Platform • Analytics

Harness powerful analytics for better business intelligence and performance

From real-time processing for baseline statistics to advanced, custom real-time analytics streaming, the Circonus Platform delivers extensive analytics designed to optimize business and operational intelligence

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Circonus Analytics Query Language

Circonus uses its own Circonus Analytics Query Language (CAQL), which allows users to create customized and powerful queries against metric data residing in the Circonus Platform. CAQL is highly efficient, enabling users to analyze years of data quickly and easily. CAQL allows data to be efficiently extracted and data pipelines are easily built on top of it.

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Gain accurate capacity planning with forecasts and usage requirements for resources

The Circonus Platform makes it easy to plan for disk space and network load requirements, based on actual history, using linear, exponential or periodic regression tools with a click of the mouse. Or better yet, let the system automatically select the tool best suited for your data. Easily forecast capacity loads for daily or weekly periods.

Linear

Use capacity planning with a linear regression to forecast growth. The graph shows memory usage on a system. There is a memory leak, so the memory will fill up quickly. On July 7th one node will use 60G of memory.

Exponential

Visualize exponential regression and use it to scale to the future. This graph shows the number of accounts is exponentially growing, so given that the current trend continues, a number of users in a given future date can be accurately predicted.

Periodic

Apply account periodicity to capacity planning. Forecast trends using a seasonal predictive model with Holt-Winters exponential smoothing over weekly periods. The graph shows web request rates and makes a forecast for the next week, forecasting trend and periodicity. We can see a slight negative trend.

Easily view anomalies when machine data doesn’t behave as expected

Analyze the behavior of any kind of metric or tag and automatically create a continuously updated forecasting model of what that data should look like over time. Easily identify problems and make faster decisions to rectify errors or make system wide adjustments.

Dynamic Alerts

Identify periods of unusual or unexpected behavior in your data with anomaly detection overlays. Instead of alerting on a threshold, the pattern-based forecast becomes the “new normal” and alerts can be based on deviations from the norm.

Constant

Directly view anomaly scores or use graphs to highlight anomalous regions. Compute an anomaly score between 0-100% for each data point – when the score reaches 100, an anomaly is detected. This graph shows API request rates that are normally constant at ~100, but experience significant deviations at certain regions. Those regions are identified and marked by the Anomaly Detection method.

Period

Recognize patterns and detect deviations from them. Monitor system performance over time to gain insight into predictable patterns as well as fluctuations from them. This graph demonstrates how the anomaly detection method adapts to period behavior and detects deviations from usual periodic patterns.

Efficiently diagnose and resolve problems in scalable distributed systems

The Circonus Platform enables you to quickly and easily visualize all collected data however you want to see it. View service level violations across multiple SLAs and use actual historical data to set SLAs that make the most sense for operations and the business.

Percentiles

Calculate percentile values for Service Level Monitoring data across selected time windows. This histogram shows API response times, measured from inside the service in 1 minute resolution. The overlay in this example shows the 99th percentile in order to check an SLA: “99% of all queries issued within an hour/day should be serviced within 200ms.”

Inverse Percentiles

Use inverse percentiles to calculate ratios of samples that are below a given threshold value. For an SLA that states “99% of all queries issued within an hour/day should be serviced within 200ms.” can be checked by verifying that the percentage of queries served within 200ms is larger than 99%. In this graph, the inverse quantile is computed over all visible data in 1 hour intervals.

Percentile Aggregation — Min-Max

Use percentile aggregation to accurately analyze large view ranges. A min-max Percentile Aggregation shows the maximal (and minimal) values that occur within the aggregated time interval. This graph shows the range of the 99th percentile over 2 days. The 99th percentiles are much larger than the displayed graph because there are not enough pixels to show all measured values (1m resolution: 1440m/day). Averages-aggregation is performed before graphing to reduce spikes significantly.

Box-Plot Aggregation

Use box plots to accurately view aggregated data. The box-plot aggregation shows the information contained in box plots as an overlay, the min, the max, and 3 percentiles: 25%, 50% (the median), and 75%. Box plots are available for histograms as well. This example shows a 3 month slice of a proxy request rate graph with box-plot percentiles added. The highlighted original graph only shows mean values over several hours. The 25% and 50% percentiles show typical variations of the graph and the min and max values show the full range of variation.

Combine multiple graphs in the same view for easy comparison and correlation

With the Circonus Platform, you can visually compare performance data in the same graph for any time period you need — daily, weekly, monthly, and even yearly as long as the data has been captured by Circonus.

Historical Comparisons

Historical graph comparison allows you to compare the current graph to historic values. In this example, we see a web request metric, with graph comparison overlays with 1 day, 1 week, and 1 month delayed. This view allows us to answer important questions. Did we see more traffic than last week? Did the traffic pattern change significantly from last week or last month? Here we can see the spike is always precisely at 19:00.

Multi-Graph Correlation

Compare a graph with an overlay of a different graph to find correlation. This example shows the web request rate graph compared to the request rates graphs of the DB cluster. Note, that during Oct 1-10, there were a large number of web requests that did not cause DB requests. We might want to check the web-server logs to see which pages were requested instead.

Put your data to work

Custom visualizations

Circonus delivers alerts, graphs, dashboards and machine-learning intelligence to optimize your operations. Visualize any data, in any application, from any system, in real time.

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Easy API integration

As your network and needs evolve, making adjustments is seamless. Easily adjust and customize the UI and extend services with third-party tools like Python, Ruby, or Java.

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Real-time alerts

Highly reliable threshold alerting ensures system, service and sensor quality across your networks. Real-time intelligence enables rapid remediation, powering exceptional user experiences and business outcomes.

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See where Circonus can take you.

Learn how our machine data intelligence and insight can drive new levels of value and impact in your business and operations.

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