---

项目拉取

原项目使用ant进行项目构建，我已经更改为Maven构建，大家直接拉取我改好后的项目即可:

• https://gitee.com/DaHuYuXiXi/simple-db-hw-2021

然后就是正常的maven项目配置,启动即可。各个lab的实现，会放在lab/分支下。

---

Lab Two

lab2必须在lab1提交的代码基础上进行开发，否则无法完成相应的练习。此外，实验还提供了源码中不存在的额外测试文件。

实现提示

开始编写代码之前，强烈建议通读整篇文档，以对SimpleDB的设计有个整体的认识，对于我们编写代码非常有帮助

建议跟着文档的练习来实现对应的代码，每个练习都标明了要实现哪个类以及通过哪些单元测试，跟着练习走即可。

下面是本实验的大致流程：

• 实现Filter和Join操作并且通过相关的单元测试验证你的实现，阅读类的Javadoc将会帮助我们实现。项目中已经提供了Project和OrderBy操作的实现，阅读其代码能够帮助我们理解其他操作是如何实现的
• 实现IntegerAggregator和StringAggregator，你将会编写对元组的某一特定列分组进行聚合操作；其中integer支持求和、求最大最小值、求数量、求平均值，string只支持count聚合操作
• 实现Aggregate操作；同其他操作一样，聚合操作也实现类OpIterator接口。注意每次调用next()的Aggregate操作的输出是整个分组的聚合值，Aggregate构造函数将会设置聚合和分组操作对应的列
• 实现BufferPool类中的插入、删除和页面丢弃策略，暂时不需要关心事务
• 实现Insert和Delete操作；与所有的操作相似，Insert和Delete实现OpIterator接口，接收用于插入或者删除的元组并输出该操作影响的元组个数；这些操作将会调用BufferPool中合适的方法用于修改磁盘上的页

注意SimpleDB没有实现一致性和完整性检查，所以它可能会插入重复的记录，并且没有方法保证主键或外键的一致性。

在本节实现的基础上，我们需要使用项目提供的SQL解析器去运行SQL语句查询。

最后，你可能会发现本实验的操作扩展Operator类而不是实现OpIterator接口。因为next/hasNext的实现总是重复的、烦人的，Operator实现了通用的逻辑操作，并且仅需要实现readNext方法。可以随意使用这种风格，或者使用OpIterator。如果要实现OpIterator接口，请移除extends Operator，并替换为implements OpIterator。

---

练习一 – Filter and Join

Filter and Join：

本节将会实现比扫描整张表更有趣的操作：

• Filter：该操作仅返回满足(构造时指定的)Predicate操作的元组；因此，它会过滤那些不符合操作的元组
• Join：该操作将会通过(构造时指定的)JoinPredicate联合两个表的元组，Join操作仅需实现一个简单的嵌套循环连接

实现如下类中的方法：

• src/java/simpledb/execution/Predicate.java
• src/java/simpledb/execution/JoinPredicate.java
• src/java/simpledb/execution/Filter.java
• src/java/simpledb/execution/Join.java

---

Predict和JoinPredict分别负责普通的断言和Join断言的操作：

Predict类核心源码如下:

/**
 * Predicate compares tuples to a specified Field value.
 * 比较元组某个特定的字段--> select * from t where t.a=1;
 */
public class Predicate implements Serializable {
    private static final long serialVersionUID = 1L;
    /**
     * 待比较字段
     */
    private final int field;
    /**
     * 操作码
     */
    private final Op op;
    /**
     * 操作数
     */
    private final Field operand;

    /**
     * Constants used for return codes in Field.compare
     */
    public enum Op implements Serializable {
        EQUALS, GREATER_THAN, LESS_THAN, LESS_THAN_OR_EQ, GREATER_THAN_OR_EQ, LIKE, NOT_EQUALS;

        /**
         * Interface to access operations by integer value for command-line
         * convenience.
         */
        public static Op getOp(int i) {
            return values()[i];
        }

        public String toString() {
            if (this == EQUALS)
                return "=";
            if (this == GREATER_THAN)
                return ">";
            if (this == LESS_THAN)
                return "<";
            if (this == LESS_THAN_OR_EQ)
                return "<=";
            if (this == GREATER_THAN_OR_EQ)
                return ">=";
            if (this == LIKE)
                return "LIKE";
            if (this == NOT_EQUALS)
                return "<>";
            throw new IllegalStateException("impossible to reach here");
        }
    }

    public Predicate(int field, Op op, Field operand) {
        this.field = field;
        this.op = op;
        this.operand = operand;
    }

    /**
     * Compares the field number of t specified in the constructor to the
     * operand field specified in the constructor using the operator specific in
     * the constructor. The comparison can be made through Field's compare
     * method.
     */
    public boolean filter(Tuple t) {
        if (t == null) {
            return false;
        }
        Field f = t.getField(this.field);
        return f.compare(this.op, this.operand);
    }
    . . .
}

JoinPredict类核心源码如下:

/**
 * JoinPredicate compares fields of two tuples using a predicate. JoinPredicate
 * is most likely used by the Join operator.
 * 用于JOIN连接断言的两个元组的某个字段 --> select * from t1 join t2 on t1.id=t2.id;
 */
public class JoinPredicate implements Serializable {
    private static final long serialVersionUID = 1L;
    /**
     * 字段1
     */
    private final int field1;
    /**
     * 操作码
     */
    private final Predicate.Op op;
    /**
     * 字段2
     */
    private final int field2;

    public JoinPredicate(int field1, Predicate.Op op, int field2) {
        this.field1 = field1;
        this.op = op;
        this.field2 = field2;
    }

    /**
     * Apply the predicate to the two specified tuples. The comparison can be
     * made through Field's compare method.
     */
    public boolean filter(Tuple t1, Tuple t2) {
        if (t1 == null) {
            return false;
        }
        if (t2 == null) {
            return false;
        }
        Field first = t1.getField(field1);
        Field second = t2.getField(field2);
        return first.compare(this.op, second);
    }
    ...
}

---

OpIterator意为可操作迭代器,在SimpleDB中的含义为: 迭代器遍历元素的时候可以同时进行一些操作，具体遍历时执行什么操作由子类决定。

操作迭代器意味着迭代器自身在遍历数据时，会根据自身实现搞点事情，Operator接口模板化了部分流程，各个需要在迭代器遍历时进行操作的子类，只需要去实现readNext这个核心方法，并且每次获取下一个元组的时候，搞点事情即可。

这里不是说子类只需要去实现readNext方法，而是说readNext是子类需要实现的核心方法，其他均为辅助方法。

Operator类的核心源码如下:

