CMU 15-445 Project #3 - Query Execution（Task #1、Task #2）

一、题目链接

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二、准备工作

Project #3 中的执行器需要通过 sqllogictest 进行测试，执行以下三条命令即可完成编译及运行：

atreus@AtreusMBP 9:18 bustub % cd build
atreus@AtreusMBP 9:18 build % make -j$(nproc) sqllogictest
atreus@AtreusMBP 9:19 build % ./bin/bustub-sqllogictest ../test/sql/p3.06-simple-agg.slt --verbose

此外也可以通过 CLion 进行运行和调试，具体配置如下，其中程序实参中的 slt 文件路径与工作目录需要根据自己的项目结构进行修改，建议自己新建一个 slt 文件专门用于调试，这样每次只要修改这个自定义 slt 文件的内容即可，而不需要修改编译参数。

其他准备工作见 CMU 15-445 Project #0 - C++ Primer 中的准备工作。

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三、SQL 语句执行流程

根据 bustub 的架构图，在收到一条 SQL 语句后，查询处理层首先会通过解析器 Parser 将 SQL 语句解析为一颗抽象句法树 AST（Abstracct Syntax Tree）。接下来绑定器 Binder 会遍历这棵句法树，将表名、列名等映射到数据库中的实际对象上，并由计划器 Planner 生成初步的查询计划。查询计划会以树的形式表示，数据从叶子节点流向父节点。最后，优化器 Optimizer 会优化生成最终的查询计划，然后交由查询执行层的执行器执行，而这里面的部分执行器需要我们来实现。

以下面的 explain select * from test_1; 为例，绑定器将表名 test_1 绑定到 oid 为 20 的 test_1 实体表中，将 * 绑定到该表的所有列上。接下来计划器会生成对应的查询计划，这个查询计划包括 Projection 和 SeqScan 两个计划节点，数据会先由 SeqScan 处理，然后交给 Projection 处理。不过最后优化器会优化掉这里的投影操作，因为要查找的是所有字段，并不需要进行投影运算。最终，查询执行层会执行 SeqScan 对应的执行器完成实际的查询。

bustub> explain select * from test_1;
=== BINDER ===
BoundSelect {
  table=BoundBaseTableRef { table=test_1, oid=20 },
  columns=[test_1.colA, test_1.colB, test_1.colC, test_1.colD],
  groupBy=[],
  having=,
  where=,
  limit=,
  offset=,
  order_by=[],
  is_distinct=false,
  ctes=,
}
=== PLANNER ===
Projection { exprs=[#0.0, #0.1, #0.2, #0.3] } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
  SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
=== OPTIMIZER ===
SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)bustub> explain select * from test_1;

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四、BusTub 表结构

以上图片来自于 https://blog.eleven.wiki/posts/cmu15-445-project3-query-execution/，这位博主对 BusTub 中的表结构进行了直观的说明。

首先，我们从所有执行器的基类抽象执行器 AbstractExecutor 入手，抽象执行器实现了一个 tuple-at-a-time 形式的火山迭代器模型，它主要包括 Init() 和 Next() 两个成员函数，tuple-at-a-time 表明每次调用 Next() 函数会从当前执行器获取一个 tuple，火山模型则表明数据从子执行器向父执行器传递，根节点的输出即为最终的执行结果。

AbstractExecutor 只有执行器上下文 ExecutorContext 这一个成员变量，它包括当前事务 Transaction、缓冲池管理器 BufferPoolManager、事务管理器 TransactionManager、锁管理器 LockManager 以及目录 Catalog。不过在抽象执行器的实现类中，除了执行器上下文往往还会有一个计划节点 AbstractPlanNode 的实现类，用于表明将要执行的计划，这个后续也会被用到。

与表管理直接相关的就是执行器上下文中的目录 Catalog（见上图），在 DBMS 中，目录通常是非持久的，主要为数据库执行引擎中的执行器而设计。它可以解决表创建、表查找、索引创建和索引查找等操作。目录中主要包含了一个表标识符到表信息 TableInfo 的映射 std::unordered_map> 和一个表名到表标识符的映射 std::unordered_map。

表信息 TableInfo 主要保存了有关表的一些元数据，其中最主要的就是 TableHeap，TableHeap 表示磁盘上的物理表，它是一个双向链表的页集合，通过 TableHeap 即可得到首个表页面 TablePage 中首个元组 Tuple（每个元组对应表中的一行数据）的迭代器，从而实现表遍历。

一个物理页面的大小为 4096 字节，其内部结构大致如下：

+-------------------+-------------------+ -----------------
|     PageID(4)     |      LSN(4)       |
+-------------------+-------------------+
|   PrevPageId(4)   |   NextPageId(4)   |
+-------------------+-------------------+
|FreeSpacePointer(4)|   TupleCount(4)   |
+-------------------+-------------------+
| Tuple_1 offset(4) |  Tuple_1 size(4)  |      HEADER
+-------------------+-------------------+
| Tuple_2 offset(4) |  Tuple_2 size(4)  |
+-------------------+-------------------+
| Tuple_3 offset(4) |  Tuple_3 size(4)  |
+-------------------+-------------------+ -----------------
|                  ...                  |
+---------------------------------------+    FREE SPACE
|                  ...                  |
+---------------------------------------+ -----------------
|                Tuple_3                |
+-------------------------+-------------+  INSERTED TUPLES
|         Tuple_2         |   Tuple_1   |
+-------------------------+-------------+ -----------------

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五、Task #1 - Access Method Executors

5.1 顺序扫描执行器

bustub> explain select * from test_1;
=== BINDER ===
BoundSelect {
  table=BoundBaseTableRef { table=test_1, oid=20 },
  columns=[test_1.colA, test_1.colB, test_1.colC, test_1.colD],
  groupBy=[],
  having=,
  where=,
  limit=,
  offset=,
  order_by=[],
  is_distinct=false,
  ctes=,
}
=== PLANNER ===
Projection { exprs=[#0.0, #0.1, #0.2, #0.3] } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
  SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
=== OPTIMIZER ===
SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)bustub> explain select * from test_1;

