Gordon  Taylor

Gordon Taylor

1616230080

Microservice Observability Patterns

In my previous  article, I talked about the importance of logs and the differences between structured and unstructured logging. Logs are easy to integrate into your application and provide the ability to represent any type of data in the form of strings.

Metrics, on the other hand, are a numerical representation of data. These are often used to count or measure a value and are aggregated over a period of time. Metrics give us insights into the historical and current state of a system. Since they are just numbers, they can also be used to perform statistical analysis and predictions about the system’s future behaviour. Metrics are also used to trigger alerts and notify you about issues in the system’s behaviour.

Logs vs. Metrics

Format

Logs are represented as strings. They can be simple texts, JSON payloads, or key-value pairs (like we discussed in structured logging).

Metrics are represented as numbers. They measure something (like CPU usage, number of errors, etc.) and are numeric in nature.

Resolution

Logs contain high-resolution data. This includes complete information about an event and can be used to correlate the flow (or path) that the event took through the system.

In case of errors, logs contain the entire stack trace of the exception, which allows us to view and debug issues originating from downstream systems as well. In short, logs can tell you what happened in the system at a certain time.

Metrics contain low-resolution data. This may include a count of parameters (such as requests, errors, etc.) and measures of resources (such as CPU and memory utilization). In short, metrics can give you a count of something that happened in the system at a certain time.

#microservices #observability #monitoring #metrics

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Microservice Observability Patterns
Einar  Hintz

Einar Hintz

1599055326

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

  • Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
  • Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
  • While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

#testing #software testing #test automation #microservice architecture #microservice #test #software test automation #microservice best practices #microservice deployment #microservice components

Samanta  Moore

Samanta Moore

1623835440

Builder Design Pattern

What is Builder Design Pattern ? Why we should care about it ?

Starting from **Creational Design Pattern, **so wikipedia says “creational design pattern are design pattern that deals with object creation mechanism, trying to create objects in manner that is suitable to the situation”.

The basic form of object creations could result in design problems and result in complex design problems, so to overcome this problem Creational Design Pattern somehow allows you to create the object.

Builder is one of the** Creational Design Pattern**.

When to consider the Builder Design Pattern ?

Builder is useful when you need to do lot of things to build an Object. Let’s imagine DOM (Document Object Model), so if we need to create the DOM, We could have to do lot of things, appending plenty of nodes and attaching attributes to them. We could also imagine about the huge XML Object creation where we will have to do lot of work to create the Object. A Factory is used basically when we could create the entire object in one shot.

As **Joshua Bloch (**He led the Design of the many library Java Collections Framework and many more) – “Builder Pattern is good choice when designing the class whose constructor or static factories would have more than handful of parameters

#java #builder #builder pattern #creational design pattern #design pattern #factory pattern #java design pattern

Tia  Gottlieb

Tia Gottlieb

1597438200

What Is a Microservice Architecture? Why Is It Important Now?

We have been building software applications for many years using various tools, technologies, architectural patterns and best practices. It is evident that many software applications become large complex monolith over a period for various reasons. A monolith software application is like a large ball of spaghetti with criss-cross dependencies among its constituent modules. It becomes more complex to develop, deploy and maintain monoliths, constraining the agility and competitive advantages of development teams. Also, let us not undermine the challenge of clearing any sort of technical debt monoliths accumulate, as changing part of monolith code may have cascading impact of destabilizing a working software in production.

Over the years, architectural patterns such as Service Oriented Architecture (SOA) and Microservices have emerged as alternatives to Monoliths.

SOA was arguably the first architectural pattern aimed at solving the typical monolith issues by breaking down a large complex software application to sub-systems or “services”. All these services communicate over a common enterprise service bus (ESB). However, these sub-systems or services are actually mid-sized monoliths, as they share the same database. Also, more and more service-aware logic gets added to ESB and it becomes the single point of failure.

