学习 Amazon OpenSearch Service 的排名 - Amazon Opensearch Service
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学习 Amazon OpenSearch Service 的排名

OpenSearch 使用名为 BM-25 的概率排名框架来计算相关性得分。如果独特的关键词在文档中出现较为频繁,BM-25 会为该文档分配更高的相关性分数。但是,此框架并没有考虑点击数据等用户行为,这可以进一步提高相关性。

学习排名是一个开源插件,可以让您使用机器学习和行为数据来调整文档的相关性。它使用 XGBoost 和 Ranklib 库中的模型来重新评分搜索结果。Elasticsearch LTR 插件最初由 OpenSource Connections 开发,且 Wikimedia Foundation、Snagajob Engineering、Bonsai 和 Yelp Engineering 做出了重大贡献。该插件的 OpenSearch 版本源自 Elasticsearch LTR 插件。有关完整文档,包括详细步骤和 API 说明,请参阅学习排名文档。

学习排名需要 OpenSearch 或 Elasticsearch 7.7 或更高版本。

注意

要使用学习排名插件,您必须具有完全的管理员权限。要了解更多信息,请参阅 修改主用户

学习排名入门

您需要提供判断列表,准备训练数据集,并在 Amazon OpenSearch Service 外训练模型。蓝色部分出现在 OpenSearch Service 之外:


        示例学习排名插件过程。

步骤 1:初始化插件

要初始化学习排名插件,请将以下请求发送到您的 OpenSearch Service 域:

PUT _ltr
{ "acknowledged" : true, "shards_acknowledged" : true, "index" : ".ltrstore" }

此命令创建一个隐藏的 .ltrstore 索引,用于存储元数据信息(如功能集和模型)。

步骤 2:创建判断列表

注意

您必须在 OpenSearch Service 之外执行此步骤。

判断列表是机器学习模型从中学习的示例集合。您的判断列表中应包含对您很重要的关键词以及每个关键词的一组分级文档。

在此示例中,我们提供了电影数据集的判断列表。等级为 4 表示完全匹配。等级为 0 表示匹配不佳。

等级 Keyword 文档编号 电影名称
4 rambo 7555 Rambo
3 rambo 1370 Rambo III
3 rambo 1369 Rambo:First Blood 第 II 部分
3 rambo 1368 First Blood

准备以下格式的判断列表:

4 qid:1 # 7555 Rambo 3 qid:1 # 1370 Rambo III 3 qid:1 # 1369 Rambo: First Blood Part II 3 qid:1 # 1368 First Blood where qid:1 represents "rambo"

有关判断列表的更完整示例,请参阅电影判断

您可以在人类注释者的帮助下手动创建此判断列表,或者从分析数据以编程方式推断它。

步骤 3:构建功能集

功能是与文档相关性相对应的字段,例如 titleoverviewpopularity score(视图数),依此类推。

为每个功能构建具有 Mustache 模板的功能集。有关功能的更多信息,请参阅使用功能

在此示例中,我们构建了包含 titleoverview 字段的 movie_features 功能集:

POST _ltr/_featureset/movie_features { "featureset" : { "name" : "movie_features", "features" : [ { "name" : "1", "params" : [ "keywords" ], "template_language" : "mustache", "template" : { "match" : { "title" : "{{keywords}}" } } }, { "name" : "2", "params" : [ "keywords" ], "template_language" : "mustache", "template" : { "match" : { "overview" : "{{keywords}}" } } } ] } }

如果您查询原始 .ltrstore 索引,则返回您的功能集:

GET _ltr/_featureset

步骤 4:记录功能值

功能值是 BM-25 为每个功能计算的相关性分数。

将功能集和判断列表组合起来记录功能值。有关日志记录功能的更多信息,请参阅日志记录功能分数

在此示例中,bool 查询使用筛选器检索分级文档,然后使用 sltr 查询选择功能集。ltr_log 查询将文档和功能组合在一起,以记录相应的功能值:

POST tmdb/_search { "_source": { "includes": [ "title", "overview" ] }, "query": { "bool": { "filter": [ { "terms": { "_id": [ "7555", "1370", "1369", "1368" ] } }, { "sltr": { "_name": "logged_featureset", "featureset": "movie_features", "params": { "keywords": "rambo" } } } ] } }, "ext": { "ltr_log": { "log_specs": { "name": "log_entry1", "named_query": "logged_featureset" } } } }

