mit6.830-lab2-常见算子和 volcano 执行模型

一、实验概览

github : https://github.com/CreatorsStack/CreatorDB

这个实验需要完成的内容有：

1. 实现过滤、连接运算符，这些类都是继承与OpIterator接口了，该实验提供了OrderBy的操作符实现，可以参考实现。最终的SQL语句解析出来都是要依靠这些运算符的；
2. 实现聚合函数，由于该数据库只有int和string两种类型，int类型可实现的聚合函数有max,min,avg,count等，string类型只需要实现count；这些与分组查询一起使用，选择进行聚合操作时，可以选择是否进行分组查询。
3. 对IntegerAggregator和StringAggregator的封装，查询计划是调用Aggregate，再去调用具体的聚合器，最后获得聚合结果。
4. 实现插入、删除记录。包括从HeapPage、HeapFile、BufferPool中删除，这里需要把三个之间的调用逻辑搞清楚，代码会好写很多。
5. 实现BufferPool的数据页淘汰策略。我在 Lab1 中已经用 Lru 实现了.

二、Guideline

Exercise1:Filter and Join

exercise1要求我们完成Filter和Join两种操作符，下面是相关描述：

• Filter: This operator only returns tuples that satisfy a Predicate that is specified as part of its constructor.
  
  Hence, it filters out any tuples that do not match the predicate.
• Join: This operator joins tuples from its two children according to a JoinPredicate that is passed in as part of
  
  its constructor. We only require a simple nested loops join, but you may explore more interesting join
  
  implementations. Describe your implementation in your lab writeup.

Filter

Filter是SQL语句中where的基础，如select * from students where id > 2, Filter 起到条件过滤的作用, 也即过滤出来所有满足 id > 2 的 tuple。

    private Predicate         predicate;
    private OpIterator        child;
    private TupleDesc         tupleDesc;

其中，predicate是断言，实现条件过滤的重要属性；而child是数据源，我们从这里获取一条一条的Tuple用predicate去过滤；td是我们返回结果元组的描述信息，在Filter中与传入的数据源是相同的，而在其它运算符中是根据返回结果的情况去创建TupleDesc的；

FetchNext 方法如下:

protected Tuple fetchNext() throws NoSuchElementException, TransactionAbortedException, DbException {
    // some code goes here
    Tuple tuple;
    while (this.child.hasNext()) {
        tuple = this.child.next();
        if (tuple != null) {
            if (this.predicate.filter(tuple)) {
                return tuple;
            }
        }
    }
    return null;
}

Predicate

我们通过it.next()去获取一条一条符合过滤条件的Tuple。而其中的过滤的实现细节就是通过Predicate来实现的：

        if (this.predicate.filter(tuple)) {
            return tuple;
        }

可以看到，每次调用fetchNext，我们是从Filter的child数据源中不断取出tuple，只要有一条Tuple满足predicate的filter的过滤条件，我们就可以返回一条Tuple，即这条Tuple是经过过滤条件筛选之后的有效Tuple。

Predicate的基本属性如下：

private int   field;
private Op    op;
private Field operand;

其中 field 代表要比较 tuple 的第几个字段，op表示具体的运算符, 包括：相等、大于、小于、等于、不等于、大于等于、小于等于、模糊查询这几种。

而operand是用于参与比较的，比如上述SQL语句select * from students where id > 2；假如id是第0个字段，这里的field = 0，op = GREATER_THAN（大于），operand = new IntField(1)。这里进行比较过滤的实现在filter方法中:

public boolean filter(Tuple t) {
    // some code goes here
    final Field field1 = t.getField(this.field);
    final Field field2 = this.operand;
    return field1.compare(this.op, field2);
}

