From a50eac24c906adc00a272f281524290454aa36f6 Mon Sep 17 00:00:00 2001 From: Brandon Morelli Date: Thu, 21 Nov 2019 09:44:33 -0800 Subject: [PATCH] [logs-ui][docs] Logs analysis docs (#50832) (#51314) --- .../images/analysis-tab-create-ml-job.png | Bin 0 -> 241031 bytes docs/logs/images/log-rate-anomalies.png | Bin 0 -> 294238 bytes docs/logs/images/log-rate-entries.png | Bin 0 -> 194260 bytes docs/logs/images/log-time-filter.png | Bin 0 -> 54418 bytes docs/logs/index.asciidoc | 2 + docs/logs/log-rate.asciidoc | 94 ++++++++++++++++++ docs/logs/using.asciidoc | 15 ++- 7 files changed, 109 insertions(+), 2 deletions(-) create mode 100644 docs/logs/images/analysis-tab-create-ml-job.png create mode 100644 docs/logs/images/log-rate-anomalies.png create mode 100644 docs/logs/images/log-rate-entries.png create mode 100644 docs/logs/images/log-time-filter.png create mode 100644 docs/logs/log-rate.asciidoc diff --git a/docs/logs/images/analysis-tab-create-ml-job.png 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include::configuring.asciidoc[] + +include::log-rate.asciidoc[] diff --git a/docs/logs/log-rate.asciidoc b/docs/logs/log-rate.asciidoc new file mode 100644 index 0000000000000..56284a1c76219 --- /dev/null +++ b/docs/logs/log-rate.asciidoc @@ -0,0 +1,94 @@ +[role="xpack"] +[[xpack-logs-analysis]] +== Detecting and inspecting log anomalies + +beta::[] + +When the {ml} {anomaly-detect} features are enabled, +you can use the **Log rate** page in the Logs app. +**Log rate** helps you to detect and inspect log anomalies and the log partitions where the log anomalies occur. +This means you can easily spot anomalous behavior without significant human intervention -- +no more manually sampling log data, calculating rates, and determining if rates are normal. + +*Log rate* automatically highlights periods of time where the log rate is outside expected bounds, +and therefore may be anomalous. +You can use this information as a basis for further investigations. +For example: + +* A significant drop in the log rate might suggest that a piece of infrastructure stopped responding, +and thus we're serving less requests. +* A spike in the log rate could denote a DDoS attack. +This may lead to an investigation of IP addresses from incoming requests. + +You can also view log anomalies directly in the <>. + +[float] +[[logs-analysis-create-ml-job]] +=== Enable log rate analysis and anomaly detection + +Create a machine learning job to enable log rate analysis and anomaly detection. + +[role="screenshot"] +image::logs/images/analysis-tab-create-ml-job.png[Create machine learning job] + +1. To enable log rate analysis and anomaly detection, +you must first create your own {kibana-ref}/xpack-spaces.html[space]. +2. Within a space, navigate to the Logs app and select *Log rate*. +Here, you'll be prompted to create a machine learning job which will carry out the log rate analysis. +3. Choose a time range for the machine learning analysis. +4. Add the Indices that contain the logs you want to analyze. +5. Click *Create ML job*. +6. You're now ready to analyze your log partitions. + +Even though the machine learning job's time range is fixed, +you can still use the time filter to adjust the results that are shown in your analysis. + +[role="screenshot"] +image::logs/images/log-time-filter.png[Log rate time filter] + +[float] +[[logs-analysis-entries-chart]] +=== Log entries chart + +The log entries chart shows an overall, color-coded visualization of the log entry rate, +partitioned according to the value of the Elastic Common Schema (ECS) +{ecs-ref}/ecs-event.html[`event.dataset`] field. +This chart helps you quickly spot increases or decreases in each partition's log rate. + +[role="screenshot"] +image::logs/images/log-rate-entries.png[Log rate entries chart] + +If you have a lot of log partitions, use the following to filter your data: + +* Hover over a time range to see the log rate for each partition. +* Click or hover on a partition name to show, hide, or highlight the partition values. + +[float] +[[logs-analysis-anomalies-chart]] +=== Anomalies charts + +The Anomalies chart shows the time range where anomalies were detected. +The typical rate values are shown in grey, while the anomalous regions are color-coded and superimposed on top. + +[role="screenshot"] +image::logs/images/log-rate-anomalies.png[Log rate entries chart] + +When a time range is flagged as anomalous, +the machine learning algorithms have detected unusual log rate activity. +This might be because: + +* The log rate is significantly higher than usual. +* The log rate is significantly lower than usual. +* Other anomalous behavior has been detected. +For example, the log rate is within bounds, but not fluctuating when it is expected to. + +The level of anomaly detected in a time period is color-coded, from red, orange, yellow, to blue. +Red indicates a critical anomaly level, while blue is a warning level. + +To help you further drill down into a potential anomaly, +you can view an anomaly chart for each individual partition: + +Anomaly scores range from 0 (no anomalies) to 100 (critical). + +To analyze the anomalies in more detail, click *Analyze in ML*, which opens the +{kibana-ref}/xpack-ml.html[Anomaly Explorer in Machine Learning]. diff --git a/docs/logs/using.asciidoc b/docs/logs/using.asciidoc index 916ad42a6d221..f191f7d746cf8 100644 --- a/docs/logs/using.asciidoc +++ b/docs/logs/using.asciidoc @@ -78,8 +78,19 @@ This opens the *Log event document details* fly-out that shows the fields associ To quickly filter the logs stream by one of the field values, in the log event details, click the *View event with filter* icon image:logs/images/logs-view-event-with-filter.png[View event icon] beside the field. This automatically adds a search filter to the logs stream to filter the entries by this field and value. -To see other actions related to the event, in the log event details, click *Actions*. -Depending on the event and the features you have installed and configured, you may also be able to: +[float] +[[view-log-anomalies]] +=== View log anomalies + +When the machine learning anomaly detection features are enabled, click *Log rate*, which allows you to +<> in your log data. + +[float] +[[logs-integrations]] +=== Logs app integrations + +To see other actions related to the event, click *Actions* in the log event details. +Depending on the event and the features you have configured, you may also be able to: * Select *View status in Uptime* to <> in the *Uptime* app. * Select *View in APM* to <> in the *APM* app.

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