consolidating their JavaScript operations.

Please use the sign-up form to join the waitlist for Snippets if you are interested in testing. We hope to begin admitting users into the closed beta early 2023.

Why build Snippets?

Over the past 18 months we have released a number of new rules products such as Transform Rules, Cache Rules, Origin Rules, Config Rules and Redirect Rules. These new products give more control to customers on how we process their traffic as it flows through our global network. The feedback on these products so far has been overwhelmingly positive. However, our customers still occasionally need the ability to do more than the out-of-the-box functionality allows.

There are always some use cases where a product doesn’t provide the functionality that a customer needs for their specific situation.  For example, whilst thousands of our customers are now using Transform Rules to solve their HTTP header modification use cases, there remains a small number of use cases that are not possible, such as setting dynamic expiry times with cookies or hashing tokens with a key.

This is where Cloudflare Snippets help. Customers will no longer need to use the full Cloudflare Workers platform to implement these relatively simple use cases. Nor will they need to wait for us to build their feature requests. Instead, they will be able to run a Snippet of JavaScript.

Migrating legacy code to Snippets

Varnish Control Language (VCL) is only used within the context of Varnish. Launched around 16 years ago, it has historically been used to configure traffic and routing for Content Delivery Networks as it was extensible to a wide range of use cases.

There are still a good number of businesses out there using VCL to perform routing and traffic modification actions. Whilst other providers are deprecating support for VCL, we want to make sure those of you comfortable using it are still supported.

Snippets won’t run pure VCL. Instead, we will convert VCL into easy to maintain rules or Snippets. To achieve this we’re building a simple-to-use, self-serve VCL converter that analyzes uploaded VCL code and auto-generates suggested Snippets, and if we can find a match, also generates suggested rules for products such as Transform Rules or Cache Rules.

This topic was initially handled via Project Turpentine, a suite of tools used by Cloudflare employees to parse a customer’s VCL into a suggested JavaScript configuration. This JavaScript could then be loaded into a Worker, or series of Workers.

Snippets takes the idea and principles of Turpentine further. Much further. By building a parser directly in the dashboard it puts the power directly into the hands of users and gives them a choice. You can tell us to migrate everything we can into Rules with the remaining code migrated into Snippets, or, you can choose to tell us to migrate everything into discrete Snippets. It’s your call.

The most programmable Supercloud with Cloudflare Snippets

We’ll give Apache htaccess and NGINX configuration files the same treatment. The goal being users simply upload the files from their websites Apache or NGINX configuration, and we generate suggested Snippets and/or rules.

The days of having to use legacy code for operational tasks are coming to an end. Snippets allow users to migrate these workloads to Cloudflare, and let them focus on the bigger problems of the business vs maintaining legacy systems.

The difference between Snippets and Workers

Most readers will already be familiar with Cloudflare Workers, our powerful developer platform which allows businesses to run and build entire products and solutions on Cloudflare’s global network. Snippets is also built on this platform, but has a few key differences.

The first major difference is that a Snippet will run as part of the Ruleset Engine as dedicated new phases, similar to Transform Rules and Cache Rules. Customers will be able to select and execute a Snippet based on any ruleset engine filter. This allows customers to run a Snippet against every request, or filter for specific HTTP traffic based on the fields we offer, such as traffic with a certain bot score, originating from a specific country, or with a specific cookie. Snippets will be additive, meaning users can have one Snippet to add an HTTP header, and another to rewrite the URL, and both will execute if they match:

The most programmable Supercloud with Cloudflare Snippets

Another major difference – Cloudflare Snippets are available for all plan levels, at no additional cost. 99% of users won’t pay a single cent, ever, to use this solution. This allows customers to migrate their simple workloads from legacy solutions like VCL to the Cloudflare platform, and actively reduce their monthly spend.

Free Plans Pro Plans Business Plans Enterprise Plans
Snippets available 5 Snippets per zone. 20 Snippets per zone. 50 Snippets per zone. 200 Snippets per zone*
(Customers can speak with their Customer Success team to have this increased).

Cloudflare Snippets are lightweight when compared with Workers, offering 5ms maximum execution time, 2MB maximum memory and 32KB total package size. This comparably small footprint allows us to offer this to 99% of users at no additional cost, whilst also being sufficient for the identified use cases like HTTP header modification, URL rewriting and traffic routing – all of which don’t need the vast resources offered by Cloudflare Workers.

Cloudflare Snippets Cloudflare Workers Unbound
(For comparison)
Runtime support JavaScript JavaScript and WASM
Execution location Global – All Cloudflare locations Global – All Cloudflare locations
Triggers supported Ruleset Engine Filters HTTP Request
HTTP Response
Cron Triggers
Maximum execution time 5ms 30 Seconds HTTP
15 Minutes (Cron Trigger)
Maximum memory 2MB 128MB
Total package size 32KB 5MB
Environment variables 8/Snippet 64/Worker
Environment variable size 1KB 5KB
Subrequests 1/request 1000/request
Terraform Support
Wrangler Support
Cron Triggers
Key Value Store
Durable Objects
R2 Integration

What will you be able to build with Cloudflare Snippets?

Snippets will allow customers to migrate their existing workloads to Cloudflare. They will also open up a number of new possible use cases for customers. We have highlighted three common examples below, however there are many more to choose from.

Example 1: Sending suspect bots to a honeypot

When creating Snippets customers will be able to access Cloudflare features available in the Workers runtime, such as the bot score field. This enables customers to forward an HTTP request to a honeypot or use the RegExp Javascript function to change the URL construct being sent back to the end user when traffic is assigned a bot score below a certain threshold, e.g. 29 and lower.

…
if (request.cf.botManagement.score < 30) {
const honeypot = "https://example.com/";
return await fetch(honeypot, request);
…
}

Another common use case we foresee Snippets addressing is cookie modification. Usage can range from simply setting an expiry in five minutes by using getTime and setTime JavaScript functions to setting a dynamic cookie based on user request attributes for A/B testing purposes.

…
{
let res = await fetch(request);
res = new Response(res.body, res);
// 24h * 60m * 60s * 1000ms = 86400000ms
const expiry = new Date(Date.now() + 7 * 86400000).toUTCString();
const group = request.headers.get("userGroup") == "premium" ? "A" : "B";
res.headers.append(
      "Set-Cookie",
`testGroup=${group}; Expires=${expiry}; path=/`
    );
…

Example 3: URI query management

Customers can also deploy Cloudflare Snippets to do complex operations such as splicing the URI query value to selectively remove or inject additional parameters. Query string manipulation is typically done using Transform Rules. However, with Transform Rules the set/ action is effectively a replace action. This action when applied to the URI query string will remove the entire value if there is one and set it to what the user specifies, thus overwriting it. This is a problem for customers who wish to selectively inject specific query parameters for matching traffic. For example,  setting an additional query, e.g. ?utm_campaign=facebook when common social media platforms are detected in the user agent. With Snippets, customers will be able to do this selective removal and insertion using a simple piece of JavaScript, e.g.

…
if (userAgent.includes("Facebook")) {
      const url = new URL(request.url);
      const params = new URLSearchParams(url.search);
      params.set("utm_campaign", "facebook");
      url.search = params.toString();
      const transformedRequest = new Request(url, request)
…
}

We are excited to see what other use cases Cloudflare Snippets unlock for our customers.

Will you stop adding actions to rulesets?

The simple answer is no! We will continue to build out our no-code actions within the ruleset engine, developing new products to solve customer needs.

It may sound obvious – but a core component to feature improvement is talking to customers. Talking to Snippet users will help us understand what real life use cases Snippets help solve and highlight feature gaps we have in our product suite. We can then review if it makes sense to productise that use case, or leave it requiring Snippets.

We also understand that not everyone is a software developer. We are therefore exploring how we can make Snippets accessible to all by creating selectable templates available in a library that can be copied and modified by customers, with minimum coding knowledge required. With Snippets, powerful won’t mean difficult.

Accessing Cloudflare Snippets

Snippets are currently under development — you can sign up here to join the waitlist for access.

We hope to begin admitting users into the closed beta in early 2023, with an open beta to follow.

Building scheduling system with Workers and Durable Objects

Post Syndicated from Adam Janiš original https://blog.cloudflare.com/building-scheduling-system-with-workers-and-durable-objects/

Building scheduling system with Workers and Durable Objects

Building scheduling system with Workers and Durable Objects

We rely on technology to help us on a daily basis – if you are not good at keeping track of time, your calendar can remind you when it’s time to prepare for your next meeting. If you made a reservation at a really nice restaurant, you don’t want to miss it! You appreciate the app to remind you a day before your plans the next evening.

However, who tells the application when it’s the right time to send you a notification? For this, we generally rely on scheduled events. And when you are relying on them, you really want to make sure that they occur. Turns out, this can get difficult. The scheduler and storage backend need to be designed with scale in mind – otherwise you may hit limitations quickly.

Workers, Durable Objects, and Alarms are actually a perfect match for this type of workload. Thanks to the distributed architecture of Durable Objects and their storage, they are a reliable and scalable option. Each Durable Object has access to its own isolated storage and alarm scheduler, both being automatically replicated and failover in case of failures.

There are many use cases where having a reliable scheduler can come in handy: running a webhook service, sending emails to your customers a week after they sign up to keep them engaged, sending invoices reminders, and more!

Today, we’re going to show you how to build a scalable service that will schedule HTTP requests on a specific schedule or as one-off at a specific time as a way to guide you through any use case that requires scheduled events.

Building scheduling system with Workers and Durable Objects

Quick intro into the application stack

Before we dive in, here are some of the tools we’re going to be using today:

The application is going to have following components:

  • Scheduling system API to accept scheduled requests and manage Durable Objects
  • Unique Durable Object per scheduled request, each with
  • Storage – keeping the request metadata, such as URL, body, or headers.
  • Alarm – a timer (trigger) to wake Durable Object up.

While we will focus on building the application, the Cloudflare global network will take care of the rest – storing and replicating our data, and making sure to wake our Durable Objects up when the time’s right. Let’s build it!

Initialize new Workers project

Get started by generating a completely new Workers project using the wrangler init command, which makes creating new projects quick & easy.

wrangler init -y durable-objects-requests-scheduler

Building scheduling system with Workers and Durable Objects

For more information on how to install, authenticate, or update Wrangler, check out https://developers.cloudflare.com/workers/wrangler/get-started

Preparing TypeScript types

From my personal experience, at least a draft of TypeScript types significantly helps to be more productive down the road, so let’s prepare and describe our scheduled request in advance. Create a file types.ts in src directory and paste the following TypeScript definitions.

src/types.ts

export interface Env {
    DO_REQUEST: DurableObjectNamespace
}
 
export interface ScheduledRequest {
  url: string // URL of the request
  triggerAt?: number // optional, unix timestamp in milliseconds, defaults to `new Date()`
  requestInit?: RequestInit // optional, includes method, headers, body
}

A scheduled request Durable Object class & alarm

Based on our architecture design, each scheduled request will be saved into its own Durable Object, effectively separating storage and alarms from each other and allowing our scheduling system to scale horizontally – there is no limit to the number of Durable Objects we create.

In the end, the Durable Object class is a matter of a couple of lines. The code snippet below accepts and saves the request body to a persistent storage and sets the alarm timer. Workers runtime will wake up the Durable Object and call the alarm() method to process the request.

The alarm method reads the scheduled request data from the storage, then processes the request, and in the end reschedules itself in case it’s configured to be executed on a recurring schedule.

src/request-durable-object.ts

import { ScheduledRequest } from "./types";
 
export class RequestDurableObject {
  id: string|DurableObjectId
  storage: DurableObjectStorage
 
  constructor(state:DurableObjectState) {
    this.storage = state.storage
    this.id = state.id
  }
 
    async fetch(request:Request) {
    // read scheduled request from request body
    const scheduledRequest:ScheduledRequest = await request.json()
     
    // save scheduled request data to Durable Object storage, set the alarm, and return Durable Object id
    this.storage.put("request", scheduledRequest)
    this.storage.setAlarm(scheduledRequest.triggerAt || new Date())
    return new Response(JSON.stringify({
      id: this.id.toString()
    }), {
      headers: {
        "content-type": "application/json"
      }
    })
  }
 
  async alarm() {
    // read the scheduled request from Durable Object storage
    const scheduledRequest:ScheduledRequest|undefined = await this.storage.get("request")
 
    // call fetch on scheduled request URL with optional requestInit
    if (scheduledRequest) {
      await fetch(scheduledRequest.url, scheduledRequest.requestInit ? webhook.requestInit : undefined)
 
      // cleanup scheduled request once done
      this.storage.deleteAll()
    }
  }
}

Wrangler configuration

Once we have the Durable Object class, we need to create a Durable Object binding by instructing Wrangler where to find it and what the exported class name is.

wrangler.toml

name = "durable-objects-request-scheduler"
main = "src/index.ts"
compatibility_date = "2022-08-02"
 
# added Durable Objects configuration
[durable_objects]
bindings = [
  { name = "DO_REQUEST", class_name = "RequestDurableObject" },
]
 
[[migrations]]
tag = "v1"
new_classes = ["RequestDurableObject"]

Scheduling system API

The API Worker will accept POST HTTP methods only, and is expecting a JSON body with scheduled request data – what URL to call, optionally what method, headers, or body to send. Any other method than POST will return 405 – Method Not Allowed HTTP error.

HTTP POST /:scheduledRequestId? will create or override a scheduled request, where :scheduledRequestId is optional Durable Object ID returned from a scheduling system API before.

src/index.ts

import { Env } from "./types"
export { RequestDurableObject } from "./request-durable-object"
 
export default {
    async fetch(
        request: Request,
        env: Env
    ): Promise<Response> {
        if (request.method !== "POST") {
            return new Response("Method Not Allowed", {status: 405})
        }
 
        // parse the URL and get Durable Object ID from the URL
        const url = new URL(request.url)
        const idFromUrl = url.pathname.slice(1)
 
        // construct the Durable Object ID, use the ID from pathname or create a new unique id
        const doId = idFromUrl ? env.DO_REQUEST.idFromString(idFromUrl) : env.DO_REQUEST.newUniqueId()
 
        // get the Durable Object stub for our Durable Object instance
        const stub = env.DO_REQUEST.get(doId)
 
        // pass the request to Durable Object instance
        return stub.fetch(request)
    },
}

It’s good to mention that the script above does not implement any listing of scheduled or processed webhooks. Depending on how the scheduling system would be integrated, you can save each created Durable Object ID to your existing backend, or write your own registry – using one of the Workers storage options.

Starting a local development server and testing our application

We are almost done! Before we publish our scheduler system to the Cloudflare edge, let’s start Wrangler in a completely local mode to run a couple of tests against it and to see it in action – which will work even without an Internet connection!

wrangler dev --local
Building scheduling system with Workers and Durable Objects

The development server is listening on localhost:8787, which we will use for scheduling our first request. The JSON request payload should match the TypeScript schema we defined in the beginning – required URL, and optional triggerEverySeconds number or triggerAt unix timestamp. When only the required URL is passed, the request will be dispatched right away.

An example of request payload that will send a GET request to https://example.com every 30 seconds.

{
	"url":  "https://example.com",
	"triggerEverySeconds": 30,
}
> curl -X POST -d '{"url": "https://example.com", "triggerEverySeconds": 30}' http://localhost:8787
{"id":"000000018265a5ecaa5d3c0ab6a6997bf5638fdcb1a8364b269bd2169f022b0f"}
Building scheduling system with Workers and Durable Objects

From the wrangler logs we can see the scheduled request ID 000000018265a5ecaa5d3c0ab6a6997bf5638fdcb1a8364b269bd2169f022b0f is being triggered in 30s intervals.

Need to double the interval? No problem, just send a new POST request and pass the request ID as a pathname.

> curl -X POST -d '{"url": "https://example.com", "triggerEverySeconds": 60}' http://localhost:8787/000000018265a5ecaa5d3c0ab6a6997bf5638fdcb1a8364b269bd2169f022b0f
{"id":"000000018265a5ecaa5d3c0ab6a6997bf5638fdcb1a8364b269bd2169f022b0f"}

Every scheduled request gets a unique Durable Object ID with its own storage and alarm. As we demonstrated, the ID becomes handy when you need to change the settings of the scheduled request, or to deschedule them completely.

Publishing to the network

Following command will bundle our Workers application, export and bind Durable Objects, and deploy it to our workers.dev subdomain.

wrangler publish
Building scheduling system with Workers and Durable Objects

That’s it, we are live! 🎉 The URL of your deployment is shown in the Workers logs. In a reasonably short period of time we managed to write our own scheduling system that is ready to handle requests at scale.

You can check full source code in Workers templates repository, or experiment from your browser without installing any dependencies locally using the StackBlitz template.

