前言
经过前面两个实验的铺垫，终于到了给数据库系统添加执行查询计划功能的时候了。给定一条 SQL 语句，我们可以将其中的操作符组织为一棵树，树中的每一个父节点都能从子节点获取 tuple 并处理成操作符想要的样子，下图的根节点 会输出最终的查询结果。
对于这样一棵树，我们获取查询结果的方式有许多种，包括：迭代模型、物化模型和向量化模型。本次实验使用的是迭代模型，每个节点都会实现一个 Next() 函数，用于向父节点提供一个 tuple。从根节点开始，每个父节点每次向子节点索取一个 tuple 并处理之后输出：
代码实现
实验主要有三个任务：目录表、执行器和用线性探测哈希表重新实现 hash join 执行器，下面会一个个介绍这几个任务的完成过程。
目录表
目录表可以根据 table_oid 或者 table_name 返回表的元数据，其中最重要的一个字段就是 table_，该字段表示一张表，用于查询、插入、修改和删除 tuple：
复制using table_oid_t = uint32_t;
using column_oid_t = uint32_t;
struct TableMetadata {
TableMetadata(Schema schema, std::string name, std::unique_ptr<TableHeap> &&table, table_oid_t oid)
: schema_(std::move(schema)), name_(std::move(name)), table_(std::move(table)), oid_(oid) {}
Schema schema_;
std::string name_;
std::unique_ptr<TableHeap> table_;
table_oid_t oid_;
};
目录表类 SimpleCatalog 中有三个要求我们实现的方法：CreateTable、GetTable(const std::string &table_name) 和 GetTable(table_oid_t table_oid)，第一个方法用于创建一个新的表，后面两个方法用于获取表：
复制class SimpleCatalog {
public:
SimpleCatalog(BufferPoolManager *bpm, LockManager *lock_manager, LogManager *log_manager)
: bpm_{bpm}, lock_manager_{lock_manager}, log_manager_{log_manager} {}
/**
* Create a new table and return its metadata.
* @param txn the transaction in which the table is being created
* @param table_name the name of the new table
* @param schema the schema of the new table
* @return a pointer to the metadata of the new table
*/
TableMetadata *CreateTable(Transaction *txn, const std::string &table_name, const Schema &schema) {
BUSTUB_ASSERT(names_.count(table_name) == 0, "Table names should be unique!");
table_oid_t oid = next_table_oid_++;
auto table = std::make_unique<TableHeap>(bpm_, lock_manager_, log_manager_, txn);
tables_[oid] = std::make_unique<TableMetadata>(schema, table_name, std::move(table), oid);
names_[table_name] = oid;
return tables_[oid].get();
}
/** @return table metadata by name */
TableMetadata *GetTable(const std::string &table_name) {
auto it = names_.find(table_name);
if (it == names_.end()) {
throw std::out_of_range("The table name doesn't exist.");
}
return GetTable(it->second);
}
/** @return table metadata by oid */
TableMetadata *GetTable(table_oid_t table_oid) {
auto it = tables_.find(table_oid);
if (it == tables_.end()) {
throw std::out_of_range("The table oid doesn't exist.");
}
return it->second.get();
}
private:
[[maybe_unused]] BufferPoolManager *bpm_;
[[maybe_unused]] LockManager *lock_manager_;
[[maybe_unused]] LogManager *log_manager_;
/** tables_ : table identifiers -> table metadata. Note that tables_ owns all table metadata. */
std::unordered_map<table_oid_t, std::unique_ptr<TableMetadata>> tables_;
/** names_ : table names -> table identifiers */
std::unordered_map<std::string, table_oid_t> names_;
/** The next table identifier to be used. */
std::atomic<table_oid_t> next_table_oid_{0};
};
折叠
测试结果如下：
执行器
执行器用于执行查询计划，该实验要求我们实现下述四种执行器：
SeqScanExecutor：顺序扫描执行器，遍历表并返回符合查询条件的 tuple，比如SELECT * FROM user where id=1通过该执行器获取查询结果InsertExecutor：插入执行器，向表格中插入任意数量的 tuple，比如INSERT INTO user VALUES (1, 2), (2, 3)HashJoinExecutor：哈希连接执行器，用于内连接查询操作，比如SELECT u.id, c.class FROM u JOIN c ON u.id = c.uidAggregationExecutor：聚合执行器，用于执行聚合操作，比如SELECT MIN(grade), MAX(grade) from user
每个执行器都继承自抽象类 AbstractExecutor ，有两个纯虚函数 Init() 和 Next(Tuple *tuple) 需要实现，其中 Init() 用于初始化执行器，比如需要在 HashJoinExecutor 的 Init() 中对 left table(outer table) 创建哈希表。AbstractExecutor 还有一个 ExecutorContext 成员，包含一些查询的元数据，比如 BufferPoolManager 和上个任务实现的 SimpleCatalog：
复制class AbstractExecutor {
public:
/**
* Constructs a new AbstractExecutor.
