Why monitor a monolithic server?
It is quite challenging to improve the performance of a monolithic server application. Change is difficult when things are tightly connected. Unlike micro services, where things are more likely to be managed by development teams. However, optimizing the performance of a monolithic application is not impossible, especially if you have the right toolkit.
There are many tools that will help, many APMs from the mainstream have left behind monolithic servers and focus on micro services and distributed systems. FusionReactor has a local APM built specifically to improve monolithic server performance. It also has a cloud viewing platform that takes care of distributed systems. Both the on-premise and cloud versions continue to be developed by the software house Intergral GmbH in Germany.
Possible solutions to monolithic application problems
- People move from one team to another or from one organization to another as time goes on. Due to the centralized nature of monolithic code bases, over time a particular portion of the code may become null and void. As a result, the code snippet may no longer belong to anyone.
- With a team working on different components of the code, there is no clear separation between services and no contract or SLA.
- The lack of separation of services makes it difficult to find the regression. As a result, it can be difficult to find the root cause when a component starts to break down.
Improve the performance of your monolith
- An API usually accumulates a lot of data as it continues to be used. As a result, these become obsolete over time, so they should be disposed of if they are not needed.
- Connecting similar tasks in parallel can be useful if their nature and performance are similar
- Caching at the request level is important in monolithic architecture because there are a lot of calls within a request, which means it has a greater chance of running it. Use the Request Scope Cache for evaluation. Therefore, the first order is cached during execution.
A good APM will allow you to improve the performance of a monolith system quickly and relatively easily. It’s hard to change everything in a monolith because things are so tightly connected to each other.
Maintenance of monolithic servers is often difficult due to code and technical debt attenuation
50% of developers use monolithic environments as well as distributed environments, according to a recent survey. This is because many old applications are essential to the infrastructure but suffer from technical obligations and code decay. They are functioning fine, so their rewriting will not be the best option, as developing new applications often provides a higher return on investment. Monolithic monitoring is equally important for monitoring distributed systems. More could be argued since monolithic apps are often written in older code, which modern developers simply no longer learn. Applications like the FusionReactor APM allow older applications to run efficiently without the need for re-encoding or transfer.
Java Virtual Machine (JVM) monitoring
The Java application architecture is built around the JVM (Java Virtual Machine). In fact, it interprets and converts Java byte code into hosted platform operations. In case the Java Virtual Machine (JVM), which is used by all Java middleware such as Tomcat, JBoss EAP, WildFly, GlassFish and WebSphere, encounters performance issues, the impact on the services they support can be significant.
JVM monitoring is an essential part of any Java APM strategy. To resolve server-side bottlenecks, IT Ops and DevOps teams use JVM performance metrics, and JVM monitoring can also help developers and architects by discovering code-level issues.
Using an APM such as FusionReactor, you can detect code-level bottlenecks such as wire synchronization problems, deadlock, memory leaks, garbage collection problems and insufficient stack memory.
How can APM help manage a monolithic server?
Automatic root cause analysis
When a critical error or deviation occurs within your monolithic server, Automatic Root Cause Analysis (RCA) capability can alert the developer immediately. In the FusionReactors event image, developers can view the full source code, stack tracking, variables, and environment status of errors at the point where they occur. As a result, you will save several bug hours and dramatically reduce the average detection time (MTTD).
Debugging in a production environment
QA stages, stage environments and automation were all used to prevent bugs from reaching production. However, sometimes bugs still get into production. Whenever they do, we need a strategy to deal with them safely and effectively.
Today’s debugging tools allow you to perform debugging safely and reliably in a production environment without affecting users or causing downtime.
What makes it safe to sift through bugs in monoliths in production?
There are a few things to keep in mind when debugging production;
- Performance is not significantly affected by debugging
- You can still use your app and debug at the same time
- Secure data is not accessible from the outside
- Bugs provides you with enough information to locate and resolve the issue as soon as possible.
When debugging, you want as much information as possible in the shortest amount of time. It only adds to the stress of dealing with a critical issue and jumping between multiple systems and trying again to fix the bugs multiple times.
Use an ongoing profile to find performance issues
Continuous profiling is a process of collecting data on app performance over time. As a result, application developers analyze this data in production environments.
An ongoing profile is used to determine which components, methods, or lines of code are the most resource-intensive. This insight can be used to improve the performance of the app with a profile and to understand behavior while running.
FusionRector Ultimate has a number of continuous profile tools included
Continuous code profile
The code profile makes it easy to run code performance analysis in your low-cost production environment. Since profiles are created automatically, you will not miss any problem.
The FusionReactor code profile provides instant insight into your application performance down to the method level.
Continuous threading profile
Through continuous wire profiling and stack trace analysis, you can detect performance issues in your monolith quickly and efficiently. With an APM that includes a thread profile, like FusionReactor, you can quickly create a single thread profile or stack to identify performance issues, deadlocks, and parallel issues.
Continuous memory profile
You can get a detailed understanding of Java memory spaces and garbage collection with continuous memory profiles. As a result, you can detect memory leaks and optimize memory usage in your production Java applications by using FusionReactor’s low overhead memory profile and gaining immediate stacking insight.
What is a memory leak?
A defect in the application causes a memory leak. As a result, when an object retains memory and cannot be collected due to its reference to another living object, it is considered a memory leak. Therefore, leaked objects are accessible from at least one GC root or are themselves GC Roots. Therefore, leaked objects have a path that begins with GC Roots and ends with the leaked object.
By using a heap, the memory profile detects possible memory leaks or creates an excessive object in real time.
Continuous CPU profile
With a CPU profile in your APM, you can find and direct inefficient processes running on your monolithic application server.
Java Profiler is a low overhead tool that displays the executed code. As a result, you can determine which functions are working that may slow down the threading.