What to see or build next

Simplifying serverless best practices with AWS Lambda Powertools for TypeScript

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/simplifying-serverless-best-practices-with-aws-lambda-powertools-for-typescript/

This blog post is written by Sara Gerion, Senior Solutions Architect.

Development teams must have a shared understanding of the workloads they own and their expected behaviors to deliver business value fast and with confidence. The AWS Well-Architected Framework and its Serverless Lens provide architectural best practices for designing and operating reliable, secure, efficient, and cost-effective systems in the AWS Cloud.

Developers should design and configure their workloads to emit information about their internal state and current status. This allows engineering teams to ask arbitrary questions about the health of their systems at any time. For example, emitting metrics, logs, and traces with useful contextual information enables situational awareness and allows developers to filter and select only what they need.

Following such practices reduces the number of bugs, accelerates remediation, and speeds up the application lifecycle into production. They can help mitigate deployment risks, offer more accurate production-readiness assessments and enable more informed decisions to deploy systems and changes.

AWS Lambda Powertools for TypeScript

AWS Lambda Powertools provides a suite of utilities for AWS Lambda functions to ease the adoption of serverless best practices. The AWS Hero Yan Cui’s initial implementation of DAZN Lambda Powertools inspired this idea.

Following the community’s adoption of AWS Lambda Powertools for Python and AWS Lambda Powertools for Java, we are excited to announce the general availability of the AWS Lambda Powertools for TypeScript.

AWS Lambda Powertools for TypeScript provides a suite of utilities for Node.js runtimes, which you can use in both JavaScript and TypeScript code bases. The library follows a modular approach similar to the AWS SDK v3 for JavaScript. Each utility is installed as standalone NPM package.

Today, the library is ready for production use with three observability features: distributed tracing (Tracer), structured logging (Logger), and asynchronous business and application metrics (Metrics).

You can instrument your code with Powertools in three different ways:

  • Manually. It provides the most granular control. It’s the most verbose approach, with the added benefit of no additional dependency and no refactoring to TypeScript Classes.
  • Middy middleware. It is the best choice if your existing code base relies on the Middy middleware engine. Powertools offers compatible Middy middleware to make this integration seamless.
  • Method decorator. Use TypeScript method decorators if you prefer writing your business logic using TypeScript Classes. If you aren’t using Classes, this requires the most significant refactoring.

The examples in this blog post use the Middy approach. To follow the examples, ensure that middy is installed:

npm i @middy/core

Logger

Logger provides an opinionated logger with output structured as JSON. Its key features include:

  • Capturing key fields from the Lambda context, cold starts, and structure logging output as JSON.
  • Logging Lambda invocation events when instructed (disabled by default).
  • Printing all the logs only for a percentage of invocations via log sampling (disabled by default).
  • Appending additional keys to structured logs at any point in time.
  • Providing a custom log formatter (Bring Your Own Formatter) to output logs in a structure compatible with your organization’s Logging RFC.

To install, run:

npm install @aws-lambda-powertools/logger

Usage example:

import { Logger, injectLambdaContext } from '@aws-lambda-powertools/logger';
 import middy from '@middy/core';

 const logger = new Logger({
    logLevel: 'INFO',
    serviceName: 'shopping-cart-api',
});

 const lambdaHandler = async (): Promise<void> => {
     logger.info('This is an INFO log with some context');
 };

 export const handler = middy(lambdaHandler)
     .use(injectLambdaContext(logger));

In Amazon CloudWatch, the structured log emitted by your application looks like:

{
     "cold_start": true,
     "function_arn": "arn:aws:lambda:eu-west-1:123456789012:function:shopping-cart-api-lambda-prod-eu-west-1",
     "function_memory_size": 128,
     "function_request_id": "c6af9ac6-7b61-11e6-9a41-93e812345678",
     "function_name": "shopping-cart-api-lambda-prod-eu-west-1",
     "level": "INFO",
     "message": "This is an INFO log with some context",
     "service": "shopping-cart-api",
     "timestamp": "2021-12-12T21:21:08.921Z",
     "xray_trace_id": "abcdef123456abcdef123456abcdef123456"
 }

Logs generated by Powertools can also be ingested and analyzed by any third-party SaaS vendor that supports JSON.

Tracer

Tracer is an opinionated thin wrapper for AWS X-Ray SDK for Node.js.

Its key features include:

  • Auto-capturing cold start and service name as annotations, and responses or full exceptions as metadata.
  • Automatically tracing HTTP(S) clients and generating segments for each request.
  • Supporting tracing functions via decorators, middleware, and manual instrumentation.
  • Supporting tracing AWS SDK v2 and v3 via AWS X-Ray SDK for Node.js.
  • Auto-disable tracing when not running in the Lambda environment.

To install, run:

npm install @aws-lambda-powertools/tracer

Usage example:

import { Tracer, captureLambdaHandler } from '@aws-lambda-powertools/tracer';
 import middy from '@middy/core'; 

 const tracer = new Tracer({
    serviceName: 'shopping-cart-api'
});

 const lambdaHandler = async (): Promise<void> => {
     /* ... Something happens ... */
 };

 export const handler = middy(lambdaHandler)
     .use(captureLambdaHandler(tracer));
AWS X-Ray segments and subsegments emitted by Powertools

AWS X-Ray segments and subsegments emitted by Powertools

Example service map generated with Powertools

Example service map generated with Powertools

Metrics

Metrics create custom metrics asynchronously by logging metrics to standard output following the Amazon CloudWatch Embedded Metric Format (EMF). These metrics can be visualized through CloudWatch dashboards or used to trigger alerts.

Its key features include:

  • Aggregating up to 100 metrics using a single CloudWatch EMF object (large JSON blob).
  • Validating your metrics against common metric definitions mistakes (for example, metric unit, values, max dimensions, max metrics).
  • Metrics are created asynchronously by the CloudWatch service. You do not need any custom stacks, and there is no impact to Lambda function latency.
  • Creating a one-off metric with different dimensions.

To install, run:

npm install @aws-lambda-powertools/metrics

Usage example:

import { Metrics, MetricUnits, logMetrics } from '@aws-lambda-powertools/metrics';
 import middy from '@middy/core';

 const metrics = new Metrics({
    namespace: 'serverlessAirline', 
    serviceName: 'orders'
});

 const lambdaHandler = async (): Promise<void> => {
     metrics.addMetric('successfulBooking', MetricUnits.Count, 1);
 };

 export const handler = middy(lambdaHandler)
     .use(logMetrics(metrics));

In CloudWatch, the custom metric emitted by your application looks like:

{
     "successfulBooking": 1.0,
     "_aws": {
     "Timestamp": 1592234975665,
     "CloudWatchMetrics": [
         {
         "Namespace": "serverlessAirline",
         "Dimensions": [
             [
             "service"
             ]
         ],
         "Metrics": [
             {
             "Name": "successfulBooking",
             "Unit": "Count"
             }
         ]
     },
     "service": "orders"
 }

Serverless TypeScript demo application

The Serverless TypeScript Demo shows how to use Lambda Powertools for TypeScript. You can find instructions on how to deploy and load test this application in the repository.

Serverless TypeScript Demo architecture

Serverless TypeScript Demo architecture

The code for the Get Products Lambda function shows how to use the utilities. The function is instrumented with Logger, Metrics and Tracer to emit observability data.

// blob/main/src/api/get-products.ts
import { APIGatewayProxyEvent, APIGatewayProxyResult} from "aws-lambda";
import { DynamoDbStore } from "../store/dynamodb/dynamodb-store";
import { ProductStore } from "../store/product-store";
import { logger, tracer, metrics } from "../powertools/utilities"
import middy from "@middy/core";
import { captureLambdaHandler } from '@aws-lambda-powertools/tracer';
import { injectLambdaContext } from '@aws-lambda-powertools/logger';
import { logMetrics, MetricUnits } from '@aws-lambda-powertools/metrics';

const store: ProductStore = new DynamoDbStore();
const lambdaHandler = async (event: APIGatewayProxyEvent): Promise<APIGatewayProxyResult> => {

  logger.appendKeys({
    resource_path: event.requestContext.resourcePath
  });

  try {
    const result = await store.getProducts();

    logger.info('Products retrieved', { details: { products: result } });
    metrics.addMetric('productsRetrieved', MetricUnits.Count, 1);

    return {
      statusCode: 200,
      headers: { "content-type": "application/json" },
      body: `{"products":${JSON.stringify(result)}}`,
    };
  } catch (error) {
      logger.error('Unexpected error occurred while trying to retrieve products', error as Error);

      return {
        statusCode: 500,
        headers: { "content-type": "application/json" },
        body: JSON.stringify(error),
      };
  }
};

const handler = middy(lambdaHandler)
    .use(captureLambdaHandler(tracer))
    .use(logMetrics(metrics, { captureColdStartMetric: true }))
    .use(injectLambdaContext(logger, { clearState: true, logEvent: true }));

export {
  handler
};

The Logger utility adds useful context to the application logs. Structuring your logs as JSON allows you to search on your structured data using Amazon CloudWatch Logs Insights. This allows you to filter out the information you don’t need.

For example, use the following query to search for any errors for the serverless-typescript-demo service.

fields resource_path, message, timestamp
| filter service = 'serverless-typescript-demo'
| filter level = 'ERROR'
| sort @timestamp desc
| limit 20
CloudWatch Logs Insights showing errors for the serverless-typescript-demo service.

CloudWatch Logs Insights showing errors for the serverless-typescript-demo service.

The Tracer utility adds custom annotations and metadata during the function invocation, which it sends to AWS X-Ray. Annotations allow you to search for and filter traces by business or application contextual information such as product ID, or cold start.

You can see the duration of the putProduct method and the ColdStart and Service annotations attached to the Lambda handler function.

putProduct trace view

putProduct trace view

The Metrics utility simplifies the creation of complex high-cardinality application data. Including structured data along with your metrics allows you to search or perform additional analysis when needed.

In this example, you can see how many times per second a product is created, deleted, or queried. You could configure alarms based on the metrics.

Metrics view

Metrics view

Code examples

You can use Powertools with many Infrastructure as Code or deployment tools. The project contains source code and supporting files for serverless applications that you can deploy with the AWS Cloud Development Kit (AWS CDK) or AWS Serverless Application Model (AWS SAM).

The AWS CDK lets you build reliable and scalable applications in the cloud with the expressive power of a programming language, including TypeScript. The AWS SAM CLI is that makes it easier to create and manage serverless applications.

You can use the sample applications provided in the GitHub repository to understand how to use the library quickly and experiment in your own AWS environment.

Conclusion

AWS Lambda Powertools for TypeScript can help simplify, accelerate, and scale the adoption of serverless best practices within your team and across your organization.

The library implements best practices recommended as part of the AWS Well-Architected Framework, without you needing to write much custom code.

Since the library relieves the operational burden needed to implement these functionalities, you can focus on the features that matter the most, shortening the Software Development Life Cycle and reducing the Time To Market.

The library helps both individual developers and engineering teams to standardize their organizational best practices. Utilities are designed to be incrementally adoptable for customers at any stage of their serverless journey, from startup to enterprise.

To get started with AWS Lambda Powertools for TypeScript, see the official documentation. For more serverless learning resources, visit Serverless Land.

Making Page Shield malicious code alerts more actionable

Post Syndicated from Simon Wijckmans original https://blog.cloudflare.com/making-page-shield-malicious-code-alerts-more-actionable/

Making Page Shield malicious code alerts more actionable

Making Page Shield malicious code alerts more actionable

Last year during CIO week, we announced Page Shield in general availability. Today, we talk about improvements we’ve made to help Page Shield users focus on the highest impact scripts and get more value out of the product. In this post we go over improvements to script status, metadata and categorization.

What is Page Shield?

Page Shield protects website owners and visitors against malicious 3rd party JavaScript. JavaScript can be leveraged in a number of malicious ways: browser-side crypto mining, data exfiltration and malware injection to mention a few.

For example a single hijacked JavaScript can expose millions of user’s credit card details across a range of websites to a malicious actor. The bad actor would scrape details by leveraging a compromised JavaScript library, skimming inputs to a form and exfiltrating this to a 3rd party endpoint under their control.

Today Page Shield partially relies on Content Security Policies (CSP), a browser native framework that can be used to control and gain visibility of which scripts are allowed to load on pages (while also reporting on any violations). We use these violation reports to provide detailed information in the Cloudflare dashboard regarding scripts being loaded by end-user browsers.

Page Shield users, via the dashboard, can see which scripts are active on their website and on which pages. Users can be alerted in case a script performs malicious activity, and monitor code changes of the script.

Script status

To help identify malicious scripts, and make it easier to take action on live threats, we have introduced a status field.

When going to the Page Shield dashboard, the customer will now only see scripts with a status of active. Active scripts are those that were seen in the last seven days and didn’t get served through the “cdn-cgi” endpoint (which is managed by Cloudflare).

We also introduced other statuses:

  • infrequent scripts are scripts that have only been seen in a negligible amount of CSP reports over a period of time. TThey normally indicate a single user’s browser using a compromised browser extension. The goal of this status is to reduce noise caused by browser plugins that inject their JavaScript in the HTML.
  • inactive scripts are those that have not been reported for seven days and therefore have likely since been removed or replaced.
  • cdn-cgi are scripts served from the ‘/cdn-cgi/’ endpoint which is managed by Cloudflare. These scripts are related to Cloudflare products like our analytics or Bot Management features. Cloudflare closely monitors these scripts, they are fairly static, so they shouldn’t require close monitoring by a customer and therefore are hidden by default unless our detections find anything suspicious.

If the customer wishes to see the full list of scripts including the non-active scripts they can still do so by clicking ‘All scripts’.

Making Page Shield malicious code alerts more actionable

Script metadata in alerts

Customers that opt into the enterprise add-on version of Page Shield can also choose to set up notifications for malicious scripts. In the previous version, we offered the script URL, first seen on and last seen on data. These alerts have been revamped to improve the experience for security analysts. Our goal is to provide all data a security analyst would manually look-up in order to validate a script. With this update we’ve made a significant step in that direction through using insights delivered by Cloudflare Radar.

At the top of the email alert you will now find where the script was seen along with other information regarding when the script was seen and the full URL (not clickable for security purposes).

As part of the enterprise add-on version of Page Shield we review the scripts through a custom machine learning classifier and a range of domain and URL threat feeds. A very common question with any machine learning scoring system is “why did it score the way it scored”. Because of this, the machine learning score generated by our system has now been split up to show the components that made up the score; currently: obfuscation and data exfiltration values. This should help to improve the ability for a customer to review a script in case of a false positive.

Threat feeds can be very helpful in detecting new attack styles that our classifier hasn’t been trained for yet. Some of these feeds offer us a range of categories of malicious endpoints such as ‘malware’, ‘spyware’ or ‘phishing’. Our enterprise add-on customers will now be able to see the categorization in our alerts (as shown above) and on the dashboard. The goal is to provide more context on why a script is considered malicious.

We also now provide information on script changes along with the “malicious code score” for each version.

Making Page Shield malicious code alerts more actionable

Right below the most essential information we have added WHOIS information on the party domain that is providing the script. In some cases the registrar may hide relevant information such as the organization’s name, however, information on the date of registration and expiration can be very useful in detecting unexpected changes in ownership. For example, we often see malicious scripts being hosted under newly registered domains.

Making Page Shield malicious code alerts more actionable

We also now offer details regarding the SSL certificates issued for this domain through certificate transparency monitoring. This can be useful in detecting a potential take over. For example, if a 3rd party script endpoint usually uses a Digicert certificate, but recently a Let’s Encrypt certificate has been issued this could be an indicator that another party is trying to take over the domain.

Making Page Shield malicious code alerts more actionable

Last but not least, we have improved our dashboard link to take users directly to the specific script details page provided by the Page Shield UI.

What’s next?

There are many ways to perform malicious activity through JavaScript. Because of this it is important that we build attack type specific detection mechanisms as well as overarching tools that will help detect anomalies. We are currently building a new component purpose built to look for signs of malicious intent in data endpoints by leveraging the connect-src CSP directive. The goal is to improve the accuracy of our Magecart-style attack detection.

We are also working on providing the ability to generate CSP policies directly through Page Shield making it easy to perform positive blocking and maintain rules over time.

Another feature we are working on is offering the ability to block scripts from accessing a user’s webcam, microphone or location through a single click.

More about this in future blog posts. Many more features to come!

XSS in JSON: Old-School Attacks for Modern Applications

Post Syndicated from Julius Callahan original https://blog.rapid7.com/2022/05/04/xss-in-json-old-school-attacks-for-modern-applications/

XSS in JSON: Old-School Attacks for Modern Applications

I recently wrote a blog post on injection-type vulnerabilities and how they were knocked down a few spots from 1 to 3 on the new OWASP Top 10 for 2022. The main focus of that article was to demonstrate how stack traces could be — and still are — used via injection attacks to gather information about an application to further an attacker’s goal. In that post, I skimmed over one of my all time favorite types of injections: cross-site scripting (XSS).

