If expressions are supported, the storage engine will changed when the function column is built automatically. Also the mv selector need a expression calculation tool to judge whether mv could be selected for query.
After Doris support the count_distinct and count, I will consider this feature. 陈明雨 <morning...@163.com> 于2020年4月16日周四 下午10:50写道： > That could be good. > > > However, if expressions are supported, the function of automatically > selecting rollup may fail. > Because the reason why Doris can automatically select the rollup is based > on keeping the column values consistent. > When the column value generates a new value through the expression, the > column will no longer be consistent. > > > Therefore, we may need a completely new design, such as the "relevant > table" conception to create views, > and maintain the data consistency of views through the function of atomic > load operation. > > > > > -- > > 此致！Best Regards > 陈明雨 Mingyu Chen > > Email: > chenmin...@apache.org > > > > > > At 2020-04-16 17:40:34, "Zhao Chun" <zh...@apache.org> wrote: > >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 > >> >