/**
 * The SeqScanExecutor executor executes a sequential table scan.
 */
class SeqScanExecutor : public AbstractExecutor {
public:
    /**
     * Construct a new SeqScanExecutor instance.
     * @param exec_ctx The executor context
     * @param plan The sequential scan plan to be executed
     */
    SeqScanExecutor(ExecutorContext *exec_ctx, const SeqScanPlanNode *plan)
            : AbstractExecutor(exec_ctx), plan_(plan), table_iter_(nullptr, RID(), nullptr) {}

    /** Initialize the sequential scan */
    void Init() override {
        TableInfo *table_info = exec_ctx_->GetCatalog()->GetTable(plan_->GetTableOid());

        table_heap_ = dynamic_cast<TableHeap *>(table_info->table_.get());
        table_iter_ = table_heap_->Begin(exec_ctx_->GetTransaction());
    }

    /**
     * Yield the next tuple from the sequential scan.
     * @param[out] tuple The next tuple produced by the scan
     * @param[out] rid The next tuple RID produced by the scan
     * @return `true` if a tuple was produced, `false` if there are no more tuples
     */
    auto Next(Tuple *tuple, RID *rid) -> bool override {
        // 遍历结束返回 false
        if (table_iter_ == table_heap_->End()) {
            return false;
        }

        // 填充元组信息及元组的 rid
        *tuple = *table_iter_;
        *rid = tuple->GetRid();
        table_iter_++;
        return true;
    }

    /** @return The output schema for the sequential scan */
    auto GetOutputSchema() const -> const Schema & override {
        return plan_->OutputSchema();
    }

private:
    /** The sequential scan plan node to be executed */
    const SeqScanPlanNode *plan_;
    /** 待扫描表的表堆 */
    TableHeap *table_heap_;
    /** 待扫描表的迭代器 */
    TableIterator table_iter_;
};

5.2 插入执行器

bustub> explain insert into test_1 values (1000, 1, 1, 1);
=== BINDER ===
BoundInsert {
  table=BoundBaseTableRef { table=test_1, oid=20 },
  select=  BoundSelect {
    table=BoundExpressionListRef { identifier=__values#0, values=[[1000, 1, 1, 1]] },
    columns=[__values#0.0, __values#0.1, __values#0.2, __values#0.3],
    groupBy=[],
    having=,
    where=,
    limit=,
    offset=,
    order_by=[],
    is_distinct=false,
    ctes=,
  }
}
=== PLANNER ===
Insert { table_oid=20 } | (__bustub_internal.insert_rows:INTEGER)
  Projection { exprs=[#0.0, #0.1, #0.2, #0.3] } | (__values#0.0:INTEGER, __values#0.1:INTEGER, __values#0.2:INTEGER, __values#0.3:INTEGER)
    Values { rows=1 } | (__values#0.0:INTEGER, __values#0.1:INTEGER, __values#0.2:INTEGER, __values#0.3:INTEGER)
=== OPTIMIZER ===
Insert { table_oid=20 } | (__bustub_internal.insert_rows:INTEGER)
  Values { rows=1 } | (__values#0.0:INTEGER, __values#0.1:INTEGER, __values#0.2:INTEGER, __values#0.3:INTEGER)

/**
 * InsertExecutor executes an insert on a table.
 * Inserted values are always pulled from a child executor.
 */
class InsertExecutor : public AbstractExecutor {
public:
    /**
     * Construct a new InsertExecutor instance.
     * @param exec_ctx The executor context
     * @param plan The insert plan to be executed
     * @param child_executor The child executor from which inserted tuples are pulled
     */
    InsertExecutor(ExecutorContext *exec_ctx, const InsertPlanNode *plan,
                   std::unique_ptr<AbstractExecutor> &&child_executor)
            : AbstractExecutor(exec_ctx), plan_(plan), child_executor_(std::move(child_executor)) {}

    /** Initialize the insert */
    void Init() override {
        child_executor_->Init();
    }

    /**
     * Yield the number of rows inserted into the table.
     * @param[out] tuple The integer tuple indicating the number of rows inserted into the table
     * @param[out] rid The next tuple RID produced by the insert (ignore, not used)
     * @return `true` if a tuple was produced, `false` if there are no more tuples
     *
     * NOTE: InsertExecutor::Next() does not use the `rid` out-parameter.
     * NOTE: InsertExecutor::Next() returns true with number of inserted rows produced only once.
     */
    auto Next([[maybe_unused]] Tuple *tuple, RID *rid) -> bool override {
        // 插入完毕返回 false
        if (has_inserted_) {
            return false;
        }
        has_inserted_ = true;

        // 获取待插入的表信息及其索引列表
        TableInfo *table_info = exec_ctx_->GetCatalog()->GetTable(plan_->TableOid());
        std::vector<IndexInfo *> index_info = exec_ctx_->GetCatalog()->GetTableIndexes(table_info->name_);

        // 从子执行器 Values 中逐个获取元组并插入到表中，同时更新所有的索引
        int insert_count = 0;
        while (child_executor_->Next(tuple, rid)) {
            table_info->table_->InsertTuple(*tuple, rid, exec_ctx_->GetTransaction());
            for (const auto &index: index_info) {
                // 根据索引的模式从数据元组中构造索引元组，并插入到索引中
                Tuple key_tuple = tuple->KeyFromTuple(child_executor_->GetOutputSchema(), index->key_schema_,
                                                      index->index_->GetMetadata()->GetKeyAttrs());
                index->index_->InsertEntry(key_tuple, *rid, exec_ctx_->GetTransaction());
            }
            insert_count++;
        }

        // 这里的 tuple 不再对应实际的数据行，而是用来存储插入操作的影响行数
        std::vector<Value> result{Value(INTEGER, insert_count)};
        *tuple = Tuple(result, &plan_->OutputSchema());

        return true;
    }

    /** @return The output schema for the insert */
    auto GetOutputSchema() const -> const Schema & override {
        return plan_->OutputSchema();
    };

private:
    /** The insert plan node to be executed */
    const InsertPlanNode *plan_;
    /** 子执行器，对于插入操作来说通常是 Values 执行器 */
    std::unique_ptr<AbstractExecutor> child_executor_;
    /** 标识是否已经完成插入操作 */
    bool has_inserted_ = false;
};

