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Outlier Identification Using Spark 3.0

Tori Tompkins takes us through principles of anomaly detection in Apache Spark 3.0:

To calculate Median Absolute Deviation (MAD) you need to calculate the difference between the value and the median. In simpler terms, you will need to calculate the median of the entire dataset, the difference between each value and this median, then take another median of all the differences.

In Spark you can use a SQL expression ‘percentile()’ to calculate any medians or quartiles in a dataframe. ‘percentile()’ expects a column and an array of percentiles to calculate (for median we can provide ‘array(0.5)’ because we want the 50% value ie median) and will return an array of results.

Like standard deviation, to use MAD to identify the outliers it needs to be a certain number of MAD’s away. This number is also referred to as the threshold and is defaulted to 3.

Read on for three measures and their implementations in PySpark.