dapper分布式跟踪系统原文_en.pdf

Modern Internet services are often implemented as complex, large-scale distributed systems. These applications are constructed from collections of software modules that may be developed by different teams, perhaps in different programming languages, and could span many thousands of machines across multiple physical facilities. Tools that aid in understanding system behavior and reasoning about performance issues are invaluable in such an environment. Here we introduce the design of Dapper, Google’s production distributed systems tracing infrastructure, and describe how our design goals of low overhead, application-level transparency, and ubiquitous deployment on a very large scale system were met. Dapper sharesconceptualsimilaritieswithothertracingsystems, particularlyMagpie[3]andX-Trace[12],butcertaindesign choices were made that have been key to its success in our environment, such as the use of sampling and restricting the instrumentation to a rather small number of common libraries. The main goal of this paper is to report on our experience building, deploying and using the system for over two years, since Dapper’s foremost measure of success has been its usefulness to developer and operations teams. Dapper began as a self-contained tracing tool but evolved into a monitoring platform which has enabled thecreationofmanydifferenttools, someofwhichwere notanticipatedbyitsdesigners. Wedescribeafewofthe analysis tools that have been built using Dapper, share statisticsaboutitsusagewithinGoogle,presentsomeexample use cases, and discuss lessons learned so far.