Hi Haisheng,
> If I didn't get it wrong, joining with scan_A means it has to read all the
> tuples from table A, this will make index meaningless, because the purpose of
> using index in that example is to avoid reading all the tuples from table A.
I meant 'rowId' as a physical address (ctid in PG or rowId in Oracle).
We can combine those rowIds in some way (by analogy with bitmap index)
and then use output rowids to fetch only those rows which we actually
need. Logically it is equivalent to join and can be implemented as a
nested loop join: we can iterate over the resulted rowids and fetch
corresponding rows from the row store.
> Perhaps you have different definition of Join, but the Join operator in
> Calcite doesn't have correlation variable, only Correlate operator has, which
> is Calcite's version of Apply. You still need the rule to transform Join to
> Correlate, aka, Apply. In Calcite, the rule is JoinToCorrelateRule. However I
> don't recommend using JoinToCorrelateRule, because it will transform Join to
> Correlate unconditionally, in case of multi-way joins with join reordering
> enabled, it will generate a lot of useless alternatives, unless you want to
> support multi-level correlation, like:
We are going to use Correlate, I just skipped this detail in the
previous mail. You are right that it can inflate the search space. May
be we'll need to think of some pruning techniques later if it become a
problem. As I can see, this approach has some advantages because it
seems more flexible to me. In addition to the ability you mentioned to
use multi-level correlated join, it provides some kind of freedom
because we are not restricted to the particular pattern of
Join2IndexApply rule. In other words: we need to match Join2IndexApply
rule on some concrete pattern like
Join
RelNode
TableScan
But what if there is a filter or project between join and scan? We can
add specific patterns for this rule
Join
RelNode
Project
TableScan
Join
RelNode
Filter
TableScan
but still it's not enough to cover all possible cases. What if there are
some other RelNodes like aggregate there? We cannot foresee all possible
cases. With Correlation approach we just try to push down the filter
with correlation varible as much as possible. If it is possible to this
filter to reach the IndexScan, it becomes a very efficient index join
with low cost. Otherwise optimizer throws this alternative away. It can
be helpful in some queries like
SELECT * FROM
deps d
JOIN
(SELECT depId, COOUNT(*) FROM emps GROUP BY depId) AS e
ON d.depId = e.depId
WHERE d.name = "AAA"
if table deps is relatively small (affter filtering) and emps is big it
will be more efficient to do correlated nested loop join:
CorrelatedJoin(d.depId = e.depId)
Filter(d.name = "AAA")
TableScan(deps)
Agg(depId, COOUNT(*))
IndexScan(emps, lookup: e.depId = d.depId[correlatedVar])
because in this case we don't have to do a full aggregation over the
'emps' table before doing a join:
HashJoin(d.depId = e.depId)
Filter(d.name = "AAA")
TableScan(deps)
Agg(depId, COOUNT(*)) <- full aggregation
TableScan(emps)
So, tradeoff between rule-based approach and MV approach is classic:
space search inflation vs more plan alternatives.
Thank you!
--
Kind Regards
Roman Kondakov
On 03.06.2020 05:42, Haisheng Yuan wrote:
>> using this approach, bitmap indexes may be represented as set operations
>> (union, intersect) over idx_a and idx_b followed by joining with scan_A
>> using 'rowId'.
>
> If I didn't get it wrong, joining with scan_A means it has to read all the
> tuples from table A, this will make index meaningless, because the purpose of
> using index in that example is to avoid reading all the tuples from table A.
>
>> and it looks like it can be implemented without special
>> Join2IndexApplyRule: we'll use correlation variable from join condition
>> as filter condition.
