SignalFx Announces Unified, AI-Driven Monitoring and Troubleshooting for Modern Applications
September 17, 2019
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SignalFx announced a series of platform updates to SignalFx Microservices APM that provide AI-driven problem detection and alerting on trace latency and error metrics.

The new enhancements enable DevOps and site reliability engineering (SRE) teams to use SignalFx’s Microservices APM to not only troubleshoot, but also monitor microservices through distributed tracing in a single solution. Combined with Splunk’s log analytics, these capabilities further enhance the industry’s first enterprise-grade end-to-end observability platform.

With the continued adoption of cloud-native technologies such as containers and microservices, applications are becoming increasingly complex to monitor. Distributed tracing has become a critical part of modern monitoring and observability strategies, helping DevOps teams understand how requests are being handled end-to-end by an application’s set of microservices. SignalFx Microservices APM is the industry’s only Application Performance Monitoring (APM) solution that metricizes traces and spans, and applies streaming analytics to those metrics. With our NoSample™ tail-based approach that observes and analyzes 100% of all transactions, alerts from SignalFx are more accurate than any other APM solution in the market.

The latest release of SignalFx Microservices APM includes:

- Real-Time, AI-Driven Application Alerts — Leveraging the industry-leading capabilities of SignalFx’s NoSample tail-based sampling, trace and span metricization, and SignalFlow® streaming analytics engine, Microservices APM now supports real-time alerts based on AI-driven problem detection on trace latency and error rate metrics. These self-configuring alerts learn and trigger on behavior changes, and remove the guesswork and back-and-forth associated with manual configuration. This helps DevOps and SRE teams detect sudden spikes and historical anomalies in services and endpoints, significantly reducing mean time to detect (MTTD) and mean time to resolution (MTTR).

- Enhanced Service Maps — Dynamic service maps have been enhanced with automatic detection of inferred services, such as databases, message queues, caches, and other third-party web services. This provides a clearer and more accurate picture of service dependencies by making otherwise untraceable services visible. Combined with existing service performance and alert status details in the service map, users can easily visualize, correlate, and pinpoint potential issues in complex microservices environments.

Database Query Metrics — SignalFx Microservices APM now has the ability with the addition of database query metrics to provide insight into the performance of databases and their impact on end-to-end transactions. This gives DevOps and SRE teams the ability to monitor, alert, and more quickly investigate database-related issues.

- New Auto-Instrumentation Options — Go and Kotlin are now supported with auto-instrumentation, a process that automatically identifies frameworks and libraries, and instruments them to capture trace spans. Along with existing auto-instrumentation support for Java, Python, Ruby, and Node.js, as well as open-standards support for OpenTracing, OpenCensus, Jaeger, and Zipkin, SignalFx is one of the leaders in offering a wide selection of instrumentation options and helping our customers avoid vendor lock-in.

“The value of distributed tracing extends beyond simple troubleshooting and root cause analysis when incidents occur,” said Arijit Mukherji, CTO, SignalFx. “Traditional APM tools are not suited for modern application environments. Since our Microservices APM supports open instrumentation, observes every single transaction, and leverages advanced statistics and algorithms for analytics, we are able to offer the industry’s most unique monitoring and alerting capabilities. No other APM solution can do this.”

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