实战案例：课程搜索引擎

本章定位：综合运用全教程所学知识，从零搭建一个完整的课程搜索引擎。覆盖 Mapping 设计、搜索查询、聚合分析、自定义排序和性能优化。

业务需求

飞翔科技的在线教育平台需要升级课程搜索功能：

  1. 搜索：按课程名称、描述、大纲搜索（支持中文分词）
  2. 筛选：按分类、教师、价格范围筛选
  3. 排序：相关性 + 报名人数 + 评分综合排序
  4. 聚合：按分类统计课程数、平均价格、平均评分
  5. 高亮：搜索结果中高亮匹配词

完整示例

第一步：Mapping 设计

PUT /courses
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "analysis": { "analyzer": { "ik_smart_analyzer": { "type": "custom", "tokenizer": "ik_smart", "filter": ["lowercase"] } } }
  },
  "mappings": {
    "dynamic": "strict",
    "properties": {
      "title": { "type": "text", "analyzer": "ik_max_word", "search_analyzer": "ik_smart", "fields": { "keyword": { "type": "keyword" } } },
      "teacher": { "type": "keyword" },
      "category": { "type": "keyword" },
      "description": { "type": "text", "analyzer": "ik_max_word" },
      "outline": { "type": "text", "analyzer": "ik_max_word", "norms": false },
      "price": { "type": "scaled_float", "scaling_factor": 100 },
      "rating": { "type": "float" },
      "enrollment_count": { "type": "integer" },
      "publish_date": { "type": "date", "format": "yyyy-MM-dd" },
      "tags": { "type": "keyword" }
    }
  }
}

第二步：综合搜索

POST /courses/_search
{
  "query": {
    "function_score": {
      "query": {
        "bool": {
          "must": [{ "multi_match": { "query": "数据", "fields": ["title^3", "description", "outline"] } }],
          "filter": [
            { "terms": { "category.keyword": ["数据科学", "数据工程"] } },
            { "range": { "price": { "gte": 10000, "lte": 50000 } } },
            { "term": { "difficulty": "中级" } }
          ]
        }
      },
      "functions": [
        { "field_value_factor": { "field": "enrollment_count", "factor": 0.1, "modifier": "log1p", "missing": 0 }, "weight": 1.5 },
        { "field_value_factor": { "field": "rating", "factor": 1, "modifier": "none", "missing": 1 }, "weight": 2 }
      ],
      "score_mode": "sum",
      "boost_mode": "multiply"
    }
  }
}

第三步：聚合分析

POST /courses/_search
{
  "size": 0,
  "aggs": {
    "by_category": {
      "terms": { "field": "category.keyword", "size": 10 },
      "aggs": {
        "avg_price": { "avg": { "field": "price" } },
        "avg_rating": { "avg": { "field": "rating" } },
        "total_enrollment": { "sum": { "field": "enrollment_count" } }
      }
    }
  }
}

小结

本实战案例覆盖了 Elasticsearch 完整技术栈：Mapping 设计（中文分词 + keyword 子字段 + norms 优化）→ 数据写入（Bulk API）→ 搜索查询（multi_match + bool + function_score）→ 聚合分析（嵌套 terms + avg）→ 运维部署（ILM + 监控）。核心心得：Mapping 设计决定搜索质量上限，function_score 让业务规则融入排序，ILM 让运维自动化。