/**
 * Abstract class for implementing operators. It handles close, next and hasNext. Subclasses only need to implement open and readNext.
 */
public abstract class Operator implements OpIterator {

    public boolean hasNext() throws DbException, TransactionAbortedException {
        if (!this.open)
            throw new IllegalStateException("Operator not yet open");
        if (next == null)
            next = fetchNext();
        return next != null;
    }

    public Tuple next() throws DbException, TransactionAbortedException,
            NoSuchElementException {
        if (next == null) {
            next = fetchNext();
            if (next == null)
                throw new NoSuchElementException();
        }
        Tuple result = next;
        next = null;
        return result;
    }
    
    protected abstract Tuple fetchNext() throws DbException,
            TransactionAbortedException;

    /**
     * Closes this iterator. If overridden by a subclass, they should call
     * super.close() in order for Operator's internal state to be consistent.
     */
    public void close() {
        // Ensures that a future call to next() will fail
        next = null;
        this.open = false;
    }

    private Tuple next = null;
    private boolean open = false;

    public void open() throws DbException, TransactionAbortedException {
        this.open = true;
    }

    /**
     * @return return the children DbIterators of this operator. If there is
     *         only one child, return an array of only one element. For join
     *         operators, the order of the children is not important. But they
     *         should be consistent among multiple calls.
     * */
    public abstract OpIterator[] getChildren();

    /**
     * Set the children(child) of this operator. If the operator has only one
     * child, children[0] should be used. If the operator is a join, children[0]
     * and children[1] should be used.
     * */
    public abstract void setChildren(OpIterator[] children);

    /**
     * @return return the TupleDesc of the output tuples of this operator
     * */
    public abstract TupleDesc getTupleDesc();
    ...
}

• 迭代器调用约定: 先调用hasNext判断是否还有下一个元素,如果有调用next获取下一个元素，并且调用hashNext前需要先调用Open。
• OpIterator操作迭代器分为两部分,一部分是原始职能: 提供数据进行迭代遍历； 另一部分是附加职能: 在原始迭代器遍历过程中进行操作。
• Operator采用装饰器模式封装原始迭代器遍历行为，并在其基础上增加了遍历时进行操作的行为。
• 装饰器模式需要有被装饰的对象，这里通过setChildren进行设置，但是这里与普通的装饰器模式不同，因为不同的操作会涉及到不同的个数的被装饰对象。
  • select * from t where t.age=18 --> 此处实际使用了单值比较过滤操作，所以只涉及单表的迭代器
  • select * from t1 join t2 on t1.age=t2.age --> 此时实际使用了两表JOIN操作，所以涉及两个表的迭代器

---

SimpleDB整个迭代器的设计思路采用了装饰器模式实现，具体如下图所示:

• Operator的实现类都是装饰器，而SeqScan是迭代器的实现，也就是被装饰的对象

迭代器模式的用法可以参考Java IO整体架构实现。

Filter用于单值比较操作，具体流程如下:

图中只展示的一层装饰，如果存在多层装饰，那么child仍然是个装饰器，可以利用多层装饰实现如: select * from t where t.age=18 and t.name="dhy"的匹配过滤。

Filter核心源码如下:

/**
 * Filter is an operator that implements a relational select.
 */
public class Filter extends Operator {
    private final Predicate predicate;
    private OpIterator child;

    /**
     * Constructor accepts a predicate to apply and a child operator to read
     * tuples to filter from.
     */
    public Filter(Predicate p, OpIterator child) {
        this.predicate = p;
        this.child = child;
    }
    
    public void open() throws DbException, NoSuchElementException,
            TransactionAbortedException {
        super.open();
        this.child.open();
    }

    public void close() {
        this.child.close();
        super.close();
    }

    public void rewind() throws DbException, TransactionAbortedException {
        this.child.rewind();
    }

    /**
     * AbstractDbIterator.readNext implementation. Iterates over tuples from the
     * child operator, applying the predicate to them and returning those that
     * pass the predicate (i.e. for which the Predicate.filter() returns true.)
     * 
     */
    protected Tuple fetchNext() throws NoSuchElementException,
            TransactionAbortedException, DbException {
        while (this.child.hasNext()) {
            Tuple tuple = this.child.next();
            if (this.predicate.filter(tuple)) {
                return tuple;
            }
        }
        return null;
    }

    @Override
    public OpIterator[] getChildren() {
        return new OpIterator[] {this.child};
    }

    @Override
    public void setChildren(OpIterator[] children) {
        this.child = children[0];
    }
    ...
}

---

Join用于连接条件判断，流程如下:

Join的核心源码如下:

/**
 * The Join operator implements the relational join operation.
 */
public class Join extends Operator {
    private static final long serialVersionUID = 1L;
    /**
     * 连接条件
     */
    private final JoinPredicate predicate;
    /**
     * 参与连接的表
     */
    private OpIterator[] children;
    
    private Tuple tuple1;

    /**
     * Constructor. Accepts two children to join and the predicate to join them
     * on
     * @param p      The predicate to use to join the children
     * @param child1 Iterator for the left(outer) relation to join
     * @param child2 Iterator for the right(inner) relation to join
     */
    public Join(JoinPredicate p, OpIterator child1, OpIterator child2) {
        this.predicate = p;
        this.children = new OpIterator[]{child1, child2};
        this.tuple1 = null;
    }

    /**
      * 返回的是两表连接后得到结果的行schema
     */
    public TupleDesc getTupleDesc() {
        return TupleDesc.merge(this.children[0].getTupleDesc(), this.children[1].getTupleDesc());
    }

    public void open() throws DbException, NoSuchElementException,
            TransactionAbortedException {
        for (OpIterator child : this.children) {
            child.open();
        }
        super.open();
    }

    public void close() {
        for (OpIterator child : this.children) {
            child.close();
        }
        super.close();
    }

    public void rewind() throws DbException, TransactionAbortedException {
        for (OpIterator child : this.children) {
            child.rewind();
        }
    }