示例响应可能与以下内容下类似:

{ "took" : 7, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 4, "relation" : "eq" }, "max_score" : 0.0, "hits" : [ { "_index" : "tmdb", "_type" : "movie", "_id" : "1368", "_score" : 0.0, "_source" : { "overview" : "When former Green Beret John Rambo is harassed by local law enforcement and arrested for vagrancy, the Vietnam vet snaps, runs for the hills and rat-a-tat-tats his way into the action-movie hall of fame. Hounded by a relentless sheriff, Rambo employs heavy-handed guerilla tactics to shake the cops off his tail.", "title" : "First Blood" }, "fields" : { "_ltrlog" : [ { "log_entry1" : [ { "name" : "1" }, { "name" : "2", "value" : 10.558305 } ] } ] }, "matched_queries" : [ "logged_featureset" ] }, { "_index" : "tmdb", "_type" : "movie", "_id" : "7555", "_score" : 0.0, "_source" : { "overview" : "When governments fail to act on behalf of captive missionaries, ex-Green Beret John James Rambo sets aside his peaceful existence along the Salween River in a war-torn region of Thailand to take action. Although he's still haunted by violent memories of his time as a U.S. soldier during the Vietnam War, Rambo can hardly turn his back on the aid workers who so desperately need his help.", "title" : "Rambo" }, "fields" : { "_ltrlog" : [ { "log_entry1" : [ { "name" : "1", "value" : 11.2569065 }, { "name" : "2", "value" : 9.936821 } ] } ] }, "matched_queries" : [ "logged_featureset" ] }, { "_index" : "tmdb", "_type" : "movie", "_id" : "1369", "_score" : 0.0, "_source" : { "overview" : "Col. Troutman recruits ex-Green Beret John Rambo for a highly secret and dangerous mission. Teamed with Co Bao, Rambo goes deep into Vietnam to rescue POWs. Deserted by his own team, he's left in a hostile jungle to fight for his life, avenge the death of a woman and bring corrupt officials to justice.", "title" : "Rambo: First Blood Part II" }, "fields" : { "_ltrlog" : [ { "log_entry1" : [ { "name" : "1", "value" : 6.334839 }, { "name" : "2", "value" : 10.558305 } ] } ] }, "matched_queries" : [ "logged_featureset" ] }, { "_index" : "tmdb", "_type" : "movie", "_id" : "1370", "_score" : 0.0, "_source" : { "overview" : "Combat has taken its toll on Rambo, but he's finally begun to find inner peace in a monastery. When Rambo's friend and mentor Col. Trautman asks for his help on a top secret mission to Afghanistan, Rambo declines but must reconsider when Trautman is captured.", "title" : "Rambo III" }, "fields" : { "_ltrlog" : [ { "log_entry1" : [ { "name" : "1", "value" : 9.425955 }, { "name" : "2", "value" : 11.262714 } ] } ] }, "matched_queries" : [ "logged_featureset" ] } ] } }

在上一个示例中,第一个功能没有功能值,因为关键字 “rambo” 未出现在 ID 等于 1368 的文档的标题字段中。训练数据中缺少功能值。

步骤 5:创建训练数据集

注意

您必须在 OpenSearch Service 之外执行此步骤。

下一步是将判断列表和功能值组合在一起以创建训练数据集。如果您的原始判断列表类似于以下内容:

4 qid:1 # 7555 Rambo 3 qid:1 # 1370 Rambo III 3 qid:1 # 1369 Rambo: First Blood Part II 3 qid:1 # 1368 First Blood

将其转换为最终的训练数据集,如下所示:

4 qid:1 1:12.318474 2:10.573917 # 7555 rambo 3 qid:1 1:10.357875 2:11.950391 # 1370 rambo 3 qid:1 1:7.010513 2:11.220095 # 1369 rambo 3 qid:1 1:0.0 2:11.220095 # 1368 rambo

您可以手动执行此步骤,也可以编写程序来自动执行该步骤。

步骤 6:选择算法并构建模型

注意

您必须在 OpenSearch Service 之外执行此步骤。

训练数据集到位后,下一步是使用 XGBoost 或 Ranklib 库构建模型。XGBoost 和 Ranklib 库允许您构建常见的模型,如 LambDamart、随机森林等。