5.3 删除执行器

bustub> explain delete from test_1 where colA = 1000;
=== BINDER ===
Delete { table=BoundBaseTableRef { table=test_1, oid=20 }, expr=(test_1.colA=1000) }
=== PLANNER ===
Delete { table_oid=20 } | (__bustub_internal.delete_rows:INTEGER)
  Filter { predicate=(#0.0=1000) } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
    SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
=== OPTIMIZER ===
Delete { table_oid=20 } | (__bustub_internal.delete_rows:INTEGER)
  SeqScan { table=test_1, filter=(#0.0=1000) } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)

/**
 * DeletedExecutor executes a delete on a table.
 * Deleted values are always pulled from a child.
 */
class DeleteExecutor : public AbstractExecutor {
public:
    /**
     * Construct a new DeleteExecutor instance.
     * @param exec_ctx The executor context
     * @param plan The delete plan to be executed
     * @param child_executor The child executor that feeds the delete
     */
    DeleteExecutor(ExecutorContext *exec_ctx, const DeletePlanNode *plan,
                   std::unique_ptr<AbstractExecutor> &&child_executor)
            : AbstractExecutor(exec_ctx), plan_(plan), child_executor_(std::move(child_executor)) {}

    /** Initialize the delete */
    void Init() override {
        child_executor_->Init();
    }

    /**
     * Yield the number of rows deleted from the table.
     * @param[out] tuple The integer tuple indicating the number of rows deleted from the table
     * @param[out] rid The next tuple RID produced by the delete (ignore, not used)
     * @return `true` if a tuple was produced, `false` if there are no more tuples
     *
     * NOTE: DeleteExecutor::Next() does not use the `rid` out-parameter.
     * NOTE: DeleteExecutor::Next() returns true with the number of deleted rows produced only once.
     */
    auto Next([[maybe_unused]] Tuple *tuple, RID *rid) -> bool override {
        // 删除完毕返回 false
        if (has_deleted_) {
            return false;
        }
        has_deleted_ = true;

        // 获取待删除的表信息及其索引列表
        TableInfo *table_info = exec_ctx_->GetCatalog()->GetTable(plan_->TableOid());
        std::vector<IndexInfo *> index_info = exec_ctx_->GetCatalog()->GetTableIndexes(table_info->name_);

        // 从子执行器 Values 中逐个获取元组并插入到表中，同时更新所有的索引
        int delete_count = 0;
        while (child_executor_->Next(tuple, rid)) {
            table_info->table_->MarkDelete(*rid, exec_ctx_->GetTransaction());
            for (const auto &index: index_info) {
                // 根据索引的模式从数据元组中构造索引元组，并从索引中删除
                Tuple key_tuple = tuple->KeyFromTuple(child_executor_->GetOutputSchema(), index->key_schema_,
                                                      index->index_->GetMetadata()->GetKeyAttrs());
                index->index_->DeleteEntry(key_tuple, *rid, exec_ctx_->GetTransaction());
            }
            delete_count++;
        }

        // 这里的 tuple 不再对应实际的数据行，而是用来存储插入操作的影响行数
        std::vector<Value> result{Value(INTEGER, delete_count)};
        *tuple = Tuple(result, &plan_->OutputSchema());

        return true;
    }

    /** @return The output schema for the delete */
    auto GetOutputSchema() const -> const Schema & override {
        return plan_->OutputSchema();
    };

private:
    /** The delete plan node to be executed */
    const DeletePlanNode *plan_;
    /** The child executor from which RIDs for deleted tuples are pulled */
    std::unique_ptr<AbstractExecutor> child_executor_;
    /** 标识是否已经完成删除操作 */
    bool has_deleted_ = false;
};

5.4 索引扫描执行器

bustub> create index index_colA on test_1(colA);
Index created with id = 0
bustub> explain select * from test_1 order by colA;
=== BINDER ===
BoundSelect {
  table=BoundBaseTableRef { table=test_1, oid=20 },
  columns=[test_1.colA, test_1.colB, test_1.colC, test_1.colD],
  groupBy=[],
  having=,
  where=,
  limit=,
  offset=,
  order_by=[BoundOrderBy { type=Default, expr=test_1.colA }],
  is_distinct=false,
  ctes=,
}
=== PLANNER ===
Sort { order_bys=[(Default, #0.0)] } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
  Projection { exprs=[#0.0, #0.1, #0.2, #0.3] } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
    SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
=== OPTIMIZER ===
IndexScan { index_oid=0 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)

/**
 * IndexScanExecutor executes an index scan over a table.
 */
class IndexScanExecutor : public AbstractExecutor {
public:
    /**
     * Creates a new index scan executor.
     * @param exec_ctx the executor context
     * @param plan the index scan plan to be executed
     */
    IndexScanExecutor(ExecutorContext *exec_ctx, const IndexScanPlanNode *plan)
            : AbstractExecutor(exec_ctx), plan_(plan) {}

    void Init() override {
        IndexInfo *index_info = exec_ctx_->GetCatalog()->GetIndex(plan_->GetIndexOid());

        tree_ = dynamic_cast<BPlusTreeIndexForOneIntegerColumn *>(index_info->index_.get());
        table_iter_ = tree_->GetBeginIterator();
        table_heap_ = exec_ctx_->GetCatalog()->GetTable(index_info->table_name_)->table_.get();
    }

    auto Next(Tuple *tuple, RID *rid) -> bool override {
        // 遍历结束返回 false
        if (table_iter_ == tree_->GetEndIterator()) {
            return false;
        }

        // 获取元组的 rid 并填充元组内容
        *rid = (*table_iter_).second;
        table_heap_->GetTuple(*rid, tuple, exec_ctx_->GetTransaction());
        ++table_iter_;
        return true;
    }

    auto GetOutputSchema() const -> const Schema & override {
        return plan_->OutputSchema();
    }

private:
    /** The index scan plan node to be executed. */
    const IndexScanPlanNode *plan_;
    /** 待扫描表的 B+ 树索引 */
    BPlusTreeIndexForOneIntegerColumn *tree_;
    /** 待扫描表的 B+ 树索引迭代器 */
    BPlusTreeIndexIteratorForOneIntegerColumn table_iter_;
    /** 待扫描表的表堆 */
    TableHeap *table_heap_;
};

