#performance metrics design pattern in microservices java

[shalcool15]

Building Applications with Spring boot in Java

Spring Boot, a powerful extension of the Spring framework, is designed to simplify the process of developing new Spring applications. It enables rapid and accessible development by providing a convention-over-configuration approach, making it a preferred choice for many developers. This essay delves into the versatility of Spring Boot, exploring the various types of applications it is commonly used for, highlighting its features, benefits, and practical applications across industries.

Origins and Philosophy

Spring Boot was created to address the complexity often associated with Spring applications. By offering a set of auto-configuration, management, and production-ready features out of the box, it reduces the need for extensive boilerplate configuration. This framework adheres to the "opinionated defaults" principle, automatically configuring Spring applications based on the dependencies present on the classpath. This approach significantly accelerates development time and lowers the entry barrier for businesses looking to hire Java developers.

Web Applications

Spring Boot is widely recognized for its efficacy in building web applications. With embedded servers like Tomcat, Jetty, or Undertow, developers can easily create standalone, production-grade web applications that are ready to run. The framework's auto-configuration capabilities, along with Spring MVC, provide a robust foundation for building RESTful web services and dynamic websites. Spring Boot also supports various templates such as Thymeleaf, making the development of MVC applications more straightforward.

Microservices

In the realm of microservices architecture, Spring Boot stands out for its ability to develop lightweight, independently deployable services. Its compatibility with Spring Cloud offers developers an array of tools for quickly building some of the common patterns in distributed systems (e.g., configuration management, service discovery, circuit breakers). This makes Spring Boot an ideal choice for organizations transitioning to a microservices architecture, as it promotes scalability, resilience, and modularity. Microservices is one important reason why businesses look to migrate to Java 11 and beyond.

Cloud-Native Applications

Spring Boot's design aligns well with cloud-native development principles, facilitating the creation of applications that are resilient, manageable, and observable. By leveraging Spring Boot's actuator module, developers gain insights into application health, metrics, and audit events, which are crucial for Java development services companies maintaining and monitoring applications deployed in cloud environments. Furthermore, Spring Boot's seamless integration with containerization tools like Docker and Kubernetes streamlines the deployment process in cloud environments.

Enterprise Applications

Spring Boot is adept at catering to the complex requirements of enterprise applications. Its seamless integration with Spring Security, Spring Data, and Spring Batch, among others, allows for the development of secure, transactional, and data-intensive applications. Whether it's managing security protocols, handling transactions across multiple databases, or processing large batches of data, Spring Boot provides the necessary infrastructure to develop and maintain robust enterprise applications.

IoT and Big Data Applications

The Internet of Things (IoT) and big data are rapidly growing fields where Spring Boot is finding its footing. By facilitating the development of lightweight, high-performance applications, Spring Boot can serve as the backbone for IoT devices' data collection and processing layers. Additionally, its compatibility with big data processing tools like Apache Kafka and Spring Data makes it suitable for building applications that require real-time data processing and analytics.

Summary

Spring Boot's versatility extends across various domains, making it a valuable tool for developing a wide range of applications—from simple CRUD applications to complex, distributed systems. Its convention-over-configuration philosophy, combined with the Spring ecosystem's power, enables developers to build resilient, scalable, and maintainable applications efficiently.

In essence, Spring Boot is not just a tool for one specific type of application; it is a comprehensive framework designed to meet the modern developer's needs. Its ability to adapt to various application requirements, coupled with the continuous support and advancements from the community, ensures that Spring Boot will remain a crucial player in the software development landscape for years to come. Whether for web applications, microservices, cloud-native applications, enterprise-level systems, or innovative fields like IoT and big data, Spring Boot offers the flexibility, efficiency, and reliability that modern projects demand. The alternative Spring cloud also offers variety of advantage for developers building microservices in java with spring boot and spring cloud.

[codeonedigest]

Performance Metrics Design Pattern Tutorial with Examples for Software Programmers

Full Video Link https://youtu.be/ciERWgfx7Tk Hello friends, new #video on #performancemetrics #designpattern for #microservices #tutorial for #programmers with #examples is published on #codeonedigest #youtube channel. Learn #performance #metr

In this video we will learn about Performance Metrics design pattern for microservices. This is the 2nd design principle in Observability design patterns category for microservices.   Microservice architecture structures an application as a set of loosely coupled microservices and each service can be developed independently in agile manner to enable continuous delivery. But how to analyse and…

[xceltecseo]

What Are the Most Popular Java Frameworks in 2022 for Enterprise App development?

The popularity of Java as the most dependable programming language seems to be escalating annually. Despite the emergence of rival platforms that are similar to Java, its popularity has mostly remained unopposed. Programmers typically like Java, especially when creating enterprise-level applications. However, it's not just Java enterprise apps. A range of software solutions, including internet and mobile apps, can be made using the framework. Java increases productivity by providing a strong operating framework. For the creation of enterprise app development in 2022 and beyond, XcelTec has introduced the most well-liked Java frameworks.

Most Popular Java Frameworks for Enterprise App Development in 2022

Spring Boot:

An open source Java web framework called Spring Boot is based on microservices. The Spring Boot framework offers a completely customizable, production-ready environment thanks to its prebuilt code. The microservice architecture, which contains embedded application servers, allows developers to design fully contained applications.