可以看到，Predicate的作用就是将传入的Tuple进行判断，而Predicate的field属性表明使用元组的第几个字段去与操作数operand进行op运算操作，比较的结果实际是调用Field类的compare方法，compare方法会根据传入的运算符和操作数进行比较，以IntField为例：

    public boolean compare(Predicate.Op op, Field val) {

        IntField iVal = (IntField) val;

        switch (op) {
            case EQUALS:
            case LIKE:
                return value == iVal.value;
            case NOT_EQUALS:
                return value != iVal.value;
            case GREATER_THAN:
                return value > iVal.value;
            case GREATER_THAN_OR_EQ:
                return value >= iVal.value;
            case LESS_THAN:
                return value < iVal.value;
            case LESS_THAN_OR_EQ:
                return value <= iVal.value;
        }

        return false;
    }

可以看到支持的运算符有相等、大于、小于、不等于、大于等于、小于等于这些运算符，这里LIKE和EQUALS都表示等于的意思。

OrderBy

实验提供了OrderBy的实现，其思路与我们实现的Filter也是相似的，区别在于对fetchNext的获取下一条tuple的实现有所不同。OrderBy的属性如下：

    private static final long serialVersionUID = 1L;
    private OpIterator        child;
    private final TupleDesc   td;
    private final List<Tuple> childTups        = new ArrayList<>();
    private final int         orderByField;
    private final String      orderByFieldName;
    private Iterator<Tuple>   it;
    private final boolean     asc;

关键的属性：

1、child：数据源，传入进行排序的所有记录Tuple；

2、childTups：OrderBy的实现思路是在open时将数据源child的所有记录存入list中，然后进行排序；

3、asc：升序还是降序，true表示升序；

4、orderByField：依据元组的第几个字段进行排序；

5、it：对childTups进行排序后childTups.iterator()返回的迭代器，原数据源child依据field字段进行排序后的所有数据。

这里主要看open的实现，因为在open中实现了排序并存入it迭代器中，后续调用fetchNext只需要在it中取就行了：

public void open() throws DbException, NoSuchElementException, TransactionAbortedException {
    child.open();
    // load all the tuples in a collection, and sort it
    while (child.hasNext())
        childTups.add(child.next());
    childTups.sort(new TupleComparator(orderByField, asc));
    it = childTups.iterator();
    super.open();
}

fetchNext就简单很多了，直接从结果迭代器中取就完事了：

protected Tuple fetchNext() throws NoSuchElementException {
    if (it != null && it.hasNext()) {
        return it.next();
    } else
        return null;
}

实际上, 在整个实现的过程中, 像 join, order by , aggregation 这些算子, 都是需要在 open 的时候就直接计算好结果.

Join

理解了上面Filter与Predicate的关系以及OrderBy的实现思路，来做Join和JoinPredicate就会容易一点点了。

Join是连接查询实现的基本操作符，我们在MySQL中会区分内连接和外连接，我们这里只实现内连接。一条连接查询的SQL语句如下：

select a.,b. from a inner join b on a.id=b.id

Join的主要属性如下：

    private JoinPredicate     joinPredicate;
    private OpIterator        child1;
    private OpIterator        child2;
    private TupleDesc         td;

    private JoinStrategy      joinStrategy;
    private TupleIterator     iterator;

其中child1，child2是参与连接查询的两个表的元数据，从里面取出tuple使用joinPredicate进行连接过滤。td是结果元组的描述信息，使用内连接我们是将两个表连起来，所以如果显示连接两个表的所有字段的记录，td可以简单理解成两个child数据源的两种tuple的td的拼接

JoinStrategy 是我自己添加的一个接口, 由不同的实现类, 包括 sortMergeJoin , nestedLoopJoin, hashJoin 等等

可以在 open 的时候指定 join strategy:

public void open() throws DbException, NoSuchElementException, TransactionAbortedException {
    // some code goes here
    this.child1.open();
    this.child2.open();
    super.open();

    // You can choose sortMerge join, hash join, or nested loop join
    this.joinStrategy = new NestedLoopJoin(child1, child2, this.td, this.joinPredicate);
    this.iterator = this.joinStrategy.doJoin();
    this.iterator.open();
}

nestedLoopJoin

我们来看一下简单的 nestedLoopJoin 算法:

    @Override
    public TupleIterator doJoin() {
        final List<Tuple> tuples = new ArrayList<>();
        try {
            child1.rewind();
            while (child1.hasNext()) {
                final Tuple lTuple = child1.next();
                child2.rewind();
                while (child2.hasNext()) {
                    final Tuple rTuple = child2.next();
                    if (this.joinPredicate.filter(lTuple, rTuple)) {
                        tuples.add(mergeTuple(lTuple, rTuple, this.td));
                    }
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
            System.out.println("Error happen when nested loop join");
        }
        return new TupleIterator(this.td, tuples);
    }

