If we want to support the expr of key, the different load plan will be need between base and rollup. The key of rollup needs to be transformed by the to_mouth function before the storage engine aggregate the value column.
>From the perspective of demand, users only need to be able to store day, month data. >From an implementation perspective, is it a good way to implement it with the current materialized view? Zhao Chun <zh...@apache.org> 于2020年4月16日周四 下午5:40写道： > 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 > > >
