Microservices are becoming increasingly popular. The idea behind them is to break a monolith application into a smaller suite of services. This approach gives rise to a range of benefits including individual scaling and individual deployments. However, it also introduces challenges regarding configuration management, load balancing, and latency analysis. Reshmi Krishna discusses how companies like Uber and Twitter analyze their microservices latency in real time and demonstrates how to integrate popular distributed tracing tools like Zipkin into an existing application with just a few lines of code.
When a request spreads across several services, it quickly becomes challenging to answer questions such as: How can we analyze the latency in real time and after the event? Which event caused it? How much does each microservice contribute to this latency? How can we better troubleshoot without manually digging through log files? Which microservices were encountered during the lifetime of the request and response?
Reshmi answers these questions, explaining how distributed tracing can help us with latency analysis both in real time and after the event, how to integrate distributed tracing into our existing applications with minimal changes and currently available enterprise-level distributed tracing tools.
Reshmi Krishna is a platform architect with Pivotal, where she works with Cloud Foundry and helps customers transform the way they build software. Previously, Reshmi was a software engineer with investment banks and startups on Wall Street. She has extensive experience of building low-latency trading systems. Reshmi supports various conferences and causes for diversity, including Women in Tech and the Society of Women Engineers, and participates in the Grace Hopper Conference for Women.
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