SortMergeJoin

主要分为几个步骤:

• 构建两个 block 缓冲块
• 对于 输入源 child1, 利用 block1 缓冲其每一个 block, 然会遍历 child2 的每一个 block, 进行 sortMergeJoin:
  • 先对两个 block 进行排序
  • 然后利用双指针算法, 进行匹配输出即可

    private Tuple[]       block1;
    private Tuple[]       block2;

    private JoinPredicate lt;
    private JoinPredicate eq;

    @Override
    public TupleIterator doJoin() {
        final List<Tuple> tupleList = new ArrayList<>();

        // fetch child1
        try {
            child1.rewind();
            while (child1.hasNext()) {
                int end1 = fetchTuples(child1, block1);
                // Fetch each block of child2, and do merge join
                child2.rewind();

                while (child2.hasNext()) {
                    int end2 = fetchTuples(child2, block2);

                    // sortMerge
                    mergeJoin(tupleList, end1, end2);
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
            System.out.println("Error happen when sort merge join:" + e.getMessage());
        }
        Arrays.fill(this.block1, null);
        Arrays.fill(this.block2, null);
        return new TupleIterator(this.td, tupleList);
    }

    private void mergeJoin(final List<Tuple> tupleList, int end1, int end2) {
        // 1.Sort each block
        final int field1 = this.joinPredicate.getField1();
        final int field2 = this.joinPredicate.getField2();
        sortTuples(block1, end1, field1);
        sortTuples(block2, end2, field2);

        // 2.Join
        int index1 = 0, index2 = 0;
        final Predicate.Op op = this.joinPredicate.getOperator();
        switch (op) {
            case EQUALS: {
                while (index1 < end1 && index2 < end2) {
                    final Tuple lTuple = this.block1[index1];
                    final Tuple rTuple = this.block2[index2];
                    if (eq.filter(lTuple, rTuple)) {
                        // If equal , we should find the right boundary that equal to lTuple in block1 and rTuple in block2
                        final JoinPredicate eq1 = new JoinPredicate(field1, Predicate.Op.EQUALS, field1);
                        final JoinPredicate eq2 = new JoinPredicate(field2, Predicate.Op.EQUALS, field2);
                        int begin1 = index1 + 1, begin2 = index2 + 1;
                        while (begin1 < end1 && eq1.filter(lTuple, this.block1[begin1]))
                            begin1++;
                        while (begin2 < end2 && eq2.filter(rTuple, this.block2[begin2]))
                            begin2++;
                        for (int i = index1; i < begin1; i++) {
                            for (int j = index2; j < begin2; j++) {
                                tupleList.add(mergeTuple(this.block1[i], this.block2[j], this.td));
                            }
                        }
                        index1 = begin1;
                        index2 = begin2;
                    } else if (lt.filter(lTuple, rTuple)) {
                        index1++;
                    } else {
                        index2++;
                    }
                }
                return;
            }
            case LESS_THAN:
            case LESS_THAN_OR_EQ: {
                while (index1 < end1) {
                    final Tuple lTuple = this.block1[index1++];
                    while (index2 < end2 && !this.joinPredicate.filter(lTuple, this.block2[index2]))
                        index2++;
                    while (index2 < end2) {
                        final Tuple rTuple = this.block2[index2++];
                        tupleList.add(mergeTuple(lTuple, rTuple, this.td));
                    }
                }
                return;
            }
            case GREATER_THAN:
            case GREATER_THAN_OR_EQ: {
                while (index1 < end1) {
                    final Tuple lTuple = this.block1[index1++];
                    while (index2 < end2 && this.joinPredicate.filter(lTuple, this.block2[index2]))
                        index2++;
                    for (int i = 0; i < index2; i++) {
                        final Tuple rTuple = this.block2[i];
                        tupleList.add(mergeTuple(lTuple, rTuple, this.td));
                    }
                }
            }
        }
    }

    private void sortTuples(final Tuple[] tuples, int field, int len) {
        final JoinPredicate lt = new JoinPredicate(field, Predicate.Op.LESS_THAN, field);
        final JoinPredicate gt = new JoinPredicate(field, Predicate.Op.GREATER_THAN, field);
        Arrays.sort(tuples, 0, len, (o1, o2) -> {
            if (lt.filter(o1, o2)) {
                return -1;
            }
            if (gt.filter(o1, o2)) {
                return 1;
            }
            return 0;
        });
    }

