Service healthcheck pattern

This is a simple pattern I’ve used on a number of projects to implement healthchecks as part of service monitoring. Whether you are making a scaled complex microservice solution or a simple web to API to database solution, having a set of consistent healthcheck URLs for all your components is extremely useful.

It requires two URLs on each web component in your architecture:

  • Healthcheck (e.g. /healthcheck) – returns 200 if the component thinks it is working correctly and can use it’s dependencies. This means checking it can hit any datastore required, reach any APIs it uses.
  • Status (e.g. /status) – returns 200 with no side effects

If either endpoint returns something other than 200 you know something is wrong, the healthcheck URL may respond with details on what’s failed but the status URL simply responds if the service is reachable and alive.

Most documentation on healthchecks only concern themselves with a healthcheck URL, which is fine when you are dealing with an individual component, but when you have a number of interconnected components which may also be interrogated by other applications (service discovery, load balancer etc.) having a status endpoint which does not trigger side effects is important.

Healthchecks can be expensive, making IO/network calls which place load on not just the component but other resources and services. A common issue is healthchecks causing cascades of requests to dependencies and other healthchecks after a release, which may cause false positives for failure when services take longer than usual to respond. Having the status URL means you can reduce the number of calls to the expensive healthcheck, offloading calls that only need to know if the component is alive.


The symmetrical architecture trap

Often when thinking about topics to write about I hesitate, as in retrospect what I’m saying seems obvious. But it’s very common to fall into simple patterns when you are in the thick of a project, doing something which the flaws only become apparent later when early chaos is over and you can think clearly.

One of these traps is symmetrical application architecture, making two similar components in your solution use the same architecture, even when they have different requirements.

A common example of this is when a web application will have a public facing external site and a more secure internal site for administration. On the surface these two components have similarities, they both serve HTML and need to access/persist data, so you may initially use the same architecture for them.

Symmetric architecture diagram

However, you soon realise the external site needs to handle much more traffic than the internal, and it’s data requirements are different (higher read or write, only needing access to specific data). You can resolve this by scaling the architecture, but it’s clunky.

Symmetric architecture with external load diagram

Then you realise the internal site has more security and auditing requirements. You can resolve with implementation changes but it would be neater to include additional layers or services.

Symmetric architecture with internal audit security diagram

The symmetry of the architecture becomes a conceptual barrier to change, changing either one appears to be introducing more complexity but in reality the implementations are diverging anyway due to their different circumstances. Looking at them individually and at how they will be hosted on less abstract infrastructure diagrams can help. Could be your external and internal sites don’t need the same data store or layers, and changing them could save resources and simplify implementation.

Asymmetric architecture diagram

Embracing asymmetry in your architecture early can help you break out of this mindset and prevent you hitting problems later when your implementation work arounds start to creak.

Session data is evil

I’ve been working in ASP.MVC recently after working in Java for a long time. One of the things that struck me was the common use of session data in web application.

Now I know that people can and do use abuse sessions in Java, but the default routing and ease of access make using it more tempting in ASP.MVC. The standard routing convention of “/Controller/Action/:id” means you need to explicitly code to use RESTful paths that give you multiple IDs in URLs like “order/2/item/3” for non-trivial scenarios, and out of the box convenience methods like “TempData” seem to offer magical persistence between requests. These incentives combine to make using session data the path of least resistance in ASP.MVC.


Any data stored in session is inherently unreliable and use of it makes load balancing and scaling your application much more difficult. Once you use it, each instance of your web application must be able to find the users session data to reliably handle requests. Since it’s now extremely common to use multiple instances even for small applications (irresponsible not to for disaster recovery and redundancy) you will need to think about this before you deploy into production.

It also adds hidden complexity to testing your application. Each endpoint which relies on state stored in session needs to be tested with that application state simulated. This means you have at least two places in your code defining and using the same semi-structured data, which makes your tests complex/fragile and your code harder to maintain.


Once you make using session data part of your architecture it’s very hard to refactor and remove it. That little innocent use of TempData to store details from the last request will spread as Developers think “If it was ok there then it’s ok here…” and “one more can’t hurt” (the broken windows theory). Now your user flows in the web application rely on session stored details to go from screen A to B to C, and refactoring them means re-writing and testing a lot of the view/controller logic to replace the data held in session.

