Key Points

References

Key Concepts

Declarative vs Imperative Coding

https://stackoverflow.com/questions/1784664/what-is-the-difference-between-declarative-and-imperative-programming

A great C# example of declarative vs. imperative programming is LINQ.

With imperative programming, you tell the compiler what you want to happen, step by step.

For example, let's start with this collection, and choose the odd numbers:

List<int> collection = new List<int> { 1, 2, 3, 4, 5 };

With imperative programming, we'd step through this, and decide what we want:

List<int> results = new List<int>();
foreach(var num in collection)
{
    if (num % 2 != 0)
          results.Add(num);
}

Here, we're saying:

1. Create a result collection
2. Step through each number in the collection
3. Check the number, if it's odd, add it to the results

With declarative programming, on the other hand, you write code that describes what you want, but not necessarily how to get it (declare your desired results, but not the step-by-step):

var results = collection.Where( num => num % 2 != 0);

Here, we're saying "Give us everything where it's odd", not "Step through the collection. Check this item, if it's odd, add it to a result collection."

G4 Pattern Summary

https://springframework.guru/gang-of-four-design-patterns/

Creational Design Patterns

• Abstract Factory. Allows the creation of objects without specifying their concrete type.
• Builder. Uses to create complex objects.
• Factory Method. Creates objects without specifying the exact class to create.
• Prototype. Creates a new object from an existing object.
• Singleton. Ensures only one instance of an object is created.

Structural Design Patterns

• Adapter. Allows for two incompatible classes to work together by wrapping an interface around one of the existing classes.
• Bridge. Decouples an abstraction so two classes can vary independently.
• Composite. Takes a group of objects into a single object.
• Decorator. Allows for an object’s behavior to be extended dynamically at run time.
• Facade. Provides a simple interface to a more complex underlying object.
• Flyweight. Reduces the cost of complex object models.
• Proxy. Provides a placeholder interface to an underlying object to control access, reduce cost, or reduce complexity.

Behavior Design Patterns

• Chain of Responsibility. Delegates commands to a chain of processing objects.
• Command. Creates objects which encapsulate actions and parameters.
• Interpreter. Implements a specialized language.
• Iterator. Accesses the elements of an object sequentially without exposing its underlying representation.
• Mediator. Allows loose coupling between classes by being the only class that has detailed knowledge of their methods.
• Memento. Provides the ability to restore an object to its previous state.
• Observer. Is a publish/subscribe pattern which allows a number of observer objects to see an event.
• State. Allows an object to alter its behavior when its internal state changes.
• Strategy. Allows one of a family of algorithms to be selected on-the-fly at run-time.
• Template Method. Defines the skeleton of an algorithm as an abstract class, allowing its sub-classes to provide concrete behavior.
• Visitor. Separates an algorithm from an object structure by moving the hierarchy of methods into one object.

Microservices.io Pattern repository

https://microservices.io/patterns/index.html

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Monolithic Application Pattern

Microservices Application Pattern

Please see the example applications developed by Chris Richardson. These examples on Github illustrate various aspects of the microservice architecture.

Resulting Context

Benefits

This solution has a number of benefits:

• Enables the continuous delivery and deployment of large, complex applications.
  • Improved maintainability - each service is relatively small and so is easier to understand and change
  • Better testability - services are smaller and faster to test
  • Better deployability - services can be deployed independently
  • It enables you to organize the development effort around multiple, autonomous teams. Each (so called two pizza) team owns and is responsible for one or more services. Each team can develop, test, deploy and scale their services independently of all of the other teams.
• Each microservice is relatively small:
  • Easier for a developer to understand
  • The IDE is faster making developers more productive
  • The application starts faster, which makes developers more productive, and speeds up deployments
• Improved fault isolation. For example, if there is a memory leak in one service then only that service will be affected. The other services will continue to handle requests. In comparison, one misbehaving component of a monolithic architecture can bring down the entire system.
• Eliminates any long-term commitment to a technology stack. When developing a new service you can pick a new technology stack. Similarly, when making major changes to an existing service you can rewrite it using a new technology stack.

Drawbacks

This solution has a number of drawbacks:

• Developers must deal with the additional complexity of creating a distributed system:
  • Developers must implement the inter-service communication mechanism and deal with partial failure
  • Implementing requests that span multiple services is more difficult
  • Testing the interactions between services is more difficult
  • Implementing requests that span multiple services requires careful coordination between the teams
  • Developer tools/IDEs are oriented on building monolithic applications and don’t provide explicit support for developing distributed applications.
• Deployment complexity. In production, there is also the operational complexity of deploying and managing a system comprised of many different services.
• Increased memory consumption. The microservice architecture replaces N monolithic application instances with NxM services instances. If each service runs in its own JVM (or equivalent), which is usually necessary to isolate the instances, then there is the overhead of M times as many JVM runtimes. Moreover, if each service runs on its own VM (e.g. EC2 instance), as is the case at Netflix, the overhead is even higher.

Issues

There are many issues that you must address.

When to use the microservice architecture?

One challenge with using this approach is deciding when it makes sense to use it. When developing the first version of an application, you often do not have the problems that this approach solves. Moreover, using an elaborate, distributed architecture will slow down development. This can be a major problem for startups whose biggest challenge is often how to rapidly evolve the business model and accompanying application. Using Y-axis splits might make it much more difficult to iterate rapidly. Later on, however, when the challenge is how to scale and you need to use functional decomposition, the tangled dependencies might make it difficult to decompose your monolithic application into a set of services.