    /**
     * Returns the next tuple generated by the join, or null if there are no
     * more tuples. Logically, this is the next tuple in r1 cross r2 that
     * satisfies the join predicate. There are many possible implementations;
     * the simplest is a nested loops join.
     * <p>
     * Note that the tuples returned from this particular implementation of Join
     * are simply the concatenation of joining tuples from the left and right
     * relation. Therefore, if an equality predicate is used there will be two
     * copies of the join attribute in the results. (Removing such duplicate
     * columns can be done with an additional projection operator if needed.)
     * <p>
     * For example, if one tuple is {1,2,3} and the other tuple is {1,5,6},
     * joined on equality of the first column, then this returns {1,2,3,1,5,6}.
     *
     * @return The next matching tuple.
     * @see JoinPredicate#filter
     */
    protected Tuple fetchNext() throws TransactionAbortedException, DbException {
        // 双重循环,将children[0]作为驱动表,children[1]作为被驱动表
        while (this.children[0].hasNext() || tuple1 != null) {
            // 获取驱动表的一行记录
            if (this.children[0].hasNext() && tuple1 == null) {
                tuple1 = this.children[0].next();
            }
            // 获取被驱动表的一行记录
            while (this.children[1].hasNext()) {
                Tuple tuple2 = this.children[1].next();
                // JoinPredicate判断join条件是否成立
                if (this.predicate.filter(tuple1, tuple2)) {
                    // 获取驱动表schema和被驱动表schema合并后的schema
                    TupleDesc tupleDesc = getTupleDesc();
                    // 用于承载合并后的行记录
                    Tuple res = new Tuple(tupleDesc);
                    int i = 0;
                    // 拿到驱动表当前行的所有字段,然后设置到res
                    Iterator<Field> fields1 = tuple1.fields();
                    while (fields1.hasNext() && i < tupleDesc.numFields()) {
                        res.setField(i++, fields1.next());
                    }
                    // 拿到被驱动表当前行的所有字段,然后设置到res
                    Iterator<Field> fields2 = tuple2.fields();
                    while (fields2.hasNext() && i < tupleDesc.numFields()) {
                        res.setField(i++, fields2.next());
                    }
                    // 被驱动表遍历完了,重置指针,同时将tuple1也重置
                    if (!this.children[1].hasNext()) {
                        this.children[1].rewind();
                        tuple1 = null;
                    }
                    // 返回捞到的记录
                    return res;
                }
            }
            // 驱动表当前行在被驱动表中没有匹配行,那么将被驱动表迭代指针复原
            this.children[1].rewind();
            tuple1 = null;
        }
        // 没有匹配记录
        return null;
    }

    @Override
    public OpIterator[] getChildren() {
        return this.children;
    }

    @Override
    public void setChildren(OpIterator[] children) {
        this.children=children;
    }

}

关于tuple1属性作用说明:

• 我们从驱动表中获取一条记录后，需要遍历被驱动表，在被驱动表中找出所有符合连接条件的行，然后拼接两表字段，然后返回结果
• fetchNext方法每调用一次，都会返回符合条件的一行记录，因此我们需要保留驱动表当前正在匹配的行，等到某一次fetchNext方法调用时，发现当前行与被驱动表每一行都进行了一次匹配后，才会从驱动表中取出下一行进行匹配。
  

---

练习二 – Aggregates

Aggregates:

本节我们应该实现如下五种聚合操作：count、sum、avg、min、max，并且支持分组聚合操作。仅支持对一个域进行聚合，对一个域进行分组即可。

为了实现聚合操作，我们使用Aggregator接口将新的元组合并到现有的聚合操作结果中。实际进行哪种聚合操作会在构造Aggregate时指明。所以，客户端代码需要为子操作的每个元组调用Aggregator.mergeTupleIntoGroup()方法，当所有的元组都被合并完成以后，客户端将会获得聚合操作的结果。

• 如果指定分组的话，那么返回结果格式为： (groupValue, aggregateValue)；
• 没有指定分组的话，返回格式为：(aggregateValue)

本节实验中，我们不需要担心分组的数量超过可用内存的限制。

实现如下类中的方法：

• src/java/simpledb/execution/IntegerAggregator.java
• src/java/simpledb/execution/StringAggregator.java
• src/java/simpledb/execution/Aggregate.java

---

Aggregator聚合器干的事情就是接收传入的Tuple,然后内部进行计算，当我们传入n个tuple后，我们可以调用聚合器的迭代器，获取当前聚合的结果:

上面给出的是不涉及分组的聚合操作，如果涉及分组的话，聚合过程如下图所示:

Aggregator聚合器接口定义如下:

/**
 * The common interface for any class that can compute an aggregate over a
 * list of Tuples.
 */
public interface Aggregator extends Serializable {
    int NO_GROUPING = -1;

    /**
     * SUM_COUNT and SC_AVG will
     * only be used in lab7, you are not required
     * to implement them until then.
     * */
    enum Op implements Serializable {
        MIN, MAX, SUM, AVG, COUNT,
        /**
         * SUM_COUNT: compute sum and count simultaneously, will be
         * needed to compute distributed avg in lab7.
         * */
        SUM_COUNT,
        /**
         * SC_AVG: compute the avg of a set of SUM_COUNT tuples,
         * will be used to compute distributed avg in lab7.
         * */
        SC_AVG;
        ...
    }

    /**
     * Merge a new tuple into the aggregate for a distinct group value;
     * creates a new group aggregate result if the group value has not yet
     * been encountered.
     *
     * @param tup the Tuple containing an aggregate field and a group-by field
     */
    void mergeTupleIntoGroup(Tuple tup);

    /**
     * Create a OpIterator over group aggregate results.
     * @see TupleIterator for a possible helper
     */
    OpIterator iterator();
    
}

对于不同类型字段的聚合有对应限制，比如: 字符串只支持COUNT统计个数聚合，不支持例如SUM，AVG等聚合操作。因此针对不兼容的类型，我们需要给出不同的聚合器实现:

• 首先来看比较简单的StringAggregator字符串聚合器，其只支持对COUNT聚合的操作

/**
 * Knows how to compute some aggregate over a set of StringFields.
 */
public class StringAggregator implements Aggregator {
    private static final IntField NO_GROUP = new IntField(-1);
    /**
     * 用于分组
     */
    private int gbfield;
    private Type gbfieldtype;
    /**
     * 用于聚合
     */
    private int afield;
    private Op what;
    /**
     * 存放结果-- 分组聚合返回的是多组键值对,分别代表分组字段不同值对应的聚合结果
     * 非分组聚合只会返回一个聚合结果,这里为了统一化处理,采用NO_GROUP做标记,进行区分
     */
    private Map<Field, Tuple> tupleMap;
    private TupleDesc desc;

    /**
     * Aggregate constructor
     *
     * @param gbfield     the 0-based index of the group-by field in the tuple, or NO_GROUPING if there is no grouping
     * @param gbfieldtype the type of the group by field (e.g., Type.INT_TYPE), or null if there is no grouping
     * @param afield      the 0-based index of the aggregate field in the tuple
     * @param what        aggregation operator to use -- only supports COUNT
     * @throws IllegalArgumentException if what != COUNT
     */

    public StringAggregator(int gbfield, Type gbfieldtype, int afield, Op what) {
        //字符串只支持COUNT聚合操作
        if (!what.equals(Op.COUNT)) {
            throw new IllegalArgumentException();
        }
        this.gbfield = gbfield;
        this.gbfieldtype = gbfieldtype;
        this.afield = afield;
        this.what = what;
        this.tupleMap = new ConcurrentHashMap<>();
        //非分组聚合返回的结果采用占位符进行统一适配
        if (gbfield == NO_GROUPING) {
            this.desc = new TupleDesc(new Type[]{Type.INT_TYPE}, new String[]{"aggregateValue"});
            Tuple tuple = new Tuple(desc);
            tuple.setField(0, new IntField(0));
            this.tupleMap.put(NO_GROUP, tuple);
        } else {
        //分组聚合返回结果Schema由两个字段组成: 分组字段和聚合结果
            this.desc = new TupleDesc(new Type[]{gbfieldtype, Type.INT_TYPE}, new String[]{"groupValue", "aggregateValue"});
        }
    }