Spring MVC:

It is a Java framework used to create web applications. It makes use of the Model-View-Controller design pattern. Additionally, it supports all of the essential Spring Framework features, such as Dependency Injection and Inversion of Control. Spring MVC provides a respectable way to use MVC in the Spring Framework with the help of DispatcherServlet. In this case, a class called DispatcherServlet is used to receive incoming requests and direct them to the proper resources, such as Controllers, Models, and Views.

Hibernate:

A Java framework called Hibernate looks after the implementations and includes an abstraction layer. The implementations involve creating a query for CRUD operations, connecting to databases, and other tasks.

Software called a framework abstracts away several different technologies, including servlets and JDBC.In order to enable the use of data for a longer period of time, Hibernate develops persistence logic that saves and processes data. It has an edge over other frameworks because it is a simple ORM tool that is also open-source.

Hadoop:

For storing, processing, and analysing massive amounts of data, Hadoop is an Apache open-source framework. A Java-based, non-OLAP database is called Hadoop (online analytical processing). It also enables batch and offline processing. It is used by numerous websites, including Facebook, Yahoo, Google, Twitter, LinkedIn, and many others.

Kafka:

A distributed streaming technique is used by the software platform Apache Kafka to process data. Data can be transferred among apps, servers, and processors using this publish-subscribe messaging mechanism. Apache Kafka was initially created by LinkedIn and later donated to the Apache Software Foundation. Confluent is now working on it as a part of the Apache Software Foundation. Apache Kafka has solved the issue of data transfer being delayed between a sender and a receiver.

Spark:

The Ruby Sinatra framework served as the model for the Spark rapid development web framework. It is less verbose than usual Java framework applications because it is based on the Java 8 Lambda Expression paradigm.

Swagger:

The industry standard for API documentation is called Swagger. Swagger is useful while implementing APIs on Azure. Programming language Swagger is mostly used to describe APIs.

Express.JS:

Express.JS is a speedy, aggressive, necessary, and modest Node.js web framework. You could think of express as a Node.js layer that helps with server and route management. It includes a wide variety of tools for building mobile and online applications.

Dropwizard:

A Java framework called Dropwizard makes it simple to quickly develop high-performance RESTful web services. To create a compact package, it incorporates a number of well-known libraries. The main libraries it uses are Jetty, Jersey, Jackson, JUnit, and Guava. It also uses a library of metrics that it has created. High performance, dependability, and stability can all be achieved with RESTful web apps.

Conclusion:

If you are wanting to create an application, XcelTec can assist you with java development services that can be genuinely helpful for enterprise app development. Hopefully, you now have a better understanding of how important java can be for your organisation.

Visit to explore more on What Are the Most Popular Java Frameworks in 2022 for Enterprise App development?

Get in touch with us for more!

Contact us on:- +91 987 979 9459 | +1 919 400 9200

Email us at:- sales@xceltec.com

[jobsaggregation2]

Sr. Backend

Job: Sr. Backend Software Engineer ? C2H Location: Austin TX Purpose: Create custom applications and back-end microservices that will data-driven decisions. Build data pipelines to generate near real-time metrics while maintaining the focus on; availability, scalability, interoperability, modifiability, performance, security, and testability. Routine: - Work on cutting-edge technologies with world-class engineers to solve challenging problems. - Design and build sophisticated, distributed, microservice architectures in the cloud. - Build highly scalable and performant data pipelines that leverage serverless and containerized compute that balance cost, latency, and duration. Qualifications: - 7+ years of hands-on software engineering experience; designed and built enterprise-level software applications. - Passionate about building software and solving hard problems using Python, Java, C#, or C++. - Design and communicate external and internal architectural perspectives of well-encapsulated systems using tools such as architecture/design patterns and sequence diagrams. - Designing and operating software in a cloud provider such as AWS, GCP, or Azure. - API design and data model implementation experience in an Agile environment. - Experienced in Continuous Integration and Continuous Deployment (CI/CD) with an emphasis on a well-maintained testing pyramid. - Product management coordination skills to ensure dependencies can be satisfied across teams and functionality. Highly-preferred: - Have experience with ?Big Data? technologies such as: Kafka, AWS EMR, Apache Spark, DataFlow or Pipeline Systems, Columnar Databases, ElasticSearch, NoSql stores. - Have experience implementing custom integrations with third party back-office solutions such as Salesforce. - Know how to identify, select, and extend 3rd party components (commercial or open source) that provide operational leverage but do not constrain our product and engineering creativity. Tech-Stack: Cloud Provider : AWS - EC2, ECS, Lambda, SQS, SNS, Kinesis, MSK, S3, Aurora, DynamoDB, KMS, CloudFront, CloudFormation, CodePipeline, etc. Event Bus : Kafka and Schema Registry 3rd Party Vendors : Salesforce, Netsuite, Pendo, Snowflake, Fivetran Deployment : Terraform, Docker (via ECS), Consul for: App Config, Service Discovery, Shared Secrets Visibility : Datadog Programming Languages : Python, Java/Kotlin, C#/.NET, JavaScript Transport Mechanisms : Avro, Protobuf, HTTP Rest/JSON CI/CD : Jenkins, CodePipeline, GitHub, Artifactory Reference : Sr. Backend jobs from Latest listings added - JobsAggregation http://jobsaggregation.com/jobs/technology/sr-backend_i9687

[nox-lathiaen]