In this post, I’ll cover this gem of an exploit in much more depth, highlighting how it has managed to adapt to the newer environments of today’s modern web applications, specifically the API and Javascript Object Notation (JSON).

I know the term API is thrown around a lot when referencing web applications these days, but for this post, I will specifically be referencing requests made from the front end of a web application to the back end via ajax (Asynchronous JavaScript and XML) or more modern approaches like the fetch method in JavaScript.

Before we begin, I’d like to give a quick recap of what XSS is and how a legacy application might handle these types of requests that could trigger XSS, then dive into how XSS still thrives today in modern web applications via the methods mentioned so far.

What is cross-site scripting?

There are many types of XSS, but for this post, I’ll only be focusing on persistent XSS, which is sometimes referred to as stored XSS.

XSS is a type of injection attack, in which malicious scripts are injected into otherwise benign and trusted websites. XSS attacks occur when an attacker uses a web application to execute malicious code — generally in the form of a browser-side script like JavaScript, for example — against an unsuspecting end user. Flaws that allow these attacks to succeed are quite widespread and occur anywhere a web application accepts an input from a user without sanitizing, validating, escaping, or encoding it.

Because the end user’s browser has no way to know not to trust the malicious script, the browser will execute the script. Because of this broken trust, attackers typically leverage these vulnerabilities to steal victims’ cookies, session tokens, or other sensitive information retained by the browser. They could also redirect to other malicious sites, install keyloggers or crypto miners, or even change the content of the website.

Now for the “stored” part. As the name implies, stored XSS generally occurs when the malicious payload has been stored on the target server, usually in a database, from input that has been submitted in a message forum, visitor log, comment field, form, or any parameter that lacks proper input sanitization.

What makes this type of XSS so much more damaging is that, unlike reflected XSS – which only affects specific targets via cleverly crafted links – stored XSS affects any and everyone visiting the compromised site. This is because the XSS has been stored in the applications database, allowing for a much larger attack surface.

Old-school apps

Now that we’ve established a basic understanding of stored XSS, let’s go back in time a few decades to when web apps were much simpler in their communications between the front-end and back-end counterparts.

Let’s say you want to change some personal information on a website, like your email address on a contacts page. When you enter in your email address and click the update button, it triggers the POST method to send the form data to the back end to update that value in a database. The database updates the value in a table, then pushes a response back to the web applications front end, or UI, for you to see. This would usually result in the entire page having to reload to display only a very minimal amount of change in content, and while it’s very inefficient, nonetheless the information would be added and updated for the end user to consume.

In the example below, clicking the update button submits a POST form request to the back-end database where the application updates and stores all the values, then provides a response back to the webpage with the updated info.

XSS in JSON: Old-School Attacks for Modern Applications

XSS in JSON: Old-School Attacks for Modern Applications

Old-school XSS

As mentioned in my previous blog post on injection, I give an example where an attacker enters in a payload of <script>alert(“This is XSS”)</script> instead of their email address and clicks the update button. Again, this triggers the POST method to take our payload and send it to the back-end database to update the email table, then pushes a response back to the front end, which gets rendered back to the UI in HTML. However, this time the email value being stored and displayed is my XSS payload, <script>alert(“This is XSS”)</script>, not an actual email address.

XSS in JSON: Old-School Attacks for Modern Applications

As seen above, clicking the “update” button submits the POST form data to the back end where the database stores the values, then pushes back a response to update the UI as HTML.

XSS in JSON: Old-School Attacks for Modern Applications

However, because our payload is not being sanitized properly, our malicious JavaScript gets executed by the browser, which causes our alert box to pop up as seen below.

XSS in JSON: Old-School Attacks for Modern Applications

While the payload used in the above example is harmless, the point to drive home here is that we were able to get the web application to store and execute our JavaScript all through a simple contact form. Anyone visiting my contact page will see this alert pop up because my XSS payload has been stored in the database and gets executed every time the page loads. From this point on, the possible damage that could be done here is endless and only limited by the attacker’s imagination… well, and their coding skills.  

New-school apps

In the first example I gave, when you updated the email address on the contact page and the request was fulfilled by the backend, the entire page would reload in order to display the newly created or updated information. You can see how inefficient this is, especially if the only thing changing on the page is a single line or a few lines of text. Here is where ajax and/or the fetch method comes in.

Ajax, or the fetch method, can be used to get data from or post data to a remote source, then update the front-end UI of that web application without having to refresh the page. Only the content from the specific request is updated, not the entire page, and that is the key difference between our first example and this one.

And a very popular format for said data being sent and received is JavaScript Object Notation, most commonly known as JSON. (Don’t worry, I’ll get back to those curly braces in just a bit.)

New-school XSS

(Well, not really, but it sounds cool.)

Now, let’s pretend we’ve traveled back to the future and our contact page has been rewritten to use ajax or the fetch method to send and receive data to and from the database. From the user’s point of view, nothing has changed — still the same ol’ form. But this time, when the email address is being updated, only the contact form refreshes. The entire page and all of its contents do not refresh like in the previous version, which is a major win for efficiency and user experience.

Below is an example of what a POST might look like formatted in JSON.

XSS in JSON: Old-School Attacks for Modern Applications

“What is JSON?” you might ask. Short for JavaScript Object Notation, it is a lightweight text format for storing and transferring data and is most commonly used when sending data to and from servers. Remember those curly braces I mentioned earlier? Well, one quick and easy way to spot JSON is the formatting and the use of curly braces.

In the example above, you can see what our new POST looks like using ajax or the fetch method in JavaScript. While the end result is no different than before, as seen in the example below, the method that was used to update the page is quite different. The key difference here is that the data we’re wanting to update is being treated as just that: data, but in the form of JSON as opposed to HTML.

XSS in JSON: Old-School Attacks for Modern Applications

Now, let’s inject the same XSS payload into the same email field and hit update.  In the example below, you can see that our POST request has been wrapped in curly braces, using JSON, and is formatted a bit differently than previously before being sent to the back end to be processed.

XSS in JSON: Old-School Attacks for Modern Applications

XSS in JSON: Old-School Attacks for Modern Applications

In the example above, you can see that the application is allowing my email address to be the XSS payload in its entirety. However, the JavaScript here is only being displayed and not being executed as code by the browser, so the alert “pop” message never gets triggered as in the previous example. That again is the key difference from the original way we were fulfilling the requests versus our new, more modern way — or in short, using JSON instead of HTML.

Now you might be asking yourself, what’s wrong with allowing the XSS payload to be the email address if it’s only being displayed and not being executed as JavaScript by the browser. That is a valid question, but hear me out.

See, I’ve been working in this industry long enough to know that the two most common responses to a question or statement regarding cybersecurity begin with either “that depends…” or “what if…”  I’m going to go with the latter here and throw a couple what-ifs at you.

Now that my XSS is stored in your database, it’s only a matter of time before this ticking time bomb goes off. Just because my XSS is being treated as JSON and not HTML now does not mean that will always be the case, and attackers are betting on this.

Here are a few scenarios.

Scenario 1

What if team B handles this data differently from team A? What if team B still uses more traditional methods of sending and receiving data to and from the back end and does leverage the use of HTML and not JSON?

In that case, the XSS would most likely eventually get executed. It might not affect the website that the XSS was originally injected into, but the stored data can be (and usually is) also used elsewhere. The XSS stored in that database is probably going to be shared and used by multiple other teams and applications at some point. The odds of all those different teams leveraging the exact same standards and best practices are slim to none, and attackers know this.  

Scenario 2

What if, down the road, a developer using more modern techniques like ajax or the fetch method to send and receive data to and from the back end decides to use the .innerHTML property instead of .innerTEXT to load that JSON into the UI? All bets are off, and the stored XSS that was previously being protected by those lovely curly braces will now most likely get executed by the browser.

Scenario 3

Lastly, what if the current app had been developed to use server-side rendering, but a decision from higher up has been made that some costs need to be cut and that the company could actually save money by recoding some of their web apps to be client-side rather than server-side?

Previously, the back end was doing all the work, including sanitizing all user input, but now the shift will be for the browser to do all the heavy lifting. Good luck spotting all the XSS stored in the DB — in its previous state, it was “harmless,” but now it could get rendered to the UI as HTML, allowing the browser to execute said stored XSS. In this scenario, a decision that was made upstream will have an unexpected security impact downstream, both figuratively and literally — a situation that is all too well-known these days.

Final thoughts

Part of my job as a security advisor is to, well, advise. And it’s these types of situations that keep me up at night. I come across XSS in applications every day, and while I may not see as many fun and exciting “pops” as in years past, I see something a bit more troubling.

This type of XSS is what I like to call a “sleeper vuln” – laying dormant, waiting for the right opportunity to be woken up. If I didn’t know any better, I’d say XSS has evolved and is aware of its new surroundings. Of course, XSS hasn’t evolved, but the applications in which it lives have.

At the end of the day, we’re still talking about the same XSS from its conception, the same XSS that has been on the OWASP Top 10 for decades — what we’re really concerned about is the lack of sanitization or handling of user input. But now, with the massive adoption of JavaScript frameworks like Angular, libraries like React, the use of APIs, and the heavy reliance on them to handle the data properly, we’ve become complacent in our duties to harden applications the proper way.

There seems to be a division in camps around XSS in JSON. On the one hand, some feel that since the JavaScript isn’t being executed by the browser, everything is fine. Who cares if an email address (or any data for that matter) is potentially dangerous — as long as it’s not being executed by the browser. And on the other hand, you have the more fundamentalist, dare I say philosophical thought that all user input should never be trusted: It should always be sanitized, regardless of whether it’s treated as data or not — and not solely because of following best coding and security practices, but also because of the “that depends” and “what if” scenarios in the world.  

I’d like to point out in my previous statement above, that “as long as” is vastly different from cannot.” “As long as” implies situational awareness and that a certain set of criteria need to be met for it to be true or false, while “cannot” is definite and fixed, regardless of the situation or criteria. “As long as the XSS is wrapped in curly braces” means it does not pose a risk in its current state but could in other states. But if input is sanitized and escaped properly, the XSS would never exist in the first place, and thus it “cannot” or could not be executed by the browser, ever.

I guess I cannot really complain too much about these differences of opinions though. The fact that I’m even having these conversations with others is already a step in the right direction. But what does concern me is that it’s 2022, and we’re still seeing XSS rampant in applications, but because it’s wrapped in JSON somehow makes it acceptable. One of the core fundamentals of my job is to find and prioritize risk, then report. And while there is always room for discussion around the severity of these types of situations, lots of factors have to be taken into consideration, a spade isn’t always a spade in application security, or cybersecurity in general for that matter. But you can rest assured if I find XSS in JSON in your environment, I will be calling it out.  

I hope there will be a future where I can look back and say, “Remember that one time when curly braces were all that prevented your website from getting hacked?” Until then, JSON or not, never trust user data, and sanitize all user input (and output for that matter). A mere { } should never be the difference between your site getting hacked or not.

Additional reading:

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Announcing native support for Stripe’s JavaScript SDK in Cloudflare Workers

Post Syndicated from Kristian Freeman original https://blog.cloudflare.com/announcing-stripe-support-in-workers/

Announcing native support for Stripe’s JavaScript SDK in Cloudflare Workers

This post is also available in 日本語, 简体中文.

Announcing native support for Stripe’s JavaScript SDK in Cloudflare Workers

Handling payments inside your apps is crucial to building a business online. For many developers, the leading choice for handling payments is Stripe. Since my first encounter with Stripe about seven years ago, the service has evolved far beyond simple payment processing. In the e-commerce example application I shared last year, Stripe managed a complete seller marketplace, using the Connect product. Stripe’s product suite is great for developers looking to go beyond accepting payments.

Earlier versions of Stripe’s SDK had core Node.js dependencies, like many popular JavaScript packages. In Stripe’s case, it interacted directly with core Node.js libraries like net/http, to handle HTTP interactions. For Cloudflare Workers, a V8-based runtime, this meant that the official Stripe JS library didn’t work; you had to fall back to using Stripe’s (very well-documented) REST API. By doing so, you’d lose the benefits of using Stripe’s native JS library — things like automatic type-checking in your editor, and the simplicity of function calls like stripe.customers.create(), instead of manually constructed HTTP requests, to interact with Stripe’s various pieces of functionality.

In April, we wrote that we were focused on increasing the number of JavaScript packages that are compatible with Workers:

We won’t stop until our users can import popular Node.js libraries seamlessly. This effort will be large-scale and ongoing for us, but we think it’s well worth it.

I’m excited to announce general availability for the stripe JS package for Cloudflare Workers. You can now use the native Stripe SDK directly in your projects! To get started, install stripe in your project: npm i stripe.

This also opens up a great opportunity for developers who have deployed sites to Cloudflare Pages to begin using Stripe right alongside their applications. With this week’s announcement of serverless function support in Cloudflare Pages, you can accept payments for your digital product, or handle subscriptions for your membership site, with a few lines of JavaScript added to your Pages project. There’s no additional configuration, and it scales automatically, just like your Pages site.

To showcase this, we’ve prepared an example open-source repository, showing how you can integrate Stripe Checkout into your Pages application. There’s no additional configuration needed — as we announced yesterday, Pages’ new Workers Functions features allows you to deploy infinitely-scalable functions just by adding a new functions folder to your project. See it in action at stripe.pages.dev, or check out the open-source repository on GitHub.

With the SDK installed, you can begin accepting payments directly in your applications. The below example shows how to initiate a new Checkout session and redirect to Stripe’s hosted checkout page:

const Stripe = require("stripe");

const stripe = Stripe(STRIPE_API_KEY, {
  httpClient: Stripe.createFetchHttpClient()
});

export default {
  async fetch(request) {
    const session = await stripe.checkout.sessions.create({
      line_items: [{
        price_data: {
          currency: 'usd',
          product_data: {
            name: 'T-shirt',
          },
          unit_amount: 2000,
        },
        quantity: 1,
      }],
      payment_method_types: [
        'card',
      ],
      mode: 'payment',
      success_url: `${YOUR_DOMAIN}/success.html`,
      cancel_url: `${YOUR_DOMAIN}/cancel.html`,
    });

    return Response.redirect(session.url)
  }
}

With support for the Stripe SDK natively in Cloudflare Workers, you aren’t just limited to payment processing either. Any JavaScript example that’s currently in Stripe’s extensive documentation works directly in Workers, without needing to make any changes.

In particular, using Workers to handle the multitude of available Stripe webhooks means that you can get better visibility into how your existing projects are working, without needing to spin up any new infrastructure. The below example shows how you can securely validate incoming webhook requests to a Workers function, and perform business logic by parsing the data inside that webhook:

const Stripe = require("stripe");

const stripe = Stripe(STRIPE_API_KEY, {
  httpClient: Stripe.createFetchHttpClient()
});

export default {
  async fetch(request) {
    const body = await request.text()
    const sig = request.headers.get('stripe-signature')
    const event = stripe.webhooks.constructEvent(body, sig, secret)

    // Handle the event
    switch (event.type) {
      case 'payment_intent.succeeded':
        const paymentIntent = event.data.object;
        // Then define and call a method to handle the successful payment intent.
        // handlePaymentIntentSucceeded(paymentIntent);
        break;
      case 'payment_method.attached':
        const paymentMethod = event.data.object;
        // Then define and call a method to handle the successful attachment of a PaymentMethod.
        // handlePaymentMethodAttached(paymentMethod);
        break;
      // ... handle other event types
      default:
        console.log(`Unhandled event type ${event.type}`);
    }

    // Return a response to acknowledge receipt of the event
    return new Response(JSON.stringify({ received: true }), {
      headers: { 'Content-type': 'application/json' }
    })
  }
}

We’re also announcing a new Workers template in partnership with Stripe. The template will help you get up and running with Stripe and Workers, using our best practices.

In less than five minutes, you can begin accepting payments for your next digital product or membership business. You can also handle and validate incoming webhooks at the edge, from a single codebase. This comes with no upfront cost, server provisioning, or any of the standard scaling headaches. Take your serverless functions, deploy them to Cloudflare’s edge, and start making money!

“We’re big fans of Cloudflare Workers over here at Stripe. Between the wild performance at the edge and fantastic serverless development experience, we’re excited to see what novel ways you all use Stripe to make amazing apps.”
Brian Holt, Stripe

We’re thrilled with this incredible update to Stripe’s JavaScript SDK. We’re also excited to see what you’ll build with native Stripe support in Workers. Join our Discord server and share what you’ve built in the #what-i-built channel — get your invite here.