* @param exec_ctx the executor context that the executor runs with
*/
explicit AbstractExecutor(ExecutorContext *exec_ctx) : exec_ctx_{exec_ctx} {}
/** Virtual destructor. */
virtual ~AbstractExecutor() = default;
/**
* Initializes this executor.
* @warning This function must be called before Next() is called!
*/
virtual void Init() = 0;
/**
* Produces the next tuple from this executor.
* @param[out] tuple the next tuple produced by this executor
* @return true if a tuple was produced, false if there are no more tuples
*/
virtual bool Next(Tuple *tuple) = 0;
/** @return the schema of the tuples that this executor produces */
virtual const Schema *GetOutputSchema() = 0;
/** @return the executor context in which this executor runs */
ExecutorContext *GetExecutorContext() { return exec_ctx_; }
protected:
ExecutorContext *exec_ctx_;
};
折叠
执行器内部会有一个代表执行计划的 AbstractPlanNode 的子类数据成员，而这些子类内部又会有一个 AbstractExpression 的子类数据成员用于判断查询条件是否成立等操作。
顺序扫描
提供的代码中为我们实现了一个 TableIterator 类，用于迭代 TableHeap，我们只要在 Next 函数中判断迭代器所指的 tuple 是否满足查询条件并递增迭代器，如果满足条件就返回该 tuple，不满足就接着迭代：
复制class SeqScanExecutor : public AbstractExecutor {
public:
/**
* Creates a new sequential scan executor.
* @param exec_ctx the executor context
* @param plan the sequential scan plan to be executed
*/
SeqScanExecutor(ExecutorContext *exec_ctx, const SeqScanPlanNode *plan);
void Init() override;
bool Next(Tuple *tuple) override;
const Schema *GetOutputSchema() override { return plan_->OutputSchema(); }
private:
/** The sequential scan plan node to be executed. */
const SeqScanPlanNode *plan_;
TableMetadata *table_metadata_;
TableIterator table_iterator_;
};
实现代码如下：
复制SeqScanExecutor::SeqScanExecutor(ExecutorContext *exec_ctx, const SeqScanPlanNode *plan)
: AbstractExecutor(exec_ctx),
plan_(plan),
table_metadata_(exec_ctx->GetCatalog()->GetTable(plan->GetTableOid())),
table_iterator_(table_metadata_->table_->Begin(exec_ctx->GetTransaction())) {}
void SeqScanExecutor::Init() {}
bool SeqScanExecutor::Next(Tuple *tuple) {
auto predicate = plan_->GetPredicate();
while (table_iterator_ != table_metadata_->table_->End()) {
*tuple = *table_iterator_++;
if (!predicate || predicate->Evaluate(tuple, &table_metadata_->schema_).GetAs<bool>()) {
return true;
}
}
return false;
}
插入
插入操作分为两种：
- raw inserts：插入数据直接来自插入执行器本身，比如
INSERT INTO tbl_user VALUES (1, 15), (2, 16) - not-raw inserts：插入的数据来自子执行器，比如
INSERT INTO tbl_user1 SELECT * FROM tbl_user2
可以根据插入计划的 IsRawInsert() 判断插入操作的类型，这个函数根据子查询器列表是否为空进行判断：
复制/** @return true if we embed insert values directly into the plan, false if we have a child plan providing tuples */
bool IsRawInsert() const { return GetChildren().empty(); }
如果是 raw inserts，我们直接根据插入执行器中的数据构造 tuple 并插入表中，否则调用子执行器的 Next 函数获取数据并插入表中：
复制class InsertExecutor : public AbstractExecutor {
public:
/**
* Creates a new insert executor.