六、Task #2 - Aggregation & Join Executors

6.1 聚合执行器

bustub> explain select sum(colA) from test_1;
=== BINDER ===
BoundSelect {
  table=BoundBaseTableRef { table=test_1, oid=20 },
  columns=[sum([test_1.colA])],
  groupBy=[],
  having=,
  where=,
  limit=,
  offset=,
  order_by=[],
  is_distinct=false,
  ctes=,
}
=== PLANNER ===
Projection { exprs=[#0.0] } | (:INTEGER)
  Agg { types=[sum], aggregates=[#0.0], group_by=[] } | (agg#0:INTEGER)
    SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
=== OPTIMIZER ===
Agg { types=[sum], aggregates=[#0.0], group_by=[] } | (:INTEGER)
  SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)

id	name	position	camp
0	Ezreal	ADC	Piltover
1	Vi	JUN	Piltover
2	Jayce	TOP	Piltover
3	Zed	MID	Ionia
4	Xayah	ADC	Ionia
5	Thresh	SUP	Shadow Isles

我们以分组聚合查询语句 select count(name) from table group by camp; 为例简要说明一下聚合执行器的执行流程：

• Init() 函数首先从子执行器中逐行获取数据，并根据每行数据构建聚合键和聚合值。其中聚合键用于标识该行数据属于哪一个聚合组，这里是按照阵营 camp 分组，因此聚合键会有 Piltover、Ionia 和 Shadow Isles 三种取值，这样所有数据被分成三个聚合组。当然，如果没 group by 子句，那么所有数据都会被分到同一个聚合组中，这个聚合组的聚合键为一个 group_bys_.size() = 0 的 AggregateKey。而聚合值就是待聚合的列的值，这里的聚合列是 name，因此这五个 Tuple 中生成的聚合值即为对应的 name 属性的值。
• 对于每个提取的数据行，Init() 函数还会通过 InsertCombine() 将相应的聚合值聚合到到相应的聚合组中。具体的聚合规则由 CombineAggregateValues() 函数中的实现来决定。
• 经过 Init() 函数的处理，以上六条数据会被整理为 [{"Piltover": 3}, {"Ionia": 2}, {"Shadow Isles": 1}] 三个聚合组（对应于聚合哈希表中的三个键值对）。
• 最后，Next() 函数会通过哈希迭代器依次获取每个聚合组的键与值，返回给父执行器。

/**
 * A simplified hash table that has all the necessary functionality for aggregations.
 */
class SimpleAggregationHashTable {
public:
    /**
     * Construct a new SimpleAggregationHashTable instance.
     * @param agg_exprs the aggregation expressions
     * @param agg_types the types of aggregations
     */
    SimpleAggregationHashTable(const std::vector<AbstractExpressionRef> &agg_exprs,
                               const std::vector<AggregationType> &agg_types)
            : agg_exprs_{agg_exprs}, agg_types_{agg_types} {}

    /** @return The initial aggregate value for this aggregation executor */
    auto GenerateInitialAggregateValue() -> AggregateValue {
        std::vector<Value> values{};
        for (const auto &agg_type: agg_types_) {
            switch (agg_type) {
                case AggregationType::CountStarAggregate:
                    // Count start starts at zero.
                    values.emplace_back(ValueFactory::GetIntegerValue(0));
                    break;
                case AggregationType::CountAggregate:
                case AggregationType::SumAggregate:
                case AggregationType::MinAggregate:
                case AggregationType::MaxAggregate:
                    // Others starts at null.
                    values.emplace_back(ValueFactory::GetNullValueByType(TypeId::INTEGER));
                    break;
            }
        }
        return {values};
    }

    /**
     * Combines the input into the aggregation result.
     * @param[out] result The output aggregate value
     * @param input The input value
     */
    void CombineAggregateValues(AggregateValue *result, const AggregateValue &input) {
        for (uint32_t i = 0; i < agg_exprs_.size(); i++) {
            auto &result_value = result->aggregates_[i];
            auto &input_value = input.aggregates_[i];
            switch (agg_types_[i]) {
                case AggregationType::CountStarAggregate:
                    // count(*) 对每一行都执行 +1 操作
                    result_value = result_value.Add(ValueFactory::GetIntegerValue(1));
                    break;
                case AggregationType::CountAggregate:
                    // count(column) 只对非空行进行 +1 操作，同时 bustub 不支持 count(distinct column) 聚合操作
                    if (!input_value.IsNull()) {
                        // 如果 result_value 为空需要从 ValueFactory 中获取
                        if (result_value.IsNull()) {
                            result_value = ValueFactory::GetIntegerValue(0);
                        }
                        result_value = result_value.Add(ValueFactory::GetIntegerValue(1));
                    }
                    break;
                case AggregationType::SumAggregate:
                    // sum(column) 只处理非空行
                    if (!input_value.IsNull()) {
                        // 如果 result_value 为空初始化为 input_value ，否则直接累加
                        if (result_value.IsNull()) {
                            result_value = input_value;
                        } else {
                            result_value = result_value.Add(input_value);
                        }
                    }
                    break;
                case AggregationType::MinAggregate:
                    // min(column) 只处理非空行
                    if (!input_value.IsNull()) {
                        // 如果 result_value 为空初始化为 input_value ，否则取最小值
                        if (result_value.IsNull()) {
                            result_value = input_value;
                        } else {
                            result_value = result_value.Min(input_value);
                        }
                    }
                    break;
                case AggregationType::MaxAggregate:
                    // max(column) 只处理非空行
                    if (!input_value.IsNull()) {
                        // 如果 result_value 为空初始化为 input_value ，否则取最大值
                        if (result_value.IsNull()) {
                            result_value = input_value;
                        } else {
                            result_value = result_value.Max(input_value);
                        }
                    }
                    break;
                default:
                    throw Exception("unknown AggregationType");
            }
        }
    }

