THE IMPACT OF DATA COMPRESSION ON THE EFFICIENCY OF DIRECT INTERACTION BETWEEN SERVICES IN A MICROSERVICE ARCHITECTURE

In modern microservice architectures, which are increasingly implemented in high-load, distributed environments with dynamic scalability, there is a growing interest in models of direct interaction between individual services without the use of traditional proxy solutionsso-called proxyless approaches.This interaction model reduces system response time, avoids delays caused by intermediary routing, optimizes infrastructure costs, decreases the number of potential points of failure, and ensures greater architectural flexibility, scalability, control over data flows, and compliance with cloud-native principles.In this context, the implementation of data compression algorithms becomes particularly important as one of the key means of optimizing information exchange between services.Compression significantly reduces the volume of transmitted data, lowers network load, minimizes request processing latency, and helps reduce resource consumption during intensive inter-service traffic.This paper provides a theoretical analysis of the impact of data compression on the quality, performance, and reliability of direct communication within a microservice architecture.The focus is on lossless algorithms such as GZIP and Snappy, which are among the most widely used in cloud-native environments supporting REST and gRPC.The analysis explores the specifics of their integration, the dependence of efficiency on data format and structure, the type of API requests (single or batch), as well as network latency levels and 2025 1164 computational overhead.The advantages of GZIP are highlighted for high-load scenarios that require deep compression, while Snappy is preferred in cases with strict latency constraints and a priority on speed.Potential limitations related to service compatibility, CPU overhead, and configuration flexibility when managing compression parameters manually are also identified.The importance of configuration consistency on both ends of service interaction is emphasized, including proper header encoding and support for the required formats.The conclusion is drawn that an adaptive and context-aware approach to selecting a compression algorithmconsidering the nature of the API, the structure of the load, the interaction topology, processing priorities, and network characteristics-is critically important to ensure stable, reliable, and efficient microservice operation within proxyless architectures of modern cloud and hybrid platforms.