JavaScript modules are now supported on Cloudflare Workers

Post Syndicated from Ashcon Partovi original https://blog.cloudflare.com/workers-javascript-modules/

JavaScript modules are now supported on Cloudflare Workers

JavaScript modules are now supported on Cloudflare Workers

We’re excited to announce that JavaScript modules are now supported on Cloudflare Workers. If you’ve ever taken look at an example Worker written in JavaScript, you might recognize the following code snippet that has been floating around the Internet the past few years:

addEventListener("fetch", (event) => {
  event.respondWith(new Response("Hello Worker!"));
}

The above syntax is known as the “Service Worker” API, and it was proposed to be standardized for use in web browsers. The idea is that you can attach a JavaScript file to a web page to modify its HTTP requests and responses, acting like a virtual endpoint. It was exactly what we needed for Workers, and it even integrated well with standard Web APIs like fetch() and caches.

Before introducing modules, we want to make it clear that we will continue to support the Service Worker API. No developer wants to get an email saying that you need to rewrite your code because an API or feature is being deprecated; and you won’t be getting one from us. If you’re interested in learning why we made this decision, you can read about our commitment to backwards-compatibility for Workers.

What are JavaScript modules?

JavaScript modules, also known as ECMAScript (abbreviated. “ES”) modules, is the standard API for importing and exporting code in JavaScript. It was introduced by the “ES6” language specification for JavaScript, and has been implemented by most Web browsers, Node.js, Deno, and now Cloudflare Workers. Here’s an example to demonstrate how it works:

// filename: ./src/util.js
export function getDate(time) {
  return new Date(time).toISOString().split("T")[0]; // "YYYY-MM-DD"
}

The “export” keyword indicates that the “getDate” function should be exported from the current module. Then, you can use “import” from another module to use that function.

// filename: ./src/index.js
import { getDate } from "./util.js"

console.log("Today’s date:", getDate());

Those are the basics, but there’s a lot more you can do with modules. It allows you to organize, maintain, and re-use your code in an elegant way that just works. While we can’t go over every aspect of modules here, if you’d like to learn more we’d encourage you to read the MDN guide on modules, or a more technical deep-dive by Lin Clark.

How can I use modules in Workers?

You can export a default module, which will represent your Worker. Instead of using “addEventListener,” each event handler is defined as a function on that module. Today, we support “fetch” for HTTP and WebSocket requests and “scheduled” for cron triggers.

export default {
  async fetch(request, environment, context) {
    return new Response("I’m a module!");
  },
  async scheduled(controller, environment, context) {
    // await doATask();
  }
}

You may also notice some other differences, such as the parameters on each event handler. Instead of a single “Event” object, the parameters that you need the most are spread out on their own. The first parameter is specific to the event type: for “fetch” it’s the Request object, and for “scheduled” it’s a controller that contains the cron schedule.

The second parameter is an object that contains your environment variables (also known as “bindings“). Previously, each variable was inserted into the global scope of the Worker. While a simple solution, it was confusing to have variables magically appear in your code. Now, with an environment object, you can control which modules and libraries get access to your environment variables. This mechanism is more secure, as it can prevent a compromised or nosy third-party library from enumerating all your variables or secrets.

The third parameter is a context object, which allows you to register background tasks using waitUntil(). This is useful for tasks like logging or error reporting that should not block the execution of the event.

When you put that all together, you can import and export multiple modules, as well as use the new event handler syntax.

// filename: ./src/error.js
export async function logError(url, error) {
  await fetch(url, {
     method: "POST",
     body: error.stack
  })
}

// filename: ./src/worker.js
import { logError } from "./error.js"

export default {
  async fetch(request, environment, context) {
    try {
       return await fetch(request);
    } catch (error) {
       context.waitUntil(logError(environment.ERROR_URL, error));
       return new Response("Oops!", { status: 500 });
    }
  }
}

Let’s not forget about Durable Objects, which became generally available earlier this week! You can also export classes, which is how you define a Durable Object class. Here’s another example with a “Counter” Durable Object, that responds with an incrementing value.

// filename: ./src/counter.js
export class Counter {
  value = 0;
  fetch() {
    this.value++;
    return new Response(this.value.toString());
  }
}

// filename: ./src/worker.js
// We need to re-export the Durable Object class in the Worker module.
export { Counter } from "./counter.js"

export default {
  async fetch(request, environment) {
    const clientId = request.headers.get("cf-connecting-ip");
    const counterId = environment.Counter.idFromName(clientId);
    // Each IP address gets its own Counter.
    const counter = environment.Counter.get(counterId);
    return counter.fetch("https://counter.object/increment");
  }
}

Are there non-JavaScript modules?

Yes! While modules are primarily for JavaScript, we also support other modules types, some of which are not yet standardized.

For instance, you can import WebAssembly as a module. Previously, with the Service Worker API, WebAssembly was included as a binding. We think that was a mistake, since WebAssembly should be represented as code and not an external resource. With modules, here’s the new way to import WebAssembly:

import module from "./lib/hello.wasm"

export default {
  async fetch(request) {
    const instance = await WebAssembly.instantiate(module);
    const result = instance.exports.hello();
    return new Response(result);
  }
}

While not supported today, we look forward to a future where WebAssembly and JavaScript modules can be more tightly integrated, as outlined in this proposal. The ergonomics improvement, demonstrated below, can go a long way to make WebAssembly more included in the JavaScript ecosystem.

import { hello } from "./lib/hello.wasm"

export default {
  async fetch(request) {
    return new Response(hello());
  }
}

We’ve also added support for text and binary modules, which allow you to import a String and ArrayBuffer, respectively. While not standardized, it allows you to easily import resources like an HTML file or an image.

<!-- filename: ./public/index.html -->
<!DOCTYPE html>
<html><body>
<p>Hello!</p>
</body></html>

import html from "../public/index.html"

export default {
  fetch(request) {
    if (request.url.endsWith("/index.html") {
       return new Response(html, {
          headers: { "Content-Type": "text/html" }
       });
    }
    return fetch(request);
  }
}

How can I get started?

There are many ways to get started with modules.

First, you can try out modules in your browser using our playground (which doesn’t require an account) or by using the dashboard quick editor. Your browser will automatically detect when you’re using modules to allow you to seamlessly switch from the Service Worker API. For now, you’ll only be able to create one JavaScript module in the browser, though supporting multiple modules is something we’ll be improving soon.

If you’re feeling adventurous and want to start a new project using modules, you can try out the beta release of wrangler 2.0, the next-generation of the command-line interface (CLI) for Workers.

For existing projects, we still recommend using wrangler 1.0 (release 1.17 or later). To enable modules, you can adapt your “wrangler.toml” configuration to the following example:

name = "my-worker"
type = "javascript"
workers_dev = true

[build.upload]
format = "modules"
dir = "./src"
main = "./worker.js" # becomes "./src/worker.js"

[[build.upload.rules]]
type = "ESModule"
globs = "**/*.js"

# Uncomment if you have a build script.
# [build]
# command = "npm run build"

We’ve updated our documentation to provide more details about modules, though some examples will still be using the Service Worker API as we transition to showing both formats. (and TypeScript as a bonus!)

If you experience an issue or notice something strange with modules, please let us know, and we’ll take a look. Happy coding, and we’re excited to see what you build with modules!

JavaScript modules are now supported on Cloudflare Workers

How We Build Micro Frontends With Lattice

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/how-we-build-micro-frontends-with-lattice-22b8635f77ea

Written by Michael Possumato, Nick Tomlin, Jordan Andree, Andrew Shim, and Rahul Pilani.

As we continue to grow here at Netflix, the needs of Revenue and Growth Engineering are rapidly evolving; and our tools must also evolve just as rapidly. The Revenue and Growth Tools (RGT) team decided to set off on a journey to build tools in an abstract manner to have solutions readily available within our organization. We identified common design patterns and architectures scattered across various tools which were all duplicating efforts in some way or another.

We needed to consolidate these tools in a way that scaled with the teams we served. It needed to have the agility of a micro frontend and the extensibility of a framework to empower our stakeholders to extend our tools. We would abstract parts of which anyone can then customize, or extend, to meet their specific business or technical requirements. The end result is Lattice: RGT’s pluggable framework for micro frontends.

A Different Approach to Our Tools

A UI composed of other dependencies is nothing new; it’s something all modern web applications do today. The traditional approach of bundling dependencies at build time lacks the flexibility we need to empower our stakeholders. We want external dependencies to be resolved on-demand from any number of sources, from another application to an engineer’s laptop.

This led us to the following high level objectives:

  • Low Friction Adoption: Encourage reuse of existing front end code and avoid creating new packages that encapsulate UI functionality. Applications can be difficult to manage when functionality must be shared across packages. We would leverage an approach that enabled applications to extend their core functionality using common, and familiar, React paradigms.
  • Weak Dependencies: Host applications could reference modules over https to a remote bundle hosted internally within Netflix. These bundles could be owned by teams outside of RGT built by already adopted standards such as with Webpack Module Federation or native JavaScript Modules.
  • Highly Aligned, Loosely Coupled: fully align with the standard frameworks and libraries used within Netflix. Plugins should be focused on delivering their core functionality without unnecessary boilerplate and have the freedom to implement without cumbersome API wrappers.
  • Metadata Driven: Plugin modules are defined from a configuration which could be injected at any point in the application lifecycle. The framework must be flexible enough to register, and unregister, plugins such that the extensions only apply when necessary.
  • Rapid Development: Reduce the development cycle by avoiding unnecessary builds and deployments. Plugins would be developed in a manner in which all of the context is available to them ahead of time via TypeScript declarations. By designing to rigid interfaces defined by a host application, both the plugin and host can be developed in parallel.

A Theoretical Example

Example Developer Dashboard Application with Embedded Lattice Plugins

Let’s take the above example — it renders and controls its own header and content areas to expose specific functionality to users. Upon release, we receive feedback that it would be nice if we could include information presented from other tools within this application. Using our new framework, Lattice, we are able to embed the existing functionality from the other applications.

A Lattice Plugin Host (which we’ll dive into later) allows us to extend the existing application by referencing two external plugins, Workflows and Spinnaker. In our example, we need to define two areas that can be extended — the application content for portal components and configurable routing.

The sequence of events in order to accomplish the above rendering process would be handled by three components — our new framework Lattice and the two plugins:

Dispatch Cycle within Lattice

First, Lattice will load both plugins asynchronously.

Next, the framework will dispatch events as they flow through the application.

In our example, Workflows will register its routes and Spinnaker will add its overlays.

An Implementation with React

In order to accomplish the above scenario, the Host Application needs to include the Lattice library and add a new PluginHost with a configuration referencing the external plugins. This host requires information about the specific application and the configuration indicating which plugins to load:

Enhancing a React Application with a Lattice Plugin Host

We’ve mocked this implementation in the example above with a useFetchPluginConfiguration hook to retrieve the metadata from an external service. Owners can then choose to add or remove plugins dynamically, outside of the application source code.

Allowing plugins access to the routing can be done using hooks defined by the Lattice framework. The usePluggableState hook will retrieve the default application routes and pass them through the Lattice framework. If any plugin responds to this AppRoutes identifier, they can choose to inject their specific routes:

Extending Existing Application State with Lattice Hooks

Plugins can inject any React element into the page with the<Pluggable /> component as illustrated below. This will allow plugins to render within this AppContent area:

Rendering Custom Children with Lattice Pluggable

The final example application snippet has been included below:

Under the Hood

Lattice is a tiny framework that provides an abstraction layer for React web applications to leverage.

Using Lattice, developers can focus on their core product, and simply wrap areas of their application that are customizable by external plugins. Developers can also extend components to use external state by using Lattice hooks.

Lattice Plugin Modules are JavaScript functions implemented by remote applications. These functions act as the “glue” between the host application and the remote component(s) being shared. Modules declare which components within their application should be exposed and how they should be rendered based on information the host provides.

A Lattice Pluggable Component allows a host application to expose a mount point through a standard React component that plugins can manipulate or override with their own content.

Lattice Custom Hooks are used to manipulate state using a state reducer pattern. These hooks allow host applications to maintain their own initial state, and modify accordingly, while also allowing plugins the opportunity to inject their own data.

Lattice Plugins

Lattice Functionality within a Host Application

The core of Lattice provides the ability to asynchronously load remote modules via Webpack Module Federation, Native ES Modules, or a custom implementation defined outside of the framework. The host application provides Lattice with basic application context and a configuration which defines the remote plugin modules to load. Once loaded, references to these plugins are stored internally within a React Context instance.

Exposing Functionality to Lattice as a Federated Module

Plugin modules can then provide new functionality, or change existing functionality, to the host application. Standard identifiers are used that all Lattice-enabled applications should implement to allow plugins to universally work across different applications. Most extensions will choose to extend existing application functionality, which will not be universal, and requires knowledge of the host’s design.

Lattice requires constant identifier values (aka “magic strings”) to understand what is being rendered. The Lattice Plugin Host will dispatch this identifier through all of the plugins which have been registered and loaded. Plugin responses are composed together, and the final returned value is what gets rendered in the component tree. Through this model, plugins can decide to extend, change, or simply ignore the event. Think of this process as an approach similar to that of Redux or Express Middleware functions.

Lattice can also be used to extend existing application functionality. In order to accomplish this, Plugins must be aware of the host identifiers and data shapes used in the host application lifecycle. While this might sound like an impossible task to maintain, we encourage host applications to publish a TypeScript declarations project which is shared between the host and plugins. Think of us as having a DefinitelyTyped repository for all of the Netflix internal tools that embrace extending via Lattice.

Using this approach, we are able to provide developers with a highly aligned, loosely coupled development environment shared between host applications and plugins. Plugins can be developed in a silo, simply adhering to the interface which has been declared.

The Possibilities are Endless

While our original approach was to extend core functionality within an application, we have found that we are able to leverage Lattice in other ways. The concept of writing a simple if statement has been replaced; we take a step back, and consider which domain in our organization should be responsible for said logic and consider moving the logic into their respective plugin.

We have also found that we can easily model more fine-grained areas within an application. For example, we can render individual form components using Lattice identifiers and have plugins be responsible for the specific UI elements. This empowers us to build these generic tools backed by metadata models and a default out-of-box experience which others can choose to override.

Most importantly, we are able to easily, and quickly, respond to conflicting requirements by simply implementing different plugins.

What’s Next?

We are only getting started with Lattice and currently gauging interest internally from other teams. By dogfooding our approach within RGT, we can work out the kinks, squash some bugs, and build a robust process for building micro frontends with Lattice. The developer experience is crucial for Lattice to be successful. Empowering developers with the ability to understand the lifecycle of Lattice events within an application, verify functionality prior to deployments, versioning, developing end-to-end test suites, and general best practices are some nuggets critical to our success.


How We Build Micro Frontends With Lattice was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Pin, Unpin, and why Rust needs them

Post Syndicated from Adam Chalmers original https://blog.cloudflare.com/pin-and-unpin-in-rust/

Pin, Unpin, and why Rust needs them

Pin, Unpin, and why Rust needs them

Using async Rust libraries is usually easy. It’s just like using normal Rust code, with a little async or .await here and there. But writing your own async libraries can be hard. The first time I tried this, I got really confused by arcane, esoteric syntax like T: ?Unpin and Pin<&mut Self>. I had never seen these types before, and I didn’t understand what they were doing. Now that I understand them, I’ve written the explainer I wish I could have read back then. In this post, we’re gonna learn

  • What Futures are
  • What self-referential types are
  • Why they were unsafe
  • How Pin/Unpin made them safe
  • Using Pin/Unpin to write tricky nested futures

What are Futures?

A few years ago, I needed to write some code which would take some async function, run it and collect some metrics about it, e.g. how long it took to resolve. I wanted to write a type TimedWrapper that would work like this:

// Some async function, e.g. polling a URL with [https://docs.rs/reqwest]
// Remember, Rust functions do nothing until you .await them, so this isn't
// actually making a HTTP request yet.
let async_fn = reqwest::get("http://adamchalmers.com");

// Wrap the async function in my hypothetical wrapper.
let timed_async_fn = TimedWrapper::new(async_fn);

// Call the async function, which will send a HTTP request and time it.
let (resp, time) = timed_async_fn.await;
println!("Got a HTTP {} in {}ms", resp.unwrap().status(), time.as_millis())

I like this interface, it’s simple and should be easy for the other programmers on my team to use. OK, let’s implement it! I know that, under the hood, Rust’s async functions are just regular functions that return a Future. The Future trait is pretty simple. It just means a type which:

  • Can be polled
  • When it’s polled, it might return “Pending” or “Ready”
  • If it’s pending, you should poll it again later
  • If it’s ready, it responds with a value. We call this “resolving”.

Here’s a really easy example of implementing a Future. Let’s make a Future that returns a random u16.

use std::{future::Future, pin::Pin, task::Context}

/// A future which returns a random number when it resolves.
#[derive(Default)]
struct RandFuture;

impl Future for RandFuture {
	// Every future has to specify what type of value it returns when it resolves.
	// This particular future will return a u16.
	type Output = u16;

	// The `Future` trait has only one method, named "poll".
fn poll(self: Pin<&mut Self>, _cx: &mut Context) -> Poll<Self::Output  {
		Poll::ready(rand::random())
	}
}

Not too hard! I think we’re ready to implement TimedWrapper.