* @param exec_ctx the executor context
* @param plan the insert plan to be executed
* @param child_executor the child executor to obtain insert values from, can be nullptr
*/
InsertExecutor(ExecutorContext *exec_ctx, const InsertPlanNode *plan,
std::unique_ptr<AbstractExecutor> &&child_executor);
const Schema *GetOutputSchema() override;
void Init() override;
// Note that Insert does not make use of the tuple pointer being passed in.
// We return false if the insert failed for any reason, and return true if all inserts succeeded.
bool Next([[maybe_unused]] Tuple *tuple) override;
private:
/** The insert plan node to be executed. */
const InsertPlanNode *plan_;
std::unique_ptr<AbstractExecutor> child_executor_;
TableMetadata *table_metadata_;
};
实现代码为：
复制InsertExecutor::InsertExecutor(ExecutorContext *exec_ctx, const InsertPlanNode *plan,
std::unique_ptr<AbstractExecutor> &&child_executor)
: AbstractExecutor(exec_ctx),
plan_(plan),
child_executor_(std::move(child_executor)),
table_metadata_(exec_ctx->GetCatalog()->GetTable(plan->TableOid())) {}
const Schema *InsertExecutor::GetOutputSchema() { return plan_->OutputSchema(); }
void InsertExecutor::Init() {}
bool InsertExecutor::Next([[maybe_unused]] Tuple *tuple) {
RID rid;
if (plan_->IsRawInsert()) {
for (const auto &values : plan_->RawValues()) {
Tuple tuple(values, &table_metadata_->schema_);
if (!table_metadata_->table_->InsertTuple(tuple, &rid, exec_ctx_->GetTransaction())) {
return false;
};
}
} else {
Tuple tuple;
while (child_executor_->Next(&tuple)) {
if (!table_metadata_->table_->InsertTuple(tuple, &rid, exec_ctx_->GetTransaction())) {
return false;
};
}
}
return true;
}
折叠
哈希连接
哈希连接执行器使用的是最基本的哈希连接算法，没有使用布隆过滤器等优化措施。该算法分为两个阶段：
- 将 left table 的 join 语句中各个条件所在列的值作为键，tuple 或者 row id 作为值构造哈希表，这一步允许将相同哈希值的 tuple 插入哈希表
- 对 right table 的 join 语句中各个条件所在列的值作为键，在哈希表中进行查询获取所以系统哈希值的 left table 中的 tuple，再使用 join 条件进行精确匹配
对 tuple 进行哈希的函数为：
复制/**
* Hashes a tuple by evaluating it against every expression on the given schema, combining all non-null hashes.
* @param tuple tuple to be hashed
* @param schema schema to evaluate the tuple on
* @param exprs expressions to evaluate the tuple with
* @return the hashed tuple
*/
hash_t HashJoinExecutor::HashValues(const Tuple *tuple, const Schema *schema, const std::vector<const AbstractExpression *> &exprs) {
hash_t curr_hash = 0;
// For every expression,
for (const auto &expr : exprs) {
// We evaluate the tuple on the expression and schema.
Value val = expr->Evaluate(tuple, schema);
// If this produces a value,
if (!val.IsNull()) {
// We combine the hash of that value into our current hash.
curr_hash = HashUtil::CombineHashes(curr_hash, HashUtil::HashValue(&val));
}
}
return curr_hash;
}
为了方便我们的测试，实验提供了一个简易的哈希表 SimpleHashJoinHashTable 用于插入 (hash, tuple) 键值对，该哈希表直接整个放入内存中，如果 tuple 很多，内存会放不下这个哈希表，所以任务三会替换为上一个实验中实现的 LinearProbeHashTable。
复制using HT = SimpleHashJoinHashTable;
class HashJoinExecutor : public AbstractExecutor {
public:
/**
* Creates a new hash join executor.