Sr. Backend

Job: Sr. Backend Software Engineer ? C2H Location: Austin TX Purpose: Create custom applications and back-end microservices that will data-driven decisions. Build data pipelines to generate near real-time metrics while maintaining the focus on; availability, scalability, interoperability, modifiability, performance, security, and testability. Routine: - Work on cutting-edge technologies with world-class engineers to solve challenging problems. - Design and build sophisticated, distributed, microservice architectures in the cloud. - Build highly scalable and performant data pipelines that leverage serverless and containerized compute that balance cost, latency, and duration. Qualifications: - 7+ years of hands-on software engineering experience; designed and built enterprise-level software applications. - Passionate about building software and solving hard problems using Python, Java, C#, or C++. - Design and communicate external and internal architectural perspectives of well-encapsulated systems using tools such as architecture/design patterns and sequence diagrams. - Designing and operating software in a cloud provider such as AWS, GCP, or Azure. - API design and data model implementation experience in an Agile environment. - Experienced in Continuous Integration and Continuous Deployment (CI/CD) with an emphasis on a well-maintained testing pyramid. - Product management coordination skills to ensure dependencies can be satisfied across teams and functionality. Highly-preferred: - Have experience with ?Big Data? technologies such as: Kafka, AWS EMR, Apache Spark, DataFlow or Pipeline Systems, Columnar Databases, ElasticSearch, NoSql stores. - Have experience implementing custom integrations with third party back-office solutions such as Salesforce. - Know how to identify, select, and extend 3rd party components (commercial or open source) that provide operational leverage but do not constrain our product and engineering creativity. Tech-Stack: Cloud Provider : AWS - EC2, ECS, Lambda, SQS, SNS, Kinesis, MSK, S3, Aurora, DynamoDB, KMS, CloudFront, CloudFormation, CodePipeline, etc. Event Bus : Kafka and Schema Registry 3rd Party Vendors : Salesforce, Netsuite, Pendo, Snowflake, Fivetran Deployment : Terraform, Docker (via ECS), Consul for: App Config, Service Discovery, Shared Secrets Visibility : Datadog Programming Languages : Python, Java/Kotlin, C#/.NET, JavaScript Transport Mechanisms : Avro, Protobuf, HTTP Rest/JSON CI/CD : Jenkins, CodePipeline, GitHub, Artifactory Reference : Sr. Backend jobs Source: http://jobrealtime.com/jobs/technology/sr-backend_i10401

[cvwing1]

Sr. Backend

Job: Sr. Backend Software Engineer ? C2H Location: Austin TX Purpose: Create custom applications and back-end microservices that will data-driven decisions. Build data pipelines to generate near real-time metrics while maintaining the focus on; availability, scalability, interoperability, modifiability, performance, security, and testability. Routine: - Work on cutting-edge technologies with world-class engineers to solve challenging problems. - Design and build sophisticated, distributed, microservice architectures in the cloud. - Build highly scalable and performant data pipelines that leverage serverless and containerized compute that balance cost, latency, and duration. Qualifications: - 7+ years of hands-on software engineering experience; designed and built enterprise-level software applications. - Passionate about building software and solving hard problems using Python, Java, C#, or C++. - Design and communicate external and internal architectural perspectives of well-encapsulated systems using tools such as architecture/design patterns and sequence diagrams. - Designing and operating software in a cloud provider such as AWS, GCP, or Azure. - API design and data model implementation experience in an Agile environment. - Experienced in Continuous Integration and Continuous Deployment (CI/CD) with an emphasis on a well-maintained testing pyramid. - Product management coordination skills to ensure dependencies can be satisfied across teams and functionality. Highly-preferred: - Have experience with ?Big Data? technologies such as: Kafka, AWS EMR, Apache Spark, DataFlow or Pipeline Systems, Columnar Databases, ElasticSearch, NoSql stores. - Have experience implementing custom integrations with third party back-office solutions such as Salesforce. - Know how to identify, select, and extend 3rd party components (commercial or open source) that provide operational leverage but do not constrain our product and engineering creativity. Tech-Stack: Cloud Provider : AWS - EC2, ECS, Lambda, SQS, SNS, Kinesis, MSK, S3, Aurora, DynamoDB, KMS, CloudFront, CloudFormation, CodePipeline, etc. Event Bus : Kafka and Schema Registry 3rd Party Vendors : Salesforce, Netsuite, Pendo, Snowflake, Fivetran Deployment : Terraform, Docker (via ECS), Consul for: App Config, Service Discovery, Shared Secrets Visibility : Datadog Programming Languages : Python, Java/Kotlin, C#/.NET, JavaScript Transport Mechanisms : Avro, Protobuf, HTTP Rest/JSON CI/CD : Jenkins, CodePipeline, GitHub, Artifactory Reference : Sr. Backend jobs from Latest listings added - cvwing http://cvwing.com/jobs/technology/sr-backend_i13427

[linkhello1]