    /**
     * Merge a new tuple into the aggregate, grouping as indicated in the constructor
     *
     * @param tup the Tuple containing an aggregate field and a group-by field
     */
    public void mergeTupleIntoGroup(Tuple tup) {
        //只支持COUNT聚合
        if (this.gbfield == NO_GROUPING) {
            Tuple tuple = tupleMap.get(NO_GROUP);
            IntField field = (IntField) tuple.getField(0);
            tuple.setField(0, new IntField(field.getValue() + 1));
            tupleMap.put(NO_GROUP, tuple);
        } else {
            Field field = tup.getField(gbfield);
            if (!tupleMap.containsKey(field)) {
                Tuple tuple = new Tuple(this.desc);
                tuple.setField(0, field);
                tuple.setField(1, new IntField(1));
                tupleMap.put(field, tuple);
            } else {
                Tuple tuple = tupleMap.get(field);
                IntField intField = (IntField) tuple.getField(1);
                tuple.setField(1, new IntField(intField.getValue() + 1));
                tupleMap.put(field, tuple);
            }
        }
    }

    /**
     * Create a OpIterator over group aggregate results.
     */
    public OpIterator iterator() {
        return new StringIterator(this);
    }

    public class StringIterator implements OpIterator {
        private StringAggregator aggregator;
        private Iterator<Tuple> iterator;

        public StringIterator(StringAggregator aggregator) {
            this.aggregator = aggregator;
            this.iterator = null;
        }

        @Override
        public void open() throws DbException, TransactionAbortedException {
            this.iterator = aggregator.tupleMap.values().iterator();
        }

        @Override
        public boolean hasNext() throws DbException, TransactionAbortedException {
            return iterator.hasNext();
        }

        @Override
        public Tuple next() throws DbException, TransactionAbortedException, NoSuchElementException {
            return iterator.next();
        }

        @Override
        public void rewind() throws DbException, TransactionAbortedException {
            iterator = aggregator.tupleMap.values().iterator();
        }

        @Override
        public TupleDesc getTupleDesc() {
            return aggregator.desc;
        }

        @Override
        public void close() {
            iterator = null;
        }
    }
    ...
}

---

• 其次来看稍微比较复杂的IntegerAggregator整数聚合器，其支持Op枚举中所有聚合操作

/**
 * Knows how to compute some aggregate over a set of IntFields.
 * <p/>
 * 针对int字段进行聚合操作,聚合得到的结果需要是个整数
 */
public class IntegerAggregator implements Aggregator {
    private static final long serialVersionUID = 1L;
    private static final Field NO_GROUP = new IntField(-1);
    /**
     * 用于分组
     */
    private int gbfield;
    private Type gbfieldType;
    /**
     * 用于聚合
     */
    private int afield;
    private Op what;
    /**
     * 存放结果
     */
    private TupleDesc tupleDesc;
    private Map<Field, Tuple> aggregate;
    /**
     * 用于非分组情况下的聚合操作
     */
    private int counts;
    private int summary;
    /**
     * 用于分组情况下的聚合操作
     */
    private Map<Field, Integer> countsMap;
    private Map<Field, Integer> sumMap;

    /**
     * Aggregate constructor
     *
     * @param gbfield     the 0-based index of the group-by field in the tuple, or
     *                    NO_GROUPING if there is no grouping
     * @param gbfieldtype the type of the group by field (e.g., Type.INT_TYPE), or null
     *                    if there is no grouping
     * @param afield      the 0-based index of the aggregate field in the tuple
     * @param what        the aggregation operator
     */

    public IntegerAggregator(int gbfield, Type gbfieldtype, int afield, Op what) {
        //分组字段
        this.gbfield = gbfield;
        //分组字段类型
        this.gbfieldType = gbfieldtype;
        //聚合得到的结果,在聚合返回结果行中的字段下标
        this.afield = afield;
        //进行什么样的聚合操作
        this.what = what;
        //存放聚合结果
        this.aggregate = new ConcurrentHashMap<>();
        // 非分组聚合
        if (gbfield == NO_GROUPING) {
            this.tupleDesc = new TupleDesc(new Type[]{Type.INT_TYPE}, new String[]{"aggregateValue"});
            Tuple tuple = new Tuple(tupleDesc);
            //占位符
            this.aggregate.put(NO_GROUP, tuple);
        } else {
            // 分组聚合,那么返回的聚合结果行由分组字段和该分组字段的聚合结果值组成
            this.tupleDesc = new TupleDesc(new Type[]{gbfieldtype, Type.INT_TYPE}, new String[]{"groupValue", "aggregateValue"});
        }
        // 如果聚合操作是AVG,那么需要初始化count和summary变量,用于存放AVG聚合中间计算状态
        if (gbfield == NO_GROUPING && what.equals(Op.AVG)) {
            this.counts = 0;
            this.summary = 0;
        } else if (gbfield != NO_GROUPING && what.equals(Op.AVG)) {
            this.countsMap = new ConcurrentHashMap<>();
            this.sumMap = new ConcurrentHashMap<>();
        }
    }

