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表连接算法概览

· 5 min read

一般连接

形式语言与 SQL

连接两个表

RθSR \bowtie_\theta S
select *
from R, S
where theta
select *
from R
join S on theta

连接多个表

θ(R1,R2,,Rn)\Large \bowtie_\theta \normalsize(R_1, R_2, \cdots, R_n)
select *
from R1, R2, ... Rn
where theta
R1θ2R2θnRnR_1 \bowtie_{\theta_2} R_2 \cdots \bowtie_{\theta_{n}} R_n
select *
from R1
join R2 on theta2
...
join Rn on thetan
θ=i=2nθi\theta=\bigwedge_{i=2}^n\theta_i

Nested Loop Join

对于一般情况,多重循环是最朴素的算法

for r in R.tuples():
for s in S.tuples():
if predicate(r, s):
yield join(r, s)

可以通过分块遍历降低 IO 的性能损失

for rb in R.blocks():
for sb in S.blocks():
for r in rb:
for s in sb:
if predicate(r, s):
yield join(r, s)

等值连接

形式语言与 SQL

一种常见的连接是等值连接

关于一个属性的等值连接

R(id,),S(id,)RR.id=S.idSR(id, \cdots), S(id, \cdots)\\ R \bowtie_{R.id=S.id} S
select *
from R, S
where R.id = S.id
select *
from R
join S on R.id = S.id
select *
from R
join S using (id)
select *
from R
natural join S

关于多个属性的等值连接

R(id1,id2,),S(id1,id2,)RR.id1=S.id1R.id2=S.id2SR(id1, id2, \cdots), S(id1, id2, \cdots)\\ R \bowtie_{R.id1=S.id1 \land R.id2=S.id2 \land \cdots} S
select *
from R, S
where R.id1 = S.id1 and R.id2 = S.id2 and ...
select *
from R
join S on R.id1 = S.id1 and R.id2 = S.id2 and ...
select *
from R
join S using (id1, id2, ...)
select *
from R
natural join S

涉及等值查询问题,呼之欲出的就是两种方法:

  • 排序方法
  • 哈希方法

这两种方法在关联容器(即有序和无序的 setdict)的构造中相当有用,在表连接中也是如此。

如果其中一个关系在连接键上有索引,则可以直接采取 Index Nested Loop Join,否则则需要进行下面两种额外的工作。

Sort-Merge Join

RRSS 按照等值连接所需键排序,随后分别遍历两个关系;当遇到左右等值时,将等值的区间取出求笛卡尔积。

import functools
import itertools


def join(p):
r, s = p
return *r, *s


def smj(R, S, cmp):
R.sort(key=functools.cmp_to_key(cmp))
S.sort(key=functools.cmp_to_key(cmp))
ri = iter(R)
si = iter(S)
r = next(ri, None)
s = next(si, None)
while True:
if r is None or s is None:
return
while (c := cmp(r, s)) != 0:
if c < 0:
if (r := next(ri, None)) is None:
return
else:
if (s := next(si, None)) is None:
return

rc = [r]
sc = [s]

while (r := next(ri, None)) is not None and cmp(rc[0], r) == 0:
rc.append(r)

while (s := next(si, None)) is not None and cmp(sc[0], s) == 0:
sc.append(s)

yield from map(join, itertools.product(rc, sc))

如果连接的键是唯一的,则可以更进一步省去笛卡尔积的部分:

def join(r, s):
return *r, *s


def smj(R, S, cmp):
ri = iter(R)
si = iter(S)
while True:
if (r := next(ri, None)) is None or (s := next(si, None)) is None:
return
while (c := cmp(r, s)) != 0:
if c < 0:
if (r := next(ri, None)) is None:
return
else:
if (s := next(si, None)) is None:
return

yield join(r, s)

如果不想编写笛卡尔积的代码,又需要考虑到键不唯一的情况,可以考虑使用迭代器标记恢复之前的过程,但需要谨慎处理其中的边界条件:

struct iterator {
int *p = nullptr, *q = nullptr;
operator bool() const {
return p < q;
}
int operator*() const {
return *p;
}
iterator& operator++() {
++p;
return *this;
}
};

void smj(iterator r, iterator s) {
iterator mark;
while (r and s) {
if (!mark) {
while (*r != *s) {
if (*r < *s) {
++r;
if (!r) return;
} else {
++s;
if (!s) return;
}
}
mark = s;
}
if (*r == *s) {
emit(*r, *s);
++s;
if (s) {
continue;
}
}
s = mark;
++r;
mark = {};
}
}

Hash Join

用等值连接所需的键的哈希值建立 RR 的哈希表,再用 SS 等值连接的键去查询这个哈希表,得到的相同的行进行连接。

def join(r, s):
return *r, *s


def hj(R, S, hsh, eq):
build = {}
for s in S:
build.setdefault(hsh(s), []).append(s)
for r in R:
for s in build.get(hsh(r), []):
if eq(r, s):
yield join(r, s)