Sr. Backend

Job: Sr. Backend Software Engineer ? C2H Location: Austin TX Purpose: Create custom applications and back-end microservices that will data-driven decisions. Build data pipelines to generate near real-time metrics while maintaining the focus on; availability, scalability, interoperability, modifiability, performance, security, and testability. Routine: - Work on cutting-edge technologies with world-class engineers to solve challenging problems. - Design and build sophisticated, distributed, microservice architectures in the cloud. - Build highly scalable and performant data pipelines that leverage serverless and containerized compute that balance cost, latency, and duration. Qualifications: - 7+ years of hands-on software engineering experience; designed and built enterprise-level software applications. - Passionate about building software and solving hard problems using Python, Java, C#, or C++. - Design and communicate external and internal architectural perspectives of well-encapsulated systems using tools such as architecture/design patterns and sequence diagrams. - Designing and operating software in a cloud provider such as AWS, GCP, or Azure. - API design and data model implementation experience in an Agile environment. - Experienced in Continuous Integration and Continuous Deployment (CI/CD) with an emphasis on a well-maintained testing pyramid. - Product management coordination skills to ensure dependencies can be satisfied across teams and functionality. Highly-preferred: - Have experience with ?Big Data? technologies such as: Kafka, AWS EMR, Apache Spark, DataFlow or Pipeline Systems, Columnar Databases, ElasticSearch, NoSql stores. - Have experience implementing custom integrations with third party back-office solutions such as Salesforce. - Know how to identify, select, and extend 3rd party components (commercial or open source) that provide operational leverage but do not constrain our product and engineering creativity. Tech-Stack: Cloud Provider : AWS - EC2, ECS, Lambda, SQS, SNS, Kinesis, MSK, S3, Aurora, DynamoDB, KMS, CloudFront, CloudFormation, CodePipeline, etc. Event Bus : Kafka and Schema Registry 3rd Party Vendors : Salesforce, Netsuite, Pendo, Snowflake, Fivetran Deployment : Terraform, Docker (via ECS), Consul for: App Config, Service Discovery, Shared Secrets Visibility : Datadog Programming Languages : Python, Java/Kotlin, C#/.NET, JavaScript Transport Mechanisms : Avro, Protobuf, HTTP Rest/JSON CI/CD : Jenkins, CodePipeline, GitHub, Artifactory Reference : Sr. Backend jobs from Latest listings added - LinkHello http://linkhello.com/jobs/technology/sr-backend_i10505

[linkhellojobs]

Sr. Backend

Job: Sr. Backend Software Engineer ? C2H Location: Austin TX Purpose: Create custom applications and back-end microservices that will data-driven decisions. Build data pipelines to generate near real-time metrics while maintaining the focus on; availability, scalability, interoperability, modifiability, performance, security, and testability. Routine: - Work on cutting-edge technologies with world-class engineers to solve challenging problems. - Design and build sophisticated, distributed, microservice architectures in the cloud. - Build highly scalable and performant data pipelines that leverage serverless and containerized compute that balance cost, latency, and duration. Qualifications: - 7+ years of hands-on software engineering experience; designed and built enterprise-level software applications. - Passionate about building software and solving hard problems using Python, Java, C#, or C++. - Design and communicate external and internal architectural perspectives of well-encapsulated systems using tools such as architecture/design patterns and sequence diagrams. - Designing and operating software in a cloud provider such as AWS, GCP, or Azure. - API design and data model implementation experience in an Agile environment. - Experienced in Continuous Integration and Continuous Deployment (CI/CD) with an emphasis on a well-maintained testing pyramid. - Product management coordination skills to ensure dependencies can be satisfied across teams and functionality. Highly-preferred: - Have experience with ?Big Data? technologies such as: Kafka, AWS EMR, Apache Spark, DataFlow or Pipeline Systems, Columnar Databases, ElasticSearch, NoSql stores. - Have experience implementing custom integrations with third party back-office solutions such as Salesforce. - Know how to identify, select, and extend 3rd party components (commercial or open source) that provide operational leverage but do not constrain our product and engineering creativity. Tech-Stack: Cloud Provider : AWS - EC2, ECS, Lambda, SQS, SNS, Kinesis, MSK, S3, Aurora, DynamoDB, KMS, CloudFront, CloudFormation, CodePipeline, etc. Event Bus : Kafka and Schema Registry 3rd Party Vendors : Salesforce, Netsuite, Pendo, Snowflake, Fivetran Deployment : Terraform, Docker (via ECS), Consul for: App Config, Service Discovery, Shared Secrets Visibility : Datadog Programming Languages : Python, Java/Kotlin, C#/.NET, JavaScript Transport Mechanisms : Avro, Protobuf, HTTP Rest/JSON CI/CD : Jenkins, CodePipeline, GitHub, Artifactory Reference : Sr. Backend jobs from Latest listings added - LinkHello http://linkhello.com/jobs/technology/sr-backend_i10505

[faizrashis1995]

What’s After the MEAN Stack?

Introduction

We reach for software stacks to simplify the endless sea of choices. The MEAN stack is one such simplification that worked very well in its time. Though the MEAN stack was great for the last generation, we need more; in particular, more scalability. The components of the MEAN stack haven’t aged well, and our appetites for cloud-native infrastructure require a more mature approach. We need an updated, cloud-native stack that can boundlessly scale as much as our users expect to deliver superior experiences.

 Stacks

When we look at software, we can easily get overwhelmed by the complexity of architectures or the variety of choices. Should I base my system on Python?  Or is Go a better choice? Should I use the same tools as last time? Or should I experiment with the latest hipster toolchain? These questions and more stymie both seasoned and newbie developers and architects.

 Some patterns emerged early on that help developers quickly provision a web property to get started with known-good tools. One way to do this is to gather technologies that work well together in “stacks.” A “stack” is not a prescriptive validation metric, but rather a guideline for choosing and integrating components of a web property. The stack often identifies the OS, the database, the web server, and the server-side programming language.

