Compression Selection for Columnar Data using Machine-Learning and Feature Engineering

Abstract: There is a continuously growing demand for improved solutions that provide both efficient storage and efficient retrieval of big data for analytical purposes. This thesis researches the use of machine-learning together with feature engineering to recommend the most cost-effective compression algorithm and encoding combination for columns in a columnar database management system (DBMS). The framework consists of a cost function calculated using compression time, decompression time, and compression ratio. An XGBoost machine-learning model is trained on labels provided by the cost function to recommend the most cost-effective combination for columnar data within a column or vector-oriented DBMS. While the methods are applied on ClickHouse, one of the most popular open-source column-oriented DBMS on the market, the results are broadly applicable to column-oriented data which share data type and characteristics with IoT telemetry data. Using billions of available rows of numeric real business data obtained at Axis Communications in Lund, Sweden, a set of features are engineered to accurately describe the characteristics of a given column. The proposed framework allows for weighting the business interests (compression time, decompression time, and compression ratio) to determine the individually optimal cost-effective solution. The model reaches an accuracy of 99% on the test dataset and an accuracy of 90.1% on unseen data by leveraging data features that are predictive of compression algorithms and encodings performances. Following ClickHouse strategies and the most suitable practices in the field, combinations of general-purpose compression algorithms and data encodings are analysed that together yield the best results in efficiently compressing the data of certain columns. Applying the unweighted recommended combinations on all columns, the framework’s performance impact was measured to increase the average compression speed by 95.46%. Reducing the time to compress the columns from 31.17 seconds to compress the data to 13.17 seconds. Additionally, the decompression speed was increased by 59.87%, reducing the time to decompress the columns from 2.63 seconds to 2.02 seconds, at the cost of decreasing the compression ratio by 66.05%. Increasing the storage requirements by 94.9 MB. In column and vector databases, chunks of data belonging to a certain column are often stored together on a disk. Therefore, choosing the right compression algorithm can lower the storage requirements and boost database throughput.