Support Using Loggly

                      Anomaly Detection

                      Anomaly Detection

                      Loggly’s anomaly detection allows you to find significant changes in event frequency. Anomalies often indicate new problems that require attention, or they can confirm that you fixed a pre-existing problem. For example, you may want to see if there is a big increase in errors after a new code deployment.

                      Accessing Anomaly Detection

                      You can access this tool on the Search or Chart?page by selecting the?Chart Type Anomalies.

                      Chart Type Anomalies

                      Using Anomaly Detection

                      The anomalies trend chart allows you to pick a field to analyze. It then shows you which values of that field have increased or decreased in frequency. It compares a change between your current search time range and a background time range. It also identifies field values that have had the most significant changes and brings them to the top of the list.

                      The gray?part of the bar shows the expected count in your current time range using the average count over the background time range. The actual values are plotted as deltas on the expected bar, and they are colored to show increases in green and decreases in red.

                      Anomaly Detection

                      In the example screenshot above, we’re analyzing the Apache status code field. We see that in the last hour the 200, 404 and 302 codes have increased, whereas the 500 and 401 code has decreased. The increased 404 code indicates that the Page not found errors are increasing. This would prevent the viewers from seeing pages on the?website. If this were a popular web store, it could be losing a significant amount of revenue.

                      Compare Against

                      This is the time range that will be used to calculate the expected count. We will compare changes relative to the actual count, which is based on your current search time range. You can also select different time?ranges in the dropdown. It might be useful to select a different time range here if there are irregularities or cyclical patterns in your data you want to take into account.

                      Split By

                      Here is an example of Anomaly Detection using Split By.

                      Anomalies Split By


                      There are also a variety of sort options. The default is significance which picks the values with the biggest changes that also had larger counts overall. You can also sort by percent difference between the actual and expected counts, by the actual count in your current time range, or the expected count from your background time range.


                      Other Settings

                      The settings menu is shown as a gear icon. It allows you to control how many bars are displayed, whether to show as log scale, and whether to show the legend.

                      Common Error Messages

                      • Time range out of bounds – the search period?is not contained with the compare against, or background, time range.
                      • Cardinality too high – the split by field has too many values. Currently we can only split by a field with less than 25 unique values.
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