CMU15445 (Fall 2019) 之 Project#3 - Query Execution 详解

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 \(\pi\) 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：

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：

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};
};

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.uid
• AggregationExecutor：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：

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_;
};

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：

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：

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：

/** @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：

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：

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;
}

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：

1. 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
2. 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：

/**
 * 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。

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;
};

根据上述的算法过程可以得到实现代码为：

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;
}

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：

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;
}

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：

 ---------------------------------------------------------------------------------------------------------
| 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）：

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; }
};

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：

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：

template class LinearProbeHashTable<hash_t, TmpTuple, HashComparator>;

还要对 HashTableBlockPage 进行模板特例化：

template class HashTableBlockPage<hash_t, TmpTuple, HashComparator>;

接着更改 HT：

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：

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;
}

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~~