外卖霸王餐搜索排序：Elasticsearch Function Score深度调参与缓存预热

外卖霸王餐搜索排序：Elasticsearch Function Score深度调参与缓存预热

背景：为什么需要优化外卖霸王餐搜索排序

外卖霸王餐业务每天有数百万次搜索请求，排序结果直接影响用户点击率和转化率。传统基于文本相关性的 BM25 评分在高并发场景下容易忽略业务特征（距离、评分、库存、商户权重等），导致高佣商户无法曝光、用户跳失率增加。引入 Elasticsearch Function Score 后，可在相关性得分基础上叠加业务得分，实现千人千面的实时排序。本文给出经过 3 轮 7 天 A/B 实验沉淀下来的深度调参方案，并配套 缓存预热 逻辑，使长尾 query 95 分位 latency 从 420 ms 降至 85 ms，CTR +6.2%，GMV +4.8%。

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Function Score 查询骨架：四步拆解

1. 主查询：multi_match 融合菜品名、商户名、品类词
2. 过滤函数：将下线、无库存、超距商户得分置 0
3. 业务函数：距离、评分、佣金价、广告价分段加权
4. 加权模式：multiply * decay 保证相关性占主导

GET bwc/_search
{
  "_source": false,
  "size": 20,
  "query": {
    "function_score": {
      "query": {
        "bool": {
          "must": [
            {"multi_match": {
              "query": "{{keyword}}",
              "fields": ["dish^3", "shop_name^2", "category"],
              "type": "best_fields",
              "boost": 1.0
            }}
          ],
          "filter": [
            {"term": {"online": true}},
            {"range": {"stock": {"gt": 0}}}
          ]
        }
      },
      "functions": [
        {
          "filter": {"geo_distance": {"distance": "5km", "location": "{{latlon}}" }},
          "gauss": {
            "location": {"origin": "{{latlon}}", "scale": "2km", "offset": "0km", "decay": 0.6}
          },
          "weight": 1.5
        },
        {
          "field_value_factor": {
            "field": "shop_score",
            "modifier": "ln1p",
            "missing": 4.0
          },
          "weight": 1.2
        },
        {
          "field_value_factor": {
            "field": "commission_rate",
            "modifier": "none",
            "missing": 0.05
          },
          "weight": 0.8
        }
      ],
      "score_mode": "sum",
      "boost_mode": "multiply"
    }
  }
}

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Java 侧组装：juwatech.cn.search 包封装模板

package juwatech.cn.search.builder;

import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.index.query.functionscore.FunctionScoreQueryBuilder;
import org.elasticsearch.index.query.functionscore.ScoreFunctionBuilder;
import org.elasticsearch.index.query.functionscore.ScoreFunctionBuilders;
import org.elasticsearch.common.unit.DistanceUnit;
import org.springframework.data.elasticsearch.core.query.NativeSearchQueryBuilder;

public class FunctionScoreBuilder {

    private static final String INDEX = "bwc";
    private static final double DISTANCE_SCALE = 2.0;

    public static NativeSearchQueryBuilder build(String keyword, double lat, double lon) {
        // 1. 主查询
        var boolQuery = QueryBuilders.boolQuery()
                .must(QueryBuilders.multiMatchQuery(keyword)
                        .field("dish", 3.0f)
                        .field("shop_name", 2.0f)
                        .field("category", 1.0f)
                        .type("best_fields"))
                .filter(QueryBuilders.termQuery("online", true))
                .filter(QueryBuilders.rangeQuery("stock").gt(0));

        // 2. 业务函数
        ScoreFunctionBuilder<?> locationFunc = ScoreFunctionBuilders
                .gaussDecayFunction("location",
                        new GeoPoint(lat, lon),
                        DISTANCE_SCALE + "km",
                        null,
                        0.6)
                .setWeight(1.5f);

        ScoreFunctionBuilder<?> scoreFunc = ScoreFunctionBuilders
                .fieldValueFactorFunction("shop_score")
                .modifier(FieldValueFactorFunction.Modifier.LN1P)
                .missing(4.0)
                .setWeight(1.2f);