Trying and failing to use nested Futures

Let’s start by defining the type.

pub struct TimedWrapper<Fut: Future> {
	start: Option<Instant>,
	future: Fut,
}

OK, so a TimedWrapper is generic over a type Fut, which must be a Future. And it will store a future of that type as a field. It’ll also have a start field which will record when it first was first polled. Let’s write a constructor:

impl<Fut: Future> TimedWrapper<Fut> {
	pub fn new(future: Fut) -> Self {
		Self { future, start: None }
	}
}

Nothing too complicated here. The new function takes a future and wraps it in the TimedWrapper. Of course, we have to set start to None, because it hasn’t been polled yet. So, let’s implement the poll method, which is the only thing we need to implement Future and make it .awaitable.

impl<Fut: Future> Future for TimedWrapper<Fut> {
	// This future will output a pair of values:
	// 1. The value from the inner future
	// 2. How long it took for the inner future to resolve
	type Output = (Fut::Output, Duration);

	fn poll(self: Pin<&mut Self>, cx: &mut Context) -> Poll<Self::Output> {
		// Call the inner poll, measuring how long it took.
		let start = self.start.get_or_insert_with(Instant::now);
		let inner_poll = self.future.poll(cx);
		let elapsed = self.elapsed();

		match inner_poll {
			// The inner future needs more time, so this future needs more time too
			Poll::Pending => Poll::Pending,
			// Success!
			Poll::Ready(output) => Poll::Ready((output, elapsed)),
		}
	}
}

OK, that wasn’t too hard. There’s just one problem: this doesn’t work.

Pin, Unpin, and why Rust needs them

So, the Rust compiler reports an error on self.future.poll(cx), which is “no method named poll found for type parameter Fut in the current scope”. This is confusing, because we know Fut is a Future, so surely it has a poll method? OK, but Rust continues: Fut doesn’t have a poll method, but Pin<&mut Fut> has one. What is this weird type?

Well, we know that methods have a “receiver”, which is some way it can access self. The receiver might be self, &self or &mut self, which mean “take ownership of self,” “borrow self,” and “mutably borrow self” respectively. So this is just a new, unfamiliar kind of receiver. Rust is complaining because we have Fut and we really need a Pin<&mut Fut>. At this point I have two questions:

  1. What is Pin?
  2. If I have a T value, how do I get a Pin<&mut T>?

The rest of this post is going to be answering those questions. I’ll explain some problems in Rust that could lead to unsafe code, and why Pin safely solves them.

Self-reference is unsafe

Pin exists to solve a very specific problem: self-referential datatypes, i.e. data structures which have pointers into themselves. For example, a binary search tree might have self-referential pointers, which point to other nodes in the same struct.

Self-referential types can be really useful, but they’re also hard to make memory-safe. To see why, let’s use this example type with two fields, an i32 called val and a pointer to an i32 called pointer.

Pin, Unpin, and why Rust needs them

So far, everything is OK. The pointer field points to the val field in memory address A, which contains a valid i32. All the pointers are valid, i.e. they point to memory that does indeed encode a value of the right type (in this case, an i32). But the Rust compiler often moves values around in memory. For example, if we pass this struct into another function, it might get moved to a different memory address. Or we might Box it and put it on the heap. Or if this struct was in a Vec<MyStruct>, and we pushed more values in, the Vec might outgrow its capacity and need to move its elements into a new, larger buffer.

Pin, Unpin, and why Rust needs them

When we move it, the struct’s fields change their address, but not their value. So the pointer field is still pointing at address A, but address A now doesn’t have a valid i32. The data that was there was moved to address B, and some other value might have been written there instead! So now the pointer is invalid. This is bad — at best, invalid pointers cause crashes, at worst they cause hackable vulnerabilities. We only want to allow memory-unsafe behaviour in unsafe blocks, and we should be very careful to document this type and tell users to update the pointers after moves.

Unpin and !Unpin

To recap, all Rust types fall into two categories.

  1. Types that are safe to move around in memory. This is the default, the norm. For example, this includes primitives like numbers, strings, bools, as well as structs or enums entirely made of them. Most types fall into this category!
  2. Self-referential types, which are not safe to move around in memory. These are pretty rare. An example is the intrusive linked list inside some Tokio internals. Another example is most types which implement Future and also borrow data, for reasons explained in the Rust async book.

Types in category (1) are totally safe to move around in memory. You won’t invalidate any pointers by moving them around. But if you move a type in (2), then you invalidate pointers and can get undefined behaviour, as we saw before. In earlier versions of Rust, you had to be really careful using these types to not move them, or if you moved them, to use unsafe and update all the pointers. But since Rust 1.33, the compiler can automatically figure out which category any type is in, and make sure you only use it safely.

Any type in (1) implements a special auto trait called Unpin. Weird name, but its meaning will become clear soon. Again, most “normal” types implement Unpin, and because it’s an auto trait (like Send or Sync or Sized1), so you don’t have to worry about implementing it yourself. If you’re unsure if a type can be safely moved, just check it on docs.rs and see if it impls Unpin!

Types in (2) are creatively named !Unpin (the ! in a trait means “does not implement”). To use these types safely, we can’t use regular pointers for self-reference. Instead, we use special pointers that “pin” their values into place, ensuring they can’t be moved. This is exactly what the Pin type does.

Pin, Unpin, and why Rust needs them

Pin wraps a pointer and stops its value from moving. The only exception is if the value impls Unpin — then we know it’s safe to move. Voila! Now we can write self-referential structs safely! This is really important, because as discussed above, many Futures are self-referential, and we need them for async/await.

Using Pin

So now we understand why Pin exists, and why our Future poll method has a pinned &mut self to self instead of a regular &mut self. So let’s get back to the problem we had before: I need a pinned reference to the inner future. More generally: given a pinned struct, how do we access its fields?

The solution is to write helper functions which give you references to the fields. These references might be normal Rust references like &mut, or they might also be pinned. You can choose whichever one you need. This is called projection: if you have a pinned struct, you can write a projection method that gives you access to all its fields.

Projecting is really just getting data into and out of Pins. For example, we get the start: Option<Duration> field from the Pin<&mut self>, and we need to put the future: Fut into a Pin so we can call its poll method). If you read the Pin methods you’ll see this is always safe if it points to an Unpin value, but requires unsafe otherwise.

// Putting data into Pin
pub        fn new          <P: Deref<Target:Unpin>>(pointer: P) -> Pin<P>;
pub unsafe fn new_unchecked<P>                     (pointer: P) -> Pin<P>;

// Getting data from Pin
pub        fn into_inner          <P: Deref<Target: Unpin>>(pin: Pin<P>) -> P;
pub unsafe fn into_inner_unchecked<P>                      (pin: Pin<P>) -> P;

I know unsafe can be a bit scary, but it’s OK to write unsafe code! I think of unsafe as the compiler saying “hey, I can’t tell if this code follows the rules here, so I’m going to rely on you to check for me.” The Rust compiler does so much work for us, it’s only fair that we do some of the work every now and then. If you want to learn how to write your own projection methods, I can highly recommend this fasterthanli.me blog post on the topic. But we’re going to take a little shortcut.

Using pin-project instead

So, OK, look, it’s time for a confession: I don’t like using unsafe. I know I just explained why it’s OK, but still, given the option, I would rather not.

I didn’t start writing Rust because I wanted to carefully think about the consequences of my actions, damnit, I just want to go fast and not break things. Luckily, someone sympathized with me and made a crate which generates totally safe projections! It’s called pin-project and it’s awesome. All we need to do is change our definition:

#[pin_project::pin_project] // This generates a `project` method
pub struct TimedWrapper<Fut: Future> {
	// For each field, we need to choose whether `project` returns an
	// unpinned (&mut T) or pinned (Pin<&mut T>) reference to the field.
	// By default, it assumes unpinned:
	start: Option<Instant>,
	// Opt into pinned references with this attribute:
	#[pin]
	future: Fut,
}

For each field, you have to choose whether its projection should be pinned or not. By default, you should use a normal reference, just because they’re easier and simpler. But if you know you need a pinned reference — for example, because you want to call .poll(), whose receiver is Pin<&mut Self> — then you can do that with #[pin].

Now we can finally poll the inner future!

fn poll(self: Pin<&mut Self>, cx: &mut Context) -> Poll<Self::Output> {
	// This returns a type with all the same fields, with all the same types,
	// except that the fields defined with #[pin] will be pinned.
	let mut this = self.project();
	
    // Call the inner poll, measuring how long it took.
	let start = this.start.get_or_insert_with(Instant::now);
	let inner_poll = this.future.as_mut().poll(cx);
	let elapsed = start.elapsed();

	match inner_poll {
		// The inner future needs more time, so this future needs more time too
		Poll::Pending => Poll::Pending,
		// Success!
		Poll::Ready(output) => Poll::Ready((output, elapsed)),
	}
}

Finally, our goal is complete — and we did it all without any unsafe code.

Summary

If a Rust type has self-referential pointers, it can’t be moved safely. After all, moving doesn’t update the pointers, so they’ll still be pointing at the old memory address, so they’re now invalid. Rust can automatically tell which types are safe to move (and will auto impl the Unpin trait for them). If you have a Pin-ned pointer to some data, Rust can guarantee that nothing unsafe will happen (if it’s safe to move, you can move it, if it’s unsafe to move, then you can’t). This is important because many Future types are self-referential, so we need Pin to safely poll a Future. You probably won’t have to poll a future yourself (just use async/await instead), but if you do, use the pin-project crate to simplify things.

I hope this helped — if you have any questions, please ask me on Twitter. And if you want to get paid to talk to me about Rust and networking protocols, my team at Cloudflare is hiring, so be sure to visit careers.cloudflare.com.

References

Micro-frontend Architectures on AWS

Post Syndicated from Bryant Bost original https://aws.amazon.com/blogs/architecture/micro-frontend-architectures-on-aws/

A microservice architecture is characterized by independent services that are focused on a specific business function and maintained by small, self-contained teams. Microservice architectures are used frequently for web applications developed on AWS, and for good reason. They offer many well-known benefits such as development agility, technological freedom, targeted deployments, and more. Despite the popularity of microservices, many frontend applications are still built in a monolithic style. For example, they have one large code base that interacts with all backend microservices, and is maintained by a large team of developers.

Monolith Frontend

Figure 1. Microservice backend with monolith frontend

What is a Micro-frontend?

The micro-frontend architecture introduces microservice development principles to frontend applications. In a micro-frontend architecture, development teams independently build and deploy “child” frontend applications. These applications are combined by a “parent” frontend application that acts as a container to retrieve, display, and integrate the various child applications. In this parent/child model, the user interacts with what appears to be a single application. In reality, they are interacting with several independent applications, published by different teams.

Micro Frontend

Figure 2. Microservice backend with micro-frontend

Micro-frontend Benefits

Compared to a monolith frontend, a micro-frontend offers the following benefits:

  • Independent artifacts: A core tenet of microservice development is that artifacts can be deployed independently, and this remains true for micro-frontends. In a micro-frontend architecture, teams should be able to independently deploy their frontend applications with minimal impact to other services. Those changes will be reflected by the parent application.
  • Autonomous teams: Each team is the expert in its own domain. For example, the billing service team members have specialized knowledge. This includes the data models, the business requirements, the API calls, and user interactions associated with the billing service. This knowledge allows the team to develop the billing frontend faster than a larger, less specialized team.
  • Flexible technology choices: Autonomy allows each team to make technology choices that are independent from other teams. For instance, the billing service team could develop their micro-frontend using Vue.js and the profile service team could develop their frontend using Angular.
  • Scalable development: Micro-frontend development teams are smaller and are able to operate without disrupting other teams. This allows us to quickly scale development by spinning up new teams to deliver additional frontend functionality via child applications.
  • Easier maintenance: Keeping frontend repositories small and specialized allows them to be more easily understood, and this simplifies long-term maintenance and testing. For instance, if you want to change an interaction on a monolith frontend, you must isolate the location and dependencies of the feature within the context of a large codebase. This type of operation is greatly simplified when dealing with the smaller codebases associated with micro-frontends.

Micro-frontend Challenges

Conversely, a micro-frontend presents the following challenges:

  • Parent/child integration: A micro-frontend introduces the task of ensuring the parent application displays the child application with the same consistency and performance expected from a monolith application. This point is discussed further in the next section.
  • Operational overhead: Instead of managing a single frontend application, a micro-frontend application involves creating and managing separate infrastructure for all teams.
  • Consistent user experience: In order to maintain a consistent user experience, the child applications must use the same UI components, CSS libraries, interactions, error handling, and more. Maintaining consistency in the user experience can be difficult for child applications that are at different stages in the development lifecycle.

Building Micro-frontends

The most difficult challenge with the micro-frontend architecture pattern is integrating child applications with the parent application. Prioritizing the user experience is critical for any frontend application. In the context of micro-frontends, this means ensuring a user can seamlessly navigate from one child application to another inside the parent application. We want to avoid disruptive behavior such as page refreshes or multiple logins. At its most basic definition, parent/child integration involves the parent application dynamically retrieving and rendering child applications when the parent app is loaded. Rendering the child application depends on how the child application was built, and this can be done in a number of ways. Two of the most popular methods of parent/child integration are:

  1. Building each child application as a web component.
  2. Importing each child application as an independent module. These modules either declares a function to render itself or is dynamically imported by the parent application (such as with module federation).

Registering child apps as web components:

<html>
    <head>
        <script src="https://shipping.example.com/shipping-service.js"></script>
        <script src="https://profile.example.com/profile-service.js"></script>
        <script src="https://billing.example.com/billing-service.js"></script>
        <title>Parent Application</title>
    </head>
    <body>
        <shipping-service />
        <profile-service />
        <billing-service />
    </body>
</html>

Registering child apps as modules:

<html>
    <head>
        <script src="https://shipping.example.com/shipping-service.js"></script>
        <script src="https://profile.example.com/profile-service.js"></script>
        <script src="https://billing.example.com/billing-service.js"></script>
     <title>Parent Application</title>
    </head>
    <body>
    </body>
    <script>
        // Load and render the child applications form their JS bundles.
    </script>
</html>

The following diagram shows an example micro-frontend architecture built on AWS.

AWS Micro Frontend

Figure 3. Micro-frontend architecture on AWS

In this example, each service team is running a separate, identical stack to build their application. They use the AWS Developer Tools and deploy the application to Amazon Simple Storage Service (S3) with Amazon CloudFront. The CI/CD pipelines use shared components such as CSS libraries, API wrappers, or custom modules stored in AWS CodeArtifact. This helps drive consistency across parent and child applications.

When you retrieve the parent application, it should prompt you to log in to an identity provider and retrieve JWTs. In this example, the identity provider is an Amazon Cognito User Pool. After a successful login, the parent application retrieves the child applications from CloudFront and renders them inside the parent application. Alternatively, the parent application can elect to render the child applications on demand, when you navigate to a specific route. The child applications should not require you to log in again to the Amazon Cognito user pool. They should be configured to use the JWT obtained by the parent app or silently retrieve a new JWT from Amazon Cognito.

Conclusion

Micro-frontend architectures introduce many of the familiar benefits of microservice development to frontend applications. A micro-frontend architecture also simplifies the process of building complex frontend applications by allowing you to manage small, independent components.

Save 2 clicks, test data preprocessing

Post Syndicated from Aigars Kadiķis original https://blog.zabbix.com/save-2-clicks-test-data-preprocessing/13249/

This topic is related to template development from scratch, bulk data input, and a lot of dependable items having different preprocessing steps each.

If these keywords resonate with you, keep reading.

Story stars back in a day when a “Test now” button was invented inside the item preprocessing section. In this way, we can simulate the entire preprocessing stack. A very cool feature to have.

Nevertheless, we tend to copy over and over again the data input:

While this is fine for small projects with simple preprocessing steps which match our knowledge league. It is not so OK in we have ambition to solve the impossible. Figure out a data preprocessing rule(s) which suit our needs.

For a template development process, the solution is to skip data input and inject a static value in the very first preprocessing step. Let me introduce the concept.

JavaScript preprocessing step 1:

return 'this is input text';

JavaScript preprocessing step 2:

return value.replace("text","data");

Now we have static input, no need to spend time to “click” the input data.

Sometimes the input is not just one line but multiple lines, and tabs, and spaces and double quotes and single quotes and special characters. To respect all these things, we must get our hands dirty with the base64 format.

