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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。

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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。

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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

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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);}...
}

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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 failnext = 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

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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;}@Overridepublic OpIterator[] getChildren() {return new OpIterator[] {this.child};}@Overridepublic void setChildren(OpIterator[] children) {this.child = children[0];}...
}

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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schemaTupleDesc 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resIterator<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resIterator<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;}@Overridepublic OpIterator[] getChildren() {return this.children;}@Overridepublic 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。
  

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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

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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;}@Overridepublic void open() throws DbException, TransactionAbortedException {this.iterator = aggregator.tupleMap.values().iterator();}@Overridepublic boolean hasNext() throws DbException, TransactionAbortedException {return iterator.hasNext();}@Overridepublic Tuple next() throws DbException, TransactionAbortedException, NoSuchElementException {return iterator.next();}@Overridepublic void rewind() throws DbException, TransactionAbortedException {iterator = aggregator.tupleMap.values().iterator();}@Overridepublic TupleDesc getTupleDesc() {return aggregator.desc;}@Overridepublic void close() {iterator = null;}}...
}

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• 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1tuple.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+1IntField 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 ageField 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;}@Overridepublic void open() throws DbException, TransactionAbortedException {this.iterator = aggregator.aggregate.values().iterator();}@Overridepublic boolean hasNext() throws DbException, TransactionAbortedException {return iterator.hasNext();}@Overridepublic Tuple next() throws DbException, TransactionAbortedException, NoSuchElementException {return iterator.next();}@Overridepublic void rewind() throws DbException, TransactionAbortedException {iterator = aggregator.aggregate.values().iterator();}@Overridepublic TupleDesc getTupleDesc() {return aggregator.tupleDesc;}@Overridepublic 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。

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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

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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0markSlotUsed(slotId, false);// 将slot对应的tuple置为nulltuples[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

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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 javadocspublic 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 javadocspublic 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

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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

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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;}@Overridepublic OpIterator[] getChildren() {return new OpIterator[]{child};}@Overridepublic void setChildren(OpIterator[] children) {this.child = children[0];}
}

装饰器模式要点有两个:

• 装饰器对象继承被装饰对象的抽象父类或者父类接口，这样我们才可以在使用时能够用基类指针接收被装饰后的对象实现
• 装饰器对象内部需要调用被装饰对象的方法获取原数据，然后再此基础上进行计算然后返回一个结果，或者在原有数据基础上增加附加信息，或者啥也不干，只进行相关信息记录。
• fetchNext方法这里就是Insert装饰器对象需要实现的方法，其内部调用被装饰器对象的next方法获取所有数据，然后执行insert操作，同时计算插入数据条数，最终返回的是插入的数据条数。

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• 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;}@Overridepublic OpIterator[] getChildren() {return new OpIterator[]{child};}@Overridepublic 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

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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。

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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);}@Overridepublic 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");}}@Overridepublic 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。

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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.field1AND 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 schemaType[] 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 joinTransactionId 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 conditionFilter 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 ittry {j.open();while (j.hasNext()) {Tuple tup = j.next();System.out.println(tup);}j.close();Database.getBufferPool().transactionComplete(tid);} catch (Exception e) {e.printStackTrace();}}
}

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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
------------------
1 102 203 304 405 505 506 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