>
> Perhaps you have different definition of Join, but the Join operator in
> Calcite doesn't have correlation variable, only Correlate operator has, which
> is Calcite's version of Apply. You still need the rule to transform Join to
> Correlate, aka, Apply. In Calcite, the rule is JoinToCorrelateRule. However I
> don't recommend using JoinToCorrelateRule, because it will transform Join to
> Correlate unconditionally, in case of multi-way joins with join reordering
> enabled, it will generate a lot of useless alternatives, unless you want to
> support multi-level correlation, like:
>
> NestedLoopOuterJoin
> -> Table Scan on A
> -> HashOuterJoin
> Join Condition: B.Y = C.W
> -> Table Scan on B
> -> Index Scan using C_Z_IDX on C
> Index Condition: C.Z = A.X
>
> or
>
> NestedLoopOuterJoin
> -> Table Scan on SmallTable1 A
> -> NestedLoopOuterJoin
> -> Table Scan on SmallTable2 B
> -> Index Scan using XYIndex on LargeTable C
> Index Condition: C.X = A.AID and C.Y = B.BID
>
> Otherwise, the join to correlate rule that matches a join with right child as
> a tablescan (or its variants) makes more sense. Transforming a Join to
> Correlate is meaningful only when there is at least 1 index can be used in
> the right relation, so it would be perfect if you can check if there is
> available index in the right relation or not, if you care about the search
> space.
>
> On 2020/06/02 20:30:41, Roman Kondakov <[email protected]> wrote:
>> Vladimir, Haisheng,
>>
>> thank you for sharing your thoughts. And special thanks to Haisheng for
>> awesome examples.
>>
>> I found Julian's reasoning about representing bitmap indexes as joins
>> very deep and interesting. As I understand this idea right, for table
>> A(a,b,c) indexes over columns, say, 'a' and 'b' can be represented as
>> relations sorted by 'a' and 'b':
>>
>> idx_a('rowId', 'a') [collation=[a]]
>> idx_b('rowId', 'b') [collation=[b]]
>>
>> Table A itself may be represented as a relation
>>
>> scan_A('rowId', 'a', 'b', 'c') [collation=[]]
>>
>> using this approach, bitmap indexes may be represented as set operations
>> (union, intersect) over idx_a and idx_b followed by joining with scan_A
>> using 'rowId'.
>>
>> Index-only scans (aka covered indexes), can be modeled as scans over
>> idx_a or idx_b without consequent join with the main table.
>>
>> @Julian, did I get your idea right?
>>
>> If one try to apply this approach directly, then the search space will
>> be quickly polluted by those index relations. But if the Lattice-like
>> framework from the Julian's mail is implemented, then it will be a great
>> feature, I guess.
>>
>> @Haisheng, BTW we are working on the indexed nested loop join right now
>> and it looks like it can be implemented without special
>> Join2IndexApplyRule: we'll use correlation variable from join condition
>> as filter condition. If there is no index there - it will be a simple
>> correlated nested loop join (or Apply). If index is present, the cost
>> will be lower because index lookup will speed up this join, and this
>> index scan will win.
>>
>>>
>>> There is another useful feature, that is IndexApply.
>>>
>>> The rule of Join2IndexApply will transform a logical join to logical index
>>> apply, then transform to physical indexed nested loop join.
>>> select * from foo join bar on foo.a = bar.f;
>>> There is an index on foo.a, but no index on bar.f.
>>
>>
>> Thank you!
>>
>>
>> --
>> Kind Regards
>> Roman Kondakov
>>
>>
>> On 02.06.2020 19:55, Julian Hyde wrote:
>>
>>> Vladimir,
>>>
>>> I feel the same way. MVs are more powerful and general, and with some
>>> effort they could be just as efficient as other approaches.
>>>
>>> One problem that needs to be solved is the “registration problem”. If you
>>> have a lot of MVs they all have to be registered in the planner’s search
>>> space, otherwise the planner will not find them.
>>>
>>> I would like to see a benchmark for this. Say we have 1,000 MVs and we plan
>>> 10,000 queries that potentially use them. The goal is that the planning
>>> cost Is comparable to only having 1 MV. We could achieve the goal by
>>> re-using the MVs across planner instances (thereby amortizing the cost of
>>> creating them) and “index” the planner’s search space.
>>>
>>> The Lattice structure essentially does this for families for join-aggregate
>>> MVs (summary tables). We need something similar for project-sort MVs
>>> (indexes).
