Hi Tim,
In Apache Ignite we've already faced this challenge. We solved it using
materialized views and FilterableTable. Let's consider your example:
> select * from users where country='UK' and some_other_column='foo';
with a primary index and a sorted secondary index (B+Tree?) over the
'country' field.
As a first step Ignite registers all indexes that might be helpful for
the query plan in VolcanoPlanner as materialized views. For now we
register all indexes in all tables that participate in the query.
Registering all indexes might be excessive, but maybe we will apply some
pruning later. So it's ok for now to register all indexes.
Index materialized view is very simple: it's just a sorted table scan:
'SELECT * from tbl ORDER BY idx_fields'
After registering indexes as materialized views, the optimizer's search
space will look like this:
Project([*])
Filter[country='UK' and some_other_column='foo']
Set0
where Set0 consists of table and index scans:
TableScan('tbl', collation=[])
IndexScan('PK', collation=[PK_field])
IndexScan('country_idx', collation=[country])
At this step we have our index scans registered in the optimizer within
the same equivalence set as a TableScan.
The next step is a pushing filters down to the scans. We do it with a
rule which is similar to 'FilterTableScanRule'. After applying this rule
we have a search space that is looking like this:
Project([*])
TableScan('tbl', collation=[], filter=[country='UK' and
some_other_column='foo'])
IndexScan('PK', collation=[PK_field], filter=[country='UK' and
some_other_column='foo'])
IndexScan('country_idx', collation=[country], filter=[country='UK' and
some_other_column='foo'])
And the final step is adjusting the cost model to make it select the
scan with the lower cost which depends on the filter conditions within
the Scan. For example, full table scan with filters
TableScan('tbl', collation=[], filter=[country='UK' and
some_other_column='foo'])
will cost, say, 100. Because it have to scan all rows and then filter
out some set of them. On the other hand the index scan that can do index
lookup instead of full scan will have a less cost. For example
IndexScan('country_idx', collation=[country], filter=[country='UK' and
some_other_column='foo'])
will have cost about ~10, or so because it has a good index condition
`country='UK'` which can be used for index lookup that that returns only
10% of rows. And therefore this IndexScan should be chosen as the best
plan by the optimizer.
We've recently implemented this approach in Apache Ignite and it works
well for us. You can find it in [1]. This PR has many changes that are
unrelated to the main topic. So particularly you can look at
`IgnitePlanner.materializations()' method which registers indexes as
materialized views and `IgniteTableScan` which performs filter
conditions assessment and index scan cost estimation.
[1] https://github.com/apache/ignite/pull/7813
--
Kind Regards
Roman Kondakov
On 29.05.2020 11:44, Tim Fox wrote:
> Hi,
>
> I'm building a query engine with Calcite - really enjoying working with
> Calcite so far!
>
> When creating a plan, it seems Calcite always creates a plan where the
> sources are table scans, however in my implementation the tables can have
> indexes on them so a table scan is not always the right choice.
>
> I was wondering if there was any way of making Calcite "index aware" - e.g.
> perhaps providing hints to the table scan instance that, actually, an index
> scan or a primary key lookup should be used instead of actually scanning
> the table. E.g. On the table meta-data if we provided information about any
> indexes on the table, then Calcite could figure out what parts of the query
> to push to the table scan and which to keep in the rest of the plan.
>
> There are two specific cases I really care about:
>
> 1. Queries that contain a primary key lookup:
>
> select * from some_table where key_column=23 AND some_other_column='foo';
>
> In the above case the 'select * from some_table where key_column=23' can be
> implemented as a simple PK lookup in the source table, not requiring a
> scan, thus leaving just the filter corresponding to
> 'some_other_column='foo'' in the rest of the plan
>
> 2. Queries with expressions on a column which has a secondary index
>
> select * from users where country='UK' and some_other_column='foo';
>
> We have many users, and let's say 10% of them are from UK (still a lot). We
> have a secondary index in the country column in the source table so we can
> do an efficient index scan to retrieve the matching records.
>
> I found this document
> https://calcite.apache.org/docs/materialized_views.html which seems like it
> might help me in some way.
>
> The idea being if I can think of my indexes as materialized views then the
> query can be written against those materialized views as sources instead of
> the original table sources. There appears to be a rule
> 'MaterializedViewRule' that does this already (?).
>
> This seems to get me a bit further, however, for this approach to work, it
> seems I would have to create materialized views _dynamically_ during
> evaluation of the query, register them, rewrite the query, execute it, then
> deregister the materialized view.
>
> E.g. for the primary key lookup example above, for the following query:
>
> select * from some_table where key_column=23 AND some_other_column='foo';
>
> I would need to dynamically create a materialized view corresponding to:
>
> select * from some_table where key_column=23
>
> Then rewrite the query using MaterializedViewRule.
>
> In the general case, in order to figure out what materialized views I need
> to dynamically create I would need to examine the query, figure out which
> columns in expressions have indexes on them and from them work out the best
> materialized view to create based on that information. This seems non
> trivial.
>
> Does anyone have any suggestions or pointers for how to implement this kind
> of thing? I suspect I'm not the first person to have tried to do this, as
> using indexes on tables seems a pretty common thing in many systems (?)
>
