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#Splunk eval max manual
#Splunk eval max series

For instance, we could measure outliers in megabytes going out of a network OR # of logins in a applications using the using the same type of Splunk query.

#Splunk eval max series

Downloading this data set is not important, any sample time series data that we would like to measure for outliers is valid for the purposes of this blog. To gain access to this data please click here. The data used in this blog is Splunk’s open sourced “Bots 2.0” dataset from 2017. Stats: Calculates Aggregate Statistics such as count, distinct count, sum, avg over all the data points in a particular field(s).This feature is undoubtedly useful to calculate ‘Moving Average’ in additional to ordering events Streamstats: Similar to ‘stats’, streamstats calculates statistics at the time the event is seen (as the name implies).It is great for generating statistics on ‘ALL’ events Eventstats: Generates statistics (such as avg,max etc) and adds them in a new field.Subsequently the ‘timechart’ and ‘chart’ function use the bin command under the hood Bin: The ‘bin’ command puts numeric values (including time) into buckets.The 4 important commands to remember are: Data ingested in Splunk with a timestamp and by using the correct ‘nf’ can be considered “Time Series” dataĪdditionally, we will leverage aggregate and statistic Splunk commands in this blog. Time Series: Data ingested in regular intervals of time.

#Splunk eval max manual

To view the manual method of standard deviation calculation click here. We will use the concept of standard substantially in today’s blog. The higher the Standard Deviation the larger the difference between data points.

Standard Deviation: Measure of spread of data.

Outlier Detection Definition: Outlier detection is a method of finding events or data that are different from the norm.

Before we jump into Splunk SPL (Search Processing Language) there are basic ‘Need-to-know’ Math terminologies and definitions we need to highlight:

Understanding some key concepts are essentials to any Outlier Detection framework.

The visual aspect of detecting outliers using averages and standard deviation as a basis will be elevated by comparing the timeline visual against the custom Outliers Chart and a custom Splunk’s Punchcard Visual. This blog will cover the widely accepted method of using averages and standard deviation for outlier detection. In this blog I will highlight a few common and simple methods that do not require Splunk MLTK (Machine Learning Toolkit) and discuss visuals (that require the MLTK) that will complement presentation of outliers in any scenario. There are multiple (almost discretely infinite) methods of outlier detection.