Oracle optimizer

26 décembre 2010 informatique No comments

Ref url: http://blogs.oracle.com/optimizer/ for more information…

….

Star transformation was introduced in Oracle 8i to process star queries efficiently. These queries are commonly used in data warehouse applications that follow the Star Schema data model. The Star Schema is so called because the data model diagram resembles a star. The center of the star consists of one or more fact tables and the points of the star are the dimension tables.

The basic idea of this transformation is to steer clear of using a full table scan access method on large tables, referred to as fact tables in the Star Schema. In a typical star query, the fact table is joined to several much smaller dimension tables. The fact table typically contains one key (referred to as foreign key) for every dimension table as well as a number of measure columns such as sales amount. The corresponding key in the dimension table is referred to as the primary key. The join is based on a foreign key of the fact table with the corresponding primary key of the dimension table. The query also contains filter predicates on other columns of the dimension tables that typically are very restrictive. The combination of these filters help to dramatically reduce the data set processed from the fact table.  The goal of star transformation is to access only this reduced set of data from the fact table.

Consider the following star query Q1. The query is to find the total sales amount in all cities in California for quarters Q1 and Q2 of year 1999 through the Internet.

Q1:

SELECT c.cust_city, t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount

FROM sales s, times t, customers c, channels ch

WHERE s.time_id = t.time_id

AND s.cust_id = c.cust_id

AND s.channel_id = ch.channel_id

AND c.cust_state_province = ‘CA’

AND ch.channel_desc = ‘Internet’

AND t.calendar_quarter_desc IN (‘1999-01′,’1999-02’)

GROUP BY c.cust_city, t.calendar_quarter_desc;

Sales is the fact table while the other tables are considered as dimension tables. The Sales table contains one row for every sale of a product and thus it may contain billions of sales records. However only a few of them are sold to customers in California through the Internet for the specified quarters. The query is transformed into Q2.

Q2:

SELECT c.cust_city, t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount

FROM sales s, times t, customers c

WHERE s.time_id = t.time_id

AND s.cust_id = c.cust_id

AND c.cust_state_province = ‘CA’

AND t.calendar_quarter_desc IN (‘1999-01′,’1999-02’)

AND s.time_id IN (SELECT time_id
FROM times
WHERE calendar_quarter_desc IN(‘1999-01′,’1999-02’))

AND s.cust_id IN (SELECT cust_id
FROM customers
WHERE cust_state_province=’CA’)

AND s.channel_id IN (SELECT channel_id
FROM channels
WHERE channel_desc = ‘Internet’)

GROUP BY c.cust_city, t.calendar_quarter_desc;
Star transformation is essentially about adding subquery predicates corresponding to the constraint dimensions. These subquery predicates are referred to as bitmap semi-join predicates. The transformation is performed when there are indexes on the fact join columns (s.timeid, s.custid…). By driving bitmap AND and OR operations (bitmaps can be from bitmap indexes or generated from regular B-Tree indexes) of the key values supplied by the subqueries, only the relevant rows from the fact table need to be retrieved.  If the filters on the dimension tables filter out a lot of data, this can be much more efficient than a full table scan on the fact table.  After the relevant rows have been retrieved from the fact table, they may need to be joined back to the dimension tables, using the original predicates. In some cases, the join back can be eliminated. We will discuss this situation later.

Table 1 shows the query plan for the transformed query. Note that the sales table has a bitmap access path instead of a full table scan. For each key value coming from the subqueries (lines 11, 16, 21), the bitmaps are retrieved from the fact table indexes (lines 12, 17, 22).  Each bit in the bitmap corresponds to a row in fact table. The bit is set if the key value from the subquery is same as the value in the row of fact table.  For example, the bitmap [1][0][1][0][0][0]…(all 0s for remaining rows) indicate that rows 1 and 3 of fact table has matching key value from subquery.  Lets say the above bitmap is for a key value from customers table subquery.

The operations in lines 9, 14, 19 iterates over the keys from the subqueries and get the corresponding bitmaps. Lets say the customers subquery produces one more key value with the  bitmap [0][1][0][0][0][0]…

The bitmaps for each subquery are merged (ORed) (lines 8, 13 and 18). In the above example, it will produce a single bitmap [1][1][1][0][0][0]… for customers subquery after merging the two bitmaps.