    /**
     * Merge a new tuple into the aggregate, grouping as indicated in the
     * constructor
     * <p>
     * 向整数聚合器中添加一行记录,进行分组计算
     * @param tup the Tuple containing an aggregate field and a group-by field
     */
    public void mergeTupleIntoGroup(Tuple tup) {
        // 从传递给聚合器的行记录中取出聚合字段的值
        IntField operationField = (IntField) tup.getField(afield);
        if (operationField == null) {
            return;
        }
        // 非分组聚合:
        if (gbfield == NO_GROUPING) {
            // 拿到承载聚合结果的元组对象
            Tuple tuple = aggregate.get(NO_GROUP);
            IntField field = (IntField) tuple.getField(0);
            // 说明是进行聚合的第一行记录
            if (field == null) {
                // 如果聚合是统计个数操作
                if (what.equals(Op.COUNT)) {
                    // 初值为1
                    tuple.setField(0, new IntField(1));
                } else if (what.equals(Op.AVG)) {
                    // 如果聚合是求平均值操作
                    // 统计参与聚合的记录个数
                    counts++;
                    // 累加每个值
                    summary = operationField.getValue();
                    // 如果参与聚合的行只存在一个,那么平均值就是当前行的值
                    tuple.setField(0, operationField);
                } else {
                    // 其他的情况: MIN,MAX,SUM在参与聚合的行只存在一个时,聚合结果就是当前行的值
                    // 所以这里可以统一处理
                    tuple.setField(0, operationField);
                }
                return;
            }
            // 判断是哪种类型的聚合
            // 非第一行记录
            switch (what) {
                //select MIN(age) from t;
                case MIN:
                    // 聚合字段的值和当前阶段已经保存的聚合结果进行比较,看谁更小
                    if (operationField.compare(Predicate.Op.LESS_THAN, field)) {
                        tuple.setField(0, operationField);
                        aggregate.put(NO_GROUP, tuple);
                    }
                    return;
                //select MAX(age) from t;
                case MAX:
                    // 聚合字段的值和当前阶段已经保存的聚合结果进行比较,看谁更大
                    if (operationField.compare(Predicate.Op.GREATER_THAN, field)) {
                        tuple.setField(0, operationField);
                        aggregate.put(NO_GROUP, tuple);
                    }
                    return;
                //select COUNT(age) from t;
                case COUNT:
                    // 计数+1
                    IntField count = new IntField(field.getValue() + 1);
                    tuple.setField(0, count);
                    aggregate.put(NO_GROUP, tuple);
                    return;
                //select SUM(age) from t;
                case SUM:
                    // 求和
                    IntField sum = new IntField(field.getValue() + operationField.getValue());
                    tuple.setField(0, sum);
                    aggregate.put(NO_GROUP, tuple);
                    return;
                //select AVG(age) from t;
                case AVG:
                    // 求平均值,每次往整数聚合器塞入一条记录时,都会将记录数和总和累加
                    counts++;
                    summary += operationField.getValue();
                    IntField avg = new IntField(summary / counts);
                    tuple.setField(0, avg);
                    aggregate.put(NO_GROUP, tuple);
                    return;
                default:
                    return;
            }
        } else {
            // 分组聚合操作:
            // 获取分组字段 --> group by age
            Field groupField = tup.getField(gbfield);
            // 如果聚合结果中还不包括当前字段值,说明当前字段是第一次出现
            // 例如: group by age --> <age=18,count=20> ,如果此次获取的age=20,那么就是第一次出现的分组值
            if (!aggregate.containsKey(groupField)) {
                Tuple value = new Tuple(this.tupleDesc);
                value.setField(0, groupField);
                if (what.equals(Op.COUNT)) {
                    value.setField(1, new IntField(1));
                } else if (what.equals(Op.AVG)) {
                    countsMap.put(groupField, countsMap.getOrDefault(groupField, 0) + 1);
                    sumMap.put(groupField, sumMap.getOrDefault(groupField, 0) + operationField.getValue());
                    value.setField(1, operationField);
                } else {
                    // 其他的情况: MIN,MAX,SUM在参与聚合的行只存在一个时,结果假设当前行的值
                    // 所以这里可以统一处理
                    value.setField(1, operationField);
                }
                aggregate.put(groupField, value);
                return;
            }
            // 当前字段不是第一次出现的分组值
            Tuple tuple = aggregate.get(groupField);
            // 获取本阶段的聚合结果
            IntField field = (IntField) tuple.getField(1);
            switch (what) {
                case MIN:
                    if (operationField.compare(Predicate.Op.LESS_THAN, field)) {
                        tuple.setField(1, operationField);
                        aggregate.put(groupField, tuple);
                    }
                    return;
                case MAX:
                    if (operationField.compare(Predicate.Op.GREATER_THAN, field)) {
                        tuple.setField(1, operationField);
                        aggregate.put(groupField, tuple);
                    }
                    return;
                case COUNT:
                    IntField count = new IntField(field.getValue() + 1);
                    tuple.setField(1, count);
                    aggregate.put(groupField, tuple);
                    return;
                case SUM:
                    IntField sum = new IntField(field.getValue() + operationField.getValue());
                    tuple.setField(1, sum);
                    aggregate.put(groupField, tuple);
                    return;
                case AVG:
                    countsMap.put(groupField, countsMap.getOrDefault(groupField, 0) + 1);
                    sumMap.put(groupField, sumMap.getOrDefault(groupField, 0) + operationField.getValue());
                    IntField avg = new IntField(sumMap.get(groupField) / countsMap.get(groupField));
                    tuple.setField(1, avg);
                    aggregate.put(groupField, tuple);
                    return;
                default:
                    return;
            }
        }
    }

    public TupleDesc getTupleDesc() {
        return tupleDesc;
    }

    /**
     * Create a OpIterator over group aggregate results.
     *
     * @return a OpIterator whose tuples are the pair (groupVal, aggregateVal)
     * if using group, or a single (aggregateVal) if no grouping. The
     * aggregateVal is determined by the type of aggregate specified in
     * the constructor.
     */
    public OpIterator iterator() {
        return new IntOpIterator(this);
    }

    public class IntOpIterator implements OpIterator {
        private Iterator<Tuple> iterator;
        private IntegerAggregator aggregator;

        public IntOpIterator(IntegerAggregator aggregator) {
            this.aggregator = aggregator;
            this.iterator = null;
        }

        @Override
        public void open() throws DbException, TransactionAbortedException {
            this.iterator = aggregator.aggregate.values().iterator();
        }

        @Override
        public boolean hasNext() throws DbException, TransactionAbortedException {
            return iterator.hasNext();
        }

        @Override
        public Tuple next() throws DbException, TransactionAbortedException, NoSuchElementException {
            return iterator.next();
        }

        @Override
        public void rewind() throws DbException, TransactionAbortedException {
            iterator = aggregator.aggregate.values().iterator();
        }

        @Override
        public TupleDesc getTupleDesc() {
            return aggregator.tupleDesc;
        }

        @Override
        public void close() {
            iterator = null;
        }
    }
}

完成本节练习之后，需要通过PredicateTest, JoinPredicateTest, FilterTest, JoinTest单元测试；并通过FilterTest和JoinTest系统测试。

---

练习三 – HeapFile Mutability

本节我们将实现修改数据库表文件的方法，我们从单独的页面和文件开始，主要实现两种操作：增加元组和移除元组

• 移除元组：为了移除一个元组，我们需要实现deleteTuple方法，元组包含RecordIDs可以帮助我们找到它们存储在哪一页，所以定位到元组对应的page并且正确修改page的headers信息就很简单了
• 增加元组：HeapFile中的insertTuple方法主要用于向数据库文件添加一个元组。为了向HeapFile中添加一个新的元组，我们需要找到带有空槽的页，如果不存在这样的页，我们需要创造一个新页并且将其添加到磁盘的文件上。我们需要确保元组的RecordID被正确更新

实现如下类中的方法：

• src/java/simpledb/storage/HeapPage.java
• src/java/simpledb/storage/HeapFile.java (Note that you do not necessarily need to implement writePage at this point).