>>>
>>> Julian
>>
>>
>>
>>
>>
>>
>> On 02.06.2020 10:05, Vladimir Ozerov wrote:
>>> Hi Roman,
>>>
>>> To me, there is no principal difference between materialized views and
>>> rule-based approaches - they are both "rules". In the materialized views
>>> approach the rule is to create all alternative access paths
>>> unconditionally, this rule is always fired first during the optimization
>>> process. A rule-based approach gives you flexibility. You may do exactly
>>> the same as with materialized views - just enumerate all access paths and
>>> add them to search space. But at the same time, you may develop more
>>> complicated things, such as pruning, bitmap index joins, etc. This is not
>>> possible with materialized views.
>>>
>>> In this sense, the difference in complexity between materialized views
>>> approach and rule-based approach with similar features (no pruning, etc) is
>>> close to zero - in both cases you just iterate over existing indexes and
>>> add them to search space. Rule-based approach becomes more complex when you
>>> add more features to it.
>>>
>>> вт, 2 июн. 2020 г. в 00:37, Haisheng Yuan <[email protected]>:
>>>
>>>> Hi Roman,
>>>>
>>>> There are some potential advantages.
>>>>
>>>> Let's still use this as example.
>>>> SELECT * FROM foo WHERE a > 100 or b < 1000;
>>>> Both column a and b have B-Tree indexes, note that it doesn't have to be
>>>> bitmap index.
>>>>
>>>> Bitmap Table Scan on foo
>>>> -> BitmapOr
>>>> -> Bitmap Index Scan on foo_index_a
>>>> Index Cond: (a > 100)
>>>> -> Bitmap Index Scan on foo_index_b
>>>> Index Cond: (b < 1000)
>>>>
>>>> filter a: --------------------
>>>> filter b: -----------------------
>>>>
>>>> The bitmap scan just needs to read the tuples of satisfying the condition
>>>> once, because the BitmapOr will eliminate duplicate tuple id. Constructing
>>>> to UNION ALL will read the intersecting part twice, and it will increase
>>>> the search space a little bit even when there is no available index.
>>>>
>>>> If we change the condition from OR to AND:
>>>> SELECT * FROM foo WHERE a > 100 and b < 1000;
>>>>
>>>> Will MV-based approach have to choose which index to use?
>>>>
>>>> I would prefer the following plan:
>>>> Bitmap Table Scan on foo
>>>> -> BitmapAnd
>>>> -> Bitmap Index Scan on foo_index_a
>>>> Index Cond: (a > 100)
>>>> -> Bitmap Index Scan on foo_index_b
>>>> Index Cond: (b < 1000)
>>>>
>>>> filter a: --------------------
>>>> filter b: -----------------------
>>>>
>>>> It would be extremely helpful when the intersecting range is very small,
>>>> because it just need to read the intersected data page only to fetch
>>>> tuples.
>>>>
>>>> This can applies to arbitrary combinations of AND/OR conditions.
>>>>
>>>> This is an attractive feature for data warehousing customers.
>>>>
>>>> Another example:
>>>> SELECT max(a) from foo;
>>>>
>>>> The query can be transformed to
>>>> SELECT a from foo order by a desc limit 1;
>>>>
>>>> If there is a index on column 'a' but with ASC order.
>>>> The collation trait request will pass to TableScan, if there is an index
>>>> on the collation key. We just need to transform to an IndexScan with
>>>> reverse index scan direction, which is already possible in current Calcite.
>>>>
>>>> Similarly, the user query may be simple but the index on a is ASC order:
>>>> SELECT * from foo order by a desc;
>>>>
>>>> MV-based approach would have to register all the tablescan again with
>>>> reversed index scan direction.
>>>>
>>>> There is another useful feature, that is IndexApply.
>>>>
>>>> The rule of Join2IndexApply will transform a logical join to logical index
>>>> apply, then transform to physical indexed nested loop join.
>>>> select * from foo join bar on foo.a = bar.f;
>>>> There is an index on foo.a, but no index on bar.f.
>>>>
>>>> Nested Loop Inner Join
>>>> Join Filter: true
>>>> -> TableScan on bar
>>>> -> Index Scan using foo_index_a on foo
>>>> Index Cond: foo.a = bar.f
>>>>
>>>> MV-based approach would have to sort table bar if it want to use the index.
>>>>
>>>> But considering Ignite is a in-memory computing platform, MV-based
>>>> approach would be just good enough to serve its purpose.
>>>>
>>>> Looking at Drill's DbScanToIndexScanPrule.java, the way Apache Drill uses
>>>> to tackle with index is not wrong, but I would not recommend to follow
>>>> Drill's example to do it.
>>>>
>>>> Thanks,
>>>> Haisheng
>>>>
>>>> On 2020/06/01 19:15:32, Julian Hyde <[email protected]> wrote:
>>>>> I'm pleased there are a variety of approaches. People should use
>>>>> whichever works for them and their use case.
>>>>>
>>>>> The so-called "rule-based" approach is definitely useful for
>>>>> OLTP-style queries where you are accessing a few rows and you need to
>>>>> plan quickly.
>>>>>
>>>>> Haisheng mentioned bitmap indexes a while back. Optimizing queries to
>>>>> use bitmap indexes is interesting because they can be combined in more
>>>>> ways than regular indexes. In this case, and many others, it is worth
>>>>> thinking about the problem at a high level. For example, combining two
>>>>> bitmap indexes can be modeled as a join, where the join keys are the
>>>>> record ids, and the record ids are sorted within each index value.
>>>>> Thinking at the high level can find plans that the rule-based approach
>>>>> will never find.
>>>>>
>>>>> Indexes-as-MVs, indexes-as-tables, and
>>>>> index-filtered-table-scan-as-join are other examples of the high-level
>>>>> approach.
>>>>>
>>>>> Julian
>>>>>
>>>>>
>>>>>
>>>>> On Mon, Jun 1, 2020 at 12:00 PM Roman Kondakov
>>>>> <[email protected]> wrote:
>>>>>>
>>>>>> Hi Xiening,
>>>>>>
>>>>>> the example was synthetic. What is still not clear for me is how to
>>>>>> exploit index sortedness with a rule based approach. As far as I
>>>>>> understand with this approach we need to write complex rules (for
>>>>>> example [1]) that should decide which index is useful and which is not
>>>>>> useful. These rules should take into account both statistics and
>>>>>> collations, so they do some part of work that should be done by a cost
>>>>>> model. And it makes writing such rules quite a difficult task.
>>>>>>
>>>>>> With a materialized views approach we can register all indexes as
>>>> scans,
>>>>>> push filters to them if needed. And the cost model, not a rule, will
>>>>>> decide which index is better based on its cost and output collation.
>>>>>>
>>>>>> So the benefits of rule based approach are not so obvious to me. I
>>>> would
>>>>>> really appreciate if you could tell me in what cases rule-based
>>>> approach
>>>>>> is better. I understand that its definitely better in scenarios when
>>>> the
>>>>>> number of indexes is very high. But may be there are some other
>>>> advantages?
>>>>>>
>>>>>> Thank you!
>>>>>>
>>>>>> [1]
>>>>>>
>>>> https://github.com/apache/drill/blob/master/exec/java-exec/src/main/java/org/apache/drill/exec/planner/index/rules/DbScanToIndexScanPrule.java
>>>>>>
>>>>>> --
>>>>>> Kind Regards
>>>>>> Roman Kondakov
>>>>>>
>>>>>>
>>>>>> On 01.06.2020 21:00, Xiening Dai wrote:
>>>>>>> Hi Roman,
>>>>>>>
>>>>>>> The example you mentioned is an advanced scenario. Note that there
>>>> are different types of index, such as clustered index, secondary index,
>>>> covered and non-covered index. In your case, typical OLTP/OLAP optimizer
>>>> would create an index-based join on top of the range table scan (or
>>>> FilteredTableScan in your term). And these transformation can definitely be
>>>> based on rules. But the difficult part is actually the statistics
>>>> estimation and cost calculation. You could end up with higher runtime cost
>>>> with index based join when join cardinality is high.
>>>>>>>
>>>>>>> But back to the original question, if we’d like to leverage index on
>>>> table scan, I think simple rule would serve the purpose. In fact, we have
>>>> FilterTableScanPredicatePushdownRule in our system which does exactly the
>>>> same thing.
>>>>>>>
>>>>>>>> On May 31, 2020, at 12:45 PM, Roman Kondakov
>>>> <[email protected]> wrote:
>>>>>>>>
>>>>>>>> Hi Vladimir,
>>>>>>>>
>>>>>>>> thank you for sharing your point. Could you please clarify some
>>>> details
>>>>>>>> with a rulse-based index selection? You said
>>>>>>>>
>>>>>>>>> the fundamental problem with "indexes as materialized
>>>>>>>>> views" approach is that you have to register them beforehand,
>>>> instead of
>>>>>>>>> using them only when needed.
>>>>>>>>
>>>>>>>> I agree, it's kind of a problem. What is not clear for me with
>>>>>>>> IndexScanRule-based approach is how to decide when and which index
>>>> we
>>>>>>>> need? I understand that is is pretty easy to do in the case like
>>>> this:
>>>>>>>>
>>>>>>>> Filter
>>>>>>>> Scan
>>>>>>>>
>>>>>>>> we can match the IndexScanRule on this pattern and do an index
>>>> lookup
>>>>>>>> using filter condition. But what to do in the more complex
>>>> scenarios?
>>>>>>>> Let's consider an example
>>>>>>>>
>>>>>>>> SELECT * FROM A JOIN B ON A.a=B.b WHERE A.c > 100
>>>>>>>>
>>>>>>>> where A.a, A.c and B.b are indexed fields. The logical plan for this
>>>>>>>> query might look like this:
>>>>>>>>
>>>>>>>> LogicalJoin(A.a=B.b)
>>>>>>>> LogicalFilter(A.c > 100)
>>>>>>>> LogicalScan(A)
>>>>>>>> LogicalScan(B)
>>>>>>>>
>>>>>>>> as I understand (please correct me if I'm wrong), with the
>>>> rule-based
>>>>>>>> approach, after allpying IndexScanRule the plan will look like this:
>>>>>>>>
>>>>>>>> LogicalJoin(A.a=B.b)
>>>>>>>> PhysicalIndexScan(A.c, lower bound = 100)
>>>>>>>> PhysicalTableScan(B)
>>>>>>>>
>>>>>>>> But in this case we lose the possibility of using index scans over
>>>> A.a
>>>>>>>> and B.b and joining them with MergeJoin, which can be more efficient
>>>>>>>> plan in terms of the cost.
>>>>>>>>
>>>>>>>> My question is: how rule-based approach handle this scenario? Will
>>>> it
>>>>>>>> re-apply IndexScanRule once again to produce PhysicalIndexScan(A.a)
>>>> and
>>>>>>>> PhysicalIndexScan(B.b)? Or am I missing the crucial point of a
>>>> rule-base
>>>>>>>> approach?
>>>>>>>>
>>>>>>>> Thank you in advance!
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Kind Regards
>>>>>>>> Roman Kondakov
>>>>>>>>
>>>>>>>>
>>>>>>>> On 31.05.2020 21:39, Vladimir Ozerov wrote:
>>>>>>>>> As already mentioned, the fundamental problem with "indexes as
>>>> materialized
>>>>>>>>> views" approach is that you have to register them beforehand,
>>>> instead of
>>>>>>>>> using them only when needed. On the other hand, the complexity of
>>>> index
>>>>>>>>> planning comes from cost estimation and predicate splitting.
>>>>>>>>> Materializations cannot help you with that anyhow. This is why I
>>>> call this
>>>>>>>>> approach (not materialized views per se) "hacky" - you reuse
>>>> several simple
>>>>>>>>> parts of the Calcite infrastructure at the cost of loss in the
>>>> flexibility
>>>>>>>>> of the planner, while the most complicated parts still need to be
>>>>>>>>> implemented by hand.
>>>>>>>>>
>>>>>>>>> Materialized views could be a good fit e.g for partial indexes
>>>> because in
>>>>>>>>> this case, Calcite could help you with complex subsumption
>>>> mechanics. But
>>>>>>>>> for standard indexes, the pros/cons balance is not that obvious.
>>>>>>>>>
>>>>>>>>> вс, 31 мая 2020 г. в 19:28, xu <[email protected]>:
>>>>>>>>>
>>>>>>>>>> Hi Tim,
>>>>>>>>>>
>>>>>>>>>> I am working on MySQL InnoDB adapter and trying to introduce this
>>>> to
>>>>>>>>>> Calcite, currently it is only in early stage, and not
>>>> approved/reviewed by
>>>>>>>>>> committers yet. Anyway, we are facing the same problem like what
>>>> index to
>>>>>>>>>> use, how to push down order by operation, etc. I have developed a
>>>> simple
>>>>>>>>>> rule based adapter to be "index aware" and being able to leverage
>>>> a MySQL
>>>>>>>>>> InnoDB storage engine written in Java. Hope this will help you to
>>>> explore
>>>>>>>>>> more options.
>>>>>>>>>>
>>>>>>>>>> https://issues.apache.org/jira/browse/CALCITE-4034
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Xu
>>>>>>>>>>
>>>>>>>>>> Haisheng Yuan <[email protected]> 于2020年5月31日周日 下午10:06写道：
>>>>>>>>>>
>>>>>>>>>>> Hi Roman,
>>>>>>>>>>>
>>>>>>>>>>> Thank you for sharing your thoughts.
>>>>>>>>>>>
>>>>>>>>>>>> It can be very tricky because the rule should consider not
>>>>>>>>>>>> only filters, but also collations. This leads to increasing the
>>>>>>>>>>>> complexity of such rules.
>>>>>>>>>>>
>>>>>>>>>>> Logical transformation rules like
>>>> FilterTableScan2IndexTableScanRule
>>>>>>>>>>> should not consider physical properties, like collation,
>>>> distribution.
>>>>>>>>>> You
>>>>>>>>>>> forgot that we just reached consensus in CALCITE-3972. :)
>>>>>>>>>>>
>>>>>>>>>>> Regarding the 2nd option that uses index b, it is indeed not
>>>> that easy
>>>>>>>>>> for
>>>>>>>>>>> Calcite 1.22.0. In latest version, it now becomes possible.
>>>> After rule
>>>>>>>>>>> transformation, during top-down trait request, the collation is
>>>> passed
>>>>>>>>>>> through Filter, down to physical TableScan, which accepts the
>>>> trait
>>>>>>>>>> request
>>>>>>>>>>> with collation on b, find there is an index on b, and return a
>>>> new
>>>>>>>>>> RelNode
>>>>>>>>>>> IndexScan. This process can be done in
>>>>>>>>>>> EnumerableTableScan#passThrough(required).
>>>>>>>>>>>
>>>>>>>>>>>> I hope, when the Cascades-style optimizer become a part of
>>>> Calcite, the
>>>>>>>>>>> search space
>>>>>>>>>>>> pollution will not be a serious issue anymore.
>>>>>>>>>>>
>>>>>>>>>>> I hope so too. Top-down style can help alleviate space pollution
>>>> by space
>>>>>>>>>>> pruning. But the space pollution caused by LogicalProject
>>>> operator and
>>>>>>>>>> its
>>>>>>>>>>> related rules still can't be avoided. :)
>>>>>>>>>>>
>>>>>>>>>>> Again, thanks for sharing your experience with us.
>>>>>>>>>>>
>>>>>>>>>>> Haisheng
>>>>>>>>>>>
>>>>>>>>>>> On 2020/05/31 09:58:36, Roman Kondakov
>>>> <[email protected]>
>>>>>>>>>>> wrote:
>>>>>>>>>>>> Hi Haisheng,
>>>>>>>>>>>>
>>>>>>>>>>>> The basic rationale behind the using materialized views for
>>>> secondary
>>>>>>>>>>>> index representation instead of special rules like mentioned
>>>>>>>>>>>> FilterTableScan2IndexTableScanRule is the simplicity of
>>>> implementation.