The merged bitmaps are ANDed (line 7). Lets say the bitmap from channels is [1][0][0][0][0][0]…  If you AND this bitmap with the bitmap from customers subquery it will produce [1][0][0][0][0]…

The corresponding rowids of the final bitmap are generated (line 6).   The fact table rows are retrieved using the rowids (line 5).  In the above example, it will generate only 1 rowid corresponding to the first row and fetches only a single row instead of scanning the entire fact table.

The representation of bitmaps in the above example is for illustration purpose only. In oracle, they are represented and stored in a compressed form.

Table 1: The plan of the transformed query

Id Operation Name
0 SELECT STATEMENT
1 HASH GROUP BY
2 HASH JOIN
3 HASH JOIN
4 PARTITION RANGE SUBQUERY
5 TABLE ACCESS BY LOCAL INDEX ROWID SALES
6 BITMAP CONVERSION TO ROWIDS
7 BITMAP AND
8 BITMAP MERGE
9 BITMAP KEY ITERATION
10 BUFFER SORT
11 TABLE ACCESS FULL CHANNELS
12 BITMAP INDEX RANGE SCAN SALES_CHANNEL_BIX
13 BITMAP MERGE
14 BITMAP KEY ITERATION
15 BUFFER SORT
16 TABLE ACCESS FULL TIMES
17 BITMAP INDEX RANGE SCAN SALES_TIME_BIX
18 BITMAP MERGE
19 BITMAP KEY ITERATION
20 BUFFER SORT
21 TABLE ACCESS FULL CUSTOMERS
22 BITMAP INDEX RANGE SCAN SALES_CUST_BIX
23 TABLE ACCESS FULL CUSTOMERS
24 TABLE ACCESS FULL TIMES

Join back elimination

The subqueries and their bitmap tree only filter the fact table based on the dimension filters, so it may still be necessary to join to the dimension table.  The join back of the dimension table is eliminated when all the predicates on dimension tables are part of the semijoin subquery predicate, the column(s) selected from the subquery are unique and the dimension columns are not in select list, group by etc.  In the above example, the table channels is not joined back to the sales table since it is not referenced outside and channel_id is unique.

Temporary table transformation

If the join back is not eliminated, Oracle stores the results of the subquery in a temporary table to avoid re-scanning the dimension table (for bitmap key generation and join back). In addition to this, the results are materialized if the query is run in parallel, so that each slave can select the results from the temporary tables instead of executing the subquery again.

For example, if Oracle materializes the results of the subquery on customers into a temporary table, the transformed query Q3 will be as follows.

Q3:
SELECT t1.c1 cust_city, t.calendar_quarter_desc calendar_quarter_desc,
sum(s.amount_sold) sales_amount

FROM sales s, sh.times t, sys_temp_0fd9d6621_e7e24 t1

WHERE s.time_id=t.time_id

AND s.cust_id=t1.c0

AND (t.calendar_quarter_desc=’1999-q1′ OR t.calendar_quarter_desc=’1999-q2′)

AND s.cust_id IN (SELECT  t1.c0 FROM sys_temp_0fd9d6621_e7e24 t1)

AND s.channel_id IN (SELECT  ch.channel_id
FROM channels ch
WHERE ch.channel_desc=’internet’)

AND s.time_id IN (SELECT t.time_id
FROM times t
WHERE t.calendar_quarter_desc=’1999-q1′
OR t.calendar_quarter_desc=’1999-q2′)

GROUP BY t1.c1,  t.calendar_quarter_desc

Note that customers is replaced by the temporary table sys_temp_0fd9d6621_e7e24 and references to columns cust_id and cust_city are replaced by the corresponding columns of the temporary table. The temporary table will be created with 2 columns –  (c0 number, c1 varchar2(30)). These columns corresponds to cust_id and cust_city of customers table. The table will be populated using the following query Q4 at the beginning of the execution of the statement Q3.