To prepare input data as base64 string, on windows systems it can be easily done with Notepad++. Just select all text and select “Plugin commands” => “Base64 Encode” (functionality is not there with a lite version of Notepad++):

After that, we need to copy all content to clipboard:

Create the first JavasSript preprocessing with the content from the clipboard. Here is the same example:

return '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';

In the next step, there must be decoding scheduled. Kindly copy the code 1:1. Configure it as a second preprocessing step:

var k = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/="
function d(e) {
    var t, n, o, r, a = "",
        i = "",
        c = "",
        l = 0;
    for (/[^A-Za-z0-9+/=]/g.exec(e) && alert("1"), e = e.replace(/[^A-Za-z0-9+/=]/g, ""); t = k.indexOf(e.charAt(l++)) << 2 | (o = k.indexOf(e.charAt(l++))) >> 4, n = (15 & o) << 4 | (r = k.indexOf(e.charAt(l++))) >> 2, i = (3 & r) << 6 | (c = k.indexOf(e.charAt(l++))), a += String.fromCharCode(t), 64 != r && (a += String.fromCharCode(n)), 64 != c && (a += String.fromCharCode(i)), t = n = i = "", o = r = c = "", l < e.length;);
    return unescape(a)
}
return d(value);

This is how it looks like:

Go to testing section and ensure the data in Zabbix is similar as it was in Notepad++:

Data has been successfully decoded. Multiple lines, quite original stuff. The tabs are not visible with a naked human eye but they are there, I promise!

Now we can “play” out the next preprocessing steps and try out different things:

When one preprocessing has been figured out, just clone the item and start to developing a next one. Sure, if we succeed the ambition, it will be required to spend 5 minutes to go through all items, remove first 2 steps and link the item to master key 😉

Ok. That is it for today. Bye.

By the way, on Linux system to have base64 string we only need:

  1. A command where the output entertains us
  2. Pipe it to ‘base64 -w0’
systemctl list-unit-files --type=service | base64 -w0

Publishing private npm packages with AWS CodeArtifact

Post Syndicated from Ryan Sonshine original https://aws.amazon.com/blogs/devops/publishing-private-npm-packages-aws-codeartifact/

This post demonstrates how to create, publish, and download private npm packages using AWS CodeArtifact, allowing you to share code across your organization without exposing your packages to the public.

The ability to control CodeArtifact repository access using AWS Identity and Access Management (IAM) removes the need to manage additional credentials for a private npm repository when developers already have IAM roles configured.

You can use private npm packages for a variety of use cases, such as:

  • Reducing code duplication
  • Configuration such as code linting and styling
  • CLI tools for internal processes

This post shows how to easily create a sample project in which we publish an npm package and install the package from CodeArtifact. For more information about pipeline integration, see AWS CodeArtifact and your package management flow – Best Practices for Integration.

Solution overview

The following diagram illustrates this solution.

Diagram showing npm package publish and install with CodeArtifact

In this post, you create a private scoped npm package containing a sample function that can be used across your organization. You create a second project to download the npm package. You also learn how to structure your npm package to make logging in to CodeArtifact automatic when you want to build or publish the package.

The code covered in this post is available on GitHub:

Prerequisites

Before you begin, you need to complete the following:

  1. Create an AWS account.
  2. Install the AWS Command Line Interface (AWS CLI). CodeArtifact is supported in these CLI versions:
    1. 18.83 or later: install the AWS CLI version 1
    2. 0.54 or later: install the AWS CLI version 2
  3. Create a CodeArtifact repository.
  4. Add required IAM permissions for CodeArtifact.

Creating your npm package

You can create your npm package in three easy steps: set up the project, create your npm script for authenticating with CodeArtifact, and publish the package.

Setting up your project

Create a directory for your new npm package. We name this directory my-package because it serves as the name of the package. We use an npm scope for this package, where @myorg represents the scope all of our organization’s packages are published under. This helps us distinguish our internal private package from external packages. See the following code:

npm init --scope=@myorg -y

{
  "name": "@myorg/my-package",
  "version": "1.0.0",
  "description": "A sample private scoped npm package",
  "main": "index.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  }
}

The package.json file specifies that the main file of the package is called index.js. Next, we create that file and add our package function to it:

module.exports.helloWorld = function() {
  console.log('Hello world!');
}

Creating an npm script

To create your npm script, complete the following steps:

  1. On the CodeArtifact console, choose the repository you created as part of the prerequisites.

If you haven’t created a repository, create one before proceeding.

CodeArtifact repository details console

  1. Select your CodeArtifact repository and choose Details to view the additional details for your repository.

We use two items from this page:

  • Repository name (my-repo)
  • Domain (my-domain)
  1. Create a script named co:login in our package.json. The package.json contains the following code:
{
  "name": "@myorg/my-package",
  "version": "1.0.0",
  "description": "A sample private scoped npm package",
  "main": "index.js",
  "scripts": {
    "co:login": "aws codeartifact login --tool npm --repository my-repo --domain my-domain",
    "test": "echo \"Error: no test specified\" && exit 1"
  }
}

Running this script updates your npm configuration to use your CodeArtifact repository and sets your authentication token, which expires after 12 hours.

  1. To test our new script, enter the following command:

npm run co:login

The following code is the output:

> aws codeartifact login --tool npm --repository my-repo --domain my-domain
Successfully configured npm to use AWS CodeArtifact repository https://my-domain-<ACCOUNT ID>.d.codeartifact.us-east-1.amazonaws.com/npm/my-repo/
Login expires in 12 hours at 2020-09-04 02:16:17-04:00
  1. Add a prepare script to our package.json to run our login command:
{
  "name": "@myorg/my-package",
  "version": "1.0.0",
  "description": "A sample private scoped npm package",
  "main": "index.js",
  "scripts": {
    "prepare": "npm run co:login",
    "co:login": "aws codeartifact login --tool npm --repository my-repo --domain my-domain",
    "test": "echo \"Error: no test specified\" && exit 1"
  }
}

This configures our project to automatically authenticate and generate an access token anytime npm install or npm publish run on the project.

If you see an error containing Invalid choice, valid choices are:, you need to update the AWS CLI according to the versions listed in the perquisites of this post.

Publishing your package

To publish our new package for the first time, run npm publish.

The following screenshot shows the output.

Terminal showing npm publish output

If we navigate to our CodeArtifact repository on the CodeArtifact console, we now see our new private npm package ready to be downloaded.

CodeArtifact console showing published npm package

Installing your private npm package

To install your private npm package, you first set up the project and add the CodeArtifact configs. After you install your package, it’s ready to use.

Setting up your project

Create a directory for a new application and name it my-app. This is a sample project to download our private npm package published in the previous step. You can apply this pattern to all repositories you intend on installing your organization’s npm packages in.

npm init -y

{
  "name": "my-app",
  "version": "1.0.0",
  "description": "A sample application consuming a private scoped npm package",
  "main": "index.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  }
}

Adding CodeArtifact configs

Copy the npm scripts prepare and co:login created earlier to your new project:

{
  "name": "my-app",
  "version": "1.0.0",
  "description": "A sample application consuming a private scoped npm package",
  "main": "index.js",
  "scripts": {
    "prepare": "npm run co:login",
    "co:login": "aws codeartifact login --tool npm --repository my-repo --domain my-domain",
    "test": "echo \"Error: no test specified\" && exit 1"
  }
}

Installing your new private npm package

Enter the following command:

npm install @myorg/my-package

Your package.json should now list @myorg/my-package in your dependencies:

{
  "name": "my-app",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",
  "scripts": {
    "prepare": "npm run co:login",
    "co:login": "aws codeartifact login --tool npm --repository my-repo --domain my-domain",
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "dependencies": {
    "@myorg/my-package": "^1.0.0"
  }
}

Using your new npm package

In our my-app application, create a file named index.js to run code from our package containing the following:

const { helloWorld } = require('@myorg/my-package');

helloWorld();

Run node index.js in your terminal to see the console print the message from our @myorg/my-package helloWorld function.

Cleaning Up

If you created a CodeArtifact repository for the purposes of this post, use one of the following methods to delete the repository:

Remove the changes made to your user profile’s npm configuration by running npm config delete registry, this will remove the CodeArtifact repository from being set as your default npm registry.

Conclusion

In this post, you successfully published a private scoped npm package stored in CodeArtifact, which you can reuse across multiple teams and projects within your organization. You can use npm scripts to streamline the authentication process and apply this pattern to save time.

About the Author

Ryan Sonshine

Ryan Sonshine is a Cloud Application Architect at Amazon Web Services. He works with customers to drive digital transformations while helping them architect, automate, and re-engineer solutions to fully leverage the AWS Cloud.

 

 

Building, bundling, and deploying applications with the AWS CDK

Post Syndicated from Cory Hall original https://aws.amazon.com/blogs/devops/building-apps-with-aws-cdk/

The AWS Cloud Development Kit (AWS CDK) is an open-source software development framework to model and provision your cloud application resources using familiar programming languages.

The post CDK Pipelines: Continuous delivery for AWS CDK applications showed how you can use CDK Pipelines to deploy a TypeScript-based AWS Lambda function. In that post, you learned how to add additional build commands to the pipeline to compile the TypeScript code to JavaScript, which is needed to create the Lambda deployment package.

In this post, we dive deeper into how you can perform these build commands as part of your AWS CDK build process by using the native AWS CDK bundling functionality.

If you’re working with Python, TypeScript, or JavaScript-based Lambda functions, you may already be familiar with the PythonFunction and NodejsFunction constructs, which use the bundling functionality. This post describes how to write your own bundling logic for instances where a higher-level construct either doesn’t already exist or doesn’t meet your needs. To illustrate this, I walk through two different examples: a Lambda function written in Golang and a static site created with Nuxt.js.

Concepts

A typical CI/CD pipeline contains steps to build and compile your source code, bundle it into a deployable artifact, push it to artifact stores, and deploy to an environment. In this post, we focus on the building, compiling, and bundling stages of the pipeline.

The AWS CDK has the concept of bundling source code into a deployable artifact. As of this writing, this works for two main types of assets: Docker images published to Amazon Elastic Container Registry (Amazon ECR) and files published to Amazon Simple Storage Service (Amazon S3). For files published to Amazon S3, this can be as simple as pointing to a local file or directory, which the AWS CDK uploads to Amazon S3 for you.

When you build an AWS CDK application (by running cdk synth), a cloud assembly is produced. The cloud assembly consists of a set of files and directories that define your deployable AWS CDK application. In the context of the AWS CDK, it might include the following:

  • AWS CloudFormation templates and instructions on where to deploy them
  • Dockerfiles, corresponding application source code, and information about where to build and push the images to
  • File assets and information about which S3 buckets to upload the files to

Use case

For this use case, our application consists of front-end and backend components. The example code is available in the GitHub repo. In the repository, I have split the example into two separate AWS CDK applications. The repo also contains the Golang Lambda example app and the Nuxt.js static site.

Golang Lambda function

To create a Golang-based Lambda function, you must first create a Lambda function deployment package. For Go, this consists of a .zip file containing a Go executable. Because we don’t commit the Go executable to our source repository, our CI/CD pipeline must perform the necessary steps to create it.

In the context of the AWS CDK, when we create a Lambda function, we have to tell the AWS CDK where to find the deployment package. See the following code:

new lambda.Function(this, 'MyGoFunction', {
  runtime: lambda.Runtime.GO_1_X,
  handler: 'main',
  code: lambda.Code.fromAsset(path.join(__dirname, 'folder-containing-go-executable')),
});

In the preceding code, the lambda.Code.fromAsset() method tells the AWS CDK where to find the Golang executable. When we run cdk synth, it stages this Go executable in the cloud assembly, which it zips and publishes to Amazon S3 as part of the PublishAssets stage.

If we’re running the AWS CDK as part of a CI/CD pipeline, this executable doesn’t exist yet, so how do we create it? One method is CDK bundling. The lambda.Code.fromAsset() method takes a second optional argument, AssetOptions, which contains the bundling parameter. With this bundling parameter, we can tell the AWS CDK to perform steps prior to staging the files in the cloud assembly.

Breaking down the BundlingOptions parameter further, we can perform the build inside a Docker container or locally.

Building inside a Docker container

For this to work, we need to make sure that we have Docker running on our build machine. In AWS CodeBuild, this means setting privileged: true. See the following code:

new lambda.Function(this, 'MyGoFunction', {
  code: lambda.Code.fromAsset(path.join(__dirname, 'folder-containing-source-code'), {
    bundling: {
      image: lambda.Runtime.GO_1_X.bundlingDockerImage,
      command: [
        'bash', '-c', [
          'go test -v',
          'GOOS=linux go build -o /asset-output/main',
      ].join(' && '),
    },
  })
  ...
});

We specify two parameters:

  • image (required) – The Docker image to perform the build commands in
  • command (optional) – The command to run within the container

The AWS CDK mounts the folder specified as the first argument to fromAsset at /asset-input inside the container, and mounts the asset output directory (where the cloud assembly is staged) at /asset-output inside the container.

After we perform the build commands, we need to make sure we copy the Golang executable to the /asset-output location (or specify it as the build output location like in the preceding example).

This is the equivalent of running something like the following code:

docker run \
  --rm \
  -v folder-containing-source-code:/asset-input \
  -v cdk.out/asset.1234a4b5/:/asset-output \
  lambci/lambda:build-go1.x \
  bash -c 'GOOS=linux go build -o /asset-output/main'

Building locally

To build locally (not in a Docker container), we have to provide the local parameter. See the following code:

new lambda.Function(this, 'MyGoFunction', {
  code: lambda.Code.fromAsset(path.join(__dirname, 'folder-containing-source-code'), {
    bundling: {
      image: lambda.Runtime.GO_1_X.bundlingDockerImage,
      command: [],
      local: {
        tryBundle(outputDir: string) {
          try {
            spawnSync('go version')
          } catch {
            return false
          }

          spawnSync(`GOOS=linux go build -o ${path.join(outputDir, 'main')}`);
          return true
        },
      },
    },
  })
  ...
});

The local parameter must implement the ILocalBundling interface. The tryBundle method is passed the asset output directory, and expects you to return a boolean (true or false). If you return true, the AWS CDK doesn’t try to perform Docker bundling. If you return false, it falls back to Docker bundling. Just like with Docker bundling, you must make sure that you place the Go executable in the outputDir.

Typically, you should perform some validation steps to ensure that you have the required dependencies installed locally to perform the build. This could be checking to see if you have go installed, or checking a specific version of go. This can be useful if you don’t have control over what type of build environment this might run in (for example, if you’re building a construct to be consumed by others).

If we run cdk synth on this, we see a new message telling us that the AWS CDK is bundling the asset. If we include additional commands like go test, we also see the output of those commands. This is especially useful if you wanted to fail a build if tests failed. See the following code:

$ cdk synth
Bundling asset GolangLambdaStack/MyGoFunction/Code/Stage...
✓  . (9ms)
✓  clients (5ms)

DONE 8 tests in 11.476s
✓  clients (5ms) (coverage: 84.6% of statements)
✓  . (6ms) (coverage: 78.4% of statements)

DONE 8 tests in 2.464s

Cloud Assembly

If we look at the cloud assembly that was generated (located at cdk.out), we see something like the following code:

$ cdk synth
Bundling asset GolangLambdaStack/MyGoFunction/Code/Stage...
✓  . (9ms)
✓  clients (5ms)

DONE 8 tests in 11.476s
✓  clients (5ms) (coverage: 84.6% of statements)
✓  . (6ms) (coverage: 78.4% of statements)

DONE 8 tests in 2.464s

It contains our GolangLambdaStack CloudFormation template that defines our Lambda function, as well as our Golang executable, bundled at asset.01cf34ff646d380829dc4f2f6fc93995b13277bde7db81c24ac8500a83a06952/main.

Let’s look at how the AWS CDK uses this information. The GolangLambdaStack.assets.json file contains all the information necessary for the AWS CDK to know where and how to publish our assets (in this use case, our Golang Lambda executable). See the following code:

{
  "version": "5.0.0",
  "files": {
    "01cf34ff646d380829dc4f2f6fc93995b13277bde7db81c24ac8500a83a06952": {
      "source": {
        "path": "asset.01cf34ff646d380829dc4f2f6fc93995b13277bde7db81c24ac8500a83a06952",
        "packaging": "zip"
      },
      "destinations": {
        "current_account-current_region": {
          "bucketName": "cdk-hnb659fds-assets-${AWS::AccountId}-${AWS::Region}",
          "objectKey": "01cf34ff646d380829dc4f2f6fc93995b13277bde7db81c24ac8500a83a06952.zip",
          "assumeRoleArn": "arn:${AWS::Partition}:iam::${AWS::AccountId}:role/cdk-hnb659fds-file-publishing-role-${AWS::AccountId}-${AWS::Region}"
        }
      }
    }
  }
}

The file contains information about where to find the source files (source.path) and what type of packaging (source.packaging). It also tells the AWS CDK where to publish this .zip file (bucketName and objectKey) and what AWS Identity and Access Management (IAM) role to use (assumeRoleArn). In this use case, we only deploy to a single account and Region, but if you have multiple accounts or Regions, you see multiple destinations in this file.