How to decompose the application into services?

Another challenge is deciding how to partition the system into microservices. This is very much an art, but there are a number of strategies that can help:

• Decompose by business capability and define services corresponding to business capabilities.
• Decompose by domain-driven design subdomain.
• Decompose by verb or use case and define services that are responsible for particular actions. e.g. a Shipping Service that’s responsible for shipping complete orders.
• Decompose by by nouns or resources by defining a service that is responsible for all operations on entities/resources of a given type. e.g. an Account Service that is responsible for managing user accounts.

Ideally, each service should have only a small set of responsibilities. (Uncle) Bob Martin talks about designing classes using the Single Responsibility Principle (SRP). The SRP defines a responsibility of a class as a reason to change, and states that a class should only have one reason to change. It make sense to apply the SRP to service design as well.

Another analogy that helps with service design is the design of Unix utilities. Unix provides a large number of utilities such as grep, cat and find. Each utility does exactly one thing, often exceptionally well, and can be combined with other utilities using a shell script to perform complex tasks.

How to maintain data consistency?

In order to ensure loose coupling, each service has its own database. Maintaining data consistency between services is a challenge because 2 phase-commit/distributed transactions is not an option for many applications. An application must instead use the Saga pattern. A service publishes an event when its data changes. Other services consume that event and update their data. There are several ways of reliably updating data and publishing events including Event Sourcing and Transaction Log Tailing.

How to implement queries?

Another challenge is implementing queries that need to retrieve data owned by multiple services.

• The API Composition and Command Query Responsibility Segregation (CQRS) patterns.

Related patterns for Microservices

There are many patterns related to the microservices pattern. The Monolithic architecture is an alternative to the microservice architecture. The other patterns address issues that you will encounter when applying the microservice architecture.

• Decomposition patterns
  • Decompose by business capability
  • Decompose by subdomain
• The Database per Service pattern describes how each service has its own database in order to ensure loose coupling.
• The API Gateway pattern defines how clients access the services in a microservice architecture.
• The Client-side Discovery and Server-side Discovery patterns are used to route requests for a client to an available service instance in a microservice architecture.
• The Messaging and Remote Procedure Invocation patterns are two different ways that services can communicate.
• The Single Service per Host and Multiple Services per Host patterns are two different deployment strategies.
• Cross-cutting concerns patterns: Microservice chassis pattern and Externalized configuration
• Testing patterns: Service Component Test and Service Integration Contract Test
• Circuit Breaker
• Access Token
• Observability patterns:
  • Log aggregation
  • Application metrics
  • Audit logging
  • Distributed tracing
  • Exception tracking
  • Health check API
  • Log deployments and changes
• UI patterns:
  • Server-side page fragment composition
  • Client-side UI composition

Interoperable Blockchain ACID transaction pattern

blockchain interoperability on atomic transactions using escrow transactions

context

assume 2 different orgs on 2 different blockchains on 2 different platforms want to execute a sale using an order (any type of asset )

blockchains implement escrow transaction interface services in a smart contract

2 independent chains A and B can send messages using a common protocol ( gRPC or ??? )

party A wants to buy X from party B for Y currency amount

problems

how to ensure only authorized parties participate in the transaction

how to ensure both parties will execute their transaction once and only once to complete the purchase transaction

how to ensure that the purchase is cancelled if either party fails to complete their part of the purchase transaction within the designated time limit

pre-requisites

network connectivity

blockchain directory services and access

identity, registration and access management services for all parties in a transaction

each party implements the escrow transaction interfaces

each party communicates using one of the supported protocols: gRPC or ?

each party digital certificates to sign and execute transactions identifying the authorized party

gateway nodes connect 2 different logical blockchain networks directly or indirectly ( blockchain networks normally not physical but virtual overlay networks across multiple provider networks )

solution

use an escrow transction with smart contracts that

records each parties performance of the obligation

This follows a saga pattern to implement the escrow transaction (eg a purchase ) as a set of linked transactions in smart contracts

Singe Use Tokens are used to guarantee that only this single reservation transaction can be executed once by the designated party

step 0 - party A agrees to buy X from party B for Y currency amount agreeing to a set of policies to execute the transaction for the buyer and the seller roles

step 1 - reservation for payment from A to B with a token,  reservation for delivery from B to A with a token

step 2 - when both reservations complete, the reservations are executable as smart contracts by each party

each party executes their reservation transaction via smart contract using the supplied single use token created for just this transaction

A executes B's reservation using the token from B

B executes A's reservation using the token from A

alternate scenarios

both reservations are not completed within the agreed time limit, all reservations and tokens are cancelled and resources revert to current owner control

the purchase policy agreed to may include automated contracts to execute actions to reverse one or both reservation executions or cancel the reservations updating the blockchain

step 3 - if both reservations are not executed as transactions within an agreed time limit, each party is notified to manage the purchase based on agreed policy

step 4 - when both reservations have been executed by each party, all parties are notified of the completion of the escrow transaction

step 5 - any dependent actors are notified of the completion of the purchase which may trigger additional transactions ( eg start an automatic purchase warranty etc )

Potential Value Opportunities

Potential Challenges

Candidate Solutions

Step-by-step guide for Example

sample code block

Recommended Next Steps

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