Variant #2.
When Histogram::point_selectivity() sees that the point value of interest
falls into one bucket, it tries to guess whether the bucket has many
different (unpopular) values or a few popular values. (The number of
rows is fixed, as it's a Height-balanced histogram).
The basis for this guess is the "width" of the value range the bucket
covers. Buckets covering wider value ranges are assumed to contain
values with proportionally lower frequencies.
This is just a [brave] guesswork. For a very narrow bucket, it may
produce an estimate that's larger than total #rows in the bucket
or even in the whole table.
Remove the guesswork and replace it with basic logic: return
either the per-table average selectivity of col=const, or selectivity
of one bucket, whichever is lower.
The MDEV-25004 test innodb_fts.versioning is omitted because ever since
commit 685d958e38 InnoDB would not allow
writes to a database where the redo log file ib_logfile0 is missing.
(Patch from Monty, slightly amended)
Fix rowid filtering optimization in best_access_path():
== Ref access + rowid filtering ==
The cost computations compare #records and index-only scan cost
(keyread_tmp) to find out the per-record advantage one will get if
they skip reading full table record.
The computations produce wrong result when:
- the #records are "clipped down" with s->worst_seeks or
thd->variables.max_seeks_for_key. keyread_tmp is not clipped
this way so the numbers are not comparable.
- access_factor is negative. This means index_only read is
cheaper than non-index-only read.
This patch makes the optimizer not to consider Rowid Filtering in
such cases.
The decision is logged in the Optimizer Trace using
"rowid_filter_skipped" name.
== Range access + rowid filtering ==
when considering to use Rowid Filter with range access, do multiply
keyread_tmp by record_count. That way, it is comparable with the
range access's estimate, which is multiplied by record_count.
MDEV-28073 Slow query performance in MariaDB when using many tables
The faster we can find a good query plan, the more options we have for
finding and pruning (ignoring) bad plans.
This patch adds sorting of plans to best_extension_by_limited_search().
The plans, from best_access_path() are sorted according to the numbers
of found rows. This allows us to faster find 'good tables' and we are
thus able to eliminate 'bad plans' faster.
One side effect of this patch is that if two tables have equal cost,
the table that which was used earlier in the query is preferred.
This allows users to improve plans by reordering eq_ref tables in the
order they would like them to be uses.
Result changes caused by the patch:
- Traces are different as now we print the cost for using tables before
we start considering them in the plan.
- Table order are changed for some plans. In most cases this is because
the plans are equal and tables are in this case sorted according to
their usage in the original query.
- A few plans was changed as the optimizer was able to find a better
plan (that was pruned by the original code).
Other things:
- Added a new statistic variable: "optimizer_join_prefixes_check_calls",
which counts number of calls to best_extension_by_limited_search().
This can be used to check the prune efficiency in greedy_search().
- Added variable "JOIN_TAB::embedded_dependent" to be able to handle
XX IN (SELECT..) in the greedy_optimizer. The idea is that we
should prune a table if any of the tables in embedded_dependent is
not yet read.
- When using many tables in a query, there will be some additional
memory usage as we need to pre-allocate table of
table_count*table_count*sizeof(POSITION) objects (POSITION is 312
bytes for now) to hold the pre-calculated best_access_path()
information. This memory usage is offset by the expected
performance improvement when using many tables in a query.
- Removed the code from an earlier patch to keep the table order in
join->best_ref in the original order. This is not needed anymore as we
are now sorting the tables for each best_extension_by_limited_search()
call.
best_extension_by_limited_search() assumes that tables should be sorted
according to size to be able to quickly disregard bad plans. However the
current usage of swap_variables() will change the table order to a not
sorted one for the next recursive call. This breaks the assumtion and
causes performance issues when using many tables (we have to examine
many more plans).
This patch fixes this by ensuring that the original table order is kept
for the not yet used tables when best_extension_by_limited_search() is
called.
This was done by always calling swap_variables() for each table and
restoring the original table order at exit.
Some test changed:
- In a majority of the test the change was that two "identical tables"
where swapped and the optimzer is now using the first/smaller table
- In few test the table order was changed. The new plan looks identical
or slighly better than the original.
In Histogram_json_hb::point_selectivity(), do return selectivity of 0.0
when the histogram says so.
The logic of "Do not return 0.0 estimate as it causes a multiply-by-zero
meltdown in cost and cardinality calculations" is moved into
records_in_column_ranges() where it is one *once* per column pair (as
opposed to doing once per range, which can cause the error to add-up
to large number when there are many ranges)
- multi_range_read_info_const now uses the new records_in_range interface
- Added handler::avg_io_cost()
- Don't calculate avg_io_cost() in get_sweep_read_cost if avg_io_cost is
not 1.0. In this case we trust the avg_io_cost() from the handler.
- Changed test_quick_select to use TIME_FOR_COMPARE instead of
TIME_FOR_COMPARE_IDX to align this with the rest of the code.
- Fixed bug when using test_if_cheaper_ordering where we didn't use
keyread if index was changed
- Fixed a bug where we didn't use index only read when using order-by-index
- Added keyread_time() to HEAP.
The default keyread_time() was optimized for blocks and not suitable for
HEAP. The effect was the HEAP prefered table scans over ranges for btree
indexes.
- Fixed get_sweep_read_cost() for HEAP tables
- Ensure that range and ref have same cost for simple ranges
Added a small cost (MULTI_RANGE_READ_SETUP_COST) to ranges to ensure
we favior ref for range for simple queries.
- Fixed that matching_candidates_in_table() uses same number of records
as the rest of the optimizer
- Added avg_io_cost() to JT_EQ_REF cost. This helps calculate the cost for
HEAP and temporary tables better. A few tests changed because of this.
- heap::read_time() and heap::keyread_time() adjusted to not add +1.
This was to ensure that handler::keyread_time() doesn't give
higher cost for heap tables than for normal tables. One effect of
this is that heap and derived tables stored in heap will prefer
key access as this is now regarded as cheap.
- Changed cost for index read in sql_select.cc to match
multi_range_read_info_const(). All index cost calculation is now
done trough one function.
- 'ref' will now use quick_cost for keys if it exists. This is done
so that for '=' ranges, 'ref' is prefered over 'range'.
- scan_time() now takes avg_io_costs() into account
- get_delayed_table_estimates() uses block_size and avg_io_cost()
- Removed default argument to test_if_order_by_key(); simplifies code
