Apache SkyWalking provided full visibility into the black hole because before using it, we could not see what was happening when a request left Amazon EKS and went to our on-premises legacy databases. Apache SkyWalking's distributed tracing correlates these two worlds in a single view, showing us that 40% of the latency was actually happening in the network hop between the cloud and the physical data center, not in the code itself. Second, it exposes hidden architectural flaws. By using the automatic dependency mapping, we discovered that some microservices were stuck in a cyclic dependency which was documented nowhere. This visual evidence allowed us to refactor the logic and immediately increased our throughput by 30%. Apache SkyWalking gave us database-level insight without database access. Through its slow query monitoring, the Java agents captured the exact SQL statements that were hanging during peak sales hours. This meant our developers could fix the exact line of code or index without needing to wait for a DBA to pull logs, reducing our mean time to resolution. There are many features that are useful to mention in this case because we obtained different benefits. Apache SkyWalking automatically drew the topology of the 600 pods where we discovered cyclic dependencies between services that no one had documented before and that were slowing down the system. Another valuable feature is resolving hybrid bottlenecks because we isolated a specific network issue between AWS and the physical data center. Without distributed tracing, infrastructure teams blame Java code and vice versa. Database tuning is also important because thanks to slow query metrics captured by the agent, we identified and rewrote the SQL queries that most impacted performance during sales peaks.
