Note: On November 14th, 2018, we announced SignalFx Microservices APM™, the first APM solution that combines NoSample™ tail-based distributed tracing with streaming analytics. Microservices APM™ extends our real-time metrics platform by adding distributed tracing capabilities that enable SignalFx users to monitor the flow of transactions through service-oriented and microservices-based applications, a need that is not addressed by traditional APM solutions.
For every modern application, infrastructure monitoring that aggregates metrics and focuses on time series analytics is essential to ensuring availability and performance in production. Infrastructure monitoring fills a large gap not previously addressed by APM (or log management): intelligent and timely alerting on service-wide issues and trends across the environment (whether in the cloud or on-prem, or a mix of legacy and new architectures).
Ultimately, the best DevOps strategy requires full visibility not only up and down the stack, but also across all stages of the application lifecycle. APM alone does not provide that, and an effective infrastructure monitoring solution should integrate APM data to provide time correlation with other infrastructure metrics and robust analytics for the most meaningful view of the entire environment.
APM tools help organizations easily instrument and identify bottlenecks in their code. APM vendors focus most of their development resources on the instrumentation part of the problem (e.g., providing the best tracing for Java applications), but have not invested in the downstream analytics, correlation, and alerting required of a general-purpose monitoring solution. Ultimately, they provide another source of insight that is tremendously valuable when combined with other operational data in a complete, modern infrastructure monitoring solution.
With the real-time insight introduced by modern infrastructure monitoring, application developers, infrastructure engineers, and operations teams can collaborate across the entire application lifecycle for the first time, from pre-production performance engineering through real-time service-level monitoring in production to post-mortem investigation of past issues.