为了实现HeapPage，在insertTuple和deleteTuple方法中你需要修改表示header的bitmap；这里将会使用到我们在实验一中实现的getNumEmptySlots()和isSlotUsed方法，markSlotUsed方法是抽象方法，并且用于填充或者清除page header的的状态信息。

注意，insertTuple和deleteTuple方法需要通过BufferPool.getPage方法访问页，否则下一个实验中关于事务的实现将无法正常工作

---

HeapPage作为数据读写的最小单位,主要负责维护Page数据组织格式和数据读写操作,其内部属性如下所示:

public class HeapPage implements Page {
    final HeapPageId pid;
    final TupleDesc td;
    final byte[] header;
    final Tuple[] tuples;
    final int numSlots;
    byte[] oldData;
    private final Byte oldDataLock = (byte) 0;
    // 本lab新增的两个属性
    private boolean dirty;
    private TransactionId tid;
    ...

本节我们需要在HeapPage中实现的方法主要包括元组的插入,删除以及脏页标记和判脏:

    /**
     * Adds the specified tuple to the page;  the tuple should be updated to reflect
     * that it is now stored on this page.
     *
     * @param t The tuple to add.
     * @throws DbException if the page is full (no empty slots) or tupledesc
     *                     is mismatch.
     */
    public void insertTuple(Tuple t) throws DbException {
        TupleDesc tupleDesc = t.getTupleDesc();
        if (getNumEmptySlots() == 0 || !tupleDesc.equals(this.td)) {
            throw new DbException("this page is full or tupledesc is mismatch");
        }
        for (int i = 0; i < numSlots; i++) {
            if (!isSlotUsed(i)) {
                markSlotUsed(i, true);
                t.setRecordId(new RecordId(this.pid, i));
                tuples[i] = t;
                break;
            }
        }
    }

    /**
     * Delete the specified tuple from the page; the corresponding header bit should be updated to reflect
     * that it is no longer stored on any page.
     *
     * @param t The tuple to delete
     * @throws DbException if this tuple is not on this page, or tuple slot is
     *                     already empty.
     */
    public void deleteTuple(Tuple t) throws DbException {
        RecordId recordId = t.getRecordId();
        int slotId = recordId.getTupleNumber();
        if (recordId.getPageId() != this.pid || !isSlotUsed(slotId)) {
            throw new DbException("tuple is not in this page");
        }
        // 将tuple对应的slot置为0
        markSlotUsed(slotId, false);
        // 将slot对应的tuple置为null
        tuples[slotId] = null;
    }
    
    /**
     * Marks this page as dirty/not dirty and record that transaction
     * that did the dirtying
     */
    public void markDirty(boolean dirty, TransactionId tid) {
        this.dirty = dirty;
        this.tid = tid;
    }

    /**
     * Returns the tid of the transaction that last dirtied this page, or null if the page is not dirty
     */
    public TransactionId isDirty() {
        return dirty ? tid : null;
    }

其他辅助的工具方法大家自行查看源码

---

HeapFile可以看做是表的实体对象，表由一堆HeadPage组成，这一堆HeadPage存放于当前表的DBFile中，这里我们主要实现元组的插入和删除方法:

    // see DbFile.java for javadocs
    public List<Page> insertTuple(TransactionId tid, Tuple t)
            throws DbException, IOException, TransactionAbortedException {
        List<Page> modified = new ArrayList<>();
        for (int i = 0; i < numPages(); i++) {
            HeapPage page = (HeapPage) bufferPool.getPage(tid, new HeapPageId(this.getId(), i), Permissions.READ_WRITE);
            if (page.getNumEmptySlots() == 0) {
                continue;
            }
            page.insertTuple(t);
            modified.add(page);
            return modified;
        }
        // 当所有的页都满时,我们需要创建新的页并写入文件中
        BufferedOutputStream outputStream = new BufferedOutputStream(new FileOutputStream(file, true));
        byte[] emptyPageData = HeapPage.createEmptyPageData();
        // 向文件末尾添加数据
        outputStream.write(emptyPageData);
        outputStream.close();
        // 加载到缓存中,使用numPages() - 1是因为此时numPages()已经变为插入后的大小了
        HeapPage page = (HeapPage) bufferPool.getPage(tid, new HeapPageId(getId(), numPages() - 1), Permissions.READ_WRITE);
        page.insertTuple(t);
        modified.add(page);
        return modified;
    }

    // see DbFile.java for javadocs
    public ArrayList<Page> deleteTuple(TransactionId tid, Tuple t) throws DbException,
            TransactionAbortedException {
        HeapPage page = (HeapPage) bufferPool.getPage(tid, t.getRecordId().getPageId(), Permissions.READ_WRITE);
        page.deleteTuple(t);
        ArrayList<Page> modified = new ArrayList<>();
        modified.add(page);
        return modified;
    }

实现BufferPool类中的如下方法：

• insertTuple()
• deleteTuple()

这些方法需要调用需要被修改的表的HeapFile中的合适的方法来实现

    public void insertTuple(TransactionId tid, int tableId, Tuple t)
            throws DbException, IOException, TransactionAbortedException {
        DbFile dbFile = Database.getCatalog().getDatabaseFile(tableId);
        updateBufferPool(dbFile.insertTuple(tid, t), tid);
    }

    public void deleteTuple(TransactionId tid, Tuple t)
            throws DbException, IOException, TransactionAbortedException {
        DbFile dbFile = Database.getCatalog().getDatabaseFile(t.getRecordId().getPageId().getTableId());
        updateBufferPool(dbFile.deleteTuple(tid, t), tid);
    }

    private void updateBufferPool(List<Page> pages, TransactionId tid) throws DbException {
        for (Page page : pages) {
            page.markDirty(true, tid);
        }
    }

完成练习后，我们的代码需要通过HeapPageWriteTest、HeapFileWriteTest和BufferPoolWriteTest单元测试

---

练习四 – Insertion & deletion

现在我们已经实现了向HeapFile添加和删除元组的机制，接下来就需要实现Insert和Delete操作

为了实现insert和delete查询，我们需要使用Insert和Delete来修改磁盘上的页，这些操作会返回被影响的元组数量

• Insert：该操作从他的子操作中读取元组加入到构造函数指定的tableid对应的表中，需要调用BufferPool.insertTuple()方法实现
• Delete：该操作从构造函数的tableid找到对应的table，并删除子操作中的元组，需要调用BufferPool.deleteTuple方法实现

实现如下类中的方法：

• src/java/simpledb/execution/Insert.java
• src/java/simpledb/execution/Delete.java

---

Insert和Delete采用的也是装饰器模式，所以这里不再多讲:

• Insert操作

/**
 * Inserts tuples read from the child operator into the tableId specified in the
 * constructor
 */
public class Insert extends Operator {
    private static final long serialVersionUID = 1L;
    private final TransactionId tid;
    private OpIterator child;
    private final int tableId;
    private final TupleDesc tupleDesc;
    private Tuple insertTuple;

    /**
     * Constructor.
     *
     * @param t       The transaction running the insert.
     * @param child   The child operator from which to read tuples to be inserted.
     * @param tableId The table in which to insert tuples.
     * @throws DbException if TupleDesc of child differs from table into which we are to
     *                     insert.
     */
    public Insert(TransactionId t, OpIterator child, int tableId)
            throws DbException {
        this.tid = t;
        this.child = child;
        this.tableId = tableId;
        this.tupleDesc = new TupleDesc(new Type[]{Type.INT_TYPE}, new String[]{"insertNums"});
        this.insertTuple = null;
    }

    public TupleDesc getTupleDesc() {
        return this.tupleDesc;
    }

    public void open() throws DbException, TransactionAbortedException {
        super.open();
        child.open();
    }

    public void close() {
        super.close();
        child.close();
    }

    public void rewind() throws DbException, TransactionAbortedException {
        child.rewind();
    }

    /**
     * Inserts tuples read from child into the tableId specified by the
     * constructor. It returns a one field tuple containing the number of
     * inserted records. Inserts should be passed through BufferPool. An
     * instances of BufferPool is available via Database.getBufferPool(). Note
     * that insert DOES NOT need check to see if a particular tuple is a
     * duplicate before inserting it.
     *
     * @return A 1-field tuple containing the number of inserted records, or
     * null if called more than once.
     * @see Database#getBufferPool
     * @see BufferPool#insertTuple
     */
    protected Tuple fetchNext() throws TransactionAbortedException, DbException {
        if (insertTuple != null) {
            return null;
        }
        BufferPool bufferPool = Database.getBufferPool();
        int insertTuples = 0;
        while (child.hasNext()) {
            try {
                bufferPool.insertTuple(tid, tableId, child.next());
                insertTuples++;
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
        //返回的是插入的元组数量
        insertTuple = new Tuple(this.tupleDesc);
        insertTuple.setField(0, new IntField(insertTuples));
        return insertTuple;
    }

    @Override
    public OpIterator[] getChildren() {
        return new OpIterator[]{child};
    }

    @Override
    public void setChildren(OpIterator[] children) {
        this.child = children[0];
    }
}

此处的装饰器模式并非等同于传统意义上的，例如此处的fetchNext会返回符合当前操作含义的结果，filter操作返回满足条件的Tuple,Insert操作返回插入的记录数。

• delete操作

/**
 * The delete operator. Delete reads tuples from its child operator and removes
 * them from the table they belong to.
 */
public class Delete extends Operator {

    private static final long serialVersionUID = 1L;
    private final TransactionId tid;
    private OpIterator child;
    private final TupleDesc tupleDesc;
    private Tuple deleteTuple;

    /**
     * Constructor specifying the transaction that this delete belongs to as
     * well as the child to read from.
     *
     * @param t     The transaction this delete runs in
     * @param child The child operator from which to read tuples for deletion
     */
    public Delete(TransactionId t, OpIterator child) {
        this.tid = t;
        this.child = child;
        this.tupleDesc = new TupleDesc(new Type[]{Type.INT_TYPE}, new String[]{"deleteNums"});
        this.deleteTuple = null;
    }

    public TupleDesc getTupleDesc() {
        return this.tupleDesc;
    }

    public void open() throws DbException, TransactionAbortedException {
        super.open();
        child.open();
    }

    public void close() {
        super.close();
        child.close();
    }

    public void rewind() throws DbException, TransactionAbortedException {
        child.rewind();
    }

    /**
     * Deletes tuples as they are read from the child operator. Deletes are
     * processed via the buffer pool (which can be accessed via the
     * Database.getBufferPool() method.
     *
     * @return A 1-field tuple containing the number of deleted records.
     * @see Database#getBufferPool
     * @see BufferPool#deleteTuple
     */
    protected Tuple fetchNext() throws TransactionAbortedException, DbException {
        if (deleteTuple != null) {
            return null;
        }
        BufferPool bufferPool = Database.getBufferPool();
        int deleteNums = 0;
        while (child.hasNext()) {
            try {
                bufferPool.deleteTuple(tid, child.next());
                deleteNums++;
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
        deleteTuple = new Tuple(tupleDesc);
        deleteTuple.setField(0, new IntField(deleteNums));
        return deleteTuple;
    }

    @Override
    public OpIterator[] getChildren() {
        return new OpIterator[]{child};
    }

    @Override
    public void setChildren(OpIterator[] children) {
        this.child = children[0];
    }

}

完成实验后需要通过InsertTest单元测试，并且通过InsertTest和DeleteTest系统测试

---

练习五 – Page eviction

在实验一中，我们没有正确的根据BufferPool构造函数中定义的numPages对BufferPool中缓存的最大页面数量进行限制，本节我们将实现拒绝策略。

当缓冲池中存在超过numPages数量的页时，我们需要在加载下一个页时选择淘汰缓冲池中现存的一个页；具体的拒绝策略我们自己选择即可。

BufferPool中包含一个flushAllPages方法，该方法不会被实际用到，只是用来进行实际的测试，我们在实际代码中不会调用此方法。

flushAllPages方法需要调用flushPage方法，并且flushPage方法需要在page离开BufferPool时将脏页写入磁盘，并且将其置为非脏。

从缓冲池中移除页面的唯一方法是evictPage，当任何脏页被丢弃时，我们需要调用flushPage方法来将其刷新到磁盘。

如果学过操作系统，那么应该了解过缓存页面丢弃策略，主要有先进先出(FIFO)、最近最少使用(LRU)和最不常用(LFU)这几种方法，我们可以选择不同的策略实现。我这里给定了一个抽象的接口，定义好方法，最后实现了FIFO和LRU页面丢弃策略，详情请看代码。

实现BufferPool的页面丢弃策略：

• src/java/simpledb/storage/BufferPool.java

我们需要实现discardPage方法去移除缓冲池中没有被刷新到磁盘上的页，本次实验不会使用该方法，但是它是未来的实验所必须的。

---

页面淘汰采用策略模式进行实现，这里只展示FIFO策略的实现，LRU可以采用哈希链表实现，具体可以参考Lab2源代码中的LRUEvict类：

public interface EvictStrategy {

    /**
     * 修改对应的数据结构以满足丢弃策略
     * @param pageId
     */
    void addPage(PageId pageId);

    /**
     * 获取要丢弃的Page的PageId信息,用于丢弃
     * @return  PageId
     */
    PageId getEvictPageId();

