BigQuery Db2 (LUW) MariaDB MySQL Oracle DB PostgreSQL SQL Server SQLite from clause window clause full aggregate support JSON Readers interested in more details may refer to the technical report “Row Pattern Recognition in SQL” (ISO/IEC TR 19075-5:2016) available for free at ISO. If you understand German, you can watch the video recording here. ![]() Time intervals: closing gaps (creating new rows) Top-N per group (might be faster than window functions!) The examples cover some typical use cases, and also some atypical use cases for row pattern recognition:Ĭonsecutive events: identifying sessions in a web-log tolerating gaps in sequences It discusses several examples in two implementation variants: with and without the new match_recognize clause. I have given a presentation about row pattern recognition. However, the match_recognize clause combines aspects of the where, group by, having and over clauses (window functions) so it is also useful in many other cases. The main use of row pattern recognition is to check time series for patterns. The pattern itself is described with a simple regular expression syntax. ![]() Rows in matched groups can be filtered, grouped, and aggregated. Row pattern recognition captures groups of rows that follow a pattern. Let’s take a look at them… Row Pattern Recognition It introduces 44 new optional features (+14%).
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