elasticsearch - Elastic Search 中带有附加条件的 Completion Suggester

Question

我有一个索引可以返回不同语言的工作。

我需要根据针对单一语言的单一文本搜索类似的工作。比方说，我已将 1 设置为英语的 LanguageId。我想搜索与帐户匹配的工作。所以如果我在下面写查询，它会获取所有不同语言的工作。所以基本上 “必须” 查询没有任何影响。

GET jobs/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "languageid": "1"
          }
        }
      ]
    }
  },
  "suggest": {
    "suggestions": {
      "text": "acce",
      "completion": {
        "field": "jobs.suggest",
        "size": 30
      }
    }
  }
}

我的映射如下所示

   "mappings": {
"jobs": {
        "properties": {
          "@timestamp": {
            "type": "date"
          },
          "@version": {
            "type": "text"
          },
          "industytype": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "jobs": {
            "properties": {
              "suggest": {
                "type": "completion",
                "analyzer": "simple",
                "preserve_separators": true,
                "preserve_position_increments": true,
                "max_input_length": 50
              }
            }
          },
          "language": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "type": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "updateddate": {
            "type": "date"
          }
        }
      }
    }
}

Answer 1

无法在查询时过滤掉建议，因为completion suggester 使用FST - 在索引时构建的特殊内存数据结构:

Suggesters in Lucene are built in-memory by loading the completion values from the index, then building the FST. This can be a slow, resource intensive process. And, as soon as the index changes, the FST needs to be rebuilt. "Real time search" is a mantra of Elasticsearch. It is not acceptable to return out of date suggestions, nor to require a full rebuild whenever the index changes. Instead of building the FST at search time, we now build an FST per-segment at index time.

所以你所能做的就是添加context对于你的建议者。上下文也在索引时与 completion 字段一起填充，因此可以在查询时在 suggest 查询中使用。另外，这个 article可能对您有用。