- Time Series
- 18. Sep
SignalFx is a cloud monitoring and alerting solution for modern enterprise infrastructures. This chart will deploy the SignalFx agent as a DaemonSet to all nodes in your cluster. It is designed to be run in only one release at a time.
See the agent docs for more information on how the agent works. The installation steps will be different since you are using Helm but the agent otherwise behaves identically.
The Smart Agent has three main components:
- Observers that discover applications and services running on the host
- Monitors that collect metrics from the host and applications
- The Writer that sends the metrics collected by monitors to SignalFx.
Observers watch the various environments that we support to discover running services and automatically configure the Smart Agent to send metrics for those services.
Monitors collect metrics from the host system and services. They are configured under the monitor's list in the Smart Agent config. For application-specific monitors, you can define discovery rules in your monitor configuration. A separate monitor instance is created for each discovered instance of applications that match a discovery rule. See Auto-Discovery for more information.
Many of the monitors are built around collected, an open source third-party monitor, and use it to collect metrics. Some other monitors do not use collected. However, either type is configured in the same way.
For a list of supported monitors and their configurations, see Monitor Config.
The Smart Agent is primarily intended to monitor services/applications running on the same host as the Smart Agent. This is in keeping with the collectd model. The main issue with monitoring services on other hosts is that the host dimension that collectd sets on all metrics will currently get set to the hostname of the machine that the Smart Agent is running on. This allows everything to have a consistent host dimension so that metrics can be matched to a specific machine during metric analysis.
The writer collects metrics emitted by the configured monitors and sends them to SignalFx on a regular basis. There are a few things that can be configured in the writer, but this is generally only necessary if you have a very large number of metrics flowing through a single agent.
Real-Time Monitoring at Cloud Scale
Analyze across 100,000’s of ephemeral components for better insight into today’s cloud environments.
- Unlimited dimensionality – easily aggregate and analyze metrics with relevant metadata (customer, id, etc)
- Easily capture custom metrics and business KPIs
- Compute sum, percentile, min and max, moving average, growth rate, standard deviation, and more with flexibility
- Model down to the host, device, or any other relevant unit
- Create charts dynamically from streaming data
- Monitor live data against historic trends in one graph
Breadth of Integrations
- Full visibility into public and private cloud IaaS, including AWS, Google Cloud, Microsoft Azure, PivotalCloud Foundry, and OpenStack.
- Monitor CPU, memory, disk and network at a 10s resolution
- See the state of your infrastructure and identify outliers enabling right-sizing
- Support for multiple data formats with no agent lock-in
- Capture custom metrics from statsd or with our client libraries for your Java, Ruby, Python, Go, and Node.js code
- Scale data pipelines with a standard performant daemon
- Send metrics from Mesos, MySQL, Kafka, Elasticsearch, Apache, Cassandra, and all the OSS in your architecture
- The instant discovery of highly ephemeral infrastructure (containers, functions)
- Analyze streaming data at ingest to eliminate latency
- Correlate your infrastructure to your applications to gain business level insight
- Built-in dashboards for Kubernetes, Docker, AWS Lambda, Azure Functions, and GCP Functions
- Keep track of your business in real-time.
- Correlate your infrastructure with your applications and monitor the business KPIs that matter to you.
Monitor your business
- Collect metrics from anywhere: system, application, or business
- Correlate across your app & business metrics
- Visualize and alert on anything, combine metrics to monitor the business KPIs that matter to you
- Keep up to date with your modern SLAs
- Cut down on service outages that affect your users
- Proactively resolve issues before your users see them
- Flexible and convenient mechanisms for sending custom app metrics
- Client libraries to instrument code directly (Java, Node.js, Python, Ruby, Go)
- Metric proxy to tap into existing metrics pipelines (e.g. Prometheus, Graphite)
- Add your own custom integration to retrieve metrics from existing APIs
Easily build custom dashboards and alerts
- No query language required – all functionality available via point and click UI
- signal flow – powerful pythons like language for advanced charts and alerts
- Harness the power of data science algorithms through built-in alerts and analytics functions
The service discovery and collects streaming metrics across every component in the cloud, replacing traditional point tools and providing real-time visibility into today’s dynamic cloud and container environments. The massive scalability of the service is optimized for the container, microservices, and function based architectures and provides powerful visualization, proactive alerting, and collaborative triage capabilities for organizations at any stages of their cloud transition.