There are disadvantages to the layered architecture approach. And some aspects of it have been deemed not architecturally pure for domain-driven design.
However, there are disadvantages to most approaches. The key is to understand both the advantages and disadvantages. Pick what architectural patterns work best for your particular problem.
Here are some factors you want to keep in mind.
The layered architecture is very database-centric. As mentioned before, since everything flows down, it’s often the database that’s the last layer. Critics of this architecture point out that your application isn’t about storing data; it’s about solving a business problem. However, when so many applications are simple CRUD apps, maybe the database is more than just a secondary player.
Scaleability can be difficult with a layered architecture. This is tied to the fact that many layered applications tend to take on monolithic properties. If you need to scale your app, you have to scale the whole app! However, that doesn’t mean your layered application has to be a monolith. Once it becomes large enough, it’s time to split it out—just like you would with any other architecture.
A layered application is harder to evolve, as changes in requirements will often touch all layers.
A layered architecture is deployed as a whole. That’s even if it’s modular and separated into good components and namespaces. But that might not be a bad thing. Unless you have separate teams working on different parts of the application, deploying all at once isn’t the worst thing you can do.
While architecting distributed cloud applications, you should assume that failures will happen and design your applications for resiliency. A Microservice ecosystem is going to fail at some point or the other and hence you need to learn embracing failures. Don’t design systems with the assumption that its going to be sunny throughout the year. Be realistic and account for the chances of having rain, snow, thunderstorms and other adverse conditions. In short, design your microservices with failure in mind. Things don’t go as per plan always and you need to be prepared for the worst case scenario.
If Service A calls Service B which in turn calls Service C, what happens when Service B is down? What is your fallback plan in such a scenario?
Can you return a pre-decided error message to the user?
Can you call another service to fetch the information?
Can you return values from cache instead?
Can you return a default value?
Microservices have their drawbacks, but one big advantage is that they tend to be concise enough that you can reason about them more clearly than kitchen sink applications. Planning ahead on potential failure modalities differentiates flaky services from robust services.
You don’t see a supertype-subtype relationship defined as such when you’re looking at the physical database. You’ll only see it explicitly in the logical data model. So what is the pattern and how do you know that you have one in your database?
This relationship exists where you have one entity that could have different attributes based on a discriminator type. One example is a person. Depending on the role of that person in relationship to the business, you will need to store different pieces of information for them. You need different information about a client than you do an employee. But you’re dealing with a person so there is shared information.
It’s a good pattern for minimizing data repetition.
The Existing System
- SQL Server DB with SPs, functions, views, and queries. SPs are non-modular in the sense they join various tables and return values specific to the calls being made.
- Connection with the DB is using basic ADO.NET and not with EF/LINQ, etc.
- ASP.NET WebAPI2.0 (Not .NET Core) and Java-based APIs.
- Native Android, iOS and Web Clients. Some portion of the web clients bypasses the API and talks to the DB directly.
- WebClients: ASP.NET and React.
- A React Native-based app.
- System has been in production for few years now and is stable.
- As the system is in a competitive space, new features are always getting added apart from usual maintenance.
Whether it makes sense to wrap our existing APIs into a GraphQL Endpoint.
For a new feature in the react app evaluate making the new .NET based APIs in GraphQL.
It’s nice that Anshulee shared this case study, especially because there’s relatively little involving GraphQL + .NET.
The real “truth” of your database schema, and the “sovereignty” over it, resides with your database. The database is the only place where the schema is defined, and all clients have a copy of the database schema, not vice versa. The data is in your database, not in your client, so it makes perfect sense to enforce the schema and its integrity in the database, right where the data is.
This is old wisdom, nothing new. Primary and unique keys are good. Foreign keys are good. Check constraints are good. Assertions (when they’re finally implemented) are good.
