NEW RELIC TIMESLICE SOFTWARE
In the software industry, events can be thought of as simply “things that occur in a system.” For example, a server setting being changed would be an event. Event dataįirst, we'll explain the definition of events from a monitoring industry perspective, and then we'll explain some specifics about how New Relic handles event data. Want to report custom metrics? See Get data into New Relic. To learn more about how this can be addressed, see Event limits and sampling. This means that sometimes, for high-throughput systems, the count may not accurately represent the total activity on that system. The downside of this approach is that there are limits on how many events a monitoring system (including ours) can report. Some New Relic charts are generated with these kinds of queries.
If you want to learn more about the structure of metric timeslice data and see some examples, expand the collapser below. Ways to explore and query metric timeslice data:įor APM: metric timeslice data is converted to dimensional metrics and can be queried via NRQL Metric timeslice data is a lightweight data type and lacks the detail that dimensional metrics have. You also have the ability to create custom metrics. For example: error rate, bandwidth usage, and garbage collection time. Our agents (APM, browser, and mobile) can collect thousands of metric timeslices per minute for a variety of performance metrics. These metrics usually follow a pattern like //. A metric timeslice consists of three parts: a metric name, the segment of time the metric represents (the "timeslice"), and a numeric value (the measurement).įor example: an APM metric timeslice for time spent in a particular transaction is named WebTransaction/URI/foo, and might have a response time of 0.793 for a one-minute time slice from 10:20am to 10:21am. New Relic's APM, browser, and mobile report and display metrics in a simple data format that we refer to as metric timeslice data. Here are some of the ways metrics are reported and stored across the New Relic platform:
NEW RELIC TIMESLICE HOW TO
In our documentation, we typically will just refer to "metrics," regardless of how that data is reported, unless there's a reason you need to know more (like understanding how to query your data). There are various ways New Relic measures and reports metrics but, in practice, when using the New Relic UI, you usually won't have to understand how exactly this happens. Metrics at New RelicĬonceptually, "metrics" is a broad, general category. One potential downside is that it can be difficult to do detailed analysis of older data that has been aggregated over time when high detail is required about specific important actions, event data can be used. Metrics are a strong solution for storing data long-term, and understanding trends over time. This approach is efficient for long-term data storage.
For example, per-minute data may be “rolled up” to per-hour aggregations after some amount of time, and eventually may be rolled up to a per-day aggregation. They can also be aggregated more and more over time. Metrics are relatively easy to report and store because a single record can represent a range of time. For example: a CPU temperature reading, or a “CPU% used” status. For example: a count of events over one minute's time, or the rate of some event per minute. Metrics are typically reported on a regular schedule. In the software monitoring industry, a metric means a numeric measurement of an application or system. Metricsįirst, we'll explain the definition of metrics from a monitoring industry perspective, and then we'll explain how New Relic handles metrics. But having a better understanding of this can help you get data into New Relic, understand the data you see in our UI, and query your data. You can use most of our features without needing to understand the underlying data structure. This doc will give you a fairly technical explanation of our core MELT data types, their structure, and how they're used in our features.
NEW RELIC TIMESLICE INSTALL
After you sign up for a free New Relic account and install any of our monitoring services, you can start working with your data.
The New Relic platform is built around the four fundamental telemetry data types we believe are necessary for complete and effective system monitoring: metrics, events, logs, and traces (often referred to as "MELT" in the observability industry).
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