Elastic Metrics
Already using the Elastic Stack for logs? Add infrastructure metrics in just a few steps. Elasticsearch takes what you love about its log search superpowers and applies them to metrics from multiple sources, across your entire infrastructure.
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Get started with tracking your infrastructure logs and metrics in the Elastic Stack.
Watch videoSee how to get your observability initiative up and running quickly with Elasticsearch Service.
Watch videoLearn the fundamentals for working with metric data and the Elastic Stack.
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7.6 adds new inventory views for AWS services, providing an overview of EC2, RDS, S3, and SQS services.
Whether you are monitoring servers, Docker containers, Kubernetes orchestration, Prometheus-style metrics, or application telemetry, there is a Metricbeat module that will get you started in minutes. And the best part is that data is natively correlated with your logs.
Run wild with dimensions, tags, cardinality, and fields. Elastic doesn’t limit or dictate how you explore your data. Instead, you can continuously and quickly explore attributes — host name, IP address, tags — at scale in any way you like, in any order you like, in the visualization you like. Plus, Beats and their modules do the collecting, parsing, and tagging for you. They can also create dashboards and machine learning jobs.
We went beyond the inverted index. We created new data types, implemented BKD trees, and added a columnar store — all of which leads to more efficiently structured data for faster searches, less memory use, and less disk use. In other words: you can access fields and values across petabyte-scale data at remarkable speeds.
Get a different view of your infrastructure with perspectives aligned with your topology. Dig into current and historical performance by CPU, memory, or network traffic. Then get granular in the Metrics Explorer and create time series charts based on the aggregation of your choice. Select the fields you’d like to plot, slice and dice your data into sub-graphs, add optional filters, then enhance your visualizations in Time Series Visual Builder.
As data scales, it's easy to lose errant data points among the streaming averages, measurements, and totals. And it’s impractical to analyze all your visualizations all the time. (Hey, we're all human here.)
The machine learning capabilities of the Elastic Stack automate anomaly detection at scale and across disparate data sources. It learns what’s normal in your data to identify what isn't, and then alerts you to it.
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Metricbeat created an index pattern in Kibana with defined fields, searches, visualizations, and dashboards. In a matter of minutes you can start viewing CPU and memory utilization, and process-level statistics.
Metricbeat modules have defaults and configurations for each system they connect to. See the documentation for supported versions and configuration options.
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