From 52ae85fbf8c65620b2f533ddfef7d0d29c63d01e Mon Sep 17 00:00:00 2001 From: tangwang Date: Thu, 5 Mar 2026 23:27:42 +0800 Subject: [PATCH] 1. ES docs 2. 修改索引配置： 向量改为bf16 --- docs/ES/1_ES安装.md | 103 ------------------------------------------------------------------------------------------------------- docs/ES/1_ES配置和使用.md | 680 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- docs/ES/2_kibana安装.md | 51 --------------------------------------------------- docs/ES/3.1_hanlp安装.md | 116 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----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- docs/ES/install.sh | 47 ----------------------------------------------- mappings/search_products.json | 9 ++++++--- 26 files changed, 3184 insertions(+), 2809 deletions(-) delete mode 100644 docs/ES/1_ES安装.md delete mode 100644 docs/ES/1_ES配置和使用.md delete mode 100644 docs/ES/2_kibana安装.md delete mode 100644 docs/ES/3.1_hanlp安装.md delete mode 100644 docs/ES/3.2_jieba插件安装.md delete mode 100644 docs/ES/3.3_ik分词器.md delete mode 100644 docs/ES/3_ansj分词插件安装.md delete mode 100644 docs/ES/4_索引和查询测试.md create mode 100644 docs/ES/ES_8.18/1_ES安装.md create mode 100644 docs/ES/ES_8.18/1_ES配置和使用.md create mode 100644 docs/ES/ES_8.18/2_kibana安装.md create mode 100644 docs/ES/ES_8.18/3.1_hanlp安装.md create mode 100644 docs/ES/ES_8.18/3.2_jieba插件安装.md create mode 100644 docs/ES/ES_8.18/3.3_ik分词器.md create mode 100644 docs/ES/ES_8.18/3_ansj分词插件安装.md create mode 100644 docs/ES/ES_8.18/4_索引和查询测试.md create mode 100644 docs/ES/ES_8.18/README__ES查询相关.md create mode 100644 docs/ES/ES_8.18/README__分词相关.md create mode 100644 docs/ES/ES_8.18/README__性能优化.md create mode 100644 docs/ES/ES_8.18/install.sh create mode 100644 docs/ES/ES_9 delete mode 100644 docs/ES/README__ES查询相关.md delete mode 100644 docs/ES/README__分词相关.md delete mode 100644 docs/ES/README__性能优化.md delete mode 100644 docs/ES/install.sh diff --git a/docs/ES/1_ES安装.md b/docs/ES/1_ES安装.md deleted file mode 100644 index 8bde5e9..0000000 --- a/docs/ES/1_ES安装.md +++ /dev/null @@ -1,103 +0,0 @@ - - - -## 一、安装 -### 1. 安装 -```shell -# 参考install_elasticsearch.sh。 - -ELASTICSEARCH_VERSION="8.18.0" - -wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo rpm --import -yum install -y https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-$ELASTICSEARCH_VERSION-x86_64.rpm - -# 设置Elasticsearch的系统服务 -systemctl daemon-reload -systemctl start elasticsearch -systemctl enable elasticsearch -``` -安装、配置好后验证命令： curl -X GET "http://localhost:9200" - - -### 3. 工程&安全方面设置 - -性能相关配置： -```shell -vi /etc/elasticsearch/jvm.options -堆内存大小： --Xms32g --Xmx32g -``` - -/etc/elasticsearch/elasticsearch.yml ： - -Fielddata Cache（用于聚合和排序操作）： -indices.fielddata.cache.size: 40% # 将 fielddata 缓存大小设置为 40% 的物理内存 -Query Cache（用于查询缓存）： -indices.queries.cache.size: 30% # 将查询缓存设置为 30% 的物理内存 - -修改的系统配置： -vi /etc/sysctl.conf -```shell -# tangwang -# 调整系统句柄限制 -fs.file-max = 1000000 -# 优化文件描述符 可以分配的vma数量 -vm.max_map_count = 655360 -# 优化脏内存页，es写入削峰 -vm.dirty_ratio=10 -vm.dirty_background_ratio=5 -vm.dirty_writeback_centisecs=200 -# 优化系统回收inode cache权重 -vm.vfs_cache_pressure=200 -vm.dirty_expire_centisecs=6000 -# 尽可能低的使用swap,内存很充裕可以设置为0，较低设置为1 -vm.swappiness=0 -``` -vi /etc/security/limits.conf -```shell -# tangwang 修改 下面都加了10倍 -# End of file -* soft nofile 655350 -* hard nofile 655350 -root soft nofile 655350 -root hard nofile 655350 -* soft nofile 655350 -* hard nofile 655350 - - -# tangwang 补充下面的 -* soft nproc 102400 -* hard nproc 409600 -# 允许进程锁定内存 -* soft memlock unlimited -* hard memlock unlimited -``` - - -### TOOD : -配置为单节点模式，discovery.type: single-node 但是还需要其他配套改动。 - -用户名 密码 安全方面 -kibana用户名密码 - -## 其他说明 -ES 8.x版本特性 -在 8.0版本 中，提供新的kNN 搜索 API。以前，Elasticsearch 仅支持使用script_score带有向量函数的查询进行精确的 kNN 搜索。虽然此方法可以保证准确的结果，但它通常会导致搜索速度缓慢，并且无法很好地适应大型数据集。作为索引速度较慢和准确性不完美的代价，新的 kNN 搜索 API 允许您以更快的速度对更大的数据集运行近似 kNN 搜索。 - -在 8.4版本中，把knn搜索加入到search API中，以支持 ANN 搜索。它由与旧 _knn_search端点相同的 Lucene ANN 功能提供支持。该knn选项可以与其他搜索功能（例如查询和聚合）结合使用。 - -在 8.7版本中，允许多个 KNN 搜索子句。某些向量搜索场景需要使用几个 kNN 子句进行相关性排名，例如，当基于多个字段进行排名时，每个字段都有自己的向量，或者当文档包含图像向量和文本向量时。用户可能希望获得基于所有这些 kNN 子句的组合的相关性排名。 - -在 8.8版本 中，将 Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search — Elastic Search Labs 模型引入到我们的机器学习模型库中，您可以开箱即用。ELSER 通过启用语义搜索来提高搜索结果的相关性。这种搜索方法考虑单词的含义，而不是仅仅依赖字面术语。ELSER 是一种预训练的域外稀疏向量模型，无需对特定源数据进行微调。它从一开始就为您提供相关的搜索结果。并且在此版本中，还提升了KNN检索语法，加入了similarity参数，允许过滤给定相似性之外的最近邻结果。 - -在8.9版本中。text_embedding query_vector_builderkNN 搜索的扩展普遍可用。 - -在8.10版本中，启用跨段并行 knn 搜索，使得 knn 查询对由多个段组成的分片更快，来优化knn搜索的性能。 - - - - - -8.15 更为丰富的工具用于微调相关性、更高的模型灵活性、矢量搜索的改进 - diff --git a/docs/ES/1_ES配置和使用.md b/docs/ES/1_ES配置和使用.md deleted file mode 100644 index 569f12f..0000000 --- a/docs/ES/1_ES配置和使用.md +++ /dev/null @@ -1,680 +0,0 @@ -# Elasticsearch 文档 - -## 相关链接 -- 接口文档：http://rap.essa.top:88/workspace/myWorkspace.do?projectId=78#2187 -- Kibana 控制台：http://120.76.41.98:5601/app/dev_tools#/console/shell - -## 分词方面 - -Ansj 分词插件安装 -ES可以用的中文分词，效果最好的是hanLP和ansj，其次是jieba。 - -我们老的搜索 solr 已经在几年前替代掉了ik，使用的是mmseg。但是我没找到mmseg的ES插件。 - -为了分词方面不至于比老版本差，这里先安装了ansj - -### 1. 下载插件 -从 [elasticsearch-analysis-ansj releases](https://github.com/NLPchina/elasticsearch-analysis-ansj/releases) 选择对应版本下载： - -- ES 8.18 版本： -```bash -wget https://github.com/NLPchina/elasticsearch-analysis-ansj/archive/refs/tags/v8.18.0.zip -``` - -- ES 8.17 版本： -```bash -wget https://github.com/NLPchina/elasticsearch-analysis-ansj/archive/refs/tags/v8.17.6.zip -``` - -### 2. 编译 -执行 `mvn package` 命令，编译成功后将在 `target/releases/` 目录生成插件压缩包： -`elasticsearch-analysis-ansj-<版本号>-release.zip` - -### 3. 安装步骤 -1. 进入 ES 安装路径（默认：`/usr/share/elasticsearch/`） -2. 执行安装命令： -```bash -bin/elasticsearch-plugin install file:///xxx/绝对路径到/elasticsearch-analysis-ansj-8.18.0.0-release.zip -``` -3. 重启服务： -```bash -systemctl restart elasticsearch -``` - -其他分词插件安装方法： -《3.1_hanlp安装.md》 -《3.2_jieba插件安装.md》 -在ES8上面安装过，但是没试过具体的版本 8.17 8.18 - -### 4. 配置说明 -停用词、同义词配置位于 `/config/elasticsearch-analysis-ansj/ansj.cfg.yml`（暂未使用） - -## 字段说明 - -```bash -需要的字段： -id 商品skuId -goods_id 商品spuId -buyer_id 所属专属采购商id -trader_buyer_ids 所属贸易商名下平台客户的专属采购商id -goods_certification_types 商品证书类型 -supplier_code 供应商编码 -supplier_name 供应商名称 -supplier_certification_code 供应商企业证书编码（列表） -auth_buyer_level_list 商品可见采购商等级（集合） -show_price_level_list 价格可见采购商等级（集合） -goods_composition 成分列表（材质） -compositions_main_secondary 物料主副（主：1，副：2），格式：物料代码_主副类型 -goods_key_word_zh 商品关键词中文 -goods_key_word_en 商品关键词英文 -goods_key_word_ru 商品关键词俄文 -goods_copyright 版权（自有、第三方、无授权、A货） -goods_main_material 主材质（字典：材质） -is_in_new_protect 是否在新品保护期（0否，1是） -goods_new_protect_date_stamp 新品保护期日期时间戳 -goods_attribute_name_zh spu属性中文（列表） -goods_attribute_name_en spu属性英文（列表） -goods_attribute_name_ru spu属性俄文（列表） -purchase_moq 采购MOQ -ts 触发索引的时间 -deliver_day 货期 -factory_no 工厂货号 -factory_no_buyer 工厂货号(客户) -fir_on_sell_time 首次上架时间 -fir_on_sell_time_stamp 首次上架时间timestamp -no 商品编码 -hs_no 宏升编码 -package_type 包装类型值（来自商品属性编码：PKG） -package_type_id 包装类型ID（来自商品属性编码：PKG） -labelId_by_skuId_essaone_* essaone商品标签，国家编码标识 -sale_goods_certificate_* 商品证书ID，国家编码标识 -labelId_by_skuId_essa_* essa商品标签，区域ID标识 -``` - -## Mapping 配置 -参考文件 `create_index.sh`： - -## 快速入门 - -### Shell -参考 [索引和查询测试](../docs/3.3_索引和查询测试.md) 包含了在ES服务器进行本地进行一些常用的查询操作。 - -### Python -- [test_index_and_search.py](../tests/test_index_and_search.py) 是一个简单的例子，创建索引，导入数据，查询数据 -- [batch_bulk_goods.py](../batch_bulk_goods.py) 功能是 通过sql 读取最近3年的所有数据，按batch（1000）通过bulk接口进行逐批入库，入库到goods索引。 - -### Kibana - -#### 分词相关 -```bash -# 索引分词 -GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝&type=index_ansj - -# 查询分词 -GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝&type=query_ansj - -# 查看配置 -GET /_cat/ansj/config -``` -#### 查询相关 -GET /goods/_search -{ - "query": { - "match_all": {} - }, - "size": 5 -} - -#### 1. 查看字段分词结果 -```bash -# 查看中文名称分词结果 -GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车&type=index_ansj - -# 查看英文名称分词结果 -GET /_cat/ansj?text=14 inch 4th generation real eye snow doll with manual cart&type=standard -``` - -#### 2. 查看索引随机10条内容 -```bash -GET /goods/_search -{ - "size": 10, - "query": { - "function_score": { - "query": { "match_all": {} }, - "random_score": {} - } - } -} -``` - -#### 3. 关键词查询 -```bash -# 简单关键词匹配 -GET /goods/_search -{ - "query": { - "match": { - "name_zh": "冰雪公仔" - } - } -} - -# 多字段关键词匹配 -GET /goods/_search -{ - "query": { - "multi_match": { - "query": "冰雪公仔", - "fields": ["name_zh", "sub_name_zh", "category_name_zh"] - } - } -} -``` - -#### 4. 向量查询 -```bash -# 使用向量相似度查询 -GET /goods/_search -{ - "query": { - "script_score": { - "query": { "match_all": {} }, - "script": { - "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0", - "params": { - "query_vector": [0.1, 0.2, ...] # 1024维向量 - } - } - } - } -} -``` - -#### 5. SKUID查询 -```bash -# 精确匹配SKUID -GET /goods/_search -{ - "query": { - "term": { - "goods_id": "2817667" - } - } -} -``` - -#### 6. 名称查询测试 -```bash -# 中文名称模糊匹配 -GET /goods/_search -{ - "query": { - "match": { - "name_zh": { - "query": "冰雪公仔", - "fuzziness": "AUTO" - } - } - } -} - -# 英文名称匹配 -GET /goods/_search -{ - "query": { - "match": { - "name_en": "snow doll" - } - } -} - -# 俄语名称匹配 -GET /goods/_search -{ - "query": { - "match": { - "name_ru": "снежная кукла" - } - } -} - -# 使用 match_phrase 进行短语匹配 -GET /goods/_search -{ - "query": { - "match_phrase": { - "name_zh": "冰雪公仔" - } - } -} - -# 使用 match_phrase 进行多语言短语匹配 -GET /goods/_search -{ - "query": { - "bool": { - "should": [ - { - "match_phrase": { - "name_zh": "冰雪公仔" - } - }, - { - "match_phrase": { - "name_en": "snow doll" - } - }, - { - "match_phrase": { - "name_ru": "снежная кукла" - } - } - ], - "minimum_should_match": 1 - } - } -} - -# 使用 match_phrase 配合 slop 参数进行模糊短语匹配 -GET /goods/_search -{ - "query": { - "match_phrase": { - "name_zh": { - "query": "冰雪公仔", - "slop": 2 - } - } - } -} - -# 多语言 match_phrase 配合 slop 参数 -GET /goods/_search -{ - "query": { - "bool": { - "should": [ - { - "match_phrase": { - "name_zh": { - "query": "冰雪公仔", - "slop": 2 - } - } - }, - { - "match_phrase": { - "name_en": { - "query": "snow doll", - "slop": 1 - } - } - }, - { - "match_phrase": { - "name_ru": { - "query": "снежная кукла", - "slop": 2 - } - } - } - ], - "minimum_should_match": 1 - } - } -} -``` - -#### 7. 多语言查询测试 -```bash -# 同时查询中英文名称 -GET /goods/_search -{ - "query": { - "bool": { - "should": [ - { - "match": { - "name_zh": "冰雪公仔" - } - }, - { - "match": { - "name_en": "snow doll" - } - } - ], - "minimum_should_match": 1 - } - } -} -``` - -#### 8. 向量索引查询测试 -注意：向量查询中的向量维度必须与索引中定义的维度匹配（1024维） -```bash -# 使用向量相似度进行商品推荐 -GET /goods/_search -{ - "query": { - "script_score": { - "query": { "match_all": {} }, - "script": { - "source": "cosineSimilarity(params.query_vector, 'ru_name') + 1.0", - "params": { - "query_vector": [0.1, 0.2, ...] # 1024维向量 - } - } - } - }, - "size": 10 -} -``` - -#### 9. 关键词+向量索引组合查询测试 -```bash -# 关键词搜索+向量相似度提权 -GET /goods/_search -{ - "query": { - "function_score": { - "query": { - "match": { - "name_zh": "冰雪公仔", - "boost": 1.0 - } - }, - "functions": [ - { - "script_score": { - "script": { - "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0", - "params": { - "query_vector": [0.1, 0.2, ...] # 1024维向量 - } - } - } - } - ], - "boost_mode": "multiply" - } - } -} - -# source 可以支持embedding为空 ： "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", - -两者乘起来： -{ - "query": { - "function_score": { - "score_mode": "sum", - "boost_mode": "multiply", - "query": { - "match": { - "content": { - "query": keywords, - "boost": 1.0 - } - } - }, - "functions": [ - { - "script_score": { - "script": { - "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", - "params": {"query_vector": context.embeddings[0][1]} - } - } - } - ] - } - } - } - -#### 9. 向量搜索+关键词搜索 -GET /goods/_search -{ - "query": { - "match": { - "content": { - "query": "玩具", - "boost": 1.0 - } - } - }, - "knn": { - "field": "name_prefix", - "query_vector": [-0.05291186273097992, ...], - "k": 5, - "num_candidates": 10, - "boost": 1.0 - } - } - - - - -参考代码： -```python - def execute_search(self, context, search_type="match_phrase", search_type_attachment=0, size=10): - query = context.query - normalized_query = context.normalized_query - core_term = context.core_term - keywords = context.keywords - knn_boost_keywords = core_term if core_term else keywords - expand = context.expand - - seen_queries = set() - unique_queries = [] - for q, weight in [(query, 1.0), (normalized_query, 1.0), (keywords, 0.5)]: - if q and q not in seen_queries: - unique_queries.append((q, weight)) - seen_queries.add(q) - - # 关于混合检索： - # knn和文本查询同时作用： - # 8.12之前query里面不能包含knn，kNN搜索作为查询已在 8.12 版本中引入: https://www.elastic.co/search-labs/blog/knn-query-elasticsearch - # { - # "size": 3, - # "query": { - # "bool": { - # "should": [ - # { - # "knn": { - # "field": "embedding", - # "query_vector": [2,2,2,0], - # "num_candidates": 10, - # "_name": "knn_query" - # } - # }, - # { - # "match": { - # "description": { - # "query": "luxury", - # "_name": "bm25query" - # } - # } - # } - # ] - # } - # - # knn里面不能包含query。 - # knn和query并列（hybrid search 混合检索），是求或的关系。 - # knn里面可以加filter，比如： "filter": {"match": {"my_label": "red"}} - - if search_type == "match_phrase": - body = { - "query": { - "bool": { - "should": [ - { - "match_phrase": { - "content": { - "query": unique_query, - "boost": weight, - "slop": search_type_attachment - } - } - } for unique_query, weight in unique_queries - ], - "minimum_should_match": 1 - } - } - } - # 纯关键词检索 2 - elif search_type == "match_keywords": - body = { - "query": { - "bool": { - "must": [ - { - "match": { - "content": {"query": core_term, "boost": 1.0} - } - } - ], - "should": [ - { - "match": { - "content": {"query": q, "boost": boost} - } - } for (q, boost) in [(keywords, 1.0), (expand, 0.6), (normalized_query, 1.0)] if q - ], - "minimum_should_match": 1 - } - } - } - # 关键词搜索+向量排序 - elif search_type == "match&boost": - body = { - "query": { - "function_score": { - "score_mode": "sum", - "boost_mode": "multiply", - "query": { - "match": { - "content": { - "query": keywords, - "boost": 1.0 - } - } - }, - "functions": [ - { - "script_score": { - "script": { - "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", - "params": {"query_vector": context.embeddings[0][1]} - } - } - } - ] - } - } - } - # 这个太慢 - elif search_type == "match&boost2": - body = { - "query": { - "script_score": { - "query": { - "match": { - "content": { - "query": keywords, - "boost": 1.0 - } - } - }, - "script": { - "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", - "params": {"query_vector": context.embeddings[0][1]} - } - } - } - } - # 向量搜索+关键词搜索 - elif search_type == "match&knn": - body = { - "query": { - "match": { - "content": { - "query": knn_boost_keywords, - "boost": 1.0 - } - } - }, - "knn": { - "field": "embedding", - "query_vector": context.embeddings[search_type_attachment][1], - "k": 5, - "num_candidates": 10, - "boost": 1.0 - } - } - # 纯向量搜索 - elif search_type == "knn": - body = { - "knn": { - "field": "embedding", - "query_vector": context.embeddings[search_type_attachment][1], - "k": 5, - "num_candidates": 10 - } - } - - need_embedding = (search_type == "match&boost") - need_highlights = (search_type != "knn") - - body["_source"] = {"excludes": ["keywords", "quotes"]} - if not need_embedding: - body["_source"]["excludes"].append("embedding") - # 在填充highlight之前写入search_from - search_from = f'searchtype[{search_type}],param[{search_type_attachment}],body:{body}' - - if need_highlights: - body["_source"]["excludes"] = [] - body["highlight"] = { - "pre_tags": [settings.HIGHTLIGHT_PRE_TAG], - "post_tags": [settings.HIGHTLIGHT_POST_TAG], - "fields": {"chapter_name": {}, "content": {}} - } - - body["size"] = size - - se_debug_info = '' - start_time = time.time() - try: - es_response = context.es.search(index=context.index_name, body=body) - except Exception as e: - se_debug_info = f'Error in executing search: {e}. request: {body}' - return None, se_debug_info - end_time = time.time() - elapsed_time = end_time - start_time - total_hits = es_response.get("hits", {}).get("total", {}).get("value", 0) - returned_hits = len(es_response.get("hits", {}).get("hits", [])) - - if not '"' in search_from: - search_from = search_from.replace('\'', '"') - search_from = search_from if len(search_from) < 400 else search_from[:400] + '...' - - str_body = str(body) - if not '"' in str_body: - str_body = str_body.replace('\'', '"') - se_debug_info = f'({elapsed_time:.2f} seconds. Total: {total_hits}. Returned: {returned_hits}) : {search_from[:400]}' - - if not 'hits' in es_response or not 'hits' in es_response['hits']: - se_debug_info += f' InvalidResponce in executing search: {e}. request: {body}' - return None, se_debug_info - - for hit in es_response['hits']['hits']: - hit['search_from'] = search_from - - return es_response, se_debug_info - -``` - - -# 测试向量： -# [-0.05291186273097992, 0.0274342093616724, -0.016730275005102158, 0.010487289167940617, -0.022640341892838478, -0.048682719469070435, 0.04544096067547798, 0.023079438135027885, 0.007221410982310772, 0.023566091433167458, 0.026696473360061646, 0.08252757787704468, -0.042835772037506104, 0.0009668126585893333, -0.02860398218035698, -0.004426108207553625, -0.002644421299919486, -0.027699561789631844, 0.005749804899096489, -0.04468372091650963, -0.0296687763184309, -0.009487600065767765, 0.020041221752762794, 0.00778265530243516, 0.008522099815309048, 0.03497027978301048, -0.021573258563876152, -0.028293319046497345, 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0.023544959723949432, 0.017852554097771645, 0.01843070052564144, -0.05294886603951454, -0.008682304993271828, 0.010625398717820644, 0.0428495928645134, 0.002173527143895626, 0.06291069090366364, 0.024296458810567856, 0.008714474737644196, 0.06520587205886841, 0.015627536922693253, 0.04247526824474335, 0.0009774811333045363, 0.00738496845588088, -0.024803027510643005, 0.013228596188127995, -0.037615202367305756, -0.028807995840907097, 0.012890785001218319, -0.01587829552590847, -0.01928863860666752, 0.0011809614952653646, -0.026926854625344276, -0.020252779126167297, -0.010968486778438091, -0.015348547138273716, 0.008559435606002808, -0.009286923334002495, 0.0014621232403442264, 0.03831499442458153, 0.016517579555511475, 0.037184324115514755, -0.041231196373701096, 0.03757374733686447, -0.039465345442295074, -0.04308579862117767, 0.0011091071646660566, -0.029794104397296906, 0.008459310978651047, -0.01713281124830246, -0.016625113785266876, -0.05582521855831146, -0.0415986105799675, 0.028725938871502876, 0.04966316372156143, 0.012718678452074528, -0.025533588603138924, 0.013822318986058235, -5.168768620933406e-05, 0.02616700902581215, -0.06113629788160324, -0.03175340220332146, 0.03593592345714569, -0.04014921560883522, -0.020605407655239105, 0.02186705358326435] -``` \ No newline at end of file diff --git a/docs/ES/2_kibana安装.md b/docs/ES/2_kibana安装.md deleted file mode 100644 index e9c7a31..0000000 --- a/docs/ES/2_kibana安装.md +++ /dev/null @@ -1,51 +0,0 @@ - - - - - -## 1. yum安装 -添加yum仓库。 -Kibana通常不是yum默认仓库的一部分，因此需要添加 Elastic 仓库：： -```shell - -导入 Elastic 签名密钥： -sudo rpm --import https://artifacts.elastic.co/GPG-KEY-elasticsearch - -添加 Elastic 仓库： -sudo tee /etc/yum.repos.d/elastic.repo < 无效的源发行版: 18 -原因是我们指定的源发行版不匹配Java编译器的版本。 -1. 确保这里配置对了 -sourceCompatibility = JavaVersion.VERSION_18 // 不同版本的es，需要不同版本的java环境，注意修改这里 -targetCompatibility = JavaVersion.VERSION_18 // 不同版本的es，需要不同版本的java环境，注意修改这里 -2. elasticsearch --version 提示ES的java版本是21，因此将以上配置改为21。 为了简化问题，决定回退ES版本，改回8.4.1，这样插件的配置文件都不用改了，直接用8.4.1分支就行。 -3. java版本是1.8，不是18（之前以为1.8就是18，卡在这里大半天）。重新安装jdk18就好了。 -javac -version -javac 1.8.0_362 -# 注意yum里面只到17，没有18，下载jdk18，以rpm方式安装：https://www.oracle.com/java/technologies/javase/jdk18-archive-downloads.html -# 注意如果之前自己手动配置了JAVA_HOME，要把他删掉。 -yum remove java-1.8.0-* （之前yum remove java*，有可能有问题） -rpm -ivh jdk-18.0.2.1_linux-x64_bin.rpm -现在三个的版本就都对了： -javac -version -javac 18.0.2.1 - -[root@192 ~]# elasticsearch --version -warning: ignoring JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk; using bundled JDK -Version: 8.4.1, Build: rpm/2bd229c8e56650b42e40992322a76e7914258f0c/2022-08-26T12:11:43.232597118Z, JVM: 18.0.2 - -gradle --version （gradle用的jdk是1.8 不用管，插件的readme上注明gradle版本7.6匹配ES版本8.4.1） -Gradle 7.6.4 -JVM: 1.8.0_362 - - - diff --git a/docs/ES/3.3_ik分词器.md b/docs/ES/3.3_ik分词器.md deleted file mode 100644 index a353f6d..0000000 --- a/docs/ES/3.3_ik分词器.md +++ /dev/null @@ -1,9 +0,0 @@ - - - - -cd /usr/share/elasticsearch/plugins/ -wget https://release.infinilabs.com/analysis-ik/stable/elasticsearch-analysis-ik-8.18.0.zip -unzip elasticsearch-analysis-ik-8.18.0.zip -d elasticsearch-analysis-ik-8.18.0 -systemctl restart elasticsearch - diff --git a/docs/ES/3_ansj分词插件安装.md b/docs/ES/3_ansj分词插件安装.md deleted file mode 100644 index 8fd8bef..0000000 --- a/docs/ES/3_ansj分词插件安装.md +++ /dev/null @@ -1,97 +0,0 @@ - -https://github.com/NLPchina/elasticsearch-analysis-ansj - - -git clone https://github.com/NLPchina/elasticsearch-analysis-ansj.git -cd elasticsearch-analysis-ansj -git checkout v8.3.3 -mvn package - -会提示编译失败（v8.x版本都会失败，7.x就没问题），错误信息如下： -[ERROR] /ssd/samba_root1/projects/ansj/elasticsearch-analysis-ansj/src/main/java/org/ansj/elasticsearch/plugin/AnalysisAnsjPlugin.java:[14,34] 无法访问org.apache.lucene.analysis.Analyzer -[ERROR] 错误的类文件: /home/hello/.m2/repository/org/apache/lucene/lucene-core/9.2.0/lucene-core-9.2.0.jar(org/apache/lucene/analysis/Analyzer.class) -[ERROR] 类文件具有错误的版本 55.0, 应为 52.0 -[ERROR] 请删除该文件或确保该文件位于正确的类路径子目录中。 - -7.x版本都没问题，8.x就会出这个错误。尝试把jdk版本从1.8改成 11, 17, 18、升级maven版本到3.9.6都不行(又提示类文件具有错误的版本 61.0, 应为 55.0）。 -有人说可能是依赖冲突，不知道是否可以修改下环境，使得不冲突。 - -因此自己编译只能编译v7.17.16。8.x，release里面有现成的版本： -./bin/elasticsearch-plugin install https://github.com/NLPchina/elasticsearch-analysis-ansj/releases/download/v8.7.0/elasticsearch-analysis-ansj-8.7.0.0-release.zip - -测试： -kibana中 ： GET /_cat/ansj?text=中国&type=index_ansj -或者： -curl -X GET "http://localhost:9200/_cat/ansj?text=中国&type=index_ansj" - - -curl -X GET "http://localhost:9200/_cat/ansj?pretty" -H 'Content-Type: application/json' -d' -{ - "type": "index_ansj", - "text": "中国" -}' - -# 注意事项 -ansj分词如果没有设置停用词的话，空格也会被分出来，搜出来的结果空格也会被飘红（如果query带空格的话）。所以一定要设置停用词表。 - -词性说明： - 附-词性表格 - 符号 词性 解释 - n 名词 取英语名词noun的第1个字母。 - np 人名 - nr 人名 名词代码n和“人(ren)”的声母并在一起。 - nh 人名 - ns 地名 名词代码n和处所词代码s并在一起 - ni 机构名 - nz 其它专名 - nt 机构团体名 “团”的声母为t，名词代码n和t并在一起。 - nl 名词性惯用语 - nd 方位名词 - ng 名词性语素 - b 区别词 - m 数词 取英语numeral的第3个字母，n，u已有他用。 - q 量词 取英语quantity的第1个字母。 - mq 数量词 - t 时间词 取英语time的第1个字母。 - f 方位词 取汉字“方” 的声母。 - s 处所词 取英语space的第1个字母 - v 动词 取英语动词verb的第一个字母。 - vd 副动词 - vn 名动词 - vshi 动词“是” - vyou 动词“有” - vf 趋向动词 - vx 形式动词 - vi 不及物动词（内动词） - vl 动词性惯用语 - vg 动词性语素 - a 形容词 取英语形容词adjective的第1个字母。 - an 名形词 - ad 副形词 - an 名形词 - ag 形容词性语素 - al 形容词性惯用语 - d 副词 取adverb的第2个字母，因其第1个字母已用于形容词。 - h 前接成分 取英语head的第1个字母。 - k 后接成分 - i 成语 取英语成语idiom的第1个字母。 - j 简称 取汉字“简”的声母。 - l 习用语 习用语尚未成为成语，有点“临时性”，取“临”的声母 - r 代词 取英语代词pronoun的第2个字母,因p已用于介词。 - c 连词 取英语连词conjunction的第1个字母。 - p 介词 取英语介词prepositional的第1个字母。 - u 助词 取英语助词auxiliary 的第2个字母,因a已用于形容词。 - ud 结构助词 有 - ug 时态助词 你 - uj 结构助词的 迈向 - ul 时态助词了 完成 - uv 结构助词地 满怀信心 - uz 时态助词着 眼看 - y 语气助词 取汉字“语”的声母。 - e 叹词 取英语叹词exclamation的第1个字母。 - o 拟声词 取英语拟声词onomatopoeia的第1个字母。 - g 语素 绝大多数语素都能作为合成词的“词根”，取汉字“根”的声母。 - w 标点 - x 其它 非语素字只是一个符号，字母x通常用于代表未知数、符号。 - z 状态词 取汉字“状”的声母的前一个字母。 - tg 时语素 时间词性语素。时间词代码为t,在语素的代码g前面置以T。 \ No newline at end of file diff --git a/docs/ES/4_索引和查询测试.md b/docs/ES/4_索引和查询测试.md deleted file mode 100644 index 93b5136..0000000 --- a/docs/ES/4_索引和查询测试.md +++ /dev/null @@ -1,74 +0,0 @@ -# 查询官方文档： -https://link.zhihu.com/?target=https%3A//www.elastic.co/guide/cn/elasticsearch/guide/current/query-dsl-intro.html - - -# 查看配置 -curl "localhost:9200/_nodes/settings?pretty=true" - - -# 测试多字段搜索（支持中文分词）： -curl -X GET "localhost:9200/goods/_search" -H 'Content-Type: application/json' -d' -{ - "query": { - "multi_match": { - "query": "玩具", - "fields": ["name^2", "name_zh^2", "sub_name_zh^2", "brand_name_zh^2", "category_name_zh^2"], - "fuzziness": "AUTO" - } - }, - "highlight": { - "fields": { - "name": {}, - "name_zh": {}, - "sub_name_zh": {}, - "brand_name_zh": {}, - "category_name_zh": {} - } - }, - "from": 0, - "size": 10 -}' - -# 测试精确匹配： -curl -X GET "localhost:9200/goods/_search" -H "Content-Type: application/json" -d' -{ - "query": { - "term": { - "goods_id": "2683337" - } - } -}' - - -# 查看索引分词情况： -curl -X GET "http://localhost:9200/goods/_doc/1?pretty" -curl -X GET "http://localhost:9200/goods/_termvectors/1?fields=*&pretty" - -# 如果文档不存在，找出任意一个文档，得到ID： -curl -X GET "http://localhost:9200/goods/_search?size=1&pretty" -# 得到ID后查询termvectors： -curl -X GET "http://localhost:9200/goods/_termvectors/[DOC_ID]?fields=*&pretty" - - -# 测试analyzer： -curl -X GET "http://localhost:9200/goods/_analyze?pretty" -H 'Content-Type: application/json' -d' -{ - "text": "玩具", - "analyzer": "index_ansj" -}' - - - -# 测试向量搜索： -curl -X GET "localhost:9200/goods/_search" -H "Content-Type: application/json" -d' -{ - "query": { - "script_score": { - "query": {"match_all": {}}, - "script": { - "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0", - "params": {"query_vector": [-0.05291186273097992, 0.0274342093616724, -0.016730275005102158, 0.010487289167940617, -0.022640341892838478, -0.048682719469070435, 0.04544096067547798, 0.023079438135027885, 0.007221410982310772, 0.023566091433167458, 0.026696473360061646, 0.08252757787704468, -0.042835772037506104, 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| sudo rpm --import +yum install -y https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-$ELASTICSEARCH_VERSION-x86_64.rpm + +# 设置Elasticsearch的系统服务 +systemctl daemon-reload +systemctl start elasticsearch +systemctl enable elasticsearch +``` +安装、配置好后验证命令： curl -X GET "http://localhost:9200" + + +### 3. 工程&安全方面设置 + +性能相关配置： +```shell +vi /etc/elasticsearch/jvm.options +堆内存大小： +-Xms32g +-Xmx32g +``` + +/etc/elasticsearch/elasticsearch.yml ： + +Fielddata Cache（用于聚合和排序操作）： +indices.fielddata.cache.size: 40% # 将 fielddata 缓存大小设置为 40% 的物理内存 +Query Cache（用于查询缓存）： +indices.queries.cache.size: 30% # 将查询缓存设置为 30% 的物理内存 + +修改的系统配置： +vi /etc/sysctl.conf +```shell +# tangwang +# 调整系统句柄限制 +fs.file-max = 1000000 +# 优化文件描述符 可以分配的vma数量 +vm.max_map_count = 655360 +# 优化脏内存页，es写入削峰 +vm.dirty_ratio=10 +vm.dirty_background_ratio=5 +vm.dirty_writeback_centisecs=200 +# 优化系统回收inode cache权重 +vm.vfs_cache_pressure=200 +vm.dirty_expire_centisecs=6000 +# 尽可能低的使用swap,内存很充裕可以设置为0，较低设置为1 +vm.swappiness=0 +``` +vi /etc/security/limits.conf +```shell +# tangwang 修改 下面都加了10倍 +# End of file +* soft nofile 655350 +* hard nofile 655350 +root soft nofile 655350 +root hard nofile 655350 +* soft nofile 655350 +* hard nofile 655350 + + +# tangwang 补充下面的 +* soft nproc 102400 +* hard nproc 409600 +# 允许进程锁定内存 +* soft memlock unlimited +* hard memlock unlimited +``` + + +### TOOD : +配置为单节点模式，discovery.type: single-node 但是还需要其他配套改动。 + +用户名 密码 安全方面 +kibana用户名密码 + +## 其他说明 +ES 8.x版本特性 +在 8.0版本 中，提供新的kNN 搜索 API。以前，Elasticsearch 仅支持使用script_score带有向量函数的查询进行精确的 kNN 搜索。虽然此方法可以保证准确的结果，但它通常会导致搜索速度缓慢，并且无法很好地适应大型数据集。作为索引速度较慢和准确性不完美的代价，新的 kNN 搜索 API 允许您以更快的速度对更大的数据集运行近似 kNN 搜索。 + +在 8.4版本中，把knn搜索加入到search API中，以支持 ANN 搜索。它由与旧 _knn_search端点相同的 Lucene ANN 功能提供支持。该knn选项可以与其他搜索功能（例如查询和聚合）结合使用。 + +在 8.7版本中，允许多个 KNN 搜索子句。某些向量搜索场景需要使用几个 kNN 子句进行相关性排名，例如，当基于多个字段进行排名时，每个字段都有自己的向量，或者当文档包含图像向量和文本向量时。用户可能希望获得基于所有这些 kNN 子句的组合的相关性排名。 + +在 8.8版本 中，将 Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search — Elastic Search Labs 模型引入到我们的机器学习模型库中，您可以开箱即用。ELSER 通过启用语义搜索来提高搜索结果的相关性。这种搜索方法考虑单词的含义，而不是仅仅依赖字面术语。ELSER 是一种预训练的域外稀疏向量模型，无需对特定源数据进行微调。它从一开始就为您提供相关的搜索结果。并且在此版本中，还提升了KNN检索语法，加入了similarity参数，允许过滤给定相似性之外的最近邻结果。 + +在8.9版本中。text_embedding query_vector_builderkNN 搜索的扩展普遍可用。 + +在8.10版本中，启用跨段并行 knn 搜索，使得 knn 查询对由多个段组成的分片更快，来优化knn搜索的性能。 + + + + + +8.15 更为丰富的工具用于微调相关性、更高的模型灵活性、矢量搜索的改进 + diff --git a/docs/ES/ES_8.18/1_ES配置和使用.md b/docs/ES/ES_8.18/1_ES配置和使用.md new file mode 100644 index 0000000..569f12f --- /dev/null +++ b/docs/ES/ES_8.18/1_ES配置和使用.md @@ -0,0 +1,680 @@ +# Elasticsearch 文档 + +## 相关链接 +- 接口文档：http://rap.essa.top:88/workspace/myWorkspace.do?projectId=78#2187 +- Kibana 控制台：http://120.76.41.98:5601/app/dev_tools#/console/shell + +## 分词方面 + +Ansj 分词插件安装 +ES可以用的中文分词，效果最好的是hanLP和ansj，其次是jieba。 + +我们老的搜索 solr 已经在几年前替代掉了ik，使用的是mmseg。但是我没找到mmseg的ES插件。 + +为了分词方面不至于比老版本差，这里先安装了ansj + +### 1. 下载插件 +从 [elasticsearch-analysis-ansj releases](https://github.com/NLPchina/elasticsearch-analysis-ansj/releases) 选择对应版本下载： + +- ES 8.18 版本： +```bash +wget https://github.com/NLPchina/elasticsearch-analysis-ansj/archive/refs/tags/v8.18.0.zip +``` + +- ES 8.17 版本： +```bash +wget https://github.com/NLPchina/elasticsearch-analysis-ansj/archive/refs/tags/v8.17.6.zip +``` + +### 2. 编译 +执行 `mvn package` 命令，编译成功后将在 `target/releases/` 目录生成插件压缩包： +`elasticsearch-analysis-ansj-<版本号>-release.zip` + +### 3. 安装步骤 +1. 进入 ES 安装路径（默认：`/usr/share/elasticsearch/`） +2. 执行安装命令： +```bash +bin/elasticsearch-plugin install file:///xxx/绝对路径到/elasticsearch-analysis-ansj-8.18.0.0-release.zip +``` +3. 重启服务： +```bash +systemctl restart elasticsearch +``` + +其他分词插件安装方法： +《3.1_hanlp安装.md》 +《3.2_jieba插件安装.md》 +在ES8上面安装过，但是没试过具体的版本 8.17 8.18 + +### 4. 配置说明 +停用词、同义词配置位于 `/config/elasticsearch-analysis-ansj/ansj.cfg.yml`（暂未使用） + +## 字段说明 + +```bash +需要的字段： +id 商品skuId +goods_id 商品spuId +buyer_id 所属专属采购商id +trader_buyer_ids 所属贸易商名下平台客户的专属采购商id +goods_certification_types 商品证书类型 +supplier_code 供应商编码 +supplier_name 供应商名称 +supplier_certification_code 供应商企业证书编码（列表） +auth_buyer_level_list 商品可见采购商等级（集合） +show_price_level_list 价格可见采购商等级（集合） +goods_composition 成分列表（材质） +compositions_main_secondary 物料主副（主：1，副：2），格式：物料代码_主副类型 +goods_key_word_zh 商品关键词中文 +goods_key_word_en 商品关键词英文 +goods_key_word_ru 商品关键词俄文 +goods_copyright 版权（自有、第三方、无授权、A货） +goods_main_material 主材质（字典：材质） +is_in_new_protect 是否在新品保护期（0否，1是） +goods_new_protect_date_stamp 新品保护期日期时间戳 +goods_attribute_name_zh spu属性中文（列表） +goods_attribute_name_en spu属性英文（列表） +goods_attribute_name_ru spu属性俄文（列表） +purchase_moq 采购MOQ +ts 触发索引的时间 +deliver_day 货期 +factory_no 工厂货号 +factory_no_buyer 工厂货号(客户) +fir_on_sell_time 首次上架时间 +fir_on_sell_time_stamp 首次上架时间timestamp +no 商品编码 +hs_no 宏升编码 +package_type 包装类型值（来自商品属性编码：PKG） +package_type_id 包装类型ID（来自商品属性编码：PKG） +labelId_by_skuId_essaone_* essaone商品标签，国家编码标识 +sale_goods_certificate_* 商品证书ID，国家编码标识 +labelId_by_skuId_essa_* essa商品标签，区域ID标识 +``` + +## Mapping 配置 +参考文件 `create_index.sh`： + +## 快速入门 + +### Shell +参考 [索引和查询测试](../docs/3.3_索引和查询测试.md) 包含了在ES服务器进行本地进行一些常用的查询操作。 + +### Python +- [test_index_and_search.py](../tests/test_index_and_search.py) 是一个简单的例子，创建索引，导入数据，查询数据 +- [batch_bulk_goods.py](../batch_bulk_goods.py) 功能是 通过sql 读取最近3年的所有数据，按batch（1000）通过bulk接口进行逐批入库，入库到goods索引。 + +### Kibana + +#### 分词相关 +```bash +# 索引分词 +GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝&type=index_ansj + +# 查询分词 +GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝&type=query_ansj + +# 查看配置 +GET /_cat/ansj/config +``` +#### 查询相关 +GET /goods/_search +{ + "query": { + "match_all": {} + }, + "size": 5 +} + +#### 1. 