What do you do if your system architecture is failing to deliver reliable performance?

I will proceed with the following steps: - Identify the root cause of the problem. - Establish a matrix of solutions to address the problem, allowing for comparison of different approaches. - Prioritize the solutions according to the specific situation and business requirements. - Implement the highest-priority solution, which in most cases will be the simplest and least effortful approach. - Evaluate the system against performance metrics and set benchmarks.

1. Analysis the system architecture to find shortcoming and if they can be fixed to make it scalable? If yes, create a laser focused effort to get it fixed with daily sync-up to avoid any last minute surprises i.e failures. 2. If architecture can not be made scalable, which can happen for many reasons then go for new scalable architecture and ensure proper transition of existing applications from old to new with little to no disruption of services.

Pinpointing the right performance indicators is crucial. Think of it as diagnosing a patient - you need to know exactly where the problem lies. Key metrics like response times, error rates, and server load can tell us a lot about where our system might be limping. Also, don’t overlook throughput and resource utilization; they give insights into how efficiently our system is running. By keeping a close eye on these indicators, we can start to unravel the mystery behind our system's performance issues and work towards a healthier, more reliable architecture. It’s all about spotting the signs early and acting swiftly!

In my experienece its worth to collect data and perform common analysis. I have seen this happening many times in my career that finding the commonalty of issues or process will gain the most effective improvement.

1. Measure and analyse: Start by understanding where my system stands right now and collecting data on how it’s performing. This helps me identify and understand what’s not working as it should. 2. Identify and Fix: Dive into the data to find the root causes. Re-assess my architectural design to see if it needs tweaking or a complete overhaul to meet current needs. Make targeted improvements based on analysis, whether it’s optimising or implementing new solutions. 3. Iterate and Share: I always keep an eye on how these changes affect performance and adjust as needed. Performance tuning is ongoing, so stay vigilant. I have also never missed documenting what I have done and sharing the outcomes.

When software engineers are in the situation to analyze the data, it is highly recommeded that: 1) Be aware of false positive: Data or log can contain false positive message, which reveals the error signature during normal exception. 2) Figure out the key signature: Log or data collected from sample or server can have a lot of information with false positive. We should have a discerning eye for which signature affects the slowness or degradation in terms of performance. 3) Practical method to reproduce: In many cases, the issue is only reproducible in the particular scenario. So it is hard to reproduce the issue in your lab. Having practical method to reproduce the performance issue is importance. Using python script is good approach.

Collecting relevant metrics should not be confined to information gathered from the system itself. The expectation of performance can be subjective from the perspective of the end-user or user application. Including artifacts from user workflows and actual processing, such as workflow and application specific logs and performance metrics, helps complete the performance picture where it really matters. This is especially true for systems that serve a variety of end-user workflows, which is common in supercomputers and other centralized processing systems.

Analyze the web server logs. Plot a graph or create a table of time taken by requests. Now filter out the requests taking most of the time. Pick one request ID. Track this request ID end to end. From originating source to Load balancer, to NGINX/Apache Server, to application, to DB/cache. Identify the component taking most of the time. Check if the servers where the application is running have enough resources. For java applications - Check if garbage collection activity is healthy.

Selective performance indicators are, definitely, the foundation, but always take a holistic approach. Context is key. Deeply understand the problem and you will be able to navigate into the solution.

Without data there is no KPI, so yes, this is mandatory even before performance degrades. It is possible that more data than the standard one should be collected to identify the performance issues. how ever precaution should be used, as to much logging can make the performance level worse

If the system architecture fails to provide reliable performance, it is important to address the issue in a timely and targeted manner. You can evaluate the scalability of the system and determine whether it can handle growing workloads effectively. If your system is not scalable enough, consider adopting more distributed architectures or implementing scalability techniques such as data replication, sharding, or the use of scalable cloud services. You can also tune system resources, including processors, memory, storage, and network, to maximize performance and efficiency. Ensure that resources are optimally allocated and that there are no hardware limitations that could compromise system performance.

1. Being transparent and clearly communicating the issue with all the stakeholders is key to resolving the problem. 2. Identifying the real root cause is very important so that we don't get sidetracked with superfluous fixes. 3. Stop the bleeding with short term tactics while you work on long term fixes. 4. Monitor the right metrics continuously and improve iteratively.

Once bottlenecks are identified, prioritize them in order of most impact to least impact. Or in order of least effort required to most effort required. Sometimes simplest solution is to throw in more resources. However, this might not be a permanent solution to the problem. But this will buy you enough time to work on better solutions.