Microservice as an architectural pattern has gathered steam due to large scale adoption by companies like Amazon, Netflix, SoundCloud, Spotify etc. It breaks downs a large software application to a number of loosely coupled microservices. Each microservice is responsible for doing specific discrete tasks, can have its own database and can communicate with other microservices through Application Programming Interfaces (APIs) to solve a large complex business problem. Each microservice can be developed, deployed and maintained independently as long as it operates without breaching a well-defined set of APIs called contract to communicate with other microservices.

#microservice architecture #microservice #scaling #thought leadership #microservices build #microservice

Gordon  Taylor

Gordon Taylor

1616230080

Microservice Observability Patterns

In my previous  article, I talked about the importance of logs and the differences between structured and unstructured logging. Logs are easy to integrate into your application and provide the ability to represent any type of data in the form of strings.

Metrics, on the other hand, are a numerical representation of data. These are often used to count or measure a value and are aggregated over a period of time. Metrics give us insights into the historical and current state of a system. Since they are just numbers, they can also be used to perform statistical analysis and predictions about the system’s future behaviour. Metrics are also used to trigger alerts and notify you about issues in the system’s behaviour.

Logs vs. Metrics

Format

Logs are represented as strings. They can be simple texts, JSON payloads, or key-value pairs (like we discussed in structured logging).

Metrics are represented as numbers. They measure something (like CPU usage, number of errors, etc.) and are numeric in nature.

Resolution

Logs contain high-resolution data. This includes complete information about an event and can be used to correlate the flow (or path) that the event took through the system.

In case of errors, logs contain the entire stack trace of the exception, which allows us to view and debug issues originating from downstream systems as well. In short, logs can tell you what happened in the system at a certain time.

Metrics contain low-resolution data. This may include a count of parameters (such as requests, errors, etc.) and measures of resources (such as CPU and memory utilization). In short, metrics can give you a count of something that happened in the system at a certain time.

#microservices #observability #monitoring #metrics

Autumn  Blick

Autumn Blick

1595335187

Microservices and Data Management - DZone Microservices

Introduction

For pure frontend developers who doesn’t have much exposure to backend or middleware technology, microservices are a vague thing. They might have high-level introduction. So, let us have some deep understanding of what microservices are, and how it is different from monolithic application data management.

Monolithic and Microservice

In a monolithic application, all the stakeholders like all the business logic, routing features, middle-wares and Database access code get used to implement all the functionalities of the application. It is basically a single unit application. It has a lot of challenges in terms of scalability and agility. On the other side, in a microservice, all the business logic, routing features, middle-wares, and database access code get used to implement a single functionality of the application. We break down the functionalities to the core level and then connect to related services. So, the functionalities are actually dependent on related services only and does not get affected if there is an issue with other services. This helps to make the application agile, flexible, and highly scalable.

Monolithic architecture

Microservices Architecture

Why Microservices

Independent DB for the Services

The very first important thing associated with microservices is that each functionality requires its own database and never connects to the database of other services. In a monolithic service, since you have a single database. if something goes wrong with it then the whole application gets crashed. But in microservice, since we have an independent database for each service, in case of any problem with any particular database, it certainly does not affect other services and your application does not crash as a whole.

No Dependency on Schema

We have many services in our application and each service requires its own database. Hence, each database has its own schema or structure. But, if any service is connected to other service and shares the data and during development, the source database changes its schema and does not update the dependent services, then the service will not function correctly and may crash. So, there should be no dependency on databases.

Performance

Depending on the nature of service, we choose the appropriate type of DB. Some services are more efficient in specific database. So, creating a single database for all the services in the application might affect performance. In Microservice, since we have individual DB for each of the service, it is quite flexible, independent, and functions efficiently.

Data Management

Unlike the monolithic approach, in microservice, each functionality or service connects to its own database and never gets connected to other database. So, the big question arises of how we communicate between two services. It is quite generic in an application that we require to get some information based on the combination of many service outputs. But as a thumb rule, services dont communicate. Then what is the solution to this issue? Let us see, how data communicates between the services.

#data management #monolith vs microservice #microservices benefits #microservices communication #microservices archiecture