>>>>>>>>>>>>
>>>>>>>>>>>> You are absolutely right that materialized views approach has an
>>>>>>>>>> obvious
>>>>>>>>>>>> drawback that it should register all indexes as materialized
>>>> views in
>>>>>>>>>>>> the optimizer's search space. But we expect our users will not
>>>> overuse
>>>>>>>>>>>> indexes because in general having too many indexes is a bad
>>>> practice:
>>>>>>>>>> on
>>>>>>>>>>>> each table update we also should update it's indexes and it can
>>>> cause
>>>>>>>>>>>> some performance degradation of the system as a whole, not only
>>>>>>>>>>>> optimizer. So we expect the number of indexes will be
>>>> relatively small.
>>>>>>>>>>>>
>>>>>>>>>>>> Ignite uses indexes not only for index lookups, but also for
>>>> exploiting
>>>>>>>>>>>> it's sortedness. In this case materialized view's approach can
>>>> show
>>>>>>>>>> some
>>>>>>>>>>>> advantages. Let's consider the example:
>>>>>>>>>>>>
>>>>>>>>>>>> SELECT * FROM foo WHERE a > 100 ORDER BY b;
>>>>>>>>>>>>
>>>>>>>>>>>> where both fields 'a' and 'b' are indexed. In this case we will
>>>> have
>>>>>>>>>> two
>>>>>>>>>>>> alternatives of the query execution:
>>>>>>>>>>>>
>>>>>>>>>>>> 1. Use index 'a' for index conditioned scan and then sort rows
>>>> by 'b'
>>>>>>>>>>>> 2. Use index scan over 'b' and then apply filter over 'a' -
>>>> here we can
>>>>>>>>>>>> avoid sorting, because index over 'b' is already sorted.
>>>>>>>>>>>>
>>>>>>>>>>>> If I understand the approach with
>>>> FilterTableScan2IndexTableScanRule
>>>>>>>>>>>> correctly, at this step the rule should make a decision about
>>>> which
>>>>>>>>>>>> index to use. It can be very tricky because the rule should
>>>> consider
>>>>>>>>>> not
>>>>>>>>>>>> only filters, but also collations. This leads to increasing the
>>>>>>>>>>>> complexity of such rules. With materialized views approach we
>>>> just
>>>>>>>>>>>> register all indexes with their caollation and let the cost
>>>> model do
>>>>>>>>>> its
>>>>>>>>>>>> job. Our rules are very simple. The complexity is encapsulated
>>>> in the
>>>>>>>>>>>> index scan cost estimation.
>>>>>>>>>>>>
>>>>>>>>>>>> As for your example with disjunctive predicate:
>>>>>>>>>>>>
>>>>>>>>>>>>> SELECT * FROM foo WHERE a > 100 or b < 1000;
>>>>>>>>>>>>
>>>>>>>>>>>> I think with materialized views approach we can do a complex
>>>> logic as
>>>>>>>>>>>> well. For example, we may implement a special rule for such
>>>> cases:
>>>>>>>>>>>> BitmapOrRule. Or, even better, we will rewrite such predicates
>>>> to a
>>>>>>>>>>>> UNION ALL clause. A very good explanation and a comparison of
>>>> both
>>>>>>>>>>>> approaches I've found in this Oracle's blog post [1].
>>>>>>>>>>>>
>>>>>>>>>>>> As a conclusion: choosing between materialized views approach
>>>> and
>>>>>>>>>>>> IndexRule-based approach is a trade-off between complexity of
>>>>>>>>>>>> implementation and search space pollution. I hope, when the
>>>>>>>>>>>> Cascades-style optimizer become a part of Calcite, the search
>>>> space
>>>>>>>>>>>> pollution will not be a serious issue anymore.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks!
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> [1]
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>> https://blogs.oracle.com/optimizer/optimizer-transformations:-or-expansion
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> Kind Regards
>>>>>>>>>>>> Roman Kondakov
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 31.05.2020 07:31, Haisheng Yuan wrote:
>>>>>>>>>>>>> Thanks Julian and Roman for sharing the experiences for
>>>> modeling
>>>>>>>>>>> indexes.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Besides using materialized views, which is already proven by
>>>> Phoenix
>>>>>>>>>>> and Ignite, there is another approach, as mentioned by Vladimir,
>>>> define
>>>>>>>>>>> your own rules and indexscan operators.
