Book Image

JavaScript Cloud Native Development Cookbook

By : John Gilbert

Book Image

JavaScript Cloud Native Development Cookbook

By: John Gilbert

Overview of this book

Cloud-native development is a modern approach to building and running applications that leverages the merits of the cloud computing model. With cloud-native development, teams can deliver faster and in a more lean and agile manner as compared to traditional approaches. This recipe-based guide provides quick solutions for your cloud-native applications. Beginning with a brief introduction, JavaScript Cloud-Native Development Cookbook guides you in building and deploying serverless, event-driven, cloud-native microservices on AWS with Node.js. You'll then move on to the fundamental patterns of developing autonomous cloud-native services and understand the tools and techniques involved in creating globally scalable, highly available, and resilient cloud-native applications. The book also covers multi-regional deployments and leveraging the edge of the cloud to maximize responsiveness, resilience, and elasticity. In the latter chapters you'll explore techniques for building fully automated, continuous deployment pipelines and gain insights into polyglot cloud-native development on popular cloud platforms such as Azure and Google Cloud Platform (GCP). By the end of the book, you'll be able to apply these skills to build powerful cloud-native solutions.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Getting Started with Cloud-Native

Getting Started with Cloud-Native

Creating a stack

Creating a function and working with metrics and logs

Creating an event stream and publishing an event

Creating a stream processor

Creating an API Gateway

Deploying a single-page application

Applying the Event Sourcing and CQRS Patterns

Applying the Event Sourcing and CQRS Patterns

Creating a data lake

Applying the event-first variant of the Event Sourcing pattern

Creating a micro event store

Applying the database-first variant of the Event Sourcing pattern with DynamoDB

Applying the database-first variant of the Event Sourcing pattern with Cognito datasets

Creating a materialized view in DynamoDB

Creating a materialized view in S3

Creating a materialized view in Elasticsearch

Creating a materialized view in a Cognito dataset

Replaying events

Indexing the data lake

Implementing bi-directional synchronization

Implementing Autonomous Services

Implementing Autonomous Services

Implementing a GraphQL CRUD BFF

Implementing a search BFF

Implementing an analytics BFF

Implementing an inbound External Service Gateway

Implementing an outbound External Service Gateway

Orchestrating collaboration between services

Implementing a Saga

Leveraging the Edge of the Cloud

Leveraging the Edge of the Cloud

Serving a single-page application from a CDN

Associating a custom domain name with a CDN

Serving a website from the CDN

Deploying a service behind a CDN

Serving static JSON from a CDN

Triggering the invalidation of content in a CDN

Executing code at the edge of the cloud

Securing Cloud-Native Systems

Securing Cloud-Native Systems

Securing your cloud account

Creating a federated identity pool

Implementing sign up, sign in, and sign out

Securing an API Gateway with OpenID Connect

Implementing a custom authorizer

Authorizing a GraphQL-based service

Implementing a JWT filter

Using envelope encryption

Creating an SSL certificate for encryption in transit

Configuring a web application firewall

Replicating the data lake for disaster recovery

Building a Continuous Deployment Pipeline

Building a Continuous Deployment Pipeline

Creating the CI/CD pipeline

Writing unit tests

Writing integration tests

Writing contract tests for a synchronous API

Writing contract tests for an asynchronous API

Assembling transitive end-to-end tests

Leveraging feature flags

Optimizing Observability

Optimizing Observability

Monitoring a cloud-native system

Implementing custom metrics

Monitoring domain events

Creating alerts

Creating synthetic transaction tests

Designing for Failure

Designing for Failure

Employing proper timeouts and retries

Implementing backpressure and rate limiting

Handling faults

Resubmitting fault events

Implementing idempotence with an inverse OpLock

Implementing idempotence with Event Sourcing

Optimizing Performance

Optimizing Performance

Tuning Function as a Service

Batching requests

Leveraging asynchronous non-blocking IO

Grouping events in stream processors

Autoscaling DynamoDB

Utilizing cache-control

Leveraging session consistency

Deploying to Multiple Regions

Deploying to Multiple Regions

Implementing latency-based routing

Creating a regional health check

Triggering regional failover

Implementing regional replication with DynamoDB

Implementing round-robin replication

Welcoming Polycloud

Welcoming Polycloud

Creating a service with Google Cloud Functions

Creating a service with Azure Functions

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

Creating a materialized view in DynamoDB

The Command Query Responsibility Segregation (CQRS) pattern is critical for designing cloud-native systems that are composed of bounded, isolated, and autonomous services with appropriate bulkheads to limit the blast radius when a service experiences an outage. These bulkheads are implemented by creating materialized views in downstream services.

Upstream services are responsible for the commands that write data using the Event Sourcing pattern. Downstream services take responsibility for their own queries by creating materialized views that are specifically tailored to their needs. This replication of data increases scalability, reduces latency, and allows services to be completely autonomous and function even when upstream source services are unavailable. In this recipe, we will implement a materialized view in AWS DynamoDB.

...