Users new to ClickHouse often struggle to fully understand its primary key concepts. Unlike B(+)-Tree-based OLTP databases, which are optimized for fast location of specific rows, ClickHouse utilizes a sparse index designed for millions of inserted rows per second and petabyte-scale datasets. In contrast to OLTP databases, this index relies on the data on disk being sorted for fast identification of groups of rows that could possibly match a query - a common requirement in analytical queries. The index, in effect, allows the matching sections of part files to be rapidly identified before they are streamed into the processing engine. For more detail on the layout of the data on disk, we highly recommend this guide.
right = confidence[:, self.num_temp_instances:]
。新收录的资料是该领域的重要参考
拓竹创作者激励计划，图源/Bambu Lab Wiki
This does something specific to the people involved. Everyone holds simultaneous financial, reputational, and identity exposure. Criticizing the project risks all three at once. The cost of discovering you are wrong is high, so people unconsciously construct protective narratives instead.