    /**
     * 检查当前表是否为空，如果为空生成默认的聚合值，对于 count(*) 来说是 0，对于其他聚合函数来说是 integer_null
     * 等指定了具体类型的空值。 之所以要进行这次额外检查，是因为空表的聚合操作不是没有返回值，而是返回了一个默认的聚合值。
     *
     * @param[out] value 对应于不同聚合操作生成的初始聚合值。
     * @return `true` 当前表为空，需要返回一个初始聚合值，`false` 不需要返回初始聚合值。
     */
    auto CheckIsNullTable(AggregateValue *value) -> bool {
        // 从未检查过或者表不为空
        if (is_checked_ || !ht_.empty()) {
            return false;
        }

        is_checked_ = true;
        // 遍历聚合表达式列表，为每个聚合函数生成初始的聚合值
        for (size_t i = 0; i < agg_exprs_.size(); i++) {
            *value = GenerateInitialAggregateValue();
        }
        return true;
    }

    /**
     * Inserts a value into the hash table and then combines it with the current aggregation.
     * @param agg_key the key to be inserted
     * @param agg_val the value to be inserted
     */
    void InsertCombine(const AggregateKey &agg_key, const AggregateValue &agg_val) {
        if (ht_.count(agg_key) == 0) {
            ht_.insert({agg_key, GenerateInitialAggregateValue()});
        }
        CombineAggregateValues(&ht_[agg_key], agg_val);
    }

    /**
     * Clear the hash table
     */
    void Clear() { ht_.clear(); }

    /** An iterator over the aggregation hash table */
    class Iterator {
    public:
        /** Creates an iterator for the aggregate map. */
        explicit Iterator(std::unordered_map<AggregateKey, AggregateValue>::const_iterator iter) : iter_{iter} {}

        /** @return The key of the iterator */
        auto Key() -> const AggregateKey & { return iter_->first; }

        /** @return The value of the iterator */
        auto Val() -> const AggregateValue & { return iter_->second; }

        /** @return The iterator before it is incremented */
        auto operator++() -> Iterator & {
            ++iter_;
            return *this;
        }

        /** @return `true` if both iterators are identical */
        auto operator==(const Iterator &other) -> bool { return this->iter_ == other.iter_; }

        /** @return `true` if both iterators are different */
        auto operator!=(const Iterator &other) -> bool { return this->iter_ != other.iter_; }

    private:
        /** Aggregates map */
        std::unordered_map<AggregateKey, AggregateValue>::const_iterator iter_;
    };

    /** @return Iterator to the start of the hash table */
    auto Begin() -> Iterator { return Iterator{ht_.cbegin()}; }

    /** @return Iterator to the end of the hash table */
    auto End() -> Iterator { return Iterator{ht_.cend()}; }

private:
    /** The hash table is just a map from aggregate keys to aggregate values */
    std::unordered_map<AggregateKey, AggregateValue> ht_{};
    /** The aggregate expressions that we have */
    const std::vector<AbstractExpressionRef> &agg_exprs_;
    /** The types of aggregations that we have */
    const std::vector<AggregationType> &agg_types_;
    /** 标识是否进行过空表检查 */
    bool is_checked_ = false;
};

/**
 * AggregationExecutor executes an aggregation operation (e.g. COUNT, SUM, MIN, MAX)
 * over the tuples produced by a child executor.
 */
class AggregationExecutor : public AbstractExecutor {
public:
    /**
     * Construct a new AggregationExecutor instance.
     * @param exec_ctx The executor context
     * @param plan The insert plan to be executed
     * @param child_executor The child executor from which inserted tuples are pulled (may be `nullptr`)
     */
    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()) {}

    /** Initialize the aggregation */
    void Init() override {
        child_->Init();

        Tuple tuple;
        RID rid;
        // 将每个 tuple 分配到指定的聚合分组，并更新该分组的聚合值
        while (child_->Next(&tuple, &rid)) {
            // 获取该 tuple 对应聚合键，这个聚合键表明了它的所属分组，聚合键的本质是用于分组的列的值
            AggregateKey agg_key = this->MakeAggregateKey(&tuple);
            // 获取该 tuple 对应的聚合值，聚合值的本质是待聚合的列的值
            AggregateValue agg_value = this->MakeAggregateValue(&tuple);
            // 将该 tuple 的聚合值合并到对应聚合分组的聚合值中，对于不同的聚合函数有不同的聚合方式
            aht_.InsertCombine(agg_key, agg_value);
        }
        aht_iterator_ = aht_.Begin();
    }

    /**
     * Yield the next tuple from the insert.
     * @param[out] tuple The next tuple produced by the aggregation
     * @param[out] rid The next tuple RID produced by the aggregation
     * @return `true` if a tuple was produced, `false` if there are no more tuples
     */
    auto Next(Tuple *tuple, RID *rid) -> bool override {
        if (aht_iterator_ == aht_.End()) {
            // 如果存在 group by 子句，则不需要对每个分组的聚合结果进行进一步的聚合
            if (!plan_->GetGroupBys().empty()) {
                return false;
            }

            AggregateValue result_value;
            // 如果对空表执行聚合操作直接返回初始化后的元组
            if (aht_.CheckIsNullTable(&result_value)) {
                *tuple = Tuple(result_value.aggregates_, &plan_->OutputSchema());
                *rid = tuple->GetRid();
                return true;
            }

            return false;
        }

        // 获取当前迭代器对应聚合分组的聚合键和和聚合值
        AggregateKey agg_key = aht_iterator_.Key();
        AggregateValue agg_value = aht_iterator_.Val();
        std::vector<Value> values;
        values.reserve(agg_key.group_bys_.size() + agg_value.aggregates_.size());

        // 向结果集中追加聚合分组的键
        for (const auto &group_by_key: agg_key.group_bys_) {
            values.emplace_back(group_by_key);
        }
        // 向结果集中追加聚合分组的聚合值
        for (const auto &group_by_value: agg_value.aggregates_) {
            values.emplace_back(group_by_value);
        }

