![]() Jaeger generates tracing information based on "spans" used to understand the complete chain of calls related to one service request. Uber uses Jaeger extensively to trace transactions between microservices. (.) The critical path in a parallel program DAG represents the components of the program speeding, which will speed the overall execution time of the parallel program. ![]() The article authors defined a critical path as follows:įormally, the critical path in a parallel computation DAG is the longest weighted (where each node has an associated weight) path through the DAG. The DAG's critical path is the key to understanding how to reduce latencies in that specific call graph. Since a microservice architecture can be considered a distributed system, that research also applies to the analysis of DAGs representing calls between microservices.Ī DAG representing the flow of parallel computational tasks with node weights proportional to their work. A rich research corpus about the study of computational graphs composed of parallel tasks already exists. Millind Chabbi and Chris Zhang, researchers, and Murali Ramanathan, engineer, pointed out that the interactions between microservices form a call graph, specifically a directed acyclic graph (DAG). However, it becomes increasingly difficult to reason about the impact of each interaction as microservice-based systems grow larger in size and complexity. Understanding which interactions have the most significant impact helps prioritise optimisations, system configuration, capacity planning or problem diagnostics. The request's end-to-end latency results from a combination of all interaction latencies. Therefore, fulfilling one request in a microservice architecture usually involves several interactions between different microservices. The use of specialised and independent deployment units that interact with each other is the fundamental characteristic of microservice architectures. Critical paths identify those operations whose optimisation benefits the overall system. Uber described an open-source tool called CRISP built to solve this problem by finding the critical paths in these graphs. Discovering which services need to be optimised to reduce end-to-end latency in a microservices-based system can be challenging because call graphs may be too complicated to read. ![]()
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