Monitoring platform for keeping systems up and running at all times.
Full stack visibility across the entire stack.
Detect and resolve any incident in record time.
Conform to industry best practices.
Dashbird continuously monitors and analyses your serverless applications to ensure reliability, cost and performance optimisation and alignment with the Well Architected Framework.
What defines a serverless system, main characteristics and how it operates
What are the types of serverless systems for computing, storage, queue processing, etc.
What are the challenges of serverless infrastructures and how to overcome them?
How systems can be reliable and the importance to cloud applications
What is a scalable system and how to handle increasing loads
Making systems easy to operate, manage and evolve
Learn the three basic concepts to build scalable and maintainable applications on serverless backends
The pros and cons of each architecture and insights to choose the best option for your projects
Battle-tested serverless patterns to make sure your cloud architecture is ready to production use
Strategies to compose functions into flexible, scalable and maintainable systems
Achieving loosely-coupled architectures with the asynchronous messaging pattern
Using message queues to manage task processing asynchronously
Asynchronous message and task processing with Pub/Sub
A software pattern to control workflows and state transitions on complex processes
The strategy and practical considerations about AWS physical infrastructure
How cloud resources are identified across the AWS stack
What makes up a Lambda function?
What is AWS Lambda and how it works
Suitable use cases and advantages of using AWS Lambda
How much AWS Lambda costs, pricing model structure and how to save money on Lambda workloads
Learn the main pros/cons of AWS Lambda, and how to solve the FaaS development challenges
Main aspects of the Lambda architecture that impact application development
Quick guide for Lambda applications in Nodejs, Python, Ruby, Java, Go, C# / .NET
Different ways of invoking a Lambda function and integrating to other services
Building fault-tolerant serverless functions with AWS Lambda
Understand how Lambda scales and deals with concurrency
How to use Provisioned Concurrency to reduce function latency and improve overall performance
What are Lambda Layers and how to use them
What are cold starts, why they happen and what to do about them
Understand the Lambda retry mechanism and how functions should be designed
Managing AWS Lambda versions and aliases
How to best allocate resources and improve Lambda performance
What is DynamoDB, how it works and the main concepts of its data model
How much DynamoDB costs and its different pricing models
Query and Scan operations and how to access data on DynamoDB
Alternative indexing methods for flexible data access patterns
How to organize information and leverage DynamoDB features for advanced ways of accessing data
Different models for throughput capacity allocation and optimization in DynamoDB
Comparing NoSQL databases: DynamoDB and Mongo
Comparing managed database services: DynamoDB vs. Mongo Atlas
How does an API gateway work and what are some of the most common usecases
Learn what are the benefits or drawbacks of using APIGateway
Picking the correct one API Gateway service provider can be difficult
Types of possible errors in an AWS Lambda function and how to handle them
Best practices for what to log in an AWS Lambda function
How to log objects and classes from the Lambda application code
Program a proactive alerting system to stay on top of the serverless stack
Monolithic vs Microservices — what’s the best architecture for a business? This is a common question especially if you design and develop software. Both seem to have their pros and cons and it can become hard to choose.
But with large companies like Netflix attributing their success to microservices, you know they can be valuable assets to your business.
The question is — are they right for you? Let’s compare monolithic and microservice architectures and understand the differences that make them unique.
By the end, you should have a fair idea of which architecture is best for you.
It’s not just the implementation, but the thought processes that are different when we compare monolithic to microservice architectures. Let’s look at some of the major aspects that monolithic architecture differs from microservices.
Traditional unified model. Self-contained and independent.
Collection of smaller, independently deployable services.
Simple to develop and deploy. Enhanced performance. Less network latency.
Agility in development. Flexible scaling. Different technology per microservice.
Difficult to manage as it grows. Lack of scalability and flexibility.
More complex. Requires skilled developers. Complicated independent deployment.
One executable file or directory makes deployment easier.
Independent deployment of individual features.
Faster due to the simplicity of having an application based on one code base.
Can be slower due to complexity and more services in more places created by multiple teams.
Cannot scale individual components.
If a microservice reaches its load capacity, new instances can be rapidly deployed to the accompanying cluster.
An error in any module could affect the entire application’s availability.
High reliability. Changes can be deployed for a specific service without threatening the entire application.
Constrained by the technologies already used in the monolith.
Allows teams the freedom to select the tools they desire.
Easier with all code located in one place.
More complicated with each microservice having its own set of logs. A single process can run across multiple machines.
Generally lower cost.
Can lead to exponential infrastructure costs for each new microservice.
Security and Latency
Less network latency and security issues.
Potential for more network latency and less security.
Generally lower as everything is centralized.
Added level of communication and collaboration needed to coordinate updates and interfaces.
