Fredy  Larson

Fredy Larson

1603420620

Debugging Microservices Networking Issues — An Introduction

Broadly speaking, many of the new debugging challenges you are expected to face with distributed microservices can be categorized as networking problems between the different parts of the infrastructure.

Note that inter-service communication in distributed systems is implemented either as a request/response synchronous communication (REST, gRPC, GraphQL) or asynchronous event-driven messaging (Kafka, AMQP, and many others).

Synchronous mechanisms are the clear winners – at least as of late 2020 – because it is much easier to develop, test, and maintain synchronous code. But they bring with them a host of problems. Let’s take a look at some of the possible friction points first, and then explore a few of the possible tools we can use to tackle them.

Inconsistent Network Layers

Your microservices might be deployed in various, different public clouds or on-prem, which means the networking layer service is based on top of can varies drastically between services. This is often the cause of sudden, non-reproducible timeouts and bursts of increased latency and low throughput. These are often a sad daily routine, the majority of which is out of your control.

Service Discovery

Microservices are dynamic, so the routing should be as well. It’s not clear to a service where exactly in the topology its companion service is located, so specialized tooling is needed to allow each service to dynamically detect its peers.

Cascading Failures and Propagated Bottlenecks

Any microservice may start responding slower to the network requests from other services because of high CPU, low memory, long-running DB queries, and other factors. This may end up causing a chain reaction that will slow down other services, causing even more bottlenecks or making them drop connections.

#security #architecture #microservices #network #observability

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Debugging Microservices Networking Issues — An Introduction
Fredy  Larson

Fredy Larson

1603420620

Debugging Microservices Networking Issues — An Introduction

Broadly speaking, many of the new debugging challenges you are expected to face with distributed microservices can be categorized as networking problems between the different parts of the infrastructure.

Note that inter-service communication in distributed systems is implemented either as a request/response synchronous communication (REST, gRPC, GraphQL) or asynchronous event-driven messaging (Kafka, AMQP, and many others).

Synchronous mechanisms are the clear winners – at least as of late 2020 – because it is much easier to develop, test, and maintain synchronous code. But they bring with them a host of problems. Let’s take a look at some of the possible friction points first, and then explore a few of the possible tools we can use to tackle them.

Inconsistent Network Layers

Your microservices might be deployed in various, different public clouds or on-prem, which means the networking layer service is based on top of can varies drastically between services. This is often the cause of sudden, non-reproducible timeouts and bursts of increased latency and low throughput. These are often a sad daily routine, the majority of which is out of your control.

Service Discovery

Microservices are dynamic, so the routing should be as well. It’s not clear to a service where exactly in the topology its companion service is located, so specialized tooling is needed to allow each service to dynamically detect its peers.

Cascading Failures and Propagated Bottlenecks

Any microservice may start responding slower to the network requests from other services because of high CPU, low memory, long-running DB queries, and other factors. This may end up causing a chain reaction that will slow down other services, causing even more bottlenecks or making them drop connections.

#security #architecture #microservices #network #observability

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

Marlon  Boyle

Marlon Boyle

1594312560

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Image for post

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.
Image for post

Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

  • Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
  • **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
  • **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
  • **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
  • Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
  • **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
  • **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
  • **Self-Report: **Automatically communicate with its environment and exchange necessary information.
  • Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

  • software-defined networking (SDN) control
  • industry-standard models and open APIs
  • Real-time analytics/telemetry
  • big data processing
  • cross-domain orchestration
  • programmable infrastructure
  • cloud-native virtualized network functions (VNF)
  • DevOps agile development process
  • everything-as-service design paradigm
  • intelligent process automation
  • edge computing
  • cloud infrastructure
  • programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
  • open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks

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

Autumn  Blick

Autumn Blick

1595342460

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