 In the earliest days, the famous stacks were the “LAMP-stack” and the “Microsoft-stack”. The LAMP stack represents Linux, Apache, MySQL, and PHP or Python. LAMP is an acronym of these product names. All the components of the LAMP stack are open source (though some of the technologies have commercial versions), so one can use them completely for free. The only direct cost to the developer is the time to build the experiment.

 The “Microsoft stack” includes Windows Server, SQL Server, IIS (Internet Information Services), and ASP (90s) or ASP.NET (2000s+). All these products are tested and sold together.

 Stacks such as these help us get started quickly. They liberate us from decision fatigue, so we can focus instead on the dreams of our start-up, or the business problems before us, or the delivery needs of internal and external stakeholders. We choose a stack, such as LAMP or the Microsoft stack, to save time.

 In each of these two example legacy stacks, we’re producing web properties. So no matter what programming language we choose, the end result of a browser’s web request is HTML, JavaScript, and CSS delivered to the browser. HTML provides the content, CSS makes it pretty, and in the early days, JavaScript was the quick form-validation experience. On the server, we use the programming language to combine HTML templates with business data to produce rendered HTML delivered to the browser.

 We can think of this much like mail merge: take a Word document with replaceable fields like first and last name, add an excel file with columns for each field, and the engine produces a file for each row in the sheet.

 As browsers evolved and JavaScript engines were tuned, JavaScript became powerful enough to make real-time, thick-client interfaces in the browser. Early examples of this kind of web application are Facebook and Google Maps.

 These immersive experiences don’t require navigating to a fresh page on every button click. Instead, we could dynamically update the app as other users created content, or when the user clicks buttons in the browser. With these new capabilities, a new stack was born: the MEAN stack.

 What is the MEAN Stack?

The MEAN stack was the first stack to acknowledge the browser-based thick client. Applications built on the MEAN stack primarily have user experience elements built in JavaScript and running continuously in the browser. We can navigate the experiences by opening and closing items, or by swiping or drilling into things. The old full-page refresh is gone.

 The MEAN stack includes MongoDB, Express.js, Angular.js, and Node.js. MEAN is the acronym of these products. The back-end application uses MongoDB to store its data as binary-encoded JavaScript Object Notation (JSON) documents. Node.js is the JavaScript runtime environment, allowing you to do backend, as well as frontend, programming in JavaScript. Express.js is the back-end web application framework running on top of Node.js. And Angular.js is the front-end web application framework, running your JavaScript code in the user’s browser. This allows your application UI to be fully dynamic.

 Unlike previous stacks, both the programming language and operating system aren’t specified, and for the first time, both the server framework and browser-based client framework are specified.

 In the MEAN stack, MongoDB is the data store. MongoDB is a NoSQL database, making a stark departure from the SQL-based systems in previous stacks. With a document database, there are no joins, no schema, no ACID compliance, and no transactions. What document databases offer is the ability to store data as JSON, which easily serializes from the business objects already used in the application. We no longer have to dissect the JSON objects into third normal form to persist the data, nor collect and rehydrate the objects from disparate tables to reproduce the view.

 The MEAN stack webserver is Node.js, a thin wrapper around Chrome’s V8 JavaScript engine that adds TCP sockets and file I/O. Unlike previous generations’ web servers, Node.js was designed in the age of multi-core processors and millions of requests. As a result, Node.js is asynchronous to a fault, easily handling intense, I/O-bound workloads. The programming API is a simple wrapper around a TCP socket.

 In the MEAN stack, JavaScript is the name of the game. Express.js is the server-side framework offering an MVC-like experience in JavaScript. Angular (now known as Angular.js or Angular 1) allows for simple data binding to HTML snippets. With JavaScript both on the server and on the client, there is less context switching when building features. Though the specific features of Express.js’s and Angular.js’s frameworks are quite different, one can be productive in each with little cross-training, and there are some ways to share code between the systems.

 The MEAN stack rallied a web generation of start-ups and hobbyists. Since all the products are free and open-source, one can get started for only the cost of one’s time. Since everything is based in JavaScript, there are fewer concepts to learn before one is productive. When the MEAN stack was introduced, these thick-client browser apps were fresh and new, and the back-end system was fast enough, for new applications, that database durability and database performance seemed less of a concern.

 The Fall of the MEAN Stack

The MEAN stack was good for its time, but a lot has happened since. Here’s an overly brief history of the fall of the MEAN stack, one component at a time.

 Mongo got a real bad rap for data durability. In one Mongo meme, it was suggested that Mongo might implement the PLEASE keyword to improve the likelihood that data would be persisted correctly and durably. (A quick squint, and you can imagine the XKCD comic about “sudo make me a sandwich.”) Mongo also lacks native SQL support, making data retrieval slower and less efficient.

 Express is aging, but is still the defacto standard for Node web apps and apis. Much of the modern frameworks — both MVC-based and Sinatra-inspired — still build on top of Express. Express could do well to move from callbacks to promises, and better handle async and await, but sadly, Express 5 alpha hasn’t moved in more than a year.

 Angular.js (1.x) was rewritten from scratch as Angular (2+). Arguably, the two products are so dissimilar that they should have been named differently. In the confusion as the Angular reboot was taking shape, there was a very unfortunate presentation at an Angular conference.