Q4:
SELECT c.cust_id, c.cust_city FROM customers WHERE c.cust_state_province = ‘CA’

Table 2 shows the plan for the transformed query.

Table 2: Plan with temporary table transformation

0 SELECT STATEMENT
1 TEMP TABLE TRANSFORMATION
2 LOAD AS SELECT sys_temp_ 0fd9d6621_e7e24
3 TABLE ACCESS FULL CUSTOMERS
4 HASH GROUP BY
5 HASH JOIN
6 HASH JOIN
7 PARTITION RANGE SUBQUERY
8 TABLE ACCESS BY LOCAL INDEX ROWID SALES
9 BITMAP CONVERSION TO ROWIDS
10 BITMAP AND
11 BITMAP MERGE
12 BITMAP KEY ITERATION
13 BUFFER SORT
14 TABLE ACCESS FULL CHANNELS
15 BITMAP INDEX RANGE SCAN SALESCHANNELBIX
16 BITMAP MERGE
17 BITMAP KEY ITERATION
18 BUFFER SORT
19 TABLE ACCESS FULL TIMES
20 BITMAP INDEX RANGE SCAN SALESTIMEBIX
21 BITMAP MERGE
22 BITMAP KEY ITERATION
23 BUFFER SORT
24 TABLE ACCESS FULL sys_temp_0fd9d6621_e7e24
25 BITMAP INDEX RANGE SCAN SALESCUSTBIX
26 TABLE ACCESS FULL sys_temp_0fd9d6621_e7e24
27 TABLE ACCESS FULL TIMES

The lines 1,2 and 3 of the plan materialize the customers subquery into the temporary table. In line 24,  it scans the temporary table (instead of the subquery) to build the bitmap from the fact table. Line 26 is for scanning the temporary table for joining back instead of scanning customers. Note that the filter on customers is not needed to be applied on the temporary table since the filter is already applied while materializing the temporary table.

Enabling the transformation

Star transformation is controlled by the star_transformation_enabled parameter.  The parameter takes 3 values.

  • TRUE – The Oracle optimizer performs transformation by identifying fact and constraint dimension tables automatically. This is done in a cost-based manner, i.e. the transformation is performed only if the cost of the transformed plan is lower than the non-transformed plan. Also the optimizer will attempt temporary table transformation automatically whenever materialization improves performance.
  • FALSE – The transformation is not tried.
  • TEMP_DISABLE – This value has similar behavior as TRUE except that temporary table transformation is not tried.

The default value of the parameter is FALSE. You have to change the parameter value and create indexes on the joining columns of the fact table to take advantage of this transformation.

Summary
Star transformation improves the performance of queries with a very big fact table joined to multiple dimension tables when the dimension tables have very selective predicates. The transformation avoids the full scan of  the fact table. It fetches only relevant rows from the fact table that will eventually join to the constraint dimension rows. The transformation is performed based on cost – only when the cost of the transformed plan is lower than that of the non-transformed plan.  If the dimension filters do not significantly reduce the amount of data to be retrieved from the fact table, then a full table scan is more efficient.

In this post we have tried to illustrate the basic ideas behind star transformation by showing  simple example queries and plans. Oracle can do star transformation in more complex cases. For example, a query with multiple fact tables, snowflakes (dimension is a join of several normalized tables instead of denormalized single table), etc.