}

public class FIFOEvict implements EvictStrategy {
    /**
     * 存储数据的队列
     */
    private final Queue<PageId> queue;

    public FIFOEvict(int numPages) {
        this.queue = new ArrayDeque<>(numPages);
    }

    @Override
    public void addPage(PageId pageId) {
        // 向尾部插入元素
        boolean offer = queue.offer(pageId);
        if (offer) {
            System.out.println("PageId: " + pageId + " 插入队列成功");
        } else {
            System.out.println("PageId: " + pageId + " 插入队列失败");
        }
    }

    @Override
    public PageId getEvictPageId() {
        // 从队列头部获取元素
        return queue.poll();
    }

}

借助淘汰策略接口和实现类，完成BufferPool中关于flushPage和evitPage相关方法:

    private final EvictStrategy evict;

    public BufferPool(int numPages) {
        this.numPages = numPages;
        this.pageCache = new ConcurrentHashMap<>();
        this.evict = new FIFOEvict(numPages);
    }
     
        /**
     * Flush all dirty pages to disk.
     * NB: Be careful using this routine -- it writes dirty data to disk so will
     * break simpledb if running in NO STEAL mode.
     */
    public synchronized void flushAllPages() throws IOException {
        pageCache.forEach((pageId, page) -> {
            try {
                flushPage(pageId);
            } catch (IOException e) {
                e.printStackTrace();
            }
        });
    } 
    
    /**
     * Remove the specific page id from the buffer pool.
     * Needed by the recovery manager to ensure that the
     * buffer pool doesn't keep a rolled back page in its
     * cache.
     * <p>
     * Also used by B+ tree files to ensure that deleted pages
     * are removed from the cache so they can be reused safely
     */
     public synchronized void discardPage(PageId pid) {
        pageCache.remove(pid);
    }

    /**
     * Flushes a certain page to disk
     *
     * @param pid an ID indicating the page to flush
     */
    private synchronized void flushPage(PageId pid) throws IOException {
        Page flush = pageCache.get(pid);
        // 通过tableId找到对应的DbFile,并将page写入到对应的DbFile中
        int tableId = pid.getTableId();
        DbFile dbFile = Database.getCatalog().getDatabaseFile(tableId);
        // 将page刷新到磁盘
        dbFile.writePage(flush);
    }

    /**
     * Discards a page from the buffer pool.
     * Flushes the page to disk to ensure dirty pages are updated on disk.
     */
    private synchronized void evictPage() throws DbException {
        PageId evictPageId = evict.getEvictPageId();
        try {
            flushPage(evictPageId);
        } catch (IOException e) {
            e.printStackTrace();
        }
        pageCache.remove(evictPageId);
    }
     
    public Page getPage(TransactionId tid, PageId pid, Permissions perm)
            throws TransactionAbortedException, DbException {
        if (!pageCache.containsKey(pid)) {
            if (pageCache.size() > numPages) {
                evictPage();
            }
            DbFile dbFile = Database.getCatalog().getDatabaseFile(pid.getTableId());
            Page page = dbFile.readPage(pid);
            pageCache.put(pid, page);
            evict.addPage(pid);
        }
        return pageCache.get(pid);
    }

完成练习之后，代码需要通过EvictionTest单元测试。

至此我们就完成本次实验了，接下来还有对实验内容的其他测试。

---

练习六 – Query walkthrough

通过我们实现的各种查询策略，来执行类似于下面SQL语句的联合查询：

SELECT *
FROM some_data_file1,
     some_data_file2
WHERE some_data_file1.field1 = some_data_file2.field1
  AND some_data_file1.id > 1

我们需要根据实验一中的方法创建两个数据库文件some_data_file1.dat和some_data_file2.dat，然后使用如下代码进行测试：

运行下面这个测试，可以得到2,2,3,3,2,4和3,3,4,5,3,7两条结果:

public class JoinTest {

    /**
     *  select * from t1,t2 where t1.f0 > 1 and t1.f1 = t2.f1 ;
     */
    public static void main (String[] args) {
        // construct a 3-column table schema
        Type[] types = new Type[]{Type.INT_TYPE, Type.INT_TYPE, Type.INT_TYPE};
        String[] names = new String[]{"f0", "f1", "f2"};

        TupleDesc td = new TupleDesc(types, names);

        // create the tables, associate them with the data files
        // and tell the catalog about the schema  the tables.
        HeapFile table1 = new HeapFile(new File("some_data_file1.dat"), td);
        Database.getCatalog().addTable(table1, "t1");

        HeapFile table2 = new HeapFile(new File("some_data_file2.dat"), td);
        Database.getCatalog().addTable(table2, "t2");

        // construct the query: we use two SeqScans, which spoonfeed
        // tuples via iterators into join
        TransactionId tid = new TransactionId();

        SeqScan ss1 = new SeqScan(tid, table1.getId(), "t1");
        SeqScan ss2 = new SeqScan(tid, table2.getId(), "t2");

        // create a filter for the where condition
        Filter sf1 = new Filter(
                new Predicate(0,
                        Predicate.Op.GREATER_THAN, new IntField(1)), ss1);

        JoinPredicate p = new JoinPredicate(1, Predicate.Op.EQUALS, 1);
        Join j = new Join(p, sf1, ss2);

        // and run it
        try {
            j.open();
            while (j.hasNext()) {
                Tuple tup = j.next();
                System.out.println(tup);
            }
            j.close();
            Database.getBufferPool().transactionComplete(tid);

        } catch (Exception e) {
            e.printStackTrace();
        }

    }
}

---

练习七 - 查询解析

本节我们将会使用SimpleDB中已经编写好的SQL解析器来实现基于SQL语句的查询

首先我们需要创建数据库表和数据库目录，其中数据库表data.txt的内容如下：

1,10
2,20
3,30
4,40
5,50
5,50

通过如下命令将其转换为二进制文件：

java -jar dist/simpledb.jar convert data.txt 2 "int,int"

接下来创建数据库目录文件catalog.txt：

data (f1 int, f2 int)

该文件会告诉SimpleDB数据库中包含一个表：data，其结构为两个int类型的列

最后，我们运行如下命令：

java -jar dist/simpledb.jar parser catalog.txt

可以看到如下输出：

Added table : data with schema INT_TYPE(f1), INT_TYPE(f2)
Computing table stats.
Done.
SimpleDB>

接着输入SQL语句即可进行查询：

SimpleDB> select d.f1, d.f2 from data d;
Started a new transaction tid = 0
Added scan of table d
Added select list field d.f1
Added select list field d.f2
The query plan is:
  π(d.f1,d.f2),card:0
  |
scan(data d)

d.f1    d.f2
------------------
1 10

2 20

3 30

4 40

5 50

5 50


 6 rows.
Transaction 0 committed.
----------------
0.10 seconds

如果没有报错的话，证明你的相关实现都是正确的