 The talk was meant to be funny, but it was not taken that way. It showed headstones for many of the core Angular.js concepts, and sought to highlight how the presenters were designing a much easier system in the new Angular.

 Sadly, this message landed really wrong. Much like the community backlash to Visual Basic’s plans they termed Visual Fred, the community was outraged. The core tenets they trusted every day for building highly interactive and profitable apps were getting thrown away, and the new system wouldn’t be ready for a long time. Much of the community moved on to React, and now Angular is struggling to stay relevant. Arguably, Angular’s failure here was the biggest factor in React’s success — much more so than any React initiative or feature.

 Nowadays many languages’ frameworks have caught up to the lean, multi-core experience pioneered in Node and Express. ASP.NET Core brings a similarly light-weight experience, and was built on top of libuv, the OS-agnostic socket framework, the same way Node was. Flask has brought light-weight web apps to Python. Ruby on Rails is one way to get started quickly. Spring Boot brought similar microservices concepts to Java. These back-end frameworks aren’t JavaScript, so there is more context switching, but their performance is no longer a barrier, and strongly-typed languages are becoming more in vogue.

 As a further deterioration of the MEAN stack, there are now frameworks named “mean,” including mean.io and meanjs.org and others. These products seek to capitalize on the popularity of the “mean” term. Sometimes it offers more options on the original MEAN products, sometimes scaffolding around getting started faster, sometimes merely looking to cash in on the SEO value of the term.

 With MEAN losing its edge, many other stacks and methodologies have emerged.

 The JAM Stack

The JAM stack is the next evolution of the MEAN stack. The JAM stack includes JavaScript, APIs, and Markup. In this stack, the back-end isn’t specified – neither the webserver, the back-end language, or the database.

 In the JAM stack we use JavaScript to build a thick client in the browser, it calls APIs, and mashes the data with Markup — likely the same HTML templates we would build in the MEAN stack. The JavaScript frameworks have evolved as well. The new top contenders are React, Vue.js, and Angular, with additional players from Svelte, Auralia, Ember, Meteor, and many others.

 The frameworks have mostly standardized on common concepts like virtual dom, 1-way data binding, and web components. Each framework then combines these concepts with the opinions and styles of the author.

 The JAM stack focuses exclusively on the thick-client browser environment, merely giving a nod to the APIs, as if magic happens behind there. This has given rise to backend-as-a-service products like Firebase, and API innovations beyond REST including gRPC and GraphQL. But, just as legacy stacks ignored the browser thick-client, the JAM stack marginalizes the backend, to our detriment.

 Maturing Application Architecture

As the web and the cloud have matured, as system architects, we have also matured in our thoughts of how to design web properties.

 As technology has progressed, we’ve gotten much better at building highly scalable systems. Microservices offer a much different application model where simple pieces are arranged into a mesh. Containers offer ephemeral hardware that’s easy to spin up and replace, leading to utility computing.

 As consumers and business users of systems, we almost take for granted that a system will be always on and infinitely scalable. We don’t even consider the complexity of geo-replication of data or latency of trans-continental communication. If we need to wait more than a second or two, we move onto the next product or the next task.

 With these maturing tastes, we now take for granted that an application can handle near infinite load without degradation to users, and that features can be upgraded and replaced without downtime. Imagine the absurdity if Google Maps went down every day at 10 pm so they could upgrade the system, or if Facebook went down if a million people or more posted at the same time.

 We now take for granted that our applications can scale, and the naive LAMP and MEAN stacks are no longer relevant.

 Characteristics of the Modern Stack

What does the modern stack look like?  What are the elements of a modern system?  I propose a modern system is cloud-native, utility-billed, infinite-scale, low-latency, user-relevant using machine learning, stores and processes disparate data types and sources, and delivers personalized results to each user. Let’s dig into these concepts.

 A modern system allows boundless scale. As a business user, I can’t handle if my system gets slow when we add more users. If the site goes viral, it needs to continue serving requests, and if the site is seasonally slow, we need to turn down the spend to match revenue. Utility billing and cloud-native scale offers this opportunity. Mounds of hardware are available for us to scale into immediately upon request. If we design stateless, distributed systems, additional load doesn’t produce latency issues.

 A modern system processes disparate data types and sources. Our systems produce logs of unstructured system behavior and failures. Events from sensors and user activity flood in as huge amounts of time-series events. Users produce transactions by placing orders or requesting services. And the product catalog or news feed is a library of documents that must be rendered completely and quickly. As users and stakeholders consume the system’s features, they don’t want or need to know how this data is stored or processed. They need only see that it’s available, searchable, and consumable.

 A modern system produces relevant information. In the world of big data, and even bigger compute capacity, it’s our task to give users relevant information from all sources. Machine learning models can identify trends in data, suggesting related activities or purchases, delivering relevant, real-time results to users. Just as easily, these models can detect outlier activities that suggest fraud. As we gain trust in the insights gained from these real-time analytics, we can empower the machines to make decisions that deliver real business value to our organization.

 MemSQL is the Modern Stack’s Database

Whether you choose to build your web properties in Java or C#, in Python or Go, in Ruby or JavaScript, you need a data store that can elastically and boundlessly scale with your application. One that solves the problems that Mongo ran into – that scales effortlessly, and that meets ACID guarantees for data durability.