We continue our series on query transformations with a two-part discussion of view merging. In these posts, we will review the Oracle terminology related to view merging, explain the different types of view merging, and discuss the reasons that a view might not be merged. The examples in these posts use the Oracle sample schemas.
We use the term view to describe a sub-query block appearing in the FROM clause. Oracle can merge several different types of views:
  1. Simple view merging, for simple select-project-join views.
  2. Outer-join view merging for outer-joined views.
  3. Complex view merging, for distinct and group by views.
In today’s post, we will discuss the first two. We »ll discuss complex view merging in the next post.
Simple View Merging
Consider a simple query with a view:
select e.first_name, e.last_name, dept_locs_v.street_address, dept_locs_v.postal_code
from employees e,
     (select d.department_id, d.department_name, l.street_address, l.postal_code
      from departments d, locations l
      where d.location_id = l.location_id) dept_locs_v
where dept_locs_v.department_id = e.department_id
and e.last_name = 'Smith';
The query joins the employees table with a view that returns the street address for each department. The view is itself a join of two tables. The query can be executed by joining departments and locations to produce the rows of the view, and then joining that result to employees. Because the query contains the view (V), the join orders that the optimizer can consider are constrained to the following:
E, V
V, E
Within the view, two join orders are considered:
D, L
L, D
So in combination, there are only four possible join orders for this form of the query. The join methods are also constrained; the index-based nested loops join is not feasible for the join order [E, V], since there is no index on the column from the view. Without view merging, the optimizer chooses the following plan:
-----------------------------------------------------------------
| Id  | Operation                    | Name        | Cost (%CPU)|
-----------------------------------------------------------------
|   0 | SELECT STATEMENT             |             |     7  (15)|
|*  1 |  HASH JOIN                   |             |     7  (15)|
|   2 |   TABLE ACCESS BY INDEX ROWID| EMPLOYEES   |     2   (0)|
|*  3 |    INDEX RANGE SCAN          | EMP_NAME_IX |     1   (0)|
|   4 |   VIEW                       |             |     5  (20)|
|*  5 |    HASH JOIN                 |             |     5  (20)|
|   6 |     TABLE ACCESS FULL        | LOCATIONS   |     2   (0)|
|   7 |     TABLE ACCESS FULL        | DEPARTMENTS |     2   (0)|
-----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("DEPT_LOCS_V"."DEPARTMENT_ID"="E"."DEPARTMENT_ID")
3 - access("E"."LAST_NAME"='Smith')
5 - access("D"."LOCATION_ID"="L"."LOCATION_ID")
View merging merges the tables from the view into the outer query block, removing the view query block. After view merging, the query looks like this:
select e.first_name, e.last_name, l.street_address, l.postal_code
from employees e, departments d, locations l
where d.location_id = l.location_id
and d.department_id = e.department_id
and e.last_name = 'Smith';
Now that all three tables appear in one query block, the optimizer is not constrained by what join orders it can consider (there are a total of 6), and the joins to employees and departments can be index-based. The following plan is chosen with view merging:
-------------------------------------------------------------------
| Id  | Operation                      | Name        | Cost (%CPU)|
-------------------------------------------------------------------
|   0 | SELECT STATEMENT               |             |     4   (0)|
|   1 |  NESTED LOOPS                  |             |            |
|   2 |   NESTED LOOPS                 |             |     4   (0)|
|   3 |    NESTED LOOPS                |             |     3   (0)|
|   4 |     TABLE ACCESS BY INDEX ROWID| EMPLOYEES   |     2   (0)|
|*  5 |      INDEX RANGE SCAN          | EMP_NAME_IX |     1   (0)|
|   6 |     TABLE ACCESS BY INDEX ROWID| DEPARTMENTS |     1   (0)|
|*  7 |      INDEX UNIQUE SCAN         | DEPT_ID_PK  |     0   (0)|
|*  8 |    INDEX UNIQUE SCAN           | LOC_ID_PK   |     0   (0)|
|   9 |   TABLE ACCESS BY INDEX ROWID  | LOCATIONS   |     1   (0)|
-------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
 5 - access("E"."LAST_NAME"='Smith')
 7 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")
 8 - access("D"."LOCATION_ID"="L"."LOCATION_ID")
Oracle uses the term « simple » to refer to select-project-join views. The example above used simple view merging to select the better plan. Such views are automatically merged if it is legal to do so, since it is generally the case that the merged view will result in a plan that is at least as good as the unmerged view would. With the additional join orders and access paths available after a view has been merged, view merging can frequently result in a much better plan. View merging can also allow other transformations to take place; for instance, a table inside of the view may allow a table outside of the view to be join eliminated after the view has been merged and both tables reside in one query block.
There are several reasons why a select-project-join view might not be merged, typically because it is not semantically valid to do so. Some of the reasons a view may not be valid for simple view merging are listed below.
  • The view contains constructs other than select, project, join, including:
    • Group by
    • Distinct
    • Outer-join
    • Spreadsheet clause
    • Connect by
    • Set operators
    • Aggregation
  • The view appears on the right side of a semi- or anti-join.
  • The view contains subqueries in the select list.
  • The outer query block contains PL/SQL functions.
Note that some of these constructs do not disallow view merging in all queries, but depend on additional validity constraints.
Outer Join View Merging
If a view is involved in an outer join with tables from the outer query block or if the view contains outer-joined tables, there are many additional restrictions on whether it is valid to merge the view. After view merging, it must be possible to express the query in terms of Oracle outer join syntax. This imposes one significant restriction on views on the left of an outer join: each table from the outer query block can be outer-joined to at most one underlying table of the view. For instance, it is currently not possible to merge the view in this query:
select e1.first_name||' '||e1.last_name emp_name, dept_managers_v.manager_name,
      dept_managers_v.department_name
from employees e1,
    (select e2.manager_id, e2.first_name||' '||e2.last_name as manager_name,
            d.department_id, d.department_name
     from departments d, employees e2
     where d.manager_id = e2.employee_id) dept_managers_v
where dept_managers_v.department_id = e1.department_id(+)
and dept_managers_v.manager_id = e1.manager_id(+);
If the view were merged, it would result in table e1 being outer joined to two tables, which is not legal in Oracle outer join. But the view in the following query can be merged:
select e1.first_name||' '||e1.last_name emp_name, dept_managers_v.manager_name,
      dept_managers_v.department_name
from employees e1,
    (select e2.manager_id, e2.first_name||' '||e2.last_name as manager_name,
            d.department_id, d.department_name
     from departments d, employees e2
     where d.manager_id = e2.employee_id) dept_managers_v
where dept_managers_v.department_id = e1.department_id(+);
The merged form of the query looks like this:
select e1.first_name||' '||e1.last_name emp_name,
      e2.first_name||' '||e2.last_name as manager_name,
      d.department_name
from employees e1, employees e2, departments d
where d.manager_id = e2.employee_id
and d.department_id = e1.department_id(+);
This allows the optimizer to consider additional join orders and access paths like we discussed earlier.
If a view appears on the right of an outer join, the view can be merged only if it contains a single table in the from-clause (which can be a table or another view). If a view contains more than one table, the semantics of the query require the join between those two tables to occur before the outer join. There are additional restrictions on merging of views participating in an outer join, but these are the most common reasons for merging of outer joined views to not be valid.
Summary
In this post we covered the basics of view merging, how it works for simple select-project-join views and views appearing in outer joins, and why one of these views might not be merged. In the next two weeks, we’ll finish up the topic with a discussion of complex view merging, and we’ll finally reveal the reason for one of the great mysteries of view merging – the VW_NWVW_* view!
Complex View Merging
We use the term « complex view merging » to describe merging of group by and distinct views. Like simple view merging, this allows the optimizer to consider additional join orders and access paths. In addition, the evaluation of the group-by/distinct operation can be delayed until after the joins have been evaluated. Delayed evaluation of group-by can make performance better or worse depending on the characteristics of the data. Delaying a group-by until after joins can result in a reduction in the data set on which the group-by operation is to be performed, if joins are filtering; on the other hand, early group-by can reduce the amount of data to be processed by subsequent joins or the joins could explode the amount of data to undergo group-by. The same is true for distinct operations. Because it is not always better to merge such a view, we choose whether to use this transformation in a cost-based manner. The two options – with and without view merging – are each costed by the optimizer, and we choose to merge the view only if it is cheaper to do so.
Consider the following group by view and query which refers to it:
create view cust_prod_totals_v as
select sum(s.quantity_sold) total, s.cust_id, s.prod_id
from sales s
group by s.cust_id, s.prod_id;