The GolangLambdaStack.template.json file that defines our Lambda resource looks something like the following code:

{
  "Resources": {
    "MyGoFunction0AB33E85": {
      "Type": "AWS::Lambda::Function",
      "Properties": {
        "Code": {
          "S3Bucket": {
            "Fn::Sub": "cdk-hnb659fds-assets-${AWS::AccountId}-${AWS::Region}"
          },
          "S3Key": "01cf34ff646d380829dc4f2f6fc93995b13277bde7db81c24ac8500a83a06952.zip"
        },
        "Handler": "main",
        ...
      }
    },
    ...
  }
}

The S3Bucket and S3Key match the bucketName and objectKey from the assets.json file. By default, the S3Key is generated by calculating a hash of the folder location that you pass to lambda.Code.fromAsset(), (for this post, folder-containing-source-code). This means that any time we update our source code, this calculated hash changes and a new Lambda function deployment is triggered.

Nuxt.js static site

In this section, I walk through building a static site using the Nuxt.js framework. You can apply the same logic to any static site framework that requires you to run a build step prior to deploying.

To deploy this static site, we use the BucketDeployment construct. This is a construct that allows you to populate an S3 bucket with the contents of .zip files from other S3 buckets or from a local disk.

Typically, we simply tell the BucketDeployment construct where to find the files that it needs to deploy to the S3 bucket. See the following code:

new s3_deployment.BucketDeployment(this, 'DeployMySite', {
  sources: [
    s3_deployment.Source.asset(path.join(__dirname, 'path-to-directory')),
  ],
  destinationBucket: myBucket
});

To deploy a static site built with a framework like Nuxt.js, we need to first run a build step to compile the site into something that can be deployed. For Nuxt.js, we run the following two commands:

  • yarn install – Installs all our dependencies
  • yarn generate – Builds the application and generates every route as an HTML file (used for static hosting)

This creates a dist directory, which you can deploy to Amazon S3.

Just like with the Golang Lambda example, we can perform these steps as part of the AWS CDK through either local or Docker bundling.

Building inside a Docker container

To build inside a Docker container, use the following code:

new s3_deployment.BucketDeployment(this, 'DeployMySite', {
  sources: [
    s3_deployment.Source.asset(path.join(__dirname, 'path-to-nuxtjs-project'), {
      bundling: {
        image: cdk.BundlingDockerImage.fromRegistry('node:lts'),
        command: [
          'bash', '-c', [
            'yarn install',
            'yarn generate',
            'cp -r /asset-input/dist/* /asset-output/',
          ].join(' && '),
        ],
      },
    }),
  ],
  ...
});

For this post, we build inside the publicly available node:lts image hosted on DockerHub. Inside the container, we run our build commands yarn install && yarn generate, and copy the generated dist directory to our output directory (the cloud assembly).

The parameters are the same as described in the Golang example we walked through earlier.

Building locally

To build locally, use the following code:

new s3_deployment.BucketDeployment(this, 'DeployMySite', {
  sources: [
    s3_deployment.Source.asset(path.join(__dirname, 'path-to-nuxtjs-project'), {
      bundling: {
        local: {
          tryBundle(outputDir: string) {
            try {
              spawnSync('yarn --version');
            } catch {
              return false
            }

            spawnSync('yarn install && yarn generate');

       fs.copySync(path.join(__dirname, ‘path-to-nuxtjs-project’, ‘dist’), outputDir);
            return true
          },
        },
        image: cdk.BundlingDockerImage.fromRegistry('node:lts'),
        command: [],
      },
    }),
  ],
  ...
});

Building locally works the same as the Golang example we walked through earlier, with one exception. We have one additional command to run that copies the generated dist folder to our output directory (cloud assembly).

Conclusion

This post showed how you can easily compile your backend and front-end applications using the AWS CDK. You can find the example code for this post in this GitHub repo. If you have any questions or comments, please comment on the GitHub repo. If you have any additional examples you want to add, we encourage you to create a Pull Request with your example!

Our code also contains examples of deploying the applications using CDK Pipelines, so if you’re interested in deploying the example yourself, check out the example repo.

 

About the author

Cory Hall

Cory is a Solutions Architect at Amazon Web Services with a passion for DevOps and is based in Charlotte, NC. Cory works with enterprise AWS customers to help them design, deploy, and scale applications to achieve their business goals.

How to add authentication to a single-page web application with Amazon Cognito OAuth2 implementation

Post Syndicated from George Conti original https://aws.amazon.com/blogs/security/how-to-add-authentication-single-page-web-application-with-amazon-cognito-oauth2-implementation/

In this post, I’ll be showing you how to configure Amazon Cognito as an OpenID provider (OP) with a single-page web application.

This use case describes using Amazon Cognito to integrate with an existing authorization system following the OpenID Connect (OIDC) specification. OIDC is an identity layer on top of the OAuth 2.0 protocol to enable clients to verify the identity of users. Amazon Cognito lets you add user sign-up, sign-in, and access control to your web and mobile apps quickly and easily. Some key reasons customers select Amazon Cognito include:

  • Simplicity of implementation: The console is very intuitive; it takes a short time to understand how to configure and use Amazon Cognito. Amazon Cognito also has key out-of-the-box functionality, including social sign-in, multi-factor authentication (MFA), forgotten password support, and infrastructure as code (AWS CloudFormation) support.
  • Ability to customize workflows: Amazon Cognito offers the option of a hosted UI where users can sign-in directly to Amazon Cognito or sign-in via social identity providers such as Amazon, Google, Apple, and Facebook. The Amazon Cognito hosted UI and workflows help save your team significant time and effort.
  • OIDC support: Amazon Cognito can securely pass user profile information to an existing authorization system following the ODIC authorization code flow. The authorization system uses the user profile information to secure access to the app.

Amazon Cognito overview

Amazon Cognito follows the OIDC specification to authenticate users of web and mobile apps. Users can sign in directly through the Amazon Cognito hosted UI or through a federated identity provider, such as Amazon, Facebook, Apple, or Google. The hosted UI workflows include sign-in and sign-up, password reset, and MFA. Since not all customer workflows are the same, you can customize Amazon Cognito workflows at key points with AWS Lambda functions, allowing you to run code without provisioning or managing servers. After a user authenticates, Amazon Cognito returns standard OIDC tokens. You can use the user profile information in the ID token to grant your users access to your own resources or you can use the tokens to grant access to APIs hosted by Amazon API Gateway. You can also exchange the tokens for temporary AWS credentials to access other AWS services.

Figure 1: Amazon Cognito sign-in flow

Figure 1: Amazon Cognito sign-in flow

OAuth 2.0 and OIDC

OAuth 2.0 is an open standard that allows a user to delegate access to their information to other websites or applications without handing over credentials. OIDC is an identity layer on top of OAuth 2.0 that uses OAuth 2.0 flows. OAuth 2.0 defines a number of flows to manage the interaction between the application, user, and authorization server. The right flow to use depends on the type of application.

The client credentials flow is used in machine-to-machine communications. You can use the client credentials flow to request an access token to access your own resources, which means you can use this flow when your app is requesting the token on its own behalf, not on behalf of a user. The authorization code grant flow is used to return an authorization code that is then exchanged for user pool tokens. Because the tokens are never exposed directly to the user, they are less likely to be shared broadly or accessed by an unauthorized party. However, a custom application is required on the back end to exchange the authorization code for user pool tokens. For security reasons, we recommend the Authorization Code Flow with Proof Key Code Exchange (PKCE) for public clients, such as single-page apps or native mobile apps.

The following table shows recommended flows per application type.

Application CFlow Description
Machine Client credentials Use this flow when your application is requesting the token on its own behalf, not on behalf of the user
Web app on a server Authorization code grant A regular web app on a web server
Single-page app Authorization code grant PKCE An app running in the browser, such as JavaScript
Mobile app Authorization code grant PKCE iOS or Android app

Securing the authorization code flow

Amazon Cognito can help you achieve compliance with regulatory frameworks and certifications, but it’s your responsibility to use the service in a way that remains compliant and secure. You need to determine the sensitivity of the user profile data in Amazon Cognito; adhere to your company’s security requirements, applicable laws and regulations; and configure your application and corresponding Amazon Cognito settings appropriately for your use case.

Note: You can learn more about regulatory frameworks and certifications at AWS Services in Scope by Compliance Program. You can download compliance reports from AWS Artifact.

We recommend that you use the authorization code flow with PKCE for single-page apps. Applications that use PKCE generate a random code verifier that’s created for every authorization request. Proof Key for Code Exchange by OAuth Public Clients has more information on use of a code verifier. In the following sections, I will show you how to set up the Amazon Cognito authorization endpoint for your app to support a code verifier.

The authorization code flow

In OpenID terms, the app is the relying party (RP) and Amazon Cognito is the OP. The flow for the authorization code flow with PKCE is as follows:

  1. The user enters the app home page URL in the browser and the browser fetches the app.
  2. The app generates the PKCE code challenge and redirects the request to the Amazon Cognito OAuth2 authorization endpoint (/oauth2/authorize).
  3. Amazon Cognito responds back to the user’s browser with the Amazon Cognito hosted sign-in page.
  4. The user signs in with their user name and password, signs up as a new user, or signs in with a federated sign-in. After a successful sign-in, Amazon Cognito returns the authorization code to the browser, which redirects the authorization code back to the app.
  5. The app sends a request to the Amazon Cognito OAuth2 token endpoint (/oauth2/token) with the authorization code, its client credentials, and the PKCE verifier.
  6. Amazon Cognito authenticates the app with the supplied credentials, validates the authorization code, validates the request with the code verifier, and returns the OpenID tokens, access token, ID token, and refresh token.
  7. The app validates the OpenID ID token and then uses the user profile information (claims) in the ID token to provide access to resources.(Optional) The app can use the access token to retrieve the user profile information from the Amazon Cognito user information endpoint (/userInfo).
  8. Amazon Cognito returns the user profile information (claims) about the authenticated user to the app. The app then uses the claims to provide access to resources.

The following diagram shows the authorization code flow with PKCE.

Figure 2: Authorization code flow

Figure 2: Authorization code flow

Implementing an app with Amazon Cognito authentication

Now that you’ve learned about Amazon Cognito OAuth implementation, let’s create a working example app that uses Amazon Cognito OAuth implementation. You’ll create an Amazon Cognito user pool along with an app client, the app, an Amazon Simple Storage Service (Amazon S3) bucket, and an Amazon CloudFront distribution for the app, and you’ll configure the app client.

Step 1. Create a user pool

Start by creating your user pool with the default configuration.

Create a user pool:

  1. Go to the Amazon Cognito console and select Manage User Pools. This takes you to the User Pools Directory.
  2. Select Create a user pool in the upper corner.
  3. Enter a Pool name, select Review defaults, and select Create pool.
  4. Copy the Pool ID, which will be used later to create your single-page app. It will be something like region_xxxxx. You will use it to replace the variable YOUR_USERPOOL_ID in a later step.(Optional) You can add additional features to the user pool, but this demonstration uses the default configuration. For more information see, the Amazon Cognito documentation.

The following figure shows you entering the user pool name.

Figure 3: Enter a name for the user pool

Figure 3: Enter a name for the user pool

The following figure shows the resulting user pool configuration.

Figure 4: Completed user pool configuration

Figure 4: Completed user pool configuration

Step 2. Create a domain name

The Amazon Cognito hosted UI lets you use your own domain name or you can add a prefix to the Amazon Cognito domain. This example uses an Amazon Cognito domain with a prefix.

Create a domain name:

  1. Sign in to the Amazon Cognito console, select Manage User Pools, and select your user pool.
  2. Under App integration, select Domain name.
  3. In the Amazon Cognito domain section, add your Domain prefix (for example, myblog).
  4. Select Check availability. If your domain isn’t available, change the domain prefix and try again.
  5. When your domain is confirmed as available, copy the Domain prefix to use when you create your single-page app. You will use it to replace the variable YOUR_COGNITO_DOMAIN_PREFIX in a later step.
  6. Choose Save changes.

The following figure shows creating an Amazon Cognito hosted domain.

Figure 5: Creating an Amazon Cognito hosted UI domain

Figure 5: Creating an Amazon Cognito hosted UI domain

Step 3. Create an app client

Now create the app client user pool. An app client is where you register your app with the user pool. Generally, you create an app client for each app platform. For example, you might create an app client for a single-page app and another app client for a mobile app. Each app client has its own ID, authentication flows, and permissions to access user attributes.

Create an app client:

  1. Sign in to the Amazon Cognito console, select Manage User Pools, and select your user pool.
  2. Under General settings, select App clients.
  3. Choose Add an app client.
  4. Enter a name for the app client in the App client name field.
  5. Uncheck Generate client secret and accept the remaining default configurations.

    Note: The client secret is used to authenticate the app client to the user pool. Generate client secret is unchecked because you don’t want to send the client secret on the URL using client-side JavaScript. The client secret is used by applications that have a server-side component that can secure the client secret.

  6. Choose Create app client as shown in the following figure.

    Figure 6: Create and configure an app client

    Figure 6: Create and configure an app client

  7. Copy the App client ID. You will use it to replace the variable YOUR_APPCLIENT_ID in a later step.

The following figure shows the App client ID which is automatically generated when the app client is created.

Figure 7: App client configuration

Figure 7: App client configuration

Step 4. Create an Amazon S3 website bucket

Amazon S3 is an object storage service that offers industry-leading scalability, data availability, security, and performance. We use Amazon S3 here to host a static website.

Create an Amazon S3 bucket with the following settings:

  1. Sign in to the AWS Management Console and open the Amazon S3 console.
  2. Choose Create bucket to start the Create bucket wizard.
  3. In Bucket name, enter a DNS-compliant name for your bucket. You will use this in a later step to replace the YOURS3BUCKETNAME variable.
  4. In Region, choose the AWS Region where you want the bucket to reside.

    Note: It’s recommended to create the Amazon S3 bucket in the same AWS Region as Amazon Cognito.

  5. Look up the region code from the region table (for example, US-East [N. Virginia] has a region code of us-east-1). You will use the region code to replace the variable YOUR_REGION in a later step.
  6. Choose Next.
  7. Select the Versioning checkbox.
  8. Choose Next.
  9. Choose Next.
  10. Choose Create bucket.
  11. Select the bucket you just created from the Amazon S3 bucket list.
  12. Select the Properties tab.
  13. Choose Static website hosting.
  14. Choose Use this bucket to host a website.
  15. For the index document, enter index.html and then choose Save.

Step 5. Create a CloudFront distribution

Amazon CloudFront is a fast content delivery network service that helps securely deliver data, videos, applications, and APIs to customers globally with low latency and high transfer speeds—all within a developer-friendly environment. In this step, we use CloudFront to set up an HTTPS-enabled domain for the static website hosted on Amazon S3.

Create a CloudFront distribution (web distribution) with the following modified default settings:

  1. Sign into the AWS Management Console and open the CloudFront console.
  2. Choose Create Distribution.
  3. On the first page of the Create Distribution Wizard, in the Web section, choose Get Started.
  4. Choose the Origin Domain Name from the dropdown list. It will be YOURS3BUCKETNAME.s3.amazonaws.com.
  5. For Restrict Bucket Access, select Yes.
  6. For Origin Access Identity, select Create a New Identity.
  7. For Grant Read Permission on Bucket, select Yes, Update Bucket Policy.
  8. For the Viewer Protocol Policy, select Redirect HTTP to HTTPS.
  9. For Cache Policy, select Managed-Caching Disabled.
  10. Set the Default Root Object to index.html.(Optional) Add a comment. Comments are a good place to describe the purpose of your distribution, for example, “Amazon Cognito SPA.”
  11. Select Create Distribution. The distribution will take a few minutes to create and update.
  12. Copy the Domain Name. This is the CloudFront distribution domain name, which you will use in a later step as the DOMAINNAME value in the YOUR_REDIRECT_URI variable.

Step 6. Create the app

Now that you’ve created the Amazon S3 bucket for static website hosting and the CloudFront distribution for the site, you’re ready to use the code that follows to create a sample app.

Use the following information from the previous steps:

  1. YOUR_COGNITO_DOMAIN_PREFIX is from Step 2.
  2. YOUR_REGION is the AWS region you used in Step 4 when you created your Amazon S3 bucket.
  3. YOUR_APPCLIENT_ID is the App client ID from Step 3.
  4. YOUR_USERPOOL_ID is the Pool ID from Step 1.
  5. YOUR_REDIRECT_URI, which is https://DOMAINNAME/index.html, where DOMAINNAME is your domain name from Step 5.

