我正在尝试从我们的数据库中清除某个客户。我注意到一种趋势,人们用同名填写他们的名字,这与他们填写公司名称的方式不同。所以一个例子看起来像:
business_name first_name
------------- ----------
locksmith taylorsville locksmith
locksmith roy locksmi
locksmith clinton locks
locksmith farmington locksmith
这些是我不希望被查询的人。他们是坏蛋。我试图将一个查询与WHERE语句放在一起(大概)将任何名字中包含至少部分匹配的人隔离到他们的公司名称,但是我很难过并且可以使用一些帮助
答案 0 :(得分:1)
您可以使用LIKE运算符:
appcompat-v7
%代表什么。
答案 1 :(得分:0)
您可以采用基于相似性的方法
尝试答案底部的代码
它产生如下结果
business_name partial_business_name first_name similarity
locksmith taylorsville locksmith locksmith 1.0
locksmith farmington locksmith locksmith 1.0
locksmith roy locksmith locksmi 0.7777777777777778
locksmith clinton locksmith locks 0.5555555555555556
因此,您将能够根据相似度值
控制要过滤的内容**代码**
SELECT business_name, partial_business_name, first_name, similarity FROM
JS( // input table
(
SELECT business_name, REGEXP_EXTRACT(business_name, r'^(\w+)') AS partial_business_name, first_name AS first_name FROM
(SELECT 'locksmith taylorsville' AS business_name, 'locksmith' AS first_name),
(SELECT 'locksmith roy' AS business_name, 'locksmi' AS first_name),
(SELECT 'locksmith clinton' AS business_name, 'locks' AS first_name),
(SELECT 'locksmith farmington' AS business_name, 'locksmith' AS first_name),
) ,
// input columns
business_name, partial_business_name, first_name,
// output schema
"[{name: 'business_name', type:'string'},
{name: 'partial_business_name', type:'string'},
{name: 'first_name', type:'string'},
{name: 'similarity', type:'float'}]
",
// function
"function(r, emit) {
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_partial_business_name;
try {
the_partial_business_name = decodeURI(r.partial_business_name).toLowerCase();
} catch (ex) {
the_partial_business_name = r.partial_business_name.toLowerCase();
}
try {
the_first_name = decodeURI(r.first_name).toLowerCase();
} catch (ex) {
the_first_name = r.first_name.toLowerCase();
}
emit({business_name: r.business_name, partial_business_name: the_partial_business_name, first_name: the_first_name,
similarity: 1 - Levenshtein.get(the_partial_business_name, the_first_name) / the_partial_business_name.length});
}"
)
ORDER BY similarity DESC
用于How to perform trigram operations in Google BigQuery?并基于https://storage.googleapis.com/thomaspark-sandbox/udf-examples/pataky.js @thomaspark,其中Levenshtein的距离用于衡量相似度
答案 2 :(得分:0)
这样可以解决问题,
从TableName中选择*,其中lower(business_name)包含lower(first_name)
使用lower()以防它们有大写字母。希望它有所帮助。