JoinPredicate

joinPredicate 和 predicate 差不多, 只不过要对两个 tuple 进行比较:

public boolean filter(Tuple t1, Tuple t2) {
    // some code goes here
    return t1.getField(this.field1).compare(this.op, t2.getField(this.getField2()));
}

Exercise2:Aggregates

exercise2的介绍：

An additional SimpleDB operator implements basic SQL aggregates with a

GROUP BY clause. You should implement the five SQL aggregates

(COUNT, SUM, AVG, MIN,

MAX) and support grouping. You only need to support aggregates over a single field, and grouping by a single field.

In order to calculate aggregates, we use an Aggregator(聚合器)

interface which merges a new tuple into the existing calculation of an aggregate. The Aggregator is told during

construction what operation it should use for aggregation. Subsequently, the client code should

call Aggregator.mergeTupleIntoGroup() for every tuple in the child iterator. After all tuples have been merged, the

client can retrieve a OpIterator of aggregation results. Each tuple in the result is a pair of the

form (groupValue, aggregateValue), unless the value of the group by field was Aggregator.NO_GROUPING, in which case

the result is a single tuple of the form (aggregateValue).

Note that this implementation requires space linear in the number of distinct groups. For the purposes of this lab, you

do not need to worry about the situation where the number of groups exceeds available memory.

exerciese2要求我们实现各种聚合运算如count、sum、avg、min、max等，并且聚合器需要拥有分组聚合的功能。如以下SQL语句：

SELECT SUM(fee) AS country_group_total_fee, country FROM member GROUP BY country

这条语句的功能是查询每个国家的费用总和及国家名称(根据国家名称进行分组)，这里用到了聚合函数SUM。其中fee是聚合字段，country是分组字段，这两个字段是我们理解聚合运算的关键点。

You only need to support aggregates over a single field, and grouping by a single field.讲义告诉我们，我们只需实现根据一个字段去分组和聚合，也就是只有一个分组字段和一个聚合字段。

exercise2的实验要求：

Implement the skeleton methods in:

---

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

---

At this point, your code should pass the unit tests IntegerAggregatorTest, StringAggregatorTest, and AggregateTest.

Furthermore, you should be able to pass the AggregateTest system test.

IntegerAggregator

IntegerAggregator的本质是一个聚合器, 其对传入的 Tuple , 根据 group by 字段进行 merge 操作, 将 tuple 的值合并到之前的信息中

为此, 我构建了一个 aggInfo 类, 用于保存 AggInfo 的信息:

private static class AggInfo {
    int cnt;
    int sum;
    int max = Integer.MIN_VALUE;
    int min = Integer.MAX_VALUE;
}

IntegerAggregator 的基本属性如下:

private Map<Field, AggInfo> groupMap;
// Group by field
private int                 gbField;
private Type                gbFieldType;

// Aggregation field
private int                 agField;

// Aggregation operation
private Op                  op;

private TupleDesc           td;

• 其中，gbField 是指依据 tuple 的第几个字段进行聚合操作，当无需分组时groupField的值为-1，在上面的SQL语句中相当于country这个字段；gbFieldType 是分组字段的类型，如果无需分组这个属性值为null；

• agField是指对tuple的第几个字段进行聚合，在上面的SQL语句中相当于fee字段；

• op是进行聚合运算的操作符，相当于上述SQL语句的SUM。

• td是结果元组的描述信息，对于有分组的聚合运算，td是一个拥有两个字段的TupleDesc，以(groupField, aggResult)的形式，保存原tuple进行分组聚合后每个分组对应的聚合结果，对于没有分组的聚合运算，td只有一个字段来保存聚合结果；

• groupMap 用于保存聚合的结果集，后面进行运算会用到。

不管是IntegerAggregator还是StringAggregator，他们的作用都是进行聚合运算（分组可选），所以他们的核心方法在于mergeTupleIntoGroup。IntegerAggregator.mergeTupleIntoGroup(Tuple tup)的实现思路是这样的：

