Harness Powerful Analytics to Accurately Forecast Operational & Business Needs
TIME SERIES DATA VISUALIZATION AND MONITORING
Discover Operational & Business Insights Over Time
Capture and organize billions of streaming data measurements per second to view a rich set of queries effectively (percentiles, counts below X, aggregations across streams) without increasing spend.
Use histograms to visualize the frequency of occurrences in a continuous stream of data. Build histograms by allocating data into interval bins to measure the changes within your data stream. Gain a better understanding of multiple influencers at specific time periods throughout your system monitoring. Assess probability distribution, find disruptions and concentrations, and discover extreme system loads or drop-offs in real-time.
Accurate Capacity Planning with Forecasts & Usage Requirements for Resources
Plan for requirements for disk space or network load, 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.
Easily View Anomalies in Data when Metrics Do Not Behave as Expected
Analyze the behavior of any kind of metric 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.
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.
Service Level Monitoring
Efficiently Diagnose & Resolve Problems in Scalable Distributed Systems
Visualize all collected data to visualize more accurate service level monitoring graphs. View service level violations across multiple SLAs. Use actual historical data to set SLAs that make the most sense for operations and the business.
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.”
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.
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 the graph and the min and max values show the full range of variation.
Combine Multiple Graphs in the Same View for Easy Comparison & Correlation
Visualize performance data comparisons in the same graph for any time period you need – daily, weekly, monthly, even yearly as long as the data has been captured by Circonus.
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 then 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.
Graph your data and use our analytics overlays and predictive algorithms to get real insight for troubleshooting potential issues, capacity planning, setting cost-effective SLAs, and more.
Reliable threshold alerting helps your team keep your systems and service quality up. Alert team members on early signs of trouble such as when data deviates from expected values.
Programmatically adjust monitors and alerts as architecture evolves. Adjust and customize the UI and extend services with third-party tools like Python, Ruby, or Java.