Monolithic Architecture is a software development approach where the entire application is built as a single unified unit. All the business logic, database access, user interface, etc. are combined into one codebase and share the same memory space.
This makes the application self-contained and independent, but also harder to change, as even small modifications may require rebuilding and deploying the entire monolith.
As the codebase grows, monolithic architectures can become complex and slow to develop due to tight coupling between components.
Microservices architecture is a way of building software applications as collections of small, independent services rather than as a single monolithic application. Each microservice focuses on completing one specific task or function.
The services communicate with each other using well-defined APIs. This makes it easy to update, modify, or scale each service independently from the rest of the application.
Learn more: The pros and cons of microservices
Monolithic architecture is a great way to get your business started. It’s easy to implement and helps you ship quicker. But as you scale, onboard more customers, and have more features to work with, the same architecture can become difficult to maintain.
The same was the case with Uber which decided to revamp its architecture using the Service-Oriented Architecture (SOA) principles — an older variant of the microservice approach.
Let’s look at the steps that they took to go from monolithic to microservices and how you can too.
If you are in the early stages of your company, a monolithic architecture can work well. But as you grow, you may start to feel limited by it. Some signs you may need to switch architectures:
For instance, Uber started with a monolithic codebase but eventually switched to microservices as their existing codebase became a bottleneck for scalability and continuous integration. The constraints of a monolithic codebase tend to become more apparent as a company and codebase grow.
The decision to switch from monolithic to microservices for your software can be a difficult one. There are a lot of people involved and months of worth of work with seemingly no returns. Considering the number of resources you need to redirect to this new project, it may also be hard to get executive buy-ins.
Uber’s monolithic codebase had grown to over 500 features that they split into 500+ services.
But switching to microservices helped Uber go from operating solely in New York to a fast-growing global ride-hailing service so quickly.
These microservices can operate independently leading to faster and more efficient development cycles giving Uber the upper hand when it comes to adding new features.
Once you’ve decided to make the move and received executive buy-in, it’s time to plan things out. You need to identify the parts that can be broken down into microservices and select the right tools and technologies. The AWS Well-Architected Framework can help here.
Uber faced challenges in areas like obviousness, safety, and resilience.
They needed a standard way of communication that provided type safety, validation, and fault tolerance. After evaluation of existing tools, they found Apache Thrift met their needs best.
Uber used Thrift, as it guarantees strict contracts that describe how to interact with each service. They also created lifecycle tooling to publish these clients to packaging systems.
As you get started, you may benefit from using a tool like Dashbird. It helps you monitor all your services and infrastructure under a single dashboard and gather relevant insights. This can help make things much easier to manage as more of your software features are split into independent services.
Safety and resilience are paramount. Uber drew inspiration from fault tolerance and latency libraries like Netflix’s Hystrix and Twitter’s Finagle. They wrote libraries to ensure clients could deal with failure scenarios successfully.
When converting features into independent services, each service needs to be built with safety and robustness in mind. We want the clients to always have access to the required features and services. And it can be difficult with a lot of microservices running.
Dashbird can help you track all the services so you can act to fix the broken ones quickly. And implementing safety checks provide redundancy to keep the connected clients up and running without interruptions in case of failures.
Migrating to microservices has tradeoffs. The benefits of scalability and faster development must be weighed against increased complexity.
Uber considered the complexity, development time, resource allocation, and the possibility of new failure points while migrating to microservices. But, their goals were clear — improve scalability, allow for faster development cycles, and enter new markets with agility.
And microservices proved to be a valuable asset to achieve these goals.
In your case, the tradeoffs might involve increased complexity in monitoring and managing multiple services, potential consistency issues, and the required investment in new tools and technologies. If you believe the switch will help you achieve the goals faster, it makes sense to allocate the resources for the big revamp.
The last step is actually making the transition happen. This is when you put everything you planned into action. Uber did it slowly, moving one feature at a time into microservices. They kept testing to make sure nothing broke during the process.
You can follow a similar process:
With planning, smart tools, and gradual implementation, moving to microservices can make systems more flexible, robust, and scalable. It’s a big project, but the outcome is a system that can grow with your business.
The choice between Monolithic and Microservices architecture will affect how software is built in your organization.
Monolithic architecture is simpler and best for smaller projects. But as your software grows bigger, Monolithic can get hard to manage.
Microservices turn your software into small independent services which now handle just one thing. This makes Microservices more flexible and easier to update as your software grows. And if you’re leaning towards microservices, tools like Dashbird can be instrumental in your journey.
Dashbird’s serverless observability and intelligence platform can provide you with the insights and monitoring capabilities needed to manage and optimize your microservices architecture.
Remember, since there are no right answers here, pick the one that makes the most sense to your business goals!
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