 We also need a database that supports the SQL standard for data retrieval. This brings two benefits: a SQL database “plays well with others,” supporting the vast number of tools out there that interface to SQL, as well as the vast number of developers and sophisticated end users who know SQL code. The decades of work that have gone into honing the efficiency of SQL implementations is also worth tapping into.

 These requirements have called forth a new class of databases, which go by a variety of names; we will use the term NewSQL here. A NewSQL database is distributed, like Mongo, but meets ACID guarantees, providing durability, along with support for SQL. CockroachDB and Google Spanner are examples of NewSQL databases.

 We believe that MemSQL brings the best SQL, distributed, and cloud-native story to the table. At the core of MemSQL is the distributed database. In the database’s control plane is a master node and other aggregator nodes responsible for splitting the query across leaf nodes, and combining the results into deterministic data sets. ACID-compliant transactions ensure each update is durably committed to the data partitions, and available for subsequent requests. In-memory skiplists speed up seeking and querying data, and completely avoid data locks.

 MemSQL Helios delivers the same boundless scale engine as a managed service in the cloud. No longer do you need to provision additional hardware or carve out VMs. Merely drag a slider up or down to ensure the capacity you need is available.

 MemSQL is able to ingest data from Kafka streams, from S3 buckets of data stored in JSON, CSV, and other formats, and deliver the data into place without interrupting real-time analytical queries. Native transforms allow shelling out into any process to transform or augment the data, such as calling into a Spark ML model.

 MemSQL stores relational data, stores document data in JSON columns, provides time-series windowing functions, allows for super-fast in-memory rowstore tables snapshotted to disk and disk-based columnstore data, heavily cached in memory.

 As we craft the modern app stack, include MemSQL as your durable, boundless cloud-native data store of choice.

 Conclusion

Stacks have allowed us to simplify the sea of choices to a few packages known to work well together. The MEAN stack was one such toolchain that allowed developers to focus less on infrastructure choices and more on developing business value.

 Sadly, the MEAN stack hasn’t aged well. We’ve moved on to the JAM stack, but this ignores the back-end completely.

 As our tastes have matured, we assume more from our infrastructure. We need a cloud-native advocate that can boundlessly scale, as our users expect us to deliver superior experiences. Try MemSQL for free today, or contact us for a personalized demo.[Source]-https://www.memsql.com/blog/whats-after-the-mean-stack/

62 Hours Mean Stack Developer Training  includes MongoDB, JavaScript, A62 angularJS Training, MongoDB, Node JS and live Project Development. Demo Mean Stack Training available.

[mikegchambers]

OpsGenie is on a journey to reap the benefits of serverless architecture

Engineers are coding and deploying new product features using the AWS Lambda service and moving existing apps to serverless

We started the OpsGenie start-up journey in 2011 with three senior full stack developers who were very experienced in building Java enterprise, on-premise products that specialized in integration, data enrichment, and management of 1st generation infrastructure monitoring tools. We saw an opportunity in the market and decided to use our expertise to build an internet service for Alert/Incident Management.

But stepping into the SaaS world would bring many unknowns. Concepts like operational complexity, high availability, scalability, security, multi-tenancy, and much more would be our challenges. The first thing we decided was that sticking with AWS technologies would help us overcome many of those challenges. Even if there were better alternatives out there, we started to use fully/half managed Amazon services for our computing, database, messaging, and other infrastructural needs that I cannot remember right now.

As many start-ups do, we started coding with a single git repository. But somehow we didn’t have a monolithic architecture. It was still a monolith, of course, in the sense that it was built from the same code repository. :) We separated customer-facing applications from the ones that did heavy calculations in the background. OpsGenie architecture was composed of the following components, in the early days:

Web: A typical backend-for-frontend server written in Grails that served OpsGenie web application and mobile Rest API’s.

Rest API: A lightweight HTTP server and in-house built framework written on top of Netty, which provided the OpsGenie Rest API.

Engine: A standalone JSE application which calculated who should receive a notification — and when.

Sender: A standalone JSE application that talked to third party providers in order to send email, mobile push, SMS and phone notifications.

We were operating in two zones of Amazon’s Oregon region, and we designed the architecture so that all types of applications had one member alive in every zone, during deployments. We put front-end servers behind Amazon Elastic Load Balancers, and all inter-process communications were made via asynchronous message passing with SQS. That provided us with great elasticity, in terms of redundancy, scalability, and availability.

Then the same old story happened. We encountered the same obstacles and opportunities on the paths that every successful startup journeys … the product aroused keen interest in the audience, which then caused us to develop many more features, handle support requests, recruit new engineers, and so on! As a result, the complexity of our infrastructure and code base increased.

Our architecture began to look like the following:

Before I mention the problems that emerged with this architecture, I’d better talk a little bit about the engineering culture we were developing:

We had embraced the evolutionary and adaptive nature of Agile software development methodologies even before we started OpsGenie. We were already performing Test Driven Development. We started to use a modified version of Scrum when our developer size exceeded eight or ten. We accepted the importance of lean startup methodologies and fast feedback loops. We committed to the work needed to continually evolve our organization, culture, and technology in order to serve better products to our customers.

Even though the term is relatively new, we embraced the technical aspects of DevOps from its earliest beginnings. We have been performing Continuous Integration and Continuous Delivery. We have continuously monitored our infrastructure, software, logs, web, and mobile applications. Also, as soon as a new developer joined the company, got his or her hands a little bit dirty with the code, and understood the flows, then he or she began to participate in on-call rotations to solve problems before our customers notice them. And we continue to honor our commitments to an engineering culture based on such ideas and practices.