* @param exec_ctx the context that the hash join should be performed in
* @param plan the hash join plan node
* @param left the left child, used by convention to build the hash table
* @param right the right child, used by convention to probe the hash table
*/
HashJoinExecutor(ExecutorContext *exec_ctx, const HashJoinPlanNode *plan, std::unique_ptr<AbstractExecutor> &&left,
std::unique_ptr<AbstractExecutor> &&right);
/** @return the JHT in use. Do not modify this function, otherwise you will get a zero. */
const HT *GetJHT() const { return &jht_; }
const Schema *GetOutputSchema() override { return plan_->OutputSchema(); }
void Init() override;
bool Next(Tuple *tuple) override;
hash_t HashValues(const Tuple *tuple, const Schema *schema, const std::vector<const AbstractExpression *> &exprs) { // 省略 }
private:
/** The hash join plan node. */
const HashJoinPlanNode *plan_;
std::unique_ptr<AbstractExecutor> left_executor_;
std::unique_ptr<AbstractExecutor> right_executor_;
/** The comparator is used to compare hashes. */
[[maybe_unused]] HashComparator jht_comp_{};
/** The identity hash function. */
IdentityHashFunction jht_hash_fn_{};
/** The hash table that we are using. */
HT jht_;
/** The number of buckets in the hash table. */
static constexpr uint32_t jht_num_buckets_ = 2;
};
折叠
根据上述的算法过程可以得到实现代码为：
复制HashJoinExecutor::HashJoinExecutor(ExecutorContext *exec_ctx, const HashJoinPlanNode *plan,
std::unique_ptr<AbstractExecutor> &&left, std::unique_ptr<AbstractExecutor> &&right)
: AbstractExecutor(exec_ctx),
plan_(plan),
left_executor_(std::move(left)),
right_executor_(std::move(right)),
jht_("join hash table", exec_ctx->GetBufferPoolManager(), jht_comp_, jht_num_buckets_, jht_hash_fn_) {}
void HashJoinExecutor::Init() {
left_executor_->Init();
right_executor_->Init();
// create hash table for left child
Tuple tuple;
while (left_executor_->Next(&tuple)) {
auto h = HashValues(&tuple, left_executor_->GetOutputSchema(), plan_->GetLeftKeys());
jht_.Insert(exec_ctx_->GetTransaction(), h, tuple);
}
}
bool HashJoinExecutor::Next(Tuple *tuple) {
auto predicate = plan_->Predicate();
auto left_schema = left_executor_->GetOutputSchema();
auto right_schema = right_executor_->GetOutputSchema();
auto out_schema = GetOutputSchema();
Tuple right_tuple;
while (right_executor_->Next(&right_tuple)) {
// get all tuples with the same hash values in left child
auto h = HashValues(&right_tuple, right_executor_->GetOutputSchema(), plan_->GetRightKeys());
std::vector<Tuple> left_tuples;
jht_.GetValue(exec_ctx_->GetTransaction(), h, &left_tuples);
// get the exact matching left tuple
for (auto &left_tuple : left_tuples) {
if (!predicate || predicate->EvaluateJoin(&left_tuple, left_schema, &right_tuple, right_schema).GetAs<bool>()) {
// create output tuple
std::vector<Value> values;
for (uint32_t i = 0; i < out_schema->GetColumnCount(); ++i) {
auto expr = out_schema->GetColumn(i).GetExpr();
values.push_back(expr->EvaluateJoin(&left_tuple, left_schema, &right_tuple, right_schema));
}
*tuple = Tuple(values, out_schema);
return true;
}
}
}
return false;
}
折叠
聚合
聚合执行器内部维护了一个哈希表 SimpleAggregationHashTable 以及哈希表迭代器 aht_iterator_，将键值对插入哈希表的时候会立刻更新聚合结果，最终的查询结果也从该哈希表获取：
复制AggregationExecutor::AggregationExecutor(ExecutorContext *exec_ctx, const AggregationPlanNode *plan,
std::unique_ptr<AbstractExecutor> &&child)
: AbstractExecutor(exec_ctx),
plan_(plan),
child_(std::move(child)),
aht_(plan->GetAggregates(), plan->GetAggregateTypes()),