1.根据构造器给定的 agField 获取在tup中的聚合字段及其值；

2.根据构造器给定的groupField获取tup中的分组字段，如果无需分组，则为null；这里需要检查获取的分组类型是否正确；

3.根据构造器给定的aggregateOp进行分组聚合运，我们将结果保存在groupMap中，key是分组字段(如果无需分组则为null)，val是聚合结果；

下面是具体代码：

    public void mergeTupleIntoGroup(Tuple tup) {
        // some code goes here
        if (this.td == null) {
            buildTupleDesc(tup.getTupleDesc());
        }
        final IntField agField = (IntField) tup.getField(this.agField);
        final Field gbField = tup.getField(this.gbField);
        if (this.gbField != NO_GROUPING) {
            doAggregation(gbField, agField.getValue());
        } else {
            doAggregation(DEFAULT_FIELD, agField.getValue());
        }
    }

    private void doAggregation(final Field key, final int value) {
        if (key != null) {
            AggInfo preInfo = this.groupMap.getOrDefault(key, new AggInfo());
            switch (this.op) {
                case MIN: {
                    preInfo.min = Math.min(preInfo.min, value);
                    break;
                }
                case MAX: {
                    preInfo.max = Math.max(preInfo.max, value);
                    break;
                }
                case AVG: {
                    preInfo.sum += value;
                    preInfo.cnt += 1;
                    break;
                }
                case SUM: {
                    preInfo.sum += value;
                    break;
                }
                case COUNT: {
                    preInfo.cnt += 1;
                    break;
                }
            }
            this.groupMap.put(key, preInfo);
        }
    }

IntegerAggregator的另一个关键的方法是iterator方法，它用于把聚合的结果封装成tuple然后以迭代器的形式返回

下面是具体实现代码：

返回的td是一个拥有两个字段的TupleDesc，以(groupField, aggResult)的形式，保存原tuple进行分组聚合后每个分组对应的聚合结果，对于没有分组的聚合运算，td只有一个字段来保存聚合结果；

    public OpIterator iterator() {
        // some code goes here
        final List<Tuple> tuples = new ArrayList<>();
        if (this.gbField != NO_GROUPING) {
            this.groupMap.forEach((key, info) -> {
                final Tuple tuple = new Tuple(this.td);
                tuple.setField(0, key);
                tuple.setField(1, new IntField(parseValue(key)));
                tuples.add(tuple);
            });
        } else {
            final Tuple tuple = new Tuple(this.td);
            tuple.setField(0, new IntField(parseValue(DEFAULT_FIELD)));
            tuples.add(tuple);
        }
        return new TupleIterator(this.td, tuples);
    }

Aggregate

上面说到，AVG运算当需要获取聚合结果时，再进行计算返回，那么在哪里会来获取聚合结果呢？在Aggregate中，因为Aggregate是真正暴露给外部执行SQL语句调用的，Aggregate会根据聚合字段的类型来选择具体的聚合器。

public void open() throws NoSuchElementException, DbException, TransactionAbortedException {
    // some code goes here
    super.open();
    this.child.open();

    TupleDesc originTd = this.child.getTupleDesc();
    // Build aggregator
    if (originTd.getFieldType(agField) == Type.INT_TYPE) {
        this.aggregator = new IntegerAggregator(this.gbField, this.gbFieldType, this.agField, this.op);
    } else {
        this.aggregator = new StringAggregator(this.gbField, this.gbFieldType, this.agField, this.op);
    }

    // Merge tuples into group
    while (this.child.hasNext()) {
        final Tuple tuple = this.child.next();
        this.aggregator.mergeTupleIntoGroup(tuple);
    }
    this.iterator = (TupleIterator) this.aggregator.iterator();
    this.iterator.open();
}

可以看到，open做的事很简单：不断的读取数据源，利用聚合器的mergeTupleIntoGroup进行聚合运算，当所有记录都聚合完成，返回聚合器的iterator，即聚合结果。

StringAggregator

理解了IntegerAggregator和Aggregate的调用关系，来写StringAggregator就很容易，因为StringAggregator只支持count运算，所以相当于IntegerAggregator的简化版了，下面是mergeTupleIntoGroup的实现：

public void mergeTupleIntoGroup(Tuple tup) {
        // some code goes here
        if (this.td == null) {
            buildTupleDesc(tup.getTupleDesc());
        }
        final Field gbField = tup.getField(this.gbField);
        final Field target = (this.gbField == NO_GROUPING ? DEFAULT_FIELD : gbField);
        this.groupMap.put(target, this.groupMap.getOrDefault(target, 0) + 1);
    }

可以看到基本思路和IntegerAggregator几乎一样。

Exercise3:HeapFile Mutability

为了在 Exercise4 中实现 insert 和 delete operator, 我们需要先实现 heapFile insert / delete

讲义介绍：

Now, we will begin to implement methods to support modifying tables. We begin at the level of individual pages and

files. There are two main sets of operations: adding tuples and removing tuples.

