Today most applications are built using microservices. This is because microservices enable rapid scalability, agility and delivery of large complex applications.
Organizations are often fighting a losing battle if real-time data observability is run with traditional monitoring. The large complex application makes traditional application performance monitoring tools built before companies standardized microservices-based applications difficult to gather relevant data insight from containers.
What is Observability?
Observability is traditional application performance monitoring and code profiling combined with ultra-rich contextual data and full end to end transaction tracing. At a granular level, observability allows the ability to track relationships and dependencies between applications, microservices and infrastructure.
Observability adds distributed tracing to provide in-depth information about known systems. An example would be a failure domain, such as when a service is connecting to another service via the HTTP. Failure domains may depend on the way people think about the service or system as a whole. For that reason, it is important to take different perspectives into account when designing the failure domains and the context associated with them, e.g. for easy debugging depending on the audience.
Organizations use pre-production environments to test applications before moving them into the cloud computing space. Enterprise leaders and software development teams understand the significant benefit associated with adopting cloud computing be it PaaS, SaaS or Iaas.
Concerns associated with moving to the cloud are tested and resolved in the pre-production environment. The different staging environments where developers can safely develop, test, implement or integrate new code are referred to as pre-production.
Observability in pre-production refers to when DevOps test their microservices -based application to prepare for application deployment. The main goal of observability in pre-production is to fix and solve any issues that arise before deploying their application. Newer Enterprise observability systems such as Instana have the ability to automate microservice tracing enabling more effective application observability in pre-production.
What does observability look like with traditional testing in pre-production?
Observability with traditional testing in pre-production consists of various pre-deployment processes. The traditional testing done in this stage consists of many testing options.
Let’s look at a few pre-production traditional testing methods:
- Unit testing: This is one of the tests done that allows you to determine if the smallest application service or endpoint behaves the way it should.
- Scenario testing: these tests are carried out to make and receive transactions. DevOps conducts a test that produces a performance baseline result and then a second test is repeated to compare with the baseline results. Normally the second set of tests involves methods such as validation testing, which focuses on specific application components and proceeds in complexity to end-end tests.
- Component testing: This is carried out to test the behavior and resilience of one microservice. It can mimic any calls to external services.
The question here is what’s the point of all this sophisticated testing if the monitoring tool used in traditional testing can’t deliver the meaningful results needed by site reliability engineers to optimize their application. True application resilience isn’t just a better testing method but requires a more effective use of the results. Pass/Fail or error detection does not provide enough data in pre-production. Enterprise Observability can trace requests and provide critical context to traces. So why aren’t we using these tools?
What does observability look like with automated testing tools such as Instana in pre-production?
We know that in order for pre-production testing to be successful to launch new features, releases or software, the observability should make the testing work across the entire application spectrum to increase the effectiveness.
Let’s look at a few automated enterprise observability functionality using instana:
Automated discovery: Every application and infrastructure component that is installed during your development process can be tracked automatically with Instana. You can focus on comparing the sendpoints, applications or even containers. Instana also allows the benchmarking of what’s important to your team at an almost instant speed.
This would allow you to save the time spent on manual configuration. Sometimes an application fails when nothing is wrong with the application. There could be a problem with the infrastructure, or there could be other services further upstream that are required by your application to run . Without correlation capabilities, your teams could burn valuable time trying to find dependencies –or worse, miss dependencies completely.
Architecture monitoring: Seeing the upstream and downstream of your architecture allows you to know the root cause of any issue that might exist within your solution. So, rather than spending time on manual system checking to understand the effect of your architecture on your application. Instana architecture monitoring gives a better view of the impact of your applications on your architectural components.
Let’s say you receive a system alert in pre-production. We know it’s one thing to understand the simple context but knowing which part of your environment is failing is what separates Instana enterprise observability from traditional APM. You know precisely what system is failing and what issues need to be resolved prior to your deployment.
To continue your journey on the path to understanding Observability in pre-production here are a few great resources: