Oracle Scratchpad

January 24, 2016

Semijoin_driver

Filed under: bitmaps,CBO,Execution plans,Oracle,subqueries — Jonathan Lewis @ 11:42 am BST Jan 24,2016

Here’s one of those odd little tricks that (a) may help in a couple of very special cases and (b) may show up at some future date – or maybe it already does – in the optimizer if it is recognised as a solution to a more popular problem. It’s about an apparent restriction on how the optimizer uses the BITMAP MERGE operation, and to demonstrate a very simple case I’ll start with a data set with just one bitmap index:


create table t1
nologging
as
with generator as (
        select  --+ materialize
                rownum id
        from dual
        connect by
                level <= 1e4
)
select
        rownum                  id,
        mod(rownum,1000)        n1,
        rpad('x',10,'x')        small_vc,
        rpad('x',100,'x')       padding
from
        generator       v1,
        generator       v2
where
        rownum <= 1e6
;
begin
        dbms_stats.gather_table_stats(
                ownname          => user,
                tabname          =>'T1',
                method_opt       => 'for all columns size 1'
        );
end;
/

create bitmap index t1_b1 on t1(n1);

select index_name, leaf_blocks, num_rows from user_indexes;

/*
INDEX_NAME           LEAF_BLOCKS   NUM_ROWS
-------------------- ----------- ----------
T1_B1                        500       1000
*/

Realistically we don’t expect to use a single bitmap index to access data from a large table, usually we expect to have queries that give the optimizer the option to choose and combine several bitmap indexes (possibly driving through dimension tables first) to reduce the target row set in the table to a cost-effective level.

In this example, though, I’ve created a column data set that many people might view as “inappropriate” as the target for a bitmap index – in one million rows I have one thousand distinct values, it’s not a “low cardinality” column – but, as Richard Foote (among others) has often had to point out, it’s wrong to think that bitmap indexes are only suitable for columns with a very small number of distinct values. Moreover, it’s the only index on the table, so no chance of combining bitmaps.

Another thing to notice about my data set is that the n1 column has been generated by the mod() function; because of this the column cycles through the 1,000 values I’ve allowed for it, and this means that the rows for any given value are scattered widely across the table, but it also means that if I find a row with the value X in it then there could well be a row with the value X+4 (say) in the same block.

I’ve reported the statistics from user_indexes at the end of the sample code. This shows you that the index holds 1,000 “rows” – i.e. each key value requires only one bitmap entry to cover the whole table, with two rows per leaf block.  (By comparison, a B-tree index oon the column was 2,077 leaf block uncompressed, or 1,538 leaf blocks when compressed).

So here’s the query I want to play with, followed by the run-time execution plan with stats (in this case from a 12.1.0.2 instance):


alter session set statistics_level = all;

select
        /*+
                qb_name(main)
        */
        max(small_vc)
from
        t1
where
        n1 in (1,5)
;

select * from table(dbms_xplan.display_cursor(null,null,'allstats last outline'));

------------------------------------------------------------------------------------------------------------------
| Id  | Operation                             | Name  | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |
------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                      |       |      1 |        |      1 |00:00:00.03 |    2006 |      4 |
|   1 |  SORT AGGREGATE                       |       |      1 |      1 |      1 |00:00:00.03 |    2006 |      4 |
|   2 |   INLIST ITERATOR                     |       |      1 |        |   2000 |00:00:00.03 |    2006 |      4 |
|   3 |    TABLE ACCESS BY INDEX ROWID BATCHED| T1    |      2 |   2000 |   2000 |00:00:00.02 |    2006 |      4 |
|   4 |     BITMAP CONVERSION TO ROWIDS       |       |      2 |        |   2000 |00:00:00.01 |       6 |      4 |
|*  5 |      BITMAP INDEX SINGLE VALUE        | T1_B1 |      2 |        |      2 |00:00:00.01 |       6 |      4 |
------------------------------------------------------------------------------------------------------------------


Predicate Information (identified by operation id):
---------------------------------------------------
   5 - access(("N1"=1 OR "N1"=5))

The query is selecting 2,000 rows from the table, for n1 = 1 and n1 = 5, and the plan shows us that the optimizer probes the bitmap index twice (operation 5), once for each value, fetching all the rows for n1 = 1, then fetching all the rows for n1 = 5. This entails 2,000 buffer gets. However, we know that for every row where n1 = 1 there is another row nearby (probably in the same block) where n1 = 5 – it would be nice if we could pick up the 1 and the 5 at the same time and do less work.

Technically the optimizer has the necessary facility to do this – it’s known as the BITMAP MERGE – Oracle can read two or more entries from a bitmap index, superimpose the bits (effectively a BITMAP OR), then convert to rowids and visit the table. Unfortunately there are cases (and it seems to be only the simple cases) where this doesn’t appear to be allowed even when we – the users – can see that it might be a very effective strategy. So can we make it happen – and since I’ve asked the question you know that the answer is almost sure to be yes.

Here’s an alternate (messier) SQL statement that achieves the same result:


select
        /*+
                qb_name(main)
                semijoin_driver(@subq)
        */
        max(small_vc)
from
        t1
where
        n1 in (
                select /*+ qb_name(subq) */
                        *
                from    (
                        select 1 from dual
                        union all
                        select 5 from dual
                        )
        )
;

-----------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name  | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
-----------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |       |      1 |        |      1 |00:00:00.02 |    1074 |       |       |          |
|   1 |  SORT AGGREGATE                      |       |      1 |      1 |      1 |00:00:00.02 |    1074 |       |       |          |
|   2 |   TABLE ACCESS BY INDEX ROWID BATCHED| T1    |      1 |   2000 |   2000 |00:00:00.02 |    1074 |       |       |          |
|   3 |    BITMAP CONVERSION TO ROWIDS       |       |      1 |        |   2000 |00:00:00.01 |       6 |       |       |          |
|   4 |     BITMAP MERGE                     |       |      1 |        |      1 |00:00:00.01 |       6 |  1024K|   512K| 8192  (0)|
|   5 |      BITMAP KEY ITERATION            |       |      1 |        |      2 |00:00:00.01 |       6 |       |       |          |
|   6 |       VIEW                           |       |      1 |      2 |      2 |00:00:00.01 |       0 |       |       |          |
|   7 |        UNION-ALL                     |       |      1 |        |      2 |00:00:00.01 |       0 |       |       |          |
|   8 |         FAST DUAL                    |       |      1 |      1 |      1 |00:00:00.01 |       0 |       |       |          |
|   9 |         FAST DUAL                    |       |      1 |      1 |      1 |00:00:00.01 |       0 |       |       |          |
|* 10 |       BITMAP INDEX RANGE SCAN        | T1_B1 |      2 |        |      2 |00:00:00.01 |       6 |       |       |          |
-----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
  10 - access("N1"="from$_subquery$_002"."1")

Key points from this plan – and I’ll comment on the SQL in a moment: The number of buffer visits is roughly halved (In many cases we picked up two rows as we visited each buffer); operation 4 shows us that we did a BITMAP MERGE, and we can see in operations 5 to 10 that we did a BITMAP KEY ITERATION (which is a bit like a nested loop join – “for each row returned by child 1 (operation 6) we executed child 2 (operation 10)”) to probe the index twice and get two strings of bits that operation 4 could merge before operation 3 converted to rowids.

For a clearer picture of how we visit the table, here are the first few rows and last few rows from a version of the two queries where we simply select the ID column rather than aggregating on the small_vc column:

select  id from ...

Original query structure
         1
      1001
      2001
      3001
...
    997005
    998005
    999005

2000 rows selected.

Modified query structure:

         1
         5
      1001
      1005
      2001
      2005
...
    998001
    998005
    999001
    999005
    
2000 rows selected.

As you can see, one query returns all the n1 = 1 rows then all the n1 = 5 rows while the other query alternates as it walks through the merged bitmap. You may recall the Exadata indexing problem (now addressed, of course) from a few years back where the order in which rows were visited after a (B-tree) index range scan made a big difference to performance. This is the same type of issue – when the optimizer’s default plan gets the right data in the wrong order we may be able to find ways of modifying the SQL to visit the data in a more efficient order. In this case we save only fractions of a second because all the data is buffered, but it’s possible that in a production environment with much larger tables many, or all, of the re-visits could turn into physical reads.

Coming back to the SQL, the key to the re-write is to turn my IN-list into a subquery, and then tell the optimizer to use that subquery as a “semijoin driver”. This is essentially the mechanism used by the Star Tranformation, where the optimizer rewrites a simple join so that each dimension table (typically) appears twice, first as an IN subquery driving the bitmap selection then as a “joinback”. But (according to the manuals) a star transformation requires at least two dimension tables to be involved in a join to the central fact table – and that may be why the semi-join approach is not considered in this (and slightly more complex) cases.

 

 

My reference: bitmap_merge.sql, star_hack3.sql

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