Create userprofile.js

Use the following text to create the userprofile.js file. Substitute the preceding pre-existing values for the variables in the text.

var myHeaders = new Headers();
myHeaders.set('Cache-Control', 'no-store');
var urlParams = new URLSearchParams(window.location.search);
var tokens;
var domain = "YOUR_COGNITO_DOMAIN_PREFIX";
var region = "YOUR_REGION";
var appClientId = "YOUR_APPCLIENT_ID";
var userPoolId = "YOUR_USERPOOL_ID";
var redirectURI = "YOUR_REDIRECT_URI";

//Convert Payload from Base64-URL to JSON
const decodePayload = payload => {
  const cleanedPayload = payload.replace(/-/g, '+').replace(/_/g, '/');
  const decodedPayload = atob(cleanedPayload)
  const uriEncodedPayload = Array.from(decodedPayload).reduce((acc, char) => {
    const uriEncodedChar = ('00' + char.charCodeAt(0).toString(16)).slice(-2)
    return `${acc}%${uriEncodedChar}`
  }, '')
  const jsonPayload = decodeURIComponent(uriEncodedPayload);

  return JSON.parse(jsonPayload)
}

//Parse JWT Payload
const parseJWTPayload = token => {
    const [header, payload, signature] = token.split('.');
    const jsonPayload = decodePayload(payload)

    return jsonPayload
};

//Parse JWT Header
const parseJWTHeader = token => {
    const [header, payload, signature] = token.split('.');
    const jsonHeader = decodePayload(header)

    return jsonHeader
};

//Generate a Random String
const getRandomString = () => {
    const randomItems = new Uint32Array(28);
    crypto.getRandomValues(randomItems);
    const binaryStringItems = randomItems.map(dec => `0${dec.toString(16).substr(-2)}`)
    return binaryStringItems.reduce((acc, item) => `${acc}${item}`, '');
}

//Encrypt a String with SHA256
const encryptStringWithSHA256 = async str => {
    const PROTOCOL = 'SHA-256'
    const textEncoder = new TextEncoder();
    const encodedData = textEncoder.encode(str);
    return crypto.subtle.digest(PROTOCOL, encodedData);
}

//Convert Hash to Base64-URL
const hashToBase64url = arrayBuffer => {
    const items = new Uint8Array(arrayBuffer)
    const stringifiedArrayHash = items.reduce((acc, i) => `${acc}${String.fromCharCode(i)}`, '')
    const decodedHash = btoa(stringifiedArrayHash)

    const base64URL = decodedHash.replace(/\+/g, '-').replace(/\//g, '_').replace(/=+$/, '');
    return base64URL
}

// Main Function
async function main() {
  var code = urlParams.get('code');

  //If code not present then request code else request tokens
  if (code == null){

    // Create random "state"
    var state = getRandomString();
    sessionStorage.setItem("pkce_state", state);

    // Create PKCE code verifier
    var code_verifier = getRandomString();
    sessionStorage.setItem("code_verifier", code_verifier);

    // Create code challenge
    var arrayHash = await encryptStringWithSHA256(code_verifier);
    var code_challenge = hashToBase64url(arrayHash);
    sessionStorage.setItem("code_challenge", code_challenge)

    // Redirtect user-agent to /authorize endpoint
    location.href = "https://"+domain+".auth."+region+".amazoncognito.com/oauth2/authorize?response_type=code&state="+state+"&client_id="+appClientId+"&redirect_uri="+redirectURI+"&scope=openid&code_challenge_method=S256&code_challenge="+code_challenge;
  } else {

    // Verify state matches
    state = urlParams.get('state');
    if(sessionStorage.getItem("pkce_state") != state) {
        alert("Invalid state");
    } else {

    // Fetch OAuth2 tokens from Cognito
    code_verifier = sessionStorage.getItem('code_verifier');
  await fetch("https://"+domain+".auth."+region+".amazoncognito.com/oauth2/token?grant_type=authorization_code&client_id="+appClientId+"&code_verifier="+code_verifier+"&redirect_uri="+redirectURI+"&code="+ code,{
  method: 'post',
  headers: {
    'Content-Type': 'application/x-www-form-urlencoded'
  }})
  .then((response) => {
    return response.json();
  })
  .then((data) => {

    // Verify id_token
    tokens=data;
    var idVerified = verifyToken (tokens.id_token);
    Promise.resolve(idVerified).then(function(value) {
      if (value.localeCompare("verified")){
        alert("Invalid ID Token - "+ value);
        return;
      }
      });
    // Display tokens
    document.getElementById("id_token").innerHTML = JSON.stringify(parseJWTPayload(tokens.id_token),null,'\t');
    document.getElementById("access_token").innerHTML = JSON.stringify(parseJWTPayload(tokens.access_token),null,'\t');
  });

    // Fetch from /user_info
    await fetch("https://"+domain+".auth."+region+".amazoncognito.com/oauth2/userInfo",{
      method: 'post',
      headers: {
        'authorization': 'Bearer ' + tokens.access_token
    }})
    .then((response) => {
      return response.json();
    })
    .then((data) => {
      // Display user information
      document.getElementById("userInfo").innerHTML = JSON.stringify(data, null,'\t');
    });
  }}}
  main();

Create the verifier.js file

Use the following text to create the verifier.js file.

var key_id;
var keys;
var key_index;

//verify token
async function verifyToken (token) {
//get Cognito keys
keys_url = 'https://cognito-idp.'+ region +'.amazonaws.com/' + userPoolId + '/.well-known/jwks.json';
await fetch(keys_url)
.then((response) => {
return response.json();
})
.then((data) => {
keys = data['keys'];
});

//Get the kid (key id)
var tokenHeader = parseJWTHeader(token);
key_id = tokenHeader.kid;

//search for the kid key id in the Cognito Keys
const key = keys.find(key =>key.kid===key_id)
if (key === undefined){
return "Public key not found in Cognito jwks.json";
}

//verify JWT Signature
var keyObj = KEYUTIL.getKey(key);
var isValid = KJUR.jws.JWS.verifyJWT(token, keyObj, {alg: ["RS256"]});
if (isValid){
} else {
return("Signature verification failed");
}

//verify token has not expired
var tokenPayload = parseJWTPayload(token);
if (Date.now() >= tokenPayload.exp * 1000) {
return("Token expired");
}

//verify app_client_id
var n = tokenPayload.aud.localeCompare(appClientId)
if (n != 0){
return("Token was not issued for this audience");
}
return("verified");
};

Create an index.html file

Use the following text to create the index.html file.

<!doctype html>

<html lang="en">
<head>
<meta charset="utf-8">

<title>MyApp</title>
<meta name="description" content="My Application">
<meta name="author" content="Your Name">
</head>

<body>
<h2>Cognito User</h2>

<p style="white-space:pre-line;" id="token_status"></p>

<p>Id Token</p>
<p style="white-space:pre-line;" id="id_token"></p>

<p>Access Token</p>
<p style="white-space:pre-line;" id="access_token"></p>

<p>User Profile</p>
<p style="white-space:pre-line;" id="userInfo"></p>
<script language="JavaScript" type="text/javascript"
src="https://kjur.github.io/jsrsasign/jsrsasign-latest-all-min.js">
</script>
<script src="js/verifier.js"></script>
<script src="js/userprofile.js"></script>
</body>
</html>

Upload the files into the Amazon S3 Bucket you created earlier

Upload the files you just created to the Amazon S3 bucket that you created in Step 4. If you’re using Chrome or Firefox browsers, you can choose the folders and files to upload and then drag and drop them into the destination bucket. Dragging and dropping is the only way that you can upload folders.

  1. Sign in to the AWS Management Console and open the Amazon S3 console.
  2. In the Bucket name list, choose the name of the bucket that you created earlier in Step 4.
  3. In a window other than the console window, select the index.html file to upload. Then drag and drop the file into the console window that lists the destination bucket.
  4. In the Upload dialog box, choose Upload.
  5. Choose Create Folder.
  6. Enter the name js and choose Save.
  7. Choose the js folder.
  8. In a window other than the console window, select the userprofile.js and verifier.js files to upload. Then drag and drop the files into the console window js folder.

    Note: The Amazon S3 bucket root will contain the index.html file and a js folder. The js folder will contain the userprofile.js and verifier.js files.

Step 7. Configure the app client settings

Use the Amazon Cognito console to configure the app client settings, including identity providers, OAuth flows, and OAuth scopes.

Configure the app client settings:

  1. Go to the Amazon Cognito console.
  2. Choose Manage your User Pools.
  3. Select your user pool.
  4. Select App integration, and then select App client settings.
  5. Under Enabled Identity Providers, select Cognito User Pool.(Optional) You can add federated identity providers. Adding User Pool Sign-in Through a Third-Party has more information about how to add federation providers.
  6. Enter the Callback URL(s) where the user is to be redirected after successfully signing in. The callback URL is the URL of your web app that will receive the authorization code. In our example, this will be the Domain Name for the CloudFront distribution you created earlier. It will look something like https://DOMAINNAME/index.html where DOMAINNAME is xxxxxxx.cloudfront.net.

    Note: HTTPS is required for the Callback URLs. For this example, I used CloudFront as a HTTPS endpoint for the app in Amazon S3.

  7. Next, select Authorization code grant from the Allowed OAuth Flows and OpenID from Allowed OAuth Scopes. The OpenID scope will return the ID token and grant access to all user attributes that are readable by the client.
  8. Choose Save changes.

Step 8. Show the app home page

Now that the Amazon Cognito user pool is configured and the sample app is built, you can test using Amazon Cognito as an OP from the sample JavaScript app you created in Step 6.

View the app’s home page:

  1. Open a web browser and enter the app’s home page URL using the CloudFront distribution to serve your index.html page created in Step 6 (https://DOMAINNAME/index.html) and the app will redirect the browser to the Amazon Cognito /authorize endpoint.
  2. The /authorize endpoint redirects the browser to the Amazon Cognito hosted UI, where the user can sign in or sign up. The following figure shows the user sign-in page.

    Figure 8: User sign-in page

    Figure 8: User sign-in page

Step 9. Create a user

You can use the Amazon Cognito user pool to manage your users or you can use a federated identity provider. Users can sign in or sign up from the Amazon Cognito hosted UI or from a federated identity provider. If you configured a federated identity provider, users will see a list of federated providers that they can choose from. When a user chooses a federated identity provider, they are redirected to the federated identity provider sign-in page. After signing in, the browser is directed back to Amazon Cognito. For this post, Amazon Cognito is the only identity provider, so you will use the Amazon Cognito hosted UI to create an Amazon Cognito user.

Create a new user using Amazon Cognito hosted UI:

  1. Create a new user by selecting Sign up and entering a username, password, and email address. Then select the Sign up button. The following figure shows the sign up screen.

    Figure 9: Sign up with a new account

    Figure 9: Sign up with a new account

  2. The Amazon Cognito sign up workflow will verify the email address by sending a verification code to that address. The following figure shows the prompt to enter the verification code.

    Figure 10: Enter the verification code

    Figure 10: Enter the verification code

  3. Enter the code from the verification email in the Verification Code text box.
  4. Select Confirm Account.

Step 10. Viewing the Amazon Cognito tokens and profile information

After authentication, the app displays the tokens and user information. The following figure shows the OAuth2 access token and OIDC ID token that are returned from the /token endpoint and the user profile returned from the /userInfo endpoint. Now that the user has been authenticated, the application can use the user’s email address to look up the user’s account information in an application data store. Based on the user’s account information, the application can grant/restrict access to paid content or show account information like order history.

Figure 11: Token and user profile information

Figure 11: Token and user profile information

Note: Many browsers will cache redirects. If your browser is repeatedly redirecting to the index.html page, clear the browser cache.

Summary

In this post, we’ve shown you how easy it is to add user authentication to your web and mobile apps with Amazon Cognito.

We created a Cognito User Pool as our user directory, assigned a domain name to the Amazon Cognito hosted UI, and created an application client for our application. Then we created an Amazon S3 bucket to host our website. Next, we created a CloudFront distribution for our Amazon S3 bucket. Then we created our application and uploaded it to our Amazon S3 website bucket. From there, we configured the client app settings with our identity provider, OAuth flows, and scopes. Then we accessed our application and used the Amazon Cognito sign-in flow to create a username and password. Finally, we logged into our application to see the OAuth and OIDC tokens.

Amazon Cognito saves you time and effort when implementing authentication with an intuitive UI, OAuth2 and OIDC support, and customizable workflows. You can now focus on building features that are important to your core business.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the Amazon Cognito forum or contact AWS Support.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.

Author

George Conti

George is a Solution Architect for the AWS Financial Services team. He is passonate about technology and helping Financial Services Companies build solutions with AWS Services.

Bullet Updates – Windowing, Apache Pulsar PubSub, Configuration-based Data Ingestion, and More

Post Syndicated from rosaliebeevm original https://yahooeng.tumblr.com/post/183315480351

yahoodevelopers:

By Akshay Sarma, Principal Engineer, Verizon Media & Brian Xiao, Software Engineer, Verizon Media

This is the first of an ongoing series of blog posts sharing releases and announcements for Bullet, an open-sourced lightweight, scalable, pluggable, multi-tenant query system.

Bullet allows you to query any data flowing through a streaming system without having to store it first through its UI or API. The queries are injected into the running system and have minimal overhead. Running hundreds of queries generally fit into the overhead of just reading the streaming data. Bullet requires running an instance of its backend on your data. This backend runs on common stream processing frameworks (Storm and Spark Streaming currently supported).

The data on which Bullet sits determines what it is used for. For example, our team runs an instance of Bullet on user engagement data (~1M events/sec) to let developers find their own events to validate their code that produces this data. We also use this instance to interactively explore data, throw up quick dashboards to monitor live releases, count unique users, debug issues, and more.

Since open sourcing Bullet in 2017, we’ve been hard at work adding many new features! We’ll highlight some of these here and continue sharing update posts for future releases.

Windowing

Bullet used to operate in a request-response fashion – you would submit a query and wait for the query to meet its termination conditions (usually duration) before receiving results. For short-lived queries, say, a few seconds, this was fine. But as we started fielding more interactive and iterative queries, waiting even a minute for results became too cumbersome.

Enter windowing! Bullet now supports time and record-based windowing. With time windowing, you can break up your query into chunks of time over its duration and retrieve results for each chunk.  For example, you can calculate the average of a field, and stream back results every second:

In the above example, the aggregation is operating on all the data since the beginning of the query, but you can also do aggregations on just the windows themselves. This is often called a Tumbling window:

image

With record windowing, you can get the intermediate aggregation for each record that matches your query (a Sliding window). Or you can do a Tumbling window on records rather than time. For example, you could get results back every three records:

image

Overlapping windows in other ways (Hopping windows) or windows that reset based on different criteria (Session windows, Cascading windows) are currently being worked on. Stay tuned!

image
image

Apache Pulsar support as a native PubSub

Bullet uses a PubSub (publish-subscribe) message queue to send queries and results between the Web Service and Backend. As with everything else in Bullet, the PubSub is pluggable. You can use your favorite pubsub by implementing a few interfaces if you don’t want to use the ones we provide. Until now, we’ve maintained and supported a REST-based PubSub and an Apache Kafka PubSub. Now we are excited to announce supporting Apache Pulsar as well! Bullet Pulsar will be useful to those users who want to use Pulsar as their underlying messaging service.

If you aren’t familiar with Pulsar, setting up a local standalone is very simple, and by default, any Pulsar topics written to will automatically be created. Setting up an instance of Bullet with Pulsar instead of REST or Kafka is just as easy. You can refer to our documentation for more details.

image

Plug your data into Bullet without code

While Bullet worked on any data source located in any persistence layer, you still had to implement an interface to connect your data source to the Backend and convert it into a record container format that Bullet understands. For instance, your data might be located in Kafka and be in the Avro format. If you were using Bullet on Storm, you would perhaps write a Storm Spout to read from Kafka, deserialize, and convert the Avro data into the Bullet record format. This was the only interface in Bullet that required our customers to write their own code. Not anymore! Bullet DSL is a text/configuration-based format for users to plug in their data to the Bullet Backend without having to write a single line of code.

Bullet DSL abstracts away the two major components for plugging data into the Bullet Backend. A Connector piece to read from arbitrary data-sources and a Converter piece to convert that read data into the Bullet record container. We currently support and maintain a few of these – Kafka and Pulsar for Connectors and Avro, Maps and arbitrary Java POJOs for Converters. The Converters understand typed data and can even do a bit of minor ETL (Extract, Transform and Load) if you need to change your data around before feeding it into Bullet. As always, the DSL components are pluggable and you can write your own (and contribute it back!) if you need one that we don’t support.

We appreciate your feedback and contributions! Explore Bullet on GitHub, use and help contribute to the project, and chat with us on Google Groups. To get started, try our Quickstarts on Spark or Storm to set up an instance of Bullet on some fake data and play around with it.