问题描述
在使用 STARTS_WITH 之类的字符串函数或 LEFT JOIN ON 中的 LIKE 之类的运算符时，我遇到了麻烦>其中任一参数都来自联接中的两个表.我没有尝试解释摘要，而是提取了一个小示例...
I'm having trouble wrapping my head around using a string function such as STARTS_WITH or an operator such as LIKE in a LEFT JOIN ON where the parameters of either come from both tables in the join. Rather than try to explain the abstracts, I've distilled a small example ...
让我们考虑一个名为 fuzzylog 的表，该表具有一个关键字段 fullname ，我想通过联接到具有以下内容的 names 表来规范化该表:同一列.Fuzzylog中的关键字段可能有点混乱或自由形式，因此不可能直接进行等式联接.这些表可能看起来像这样:
Let's consider a table called fuzzylog that has a key field fullname that I want to canonicalize by joining to a names table that has the same column. The key field in fuzzylog may be a bit messy or free-form so a direct equality join is not possible. These tables might look something like this:
fuzzylog 表:
名称表:
名称表尝试通过提供一个 contains 字段来容纳模糊性，如果完全匹配失败，我想使用 LIKE 加入该字段:
The names table tries to accomodate the fuzziness by providing a contains field which I want to join on using LIKE should the exact match fail:
#standardSQL
SELECT l.id, n.fullname, n.nameid,
l.fullname AS logged_fullname
FROM `neilotemp.fuzzylog` l
LEFT JOIN `neilotemp.names` n
ON l.fullname = n.fullname
OR l.fullname LIKE CONCAT('%', n.contains, '%')
不幸的是，最后一行(我真正需要的那一行)会导致错误:如果没有连接两端的字段相等的条件，则不能使用LEFT OUTER JOIN.我要解决的问题.
Unfortunately that last line, the one I really need, causes the error: LEFT OUTER JOIN cannot be used without a condition that is an equality of fields from both sides of the join. This is really what I'm trying to solve.
我已经进行了很多搜索，但是还找不到能使它点击的任何东西.我必须在正确的道路上迈出一步.
I've done quite a few searches but haven't been able to find anything that is getting it to click in my head. I must be one conceptual step off from the right path.
推荐答案
老实说，我认为在这里使用 contains 字段不是最好的主意
而是考虑使用Levenshtein [edit]距离
Honestly, I think using contains field is not the best idea here
Instead consider below approach with using Levenshtein [edit] distance
#standardSQL
CREATE TEMPORARY FUNCTION EDIT_DISTANCE(string1 STRING, string2 STRING)
RETURNS INT64
LANGUAGE js AS """
var _extend = function(dst) {
var sources = Array.prototype.slice.call(arguments, 1);
for (var i=0; i<sources.length; ++i) {
var src = sources[i];
for (var p in src) {
if (src.hasOwnProperty(p)) dst[p] = src[p];
}
}
return dst;
};
var Levenshtein = {
/**
* Calculate levenshtein distance of the two strings.
*
* @param str1 String the first string.
* @param str2 String the second string.
* @return Integer the levenshtein distance (0 and above).
*/
get: function(str1, str2) {
// base cases
if (str1 === str2) return 0;
if (str1.length === 0) return str2.length;
if (str2.length === 0) return str1.length;
// two rows
var prevRow = new Array(str2.length + 1),
curCol, nextCol, i, j, tmp;
// initialise previous row
for (i=0; i<prevRow.length; ++i) {
prevRow[i] = i;
}
// calculate current row distance from previous row
for (i=0; i<str1.length; ++i) {
nextCol = i + 1;
for (j=0; j<str2.length; ++j) {
curCol = nextCol;
// substution
nextCol = prevRow[j] + ( (str1.charAt(i) === str2.charAt(j)) ? 0 : 1 );
// insertion
tmp = curCol + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// deletion
tmp = prevRow[j + 1] + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// copy current col value into previous (in preparation for next iteration)
prevRow[j] = curCol;
}
// copy last col value into previous (in preparation for next iteration)
prevRow[j] = nextCol;
}
return nextCol;
}
};
var the_string1;
try {
the_string1 = decodeURI(string1).toLowerCase();
} catch (ex) {
the_string1 = string1.toLowerCase();
}
try {
the_string2 = decodeURI(string2).toLowerCase();
} catch (ex) {
the_string2 = string2.toLowerCase();
}
return Levenshtein.get(the_string1, the_string2)
""";
WITH notrmalized_fuzzylog as (
select id, fullname,
(select string_agg(part, ' ' order by part) from unnest(split(fullname, ' ')) part) ordered_fullname
from `project.dataset.fuzzylog`
), normalized_names as (
select nameid, fullname,
(select string_agg(part, ' ' order by part) from unnest(split(fullname, ' ')) part) ordered_fullname
from `project.dataset.names`
)
select
id, l.fullname AS logged_fullname,
ARRAY_AGG(
STRUCT(n.nameid, n.fullname)
ORDER BY EDIT_DISTANCE(l.ordered_fullname, n.ordered_fullname) LIMIT 1
)[OFFSET(0)].*
FROM notrmalized_fuzzylog l
CROSS JOIN normalized_names n
GROUP BY 1, 2
您可以使用下面的问题中的虚拟数据进行测试，操作
You can test, play with above using dummy data from your question as below
#standardSQL
CREATE TEMPORARY FUNCTION EDIT_DISTANCE(string1 STRING, string2 STRING)
RETURNS INT64
LANGUAGE js AS """
var _extend = function(dst) {
var sources = Array.prototype.slice.call(arguments, 1);
for (var i=0; i<sources.length; ++i) {
var src = sources[i];
for (var p in src) {
if (src.hasOwnProperty(p)) dst[p] = src[p];
}
}
return dst;
};
var Levenshtein = {
/**
* Calculate levenshtein distance of the two strings.
