Observability is a buzzword that has become all the rage in the DevOps community in recent years, as the Google Trends graph below shows.
Observability has gained immense importance in the IT industry because it informs teams of both when downtime or an error has occurred and why. With the advent of new technologies such as cloud computing, microservices, containers, virtualization and many other systems, new challenges arise (as expected).
- Distributed Tracing - the flow of data between independent systems can be non-trivial. For example, one system might execute payment by communicating with a bank, but a request to another system might be the one that persists in internal databases. Failure to register purchases in an internal database might require deep familiarity with both systems and the flow of data between them. - Data Consistency - data written to the databases must be valid according to all defined rules between components, breaking of these rules might affect correctness of behaviour of distributed systems. - Network Failures - faults in one operational node of a complex network can produce unexpected consequences, for example, an outage of a payment system can make an online shop unusable. - Operational Overhead - maintaining procured resources for independent systems. For example giving less memory to one node can significantly impact the whole network - if a database procured for an online shop is a relatively small instance it might affect user experience, as each load of store inventory can take a long time.
Observability is the key to fully understanding what is actually happening under the hood of business applications.
Introduction to Observability
Observability provides deep insight into all layers of the IT infrastructure. Observability means gathering all fragments of many monitoring tools and then arranging them in a way that allows teams to form a picture of the current state of the system,helping IT operational teams to quickly solve critical problems. Observability analyzes complex systems, assessing why issues occur, what is causing them, how we can get ahead of them occurring and even fix them before they come to the surface.
Why Bother with Observability?
Network observability matters. It:
·Improves the application development environment by providing the data needed to improve it
·Helps teams understand what is happening behind the scenes for most IT issues
·Captures unknown problems and helps teams to better understand how to correct, predict and manage them in the future
·Puts DevOps teams into a smoother flow thanks to intelligent analysis of the programming environment
Detecting IT problems without observability is inefficient and time consuming.
Observability vs Monitoring
Observation and monitoring are two different concepts, which are often mistaken for one another.
There are several common use cases:
- Having alarms and metrics that alert of problems
- Finding a solution to the problem
- Finding the answer to "why something went wrong."
- Continuous improvement
- Reporting and documentation
Essentially, observability is a sort of a superset of monitoring. Monitoring can alert teams to something as simple as running out of disk space on a host to something as complex API endpoints reaching thresholds, for example not being able to support more than 50 concurrent users. By adding observability into the mix, it also allows teams to make intelligent assumptions about the system, identify situations that cause the system to run out of disk space or time periods where our systems are heavily used (for example streaming services can expect more than an average number of users after uploading new episodes of highly popular TV shows) and be able to remedy them before they occur.
More Benefits of Observability
Observability has many other benefits. It:
- Helps DevOps teams understand what is really happening in the development phase
- Monitors software and application operation and how it works
- Helps identify the root causes of problems
- Enables self-healing infrastructure also known as IT automation
Thanks to intuitive dashboards, it also helps NetOps teams observe what is happening in real time
How Can Teams Adapt to Implementing Observability?
Observation is essential for IT departments and DevOps teams. To apply observability together with events, logs, flow and tracking, DevOps teams need to add code to the application to be able to provide additional data that can be used to enable alerts. DevOps runs automatic tests before and after the software is deployed to see if something has broken. To minimize the effort simply integrating an observability platform for use in their system is key and an easy solution.
How Do Observability Platforms Work?
Achieving observability is a desirable end state of our product. For this very purpose a multitude of platforms are available as commercial solutions. How do they work?
The observability platform is a unified umbrella platform that saves IT professionals from a threat of downtime and breakdowns. It gives the customers the ability to view events, metrics and traces along with full monitoring of IT infrastructure.
To gain insight, the observation platform analyzes the logs, flow, metrics and traces for intelligence that can be used. After aggregating data, it manages and monitors suspicious activities.
In addition to logging, the observability platform has a correlation engine that correlates alerts generated from network monitoring, log management and network traffic.
UP9 provides an out-of-the-box test automation for microservices, kubernetes and cloud-native, replacing the need for developers to constantly build and maintain tests, while providing comprehensive service test-coverage.
- Automatic generation and maintenance of CI-ready test-code, based on service traffic
- Observability into API-contracts, business-logic and service architecture
- Automatic reliability, test-coverage and root-cause analysis
- Machine generated tests include functional, regression, performance and edge-case test-cases, covering all services and all service-endpoints
UP9 offloads the microservice testing workload from developers giving them precious time back.