Removing tuples: To remove a tuple, you will need to implement

deleteTuple. Tuples contain RecordIDs which allow you to find the page they reside on, so this should be as simple

as locating the page a tuple belongs to and modifying the headers of the page appropriately.

Adding tuples: The insertTuple method in

HeapFile.java is responsible for adding a tuple to a heap file. To add a new tuple to a HeapFile, you will have to

find a page with an empty slot. If no such pages exist in the HeapFile, you need to create a new page and append it to

the physical file on disk. You will need to ensure that the RecordID in the tuple is updated correctly.

需要实现的内容：

Implement the remaining skeleton methods in:

---

• 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).

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

  • insertTuple()
  • deleteTuple()

---

简单来说，exercise3需要我们实现HeapPage、HeapFile、BufferPool的插入元组和删除元组的方法。

HeapPage

我们要在HeapPage中插入元组，要做的第一件事就是找空槽位然后进行插入，再处理相关细节；

我们要在HeapPage删除tuple，首先需要找到tuple在哪个slot，再进行删除即可。

插入元组的思路：找到一个空的slot，然后进行插入，并标记slot已经被使用。代码如下：

   public void insertTuple(Tuple t) throws DbException {
        // some code goes here
        // not necessary for lab1
        if (!t.getTupleDesc().equals(this.td)) {
            throw new DbException("Tuple desc is not match");
        }
        for (int i = 0; i < getNumTuples(); i++) {
            if (!isSlotUsed(i)) {
                markSlotUsed(i, true);
                t.setRecordId(new RecordId(this.pid, i));
                this.tuples[i] = t;
                return;
            }
        }
        throw new DbException("The page is full");
    }

删除元组的思路：找到元组对应的slot，标记slot为使用，并将tuples数组对应slot的tuple置为空，具体实现代码：

   public void deleteTuple(Tuple t) throws DbException {
        // some code goes here
        // not necessary for lab1
        final RecordId recordId = t.getRecordId();
        final HeapPageId pageId = (HeapPageId) recordId.getPageId();
        final int tn = recordId.getTupleNumber();
        if (!pageId.equals(this.pid)) {
            throw new DbException("Page id not match");
        }
        if (!isSlotUsed(tn)) {
            throw new DbException("Slot is not used");
        }
        markSlotUsed(tn, false);
        this.tuples[tn] = null;
    }

HeapFile

实际我们插入和删除元组，都是以HeapFile为入口的，以插入元组为例，HeapFile和HeapPage的调用关系应该是这样的：

1.调用HeapFile的insertTuple

2.HeapFile的insertTuple遍历所有数据页（用BufferPool.getPage()获取，getPage会先从BufferPool再从磁盘获取），然后判断数据页是否有空slot，有的话调用对应有空slot的page的insertTuple方法去插入页面；

3.如果遍历完所有数据页，没有找到空的slot，这时应该在磁盘中创建一个空的数据页，并且先调用 writePage() 写入该数据页到磁盘, 并通过 bufferPool 来获取该数据也

