Real-time Method of Accurate Unique IPs Counting Across High Number of Distinct Dimensions and distinct Time Frames for Big Data Systems

A.V. Valialkin
VertaMedia Company
(224 West 35th St., Suite 1102-5, New York, NY 10001, USA,
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O.I. Konashevych, post-graduate
Pukhov Institute for Modelling in Energy Engineering
(15, General Naumov St., Kyiv, 03164, Ukraine,
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Èlektron. model. 2018, 38(3):63-74


The article describes a method which allows counting unique IP addresses within 10 bln of system events per day across high number of distinct dimensions (tuples). Log-based and probability-based methods showed unsatisfactory results. The proposed method allows avoiding excessive resource usage (RAM, CPU and persistent storage) as it appeared in a raw logs method and a probability method of counting. The method also avoids high statistic error for low cardinality as it appeared in a probability method. The main idea is to count unique IP addresses in distinct tuples in real time using RAM for short data interval processing, then flushing it to persistent
storage, using merge algorithms to process and store unique IP counts in ordinary database from 5 minute, hourly, daily, weekly and monthly interval files.


probability method, statistics, information technologies, queueing theory, big data, statistical process control.


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