There are acceptable uses for session data in web application, authentication is the obivous one. What they have in common is having alternative flows to cope if session data is not found without breaking functionality.

If you have an over reliance of session in your application you are making a flakey, hard to scale and maintain application that will at best limp into production. At worst it will fall over and take your users data with it.

There are common patterns and methods to avoid needing session data, below are some links to help:

Design for Devs – Change sequence diagrams

I’ve been asked a few times by junior developers how to get started in designing code, as if it’s some sort of art technique. In truth every developer is doing design, no one spontaneously writes complex formal logic. Most just do it in their head based on experience and patterns they’ve used before. For small well understood problems this is fine, but when you are dealing with larger or more complex changes doing a little design work up-front can really help clarify things and ultimately save time while giving a better solution.

I’m writing this to document a simple type of design I’ve used on many projects, a change sequence diagram, one you can do quickly on paper or on a whiteboard in ten minutes and I’ve found to be very helpful in thinking about what changes are required, the size of the overall change and promoting re-use of existing code.

Here’s an example:

It’s a pretty simple variation of a sequence diagram, where you show the sequence of events which should occur as a series of interactions between the involved people/components. It normally starts with person, like someone clicking a link on web page, then shows how the system responds. The change part is about highlighting what components in each part of the system need to change to handle the functionality, what parts need to be added/updated/removed.

Doing this forces you to think up-front about what you will need to change and how the system will interact to get the end result. It ties the individual component changes to the overall user requirement, e.g. you’re not just adding a new database column and view field, you’re adding them so the user can see and update their middle name on the personal details screen. This helps you understand how the parts of your system interact and use consistent implementations and design patterns in your changes, plus identify the unit tests and test scenarios.

When you are done, the number and type of changes shows the scale of the overall change, useful for estimates, and breaks it down into manageable chunks of work. You’ll get the best results if you do it paired with someone or get someone else to review your design. Doing this checks that you aren’t breaking existing patterns in the code or missing something that could increase or decrease the complexity. You can expand it to include alternate flows and consider NFR’s for security and performance.

Next time you’re looking at a new requirement or user story give this a try, you’ll be surprised how easy it is to do and what you’ll get out of it.

The incremental complexity trap

This is a common problem which developers face, particularly in agile projects when it’s normal to make user stories related to previous user stories to add new functions to existing screens and flows.

It happens all the time. You start with a something like a simple screen and do the necessary work to make it. Then you get a few new requirements, add some more fields and new validation for alternate user flow etc. You do the same, add the fields to your screens and logic to the controllers. Then it happens again, then again and again. More user flows are added, more fields appearing in some of those flows, more complex validation. Your original simple screen, controller and validation logic is now a monster, unmaintainable and a nightmare to debug.

Often teams don’t do anything to solve it, just live with the problems. Commonly by the time they realise what’s happening it’s easier just to keep layering on the complexity rather than deal with it properly. No one wants to be holding the bag when it’s time to stop and refactor everything.

This can be particularly bad if it’s happening in multiple places at the same time during a project. The effects of shortsighted decisions start to snowball, affecting development speed and increasing regression issues, and it’s not feasible to refactor everything (try selling a sprint full of that to a Product Owner).

So how do you avoid the trap?

  • Keep it simple

Establish code patterns that encourage separation of concerns, make the team aware of them and how to repeat the patterns.

  • Unit tests

Test complexity helps highlight when things are getting out of hand before it’s too late and make it much easier to refactor by reducing the chance of regression issues.

  • Anticipate it before it happens and design

This is the job of the Technical Architect, to know what requirements and stories are coming and how they will be implemented. If an area is going to get a lot of complexity it needs to be handled or you will end up in the trap.

What can you do if you are already in it?

  • Stop making it worse

For complex classes stop adding new layers of logic. Obvious, but the temptation will be there. Stop and plan a better approach, as the sooner you do this the easier it will be.

  • Don’t try for perfection and refactor everything

Big bang refactors are risky and time consuming. Take part of the complexity and split it out, establishing a pattern to remove more.

  • Focus on your goals

Overly complex classes aren’t bad out of principle. They are bad because they slow development and give bugs places to hide. Refactoring and creating a framework to add new functionality can speed development and reduce occurance of defects, which also eat time. You should focus your effort on refactoring areas which will need more complexity later, investing time now to save more later.