cost_model.h

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#ifndef SQL_JOIN_OPTIMIZER_COST_MODEL_H_
#define SQL_JOIN_OPTIMIZER_COST_MODEL_H_
 
#include "my_base.h"
#include "sql/join_optimizer/access_path.h"
#include "sql/join_optimizer/relational_expression.h"
#include "sql/mem_root_array.h"
 
struct AccessPath;
struct ContainedSubquery;
class Item;
class Query_block;
class THD;
struct TABLE;
 
/**
   When we make cost estimates, we use this as the maximal length the
   values we get from evaluating an Item (in bytes). Actual values of
   e.g. blobs may be much longer, but even so we use this as an upper
   limit when doing cost calculations. (For context, @see Item#max_length .)
*/
constexpr size_t kMaxItemLengthEstimate = 4096;
 
// These are extremely arbitrary cost model constants. We should revise them
// based on actual query times (possibly using linear regression?), and then
// put them into the cost model to make them user-tunable.
constexpr double kApplyOneFilterCost = 0.1;
constexpr double kAggregateOneRowCost = 0.1;
constexpr double kSortOneRowCost = 0.1;
constexpr double kHashBuildOneRowCost = 0.1;
constexpr double kHashProbeOneRowCost = 0.1;
constexpr double kHashReturnOneRowCost = 0.07;
constexpr double kMaterializeOneRowCost = 0.1;
constexpr double kWindowOneRowCost = 0.1;
 
/// A fallback cardinality estimate that is used in case the storage engine
/// cannot provide one (like for table functions). It's a fairly arbitrary
/// non-zero value.
constexpr ha_rows kRowEstimateFallback = 1000;
 
/// See EstimateFilterCost.
struct FilterCost {
  /// Cost of evaluating the filter for all rows if subqueries are not
  /// materialized.  (Note that this includes the contribution from
  /// init_cost_if_not_materialized.)
  double cost_if_not_materialized{0.0};
 
  /// Initial cost before the filter can be applied for the first time.
  /// Typically the cost of executing 'independent subquery' in queries like:
  /// "SELECT * FROM tab WHERE field = <independent subquery>".
  /// (That corresponds to the Item_singlerow_subselect class.)
  double init_cost_if_not_materialized{0.0};
 
  /// Cost of evaluating the filter for all rows if all subqueries in
  /// it have been materialized beforehand. If there are no subqueries
  /// in the condition, equals cost_if_not_materialized.
  double cost_if_materialized{0.0};
 
  /// Cost of materializing all subqueries present in the filter.
  /// If there are no subqueries in the condition, equals zero.
  double cost_to_materialize{0.0};
};
 
/// Used internally by EstimateFilterCost() only.
void AddCost(THD *thd, const ContainedSubquery &subquery, double num_rows,
             FilterCost *cost);
 
/**
  Estimate the cost of evaluating “condition”, “num_rows” times.
  This is a fairly rudimentary estimation, _but_ it includes the cost
  of any subqueries that may be present and that need evaluation.
 */
FilterCost EstimateFilterCost(THD *thd, double num_rows, Item *condition,
                              const Query_block *outer_query_block);
 
/**
  A cheaper overload of EstimateFilterCost() that assumes that all
  contained subqueries have already been extracted (ie., it skips the
  walking, which can be fairly expensive). This data is typically
  computed by FindContainedSubqueries().
 */
inline FilterCost EstimateFilterCost(
    THD *thd, double num_rows,
    const Mem_root_array<ContainedSubquery> &contained_subqueries) {
  FilterCost cost;
  cost.cost_if_not_materialized = num_rows * kApplyOneFilterCost;
  cost.cost_if_materialized = num_rows * kApplyOneFilterCost;
 
  for (const ContainedSubquery &subquery : contained_subqueries) {
    AddCost(thd, subquery, num_rows, &cost);
  }
  return cost;
}
 
double EstimateCostForRefAccess(THD *thd, TABLE *table, unsigned key_idx,
                                double num_output_rows);
void EstimateSortCost(AccessPath *path);
void EstimateMaterializeCost(THD *thd, AccessPath *path);
 
/**
   Estimate costs and result row count for an aggregate operation.
   @param[in,out] path The AGGREGATE path.
   @param[in] query_block The Query_block to which 'path' belongs.
   @param[in,out] trace Optimizer trace text.
 */
void EstimateAggregateCost(AccessPath *path, const Query_block *query_block,
                           std::string *trace = nullptr);
void EstimateDeleteRowsCost(AccessPath *path);
void EstimateUpdateRowsCost(AccessPath *path);
 
/// Estimate the costs and row count for a STREAM AccessPath.
void EstimateStreamCost(AccessPath *path);
 
/// Estimate the costs and row count for a LIMIT_OFFSET AccessPath.
void EstimateLimitOffsetCost(AccessPath *path);
 
/// Estimate the costs and row count for a WINDOW AccessPath.
void EstimateWindowCost(AccessPath *path);
 
inline double FindOutputRowsForJoin(double left_rows, double right_rows,
                                    const JoinPredicate *edge) {
  double fanout = right_rows * edge->selectivity;
  if (edge->expr->type == RelationalExpression::LEFT_JOIN) {
    // For outer joins, every outer row produces at least one row (if none
    // are matching, we get a NULL-complemented row).
    // Note that this can cause inconsistent row counts; see bug #33550360
    // and/or JoinHypergraph::has_reordered_left_joins.
    fanout = std::max(fanout, 1.0);
  } else if (edge->expr->type == RelationalExpression::SEMIJOIN) {
    // Semi- and antijoin estimation is pretty tricky, since we want isn't
    // really selectivity; we want the probability that at least one row
    // is matching, which is something else entirely. However, given that
    // we only have selectivity to work with, we don't really have anything
    // better than to estimate it as a normal join and cap the result
    // at selectivity 1.0 (ie., each outer row generates at most one inner row).
    // Note that this can cause inconsistent row counts; see bug #33550360 and
    // CostingReceiver::has_semijoin_with_possibly_clamped_child.
    fanout = std::min(fanout, 1.0);
  } else if (edge->expr->type == RelationalExpression::ANTIJOIN) {
    // Antijoin are estimated as simply the opposite of semijoin (see above),
    // but wrongly estimating 0 rows (or, of course, a negative amount) could be
    // really bad, so we assume at least 10% coming out as a fudge factor.
    // It's better to estimate too high than too low here.
    fanout = std::max(1.0 - fanout, 0.1);
  }
  return left_rows * fanout;
}
 
#endif  // SQL_JOIN_OPTIMIZER_COST_MODEL_H_