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Kodiak: Leveraging Materialized Views for Very Low-latency Analytics Over High-dimensional Web-scale Data by Shaosu Liu; Bin Song; Sriharsha Gangam; Lawrence Lo; Khaled Elmeleegy is a Computer Science article available to read on EtoBox.

What is Kodiak: Leveraging Materialized Views for Very Low-latency Analytics Over High-dimensional Web-scale Data about?

Turn's online advertising campaigns produce petabytes of data. This data is composed of trillions of events, e.g. impressions, clicks, etc., spanning multiple years. In addition to a timestamp, each event includes hundreds of fields describing the user's attributes, campaign's attributes, attributes of where the ad was served, etc. Advertisers need advanced analytics to monitor their running campaigns' performance, as well as to optimize future campaigns. This involves slicing and dicing the data over tens of dimensions over arbitrary time ranges. Many of these queries need to power the web portal to provide reports and dashboards. For an interactive response time, they have to have tens of milliseconds latency. At Turn's scale of operations, no existing system was able to deliver this performance in a cost effective manner. Kodiak, a distributed analytical data platform for web-scale high-dimensional data, was built to serve this need. It relies on pre-computations to materialize thousands of views to serve these advanced queries. These views are partitioned and replicated across Kodiak's storage nodes for scalability and reliability. They are system maintained as new events arriv

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Author

Shaosu Liu; Bin Song; Sriharsha Gangam; Lawrence Lo; Khaled Elmeleegy

Published

2016

Language

EN

Field

Computer Science (Physical Sciences)