        *tuple = Tuple(values, &plan_->OutputSchema());
        *rid = tuple->GetRid();
        ++aht_iterator_;
        return true;
    }

    /** @return The output schema for the aggregation */
    auto GetOutputSchema() const -> const Schema & override { return plan_->OutputSchema(); };

    /** Do not use or remove this function, otherwise you will get zero points. */
    auto GetChildExecutor() const -> const AbstractExecutor * { return child_.get(); }

private:
    /** @return The tuple as an AggregateKey */
    auto MakeAggregateKey(const Tuple *tuple) -> AggregateKey {
        std::vector<Value> keys;
        for (const auto &expr: plan_->GetGroupBys()) {
            keys.emplace_back(expr->Evaluate(tuple, child_->GetOutputSchema()));
        }
        return {keys};
    }

    /** @return The tuple as an AggregateValue */
    auto MakeAggregateValue(const Tuple *tuple) -> AggregateValue {
        std::vector<Value> vals;
        for (const auto &expr: plan_->GetAggregates()) {
            vals.emplace_back(expr->Evaluate(tuple, child_->GetOutputSchema()));
        }
        return {vals};
    }

private:
    /** The aggregation plan node */
    const AggregationPlanNode *plan_;
    /** The child executor that produces tuples over which the aggregation is computed */
    std::unique_ptr<AbstractExecutor> child_;
    /** Simple aggregation hash table */
    SimpleAggregationHashTable aht_;
    /** Simple aggregation hash table iterator */
    SimpleAggregationHashTable::Iterator aht_iterator_;
};

一个用于演示分组聚合和空表聚合执行流程的测试用例：

statement ok
create table legends(name varchar(128), camp varchar(128));

statement ok
insert into legends values ('Ezreal', 'Piltover'), ('Vi', 'Piltover'), ('Jayce', 'Piltover'), ('Zed', 'Ionia'), ('Xayah', 'Ionia');

statement ok
select count(name) from legends group by camp;

statement ok
delete from legends;

query
select count(*) from legends;
----
0

query
select count(camp) from legends;
----
integer_null

6.2 循环连接执行器

bustub> EXPLAIN SELECT * FROM test_1 LEFT OUTER JOIN test_2 ON test_1.colA = test_2.colA;
=== BINDER ===
BoundSelect {
  table=BoundJoin { type=Left, left=BoundBaseTableRef { table=test_1, oid=20 }, right=BoundBaseTableRef { table=test_2, oid=21 }, condition=(test_1.colA=test_2.colA) },
  columns=[test_1.colA, test_1.colB, test_1.colC, test_1.colD, test_2.colA, test_2.colB, test_2.colC],
  groupBy=[],
  having=,
  where=,
  limit=,
  offset=,
  order_by=[],
  is_distinct=false,
  ctes=,
}
=== PLANNER ===
Projection { exprs=[#0.0, #0.1, #0.2, #0.3, #0.4, #0.5, #0.6] } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER, test_2.colA:INTEGER, test_2.colB:INTEGER, test_2.colC:INTEGER)
  NestedLoopJoin { type=Left, predicate=(#0.0=#1.0) } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER, test_2.colA:INTEGER, test_2.colB:INTEGER, test_2.colC:INTEGER)
    SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
    SeqScan { table=test_2 } | (test_2.colA:INTEGER, test_2.colB:INTEGER, test_2.colC:INTEGER)
=== OPTIMIZER ===
HashJoin { type=Left, left_key=#0.0, right_key=#0.0 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER, test_2.colA:INTEGER, test_2.colB:INTEGER, test_2.colC:INTEGER)
  SeqScan { table=test_1 } | (test_1.colA:INTEGER, test_1.colB:INTEGER, test_1.colC:INTEGER, test_1.colD:INTEGER)
  SeqScan { table=test_2 } | (test_2.colA:INTEGER, test_2.colB:INTEGER, test_2.colC:INTEGER)

数据库中的连接运算

/**
 * NestedLoopJoinExecutor executes a nested-loop JOIN on two tables.
 */
class NestedLoopJoinExecutor : public AbstractExecutor {
public:
    /**
     * Construct a new NestedLoopJoinExecutor instance.
     * @param exec_ctx The executor context
     * @param plan The NestedLoop join plan to be executed
     * @param left_executor The child executor that produces tuple for the left side of join
     * @param right_executor The child executor that produces tuple for the right side of join
     */
    NestedLoopJoinExecutor(ExecutorContext *exec_ctx, const NestedLoopJoinPlanNode *plan,
                           std::unique_ptr<AbstractExecutor> &&left_executor,
                           std::unique_ptr<AbstractExecutor> &&right_executor)
            : AbstractExecutor(exec_ctx),
              plan_(plan),
              left_executor_(std::move(left_executor)),
              right_executor_(std::move(right_executor)) {}

    /** Initialize the join */
    void Init() override {
        left_executor_->Init();
        right_executor_->Init();

        // 从 left_executor_ 和 right_executor_ 中获取两张表中的所有元组
        Tuple tuple;
        RID rid;
        std::vector<Tuple> left_tuples;
        std::vector<Tuple> right_tuples;
        while (left_executor_->Next(&tuple, &rid)) {
            left_tuples.emplace_back(tuple);
        }
        while (right_executor_->Next(&tuple, &rid)) {
            right_tuples.emplace_back(tuple);
        }

        // 依次连接 left_tuples 和 right_tuples 中的所有元组
        Schema left_schema = left_executor_->GetOutputSchema();
        Schema right_schema = right_executor_->GetOutputSchema();
        for (auto &left_tuple: left_tuples) {
            // 标识 left_tuple 是否需要进行左外连接
            bool need_left_join = true;

            for (auto &right_tuple: right_tuples) {
                // 如果 left_tuple 中的连接字段与 right_tuple 中的连接字段的值相匹配，则 left_tuple 不需要进行左外连接
                Value join_result = plan_->predicate_->EvaluateJoin(&left_tuple, left_schema, &right_tuple, right_schema);
                if (!join_result.IsNull() && join_result.GetAs<bool>()) {
                    // left_tuple 不需要进行左外连接
                    need_left_join = false;

