To address performance bottlenecks in a distributed system, prioritize based on business impact and key metrics (e.g., latency, throughput). Use a reactive approach for non-blocking operations and gRPC with connection pooling for efficient communication. Apply CQRS to separate read and write operations, improving scalability and query performance. Optimize database queries with proper indexing, avoid N+1 issues, and use caching (Redis) for read-heavy data. Implement partitioning(sharding) for large datasets and ensure efficient auto-scaling . Improve network performance with batching and asynchronous processing(Kafka). Use proper logging, metrics, and distributed tracing to track issues effectively.

When managing a distributed system, deciding which bottlenecks to tackle first is all about impact. I usually start by identifying the parts of the system that affect the most critical user experiences or business processes. For instance, during one project, we had a lag in data sync across services that slowed down the entire user workflow. Rather than chasing minor inefficiencies, we focused on that bottleneck first, reducing latency and improving overall performance where it mattered most. It’s like triage: fix the issues that hurt the system’s core functionality before chasing smaller optimizations.

Managing the configuration of a distributed system requires careful attention to performance bottlenecks, as they can significantly affect the overall performance of the system. Deciding which bottlenecks to address first requires a systematic approach based on data analysis and evaluation of business priorities. It is necessary to collect comprehensive data on the behavior of the system. Addressing bottlenecks in a distributed system requires a methodical approach, based on real data and strategic priorities. Identifying and solving the problems with the greatest impact on performance, balancing the criticality and difficulty of resolution, allows you to gradually improve the system without compromising its stability or future growth.

As with any distributed system data fragmentation and duplication are the usual suspects to cause performance issues. Couple that with a suboptimal network and infra setup and you have a major problem on your hands. Systems usually become slow due to data mismanagement which is why it is important to ensure your data is maintained as cleanly and efficiently as possible. Also it's easier to decouple systems using event driven architectures and Domain driven design. The smaller the memory footprint of your application the better. Always maintain SRE and observability to spot performance bottlenecks. Do database maintenance regularly. Have an archival strategy in place for old data. Create Data warehouses when needed.

The question is pretty wide, and depends entirely on what the system is doing. Firstly I will start by closely monitoring the system's behavior. Track performance across different components to detect where slowdowns occur. Analyze logs and metrics to narrow down the problem areas. Here are some things I've seen in systems to reduce bottlenecks also and it also helps. Using caches, message queries, delay computation and parallel processing.

Gerenciar sistemas distribuídos e identificar gargalos exige monitoramento, análise de impacto e causa raiz. Ferramentas de monitoramento e priorização com base no impacto no negócio são essenciais!

The system itself should have a self healing capability and be able to proactively detect degradation of performance by raising alerts. One has to have insight into single points of failure, build redundancy for each of these failure points. Next, understand what are the min and max requirements. Design the infrastructure to handle the 80-90% of (min, max). You need clear observability from the Compute, storage and network layer. One also needs to understand the boundaries of the distributed system from a security perspective and ensure how secure the perimeter is. Lats but not the least, understand if your distributed system can ever run into a noisy neighbor problem and ensure the distributed system gets guaranteed resources to operate.

Depending on the specifics of the performance bottlenecks I believe the priority should be on issues affecting the operational uptime, loss of business and pressure from competition. Personally I would also consider other soft factors such as loss of talent and employee frustration as these can have major long-term consequences if prioritizing business over workload. For example how setting up a performant deployment pipeline with good automatic testing may be favorable to short term support work even if cost-wise it is optimal.

The relevance of a bottleneck is given by the machine use profile. Question like the workload type and scale are central to identify what to tackle first or if a bottleneck would be object of rework at all. If you offer the industry standards, engineers and scientists will successfully adapt their softwares and processes.