*
* @param str1 String the first string.
* @param str2 String the second string.
* @return Integer the levenshtein distance (0 and above).
*/
get: function(str1, str2) {
// base cases
if (str1 === str2) return 0;
if (str1.length === 0) return str2.length;
if (str2.length === 0) return str1.length;
// two rows
var prevRow = new Array(str2.length + 1),
curCol, nextCol, i, j, tmp;
// initialise previous row
for (i=0; i<prevRow.length; ++i) {
prevRow[i] = i;
}
// calculate current row distance from previous row
for (i=0; i<str1.length; ++i) {
nextCol = i + 1;
for (j=0; j<str2.length; ++j) {
curCol = nextCol;
// substution
nextCol = prevRow[j] + ( (str1.charAt(i) === str2.charAt(j)) ? 0 : 1 );
// insertion
tmp = curCol + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// deletion
tmp = prevRow[j + 1] + 1;
if (nextCol > tmp) {
nextCol = tmp;
}
// copy current col value into previous (in preparation for next iteration)
prevRow[j] = curCol;
}
// copy last col value into previous (in preparation for next iteration)
prevRow[j] = nextCol;
}
return nextCol;
}
};
var the_string1;
try {
the_string1 = decodeURI(string1).toLowerCase();
} catch (ex) {
the_string1 = string1.toLowerCase();
}
try {
the_string2 = decodeURI(string2).toLowerCase();
} catch (ex) {
the_string2 = string2.toLowerCase();
}
return Levenshtein.get(the_string1, the_string2)
""";
WITH `project.dataset.fuzzylog` AS (
SELECT 1 id, 'John Smith' fullname UNION ALL
SELECT 2, 'Jane Doe' UNION ALL
SELECT 3, 'Ms. Jane Doe' UNION ALL
SELECT 4, 'Mr. John Smith' UNION ALL
SELECT 5, 'Smith, John' UNION ALL
SELECT 6, 'J.Smith' UNION ALL
SELECT 7, 'J. Doe'
), `project.dataset.names` AS (
SELECT 1 nameid, 'John Smith' fullname, 'smith' match UNION ALL
SELECT 2, 'Jane Doe', 'doe'
), notrmalized_fuzzylog as (
select id, fullname,
(select string_agg(part, ' ' order by part) from unnest(split(fullname, ' ')) part) ordered_fullname
from `project.dataset.fuzzylog`
), normalized_names as (
select nameid, fullname,
(select string_agg(part, ' ' order by part) from unnest(split(fullname, ' ')) part) ordered_fullname
from `project.dataset.names`
)
select
id, l.fullname AS logged_fullname,
ARRAY_AGG(
STRUCT(n.nameid, n.fullname)
ORDER BY EDIT_DISTANCE(l.ordered_fullname, n.ordered_fullname) LIMIT 1
)[OFFSET(0)].*
FROM notrmalized_fuzzylog l
CROSS JOIN normalized_names n
GROUP BY 1, 2
-- ORDER BY 1
结果:
Row id logged_fullname nameid fullname
1 1 John Smith 1 John Smith
2 2 Jane Doe 2 Jane Doe
3 3 Ms. Jane Doe 2 Jane Doe
4 4 Mr. John Smith 1 John Smith
5 5 Smith, John 1 John Smith
6 6 J.Smith 1 John Smith
7 7 J. Doe 2 Jane Doe
正如您在此解决方案中看到的那样，我们完全忽略/删除了任何额外的人工列(例如 contains )，而是应用Levenshtein距离直接测量两个全名之间的相似性.如您所见，在执行此操作之前，我们对全名进行了重新排序/规范化以对其各部分进行排序如果此方法对您有用-您应该考虑通过先删除/替换所有标点(例如点，逗号等)并留有空格来改善效果，以改善效果
As you can see in this solution we totally ignore/remove use of any extra artificial columns (like contains) and rather applying Levenshtein distance to measure similarity directly between two fullnames. And as you can see before doing this we reorder/normalizing fullnames to order their parts
If this approach will work for you - you should consider improving that reordering by first removing/replacing all punctuation like dots, commas, etc with space for better result
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