aht_iterator_(aht_.Begin()) {}
const AbstractExecutor *AggregationExecutor::GetChildExecutor() const { return child_.get(); }
const Schema *AggregationExecutor::GetOutputSchema() { return plan_->OutputSchema(); }
void AggregationExecutor::Init() {
child_->Init();
// initialize aggregation hash table
Tuple tuple;
while (child_->Next(&tuple)) {
aht_.InsertCombine(MakeKey(&tuple), MakeVal(&tuple));
}
aht_iterator_ = aht_.Begin();
}
bool AggregationExecutor::Next(Tuple *tuple) {
auto having = plan_->GetHaving();
auto out_schema = GetOutputSchema();
while (aht_iterator_ != aht_.End()) {
auto group_bys = aht_iterator_.Key().group_bys_;
auto aggregates = aht_iterator_.Val().aggregates_;
if (!having || having->EvaluateAggregate(group_bys, aggregates).GetAs<bool>()) {
std::vector<Value> values;
for (uint32_t i = 0; i < out_schema->GetColumnCount(); ++i) {
auto expr = out_schema->GetColumn(i).GetExpr();
values.push_back(expr->EvaluateAggregate(group_bys, aggregates));
}
*tuple = Tuple(values, out_schema);
++aht_iterator_;
return true;
}
++aht_iterator_;
}
return false;
}
折叠
测试
测试结果如下图所示，成功通过所有测试用例：
线性探测哈希表
这个任务要求将哈希连接中的 SimpleHashJoinHashTable 更换成 LinearProbeHashTable，这样就能在磁盘中保存 left table 的哈希表。实验还提示我们可以实现 TmpTuplePage，用于保存 left table 的 tuple，其实我们完全可以用代码中写好的 TablePage 来实现该目的，但是 TmpTuplePage 结构更为精简，可以搭配 Tuple::DeserializeFrom 食用，通过实现 TmpTuplePage，我们也能加深对 tuple 存储方式的理解。
TmpTuplePage 的格式如下所示：
复制 ---------------------------------------------------------------------------------------------------------
| PageId (4) | LSN (4) | FreeSpace (4) | (free space) | TupleSize2 | TupleData2 | TupleSize1 | TupleData1 |
---------------------------------------------------------------------------------------------------------
\-----------------V------------------/ ^
header free space pointer
前 12 个字节是 header，记录了 page id、lsn 和 free space pointer，此处的 free space pointer 是相对 page id 的地址而言的。如果表中一个 tuple 都没有，且表大小为 PAGE_SIZE，那么 free space pointer 的值就是 PAGE_SIZE。tuple 从末尾开始插入，每个 tuple 后面跟着 tuple 的大小（占用 4 字节），也就是说插入一个 tuple 占用的空间大小为 tuple.size_ + 4。
理解上述内容后，实现 TmpTupleHeader 就很简单了，模仿 TablePage 的写法即可（需要将 TmpTuplePage 声明为 Tuple 的友元）：
复制class TmpTuplePage : public Page {
public:
void Init(page_id_t page_id, uint32_t page_size) {
memcpy(GetData(), &page_id, sizeof(page_id));
SetFreeSpacePointer(page_size);
}
/** @return the page ID of this temp table page */
page_id_t GetTablePageId() { return *reinterpret_cast<page_id_t *>(GetData()); }
bool Insert(const Tuple &tuple, TmpTuple *out) {
// determine whether there is enough space to insert tuple
if (GetFreeSpaceRemaining() < tuple.size_ + SIZE_TUPLE) {
return false;
}
// insert tuple and its size
SetFreeSpacePointer(GetFreeSpacePointer() - tuple.size_);
memcpy(GetData() + GetFreeSpacePointer(), tuple.data_, tuple.size_);
SetFreeSpacePointer(GetFreeSpacePointer() - SIZE_TUPLE);
memcpy(GetData() + GetFreeSpacePointer(), &tuple.size_, SIZE_TUPLE);
out->SetPageId(GetPageId());
out->SetOffset(GetFreeSpacePointer());
return true;
}
private:
static_assert(sizeof(page_id_t) == 4);
static constexpr size_t SIZE_TABLE_PAGE_HEADER = 12;
static constexpr size_t SIZE_TUPLE = 4;
static constexpr size_t OFFSET_FREE_SPACE = 8;
/** @return pointer to the end of the current free space, see header comment */
uint32_t GetFreeSpacePointer() { return *reinterpret_cast<uint32_t *>(GetData() + OFFSET_FREE_SPACE); }
/** set the pointer of the end of current free space.