After this brief insight, I hope that the problems that we have faced seem more understandable. Here they are:

Common code base: Although we strive to follow object-oriented best practices and SOLID principles, it becomes inevitable that your code base gets messy and dirty, as your product and its features grow bigger and bigger. When competition is tough, business pressure increases — which then raises your technical debt and bugs.

New engineer onboarding: Bringing on new engineers is made even more difficult by the situation above. When a junior — or even a senior — developer joins the company, he or she immediately encounters a huge technology stack and a complex code base. Dealing with that can result in significant delays before a new developer becomes productive.

Complex release cycles: When many changes are shipped in each release, the release process can become risky and uncomfortable.

Slower deployment: As your application grows, the time needed to deploy, start, and stop it increases.

Difficulty diagnosing production problems: When your application sends signals of slowing down or experiences resource shortages, it becomes difficult to find where the problem originated — especially if your application performs many heavy tasks and sophisticated business flows.

Inefficient scalability: If you detect that a business flow is slowing down and needs to be scaled, you need to scale the whole application, which is highly inefficient, in terms of resource utilization.

Failures effect entire system: When your application crashes, or its container goes down, all flows running in it also crash.

Lack of ownership: When a large group of developers participates in the implementation of your software, the level of their ownership decreases as your application goes through many different phases of development, testing, deployment, and operations. This can negatively effect your company’s Mean Time to Resolve (MTTR) performance.

As I mentioned before, we were not the first internet service company facing these kinds of challenges. Many more out there survived and succeeded on a massive scale. All we had to do was learn from their experiences and figure out the way that was most appropriate for us.

Microservices

Much has been said and discussed about microservices. There are floods of articles, blogs, books, tutorials, and talks about them on the internet. So I have no need or desire to explain the term, “microservices.”

Although pioneering companies like Amazon and Netflix switched to this architectural style in the previous decade, I think use of the term “microservices” exploded when Martin Fowler first wrote his blog post about the concept in 2014. Amazon CTO Verner Vogels mentioned their patterns as SOA in his interview published in 2006.

Instead of giving a complete definition, Martin Fowler addressed nine common characteristics of a microservices architecture:

Componentization via services

Organized around business capabilities

Products not projects

Smart endpoints and dumb pipes

Decentralized governance

Decentralized data management

Infrastructure automation

Design for failure

Evolutionary design

When we looked at our architecture, we realized that we were not too far away from those ideals to move to a microservices-oriented architecture. Our most critical need was to organize as cross-functional teams in order to implement different business capabilities in different code bases. We already had at least some organizational expertise with the other characteristics Fowler described.

Serverless (FaaS)

So, why are we moving to a serverless architecture instead of simply implementing microservices? There are a couple of advantages in using AWS Lambda instead of building Dockerized applications on top of AWS ECS — or deploying them to a PaaS platform like Heroku:

Capacity Management: The most important advantage of serverless technologies is that they can automatically scale your functions according to load. Although it is possible to configure PaaS or CaaS solutions to scale up and down, according to thresholds that you can define on some metrics, it is completely transparent in the FaaS world. Even if you have one customer today and thousands of customers tomorrow, you have nothing to worry about. AWS Lambda handles the load seamlessly, which greatly reduces operational complexity. Also, scaling happens at a micro-level, which optimizes your resource consumption.

Pay-as-you-use: AWS charges you at the invocation level. If your functions do not receive a load, you pay nothing. With micro-level capacity management and a pay-as-you-use model, it is evident that your costs will be optimized, too. That also creates some interesting opportunities… For example, you can easily determine if a particular product feature of yours is profitable or not.

Automatic Recovery: With sub-second startup times — well, not in JAVA — you don’t have to worry about things like failover, load balancing, etc. If your function crashes for some reason, AWS immediately spins up a new container for you — and the whole process happens entirely behind the scenes.

Versioning: When you deploy a Lambda function, you can assign a version to it, and different versions of your functions can co-exist. You can call whichever version of your function you want from your client code. AWS Lambda also has alias support for your functions so that you may not need to change your client code in order to execute different versions. This helps you to easily implement Blue/Green Deployment, Canary Release, or A/B testing in the backend.

Centralized Log Management: All logs of your Lambda functions go directly to AWS CloudWatch — with zero configuration.

Small Isolated Modules: It forces you to implement your software in highly cohesive and loosely coupled modules that eliminate lots of technical debt.

At the beginning of 2017, we recruited three senior engineers who had no previous knowledge of OpsGenie’s infrastructure and code base — and very little experience with cloud technologies. They started to code an entirely new product feature in a separate code base to be deployed to AWS Lambda service. In four months, they did an excellent job.

They prepared our development, testing, and deployment base — as well as implementing a brand new product extension. What they accomplished was a full blown application — not simple CRUD functions, database triggers, or any other officially-referenced architectural pattern. As I write these lines, they are sending it to production, to open for some Beta customers.

When we feel safe, and our delivery pipeline stabilizes, we plan to split our applications — domain by domain — and move them to serverless. And we will keep sharing our experiences in our engineering blog.

OpsGenie is on a journey to reap the benefits of serverless architecture was originally published in A Cloud Guru on Medium, where people are continuing the conversation by highlighting and responding to this story.

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