Hi Ling Very glad to see this proposal.
We can discuss whether to support expression operations. For the case that expressions are applied on the KEY column. For example we can easily get a monthly table through a rollup which is defined by "group by to_month(day_column)". For the case that expression are applied on the value column. For example we can get a PV aggregate table through a rollup which is defined by "sum(case event_column when "page_view" then 1 else 0 end)". Thanks, Zhao Chun ling miao <emmymia...@gmail.com> 于2020年4月16日周四 下午5:13写道： > Hi everyone, > > *The status of materialized view 1.0* > In the present, we have supported the materialized views in Doris 0.12 > version. The materialized view selector supports to select the most > efficient mv and rewrite the SQL to query against the selected mv instead > of the base table. > For query results contain a small number of rows where the original table > has a large amount of data, the performance can reach the 5X to 100X times > depends on the cardinality of the data. > The aggregate functions supported by the materialized view in 0.12 include: > sum, min, max. > > However, the aggregate functions supported by the current materialized view > are not rich enough to fully cover the user's scene. > For example, in the `Order` scenario, user needs to analyze the number of > orders in different dimensions. > Another example is the count_distinct function is used for analyzing PV and > UV data in website traffic. > > *The goal of materialized view 2.0* > In order to support more scenarios, the materialized view 2.0 will support > the following functions: > > 1. Materialized view supports aggregate functions: count, count_distinct > (bitmap and hll) > 2. Support to create materialized views of the same column with different > aggregate functions. For example: select k1, sum (v1), min (v1) from table > group by k1 > > > What features do you want the materialized view 2.0 to support? > > Looking forward to your idea~ > > LingMiao >