* @param free_space_ptr the pointer relative to data_
*/
void SetFreeSpacePointer(uint32_t free_space_ptr) {
memcpy(GetData() + OFFSET_FREE_SPACE, &free_space_ptr, sizeof(uint32_t));
}
/** @return the size of free space */
uint32_t GetFreeSpaceRemaining() { return GetFreeSpacePointer() - SIZE_TABLE_PAGE_HEADER; }
};
折叠
在 Insert 函数中更新了 TmpTuple 的参数，我们会将 TmpTuple 作为 left table 哈希表的值，而 tuple 放在 TmpTuplePage 中，根据 TmpTuple 中保存的 offset 获取 tuple：
复制class TmpTuple {
public:
TmpTuple(page_id_t page_id, size_t offset) : page_id_(page_id), offset_(offset) {}
inline bool operator==(const TmpTuple &rhs) const { return page_id_ == rhs.page_id_ && offset_ == rhs.offset_; }
page_id_t GetPageId() const { return page_id_; }
size_t GetOffset() const { return offset_; }
void SetPageId(page_id_t page_id) { page_id_ = page_id; }
void SetOffset(size_t offset) { offset_ = offset; }
private:
page_id_t page_id_;
size_t offset_;
};
接着需要将哈希表更换为 LinearProbeHashTable，在 linear_probe_hash_table.cpp 中需要进行模板特例化：
复制template class LinearProbeHashTable<hash_t, TmpTuple, HashComparator>;
还要对 HashTableBlockPage 进行模板特例化：
复制template class HashTableBlockPage<hash_t, TmpTuple, HashComparator>;
接着更改 HT：
复制using HashJoinKeyType = hash_t;
using HashJoinValType = TmpTuple;
using HT = LinearProbeHashTable<HashJoinKeyType, HashJoinValType, HashComparator>;
由于 tuple 可能很多，将 jht_num_buckets_ 设置为 1000 可以减少调整大小的次数，最后是实现代码：
复制void HashJoinExecutor::Init() {
left_executor_->Init();
right_executor_->Init();
// create temp tuple page
auto buffer_pool_manager = exec_ctx_->GetBufferPoolManager();
page_id_t tmp_page_id;
auto tmp_page = reinterpret_cast<TmpTuplePage *>(buffer_pool_manager->NewPage(&tmp_page_id)->GetData());
tmp_page->Init(tmp_page_id, PAGE_SIZE);
// create hash table for left child
Tuple tuple;
TmpTuple tmp_tuple(tmp_page_id, 0);
while (left_executor_->Next(&tuple)) {
auto h = HashValues(&tuple, left_executor_->GetOutputSchema(), plan_->GetLeftKeys());
// insert tuple to page, creata a new temp tuple page if page if full
if (!tmp_page->Insert(tuple, &tmp_tuple)) {
buffer_pool_manager->UnpinPage(tmp_page_id, true);
tmp_page = reinterpret_cast<TmpTuplePage *>(buffer_pool_manager->NewPage(&tmp_page_id)->GetData());
tmp_page->Init(tmp_page_id, PAGE_SIZE);
// try inserting tuple to page again
tmp_page->Insert(tuple, &tmp_tuple);
}
jht_.Insert(exec_ctx_->GetTransaction(), h, tmp_tuple);
}
buffer_pool_manager->UnpinPage(tmp_page_id, true);
}
bool HashJoinExecutor::Next(Tuple *tuple) {
auto buffer_pool_manager = exec_ctx_->GetBufferPoolManager();
auto left_schema = left_executor_->GetOutputSchema();
auto right_schema = right_executor_->GetOutputSchema();
auto predicate = plan_->Predicate();
auto out_schema = GetOutputSchema();
Tuple right_tuple;
while (right_executor_->Next(&right_tuple)) {
// get all tuples with the same hash values in left child
auto h = HashValues(&right_tuple, right_executor_->GetOutputSchema(), plan_->GetRightKeys());
std::vector<TmpTuple> tmp_tuples;
jht_.GetValue(exec_ctx_->GetTransaction(), h, &tmp_tuples);
// get the exact matching left tuple
for (auto &tmp_tuple : tmp_tuples) {
// convert tmp tuple to left tuple
auto page_id = tmp_tuple.GetPageId();
auto tmp_page = buffer_pool_manager->FetchPage(page_id);
Tuple left_tuple;
left_tuple.DeserializeFrom(tmp_page->GetData() + tmp_tuple.GetOffset());
buffer_pool_manager->UnpinPage(page_id, false);
if (!predicate || predicate->EvaluateJoin(&left_tuple, left_schema, &right_tuple, right_schema).GetAs<bool>()) {
// create output tuple
std::vector<Value> values;
for (uint32_t i = 0; i < out_schema->GetColumnCount(); ++i) {
auto expr = out_schema->GetColumn(i).GetExpr();
values.push_back(expr->EvaluateJoin(&left_tuple, left_schema, &right_tuple, right_schema));
}
*tuple = Tuple(values, out_schema);
return true;
}
}
}
return false;
}
折叠
测试结果如下：
总结
通过这次实验，可以加深对目录、查询计划、迭代模型和 tuple 页布局的理解，算是收获满满的一次实验了，以上~~