select c.cust_id, c.cust_first_name, c.cust_last_name, c.cust_email
from customers c, products p, cust_prod_totals_v
where c.country_id = 'US'
and c.cust_id = cust_prod_totals_v.cust_id
and cust_prod_totals_v.total > 100
and cust_prod_totals_v.prod_id = p.prod_id
and p.prod_name = 'T3 Faux Fur-Trimmed Sweater';
This query finds all of the customers from the US who have bought at least 100 of a particular item. The view is eligible for complex view merging. After merging, the query looks like this:
select c.cust_id, cust_first_name, cust_last_name, cust_email
from customers c, products p, sales s
where c.country_id = 'US'
and c.cust_id = s.cust_id
and s.prod_id = p.prod_id
and p.prod_name = 'T3 Faux Fur-Trimmed Sweater'
group by s.cust_id, s.prod_id, p.rowid, c.rowid,
c.cust_email, c.cust_last_name, c.cust_first_name, c.cust_id
having sum(s.quantity_sold) > 100;
The transformed query is cheaper than the untransformed query, so the optimizer chooses to merge the view. Why is the transformed query cheaper? In the untransformed query, the group by operator applies to the entire sales table in the view. In the transformed query, the joins to products and customers (especially products) filter out a large portion of the rows from the sales table, so the group by operation is much cheaper. The join is more expensive because the sales table has not been reduced, but it is not that much more expensive, since the group-by operation does not reduce the data size that much in the original query. If any of these characteristics were to change, it may no longer be cheaper to merge the view. Hence the need for a cost-based decision. The final plan is as follows:

