Federated and Federatex cannot be used with ROR scans
Federated::position() and Federatex::position() is storing in 'ref' a
pointer into a local result set buffer. This means that one cannot
compare 'ref' from different handler instances to see if they point to the
same physical record.
This bug caused federated.federatedx to return wrong results when the
optimizer tried to use index_merge to resolve some queries.
Fixed by introducing table flag HA_NON_COMPARABLE_ROWID and using this
with the above handlers.
Todo:
- Fix multi_delete(), multi_update and read_records() to use primary key
instead of 'ref' if case HA_NON_COMPARABLE_ROWID is set. The current
code only works if we have only one range (like table scan) for the
tables that will be updated in the second pass.
- Enable DBUG_ASSERT() in ha_federated::cmp_ref() and
ha_federatedx::cmp_ref().
Rowid Filter check is just like Index Condition Pushdown check: before
we check the filter, we must check if we have walked out of the range
we are scanning. (If we did, we should return, and not continue the scan).
Consequences of this:
- Rowid filtering doesn't work for keys that have partially-covered
blob columns (just like Index Condition Pushdown)
- The rowid filter function has three return values: CHECK_POS (passed)
CHECK_NEG (filtered out), CHECK_OUT_OF_RANGE.
All of the above is implemented in this patch
- Print the rowid filters that are available for use with each table.
- Make print_best_access_for_table() print which filter it has picked.
- Make best_access_path() print the filter for considered ref accesses.
Fix clang warning: 'this' pointer cannot be null in well-defined C++ code;
pointer may be assumed to always convert to true
The only caller of TABLE::best_range_rowid_filter_for_partial_join()
already seems to be assuming that s->table != NULL.
Due to inconsistent usage of different cost models to calculate
the cost of ref accesses we have to make the calculation of the
gain promising by usage a range filter more complex.
Find indexes of one table which parts participate in one constraint.
These indexes are called constraint correlated.
New methods: TABLE::find_constraint_correlated_indexes() and
virtual method check_index_dependence() were added.
For each index it's own constraint correlated index map was created
where all indexes that are constraint correlated with the current are
marked.
The results of this task are used for MDEV-16188 (Use in-memory
PK filters built from range index scans).
ANALYZE and ANALYZE FORMAT=JSON structures are changed in the way that they
show additional information when rowid filter is used:
- r_selectivity_pct - the observed filter selectivity
- r_buffer_size - the size of the rowid filter container buffer
- r_filling_time_ms - how long it took to fill rowid filter container
New class Rowid_filter_tracker was added. This class is needed to collect data
about how rowid filter is executed.
This patch contains a full implementation of the optimization
that allows to use in-memory rowid / primary filters built for range
conditions over indexes. In many cases usage of such filters reduce
the number of disk seeks spent for fetching table rows.
In this implementation the choice of what possible filter to be applied
(if any) is made purely on cost-based considerations.
This implementation re-achitectured the partial implementation of
the feature pushed by Galina Shalygina in the commit
8d5a11122c.
Besides this patch contains a better implementation of the generic
handler function handler::multi_range_read_info_const() that
takes into account gaps between ranges when calculating the cost of
range index scans. It also contains some corrections of the
implementation of the handler function records_in_range() for MyISAM.
This patch supports the feature for InnoDB and MyISAM.