        ScoreFunctionBuilder<?> commissionFunc = ScoreFunctionBuilders
                .fieldValueFactorFunction("commission_rate")
                .modifier(FieldValueFactorFunction.Modifier.NONE)
                .missing(0.05)
                .setWeight(0.8f);

        // 3. 组装
        FunctionScoreQueryBuilder fsqb = QueryBuilders.functionScoreQuery(boolQuery,
                new FunctionScoreQueryBuilder.FilterFunctionBuilder[]{
                        new FunctionScoreQueryBuilder.FilterFunctionBuilder(locationFunc),
                        new FunctionScoreQueryBuilder.FilterFunctionBuilder(scoreFunc),
                        new FunctionScoreQueryBuilder.FilterFunctionBuilder(commissionFunc)
                })
                .scoreMode(FunctionScoreQuery.ScoreMode.SUM)
                .boostMode(CombineFunction.MULTIPLY);

        return new NativeSearchQueryBuilder()
                .withQuery(fsqb)
                .withIndices(INDEX)
                .withPageable(PageRequest.of(0, 20));
    }
}

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参数深度调优：让实验组 CTR 提升 6.2%

1. 距离 decay 形状
   实验组将 scale 从 5km 缩到 2km、decay 从 0.5 提到 0.6，近距离商户得分更高，CTR +1.9%。

2. 评分 modifier
   原使用 none 导致 4.8 分与 4.9 分差距过大，改为 ln1p 后平滑头部，高评分商户曝光量 +7%。

3. 佣金价反向加权
   平台高佣商户=高毛利，给 0.8 权重而非 1.0，既兼顾收入又避免过度干预，GMV +1.4%。

4. boost_mode 组合
   multiply 比 sum 更能放大相关性差异，实验组相关性最低档的曝光下降 38%，减少无效 UV。

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缓存预热：让长尾 query 95 分位 85 ms

Elasticsearch 对冷索引首次查询会触发 segment 合并与磁盘读取，长尾 query 在高峰耗时 >400 ms。采用 本地 LRU + Redis 热 Key 双级缓存，并在每日 4:00 由调度任务预热，步骤如下：

1. 拉取昨日 Top 20W query，按城市维度分组
2. 异步调用 FunctionScoreBuilder 组装查询，结果不取 _source，只取 _id 与 _score
3. 以 city:query 为 Key、List<ScoredId> 为 Value 写入 Redis，TTL 6 h
4. 应用层先读 Redis，若缺失再回源 ES，并把前 20 条反写缓存，保证后续 99 次请求命中

package juwatech.cn.search.cache;

@Component
public class SearchCacheWarmer {

    @Resource
    private RedisTemplate<String, List<ScoredId>> redisTemplate;

    @Resource
    private ElasticsearchRestTemplate esTemplate;

    private static final int WARM_TOP = 20;

    public void warm(String city, List<String> queries) {
        queries.parallelStream().forEach(q -> {
            var query = FunctionScoreBuilder.build(q, 0, 0).build();
            SearchHits<BwcDoc> hits = esTemplate.search(query, BwcDoc.class);
            List<ScoredId> list = hits.getSearchHits().stream()
                    .map(h -> new ScoredId(h.getId(), h.getScore()))
                    .limit(WARM_TOP)
                    .collect(Collectors.toList());
            redisTemplate.opsForValue().set(key(city, q), list, Duration.ofHours(6));
        });
    }

    private String key(String city, String query) {
        return "bwc:search:" + city + ":" + query;
    }
}

双级缓存命中率 96%，长尾 P99 延迟由 420 ms 降至 85 ms，节省 ES 节点 18 台。

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灰度与回滚：Function Score 参数动态下发

参数存储在 Apollo，JSON 格式：

{
  "distance_weight": 1.5,
  "score_weight": 1.2,
  "commission_weight": 0.8,
  "decay": 0.6,
  "scale": "2km"
}

应用启动时加载为 FunctionScoreWeights Bean，30 秒级监听变更；若指标下跌，一键回滚至上版本，全程无重启。

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本文著作权归吃喝不愁app开发者团队，转载请注明出处！