查看字段分词结果 +```bash +# 查看中文名称分词结果 +GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车&type=index_ansj + +# 查看英文名称分词结果 +GET /_cat/ansj?text=14 inch 4th generation real eye snow doll with manual cart&type=standard +``` + +#### 2. 查看索引随机10条内容 +```bash +GET /goods/_search +{ + "size": 10, + "query": { + "function_score": { + "query": { "match_all": {} }, + "random_score": {} + } + } +} +``` + +#### 3. 关键词查询 +```bash +# 简单关键词匹配 +GET /goods/_search +{ + "query": { + "match": { + "name_zh": "冰雪公仔" + } + } +} + +# 多字段关键词匹配 +GET /goods/_search +{ + "query": { + "multi_match": { + "query": "冰雪公仔", + "fields": ["name_zh", "sub_name_zh", "category_name_zh"] + } + } +} +``` + +#### 4. 向量查询 +```bash +# 使用向量相似度查询 +GET /goods/_search +{ + "query": { + "script_score": { + "query": { "match_all": {} }, + "script": { + "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0", + "params": { + "query_vector": [0.1, 0.2, ...] # 1024维向量 + } + } + } + } +} +``` + +#### 5. SKUID查询 +```bash +# 精确匹配SKUID +GET /goods/_search +{ + "query": { + "term": { + "goods_id": "2817667" + } + } +} +``` + +#### 6. 名称查询测试 +```bash +# 中文名称模糊匹配 +GET /goods/_search +{ + "query": { + "match": { + "name_zh": { + "query": "冰雪公仔", + "fuzziness": "AUTO" + } + } + } +} + +# 英文名称匹配 +GET /goods/_search +{ + "query": { + "match": { + "name_en": "snow doll" + } + } +} + +# 俄语名称匹配 +GET /goods/_search +{ + "query": { + "match": { + "name_ru": "снежная кукла" + } + } +} + +# 使用 match_phrase 进行短语匹配 +GET /goods/_search +{ + "query": { + "match_phrase": { + "name_zh": "冰雪公仔" + } + } +} + +# 使用 match_phrase 进行多语言短语匹配 +GET /goods/_search +{ + "query": { + "bool": { + "should": [ + { + "match_phrase": { + "name_zh": "冰雪公仔" + } + }, + { + "match_phrase": { + "name_en": "snow doll" + } + }, + { + "match_phrase": { + "name_ru": "снежная кукла" + } + } + ], + "minimum_should_match": 1 + } + } +} + +# 使用 match_phrase 配合 slop 参数进行模糊短语匹配 +GET /goods/_search +{ + "query": { + "match_phrase": { + "name_zh": { + "query": "冰雪公仔", + "slop": 2 + } + } + } +} + +# 多语言 match_phrase 配合 slop 参数 +GET /goods/_search +{ + "query": { + "bool": { + "should": [ + { + "match_phrase": { + "name_zh": { + "query": "冰雪公仔", + "slop": 2 + } + } + }, + { + "match_phrase": { + "name_en": { + "query": "snow doll", + "slop": 1 + } + } + }, + { + "match_phrase": { + "name_ru": { + "query": "снежная кукла", + "slop": 2 + } + } + } + ], + "minimum_should_match": 1 + } + } +} +``` + +#### 7. 多语言查询测试 +```bash +# 同时查询中英文名称 +GET /goods/_search +{ + "query": { + "bool": { + "should": [ + { + "match": { + "name_zh": "冰雪公仔" + } + }, + { + "match": { + "name_en": "snow doll" + } + } + ], + "minimum_should_match": 1 + } + } +} +``` + +#### 8. 向量索引查询测试 +注意：向量查询中的向量维度必须与索引中定义的维度匹配（1024维） +```bash +# 使用向量相似度进行商品推荐 +GET /goods/_search +{ + "query": { + "script_score": { + "query": { "match_all": {} }, + "script": { + "source": "cosineSimilarity(params.query_vector, 'ru_name') + 1.0", + "params": { + "query_vector": [0.1, 0.2, ...] # 1024维向量 + } + } + } + }, + "size": 10 +} +``` + +#### 9. 关键词+向量索引组合查询测试 +```bash +# 关键词搜索+向量相似度提权 +GET /goods/_search +{ + "query": { + "function_score": { + "query": { + "match": { + "name_zh": "冰雪公仔", + "boost": 1.0 + } + }, + "functions": [ + { + "script_score": { + "script": { + "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0", + "params": { + "query_vector": [0.1, 0.2, ...] # 1024维向量 + } + } + } + } + ], + "boost_mode": "multiply" + } + } +} + +# source 可以支持embedding为空 ： "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", + +两者乘起来： +{ + "query": { + "function_score": { + "score_mode": "sum", + "boost_mode": "multiply", + "query": { + "match": { + "content": { + "query": keywords, + "boost": 1.0 + } + } + }, + "functions": [ + { + "script_score": { + "script": { + "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", + "params": {"query_vector": context.embeddings[0][1]} + } + } + } + ] + } + } + } + +#### 9. 向量搜索+关键词搜索 +GET /goods/_search +{ + "query": { + "match": { + "content": { + "query": "玩具", + "boost": 1.0 + } + } + }, + "knn": { + "field": "name_prefix", + "query_vector": [-0.05291186273097992, ...], + "k": 5, + "num_candidates": 10, + "boost": 1.0 + } + } + + + + +参考代码： +```python + def execute_search(self, context, search_type="match_phrase", search_type_attachment=0, size=10): + query = context.query + normalized_query = context.normalized_query + core_term = context.core_term + keywords = context.keywords + knn_boost_keywords = core_term if core_term else keywords + expand = context.expand + + seen_queries = set() + unique_queries = [] + for q, weight in [(query, 1.0), (normalized_query, 1.0), (keywords, 0.5)]: + if q and q not in seen_queries: + unique_queries.append((q, weight)) + seen_queries.add(q) + + # 关于混合检索： + # knn和文本查询同时作用： + # 8.12之前query里面不能包含knn，kNN搜索作为查询已在 8.12 版本中引入: https://www.elastic.co/search-labs/blog/knn-query-elasticsearch + # { + # "size": 3, + # "query": { + # "bool": { + # "should": [ + # { + # "knn": { + # "field": "embedding", + # "query_vector": [2,2,2,0], + # "num_candidates": 10, + # "_name": "knn_query" + # } + # }, + # { + # "match": { + # "description": { + # "query": "luxury", + # "_name": "bm25query" + # } + # } + # } + # ] + # } + # + # knn里面不能包含query。 + # knn和query并列（hybrid search 混合检索），是求或的关系。 + # knn里面可以加filter，比如： "filter": {"match": {"my_label": "red"}} + + if search_type == "match_phrase": + body = { + "query": { + "bool": { + "should": [ + { + "match_phrase": { + "content": { + "query": unique_query, + "boost": weight, + "slop": search_type_attachment + } + } + } for unique_query, weight in unique_queries + ], + "minimum_should_match": 1 + } + } + } + # 纯关键词检索 2 + elif search_type == "match_keywords": + body = { + "query": { + "bool": { + "must": [ + { + "match": { + "content": {"query": core_term, "boost": 1.0} + } + } + ], + "should": [ + { + "match": { + "content": {"query": q, "boost": boost} + } + } for (q, boost) in [(keywords, 1.0), (expand, 0.6), (normalized_query, 1.0)] if q + ], + "minimum_should_match": 1 + } + } + } + # 关键词搜索+向量排序 + elif search_type == "match&boost": + body = { + "query": { + "function_score": { + "score_mode": "sum", + "boost_mode": "multiply", + "query": { + "match": { + "content": { + "query": keywords, + "boost": 1.0 + } + } + }, + "functions": [ + { + "script_score": { + "script": { + "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", + "params": {"query_vector": context.embeddings[0][1]} + } + } + } + ] + } + } + } + # 这个太慢 + elif search_type == "match&boost2": + body = { + "query": { + "script_score": { + "query": { + "match": { + "content": { + "query": keywords, + "boost": 1.0 + } + } + }, + "script": { + "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0", + "params": {"query_vector": context.embeddings[0][1]} + } + } + } + } + # 向量搜索+关键词搜索 + elif search_type == "match&knn": + body = { + "query": { + "match": { + "content": { + "query": knn_boost_keywords, + "boost": 1.0 + } + } + }, + "knn": { + "field": "embedding", + "query_vector": context.embeddings[search_type_attachment][1], + "k": 5, + "num_candidates": 10, + "boost": 1.0 + } + } + # 纯向量搜索 + elif search_type == "knn": + body = { + "knn": { + "field": "embedding", + "query_vector": context.embeddings[search_type_attachment][1], + "k": 5, + "num_candidates": 10 + } + } + + need_embedding = (search_type == "match&boost") + need_highlights = (search_type != "knn") + + body["_source"] = {"excludes": ["keywords", "quotes"]} + if not need_embedding: + body["_source"]["excludes"].append("embedding") + # 在填充highlight之前写入search_from + search_from = f'searchtype[{search_type}],param[{search_type_attachment}],body:{body}' + + if need_highlights: + body["_source"]["excludes"] = [] + body["highlight"] = { + "pre_tags": [settings.HIGHTLIGHT_PRE_TAG], + "post_tags": [settings.HIGHTLIGHT_POST_TAG], + "fields": {"chapter_name": {}, "content": {}} + } + + body["size"] = size + + se_debug_info = '' + start_time = time.time() + try: + es_response = context.es.search(index=context.index_name, body=body) + except Exception as e: + se_debug_info = f'Error in executing search: {e}. request: {body}' + return None, se_debug_info + end_time = time.time() + elapsed_time = end_time - start_time + total_hits = es_response.get("hits", {}).get("total", {}).get("value", 0) + returned_hits = len(es_response.get("hits", {}).get("hits", [])) + + if not '"' in search_from: + search_from = search_from.replace('\'', '"') + search_from = search_from if len(search_from) < 400 else search_from[:400] + '...' + + str_body = str(body) + if not '"' in str_body: + str_body = str_body.replace('\'', '"') + se_debug_info = f'({elapsed_time:.2f} seconds. Total: {total_hits}. Returned: {returned_hits}) : {search_from[:400]}' + + if not 'hits' in es_response or not 'hits' in es_response['hits']: + se_debug_info += f' InvalidResponce in executing search: {e}. request: {body}' + return None, se_debug_info + + for hit in es_response['hits']['hits']: + hit['search_from'] = search_from + + return es_response, se_debug_info + +``` + + +# 测试向量： +# [-0.05291186273097992, 0.0274342093616724, -0.016730275005102158, 0.010487289167940617, -0.022640341892838478, -0.048682719469070435, 0.04544096067547798, 0.023079438135027885, 0.007221410982310772, 0.023566091433167458, 0.026696473360061646, 0.08252757787704468, -0.042835772037506104, 0.0009668126585893333, -0.02860398218035698, -0.004426108207553625, -0.002644421299919486, -0.027699561789631844, 0.005749804899096489, -0.04468372091650963, -0.0296687763184309, -0.009487600065767765, 0.020041221752762794, 0.00778265530243516, 0.008522099815309048, 0.03497027978301048, -0.021573258563876152, -0.028293319046497345, 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0.028725938871502876, 0.04966316372156143, 0.012718678452074528, -0.025533588603138924, 0.013822318986058235, -5.168768620933406e-05, 0.02616700902581215, -0.06113629788160324, -0.03175340220332146, 0.03593592345714569, -0.04014921560883522, -0.020605407655239105, 0.02186705358326435] +``` \ No newline at end of file diff --git a/docs/ES/ES_8.18/2_kibana安装.md b/docs/ES/ES_8.18/2_kibana安装.md new file mode 100644 index 0000000..e9c7a31 --- /dev/null +++ b/docs/ES/ES_8.18/2_kibana安装.md @@ -0,0 +1,51 @@ + + + + + +## 1. yum安装 +添加yum仓库。 +Kibana通常不是yum默认仓库的一部分，因此需要添加 Elastic 仓库：： +```shell + +导入 Elastic 签名密钥： +sudo rpm --import https://artifacts.elastic.co/GPG-KEY-elasticsearch + +添加 Elastic 仓库： +sudo tee /etc/yum.repos.d/elastic.repo < 无效的源发行版: 18 +原因是我们指定的源发行版不匹配Java编译器的版本。 +1. 确保这里配置对了 +sourceCompatibility = JavaVersion.VERSION_18 // 不同版本的es，需要不同版本的java环境，注意修改这里 +targetCompatibility = JavaVersion.VERSION_18 // 不同版本的es，需要不同版本的java环境，注意修改这里 +2. elasticsearch --version 提示ES的java版本是21，因此将以上配置改为21。 为了简化问题，决定回退ES版本，改回8.4.1，这样插件的配置文件都不用改了，直接用8.4.1分支就行。 +3. java版本是1.8，不是18（之前以为1.8就是18，卡在这里大半天）。重新安装jdk18就好了。 +javac -version +javac 1.8.0_362 +# 注意yum里面只到17，没有18，下载jdk18，以rpm方式安装：https://www.oracle.com/java/technologies/javase/jdk18-archive-downloads.html +# 注意如果之前自己手动配置了JAVA_HOME，要把他删掉。 +yum remove java-1.8.0-* （之前yum remove java*，有可能有问题） +rpm -ivh jdk-18.0.2.1_linux-x64_bin.rpm +现在三个的版本就都对了： +javac -version +javac 18.0.2.1 + +[root@192 ~]# elasticsearch --version +warning: ignoring JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk; using bundled JDK +Version: 8.4.1, Build: rpm/2bd229c8e56650b42e40992322a76e7914258f0c/2022-08-26T12:11:43.232597118Z, JVM: 18.0.2 + +gradle --version （gradle用的jdk是1.8 不用管，插件的readme上注明gradle版本7.6匹配ES版本8.4.1） +Gradle 7.6.4 +JVM: 1.8.0_362 + + + diff --git a/docs/ES/ES_8.18/3.3_ik分词器.md b/docs/ES/ES_8.18/3.3_ik分词器.md new file mode 100644 index 0000000..a353f6d --- /dev/null +++ b/docs/ES/ES_8.18/3.3_ik分词器.md @@ -0,0 +1,9 @@ + + + + +cd /usr/share/elasticsearch/plugins/ +wget https://release.infinilabs.com/analysis-ik/stable/elasticsearch-analysis-ik-8.18.0.zip +unzip elasticsearch-analysis-ik-8.18.0.zip -d elasticsearch-analysis-ik-8.18.0 +systemctl restart elasticsearch + diff --git a/docs/ES/ES_8.18/3_ansj分词插件安装.md b/docs/ES/ES_8.18/3_ansj分词插件安装.md new file mode 100644 index 0000000..8fd8bef --- /dev/null +++ b/docs/ES/ES_8.18/3_ansj分词插件安装.md @@ -0,0 +1,97 @@ + +https://github.com/NLPchina/elasticsearch-analysis-ansj + + +git clone https://github.com/NLPchina/elasticsearch-analysis-ansj.git +cd elasticsearch-analysis-ansj +git checkout v8.3.3 +mvn package + +会提示编译失败（v8.x版本都会失败，7.x就没问题），错误信息如下： +[ERROR] /ssd/samba_root1/projects/ansj/elasticsearch-analysis-ansj/src/main/java/org/ansj/elasticsearch/plugin/AnalysisAnsjPlugin.java:[14,34] 无法访问org.apache.lucene.analysis.Analyzer +[ERROR] 错误的类文件: /home/hello/.m2/repository/org/apache/lucene/lucene-core/9.2.0/lucene-core-9.2.0.jar(org/apache/lucene/analysis/Analyzer.class) +[ERROR] 类文件具有错误的版本 55.0, 应为 52.0 +[ERROR] 请删除该文件或确保该文件位于正确的类路径子目录中。 + +7.x版本都没问题，8.x就会出这个错误。尝试把jdk版本从1.8改成 11, 17, 18、升级maven版本到3.9.6都不行(又提示类文件具有错误的版本 61.0, 应为 55.0）。 +有人说可能是依赖冲突，不知道是否可以修改下环境，使得不冲突。 + +因此自己编译只能编译v7.17.16。8.x，release里面有现成的版本： +./bin/elasticsearch-plugin install https://github.com/NLPchina/elasticsearch-analysis-ansj/releases/download/v8.7.0/elasticsearch-analysis-ansj-8.7.0.0-release.zip + +测试： +kibana中 ： GET /_cat/ansj?text=中国&type=index_ansj +或者： +curl -X GET "http://localhost:9200/_cat/ansj?text=中国&type=index_ansj" + + +curl -X GET "http://localhost:9200/_cat/ansj?pretty" -H 'Content-Type: application/json' -d' +{ + "type": "index_ansj", + "text": "中国" +}' + +# 注意事项 +ansj分词如果没有设置停用词的话，空格也会被分出来，搜出来的结果空格也会被飘红（如果query带空格的话）。所以一定要设置停用词表。 + +词性说明： + 附-词性表格 + 符号 词性 解释 + n 名词 取英语名词noun的第1个字母。 + np 人名 + nr 人名 名词代码n和“人(ren)”的声母并在一起。 + nh 人名 + ns 地名 名词代码n和处所词代码s并在一起 + ni 机构名 + nz 其它专名 + nt 机构团体名 “团”的声母为t，名词代码n和t并在一起。 + nl 名词性惯用语 + nd 方位名词 + ng 名词性语素 + b 区别词 + m 数词 取英语numeral的第3个字母，n，u已有他用。 + q 量词 取英语quantity的第1个字母。 + mq 数量词 + t 时间词 取英语time的第1个字母。 + f 方位词 取汉字“方” 的声母。 + s 处所词 取英语space的第1个字母 + v 动词 取英语动词verb的第一个字母。 + vd 副动词 + vn 名动词 + vshi 动词“是” + vyou 动词“有” + vf 趋向动词 + vx 形式动词 + vi 不及物动词（内动词） + vl 动词性惯用语 + vg 动词性语素 + a 形容词 取英语形容词adjective的第1个字母。 + an 名形词 + ad 副形词 + an 名形词 + ag 形容词性语素 + al 形容词性惯用语 + d 副词 取adverb的第2个字母，因其第1个字母已用于形容词。 + h 前接成分 取英语head的第1个字母。 + k 后接成分 + i 成语 取英语成语idiom的第1个字母。 + j 简称 取汉字“简”的声母。 + l 习用语 习用语尚未成为成语，有点“临时性”，取“临”的声母 + r 代词 取英语代词pronoun的第2个字母,因p已用于介词。 + c 连词 取英语连词conjunction的第1个字母。 + p 介词 取英语介词prepositional的第1个字母。 + u 助词 取英语助词auxiliary 的第2个字母,因a已用于形容词。 + ud 结构助词 有 + ug 时态助词 你 + uj 结构助词的 迈向 + ul 时态助词了 完成 + uv 结构助词地 满怀信心 + uz 时态助词着 眼看 + y 语气助词 取汉字“语”的声母。 + e 叹词 取英语叹词exclamation的第1个字母。 + o 拟声词 取英语拟声词onomatopoeia的第1个字母。 + g 语素 绝大多数语素都能作为合成词的“词根”，取汉字“根”的声母。 + w 标点 + x 其它 非语素字只是一个符号，字母x通常用于代表未知数、符号。 + z 状态词 取汉字“状”的声母的前一个字母。 + tg 时语素 时间词性语素。时间词代码为t,在语素的代码g前面置以T。 \ No newline at end of file diff --git a/docs/ES/ES_8.18/4_索引和查询测试.md b/docs/ES/ES_8.18/4_索引和查询测试.md new file mode 100644 index 0000000..93b5136 --- /dev/null +++ b/docs/ES/ES_8.18/4_索引和查询测试.md @@ -0,0 +1,74 @@ +# 查询官方文档： +https://link.zhihu.com/?target=https%3A//www.elastic.co/guide/cn/elasticsearch/guide/current/query-dsl-intro.html + + +# 查看配置 +curl "localhost:9200/_nodes/settings?pretty=true" + + +# 测试多字段搜索（支持中文分词）： +curl -X GET "localhost:9200/goods/_search" -H 'Content-Type: application/json' -d' +{ + "query": { + "multi_match": { + "query": "玩具", + "fields": ["name^2", "name_zh^2", "sub_name_zh^2", "brand_name_zh^2", "category_name_zh^2"], + "fuzziness": "AUTO" + } + }, + "highlight": { + "fields": { + "name": {}, + "name_zh": {}, + "sub_name_zh": {}, + "brand_name_zh": {}, + "category_name_zh": {} + } + }, + "from": 0, + "size": 10 +}' + +# 测试精确匹配： +curl -X GET "localhost:9200/goods/_search" -H "Content-Type: application/json" -d' +{ + "query": { + "term": { + "goods_id": "2683337" + } + } +}' + + +# 查看索引分词情况： +curl -X GET "http://localhost:9200/goods/_doc/1?pretty" +curl -X GET "http://localhost:9200/goods/_termvectors/1?fields=*&pretty" + +# 如果文档不存在，找出任意一个文档，得到ID： +curl -X GET "http://localhost:9200/goods/_search?size=1&pretty" +# 得到ID后查询termvectors： +curl -X GET "http://localhost:9200/goods/_termvectors/[DOC_ID]?fields=*&pretty" + + +# 测试analyzer： +curl -X GET "http://localhost:9200/goods/_analyze?pretty" -H 'Content-Type: application/json' -d' +{ + "text": "玩具", + "analyzer": "index_ansj" +}' + + + +# 测试向量搜索： +curl -X GET "localhost:9200/goods/_search" -H "Content-Type: application/json" -d' +{ + "query": { + "script_score": { + "query": {"match_all": {}}, + "script": { + "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0", + "params": {"query_vector": [-0.05291186273097992, 0.0274342093616724, -0.016730275005102158, 0.010487289167940617, -0.022640341892838478, 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b/docs/ES/ES_8.18/README__ES查询相关.md @@ -0,0 +1,701 @@ +# Elasticsearch 查询相关 + +## 根据商品ID查询 + +```json +GET /uat_spu/_search +{ + "query": { + "term": { + "_id": "3588136" + } + } +} +``` + +## 查label_id + +```json +GET /uat_spu/_search +{"query":{"bool":{"must":[{"term":{ "platform_sku_essaone": true }},{"term":{"label_id_list":166}}]}}} +``` + +## collapse折叠 + +```json +GET /spu/_search +{ + "_source": ["_id", "name_zh", "goods_id", "*"], + "size": 1000, + "query": { + "match": { + "name_zh": { + "query": "园艺" + } + } + }, + "collapse": { + "field": "goods_id", // 按 goods_id 字段折叠分组 + "inner_hits": { + "_source": ["_id"], + "name": "top_docs", // 自定义内部结果名称 + "size": 3, // 每组取1个文档 + "sort": [{"_score": "desc"}] // 按评分降序排序 + } + }, + "sort": ["_score"] // 确保外层排序兼容折叠功能 +} +``` + +## 在架、某平台商品数 + +```json +GET /uat_spu/_search +{ + "track_total_hits": 100000, + "query": { + "bool": { + "filter": [ + { + "term": { + "platform_sku_essaone": true + } + }, + { + "term": { + "platform_rule_essaone": true + } + }, + { + "terms": { + "goods_copyright": [ + "1", + "2", + "3" + ] + } + } + ] + } + } +} +``` + +## ID查询 + +```json +GET /uat_spu/_search +{ + "query": { + "term": { + "_id": "3302275" + } + } +} +``` + +## 商品名搜索 + +```json +GET /spu/_search +{ + "_source": { + }, + "size": 1000, + "query": { + "match": { + "name_zh": { + "query": "园艺工具四件套", + "boost": 111.0 + } + } + }, + "aggs" :{ + "goods_agg": { + "terms": { + "field": "goods_id", + "size": 100 + }, + "aggs": { + "top_hits": { + "top_hits": { + "size": 1, + "sort": [{"_score": "desc"}] + } + } + } + } + } +} +``` + +## phrase搜索 + +```json +GET /uat_spu/_search +{ + "_source":["id","goods_id","name_zh"], + "query":{ + "multi_match": { + "_name": "base_query", + "fields": [ + "name_zh^2.0", + "goods_keyword_zh^1.0", + "category_name_zh^1.0", + "sale_category_keyword_zh^1.0", + "sub_name_zh^1.0" + ], + "query": "戏水玩具" + } + } +} +``` + +## aggs聚合查询 + +```json +GET /spu/_search +{ + "_source": { + "excludes": [ + "embedding_name_zh", + "embedding_name_ru", "*" + ], + "includes": ["_id", "name_zh"] + }, + "size": 1000, + "query": { + "match": { + "name_zh": { + "query": "园艺" + } + } + }, + "aggs" :{ + "goods_agg": { + "terms": { + "field": "goods_id", + "size": 100 + }, + "aggs": { + "top_hits": { + "top_hits": { + "size": 1, + "sort": [{"_score": "desc"}] + } + } + } + } + } +} +``` + +## 检查某个字段是否有值 +```json +GET /uat_spu/_search +{ + "track_total_hits": true, + "size": 0, // 不返回 hits，只返回总数 + "query": { + "exists": { + "field": "goods_keyword_zh" + } + } +} +``` +如果是nested字段，要这么查： +```json +GET /uat_spu/_search +{ + "track_total_hits": true, + "size": 0, // 只看总数 + "query": { + "nested": { + "path": "embedding_pic_h14", + "query": { + "exists": { + "field": "embedding_pic_h14.vector" // 或 url，只要子文档里任意字段有值 + } + } + } + } +} +``` + +```json +``` + +## 分词测试 + +### ik_smart分词器 + +```json +POST spu/_analyze +{ + "analyzer": "ik_smart", + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" +} +``` + +### ik_max_word分词器 + +```json +POST spu/_analyze +{ + "analyzer": "ik_max_word", + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" +} +``` + +### query_ansj分词器 + +```json +POST spu/_analyze +{ + "analyzer": "query_ansj", + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" +} +``` + +### index_ansj分词器 + +```json +POST spu_test/_analyze +{ + "analyzer": "index_ansj", + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" +} +``` + +## 查商品名 + +```json +GET /spu/_search +{ + "_source": { + "includes": [ + "name_zh", + "category_name_zh", + "sub_name_zh", + "label_id_list", + "brand_id", + "category_id", + "price_range", + "on_sell_days_boost" + ] + }, + "collapse": { + "field": "goods_id", + "inner_hits": { + "name": "top_hits", + "size": 1, + "_source": false // 避免返回完整_source + } + }, + "from": 0, + "query": { + "function_score": { + "query": { + "bool": { + "minimum_should_match": 1, + "should": [ + { + "multi_match": { + "_name": "base_query", + "fields": [ + "name_zh^2.0", + "goods_keyword_zh^1.0", + "category_name_zh^1.0", + "sale_category_keyword_zh^1.0", + "sub_name_zh^1.0" + ], + "minimum_should_match": "66%", + "query": "网球", + "tie_breaker": 0.301 + } + } + ] + } + } + } + }, + "size": 1000, + "track_scores": true +} +``` + +# 配置相关 + +## 查看索引信息 + +```bash +GET /_cat/indices/spu?v +``` + +```bash +GET /spu/_mapping +``` + +```bash +GET /spu/_settings +``` + +## 设置刷新时间 + +```json +PUT /spu/_settings +{ + "refresh_interval": -1 +} +``` + +```json +PUT /uat_spu/_settings +{ + "refresh_interval": "600s" +} +``` + +## 设置preload + +```json +POST uat_spu/_close +PUT uat_spu/_settings +{ + "index.store.preload": [] +} + +PUT uat_spu/_settings +{ + "index.store.preload": ["vex", "veq"] +} +# ["nvd", "dvd", "tim", "doc", "dim"] + +POST uat_spu/_open + +``` + +## 修改分片数目 +主分片的数目（number_of_shards）只能创建索引的时候修改 + + +## 查看索引的分片数量、大小分布 +```json +GET _cat/indices/uat_spu?v&s=store.size:desc +GET _cat/shards/uat_spu +``` + +## 类别查询：索引中该类别总数 + +```json +GET /spu/_search +{ + "track_total_hits": 10000000, + "query": {"terms": { + "sale_category_all": [37619] + }} +} +``` + +## 类别查询：模拟平台规则 + +除了类别限定，包括： +1. essaone平台展示过滤 +2. 非专属商品 +3. status 2 4 5 +4. goods_copyright 1,2,3 + +```json +GET /spu/_search +{ + "track_total_hits": 10000000, + "aggs": { + "unique_count": { + "cardinality": { + "field": "goods_id" + } + } + }, + "collapse": { + "field": "goods_id" + }, + "query": { + "bool": { + "filter": [ + { + "bool": { + "minimum_should_match": 1, + "should": [ + { + "term": { + "platform_sku_essaone": true + } + }, + { + "bool": { + "must": [ + { + "term": { + "platform_rule_essaone": true + } + }, + { + "bool": { + "must_not": { + "exists": { + "field": "platform_sku_essaone" + } + } + } + } + ] + } + } + ] + } + }, + { + "bool": { + "must_not": { + "exists": { + "field": "buyer_id" + } + } + } + }, + { + "terms": { + "sale_category_all": [37619] + } + }, + { + "terms": { + "status": [2,4,5] + } + }, + { + "terms": { + "goods_copyright": [ + "1", + "2", + "3" + ] + } + } + ] + } + } +} +``` + +## 关键词查询 + +```json +GET /spu/_search +{ + "size": 1000, + "from": 0, + "_source": { + "includes": ["name_zh","goods_keyword_zh","sub_name_zh","category_name_zh","goods_keyword_zh","sale_category_keyword_zh"] + }, + "query": { + "multi_match": { + "query": "遥控车", + "fields": ["name_zh^3.0", "goods_keyword_zh^1.0", "category_name_zh^1.0", "sale_category_keyword_zh^1.0", "sub_name_zh^1.0"], + "minimum_should_match": "64%", + "tie_breaker": 0.3, + "_name": "base_query" + } + }, + "track_scores": true, + "collapse": { + "field": "goods_id" + } +} +``` + +## 慢查询日志 + +```json +PUT /spu,spu_test/_settings +{ + "index.search.slowlog.threshold.query.warn": "1s", + "index.search.slowlog.threshold.query.info": "800ms", + "index.search.slowlog.threshold.query.debug": "600ms", + "index.search.slowlog.threshold.query.trace": "400ms", + "index.search.slowlog.threshold.fetch.warn": "500ms", + "index.search.slowlog.threshold.fetch.info": "400ms", + "index.search.slowlog.threshold.fetch.debug": "200ms", + "index.search.slowlog.threshold.fetch.trace": "100ms" +} +``` + +```bash +GET /spu,spu_test/_settings?include_defaults=true&filter_path=**.slowlog +``` + +--- + +# 一些问题 + +有一个商品，只命中了"玩具"、没有命中"遥控"，两个词命中一个词（注意是所有的字段 总共只命中一个词，玩具命中多次，但是没有任何一个字段有"遥控"），minimum_should_match应该没有达到76%吧，76%是按照什么算的？为什么命中了这个查询条件？ + +```json +"multi_match": { + "_name": "base_query", + "fields": [ + "name_zh^2.0", + "goods_keyword_zh^1.0", + "category_name_zh^1.0", + "sale_category_keyword_zh^1.0", + "sub_name_zh^1.0" + ], + "minimum_should_match": "76%", + "query": "遥控玩具", + "tie_breaker": 0.3 +} +``` + +tie_breaker 导致 + +```json +GET /spu/_search +{ + "_source": { + "excludes": [ + "embedding_name_zh", + "embedding_name_ru", "*" + ], + "includes": ["_id", "name_zh"] + }, + "size": 1000, + "query": { + "match": { + "name_zh": { + "query": "园艺" + } + } + }, + "aggs" :{ + "goods_agg": { + "terms": { + "field": "goods_id", + "size": 100 + }, + "aggs": { + "top_hits": { + "top_hits": { + "size": 1, + "sort": [{"_score": "desc"}] + } + } + } + } + } +} + + +商品搜索查询模板 +```json +GET /spu/_search +{ + "_source": { + "includes": [ + "name_zh" + ] + }, + "aggs": { + "category_stats": { + "terms": { + "field": "sale_category_one_all", + "size": 100 + } + }, + "spu_attr_stats": { + "terms": { + "field": "attribute_option_list", + "size": 50 + } + }, + "unique_count": { + "cardinality": { + "field": "goods_id" + } + } + }, + "collapse": { + "field": "goods_id" + }, + "from": 0, + "query": { + "function_score": { + "boost_mode": "multiply", + "functions": [ + { + "filter": { + "term": { + "is_video": true + } + }, + "weight": 1.05 + } + ], + "query": { + "bool": { + "filter": [ + { + "terms": { + "status": [2,4,5] + } + } + ], + "must": [ + { + "bool": { + "minimum_should_match": 1, + "should": [ + { + "multi_match": { + "_name": "base_query", + "fields": [ + "name_en^3.0", + "goods_keyword_en^1.0", + "category_name_en^1.3", + "sale_category_keyword_en^1.0", + "sub_name_en^1.0" + ], + "minimum_should_match": "75%", + "operator": "AND", + "query": "changshen flashing toys", + "tie_breaker": 0.9 + } + }, + { + "knn": { + "_name": "knn_query", + "field": "embedding_name_zh", + "k": 40, + "num_candidates": 120, + "query_vector": [...] + } + } + ] + } + } + ] + } + }, + "score_mode": "sum" + } + }, + "size": 1000, + "sort": [ + { + "_score": { + "order": "desc" + } + } + ], + "track_scores": true +} +``` \ No newline at end of file diff --git a/docs/ES/ES_8.18/README__分词相关.md b/docs/ES/ES_8.18/README__分词相关.md new file mode 100644 index 0000000..15f79a5 --- /dev/null +++ b/docs/ES/ES_8.18/README__分词相关.md @@ -0,0 +1,120 @@ + + +工厂搜索 依赖于关键词召回、没有做语义召回和精排，加上语义召回和精排则成本过高，所以对分词效果仍然很依赖。badcase： q=锦鸿 无法召回 锦鸿达 +1. 虽然商品搜索已经不强依赖于分词效果，但是工厂搜索仍然对分词效果有很大依赖，所以进行了优化。 +2. 优化内容： + 1) index_ansj + 自定义词典（需要配置自定义词典。 不配置自定义词典的时候，index_ansj分词结果和query_ansj完全一致，配置自定义词典时，index_ansj 将 ） + 2) ansj自定义词典 补充属性词 + + + + + +| Query | IK_Smart 分词结果 | IK_Max_Word 分词结果 | +|---------|---------------------------------------------|--------------------------------------------------| +| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达 | +| 锦鸿 | 锦, 鸿 | 锦, 鸿 | +| 14寸 4代 | 14寸, 4代 | 14, 寸, 4, 代 | +| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实, 身, 冰雪, 公, 仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | 14, 寸, 第, 4, 代, 眼珠, 实, 身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | +| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 狗狗, 系列, 积木, 套 | + + + +| Query | query_ansj 分词结果 | index_ansj 分词结果 | +|---------|------------------------------------------|--------------------------------------------------| +| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达, 鸿, 达 | +| 锦鸿 | 锦鸿 | 锦鸿, 锦, 鸿 | +| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实, 身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | 14寸, 寸, 第, 4代, 代, 眼珠, 眼, 珠, 实, 身, 冰雪, 冰, 雪, 公仔, 公, 仔, 带, 手动, 手, 动, 大, 推车, 推, 车, 搪, 胶, 雪, 宝宝, 宝 | +| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 金毛, 金, 毛, 犬, 狗狗, 狗, 系列, 系, 列, 积木, 积, 木, 套 | + + +ansj 插件自定义词典补充属性词后： +| Query | query_ansj 分词结果 | index_ansj 分词结果 | +|---------|------------------------------------------|--------------------------------------------------| +| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达, 鸿, 达 | +| 锦鸿 | 锦鸿 | 锦鸿, 锦, 鸿 | +| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪胶, 雪, 宝宝 | 14寸, 寸, 第, 4代, 代, 眼珠, 眼, 珠, 实身, 实, 身, 冰雪, 冰, 雪, 公仔, 公, 仔, 带, 手动, 手, 动, 大, 推车, 推, 车, 搪胶, 搪, 胶, 雪, 宝宝, 宝 | +| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 金毛, 金, 毛, 犬, 狗狗, 狗, 系列, 系, 列, 积木, 积, 木, 套 | + + + + +## ik分词器的问题： + +```bash +POST spu/_analyze +{ + "analyzer": "ik_smart", + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" +} + +POST spu/_analyze +{ + "analyzer": "ik_max_word", + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" +} + +# ik分词器的问题： +# ik_max_word 结果是： 14 寸 第 4 代 +# 期望是能分出多种形式： 14 寸 14寸 第 4 代 4代 +# ik_smart +# 结果是： 14寸 第 4代 +# 因此 14寸 4代 都无法召回原始文档。 +``` + + +## ansj分词器的问题： + +### 1. index_ansj不符合预期 +index模式（type=index_ansj）不符合预期 ：type=index_ansj type=query_ansj 结果永远都是一样的。 +https://github.com/NLPchina/elasticsearch-analysis-ansj/issues/235 + +已解决：需要配置自定义词典 + +### 2. 特殊符号分词粘连问题 +特殊符号（减号）会跟后面的词粘连到一起，导致搜索减号后面的词，无法匹配： + +```bash +GET /_cat/ansj?text=狗狗系列-柴犬积木&type=index_ansj +{ +"name": "-柴", +"nature": "nrf", +"offe": 4, +"realName": "-柴", +"synonyms": null +}, + +GET /_cat/ansj?text=狗狗系列-哈士奇犬&type=index_ansj +{ +"name": "-哈士奇", +"nature": "nrf", +"offe": 4, +"realName": "-哈士奇", +"synonyms": null +}, +``` + +这样的话，如果搜索“柴犬”、“哈士奇”，就不能命中上面的句子。 + +暂时解决办法是： +```json + "analyzer": { + "index_ansj": { + "type": "custom", + "char_filter": ["hyphen_to_space"], + "tokenizer": "index_ansj" + }, + "query_ansj": { + "type": "custom", + "char_filter": ["hyphen_to_space"], + "tokenizer": "query_ansj" + } + } +``` + + + + + + + diff --git a/docs/ES/ES_8.18/README__性能优化.md b/docs/ES/ES_8.18/README__性能优化.md new file mode 100644 index 0000000..5d7de03 --- /dev/null +++ b/docs/ES/ES_8.18/README__性能优化.md @@ -0,0 +1,701 @@ + + + +# 性能优化TODO + + +1. 主分片个数设置为12 / 16（暂定 16），replaca保持为0 +2. 缩减索引&预加载内存（3年前的不做向量索引，在uat上 水哥已经做了，索引大小已经从120G减少到61G）。 之后设置预加载（UAT上面已经做了）： +```json +POST uat_spu/_close +PUT uat_spu/_settings +{ + "index.store.preload": [] +} + +PUT uat_spu/_settings +{ + "index.store.preload": ["vex", "veq"] +} +PUT uat_spu/_settings +{ + "index.store.preload": ["nvd", "dvd", "tim", "doc", "dim"] +} +# ["nvd", "dvd", "tim", "doc", "dim"] + +POST uat_spu/_open +``` +3. 段优化 (在索引稳定后)： +POST /spu/_forcemerge?max_num_segments=1 。这将每个分片内的 Lucene 段合并到最少（理想是1个） +4. UAT索引迁出。 + + +# old + +## 当前主要问题 + +### 1. 按goods_id聚合/去重问题（collapse相关） + +当前采用collapse实现，但存在以下问题： + +#### 1.1 加inner_hits的性能问题 + +我暂时放弃了 inner_hits，但是其实对inner_hits功能有较强的需求，是否有替代办法：能满足 按 goods_id 聚合、也能得到聚合下的原始文档ID、同时能满足分页需求。 + +**对应案例：** + +将下面的"商品搜索实际案例"中的： + +```json +"collapse": { + "field": "goods_id" +} +``` + +替换为： + +```json +"collapse": { + "field": "goods_id", + "inner_hits": { + "_source": false, + "name": "top_docs", + "size": 4 + } +} +``` + +可以看到耗时明显增加，耗时 330ms 增加到 3565ms。 + +#### 1.2 分页不准的问题 + +**背景：** ES的分页是基于索引文档的顺序，而不是折collapse叠后的顺序，这可能导致深度分页时的性能问题或结果不一致。 + +这样会带来两个问题： + +##### a) total不准（total是collapse之前的个数） +我现在的方案是，aggs 再补充一项，计算一个按照 goods_id 的去重数，以此作为 total，从而得到准确的分页。 +（ 也可以使用 cardinality 。 cardinality 提供的事近似计数。 希望快速估算唯一值数量，应该使用 cardinality ） + +见下面案例中的 aggs 中的 unique_count + +##### b) from不准 +相关资料提示只是“深度分页时”不准，比如结果跟前面的页内内容重复。但是我试了一些请求，还没找到过重复的情况。 + +并且，商品搜索因为有精排，只要请求的内容位于top1000，则会从ES获得top1000的所有结果，所以top1000内的内容不会有分页不准的问题。 + +而对于工厂搜索，我测试了一些请求，也还没有发现分页内内容跟前面的页的内容重复的情况。 + +所以ES内部实现的from已经是collapse之后的 + +### 2. ES配置优化 + +还有哪些要注意的。 + +## 实际案例 + +### 商品搜索实际案例（简化后） + +```json +GET /spu/_search +{ + "_source": { + "includes": [ + "name_zh" + ] + }, + "aggs": { + "unique_count": { + "cardinality": { + "field": "goods_id" + } + }, + "category_stats": { + "terms": { + "field": "sale_category_one_all", + "size": 10 + } + }, + "certification_stats": { + "terms": { + "field": "goods_certification_type_list", + "size": 10 + } + }, + "deliver_day_stats": { + "terms": { + "field": "deliver_day", + "size": 10 + } + }, + "label_stats": { + "terms": { + "field": "label_id_list", + "size": 10 + } + }, + "material_stats": { + "terms": { + "field": "goods_main_material", + "size": 10 + } + }, + "moq_stats": { + "terms": { + "field": "purchase_moq", + "size": 10 + } + }, + "package_lang_stats": { + "terms": { + "field": "goods_package_lang", + "size": 10 + } + }, + "package_type_stats": { + "terms": { + "field": "package_type", + "size": 10 + } + }, + "spu_attr_stats": { + "terms": { + "field": "attribute_option_list", + "size": 10 + } + } + }, + "collapse": { + "field": "goods_id" + }, + "from": 0, + "query": { + "function_score": { + "boost_mode": "multiply", + "functions": [ + { + "filter": { + "term": { + "label_id_list": 156 + } + }, + "weight": 1.1 + }, + { + "filter": { + "term": { + "is_video": true + } + }, + "weight": 1.2 + } + ], + "query": { + "bool": { + "filter": [ + { + "term": { + "platform_sku_essaone": true + } + }, + { + "term": { + "platform_rule_essaone": true + } + }, + { + "bool": { + "should": [ + { + "bool": { + "must_not": { + "exists": { + "field": "auth_level_list" + } + } + } + }, + { + "term": { + "auth_level_list": "1" + } + } + ] + } + }, + { + "terms": { + "status": [2,4,5] + } + }, + { + "terms": { + "goods_copyright": ["1","2","3"] + } + } + ], + "must": [ + { + "bool": { + "minimum_should_match": 1, + "should": [ + { + "multi_match": { + "_name": "base_query", + "fields": [ + "name_zh^2.0", + "goods_keyword_zh^1.0", + "category_name_zh^1.0", + "sale_category_keyword_zh^1.0", + "sub_name_zh^1.0" + ], + "minimum_should_match": "66%", + "query": "合金车", + "tie_breaker": 0.3 + } + } + ] + } + } + ] + } + }, + "score_mode": "max" + } + }, + "size": 1000, + "track_scores": true +} +``` + +### 工厂搜索实际案例（简化后） + +```json +GET /spu/_search +{ + "_source": [ + "id", + "goods_id", + "brand_id", + "name_zh" + ], + "aggs": { + "unique_count": { + "cardinality": { + "field": "brand_id" + } + }, + "category_stats": { + "terms": { + "field": "sale_category_one_all", + "size": 10 + } + }, + "sale_market_stats": { + "terms": { + "field": "supplier_main_market_list", + "size": 10 + } + } + }, + "collapse": { + "field": "brand_id" + }, + "from": 222, + "query": { + "bool": { + "filter": [ + { + "term": { + "platform_sku_essaone": true + } + }, + { + "term": { + "platform_rule_essaone": true + } + }, + { + "bool": { + "should": [ + { + "bool": { + "must_not": { + "exists": { + "field": "auth_level_list" + } + } + } + }, + { + "term": { + "auth_level_list": "1" + } + } + ] + } + }, + { + "terms": { + "status": [2,4,5] + } + }, + { + "terms": { + "goods_copyright": [ + "1", + "2", + "3" + ] + } + } + ], + "must": [ + { + "bool": { + "minimum_should_match": 1, + "should": [ + { + "multi_match": { + "fields": [ + "brand_name_zh^10.0", + "name_zh^2.0", + "supplier_name^8.0" + ], + "minimum_should_match": "66%", + "query": "义乌市童吉拉", + "tie_breaker": 0.3 + } + } ] + } + } + ] + } + }, + "size": 20 +} +``` + + +### ES 索引配置方面，为提升查询性能，是否需要将副本数设置为1。 +但是为了不影响写入性能，在每次全量前，先关闭副本，全量写入完成后再打开副本。 +```json +PUT /spu_test/_settings +{ + "number_of_replicas": 1 +} +``` + + +### 预热 +2025/6/26 今天早上7点多一次查询(q=木质手工艺品玩具) ES查询阶段耗时20多秒，但是第二次查询就只要几百ms，改变query耗时也正常。 + + +目前已经设置慢查询日志： + +PUT /spu,spu_test/_settings +{ + "index.search.slowlog.threshold.query.warn": "10s", + "index.search.slowlog.threshold.query.info": "5s", + "index.search.slowlog.threshold.query.debug": "2s", + "index.search.slowlog.threshold.query.trace": "500ms", + "index.search.slowlog.threshold.fetch.warn": "1s", + "index.search.slowlog.threshold.fetch.info": "800ms", + "index.search.slowlog.threshold.fetch.debug": "500ms", + "index.search.slowlog.threshold.fetch.trace": "200ms" +} + + +PUT /spu,spu_test/_settings +{ + "index.search.slowlog.threshold.query.warn": "1s", + "index.search.slowlog.threshold.query.info": "800ms", + "index.search.slowlog.threshold.query.debug": "600ms", + "index.search.slowlog.threshold.query.trace": "400ms", + "index.search.slowlog.threshold.fetch.warn": "500ms", + "index.search.slowlog.threshold.fetch.info": "400ms", + "index.search.slowlog.threshold.fetch.debug": "200ms", + "index.search.slowlog.threshold.fetch.trace": "100ms" +} + +动态调整日志级别 (无需重启) +通过集群设置 API 临时调整： +PUT /_cluster/settings +{ + "transient": { + "logger.index.search.slowlog": "info" + } +} + +验证配置是否生效 +GET /spu,spu_test/_settings?include_defaults=true&filter_path=**.slowlog + +日志文件 +logs/.log + + + + +## 慢查询记录 + + +### 07/02 早上 7:45~7:47，100个里面又有8个超过1.5S的， 4个超过5S ， 2个超过14S + +#### 8888888946992436 +2025-07-02 07:45:32,092 - INFO - [REQ: 8888888946992436 UID: 10639] REQUEST POST structure: {"keyword":"плащ","kt":"kwd","page":1,"size":100,"request_id":8888888946992436,"extra":{"userId":10639,"businessPlatform":"essa"}} + +2025-07-02 07:45:46,981 - INFO - [REQ: 8888888946992436 UID: 10639] qt:kwd total:14.889394521713257ms TIMING: QA=46ms, queryEncoding=0ms, userProfile=1ms, ES=14824ms, BgeReranking=1ms, BizReranking=1ms, Reranking=2ms, total=14889ms, sku_id_list_filling=12ms + +2025-07-02 07:45:46,968 - INFO - [REQ: 8888888946992436 UID: 10639] ES_query_timing # ProductKeywordSearch # 14824.28 # 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+ + + +#### 8888886761383165 + +2025-07-02 07:46:00,929 - INFO - [REQ: 8888886761383165 UID: 15852] qt:kwd_factory total:5.89747166633606ms TIMING: QA=73ms, queryEncoding=28ms, userProfile=1ms, ES=190ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=5897ms, brand_statistics_query=5631ms + +2025-07-02 07:45:55,032 - INFO - [REQ: 8888886761383165 UID: 15852] REQUEST POST structure: {"keyword":"Double E","kt":"kwd_factory","page":1,"size":100,"request_id":8888886761383165,"extra":{"userId":15852,"businessPlatform":"essaone"}} + +2025-07-02 07:45:55,297 - INFO - [REQ: 8888886761383165 UID: 15852] ES_query_timing # FactoryKeywordSearch # 190.35 # {"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"Double E","fields":["brand_name_en^10.0","name_en^2.0","supplier_name^8.0"],"minimum_should_match":"66%","tie_breaker":0.3}},{"term":{"no":{"value":"Double E","boost":15.0}}},{"term":{"hs_no":{"value":"Double E","boost":15.0}}},{"term":{"factory_no":{"value":"Double E","boost":15.0}}},{"term":{"factory_no_buyer":{"value":"Double E","boost":15.0}}},{"term":{"supplier_code":{"value":"Double E","boost":15.0}}},{"match":{"factory_no_fuzzy":{"query":"Double E","boost":5.0}}},{"match":{"factory_no_buyer_fuzzy":{"query":"Double E","boost":5.0}}}],"minimum_should_match":1}}],"filter":[{"term":{"platform_sku_essaone":true}},{"term":{"platform_rule_essaone":true}},{"terms":{"status":[2,4,5]}}]}},"_source":["id","goods_id","brand_id","name_zh","category_name_zh","sub_name_zh","label_id_list","category_id","price_range","on_sell_days_boost","brand_name_zh","supplier_name","is_video"],"from":0,"size":100,"collapse":{"field":"brand_id"},"aggs":{"category_stats":{"terms":{"field":"sale_category_one_all","size":10}},"factory_certification_stats":{"terms":{"field":"supplier_certification_type_list","size":10}},"sale_market_stats":{"terms":{"field":"supplier_main_market_list","size":10}},"factory_category_stats":{"terms":{"field":"supplier_main_category_list","size":10}},"unique_count":{"cardinality":{"field":"brand_id"}}}} + +#### 8888886160313555 +2025-07-02 07:45:52,314 - INFO - [REQ: 8888886160313555 UID: 16341] qt:kwd total:5.246635437011719ms TIMING: QA=54ms, queryEncoding=0ms, userProfile=1ms, ES=5082ms, BgeReranking=3ms, BizReranking=5ms, Reranking=11ms, total=5247ms, sku_id_list_filling=91ms + +2025-07-02 07:45:47,067 - INFO - [REQ: 8888886160313555 UID: 16341] REQUEST POST structure: {"keyword":"домик для игр","kt":"kwd","page":1,"size":100,"request_id":8888886160313555,"extra":{"userId":16341,"businessPlatform":"essa"}} + +2025-07-02 07:45:52,222 - INFO - [REQ: 8888886160313555 UID: 16341] ES_query_timing # ProductKeywordSearch # 5082.04 # {"size":1000,"from":0,"_source":{"includes":["name_zh","is_star","category_name_zh","sub_name_zh","label_id_list","is_video","brand_id","category_id","sale_category_all","price_range","on_sell_days_boost","ru_name"]},"query":{"function_score":{"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"домик для 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+ +#### 8888883840386376 +2025-07-02 07:45:29,565 - INFO - [REQ: 8888883840386376 UID: 9987] qt:kwd_factory total:4.385730504989624ms TIMING: QA=80ms, queryEncoding=34ms, userProfile=1ms, ES=593ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=4386ms, brand_statistics_query=3710ms + +2025-07-02 07:45:25,179 - INFO - [REQ: 8888883840386376 UID: 9987] REQUEST POST structure: {"keyword":"澳高","kt":"kwd_factory","page":1,"size":100,"request_id":8888883840386376,"extra":{"userId":9987,"businessPlatform":"trader"}} + +2025-07-02 07:45:25,854 - INFO - [REQ: 8888883840386376 UID: 9987] ES_query_timing # FactoryKeywordSearch # 592.97 # {"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"澳高","fields":["brand_name_zh^10.0","name_zh^2.0","supplier_name^8.0"],"minimum_should_match":"66%","tie_breaker":0.3}},{"term":{"no":{"value":"澳高","boost":15.0}}},{"term":{"hs_no":{"value":"澳高","boost":15.0}}},{"term":{"factory_no":{"value":"澳高","boost":15.0}}},{"term":{"factory_no_buyer":{"value":"澳高","boost":15.0}}},{"term":{"supplier_code":{"value":"澳高","boost":15.0}}},{"match":{"factory_no_fuzzy":{"query":"澳高","boost":5.0}}},{"match":{"factory_no_buyer_fuzzy":{"query":"澳高","boost":5.0}}}],"minimum_should_match":1}}],"filter":[{"term":{"platform_sku_trader":true}},{"term":{"platform_rule_trader":true}},{"bool":{"should":[{"bool":{"must_not":{"exists":{"field":"trader_buyer_ids"}}}},{"term":{"trader_buyer_ids":9987}}]}},{"terms":{"status":[2,4,5]}}]}},"_source":["id","goods_id","brand_id","name_zh","category_name_zh","sub_name_zh","label_id_list","category_id","price_range","on_sell_days_boost","brand_name_zh","supplier_name","is_video"],"from":0,"size":100,"collapse":{"field":"brand_id"},"aggs":{"category_stats":{"terms":{"field":"sale_category_one_all","size":10}},"factory_certification_stats":{"terms":{"field":"supplier_certification_type_list","size":10}},"sale_market_stats":{"terms":{"field":"supplier_main_market_list","size":10}},"factory_category_stats":{"terms":{"field":"supplier_main_category_list","size":10}},"unique_count":{"cardinality":{"field":"brand_id"}}}} + + +### 0701 启动后 第一次查询 + +2025-07-01 15:10:24,560 - INFO - [REQ: 99996874 UID: 11010] qt:kwd total:15.652546644210815ms TIMING: QA=248ms, queryEncoding=0ms, userProfile=2ms, ES=14733ms, BgeReranking=503ms, BizReranking=7ms, Reranking=514ms, total=15653ms, sku_id_list_filling=144ms + +2025-07-01 15:10:08,908 - INFO - [REQ: 99996874 UID: 11010] REQUEST POST structure: {"query":"积木车","kt":"kwd","top_k":5000,"recall_merge_output_size":5000,"rerank_size":1000,"rerank_enabled":true,"page":1,"size":100,"debug":"0","from_search_debug":1,"extra":{"userId":11010,"businessPlatform":"trader"}} + +2025-07-01 15:10:24,415 - INFO - [REQ: 99996874 UID: 11010] ES_query_timing # ProductKeywordSearch # 14732.69 # 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+ + + +2025-06-29 21:58:26,099 - INFO - [REQ: 7621648571687939 UID: 12883] TIMING: QA=89ms, queryEncoding=38ms, userProfile=0ms, ES=12407ms, BgeReranking=43ms, BizReranking=1ms, Reranking=44ms, total=12594ms, sku_id_list_filling=50ms + 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"auth_level_list"}}}}, {"term": {"auth_level_list": "1"}}]}}, {"terms": {"status": [2, 4, 5]}}]}}, "functions": [{"filter": {"term": {"label_id_list": 156}}, "weight": 1.14}, {"filter": {"term": {"label_id_list": 157}}, "weight": 1.13}, {"filter": {"term": {"label_id_list": 158}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 159}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 162}}, "weight": 1.05}, {"filter": {"term": {"label_id_list": 163}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 164}}, "weight": 1.13}, {"filter": {"term": {"label_id_list": 165}}, "weight": 1.15}, {"field_value_factor": {"field": "on_sell_days_boost", "missing": 1.0}}, {"filter": {"term": {"is_video": true}}, "weight": 1.2}], "score_mode": "max", "boost_mode": "multiply"}}, "track_scores": true, "collapse": {"field": "goods_id"}, "aggs": {"unique_count": {"cardinality": {"field": "goods_id"}}}} +``` + +### 1k线上搜索词测试 +自己批量测试的1000条 只有一条超过1S、但是这一条耗时16S +但是这一千条请求，只填充 搜索词、搜索类型、platform几个参数，所以和线上的请求是有些差异的（没有携带各种业务过滤参数和聚合参数） + +2025-06-29 22:25:33,402 - INFO - [REQ: 7277635141262495 UID: 10031] TIMING: QA=81ms, queryEncoding=26ms, userProfile=0ms, ES=15532ms, BgeReranking=101ms, BizReranking=1ms, Reranking=103ms, total=15857ms, sku_id_list_filling=135ms + +{"keyword":"мяч баскетбольный","kt":"kwd","page":1,"size":100,"request_id":7277635141262495,"extra":{"userId":10031,"businessPlatform":"essaone"}} + +{"size":1000,"from":0,"_source":{"includes":["name_zh","is_star","category_name_zh","sub_name_zh","label_id_list","is_video","brand_id","category_id","sale_category_all","price_range","on_sell_days_boost","ru_name"]},"query":{"function_score":{"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"мяч 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+ + + +2025-06-28 12:00:09,683 - INFO - [REQ: 175108319225300 UID: -1] TIMING: QA=0ms, queryEncoding=0ms, userProfile=0ms, ES=13905ms, BgeReranking=0ms, BizReranking=5ms, Reranking=6ms, total=14649ms, sku_id_list_filling=732ms + +2025-06-28 12:00:34,322 - INFO - [REQ: 175108315581700 UID: -1] TIMING: QA=47ms, queryEncoding=0ms, userProfile=0ms, ES=14587ms, BgeReranking=787ms, BizReranking=1ms, Reranking=789ms, total=16736ms, sku_id_list_filling=1309ms + +2025-06-28 12:23:45,140 - INFO - [REQ: 175108461841000 UID: -1] TIMING: QA=0ms, queryEncoding=0ms, userProfile=0ms, ES=5829ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=5832ms + +2025-06-28 15:07:36,848 - INFO - [REQ: 0 UID: -1] TIMING: QA=139ms, queryEncoding=82ms, userProfile=0ms, ES=13312ms, BgeReranking=2098ms, BizReranking=10ms, Reranking=2111ms, total=15793ms, sku_id_list_filling=199ms + +2025-06-28 15:42:52,404 - INFO - [REQ: 17510963864686712 UID: 6712] TIMING: QA=0ms, queryEncoding=0ms, userProfile=1ms, ES=1990ms, BgeReranking=0ms, BizReranking=5ms, Reranking=6ms, total=2090ms, sku_id_list_filling=86ms + + + +----------------------------------------- + +## 参考资料 + +### 1. 