>>>>>>>>>>>>>
>>>>>>>>>>>>> FilterTableScan2IndexScanRule and its variances, which match
>>>> Filter
>>>>>>>>>>> over TableScan, create an IndexScan if the table has
>>>> corresponding index
>>>>>>>>>> on
>>>>>>>>>>> the filter column. You don't have to register different
>>>> tablescans for
>>>>>>>>>> all
>>>>>>>>>>> the indexes, in case you have nearly thousands indexes for a
>>>> table, say
>>>>>>>>>>> 999, which is allowed in SQL Server 2016. It can also support
>>>> more
>>>>>>>>>> complex
>>>>>>>>>>> scenario, e.g.:
>>>>>>>>>>>>>
>>>>>>>>>>>>> SELECT * FROM foo WHERE a > 100 or b < 1000;
>>>>>>>>>>>>> If there is an index on column a, and another index on column
>>>> b, we
>>>>>>>>>>> may need to utilize both indexes, through Bitmap TableScan.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Bitmap Table Scan on foo
>>>>>>>>>>>>> -> BitmapOR
>>>>>>>>>>>>> -> Bitmap Index Scan on foo_index_a
>>>>>>>>>>>>> Index Condition: (a > 100)
>>>>>>>>>>>>> -> Bitmap Index Scan on foo_index_b
>>>>>>>>>>>>> Index Condition: (b < 1000)
>>>>>>>>>>>>>
>>>>>>>>>>>>> But still, this approach requires some non-trivial work to do.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Hi Roman, I believe you definitely have consulted both
>>>> approaches for
>>>>>>>>>>> Apache Ignite to work with indexes, you decided to go with
>>>> materialized
>>>>>>>>>>> views, there are some reasons and tradeoffs to consider behind
>>>> your
>>>>>>>>>>> decision. Do you mind sharing with us?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>> Haisheng
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 2020/05/29 17:34:27, Julian Hyde <[email protected]> wrote:
>>>>>>>>>>>>>> Materialized views are not a hack, as Vladimir claims.
>>>> Materialized
>>>>>>>>>>> views are a fundamental concept in relational algebra, and they
>>>> are an
>>>>>>>>>>> elegant way - in my opinion the correct way - to model indexes
>>>> (and many
>>>>>>>>>>> other structures).
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> In Calcite materialized views are a feature in the planner
>>>> that
>>>>>>>>>>> allows you to declare a table as equivalent to a query. (You do
>>>> not need
>>>>>>>>>> to
>>>>>>>>>>> issue a CREATE MATERIALIZED VIEW statement. They are internal.)
>>>> Then you
>>>>>>>>>>> can use algebra to substitute one for the other, and apply
>>>> cost-based
>>>>>>>>>>> optimization to choose between plans with & without the
>>>> materialized
>>>>>>>>>> view.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Other people have used this approach in the past. Search this
>>>> list
>>>>>>>>>>> and you should find discussions. Also I have a talk with Maryann
>>>> Xue
>>>>>>>>>> about
>>>>>>>>>>> planning for indexes in Apache Phoenix[1].
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Julian
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [1]
>>>>>>>>>>>
>>>>>>>>>>
>>>> https://www.slideshare.net/julianhyde/costbased-query-optimization-in-apache-phoenix-using-apache-calcite
>>>>>>>>>>> <
>>>>>>>>>>>
>>>>>>>>>>
>>>> https://www.slideshare.net/julianhyde/costbased-query-optimization-in-apache-phoenix-using-apache-calcite
>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On May 29, 2020, at 5:28 AM, Roman Kondakov
>>>>>>>>>>> <[email protected]> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 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
>>>>>>>>>>> (?)
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>>
>>>>>>>>>> Best regards,
>>>>>>>>>>
>>>>>>>>>> Xu
>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>
>>>
>>
