如何在 Google BigQuery 中執行三元組運算？

Question

我確實使用 PostgreSQL 中的pg_trgm模塊來使用三元組計算兩個字符串之間的相似度。 特別是我使用：

similarity(text, text)

哪個返回返回一個數字，指示兩個參數的相似程度（在 0 和 1 之間）。

如何在 Google BigQuery 上執行相似函數（或等效函數）？

Answer 1

試試下面。 至少作為增強的藍圖

SELECT text1, text2, similarity FROM 
JS(
// input table
(
  SELECT * FROM 
  (SELECT 'mikhail' AS text1, 'mikhail' AS text2),
  (SELECT 'mikhail' AS text1, 'mike' AS text2),
  (SELECT 'mikhail' AS text1, 'michael' AS text2),
  (SELECT 'mikhail' AS text1, 'javier' AS text2),
  (SELECT 'mikhail' AS text1, 'thomas' AS text2)
) ,
// input columns
text1, text2,
// output schema
"[{name: 'text1', type:'string'},
  {name: 'text2', 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_text1;

  try {
    the_text1 = decodeURI(r.text1).toLowerCase();
  } catch (ex) {
    the_text1 = r.text1.toLowerCase();
  }

  try {
    the_text2 = decodeURI(r.text2).toLowerCase();
  } catch (ex) {
    the_text2 = r.text2.toLowerCase();
  }

  emit({text1: the_text1, text2: the_text2,
        similarity: 1 - Levenshtein.get(the_text1, the_text2) / the_text1.length});

  }"
)
ORDER BY similarity DESC

這是基於https://storage.googleapis.com/thomaspark-sandbox/udf-examples/pataky.js by @thomaspark 的輕微修改

Answer 2

我是這樣做的：

CREATE TEMP FUNCTION trigram_similarity(a STRING, b STRING) AS (
  (
    WITH a_trigrams AS (
      SELECT
        DISTINCT tri_a
      FROM
        unnest(ML.NGRAMS(SPLIT(LOWER(a), ''), [3,3])) AS tri_a
    ),
    b_trigrams AS (
      SELECT
        DISTINCT tri_b
      FROM
        unnest(ML.NGRAMS(SPLIT(LOWER(b), ''), [3,3])) AS tri_b
    )
    SELECT
      COUNTIF(tri_b IS NOT NULL) / COUNT(*)
    FROM
      a_trigrams
      LEFT JOIN b_trigrams ON tri_a = tri_b
  )
);

這是與Postgres 的 pg_trgm的比較：

select trigram_similarity('saemus', 'seamus');
-- 0.25 vs. pg_trgm 0.272727

select trigram_similarity('shamus', 'seamus');
-- 0.5 vs. pg_trgm 0.4