And that’s not where it ends. For instance, if you’re using Oracle, you may want to specify:
- In what tablespace your table resides
- What PCTFREE value it has
- What the cache size of your sequence (behind the identity) is
Maybe, all of this doesn’t matter in small systems, but you don’t have to go “big data” before you can profit from vendor-specific storage optimisations as the above. None of the ORMs I’ve ever seen (including jOOQ) will allow you to use the full set of DDL options that you may want to use on your database. ORMs offer some tools to help you write DDL.
But ultimately, a well-designed schema is hand written in DDL. All generated DDL is only an approximation of that.
It’s a great post. Also check out Lukas’s responses in the comments section.
The first type of NoSQL database is the Columnar databases which is optimized for reading and writing columns of data as opposed to rows of data. Column-oriented storage for database tables is an help drive down the input/output requirements for database. Since the I/O profile is lowered, overall storage footprint is lowered. One main feature of Columnar Databases is their ability to compress data. Instead of data being written in traditional row orientation, Columnar databases use column orientation. Each column will be associated with column key. Checkout this example from my HBase Blog Post.
He then goes on to describe the other three types. I agree with the taxonomy he uses.
Implementing a Power BI solution is not just about developing reports, creating a data model, or using visuals. Power BI, like any other technologies, can be used in a correct, or incorrect way. Any technology can be used more effective if it harnesses the right architecture. A right architecture can be achieved after a requirement gathering and designing aspects and components of the technology to fit the requirement. In this post, you will learn about some of the most common architectures to use Power BI. You will learn about using Power BI in different architecture guidelines;
Read on to learn more about these three patterns.
Stretch databases were going to provide “Cost-effective” availability for cold data, and unlike typical cold data storage, our data would always be online and available to query. Applications would not need to be modified to work with the seamless design of the stretch database. Run a query, and the data was there being pulled from the cloud when needed. Streamlining on-premises data maintenance by reducing the local footprint of the data files as well as the size of backups! It was even going to be possible to keep data secure via encrypted connections to the cloud and in theory, make a migration to the cloud even easier.
It was destined to be a major win!
Then the price was mentioned.
Do you know anyone using stretch databases today?
Yeah, me neither.
It’s an interesting workaround with several moving parts.
The Events Pipeline team is responsible for plumbing some of New Relic’s core data streams-specifically, event data. These are fine-grained nuggets of monitoring data that record a single event at a particular moment in time. For example, an event could be an error thrown by an application, a page view on a browser, or an e-commerce shopping cart transaction.
In this post, we show how we built our Kafka pipeline so that it stitches together microservices and serves as a changelog and “durable cache,” all with the idea of processing data streams as smoothly and effectively as possible at our scale. In an upcoming post, we’ll share thoughts on how we manage topic partitions in this pipeline.
If you’re wondering if Kafka might be right for you, check out this post for several scenarios which fit.
There’s a number of great introductory articles, so this is going to be a very brief introduction. With event sourcing, instead of storing the “current” state of the entities that are used in our system, we store a stream of events that relate to these entities. Each event is a fact, it describes a state change that occurred to the entity (past tense!). As we all know, facts are indisputable and immutable. For example, suppose we had an application that saved a customer’s details. If we took an event sourcing approach, we would store every change made to that customer’s information as a stream, with the current state derived from a composition of the changes, much like a version control system does. Each individual change record in that stream would be an immutable, indisputable fact.
Having a stream of such events, it’s possible to find out what’s the current state of an entity by folding all events relating to that entity; note, however, that it’s not possible the other way round — when storing the current state only, we discard a lot of valuable historical information.
Event sourcing can peacefully co-exist with more traditional ways of storing state. A system typically handles a number of entity types (e.g. users, orders, products, …), and it’s quite possible that event sourcing is beneficial for only some of them. It’s important to remember that it’s not an all-or-nothing choice, but an additional possibility when it comes to choosing how state is managed in our application.
It’s a helpful article and works hand in hand with a CQRS pattern.