UPDATE contains wrong data if the SELECT employs a temporary table.
If the UPDATE values of the INSERT .. SELECT .. ON DUPLICATE KEY UPDATE
statement contains fields from the SELECT part and the select employs a
temporary table then those fields will contain wrong values because they
aren't corrected to get data from the temporary table.
The solution is to add these fields to the selects all_fields list,
to store pointers to those fields in the selects ref_pointer_array and
to access them via Item_ref objects.
The substitution for Item_ref objects is done in the new function called
Item_field::update_value_transformer(). It is called through the
item->transform() mechanism at the end of the select_insert::prepare()
function.
View check option clauses were ignored for updates of multi-table
views when the updates could not be performed on fly and the rows
to update had to be put into temporary tables first.
with a column of the DATETIME type could return a wrong
result set if the WHERE clause included a BETWEEN condition
on the column.
Fixed the method Item_func_between::fix_length_and_dec
where the aggregation type for BETWEEN predicates calculated
incorrectly if the first argument was a view column of the
DATETIME type.
updated.
INSERT ... ON DUPLICATE KEY UPDATE reports that a record was updated when
the duplicate key occurs even if the record wasn't actually changed
because the update values are the same as those in the record.
Now the compare_record() function is used to check whether the record was
changed and the update of a record reported only if the record differs
from the original one.
Ignoring error codes from type conversion allows default (wrong) values to
go unnoticed in the formation of index search conditions.
Fixed by correctly checking for conversion errors.
This performance degradation for UPDATEs could be observed in the update
statements for which the search key cannot be converted to any valid
value of the type of the search column, like for a the condition
int_fld=99999999999999999999999999, though it can be guaranteed here
that there is no row with such a key value.
The bug could cause choosing a sub-optimal execution plan for
a single-table query if a unique index with many null keys were
defined for the table.
It happened because the code of the check_quick_keys function
made an assumption that any key may occur in an unique index
only once. Yet this is not true for keys with nulls that may
have multiple occurrences in the index.
Two problems here:
Problem 1:
While constructing the join columns list the optimizer does as follows:
1. Sets the join_using_fields/natural_join members of the right JOIN
operand.
2. Makes a "table reference" (TABLE_LIST) to parent the two tables.
3. Assigns the join_using_fields/is_natural_join of the wrapper table
using join_using_fields/natural_join of the rightmost table
4. Sets join_using_fields to NULL for the right JOIN operand.
5. Passes the parent table up to the same procedure on the upper
level.
Step 1 overrides the the join_using_fields that are set for a nested
join wrapping table in step 4.
Fixed by making a designated variable SELECT_LEX::prev_join_using to
pass the data from step 1 to step 4 without destroying the wrapping
table data.
Problem 2:
The optimizer checks for ambiguous columns while transforming
NATURAL JOIN/JOIN USING to JOIN ON. While doing that there was no
distinction between columns that are used in the generated join
condition (where ambiguity can be checked) and the other columns
(where ambiguity can be checked only when resolving references
coming from outside the JOIN construct itself).
Fixed by allowing the non-USING columns to be present in multiple
copies in both sides of the join and moving the ambiguity check
to the place where unqualified references to the join columns are
resolved (find_field_in_natural_join()).
When a merge table is opened compare column and key definition of
underlying tables against column and key definition of merge table.
If any of underlying tables have different column/key definition
refuse to open merge table.
The optimizer takes away columns from GROUP BY/DISTINCT if they constitute
all the parts of an unique index.
However if some of the columns can contain NULLs this cannot be done
(because an UNIQUE index can have multiple rows with NULL values).
Fixed by not using UNIQUE indexes with nullable columns to remove
grouping columns from GROUP BY/DISTINCT.
Checking for NULL before calling the val_xxx()
methods only checks for such arguments that are
known to be NULLs at compile time.
The arguments that may or may not contain
NULLs (e.g. function calls and possibly others)
are not checked at all.
Fixed by first calling the val_xxx() method and
then checking for null in SEC_TO_TIME().
In addition QUARTER() was not returning 0 (as all the
val_int() functions do when processing a NULL value).
Objects of the classes Item_func_is_not_null_test and Item_func_trig_cond
must be transparent for the method Item::split_sum_func2 as these classes
are pure helpers. It means that the method Item::split_sum_func2 should
look at those objects as at pure wrappers.
The bug report has demonstrated the following two problems.
1. If an ORDER/GROUP BY list includes a constant expression being
optimized away and, at the same time, containing single-row
subselects that return more that one row, no error is reported.
Strictly speaking the standard allows to ignore error in this case.
Yet, now a corresponding fatal error is reported in this case.
2. If a query requires sorting by expressions containing single-row
subselects that, however, return more than one row, then the execution
of the query may cause a server crash.
To fix this some code has been added that blocks execution of a subselect
item in case of a fatal error in the method Item_subselect::exec.