                    // 依次将 left_tuple 和 right_tuple 中的值追加到结果集中
                    std::vector<Value> values;
                    for (size_t i = 0; i < left_schema.GetColumnCount(); i++) {
                        values.emplace_back(left_tuple.GetValue(&left_schema, i));
                    }
                    for (size_t i = 0; i < right_schema.GetColumnCount(); i++) {
                        values.emplace_back(right_tuple.GetValue(&right_schema, i));
                    }
                    results_.emplace(values, &plan_->OutputSchema());
                }
            }

            // 对 left_tuple 进行左外连接，right_tuple 中的字段均以对应的空值追加到结果集中
            if (need_left_join && plan_->GetJoinType() == JoinType::LEFT) {
                std::vector<Value> values;
                for (size_t i = 0; i < left_schema.GetColumnCount(); i++) {
                    values.emplace_back(left_tuple.GetValue(&left_schema, i));
                }
                for (size_t i = 0; i < right_schema.GetColumnCount(); i++) {
                    values.emplace_back(ValueFactory::GetNullValueByType(right_schema.GetColumn(i).GetType()));
                }
                results_.emplace(values, &plan_->OutputSchema());
            }
        }
    }

    /**
     * Yield the next tuple from the join.
     * @param[out] tuple The next tuple produced by the join
     * @param[out] rid The next tuple RID produced, not used by nested loop join.
     * @return `true` if a tuple was produced, `false` if there are no more tuples.
     */
    auto Next(Tuple *tuple, RID *rid) -> bool override {
        // 从结果集队列中依次获取所有连接后的元组
        if (results_.empty()) {
            return false;
        }
        *tuple = results_.front();
        results_.pop();
        *rid = tuple->GetRid();
        return true;
    }

    /** @return The output schema for the insert */
    auto GetOutputSchema() const -> const Schema & override { return plan_->OutputSchema(); };

private:
    /** The NestedLoopJoin plan node to be executed. */
    const NestedLoopJoinPlanNode *plan_;
    /** 左表子执行器，对于循环连接操作来说通常是左表上的 Scan 执行器 */
    std::unique_ptr<AbstractExecutor> left_executor_;
    /** 右表子执行器，对于循环连接操作来说通常是右表上的 Scan 执行器 */
    std::unique_ptr<AbstractExecutor> right_executor_;
    /** 连接结果集 */
    std::queue<Tuple> results_;
};

statement ok
create table provinces
(
    province_name varchar(255),
    country_name  varchar(255),
    capital_name  varchar(255)
);

statement ok
insert into provinces
values ('Guangdong', 'China', 'Guangzhou'),
       ('Sichuan', 'China', 'Chengdu'),
       ('Jiangsu', 'China', 'Nanjing'),
       ('California', 'USA', 'Sacramento'),
       ('Hawaii', 'USA', 'Honolulu'),
       ('Texas', 'USA', 'Houston');

statement ok
create table capital
(
    capital_name varchar(255),
    population   int
);

statement ok
insert into capital
values ('Guangzhou', 15000000),
       ('Nanjing', 8000000),
       ('Sacramento', 500000),
       ('Honolulu', 380000),
       ('Tokyo', 14000000),
       ('London', 9000000);

statement ok
create index capital_name_index on capital(capital_name);

query rowsort
select *
from provinces
join capital on provinces.capital_name = capital.capital_name;
----
Guangdong China Guangzhou Guangzhou 15000000
Jiangsu China Nanjing Nanjing 8000000
California USA Sacramento Sacramento 500000
Hawaii USA Honolulu Honolulu 380000

6.3 索引连接执行器

bustub> CREATE TABLE t1(v1 int, v2 int);
Table created with id = 22
bustub> CREATE TABLE t2(v3 int, v4 int);
Table created with id = 23
bustub> CREATE INDEX t2v3 on t2(v3);
Index created with id = 0
bustub> EXPLAIN SELECT * FROM t2 INNER JOIN t1 ON v1 = v3;
=== BINDER ===
BoundSelect {
  table=BoundJoin { type=Inner, left=BoundBaseTableRef { table=t2, oid=23 }, right=BoundBaseTableRef { table=t1, oid=22 }, condition=(t1.v1=t2.v3) },
  columns=[t2.v3, t2.v4, t1.v1, t1.v2],
  groupBy=[],
  having=,
  where=,
  limit=,
  offset=,
  order_by=[],
  is_distinct=false,
  ctes=,
}
=== PLANNER ===
Projection { exprs=[#0.0, #0.1, #0.2, #0.3] } | (t2.v3:INTEGER, t2.v4:INTEGER, t1.v1:INTEGER, t1.v2:INTEGER)
  NestedLoopJoin { type=Inner, predicate=(#1.0=#0.0) } | (t2.v3:INTEGER, t2.v4:INTEGER, t1.v1:INTEGER, t1.v2:INTEGER)
    SeqScan { table=t2 } | (t2.v3:INTEGER, t2.v4:INTEGER)
    SeqScan { table=t1 } | (t1.v1:INTEGER, t1.v2:INTEGER)
=== OPTIMIZER ===
HashJoin { type=Inner, left_key=#0.0, right_key=#0.0 } | (t2.v3:INTEGER, t2.v4:INTEGER, t1.v1:INTEGER, t1.v2:INTEGER)
  SeqScan { table=t2 } | (t2.v3:INTEGER, t2.v4:INTEGER)
  SeqScan { table=t1 } | (t1.v1:INTEGER, t1.v2:INTEGER)
bustub> EXPLAIN SELECT * FROM t1 INNER JOIN t2 ON v1 = v3;
=== BINDER ===
BoundSelect {
  table=BoundJoin { type=Inner, left=BoundBaseTableRef { table=t1, oid=22 }, right=BoundBaseTableRef { table=t2, oid=23 }, condition=(t1.v1=t2.v3) },
  columns=[t1.v1, t1.v2, t2.v3, t2.v4],
  groupBy=[],
  having=,
  where=,
  limit=,
  offset=,
  order_by=[],
  is_distinct=false,
  ctes=,
}
=== PLANNER ===
Projection { exprs=[#0.0, #0.1, #0.2, #0.3] } | (t1.v1:INTEGER, t1.v2:INTEGER, t2.v3:INTEGER, t2.v4:INTEGER)
  NestedLoopJoin { type=Inner, predicate=(#0.0=#1.0) } | (t1.v1:INTEGER, t1.v2:INTEGER, t2.v3:INTEGER, t2.v4:INTEGER)
    SeqScan { table=t1 } | (t1.v1:INTEGER, t1.v2:INTEGER)
    SeqScan { table=t2 } | (t2.v3:INTEGER, t2.v4:INTEGER)
=== OPTIMIZER ===
NestedIndexJoin { type=Inner, key_predicate=#0.0, index=t2v3, index_table=t2 } | (t1.v1:INTEGER, t1.v2:INTEGER, t2.v3:INTEGER, t2.v4:INTEGER)
  SeqScan { table=t1 } | (t1.v1:INTEGER, t1.v2:INTEGER)

