{'fraction_non_alphanumeric': 0.021305841924398626, 'fraction_numerical': 0.005498281786941581, 'mean_word_length': 4.847389558232932, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
AntsBOA: A New Time Series Pipeline for Big Data Processing, Analyzing and Querying in Online Advertising Application

This paper presents a new pipeline AntsBOA for big data analyzing, processing and querying. This pipeline is initially designed for online advertising application. However, it is easy to extend to other big data applications. The main idea is that AntsBOA is based on time series technology. The data processing of AntsBOA includes three levels, aggregation, time series and cache. Time series data and cache data are loading to a distributed database system, named Kodiak. Query server then queries these data in Kodiak and replies the result. This pipeline has been run in production for half a year. In our production, prior 16 months performance data is able to populate in less than half an hour. The response time of querying the 16 months performance data is less than several milliseconds in average. In addition, from our production results, cache level speeds up tens of times than aggregation level in term of query time. Time series cache level has a speedup 50% than cache level in term of Hadoop resource. And Time series loading performance speeds up about 10 times than traditional loading. Also our production system is monitored to guarantee in a healthy and stable state. In summary, AntsBOA is an efficient, accurate, recoverable, scalable and fault tolerant pipeline for big data processing, analyzing and querying.