Cuando la arquitectura de un sistema no ofrece un rendimiento fiable, es crucial actuar de manera estratégica. Realiza un análisis exhaustivo para identificar los problemas clave. Luego, optimiza el código, considera el escalamiento horizontal o vertical según sea necesario y asegúrate de utilizar las tecnologías más adecuadas. Implementa una monitorización sólida para identificar problemas en tiempo real y diseña para la escalabilidad y la tolerancia a fallos. Recuerda, mejorar el rendimiento es un proceso continuo.

By analyzing all the information gathered, pinpoint the specific problem causing the performance issues. Critical issues affecting core functionalities take top priority. Depending on the cause, solutions could involve: 1. Infrastructure Optimization, helps to scale resources (adding more servers or increasing their capacity), optimizing resource allocation, or leveraging caching mechanisms to reduce load. & 2. Code Optimization, helps to refactor code to improve efficiency, potentially using more efficient algorithms, or removing unnecessary operations. Finally, 3. Database Optimization, helps to optimize database queries, indexing data for faster retrieval, or potentially migrating to a more suitable database solution.

Setting up SLO and Error budget is key KPI to identify reliability bottleneck of system. If the error budget is breached, then we should stop the new feature deployment. Start identifying the issues on various observability signals like health check, alerts, events, anomaly detection, distributed tracing , logs etc.one can focus one Google golden signals, USE or RED metrics to methodically troubleshooting it. Keep reliability issues into reliability backlogs and prioritise it . High risk issues based on severity and likelihood should be taken high priority. Take incremental and timeboxed approach to solve it. Review architecture and design with FMEA analysis. Embrace chaos testing for validation.

the solution cand be point 4, review and refine system architecture... review means assesment, it is related with point 2, collect data ... refine is about modifying to fix performance issue

Cuando la arquitectura de un sistema no ofrece un rendimiento fiable, es crucial actuar de manera estratégica. Realiza un análisis exhaustivo para identificar los problemas clave. Luego, optimiza el código, considera el escalamiento horizontal o vertical según sea necesario y asegúrate de utilizar las tecnologías más adecuadas. Implementa una monitorización sólida para identificar problemas en tiempo real y diseña para la escalabilidad y la tolerancia a fallos. Recuerda, mejorar el rendimiento es un proceso continuo.

When a system architecture fails to deliver reliable performance, it's crucial to diagnose the root cause. You might need to reassess the design, optimize components, or scale resources. Conduct thorough testing, gather performance data, and consider implementing solutions such as load balancing, caching, or improving algorithms. Regular monitoring and iterative improvements are key to maintaining reliability.

With time applications become inefficient. Multiple developers working on the same codebase, API calls that might not be required anymore - adding to latency, Older inefficient versions of tools, etc will add up. Hence it is crucial to review the architecture and design from time to time so that the application is up to date and follows guidelines.

You’ll need to work closely with stakeholders - ensure they understand the constraints to prevent further degradation in QoS for current users. Don’t make problems worse for them. There will invariably be multiple options & solutions. You’ll need to work through the pros & cons of each. Be prepared to accept hacks & workarounds as interim solutions to fix fast for least disruption. Consider the technical debt and whether it’s worth the effort of updating versus developing from scratch with new information and lessons learnt. It’s vital to always design architecture with both current and future needs in mind. Consider the path of evolution over time to cater for early value realisation & growth. Architect processes & systems together.

- May be application needs a revamp. - DB/Elastic/Java version upgradation - More resources if traffic has increased - Sometimes a simple restart may do the magic :)

The first thing is to visualize (mentally go through) the conrol flow and data flow with its fullest extend - size, timing & resource constraints, in all probable use cases. Draw the blocks describing them and identify the bottlenecks. Now start to analyze and confirm through profiling tools and re-arrange/re-construct the blocks in the drowing to meet the performance.

Observability - that's the word of the moment. Whether you gather insights by analyzing raw logs, using Zabbix dashboards, or monitoring your application via APM, it's essential to establish event collection points on critical assets and define how to handle and present these events. With metrics and events in hand, you can identify why the system is degraded and precisely address the point of failure. It's also important to consider that excessive generation of log events can further degrade an already compromised architecture, so discretion and caution are necessary during troubleshooting.

If your architecture is not delivering reliable performance, you need to consider the architecture itself. Architectures should be robust, timeless, and elemental. It is rare to innovate at an architecture level. Taking a hard look at the classics can give you insight. Often you need to abstract your architecture until it distills down to a previously known solution.