/**
 * IndexJoinExecutor executes index join operations.
 */
class NestIndexJoinExecutor : public AbstractExecutor {
public:
    /**
     * Creates a new nested index join executor.
     * @param exec_ctx the context that the hash join should be performed in
     * @param plan the nested index join plan node
     * @param child_executor the outer table
     */
    NestIndexJoinExecutor(ExecutorContext *exec_ctx, const NestedIndexJoinPlanNode *plan,
                          std::unique_ptr<AbstractExecutor> &&child_executor)
            : AbstractExecutor(exec_ctx), plan_(plan), left_executor_(std::move(child_executor)) {}

    auto GetOutputSchema() const -> const Schema & override {
        return plan_->OutputSchema();
    }

    void Init() override {
        left_executor_->Init();

        Schema left_schema = left_executor_->GetOutputSchema();

        // 获取右表中的索引
        IndexInfo *right_index_info = exec_ctx_->GetCatalog()->GetIndex(plan_->GetIndexOid());
        Index *right_index = right_index_info->index_.get();

        TableInfo *right_table_info = exec_ctx_->GetCatalog()->GetTable(plan_->GetInnerTableOid());
        Schema right_schema = right_table_info->schema_;
        TableHeap *right_table = right_table_info->table_.get();

        Tuple left_tuple;
        RID left_rid;
        while (left_executor_->Next(&left_tuple, &left_rid)) {
            // 标识 left_tuple 是否需要进行左外连接
            bool need_left_join = true;

            // 根据左表中的连接字段的值在 right_index 中查找所有匹配的 RID
            Value left_key_value = plan_->KeyPredicate()->Evaluate(&left_tuple, left_schema);
            Tuple left_key_tuple = Tuple(std::vector<Value>{left_key_value}, &right_index_info->key_schema_);
            std::vector<RID> result_rids;
            right_index->ScanKey(left_key_tuple, &result_rids, nullptr);

            // 依次处理右表中所有匹配的 RID
            for (auto &right_rid: result_rids) {
                // 当前左元组不需要进行左外连接
                need_left_join = false;

                // 依次将左元组和右元组中的值追加到结果集中
                Tuple right_tuple;
                right_table->GetTuple(right_rid, &right_tuple, exec_ctx_->GetTransaction());
                std::vector<Value> values;
                for (size_t i = 0; i < left_schema.GetColumnCount(); i++) {
                    values.emplace_back(left_tuple.GetValue(&left_schema, i));
                }
                for (size_t i = 0; i < right_schema.GetColumnCount(); i++) {
                    values.emplace_back(right_tuple.GetValue(&right_schema, i));
                }
                results_.emplace(values, &plan_->OutputSchema());
            }

            // 对当前左元组进行左外连接
            if (need_left_join && plan_->join_type_ == JoinType::LEFT) {
                std::vector<Value> values;
                for (size_t i = 0; i < left_schema.GetColumnCount(); i++) {
                    values.emplace_back(left_tuple.GetValue(&left_schema, i));
                }
                for (size_t i = 0; i < right_schema.GetColumnCount(); i++) {
                    values.emplace_back(ValueFactory::GetNullValueByType(right_schema.GetColumn(i).GetType()));
                }
                results_.emplace(values, &plan_->OutputSchema());
            }
        }
    }

    auto Next(Tuple *tuple, RID *rid) -> bool override {
        // 从结果集队列中依次获取所有连接后的元组
        if (results_.empty()) {
            return false;
        }
        *tuple = results_.front();
        results_.pop();
        *rid = tuple->GetRid();
        return true;
    }

private:
    /** The nested index join plan node. */
    const NestedIndexJoinPlanNode *plan_;
    /** 左表子执行器，对于索引连接操作来说通常是左表上的 Scan 执行器 */
    std::unique_ptr<AbstractExecutor> left_executor_;
    /** 连接结果集 */
    std::queue<Tuple> results_;
};

statement ok
create table provinces
(
    province_name varchar(255),
    country_name  varchar(255),
    capital_id    int
);

statement ok
insert into provinces
values ('Guangdong', 'China', 1),
       ('Sichuan', 'China', 2),
       ('Jiangsu', 'China', 3),
       ('California', 'USA', 4),
       ('Hawaii', 'USA', 5),
       ('Texas', 'USA', 6);

statement ok
create table capital
(
    capital_id int,
    population int
);

statement ok
insert into capital
values (1, 15000000),
       (3, 8000000),
       (4, 500000),
       (5, 380000),
       (7, 14000000),
       (8, 9000000);

statement ok
create index capital_id_index on capital(capital_id);

query rowsort
select *
from provinces
join capital on provinces.capital_id = capital.capital_id;
----
Guangdong China 1 1 15000000
Jiangsu China 3 3 8000000
California USA 4 4 500000
Hawaii USA 5 5 380000

七、评测结果

---

参考：

https://blog.eleven.wiki/posts/cmu15-445-project3-query-execution/
https://blog.csdn.net/AntiO2/article/details/131147264
https://www.bilibili.com/video/BV1184y137rG/