Consistently faster and smaller compressed bitmaps with Roaring

Compressed bitmap indexes are used in databases and search engines. Many bitmap compression techniques have been proposed, almost all relying primarily on run-length encoding (RLE). However, on unsorted data, we can get superior performance with a hybrid compression technique that uses both uncompressed bitmaps and packed arrays inside a two-level tree. An instance of this technique, Roaring, has recently been proposed. Due to its good performance, it has been adopted by several production platforms (e.g., Apache Lucene, Apache Spark, Apache Kylin and Druid). Yet there are cases where run-length encoded bitmaps are smaller than the original Roaring bitmaps---typically when the data is sorted so that the bitmaps contain long compressible runs. To better handle these cases, we build a new Roaring hybrid that combines uncompressed bitmaps, packed arrays and RLE compressed segments. The result is a new Roaring format that compresses better. Overall, our new implementation of Roaring can be several times faster (up to two orders of magnitude) than the implementations of traditional RLE-based alternatives (WAH, Concise, EWAH) while compressing better. We review the design choices and optimizations that make these good results possible

Faster Multidimensional Data Queries on Infrastructure Monitoring Systems

The analytics in online performance monitoring systems have often been limited due to the query performance of large scale multidimensional data. In this paper, we introduce a faster query approach using the bit-sliced index (BSI). Our study covers multidimensional grouping and preference top-k queries with the BSI, algorithms design, time complexity evaluation, and the query time comparison on a real-time production performance monitoring system. Our research work extended the BSI algorithms to cover attributes filtering and multidimensional grouping. We evaluated the query time with the single attribute, multiple attributes, feature filtering, and multidimensional grouping. To compare with the existing prior arts, we made a benchmarking comparison with the bitmap indexing, sequential scan, and collection streaming grouping. In the result of our experiments with large scale production data, the proposed BSI approach outperforms the existing prior arts: 3 times faster than the bitmap indexing approach on single attribute top-k queries, 10 times faster than the collection stream approach on the multidimensional grouping. While comparing with the baseline sequential scan approach, our proposed algorithm BSI approach outperforms the sequential scan approach with a factor of 10 on multiple attributes queries and a factor of 100 on single attribute queries. In the previous research, we had evaluated the BSI time complexity and space complexity on simulation data with various distributions, this research work further studied, evaluated, and concluded the BSI approach query performance with real production data