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.