--------------------------------------------------------
| Id  | Operation             | Name      | Cost (%CPU)|
--------------------------------------------------------
|   0 | SELECT STATEMENT      |           |  2101  (18)|
|*  1 |  FILTER               |           |            |
|   2 |   HASH GROUP BY       |           |  2101  (18)|
|*  3 |    HASH JOIN          |           |  2099  (18)|
|*  4 |     HASH JOIN         |           |  1801  (19)|
|*  5 |      TABLE ACCESS FULL| PRODUCTS  |    96   (5)|
|   6 |      TABLE ACCESS FULL| SALES     |  1620  (15)|
|*  7 |     TABLE ACCESS FULL | CUSTOMERS |   296  (11)|
--------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter(SUM("QUANTITY_SOLD")>100)
3 - access("C"."CUST_ID"="CUST_ID")
4 - access("PROD_ID"="P"."PROD_ID")
5 - filter("P"."PROD_NAME"='T3 Faux Fur-Trimmed Sweater')
7 - filter("C"."COUNTRY_ID"='US')

There is no view in the plan above, which is what one would expect after the view has been merged. However, there are some cases where a view will still appear in the plan even after view merging, with a name like VW_NWVW_1. We’ll discuss the reasons why in a moment, but first let’s look at an example. This also gives us a chance to look at an example of distinct view merging. Consider this query to find customers in the US that bought a particular product:

select c.cust_id, c.cust_first_name, c.cust_last_name, c.cust_email
from customers c, products p,
(select distinct s.cust_id, s.prod_id
from sales s) cust_prod_v
where c.country_id = 'US'
and c.cust_id = cust_prod_v.cust_id
and cust_prod_v.prod_id = p.prod_id
and p.prod_name = 'T3 Faux Fur-Trimmed Sweater';

The view can be merged, though it is based on cost, since the reduction in data due to distinct may make the join cheaper. In this case, however, it is cheaper to merge the view, so we get this equivalent query:

select nwvw.cust_id, nwvw.cust_first_name, nwvw.cust_last_name, nwvw.cust_email
from (select distinct c.rowid, p.rowid, s.prod_id, s.cust_id, c.cust_id,
c.cust_first_name, c.cust_last_name, c.cujst_email
from customers c, products p, sales s
where c.country_id = 'US'
and c.cust_id = s.cust_id
and s.prod_id = p.prod_id
and p.prod_name = 'T3 Faux Fur-Trimmed Sweater') nwvw;

and this plan:

-------------------------------------------
| Id  | Operation             | Name      |
-------------------------------------------
|   0 | SELECT STATEMENT      |           |
|   1 |  VIEW                 | VM_NWVW_1 |
|   2 |   HASH UNIQUE         |           |
|*  3 |    HASH JOIN          |           |
|*  4 |     HASH JOIN         |           |
|*  5 |      TABLE ACCESS FULL| PRODUCTS  |
|   6 |      TABLE ACCESS FULL| SALES     |
|*  7 |     TABLE ACCESS FULL | CUSTOMERS |
-------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
  3 - access("C"."CUST_ID"="S"."CUST_ID")
  4 - access("S"."PROD_ID"="P"."PROD_ID")
  5 - filter("P"."PROD_NAME"='T3 Faux Fur-Trimmed Sweater')
  7 - filter("C"."COUNTRY_ID"='US')

So why do we still have a view after we’ve supposedly merged the view? The new view is what we call a « projection view ». When we merge the view, we move the distinct to the outer query block. But when we move the distinct, we have to add several additional columns, in order to maintain semantic equivalence with the original query. So we put all of that into a new view, so we can select out just the columns we want in the outer query block’s select list. But we still get all of the benefits we promised from merging the view — all of the tables are in one query block and the optimizer is free to permute them as it desires in the final join order, and the distinct operation has been delayed until after all of the joins are completed. These projection views appear in queries where a distinct view has been merged, or a group by view is merged into an outer query block which also contains group by, having, and/or aggregates. In the latter case, the projection view contains the group by, having, and aggregates from the original outer query block.

Now that this great mystery has been revealed, let’s look at some of the reasons a group by or distinct view might not be merged. Aside from cost, there are several other reasons, including:
  • The outer query tables do not have a rowid or unique column
  • View appears in a connect by query block
  • View contains grouping sets, rollup, pivot
  • View or outer query block contains spreadsheet clause
Summary
View merging can improve plans by allowing additional join orders, access methods, and other transformations to be considered. In cases where view merging should always lead to a better plan, Oracle automatically merges a view; in other cases, this is determined based on cost. There are many reasons why a view may not be merged, including cost or validity restrictions. Note that view merging that is rejected on the basis of cost or heuristics can be overridden with hints; but view merging that is rejected based on validity may not.
…..

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Google Maps:
Plutôt que d’écrater l’index du pouce sur l’écran pour zoomer, vous pouvez simplement « double taper» sur l’endroit désiré. Ça zoome…
Pour dézoomer, double tap avec deux doigts collés. (ça fonctionne avec la plupart des applications qui zooment et dézooment, comme les images, par exemple)

Sauvegarder une photo d’un mail ou du net:
Vous pouvez sauvegarder les images en maintenant appuyé une seconde le doigt sur l’image. Un petit menu apparait vous permettant d’ouvrir l’image en grand (dans une nouvelle page) ou de la sauvegarder dans votre bibliothèque.

Taper des majuscules:
Plutôt que d’appuyer sur la touche SHIFT, puis de sélectionner la lettre, il vous suffit d’appuyer sur Shift, et sans lever le doigt, glisser jusqu’à la lettre désirée. Là, seulement, vous soulevez le doigt…
C’est plus précis, et on gagne un temps précieux.

Prendre un auto-portrait:
1° Vous pouvez vous regarder dans le symbole argenté de la pomme qui fait office de miroir.
2° Maintenez appyé le bouton « Prendre une photo» , et relâchez-le seulement quand vous estimez être on ne peut plus beau Car, facilement, si on ne procède pas de cette façon, on tremble un peu en appuyant sur la touche et la photo est floue…

Annuler une saisie de texte
Vous venez de faire une erreur en entrant votre texte ou en collant une données ? Secouez votre iPhone. Celà fera apparaitre un menu « Annuler »

Déplacez les applications de page plus rapidement
Depuis la version 3.0 du firmware, il est possible de déplacer rapidement les applications d’une page à l’autre. Sélectionnez une application et maintenez la pression. Lorsqu’elle « danse », appuyez sur l’application avec votre doigt et glissez-la dans le coin supérieur gauche ou droit de la page (sur l’heure ou sur le logo d’opérateur). Il ne vous reste plus qu’à sélectionner la page où vous désirez la déposer.

Calculatrice scientifique
Pour afficher la calculatrice scientifique du iPhone, démarrez la calculatrice et faites pivoter votre iPhone en mode paysage.

