PlanContext LogicalFilter::GetPlanContext() {
/** In the currently implementation, we assume that the boolean operator
* between each AttributeComparator is "AND".
*/
PlanContext plan_context = child_->GetPlanContext();
if (plan_context.IsHashPartitioned()) {
for (unsigned i = 0;
i < plan_context.plan_partitioner_.GetNumberOfPartitions(); ++i) {
if (CanBeHashPruned(i, plan_context.plan_partitioner_)) {
// Is filtered.
plan_context.plan_partitioner_.GetPartition(i)->set_filtered();
} else { // Call predictSelectivilty() to alter cardinality.
/**
* Should predict the volume of data that passes the filter.
* TODO(wangli): A precious prediction is needed based on the statistic
* of the input data, which may be maintained in the
* catalog module.
*/
const unsigned before_filter_cardinality =
plan_context.plan_partitioner_.GetPartition(i)->get_cardinality();
const unsigned after_filter_cardinality =
before_filter_cardinality * PredictSelectivity();
plan_context.plan_partitioner_.GetPartition(i)
->set_cardinality(after_filter_cardinality);
}
}
}
set_column_id(plan_context);
Schema* input_ = GetSchema(plan_context.attribute_list_);
for (int i = 0; i < condi_.size(); ++i) {
// Initialize expression of logical execution plan.
InitExprAtLogicalPlan(condi_[i], t_boolean, column_id_, input_);
}
return plan_context;
}
PlanContext LogicalFilter::GetPlanContext() {
/** In the currently implementation, we assume that the boolean operator
* between each AttributeComparator is "AND".
*/
lock_->acquire();
if (NULL != plan_context_) {
lock_->release();
return *plan_context_;
}
PlanContext plan_context = child_->GetPlanContext();
if (plan_context.IsHashPartitioned()) {
for (unsigned i = 0;
i < plan_context.plan_partitioner_.GetNumberOfPartitions(); ++i) {
if (CanBeHashPruned(i, plan_context.plan_partitioner_)) {
// Is filtered.
plan_context.plan_partitioner_.GetPartition(i)->set_filtered();
} else { // Call predictSelectivilty() to alter cardinality.
/**
* Should predict the volume of data that passes the filter.
* TODO(wangli): A precious prediction is needed based on the statistic
* of the input data, which may be maintained in the
* catalog module.
*/
const unsigned before_filter_cardinality =
plan_context.plan_partitioner_.GetPartition(i)->get_cardinality();
const unsigned after_filter_cardinality =
before_filter_cardinality * PredictSelectivity();
plan_context.plan_partitioner_.GetPartition(i)
->set_cardinality(after_filter_cardinality);
}
}
}
// std::map<std::string, int> column_to_id;
// GetColumnToId(plan_context.attribute_list_, column_to_id);
// Schema* input_schema = GetSchema(plan_context.attribute_list_);
#ifdef NEWCONDI
for (int i = 0; i < condi_.size(); ++i) {
// Initialize expression of logical execution plan.
InitExprAtLogicalPlan(condi_[i], t_boolean, column_to_id, input_schema);
}
#else
LogicInitCnxt licnxt;
GetColumnToId(plan_context.attribute_list_, licnxt.column_id0_);
licnxt.schema0_ = plan_context.GetSchema();
for (int i = 0; i < condition_.size(); ++i) {
licnxt.return_type_ = t_boolean;
condition_[i]->InitExprAtLogicalPlan(licnxt);
}
#endif
plan_context_ = new PlanContext();
*plan_context_ = plan_context;
plan_context_->attribute_list_.assign(plan_context.attribute_list_.begin(),
plan_context.attribute_list_.end());
lock_->release();
return *plan_context_;
}