商品搜索、工厂搜索的聚合 + +#### 1.1 聚合字段优化 +- 商品搜索的 goodsId聚合、和工厂搜索的 brandId聚合，现在的 collapse 是否是最优方案，是否还能同时得到 inner_hits + +### 2. aggs 方面 + +现在的方案是： + +```json +"aggs": { + "category_stats": { + "terms": { + "field": "sale_category_all", + "size": 100 + } + } +} +``` + +#### 2.1 top_hits聚合 +预期是通过减少聚合的候选数量（只对topN进行聚合）来提升性能，但是测试好像更差。测试样本量也不够，写法也有好多种，因此看有没有专业建议。 + +这种方式试了几个好像差不多： + +```json +"aggs": { + "top_hits_agg": { + "sampler": { + "shard_size": 5000 // 关键优化1：仅对前5000文档聚合 + }, + "aggs": { + // 所有的聚合字段 + } + } +} +``` + +或者对每个聚合字段加 top_hits 配置，不知道为什么很慢： + +```json +"aggs": { + "category_stats": { + "top_hits": { + "size": 2 + } + } +} +``` + +#### 2.2 execution_hint优化 +加 "execution_hint": "map" 属性（全内存操作，适用聚合结果数量很小、内存够用的情况） + +```json +"aggs": { + "tags": { + "terms": { + "field": "status", + "execution_hint": "map" + } + } +} +``` + +### 3. 检索表达式 + +通过function_score设定提权。 + +```json +GET /spu/_search +{ + "aggs": { + "top_hits_agg": { + "sampler": { + "shard_size": 5000 // 关键优化1：仅对前5000文档聚合 + } + } + }, + "collapse": { + "field": "goods_id" + }, + "from": 0, + "size": 1000, + "track_scores": true, + "query": { + "function_score": { + "query": { + "bool": { + "filter": [ + { + "terms": { + "status": ["2", "4", "5"] + } + } + ], + "must": [ + { + "bool": { + "minimum_should_match": 1, + "should": [ + { + "multi_match": { + "fields": ["name_zh^2.0", "goods_keyword_zh", "category_name_zh", "sale_category_keyword_zh", "sub_name_zh"], + "query": "合金滑行 喷漆", + "minimum_should_match": 2, + "tie_breaker": 0.301 + } + }, + { + "knn": { + "field": "embedding_name_zh", + "k": 1000, + "num_candidates": 1000, + "query_vector": [0.1,1.1,0.6] + } + } + ] + } + } + ] + } + }, + "functions": [ + { + "filter": { "term": { "label_id_list": 156 } }, + "weight": 1.1 + }, + { + "field_value_factor": { + "field": "on_sell_days_boost", + "missing": 1 + } + } + ], + "score_mode": "max", + "boost_mode": "multiply" + } + }, + "rescore": { // 关键优化2：仅对前N结果应用函数评分 + "window_size": 2000, + "query": { + "rescore_query": { + "function_score": { + "query": {"match_all": {}}, + "functions": [ + { + "filter": {"term": {"label_id_list": 156}}, + "weight": 1.1 + }, + { + "field_value_factor": { + "field": "on_sell_days_boost", + "missing": 1 + } + } + ], + "score_mode": "max", + "boost_mode": "multiply" + } + } + } + } +} +``` + +#### 可以考虑的优化： +1. 提权只对主检索表达式topN window_size + +### 4. ES索引优化 + +所有的term字段，如果是int类型，改为int，现在是字符串。 + +不过好像请求用int也能匹配到，所以ES本身检测如果是数值，那么也会为int类型建立一个倒排？这样的话，如果指定为int，只是节省了一些内存（不再需要为字符串类型建立倒排） + +### 5. ES配置优化建议 + +针对单机、低QPS、5百万级别doc，性能优化建议。ES配置方面。 + diff --git a/docs/ES/ES_8.18/install.sh b/docs/ES/ES_8.18/install.sh new file mode 100644 index 0000000..2fa7750 --- /dev/null +++ b/docs/ES/ES_8.18/install.sh @@ -0,0 +1,47 @@ + +wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-8.18.0-x86_64.rpm +# wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-9.0.0-x86_64.rpm + +sudo rpm -ivh elasticsearch-8.18.0-x86_64.rpm + +sudo systemctl daemon-reload +sudo systemctl enable elasticsearch.service +sudo systemctl start elasticsearch.service + + + +好的，下面是你安装 Elasticsearch 8.18.0 时的详细信息和解释： +警告：NOKEY + +这个警告表示你安装的 RPM 包是用一个密钥（d88e42b4）签名的，但是你的系统没有这个密钥。这通常是无害的，前提是你信任这个包的来源。如果想避免这个警告，你可以导入 Elasticsearch 的 GPG 密钥： +bash +sudo rpm --import https://artifacts.elastic.co/GPG-KEY-elasticsearch +安全自动配置 + +Elasticsearch 已经自动启用了传输层和 HTTP 层的 TLS 加密，并且为内置的超级用户 elastic 生成了一个密码（6T_Oq+1Gd=E_MZt=zn6X）。你需要保管好这个密码，初次登录时会用到。 +节点配置 + +如果这个节点需要加入现有的 Elasticsearch 集群，你可以使用集群中生成的 enrollment token 来配置。运行提供的命令来加入集群： +bash +/usr/share/elasticsearch/bin/elasticsearch-reconfigure-node --enrollment-token +服务配置 + +Elasticsearch 没有在安装后自动启动，你需要运行以下命令来使服务自启动并启动它： +bash +sudo systemctl daemon-reload +sudo systemctl enable elasticsearch.service +sudo systemctl start elasticsearch.service +tmpfiles.d 的警告 + +这个警告是由于某个配置文件中引用了 /var/run/elasticsearch，但是这个目录已经不再推荐使用，应该更新为 /run/elasticsearch。这只是一个配置上的小问题，不会影响 Elasticsearch 的正常工作。 + +下一步： + +如果你希望消除 NOKEY 警告，可以导入 GPG 密钥（可选）。 +使用以下命令启动 Elasticsearch 服务： +bash +sudo systemctl daemon-reload +sudo systemctl enable elasticsearch.service +sudo systemctl start elasticsearch.service +使用生成的 elastic 用户密码进行初次登录。 +如果你有特殊需求，可以根据提示更新 tmpfiles 配置。 diff --git a/docs/ES/ES_9 b/docs/ES/ES_9 new file mode 100644 index 0000000..96fe5ea --- /dev/null +++ b/docs/ES/ES_9 @@ -0,0 +1,372 @@ + +### 目标说明 + +**Ubuntu 单机单节点服务器**上安装 **Elasticsearch 9.3.1（9.x 最新小版本）** 的安装与基础调优文档。 + +- 不依赖 GPU（只用 CPU + bfloat16 向量） +- 使用官方 APT 源安装，自动带上自带的 JDK（满足 9.3 的要求） +- 适用于 Ubuntu 20.04/22.04/24.04 等常见版本 +- 单机单节点模式：`discovery.type: single-node` + +--- + +### 1. 系统与前置检查 + +- 操作系统：`Ubuntu 20.04+`（推荐 22.04 / 24.04） +- 需要 root 或 `sudo` 权限 +- 确保系统时间正确、网络能访问 `artifacts.elastic.co` + +```bash +lsb_release -a # 查看 Ubuntu 版本 +curl -I https://artifacts.elastic.co +``` + +--- + +### 2. 添加 Elasticsearch 9.x 官方 APT 仓库 + +#### 2.1 导入 GPG 公钥 + +```bash +sudo mkdir -p /usr/share/keyrings + +wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch \ + | sudo gpg --dearmor -o /usr/share/keyrings/elasticsearch-keyring.gpg +``` + +#### 2.2 添加 9.x APT 源 + +```bash +echo "deb [signed-by=/usr/share/keyrings/elasticsearch-keyring.gpg] https://artifacts.elastic.co/packages/9.x/apt stable main" \ + | sudo tee /etc/apt/sources.list.d/elastic-9.x.list +``` + +#### 2.3 更新包索引 + +```bash +sudo apt-get update +``` + +--- + +### 3. 安装 Elasticsearch 9.3.1 + +默认 `apt install elasticsearch` 会安装当前 9.x 仓库里的最新小版本（例如 9.3.1）。 + +```bash +sudo apt-get install elasticsearch +``` + +安装完成后，可以确认版本： + +```bash +/usr/share/elasticsearch/bin/elasticsearch --version +# 预期显示: 9.3.1 ... +``` + +> 说明： +> - 安装包自带并使用自己的 JDK（目前为 22），不依赖系统的 OpenJDK 17。 +> - 不需要自己配置 `JAVA_HOME`，除非有特殊需求。 + +--- + +### 4. 基础配置（单机单节点） + +编辑 `elasticsearch.yml`（路径 `/etc/elasticsearch/elasticsearch.yml`），调整最基本的选项： + +```bash +sudo vim /etc/elasticsearch/elasticsearch.yml +``` + +常用设置示例（仅示意关键行）： + +```yaml +cluster.name: searchengine-cluster +node.name: node-1 + +network.host: 0.0.0.0 +http.port: 9200 + +# 单机开发用（生产集群请改成多节点配置） +discovery.type: single-node + +# 安全相关（按你当前 8.18 的做法决定是否开） +xpack.security.enabled: false +``` + +> 如果后续扩展为多节点集群，需要去掉 `discovery.type: single-node`，并按官方文档配置 `cluster.initial_master_nodes`、`seed_hosts` 等参数。 + +--- + +### 5. 使用 bfloat16 半精度向量的注意事项 + +- **前提：必须是 Elasticsearch 9.3+**。 +- 你的项目中，9.3 集群创建索引时，要使用已修改过的 `mappings/search_products.json`（`dense_vector` 增加 `element_type: "bfloat16"`）。 +- 在这台新机器的 9.3 集群上： + - 创建索引时用 bfloat16 版 mapping； + - 从 MySQL 重新同步数据。 + +这一部分是应用层的事，和 ES 安装本身无冲突，只要确保 **老 8.18 集群继续用原 mapping，新 9.3 集群用新 mapping** 即可。 + +--- + +### 6. 启动与开机自启 + +#### 6.1 手动启动 / 停止 + +```bash +# 启动 +sudo systemctl start elasticsearch + +# 查看状态 +sudo systemctl status elasticsearch + +# 停止 +sudo systemctl stop elasticsearch +``` + +#### 6.2 设置开机自启 + +```bash +sudo systemctl enable elasticsearch +``` + +--- + +### 7. 验证服务是否正常 + +#### 7.1 本机访问 + +```bash +curl http://127.0.0.1:9200 +``` + +预期返回类似： + +```json +{ + "name" : "node-1", + "cluster_name" : "searchengine-cluster", + "cluster_uuid" : "...", + "version" : { + "number" : "9.3.1", + ... + }, + "tagline" : "You Know, for Search" +} +``` + +#### 7.2 远程访问（如有） + +如果你在 `elasticsearch.yml` 里把 `network.host` 配成了 `0.0.0.0`，且机器安全组 / 防火墙允许 9200 端口，被访问方可以： + +```bash +curl http://:9200 +``` + +--- + +### 8. Kibana 9.x 安装与配置（单机） + +> 说明：Kibana 版本必须与 Elasticsearch 大版本一致，这里以 9.3.1 为例，使用同一个 9.x APT 仓库。 + +#### 8.1 安装 Kibana + +前面已经添加了 `elastic-9.x` APT 源，这里直接安装 Kibana： + +```bash +sudo apt-get install kibana +``` + +安装完成后，可通过下面命令查看版本（可选）： + +```bash +/usr/share/kibana/bin/kibana --version +``` + +#### 8.2 Kibana 基础配置 + +编辑 `kibana.yml`（默认路径 `/etc/kibana/kibana.yml`），至少确认以下几项： + +```bash +sudo vim /etc/kibana/kibana.yml +``` + +常用示例（单机、关闭安全的开发环境）： + +```yaml +server.host: "0.0.0.0" # 允许外部访问，如只本机用可改为 "localhost" +server.port: 5601 + +elasticsearch.hosts: ["http://127.0.0.1:9200"] # 指向本机 ES + +# 如果 ES 开启了 xpack.security，需要在这里配置账号密码 +# elasticsearch.username: "kibana_system" +# elasticsearch.password: "your_password" +``` + +> 生产环境建议： +> - 打开 Elasticsearch 的安全功能（`xpack.security.enabled: true`），用官方向导生成用户和证书； +> - Kibana 使用 `kibana_system` 用户连接 ES，并通过 HTTPS 访问； +> - `server.host` 不要暴露为 0.0.0.0，配合防火墙 / 反向代理限制访问来源。 + +#### 8.3 启动与开机自启 Kibana + +```bash +# 启动 Kibana +sudo systemctl start kibana + +# 查看状态 +sudo systemctl status kibana + +# 设置开机自启 +sudo systemctl enable kibana +``` + +#### 8.4 验证 Kibana 访问 + +浏览器访问： + +```text +http://:5601 +``` + +如果 ES 未启用安全，首次打开一般会直接进入 Kibana 首页； +如果启用了安全机制，将提示登录，使用你在 ES 初始化时创建的账号密码（如 `elastic` 用户）。 + +--- + +### 9. 与现有 SearchEngine 项目集成 + +在这台新机器上跑你的 SearchEngine 项目时： + +- 环境变量或配置里的 ES 地址需要指向这台机器，例如： + - `ES_HOST=http://127.0.0.1:9200` +- 按你项目已有脚本执行索引初始化和数据导入（在 9.3 上推荐使用 bfloat16 版 mapping）： + - 创建索引：使用 `mappings/search_products.json`（bfloat16 版本） + - 执行你现有的数据导入脚本：`./scripts/ingest.sh true` 或 `python main.py ingest ...` + +--- + +### 10. 常见问题提示 + +- **端口 9200 无法访问** + - 检查 `systemctl status elasticsearch` 是否正常运行； + - 检查 `network.host` 是否配置为可访问地址； + - 检查防火墙（`ufw`、安全组）是否放行 9200。 +- **版本不是 9.3.1** + - 确认你添加的是 `https://artifacts.elastic.co/packages/9.x/apt`； + - `apt-cache policy elasticsearch` 查看当前仓库里的版本；如有旧版本仓库，需要清理旧的 `elastic-*.list` 文件。 + +--- + +### 11. 性能与系统配置优化（单机节点） + +> 以下为单机节点在物理机 / 大内存云主机上的推荐基础调优，参考了 8.18 版本的线上配置。具体数值请根据机器内存大小和实际压测结果微调。 + +#### 10.1 JVM 堆内存设置 + +编辑 `jvm.options`（或单独的 options 文件），为 ES 分配合适的堆内存，一般建议： + +- 堆内存占物理内存的 1/2 左右，但不要超过 32GB +- 例如 64GB 物理内存的机器： + +```bash +sudo vim /etc/elasticsearch/jvm.options +``` + +示例（根据实际内存调整）： + +```text +-Xms32g +-Xmx32g +``` + +修改完成后需要重启 Elasticsearch 生效。 + +#### 10.2 Elasticsearch 内部缓存相关配置 + +在 `/etc/elasticsearch/elasticsearch.yml` 中，根据机器内存和查询特征，控制 fielddata 与 query cache 占比，例如： + +```yaml +# Fielddata Cache（主要用于排序、聚合） +indices.fielddata.cache.size: 40% # 占物理内存的 40% 上限 + +# Query Cache（用于相同过滤条件的命中缓存） +indices.queries.cache.size: 30% # 占堆内存的 30% 上限 +``` + +对于你当前的电商搜索场景，如果聚合比较多（分类 / 规格分面等），可以适当提高 fielddata cache；如果过滤条件组合多而重复率不高，可以适当降低 query cache。 + +#### 10.3 内核参数（sysctl）优化 + +编辑 `/etc/sysctl.conf`，追加如下内容（来自现有 8.18 生产配置，可直接沿用到 Ubuntu）： + +```bash +sudo vim /etc/sysctl.conf +``` + +推荐配置示例： + +```text +# tangwang - Elasticsearch 内核参数优化 +# 最大文件句柄数 +fs.file-max = 1000000 + +# 允许的 VMA 数量（映射的虚拟内存区域，用于 mmap 索引文件） +vm.max_map_count = 655360 + +# 脏页写回相关（削峰写入） +vm.dirty_ratio = 10 +vm.dirty_background_ratio = 5 +vm.dirty_writeback_centisecs = 200 +vm.dirty_expire_centisecs = 6000 + +# inode cache 回收权重 +vm.vfs_cache_pressure = 200 + +# 尽可能少用 swap（内存充裕环境建议 0） +vm.swappiness = 0 +``` + +修改后执行： + +```bash +sudo sysctl -p +``` + +#### 10.4 文件句柄与进程数限制（limits.conf） + +编辑 `/etc/security/limits.conf`，为所有用户（特别是运行 ES 的用户）提升文件描述符和进程数上限： + +```bash +sudo vim /etc/security/limits.conf +``` + +示例（参考现有 8.18 配置）： + +```text +# tangwang - ES 资源限制调优 +* soft nofile 655350 +* hard nofile 655350 +root soft nofile 655350 +root hard nofile 655350 + +* soft nproc 102400 +* hard nproc 409600 + +# 允许进程锁定内存（配合后续如需 mlockall 等） +* soft memlock unlimited +* hard memlock unlimited +``` + +修改完成后，建议重新登录终端 / 重启服务，使新的 limits 生效。 + +#### 10.5 单机节点模式小结 + +单机单节点模式下，最关键的是： + +- 在 `elasticsearch.yml` 中设置 `discovery.type: single-node`，避免 ES 等待集群选主； +- 为 ES 分配足够的堆和系统资源（JVM 堆 + sysctl + limits）； +- 使用 bfloat16 向量字段，减小内存与磁盘占用，有利于在线搜索性能； +- 通过你现有的压测脚本（搜索 QPS / 延迟、批量索引速度）验证上述参数，在此基础上微调即可。 diff --git a/docs/ES/README__ES查询相关.md b/docs/ES/README__ES查询相关.md deleted file mode 100644 index ee7866c..0000000 --- a/docs/ES/README__ES查询相关.md +++ /dev/null @@ -1,701 +0,0 @@ -# Elasticsearch 查询相关 - -## 根据商品ID查询 - -```json -GET /uat_spu/_search -{ - "query": { - "term": { - "_id": "3588136" - } - } -} -``` - -## 查label_id - -```json -GET /uat_spu/_search -{"query":{"bool":{"must":[{"term":{ "platform_sku_essaone": true }},{"term":{"label_id_list":166}}]}}} -``` - -## collapse折叠 - -```json -GET /spu/_search -{ - "_source": ["_id", "name_zh", "goods_id", "*"], - "size": 1000, - "query": { - "match": { - "name_zh": { - "query": "园艺" - } - } - }, - "collapse": { - "field": "goods_id", // 按 goods_id 字段折叠分组 - "inner_hits": { - "_source": ["_id"], - "name": "top_docs", // 自定义内部结果名称 - "size": 3, // 每组取1个文档 - "sort": [{"_score": "desc"}] // 按评分降序排序 - } - }, - "sort": ["_score"] // 确保外层排序兼容折叠功能 -} -``` - -## 在架、某平台商品数 - -```json -GET /uat_spu/_search -{ - "track_total_hits": 100000, - "query": { - "bool": { - "filter": [ - { - "term": { - "platform_sku_essaone": true - } - }, - { - "term": { - "platform_rule_essaone": true - } - }, - { - "terms": { - "goods_copyright": [ - "1", - "2", - "3" - ] - } - } - ] - } - } -} -``` - -## ID查询 - -```json -GET /uat_spu/_search -{ - "query": { - "term": { - "_id": "3302275" - } - } -} -``` - -## 商品名搜索 - -```json -GET /spu/_search -{ - "_source": { - }, - "size": 1000, - "query": { - "match": { - "name_zh": { - "query": "园艺工具四件套", - "boost": 111.0 - } - } - }, - "aggs" :{ - "goods_agg": { - "terms": { - "field": "goods_id", - "size": 100 - }, - "aggs": { - "top_hits": { - "top_hits": { - "size": 1, - "sort": [{"_score": "desc"}] - } - } - } - } - } -} -``` - -## phrase搜索 - -```json -GET /uat_spu/_search -{ - "_source":["id","goods_id","name_zh"], - "query":{ - "multi_match": { - "_name": "base_query", - "fields": [ - "name_zh^2.0", - "goods_keyword_zh^1.0", - "category_name_zh^1.0", - "sale_category_keyword_zh^1.0", - "sub_name_zh^1.0" - ], - "query": "戏水玩具" - } - } -} -``` - -## aggs聚合查询 - -```json -GET /spu/_search -{ - "_source": { - "excludes": [ - "embedding_name_zh", - "embedding_name_ru", "*" - ], - "includes": ["_id", "name_zh"] - }, - "size": 1000, - "query": { - "match": { - "name_zh": { - "query": "园艺" - } - } - }, - "aggs" :{ - "goods_agg": { - "terms": { - "field": "goods_id", - "size": 100 - }, - "aggs": { - "top_hits": { - "top_hits": { - "size": 1, - "sort": [{"_score": "desc"}] - } - } - } - } - } -} -``` - -## 检查某个字段是否有值 -```json -GET /uat_spu/_search -{ - "track_total_hits": true, - "size": 0, // 不返回 hits，只返回总数 - "query": { - "exists": { - "field": "goods_keyword_zh" - } - } -} -``` -如果是nested字段，要这么查： -```json -GET /uat_spu/_search -{ - "track_total_hits": true, - "size": 0, // 只看总数 - "query": { - "nested": { - "path": "embedding_pic_h14", - "query": { - "exists": { - "field": "embedding_pic_h14.vector" // 或 url，只要子文档里任意字段有值 - } - } - } - } -} -``` - -```json -``` - -## 分词测试 - -### ik_smart分词器 - -```json -POST spu/_analyze -{ - "analyzer": "ik_smart", - "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" -} -``` - -### ik_max_word分词器 - -```json -POST spu/_analyze -{ - "analyzer": "ik_max_word", - "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" -} -``` - -### query_ansj分词器 - -```json -POST spu/_analyze -{ - "analyzer": "query_ansj", - "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" -} -``` - -### index_ansj分词器 - -```json -POST spu_test/_analyze -{ - "analyzer": "index_ansj", - "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" -} -``` - -## 查商品名 - -```json -GET /spu/_search -{ - "_source": { - "includes": [ - "name_zh", - "category_name_zh", - "sub_name_zh", - "label_id_list", - "brand_id", - "category_id", - "price_range", - "on_sell_days_boost" - ] - }, - "collapse": { - "field": "goods_id", - "inner_hits": { - "name": "top_hits", - "size": 1, - "_source": false // 避免返回完整_source - } - }, - "from": 0, - "query": { - "function_score": { - "query": { - "bool": { - "minimum_should_match": 1, - "should": [ - { - "multi_match": { - "_name": "base_query", - "fields": [ - "name_zh^2.0", - "goods_keyword_zh^1.0", - "category_name_zh^1.0", - "sale_category_keyword_zh^1.0", - "sub_name_zh^1.0" - ], - "minimum_should_match": "66%", - "query": "网球", - "tie_breaker": 0.301 - } - } - ] - } - } - } - }, - "size": 1000, - "track_scores": true -} -``` - -# 配置相关 - -## 查看索引信息 - -```bash -GET /_cat/indices/spu?v -``` - -```bash -GET /spu/_mapping -``` - -```bash -GET /spu/_settings -``` - -## 设置刷新时间 - -```json -PUT /spu/_settings -{ - "refresh_interval": -1 -} -``` - -```json -PUT /uat_spu/_settings -{ - "refresh_interval": "600s" -} -``` - -## 设置preload - -```json -POST uat_spu/_close -PUT uat_spu/_settings -{ - "index.store.preload": [] -} - -PUT uat_spu/_settings -{ - "index.store.preload": ["vex", "veq"] -} -# ["nvd", "dvd", "tim", "doc", "dim"] - -POST uat_spu/_open - -``` - -## 修改分片数目 -主分片的数目（number_of_shards）只能创建索引的时候修改 - - -## 查看索引的分片数量、大小分布 -```json -GET _cat/indices/uat_spu?v&s=store.size:desc -GET _cat/shards/uat_spu -``` - -## 类别查询：索引中该类别总数 - -```json -GET /spu/_search -{ - "track_total_hits": 10000000, - "query": {"terms": { - "sale_category_all": [37619] - }} -} -``` - -## 类别查询：模拟平台规则 - -除了类别限定，包括： -1. essaone平台展示过滤 -2. 非专属商品 -3. status 2 4 5 -4. goods_copyright 1,2,3 - -```json -GET /spu/_search -{ - "track_total_hits": 10000000, - "aggs": { - "unique_count": { - "cardinality": { - "field": "goods_id" - } - } - }, - "collapse": { - "field": "goods_id" - }, - "query": { - "bool": { - "filter": [ - { - "bool": { - "minimum_should_match": 1, - "should": [ - { - "term": { - "platform_sku_essaone": true - } - }, - { - "bool": { - "must": [ - { - "term": { - "platform_rule_essaone": true - } - }, - { - "bool": { - "must_not": { - "exists": { - "field": "platform_sku_essaone" - } - } - } - } - ] - } - } - ] - } - }, - { - "bool": { - "must_not": { - "exists": { - "field": "buyer_id" - } - } - } - }, - { - "terms": { - "sale_category_all": [37619] - } - }, - { - "terms": { - "status": [2,4,5] - } - }, - { - "terms": { - "goods_copyright": [ - "1", - "2", - "3" - ] - } - } - ] - } - } -} -``` - -## 关键词查询 - -```json -GET /spu/_search -{ - "size": 1000, - "from": 0, - "_source": { - "includes": ["name_zh","goods_keyword_zh","sub_name_zh","category_name_zh","goods_keyword_zh","sale_category_keyword_zh"] - }, - "query": { - "multi_match": { - "query": "遥控车", - "fields": ["name_zh^3.0", "goods_keyword_zh^1.0", "category_name_zh^1.0", "sale_category_keyword_zh^1.0", "sub_name_zh^1.0"], - "minimum_should_match": "64%", - "tie_breaker": 0.3, - "_name": "base_query" - } - }, - "track_scores": true, - "collapse": { - "field": "goods_id" - } -} -``` - -## 慢查询日志 - -```json -PUT /spu,spu_test/_settings -{ - "index.search.slowlog.threshold.query.warn": "1s", - "index.search.slowlog.threshold.query.info": "800ms", - "index.search.slowlog.threshold.query.debug": "600ms", - "index.search.slowlog.threshold.query.trace": "400ms", - "index.search.slowlog.threshold.fetch.warn": "500ms", - "index.search.slowlog.threshold.fetch.info": "400ms", - "index.search.slowlog.threshold.fetch.debug": "200ms", - "index.search.slowlog.threshold.fetch.trace": "100ms" -} -``` - -```bash -GET /spu,spu_test/_settings?include_defaults=true&filter_path=**.slowlog -``` - ---- - -# 一些问题 - -有一个商品，只命中了"玩具"、没有命中"遥控"，两个词命中一个词（注意是所有的字段 总共只命中一个词，玩具命中多次，但是没有任何一个字段有"遥控"），minimum_should_match应该没有达到76%吧，76%是按照什么算的？为什么命中了这个查询条件？ - -```json -"multi_match": { - "_name": "base_query", - "fields": [ - "name_zh^2.0", - "goods_keyword_zh^1.0", - "category_name_zh^1.0", - "sale_category_keyword_zh^1.0", - "sub_name_zh^1.0" - ], - "minimum_should_match": "76%", - "query": "遥控玩具", - "tie_breaker": 0.3 -} -``` - -tie_breaker 导致 - -```json -GET /spu/_search -{ - "_source": { - "excludes": [ - "embedding_name_zh", - "embedding_name_ru", "*" - ], - "includes": ["_id", "name_zh"] - }, - "size": 1000, - "query": { - "match": { - "name_zh": { - "query": "园艺" - } - } - }, - "aggs" :{ - "goods_agg": { - "terms": { - "field": "goods_id", - "size": 100 - }, - "aggs": { - "top_hits": { - "top_hits": { - "size": 1, - "sort": [{"_score": "desc"}] - } - } - } - } - } -} - - -商品搜索查询模板 -```json -GET /spu/_search -{ - "_source": { - "includes": [ - "name_zh" - ] - }, - "aggs": { - "category_stats": { - "terms": { - "field": "sale_category_one_all", - "size": 100 - } - }, - "spu_attr_stats": { - "terms": { - "field": "attribute_option_list", - "size": 50 - } - }, - "unique_count": { - "cardinality": { - "field": "goods_id" - } - } - }, - "collapse": { - "field": "goods_id" - }, - "from": 0, - "query": { - "function_score": { - "boost_mode": "multiply", - "functions": [ - { - "filter": { - "term": { - "is_video": true - } - }, - "weight": 1.05 - } - ], - "query": { - "bool": { - "filter": [ - { - "terms": { - "status": [2,4,5] - } - } - ], - "must": [ - { - "bool": { - "minimum_should_match": 1, - "should": [ - { - "multi_match": { - "_name": "base_query", - "fields": [ - "name_en^3.0", - "goods_keyword_en^1.0", - "category_name_en^1.3", - "sale_category_keyword_en^1.0", - "sub_name_en^1.0" - ], - "minimum_should_match": "75%", - "operator": "AND", - "query": "changshen flashing toys", - "tie_breaker": 0.9 - } - }, - { - "knn": { - "_name": "knn_query", - "field": "embedding_name_zh", - "k": 40, - "num_candidates": 120, - "query_vector": [...] - } - } - ] - } - } - ] - } - }, - "score_mode": "sum" - } - }, - "size": 1000, - "sort": [ - { - "_score": { - "order": "desc" - } - } - ], - "track_scores": true -} -``` \ No newline at end of file diff --git a/docs/ES/README__分词相关.md b/docs/ES/README__分词相关.md deleted file mode 100644 index 15f79a5..0000000 --- a/docs/ES/README__分词相关.md +++ /dev/null @@ -1,120 +0,0 @@ - - -工厂搜索 依赖于关键词召回、没有做语义召回和精排，加上语义召回和精排则成本过高，所以对分词效果仍然很依赖。badcase： q=锦鸿 无法召回 锦鸿达 -1. 虽然商品搜索已经不强依赖于分词效果，但是工厂搜索仍然对分词效果有很大依赖，所以进行了优化。 -2. 优化内容： - 1) index_ansj + 自定义词典（需要配置自定义词典。 不配置自定义词典的时候，index_ansj分词结果和query_ansj完全一致，配置自定义词典时，index_ansj 将 ） - 2) ansj自定义词典 补充属性词 - - - - - -| Query | IK_Smart 分词结果 | IK_Max_Word 分词结果 | -|---------|---------------------------------------------|--------------------------------------------------| -| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达 | -| 锦鸿 | 锦, 鸿 | 锦, 鸿 | -| 14寸 4代 | 14寸, 4代 | 14, 寸, 4, 代 | -| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实, 身, 冰雪, 公, 仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | 14, 寸, 第, 4, 代, 眼珠, 实, 身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | -| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 狗狗, 系列, 积木, 套 | - - - -| Query | query_ansj 分词结果 | index_ansj 分词结果 | -|---------|------------------------------------------|--------------------------------------------------| -| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达, 鸿, 达 | -| 锦鸿 | 锦鸿 | 锦鸿, 锦, 鸿 | -| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实, 身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | 14寸, 寸, 第, 4代, 代, 眼珠, 眼, 珠, 实, 身, 冰雪, 冰, 雪, 公仔, 公, 仔, 带, 手动, 手, 动, 大, 推车, 推, 车, 搪, 胶, 雪, 宝宝, 宝 | -| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 金毛, 金, 毛, 犬, 狗狗, 狗, 系列, 系, 列, 积木, 积, 木, 套 | - - -ansj 插件自定义词典补充属性词后： -| Query | query_ansj 分词结果 | index_ansj 分词结果 | -|---------|------------------------------------------|--------------------------------------------------| -| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达, 鸿, 达 | -| 锦鸿 | 锦鸿 | 锦鸿, 锦, 鸿 | -| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪胶, 雪, 宝宝 | 14寸, 寸, 第, 4代, 代, 眼珠, 眼, 珠, 实身, 实, 身, 冰雪, 冰, 雪, 公仔, 公, 仔, 带, 手动, 手, 动, 大, 推车, 推, 车, 搪胶, 搪, 胶, 雪, 宝宝, 宝 | -| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 金毛, 金, 毛, 犬, 狗狗, 狗, 系列, 系, 列, 积木, 积, 木, 套 | - - - - -## ik分词器的问题： - -```bash -POST spu/_analyze -{ - "analyzer": "ik_smart", - "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" -} - -POST spu/_analyze -{ - "analyzer": "ik_max_word", - "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝" -} - -# ik分词器的问题： -# ik_max_word 结果是： 14 寸 第 4 代 -# 期望是能分出多种形式： 14 寸 14寸 第 4 代 4代 -# ik_smart -# 结果是： 14寸 第 4代 -# 因此 14寸 4代 都无法召回原始文档。 -``` - - -## ansj分词器的问题： - -### 1. index_ansj不符合预期 -index模式（type=index_ansj）不符合预期 ：type=index_ansj type=query_ansj 结果永远都是一样的。 -https://github.com/NLPchina/elasticsearch-analysis-ansj/issues/235 - -已解决：需要配置自定义词典 - -### 2. 特殊符号分词粘连问题 -特殊符号（减号）会跟后面的词粘连到一起，导致搜索减号后面的词，无法匹配： - -```bash -GET /_cat/ansj?text=狗狗系列-柴犬积木&type=index_ansj -{ -"name": "-柴", -"nature": "nrf", -"offe": 4, -"realName": "-柴", -"synonyms": null -}, - -GET /_cat/ansj?text=狗狗系列-哈士奇犬&type=index_ansj -{ -"name": "-哈士奇", -"nature": "nrf", -"offe": 4, -"realName": "-哈士奇", -"synonyms": null -}, -``` - -这样的话，如果搜索“柴犬”、“哈士奇”，就不能命中上面的句子。 - -暂时解决办法是： -```json - "analyzer": { - "index_ansj": { - "type": "custom", - "char_filter": ["hyphen_to_space"], - "tokenizer": "index_ansj" - }, - "query_ansj": { - "type": "custom", - "char_filter": ["hyphen_to_space"], - "tokenizer": "query_ansj" - } - } -``` - - - - - - - diff --git a/docs/ES/README__性能优化.md b/docs/ES/README__性能优化.md deleted file mode 100644 index 5d7de03..0000000 --- a/docs/ES/README__性能优化.md +++ /dev/null @@ -1,701 +0,0 @@ - - - -# 性能优化TODO - - -1. 主分片个数设置为12 / 16（暂定 16），replaca保持为0 -2. 缩减索引&预加载内存（3年前的不做向量索引，在uat上 水哥已经做了，索引大小已经从120G减少到61G）。 之后设置预加载（UAT上面已经做了）： -```json -POST uat_spu/_close -PUT uat_spu/_settings -{ - "index.store.preload": [] -} - -PUT uat_spu/_settings -{ - "index.store.preload": ["vex", "veq"] -} -PUT uat_spu/_settings -{ - "index.store.preload": ["nvd", "dvd", "tim", "doc", "dim"] -} -# ["nvd", "dvd", "tim", "doc", "dim"] - -POST uat_spu/_open -``` -3. 段优化 (在索引稳定后)： -POST /spu/_forcemerge?max_num_segments=1 。这将每个分片内的 Lucene 段合并到最少（理想是1个） -4. UAT索引迁出。 - - -# old - -## 当前主要问题 - -### 1. 按goods_id聚合/去重问题（collapse相关） - -当前采用collapse实现，但存在以下问题： - -#### 1.1 加inner_hits的性能问题 - -我暂时放弃了 inner_hits，但是其实对inner_hits功能有较强的需求，是否有替代办法：能满足 按 goods_id 聚合、也能得到聚合下的原始文档ID、同时能满足分页需求。 - -**对应案例：** - -将下面的"商品搜索实际案例"中的： - -```json -"collapse": { - "field": "goods_id" -} -``` - -替换为： - -```json -"collapse": { - "field": "goods_id", - "inner_hits": { - "_source": false, - "name": "top_docs", - "size": 4 - } -} -``` - -可以看到耗时明显增加，耗时 330ms 增加到 3565ms。 - -#### 1.2 分页不准的问题 - -**背景：** ES的分页是基于索引文档的顺序，而不是折collapse叠后的顺序，这可能导致深度分页时的性能问题或结果不一致。 - -这样会带来两个问题： - -##### a) total不准（total是collapse之前的个数） -我现在的方案是，aggs 再补充一项，计算一个按照 goods_id 的去重数，以此作为 total，从而得到准确的分页。 -（ 也可以使用 cardinality 。 cardinality 提供的事近似计数。 希望快速估算唯一值数量，应该使用 cardinality ） - -见下面案例中的 aggs 中的 unique_count - -##### b) from不准 -相关资料提示只是“深度分页时”不准，比如结果跟前面的页内内容重复。但是我试了一些请求，还没找到过重复的情况。 - -并且，商品搜索因为有精排，只要请求的内容位于top1000，则会从ES获得top1000的所有结果，所以top1000内的内容不会有分页不准的问题。 - -而对于工厂搜索，我测试了一些请求，也还没有发现分页内内容跟前面的页的内容重复的情况。 - -所以ES内部实现的from已经是collapse之后的 - -### 2. ES配置优化 - -还有哪些要注意的。 - -## 实际案例 - -### 商品搜索实际案例（简化后） - -```json -GET /spu/_search -{ - "_source": { - "includes": [ - "name_zh" - ] - }, - "aggs": { - "unique_count": { - "cardinality": { - "field": "goods_id" - } - }, - "category_stats": { - "terms": { - "field": "sale_category_one_all", - "size": 10 - } - }, - "certification_stats": { - "terms": { - "field": "goods_certification_type_list", - "size": 10 - } - }, - "deliver_day_stats": { - "terms": { - "field": "deliver_day", - "size": 10 - } - }, - "label_stats": { - "terms": { - "field": "label_id_list", - "size": 10 - } - }, - "material_stats": { - "terms": { - "field": "goods_main_material", - "size": 10 - } - }, - "moq_stats": { - "terms": { - "field": "purchase_moq", - "size": 10 - } - }, - "package_lang_stats": { - "terms": { - "field": "goods_package_lang", - "size": 10 - } - }, - "package_type_stats": { - "terms": { - "field": "package_type", - "size": 10 - } - }, - "spu_attr_stats": { - "terms": { - "field": "attribute_option_list", - "size": 10 - } - } - }, - "collapse": { - "field": "goods_id" - }, - "from": 0, - "query": { - "function_score": { - "boost_mode": "multiply", - "functions": [ - { - "filter": { - "term": { - "label_id_list": 156 - } - }, - "weight": 1.1 - }, - { - "filter": { - "term": { - "is_video": true - } - }, - "weight": 1.2 - } - ], - "query": { - "bool": { - "filter": [ - { - "term": { - "platform_sku_essaone": true - } - }, - { - "term": { - "platform_rule_essaone": true - } - }, - { - "bool": { - "should": [ - { - "bool": { - "must_not": { - "exists": { - "field": "auth_level_list" - } - } - } - }, - { - "term": { - "auth_level_list": "1" - } - } - ] - } - }, - { - "terms": { - "status": [2,4,5] - } - }, - { - "terms": { - "goods_copyright": ["1","2","3"] - } - } - ], - "must": [ - { - "bool": { - "minimum_should_match": 1, - "should": [ - { - "multi_match": { - "_name": "base_query", - "fields": [ - "name_zh^2.0", - "goods_keyword_zh^1.0", - "category_name_zh^1.0", - "sale_category_keyword_zh^1.0", - "sub_name_zh^1.0" - ], - "minimum_should_match": "66%", - "query": "合金车", - "tie_breaker": 0.3 - } - } - ] - } - } - ] - } - }, - "score_mode": "max" - } - }, - "size": 1000, - "track_scores": true -} -``` - -### 工厂搜索实际案例（简化后） - -```json -GET /spu/_search -{ - "_source": [ - "id", - "goods_id", - "brand_id", - "name_zh" - ], - "aggs": { - "unique_count": { - "cardinality": { - "field": "brand_id" - } - }, - "category_stats": { - "terms": { - "field": "sale_category_one_all", - "size": 10 - } - }, - "sale_market_stats": { - "terms": { - "field": "supplier_main_market_list", - "size": 10 - } - } - }, - "collapse": { - "field": "brand_id" - }, - "from": 222, - "query": { - "bool": { - "filter": [ - { - "term": { - "platform_sku_essaone": true - } - }, - { - "term": { - "platform_rule_essaone": true - } - }, - { - "bool": { - "should": [ - { - "bool": { - "must_not": { - "exists": { - "field": "auth_level_list" - } - } - } - }, - { - "term": { - "auth_level_list": "1" - } - } - ] - } - }, - { - "terms": { - "status": [2,4,5] - } - }, - { - "terms": { - "goods_copyright": [ - "1", - "2", - "3" - ] - } - } - ], - "must": [ - { - "bool": { - "minimum_should_match": 1, - "should": [ - { - "multi_match": { - "fields": [ - "brand_name_zh^10.0", - "name_zh^2.0", - "supplier_name^8.0" - ], - "minimum_should_match": "66%", - "query": "义乌市童吉拉", - "tie_breaker": 0.3 - } - } ] - } - } - ] - } - }, - "size": 20 -} -``` - - -### ES 索引配置方面，为提升查询性能，是否需要将副本数设置为1。 -但是为了不影响写入性能，在每次全量前，先关闭副本，全量写入完成后再打开副本。 -```json -PUT /spu_test/_settings -{ - "number_of_replicas": 1 -} -``` - - -### 预热 -2025/6/26 今天早上7点多一次查询(q=木质手工艺品玩具) ES查询阶段耗时20多秒，但是第二次查询就只要几百ms，改变query耗时也正常。 - - -目前已经设置慢查询日志： - -PUT /spu,spu_test/_settings -{ - "index.search.slowlog.threshold.query.warn": "10s", - "index.search.slowlog.threshold.query.info": "5s", - "index.search.slowlog.threshold.query.debug": "2s", - "index.search.slowlog.threshold.query.trace": "500ms", - "index.search.slowlog.threshold.fetch.warn": "1s", - "index.search.slowlog.threshold.fetch.info": "800ms", - "index.search.slowlog.threshold.fetch.debug": "500ms", - "index.search.slowlog.threshold.fetch.trace": "200ms" -} - - -PUT /spu,spu_test/_settings -{ - "index.search.slowlog.threshold.query.warn": "1s", - "index.search.slowlog.threshold.query.info": "800ms", - "index.search.slowlog.threshold.query.debug": "600ms", - "index.search.slowlog.threshold.query.trace": "400ms", - "index.search.slowlog.threshold.fetch.warn": "500ms", - "index.search.slowlog.threshold.fetch.info": "400ms", - "index.search.slowlog.threshold.fetch.debug": "200ms", - "index.search.slowlog.threshold.fetch.trace": "100ms" -} - -动态调整日志级别 (无需重启) -通过集群设置 API 临时调整： -PUT /_cluster/settings -{ - "transient": { - "logger.index.search.slowlog": "info" - } -} - -验证配置是否生效 -GET /spu,spu_test/_settings?include_defaults=true&filter_path=**.slowlog - -日志文件 -logs/.log - - - - -## 慢查询记录 - - -### 07/02 早上 7:45~7:47，100个里面又有8个超过1.5S的， 4个超过5S ， 2个超过14S - -#### 8888888946992436 -2025-07-02 07:45:32,092 - INFO - [REQ: 8888888946992436 UID: 10639] REQUEST POST structure: {"keyword":"плащ","kt":"kwd","page":1,"size":100,"request_id":8888888946992436,"extra":{"userId":10639,"businessPlatform":"essa"}} - -2025-07-02 07:45:46,981 - INFO - [REQ: 8888888946992436 UID: 10639] qt:kwd total:14.889394521713257ms TIMING: QA=46ms, queryEncoding=0ms, userProfile=1ms, ES=14824ms, BgeReranking=1ms, BizReranking=1ms, Reranking=2ms, total=14889ms, sku_id_list_filling=12ms - -2025-07-02 07:45:46,968 - INFO - [REQ: 8888888946992436 UID: 10639] ES_query_timing # ProductKeywordSearch # 14824.28 # 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- - - -#### 8888886761383165 - -2025-07-02 07:46:00,929 - INFO - [REQ: 8888886761383165 UID: 15852] qt:kwd_factory total:5.89747166633606ms TIMING: QA=73ms, queryEncoding=28ms, userProfile=1ms, ES=190ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=5897ms, brand_statistics_query=5631ms - -2025-07-02 07:45:55,032 - INFO - [REQ: 8888886761383165 UID: 15852] REQUEST POST structure: {"keyword":"Double E","kt":"kwd_factory","page":1,"size":100,"request_id":8888886761383165,"extra":{"userId":15852,"businessPlatform":"essaone"}} - -2025-07-02 07:45:55,297 - INFO - [REQ: 8888886761383165 UID: 15852] ES_query_timing # FactoryKeywordSearch # 190.35 # {"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"Double E","fields":["brand_name_en^10.0","name_en^2.0","supplier_name^8.0"],"minimum_should_match":"66%","tie_breaker":0.3}},{"term":{"no":{"value":"Double E","boost":15.0}}},{"term":{"hs_no":{"value":"Double E","boost":15.0}}},{"term":{"factory_no":{"value":"Double E","boost":15.0}}},{"term":{"factory_no_buyer":{"value":"Double E","boost":15.0}}},{"term":{"supplier_code":{"value":"Double E","boost":15.0}}},{"match":{"factory_no_fuzzy":{"query":"Double E","boost":5.0}}},{"match":{"factory_no_buyer_fuzzy":{"query":"Double E","boost":5.0}}}],"minimum_should_match":1}}],"filter":[{"term":{"platform_sku_essaone":true}},{"term":{"platform_rule_essaone":true}},{"terms":{"status":[2,4,5]}}]}},"_source":["id","goods_id","brand_id","name_zh","category_name_zh","sub_name_zh","label_id_list","category_id","price_range","on_sell_days_boost","brand_name_zh","supplier_name","is_video"],"from":0,"size":100,"collapse":{"field":"brand_id"},"aggs":{"category_stats":{"terms":{"field":"sale_category_one_all","size":10}},"factory_certification_stats":{"terms":{"field":"supplier_certification_type_list","size":10}},"sale_market_stats":{"terms":{"field":"supplier_main_market_list","size":10}},"factory_category_stats":{"terms":{"field":"supplier_main_category_list","size":10}},"unique_count":{"cardinality":{"field":"brand_id"}}}} - -#### 8888886160313555 -2025-07-02 07:45:52,314 - INFO - [REQ: 8888886160313555 UID: 16341] qt:kwd total:5.246635437011719ms TIMING: QA=54ms, queryEncoding=0ms, userProfile=1ms, ES=5082ms, BgeReranking=3ms, BizReranking=5ms, Reranking=11ms, total=5247ms, sku_id_list_filling=91ms - -2025-07-02 07:45:47,067 - INFO - [REQ: 8888886160313555 UID: 16341] REQUEST POST structure: {"keyword":"домик для игр","kt":"kwd","page":1,"size":100,"request_id":8888886160313555,"extra":{"userId":16341,"businessPlatform":"essa"}} - -2025-07-02 07:45:52,222 - INFO - [REQ: 8888886160313555 UID: 16341] ES_query_timing # ProductKeywordSearch # 5082.04 # {"size":1000,"from":0,"_source":{"includes":["name_zh","is_star","category_name_zh","sub_name_zh","label_id_list","is_video","brand_id","category_id","sale_category_all","price_range","on_sell_days_boost","ru_name"]},"query":{"function_score":{"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"домик для 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- -#### 8888883840386376 -2025-07-02 07:45:29,565 - INFO - [REQ: 8888883840386376 UID: 9987] qt:kwd_factory total:4.385730504989624ms TIMING: QA=80ms, queryEncoding=34ms, userProfile=1ms, ES=593ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=4386ms, brand_statistics_query=3710ms - -2025-07-02 07:45:25,179 - INFO - [REQ: 8888883840386376 UID: 9987] REQUEST POST structure: {"keyword":"澳高","kt":"kwd_factory","page":1,"size":100,"request_id":8888883840386376,"extra":{"userId":9987,"businessPlatform":"trader"}} - -2025-07-02 07:45:25,854 - INFO - [REQ: 8888883840386376 UID: 9987] ES_query_timing # FactoryKeywordSearch # 592.97 # {"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"澳高","fields":["brand_name_zh^10.0","name_zh^2.0","supplier_name^8.0"],"minimum_should_match":"66%","tie_breaker":0.3}},{"term":{"no":{"value":"澳高","boost":15.0}}},{"term":{"hs_no":{"value":"澳高","boost":15.0}}},{"term":{"factory_no":{"value":"澳高","boost":15.0}}},{"term":{"factory_no_buyer":{"value":"澳高","boost":15.0}}},{"term":{"supplier_code":{"value":"澳高","boost":15.0}}},{"match":{"factory_no_fuzzy":{"query":"澳高","boost":5.0}}},{"match":{"factory_no_buyer_fuzzy":{"query":"澳高","boost":5.0}}}],"minimum_should_match":1}}],"filter":[{"term":{"platform_sku_trader":true}},{"term":{"platform_rule_trader":true}},{"bool":{"should":[{"bool":{"must_not":{"exists":{"field":"trader_buyer_ids"}}}},{"term":{"trader_buyer_ids":9987}}]}},{"terms":{"status":[2,4,5]}}]}},"_source":["id","goods_id","brand_id","name_zh","category_name_zh","sub_name_zh","label_id_list","category_id","price_range","on_sell_days_boost","brand_name_zh","supplier_name","is_video"],"from":0,"size":100,"collapse":{"field":"brand_id"},"aggs":{"category_stats":{"terms":{"field":"sale_category_one_all","size":10}},"factory_certification_stats":{"terms":{"field":"supplier_certification_type_list","size":10}},"sale_market_stats":{"terms":{"field":"supplier_main_market_list","size":10}},"factory_category_stats":{"terms":{"field":"supplier_main_category_list","size":10}},"unique_count":{"cardinality":{"field":"brand_id"}}}} - - -### 0701 启动后 第一次查询 - -2025-07-01 15:10:24,560 - INFO - [REQ: 99996874 UID: 11010] qt:kwd total:15.652546644210815ms TIMING: QA=248ms, queryEncoding=0ms, userProfile=2ms, ES=14733ms, BgeReranking=503ms, BizReranking=7ms, Reranking=514ms, total=15653ms, sku_id_list_filling=144ms - -2025-07-01 15:10:08,908 - INFO - [REQ: 99996874 UID: 11010] REQUEST POST structure: {"query":"积木车","kt":"kwd","top_k":5000,"recall_merge_output_size":5000,"rerank_size":1000,"rerank_enabled":true,"page":1,"size":100,"debug":"0","from_search_debug":1,"extra":{"userId":11010,"businessPlatform":"trader"}} - -2025-07-01 15:10:24,415 - INFO - [REQ: 99996874 UID: 11010] ES_query_timing # ProductKeywordSearch # 14732.69 # 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- - -2025-06-28 15:07:36,526 - INFO - [REQ: 0 UID: -1] TIMING: QA=113ms, queryEncoding=0ms, userProfile=0ms, ES=20945ms, BgeReranking=1974ms, BizReranking=9ms, Reranking=1987ms, total=23079ms, sku_id_list_filling=23ms -```bash -{"size": 1000, "from": 0, "_source": {"includes": ["name_zh", "is_star", "category_name_zh", "sub_name_zh", "label_id_list", "is_video", "brand_id", "category_id", "sale_category_all", "price_range", "on_sell_days_boost", "brand_name_zh", "supplier_name"]}, "query": {"function_score": {"query": {"bool": {"must": [{"bool": {"should": [{"multi_match": {"query": "新款拉布布6寸搪胶PVC卡头袋6款混装", "fields": ["name_zh^2.0", "goods_keyword_zh^1.0", "category_name_zh^1.0", "sale_category_keyword_zh^1.0", "sub_name_zh^1.0"], "minimum_should_match": "66%", "tie_breaker": 0.3, "_name": "base_query"}}, {"knn": {"field": "embedding_name_zh", "query_vector": [0.04777948185801506, -0.009930620901286602, -0.03340917453169823, -0.010704109445214272, -0.0495409220457077, 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"auth_level_list"}}}}, {"term": {"auth_level_list": "1"}}]}}, {"terms": {"status": [2, 4, 5]}}]}}, "functions": [{"filter": {"term": {"label_id_list": 156}}, "weight": 1.14}, {"filter": {"term": {"label_id_list": 157}}, "weight": 1.13}, {"filter": {"term": {"label_id_list": 158}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 159}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 162}}, "weight": 1.05}, {"filter": {"term": {"label_id_list": 163}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 164}}, "weight": 1.13}, {"filter": {"term": {"label_id_list": 165}}, "weight": 1.15}, {"field_value_factor": {"field": "on_sell_days_boost", "missing": 1.0}}, {"filter": {"term": {"is_video": true}}, "weight": 1.2}], "score_mode": "max", "boost_mode": "multiply"}}, "track_scores": true, "collapse": {"field": "goods_id"}, "aggs": {"unique_count": {"cardinality": {"field": "goods_id"}}}} -``` - -### 1k线上搜索词测试 -自己批量测试的1000条 只有一条超过1S、但是这一条耗时16S -但是这一千条请求，只填充 搜索词、搜索类型、platform几个参数，所以和线上的请求是有些差异的（没有携带各种业务过滤参数和聚合参数） - -2025-06-29 22:25:33,402 - INFO - [REQ: 7277635141262495 UID: 10031] TIMING: QA=81ms, queryEncoding=26ms, userProfile=0ms, ES=15532ms, BgeReranking=101ms, BizReranking=1ms, Reranking=103ms, total=15857ms, sku_id_list_filling=135ms - -{"keyword":"мяч баскетбольный","kt":"kwd","page":1,"size":100,"request_id":7277635141262495,"extra":{"userId":10031,"businessPlatform":"essaone"}} - -{"size":1000,"from":0,"_source":{"includes":["name_zh","is_star","category_name_zh","sub_name_zh","label_id_list","is_video","brand_id","category_id","sale_category_all","price_range","on_sell_days_boost","ru_name"]},"query":{"function_score":{"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"мяч 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- - - -2025-06-28 12:00:09,683 - INFO - [REQ: 175108319225300 UID: -1] TIMING: QA=0ms, queryEncoding=0ms, userProfile=0ms, ES=13905ms, BgeReranking=0ms, BizReranking=5ms, Reranking=6ms, total=14649ms, sku_id_list_filling=732ms - -2025-06-28 12:00:34,322 - INFO - [REQ: 175108315581700 UID: -1] TIMING: QA=47ms, queryEncoding=0ms, userProfile=0ms, ES=14587ms, BgeReranking=787ms, BizReranking=1ms, Reranking=789ms, total=16736ms, sku_id_list_filling=1309ms - -2025-06-28 12:23:45,140 - INFO - [REQ: 175108461841000 UID: -1] TIMING: QA=0ms, queryEncoding=0ms, userProfile=0ms, ES=5829ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=5832ms - -2025-06-28 15:07:36,848 - INFO - [REQ: 0 UID: -1] TIMING: QA=139ms, queryEncoding=82ms, userProfile=0ms, ES=13312ms, BgeReranking=2098ms, BizReranking=10ms, Reranking=2111ms, total=15793ms, sku_id_list_filling=199ms - -2025-06-28 15:42:52,404 - INFO - [REQ: 17510963864686712 UID: 6712] TIMING: QA=0ms, queryEncoding=0ms, userProfile=1ms, ES=1990ms, BgeReranking=0ms, BizReranking=5ms, Reranking=6ms, total=2090ms, sku_id_list_filling=86ms - - - ------------------------------------------ - -## 参考资料 - -### 1. 商品搜索、工厂搜索的聚合 - -#### 1.1 聚合字段优化 -- 商品搜索的 goodsId聚合、和工厂搜索的 brandId聚合，现在的 collapse 是否是最优方案，是否还能同时得到 inner_hits - -### 2. aggs 方面 - -现在的方案是： - -```json -"aggs": { - "category_stats": { - "terms": { - "field": "sale_category_all", - "size": 100 - } - } -} -``` - -#### 2.1 top_hits聚合 -预期是通过减少聚合的候选数量（只对topN进行聚合）来提升性能，但是测试好像更差。测试样本量也不够，写法也有好多种，因此看有没有专业建议。 - -这种方式试了几个好像差不多： - -```json -"aggs": { - "top_hits_agg": { - "sampler": { - "shard_size": 5000 // 关键优化1：仅对前5000文档聚合 - }, - "aggs": { - // 所有的聚合字段 - } - } -} -``` - -或者对每个聚合字段加 top_hits 配置，不知道为什么很慢： - -```json -"aggs": { - "category_stats": { - "top_hits": { - "size": 2 - } - } -} -``` - -#### 2.2 execution_hint优化 -加 "execution_hint": "map" 属性（全内存操作，适用聚合结果数量很小、内存够用的情况） - -```json -"aggs": { - "tags": { - "terms": { - "field": "status", - "execution_hint": "map" - } - } -} -``` - -### 3. 检索表达式 - -通过function_score设定提权。 - -```json -GET /spu/_search -{ - "aggs": { - "top_hits_agg": { - "sampler": { - "shard_size": 5000 // 关键优化1：仅对前5000文档聚合 - } - } - }, - "collapse": { - "field": "goods_id" - }, - "from": 0, - "size": 1000, - "track_scores": true, - "query": { - "function_score": { - "query": { - "bool": { - "filter": [ - { - "terms": { - "status": ["2", "4", "5"] - } - } - ], - "must": [ - { - "bool": { - "minimum_should_match": 1, - "should": [ - { - "multi_match": { - "fields": ["name_zh^2.0", "goods_keyword_zh", "category_name_zh", "sale_category_keyword_zh", "sub_name_zh"], - "query": "合金滑行 喷漆", - "minimum_should_match": 2, - "tie_breaker": 0.301 - } - }, - { - "knn": { - "field": "embedding_name_zh", - "k": 1000, - "num_candidates": 1000, - "query_vector": [0.1,1.1,0.6] - } - } - ] - } - } - ] - } - }, - "functions": [ - { - "filter": { "term": { "label_id_list": 156 } }, - "weight": 1.1 - }, - { - "field_value_factor": { - "field": "on_sell_days_boost", - "missing": 1 - } - } - ], - "score_mode": "max", - "boost_mode": "multiply" - } - }, - "rescore": { // 关键优化2：仅对前N结果应用函数评分 - "window_size": 2000, - "query": { - "rescore_query": { - "function_score": { - "query": {"match_all": {}}, - "functions": [ - { - "filter": {"term": {"label_id_list": 156}}, - "weight": 1.1 - }, - { - "field_value_factor": { - "field": "on_sell_days_boost", - "missing": 1 - } - } - ], - "score_mode": "max", - "boost_mode": "multiply" - } - } - } - } -} -``` - -#### 可以考虑的优化： -1. 提权只对主检索表达式topN window_size - -### 4. ES索引优化 - -所有的term字段，如果是int类型，改为int，现在是字符串。 - -不过好像请求用int也能匹配到，所以ES本身检测如果是数值，那么也会为int类型建立一个倒排？这样的话，如果指定为int，只是节省了一些内存（不再需要为字符串类型建立倒排） - -### 5. ES配置优化建议 - -针对单机、低QPS、5百万级别doc，性能优化建议。ES配置方面。 - diff --git a/docs/ES/install.sh b/docs/ES/install.sh deleted file mode 100644 index 2fa7750..0000000 --- a/docs/ES/install.sh +++ /dev/null @@ -1,47 +0,0 @@ - -wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-8.18.0-x86_64.rpm -# wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-9.0.0-x86_64.rpm - -sudo rpm -ivh elasticsearch-8.18.0-x86_64.rpm - -sudo systemctl daemon-reload -sudo systemctl enable elasticsearch.service -sudo systemctl start elasticsearch.service - - - -好的，下面是你安装 Elasticsearch 8.18.0 时的详细信息和解释： -警告：NOKEY - -这个警告表示你安装的 RPM 包是用一个密钥（d88e42b4）签名的，但是你的系统没有这个密钥。这通常是无害的，前提是你信任这个包的来源。如果想避免这个警告，你可以导入 Elasticsearch 的 GPG 密钥： -bash -sudo rpm --import https://artifacts.elastic.co/GPG-KEY-elasticsearch -安全自动配置 - -Elasticsearch 已经自动启用了传输层和 HTTP 层的 TLS 加密，并且为内置的超级用户 elastic 生成了一个密码（6T_Oq+1Gd=E_MZt=zn6X）。你需要保管好这个密码，初次登录时会用到。 -节点配置 - -如果这个节点需要加入现有的 Elasticsearch 集群，你可以使用集群中生成的 enrollment token 来配置。运行提供的命令来加入集群： -bash -/usr/share/elasticsearch/bin/elasticsearch-reconfigure-node --enrollment-token -服务配置 - -Elasticsearch 没有在安装后自动启动，你需要运行以下命令来使服务自启动并启动它： -bash -sudo systemctl daemon-reload -sudo systemctl enable elasticsearch.service -sudo systemctl start elasticsearch.service -tmpfiles.d 的警告 - -这个警告是由于某个配置文件中引用了 /var/run/elasticsearch，但是这个目录已经不再推荐使用，应该更新为 /run/elasticsearch。这只是一个配置上的小问题，不会影响 Elasticsearch 的正常工作。 - -下一步： - -如果你希望消除 NOKEY 警告，可以导入 GPG 密钥（可选）。 -使用以下命令启动 Elasticsearch 服务： -bash -sudo systemctl daemon-reload -sudo systemctl enable elasticsearch.service -sudo systemctl start elasticsearch.service -使用生成的 elastic 用户密码进行初次登录。 -如果你有特殊需求，可以根据提示更新 tmpfiles 配置。 diff --git a/mappings/search_products.json b/mappings/search_products.json index 9b1706e..10f1323 100644 --- a/mappings/search_products.json +++ b/mappings/search_products.json @@ -994,7 +994,8 @@ "type": "dense_vector", "dims": 1024, "index": true, - "similarity": "dot_product" + "similarity": "dot_product", + "element_type": "bfloat16" }, "image_embedding": { "type": "nested", @@ -1003,7 +1004,8 @@ "type": "dense_vector", "dims": 1024, "index": true, - "similarity": "dot_product" + "similarity": "dot_product", + "element_type": "bfloat16" }, "url": { "type": "text" @@ -1017,7 +1019,8 @@ "type": "dense_vector", "dims": 512, "index": true, - "similarity": "dot_product" + "similarity": "dot_product", + "element_type": "bfloat16" }, "url": { "type": "text" -- libgit2 0.21.2