Envoyez un courrier et téléphonez depuis Safari
Lorsque vous surfez sur le Web en utilisant Safari, vous pouvez en tout temps cliquer sur un numéro de téléphone ou une adresse courriel pour faire un appel ou envoyer un courriel.

Changer de chanson
Il est évidemment possible d’écouter de la musique tout en utilisant la plupart des logiciels. Pour contrôler cette musique depuis un autre logiciel, appuyez deux fois sur le bouton home, ce qui fait apparaître un mini menu de contrôle de l’iPod (piste suivante, précédente, pause, volume).
Vous pouvez également secouer votre iPhone pour passer à la chanson suivante (si l’option est activée dans les paramètres)

Accents et extension de noms de domaine
Pour entrer un accent, rien de plus simple: Maintenez la lettre désirée enfoncée un instant de plus, ce qui laisse apparaitre tous les choix d’accents possibles pour cette lettre.
Procédez de la même manière sur la touche « .com»  pour laisser apparaître les .net, .org, etc…
Notez que si le nom de domaine est en .com, vous n’êtes pas obligés d’entrer le « .com»  (ex.: « flickr»  suffit) Il n’est jamais nécessaire d’entrer le « WWW. » d’ailleurs.

Sauvegarder une image depuis Safari/Application Courriel
Pour enregistrer une image trouvée sur une page Web ou en pièce jointe d’un courriel sur votre iPhone, maintenez la pression sur l’image que vous voulez enregistrer avec votre doigt pendant quelques secondes. Un menu se nommant « Enregistrer l’image » apparaîtra. Sélectionnez-le, vous trouverez ensuite votre image dans l’application Photos.

Appels entrants
Pour faire taire la sonnerie d’un appel entrant, appuyez une fois sur le bouton d’allumage (en haut à droite)
Pour envoyer l’appel directement vers le répondeur, appuyez deux fois de suite.

Basculer rapidement d’un écran d’accueil à l’autre.
Lorsque vous avez plusieurs applications sur votre iPhone ou iPod Touch, elles s’installeront sur de nouveaux écrans d’accueil qui se créeront automatiquement. Pour naviguer d’un écran à l’autre, plutôt que de « balayer » la page de gauche à droite, vous pouvez aussi, tout simplement, appuyer à gauche ou à droite des points qui apparaissent au-dessus du Dock.

Surfer en parlant
Si vous êtes en Wi-fi (ça ne fonctionne pas avec le edge et 3G), vous pouvez surfer sur le net tout en parlant avec votre interlocuteur (en mains libres, par ex.)
Pour celà, durant un appel, appuyez sur le bouton « Home»  et démarrez Firefox… heuu Safari, tout simplement…

Tout en majuscules (Caps lock)
Double tapez sur la touche SHIFT pour passer en mode Caps-ON (vérifiez dans les options générales si l’option est bien activée)

Supprimez rapidement un courriel
Pour supprimer un courriel rapidement, dans la liste des messages, sélectionnez votre message et glissez votre doigt de gauche à droite. Un bouton « supprimer » apparaîtra.
Notez que cette action est disponible dans de nombreuses applications officielles et non officielles.

Les contacts dans google maps
Si vous double tapez sur l’adresse de l’un de vos contacts, Google maps s’ouvre, vous affichant la carte du contact.

Safari: Zoom intelligent
Lorsque vous voulez lire un paragraphe d’une page web, « double tapez»  sur ce paragraphe pour que Safari vous l’affiche en plein écran. Recommencez l’opération pour dézoomer.

« Tuer»  une application
Lorsque vous quittez une application, dans certains cas, elle reste active en arrière plan, consommant de la mémoire, mais surtout de la batterie. En maintenant 4-8 secondes le bouton « Home»  dans l’application que vous voulez fermer, cette dernière se fermera complètement.

Revenir à la première page du menu rapidement
Quand vous avez plusieurs pages d’applications, vous pouvez rapidement revenir à la première page (page principale) en appuyant deux fois sur le bouton principal. Vous devez par contre activer cette fonction dans l’application Réglages (« Général » puis « Bouton principal  ». Sélectionnez « le menu principal »)

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