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https://issues.apache.org/jira/browse/IGNITE-6025?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Vladimir Ozerov updated IGNITE-6025:
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    Component/s: persistence

> SQL: improve CREATE INDEX performance
> -------------------------------------
>
>                 Key: IGNITE-6025
>                 URL: https://issues.apache.org/jira/browse/IGNITE-6025
>             Project: Ignite
>          Issue Type: Improvement
>          Components: persistence, sql
>    Affects Versions: 2.1
>            Reporter: Vladimir Ozerov
>              Labels: performance
>             Fix For: 2.2
>
>
> When bulk data load is performed, it is considered a good practice to bypass 
> certain facilities of underlying engine to achieve greater throughput. E.g., 
> TX or MVCC managers can by bypassed, global table lock can be held instead of 
> fine-grained page/row/field locks, etc.. 
> Another widely used technique is to drop table indexes and re-build them form 
> scratch when load finished. This is now possible with help of {{CREATE 
> INDEX}} command which could be executed in runtime. However, experiments with 
> large data sets shown that {{DROP INDEX}} -> load -> {{CREATE INDEX}} is 
> *much slower* than simple load to indexed table. The reasons for this are 
> both inefficient implementation of {{CREATE INDEX}} command, as well as some 
> storage architectural decisions.
> 1) Index is created by a single thread; probably multiple threads could speed 
> it up and the cost of higher CPU usage. But how to split iteration between 
> several threads?
> 2) Cache iteration happens through primary index. So we read an index page, 
> but to read entries we have to navigate to data page. If single data page is 
> referenced from N places in the index tree, we will read it N times. This 
> leads to bad cache locality in memory-only case, and to excessive disk IO in 
> case of persistence. This could be avoided, if we iterate over data pages, 
> and index all data from a single page at once.
> 3) Another widely used technique is building BTree in bottom-up approach. 
> That is, we sort all data rows first, then build leafs, then go one level up, 
> etc.. This approach could give us the best build performance possible, 
> especially if p.2 is implemented. However it is the most difficult 
> optimization, which will require implementation of spilling to disk if result 
> set is too large.



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