The reference generally takes less space than the sequence itself, which decreases the compressed file size. The static dictionary feature is used to a limited extent starting with level 5, and to the full extent only at levels 10 and 11, due to the high CPU cost of this feature. In fact, we use it everyday in English. There are some advantages in using dictionary encoding: These two aspects are of paramount importance for VertiPaq. Additionally, hash lookups can be expensive due to cache misses. Later we can review a little more … You do so by providing a good sample in the first partition processed. Our approach uses three data structures to find a matching word directly. Moreover, it basically means that VertiPaq is datatype independent. Compression improvement is defined as 1 - (compressed size using the reduced dictionary / compressed size without dictionary). A relationship is a data structure that maps IDs in one table to row numbers in another table. Why spend money on 1 TB of RAM when the same model, once compressed, can be hosted in 256 GB? Compute the frequencies of the words in some corpus of English texts. Examples of lossless compression methods include Run Length Encoding and Dictionary Based Methods. Each pattern can has an ID number. The open sourced brotli library, that implements an encoder and decoder for brotli, has 11 predefined quality levels for the encoder, with higher quality level demanding more CPU in exchange for a better compression ratio. If, on the other hand, you store the same time column at the hour granularity, then you have not only reduced the cardinality, but you have also introduced repeating values (3.600 seconds converts to the same hour), resulting in a much better compression ratio. The dictionary has 2,200 pages with less than 256 entries per We improve on the limited dictionary use approach and add optimizations to improve the compression at levels 5 through 9 at a negligible performance impact when compressing web content. Now our restaurant has three things on the menu: Rather than writing down “pizza” or “fries” everytime someone order’s pizza or fries, we can assign each item a unique code. Please note that the actual details of the compression algorithm of VertiPaq are proprietary and, of course, we cannot publish them in a book. If every time it needs to move a filter from Product to Sales, VertiPaq had to retrieve values of Product[ProductKey], search them in the dictionary of Sales[ProductKey], and finally retrieve the IDs of Sales[ProductKey] to place the filter, then it would result in slow queries. Static dictionary techniques are quite straightforward to explain.