4.插入的页面保存到list中并返回，表明这是脏页，后续事务部分会用到。

HeapFile.insertTuple(Tuple tup)实现代码如下：

    public ArrayList<Page> insertTuple(TransactionId tid, Tuple t) throws DbException, IOException,
                                                                  TransactionAbortedException {
        // some code goes here
        final ArrayList<Page> dirtyPageList = new ArrayList<>();
        for (int i = 0; i < this.numPages(); i++) {
            final HeapPage page = (HeapPage) Database.getBufferPool().getPage(tid, new HeapPageId(getId(), i),
                Permissions.READ_WRITE);
            if (page != null && page.getNumEmptySlots() > 0) {
                page.insertTuple(t);
                page.markDirty(true, tid);
                dirtyPageList.add(page);
                break;
            }
        }
        // That means all pages are full, we should create a new page
        if (dirtyPageList.size() == 0) {
            final HeapPageId heapPageId = new HeapPageId(getId(), this.numPages());
            HeapPage newPage = new HeapPage(heapPageId, HeapPage.createEmptyPageData());
            writePage(newPage);
            // Through buffer pool to get newPage
            newPage = (HeapPage) Database.getBufferPool().getPage(tid, heapPageId, Permissions.READ_WRITE);
            newPage.insertTuple(t);
            newPage.markDirty(true, tid);
            dirtyPageList.add(newPage);
        }
        return dirtyPageList;
    }

而删除更加简单，只需要根据tuple得到对应的数据页HeapPage，然后调用数据页的deleteTuple即可：

    // see DbFile.java for javadocs
    public ArrayList<Page> deleteTuple(TransactionId tid, Tuple t) throws DbException, TransactionAbortedException {
        // some code goes here
        final ArrayList<Page> dirtyPageList = new ArrayList<>();
        final RecordId recordId = t.getRecordId();
        final PageId pageId = recordId.getPageId();
        final HeapPage page = (HeapPage) Database.getBufferPool().getPage(tid, pageId, Permissions.READ_WRITE);
        if (page != null && page.isSlotUsed(recordId.getTupleNumber())) {
            page.deleteTuple(t);
            dirtyPageList.add(page);
        }
        return dirtyPageList;
    }

BufferPool

以插入元组为例，BufferPool与HeapFile的调用关系：

1.BufferPool插入元组，会先调用Database.getCatalog().getDatabaseFile(tableId)获取HeapFile即表文件；

2.执行HeapFile.insertTuple()，插入元组并返回插入成功的页面；

3.使用HeapPage的markDirty方法，将返回的页面标记为脏页，并放入缓存池中

实现代码如下：

    public void insertTuple(TransactionId tid, int tableId, Tuple t) throws DbException, IOException,
                                                                    TransactionAbortedException {
        // some code goes here
        // not necessary for lab1
        final DbFile table = Database.getCatalog().getDatabaseFile(tableId);
        final List<Page> dirtyPages = table.insertTuple(tid, t);
        for (final Page page : dirtyPages) {
            page.markDirty(true, tid);
            this.lruCache.put(page.getId(), page);
        }
    }

删除元组也是同样的套路：

    public void deleteTuple(TransactionId tid, Tuple t) throws DbException, IOException, TransactionAbortedException {
        // some code goes here
        // not necessary for lab1
        int tableId = t.getRecordId().getPageId().getTableId();
        DbFile table = Database.getCatalog().getDatabaseFile(tableId);
        final List<Page> dirtyPages = table.deleteTuple(tid, t);
        for (final Page page : dirtyPages) {
            page.markDirty(true, tid);
            this.lruCache.put(page.getId(), page);
        }
    }

Exercise4:Insertion and deletion

exercise4要求我们实现Insertion and deletion两个操作符，实际上就是两个迭代器，实现方式与exercise1相似，将传入的数据源进行处理，并返回处理结果，而处理并返回结果一般都是写在fetchNext中。这里的处理结果元组，只有一个字段，那就是插入或删除影响的行数，与MySQL相似。具体实现插入和删除，需要调用我们exercise3实现的插入删除元组相关方法。

Insert

    protected Tuple fetchNext() throws TransactionAbortedException, DbException {
        // some code goes here
        int cnt = 0;
        while (this.child.hasNext()) {
            final Tuple next = this.child.next();
            try {
                Database.getBufferPool().insertTuple(this.tid, this.tableId, next);
            } catch (Exception e) {
                e.printStackTrace();
                System.out.println("Error happen when insert tuple:" + e.getMessage());
            }
            cnt++;
        }
        if (cnt == 0 && isFetched) {
            return null;
        }
        isFetched = true;
        final Tuple result = new Tuple(this.td);
        result.setField(0, new IntField(cnt));
        return result;
    }

Delete

类似的，Delete操作符的实现也很简单

Exercise5: Page eviction

exercise5要求我们实现一种BufferPool的页面淘汰策略, 我在 lab1 中已经实现了.