有关使用 XGBoost 和 Ranklib 构建模型的步骤,请参阅 XGBoostRankLib 文档。要使用 Amazon SageMaker 构建 XGBoost 模型,请参阅 XGBoost 算法

步骤 7:部署模型

构建模型后,将其部署到学习排名插件中。有关部署模型的更多信息,请参阅上传已训练模型

在此示例中,我们使用 Ranklib 库构建了 my_ranklib_model 模型。

POST _ltr/_featureset/movie_features/_createmodel?pretty { "model": { "name": "my_ranklib_model", "model": { "type": "model/ranklib", "definition": """## LambdaMART ## No. of trees = 10 ## No. of leaves = 10 ## No. of threshold candidates = 256 ## Learning rate = 0.1 ## Stop early = 100 <ensemble> <tree id="1" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-2.0</output> </split> <split pos="right"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <output>-2.0</output> </split> <split pos="right"> <output>-2.0</output> </split> </split> </split> <split pos="right"> <output>2.0</output> </split> </split> </tree> <tree id="2" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.67031991481781</output> </split> <split pos="right"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <output>-1.67031991481781</output> </split> <split pos="right"> <output>-1.6703200340270996</output> </split> </split> </split> <split pos="right"> <output>1.6703201532363892</output> </split> </split> </tree> <tree id="3" weight="0.1"> <split> <feature>2</feature> <threshold>10.573917</threshold> <split pos="left"> <output>1.479954481124878</output> </split> <split pos="right"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.4799546003341675</output> </split> <split pos="right"> <output>-1.479954481124878</output> </split> </split> <split pos="right"> <output>-1.479954481124878</output> </split> </split> </split> </tree> <tree id="4" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.3569872379302979</output> </split> <split pos="right"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <output>-1.3569872379302979</output> </split> <split pos="right"> <output>-1.3569872379302979</output> </split> </split> </split> <split pos="right"> <output>1.3569873571395874</output> </split> </split> </tree> <tree id="5" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.2721362113952637</output> </split> <split pos="right"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <output>-1.2721363306045532</output> </split> <split pos="right"> <output>-1.2721363306045532</output> </split> </split> </split> <split pos="right"> <output>1.2721362113952637</output> </split> </split> </tree> <tree id="6" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.2110036611557007</output> </split> <split pos="right"> <output>-1.2110036611557007</output> </split> </split> <split pos="right"> <output>-1.2110037803649902</output> </split> </split> <split pos="right"> <output>1.2110037803649902</output> </split> </split> </tree> <tree id="7" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.165616512298584</output> </split> <split pos="right"> <output>-1.165616512298584</output> </split> </split> <split pos="right"> <output>-1.165616512298584</output> </split> </split> <split pos="right"> <output>1.165616512298584</output> </split> </split> </tree> <tree id="8" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.131177544593811</output> </split> <split pos="right"> <output>-1.131177544593811</output> </split> </split> <split pos="right"> <output>-1.131177544593811</output> </split> </split> <split pos="right"> <output>1.131177544593811</output> </split> </split> </tree> <tree id="9" weight="0.1"> <split> <feature>2</feature> <threshold>10.573917</threshold> <split pos="left"> <output>1.1046180725097656</output> </split> <split pos="right"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.1046180725097656</output> </split> <split pos="right"> <output>-1.1046180725097656</output> </split> </split> <split pos="right"> <output>-1.1046180725097656</output> </split> </split> </split> </tree> <tree id="10" weight="0.1"> <split> <feature>1</feature> <threshold>10.357875</threshold> <split pos="left"> <feature>1</feature> <threshold>7.010513</threshold> <split pos="left"> <feature>1</feature> <threshold>0.0</threshold> <split pos="left"> <output>-1.0838804244995117</output> </split> <split pos="right"> <output>-1.0838804244995117</output> </split> </split> <split pos="right"> <output>-1.0838804244995117</output> </split> </split> <split pos="right"> <output>1.0838804244995117</output> </split> </split> </tree> </ensemble> """ } } }

要查看模型,请发送以下请求:

GET _ltr/_model/my_ranklib_model

步骤 8:通过学习排名进行搜索

部署模型后,您已准备好进行搜索。

通过您正在使用的功能和您要执行的模型执行 sltr 查询:

POST tmdb/_search { "_source": { "includes": ["title", "overview"] }, "query": { "multi_match": { "query": "rambo", "fields": ["title", "overview"] } }, "rescore": { "query": { "rescore_query": { "sltr": { "params": { "keywords": "rambo" }, "model": "my_ranklib_model" } } } } }

通过学习排名,您会看到 “Rambo” 作为第一个结果,因为我们已经为它分配了判断列表中的最高分数:

{ "took" : 12, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 7, "relation" : "eq" }, "max_score" : 13.096414, "hits" : [ { "_index" : "tmdb", "_type" : "movie", "_id" : "7555", "_score" : 13.096414, "_source" : { "overview" : "When governments fail to act on behalf of captive missionaries, ex-Green Beret John James Rambo sets aside his peaceful existence along the Salween River in a war-torn region of Thailand to take action. Although he's still haunted by violent memories of his time as a U.S. soldier during the Vietnam War, Rambo can hardly turn his back on the aid workers who so desperately need his help.", "title" : "Rambo" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "1370", "_score" : 11.17245, "_source" : { "overview" : "Combat has taken its toll on Rambo, but he's finally begun to find inner peace in a monastery. When Rambo's friend and mentor Col. Trautman asks for his help on a top secret mission to Afghanistan, Rambo declines but must reconsider when Trautman is captured.", "title" : "Rambo III" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "1368", "_score" : 10.442155, "_source" : { "overview" : "When former Green Beret John Rambo is harassed by local law enforcement and arrested for vagrancy, the Vietnam vet snaps, runs for the hills and rat-a-tat-tats his way into the action-movie hall of fame. Hounded by a relentless sheriff, Rambo employs heavy-handed guerilla tactics to shake the cops off his tail.", "title" : "First Blood" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "1369", "_score" : 10.442155, "_source" : { "overview" : "Col. Troutman recruits ex-Green Beret John Rambo for a highly secret and dangerous mission. Teamed with Co Bao, Rambo goes deep into Vietnam to rescue POWs. Deserted by his own team, he's left in a hostile jungle to fight for his life, avenge the death of a woman and bring corrupt officials to justice.", "title" : "Rambo: First Blood Part II" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "31362", "_score" : 7.424202, "_source" : { "overview" : "It is 1985, and a small, tranquil Florida town is being rocked by a wave of vicious serial murders and bank robberies. Particularly sickening to the authorities is the gratuitous use of violence by two “Rambo” like killers who dress themselves in military garb. Based on actual events taken from FBI files, the movie depicts the Bureau’s efforts to track down these renegades.", "title" : "In the Line of Duty: The F.B.I. Murders" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "13258", "_score" : 6.43182, "_source" : { "overview" : """Will Proudfoot (Bill Milner) is looking for an escape from his family's stifling home life when he encounters Lee Carter (Will Poulter), the school bully. Armed with a video camera and a copy of "Rambo: First Blood", Lee plans to make cinematic history by filming his own action-packed video epic. Together, these two newfound friends-turned-budding-filmmakers quickly discover that their imaginative ― and sometimes mishap-filled ― cinematic adventure has begun to take on a life of its own!""", "title" : "Son of Rambow" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "61410", "_score" : 3.9719706, "_source" : { "overview" : "It's South Africa 1990. Two major events are about to happen: The release of Nelson Mandela and, more importantly, it's Spud Milton's first year at an elite boys only private boarding school. John Milton is a boy from an ordinary background who wins a scholarship to a private school in Kwazulu-Natal, South Africa. Surrounded by boys with nicknames like Gecko, Rambo, Rain Man and Mad Dog, Spud has his hands full trying to adapt to his new home. Along the way Spud takes his first tentative steps along the path to manhood. (The path it seems could be a rather long road). Spud is an only child. He is cursed with parents from well beyond the lunatic fringe and a senile granny. His dad is a fervent anti-communist who is paranoid that the family domestic worker is running a shebeen from her room at the back of the family home. His mom is a free spirit and a teenager's worst nightmare, whether it's shopping for Spud's underwear in the local supermarket", "title" : "Spud" } } ] } }

如果您没有使用学习排名插件进行搜索,OpenSearch 会返回不同的结果:

POST tmdb/_search { "_source": { "includes": ["title", "overview"] }, "query": { "multi_match": { "query": "Rambo", "fields": ["title", "overview"] } } }
{ "took" : 5, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 5, "relation" : "eq" }, "max_score" : 11.262714, "hits" : [ { "_index" : "tmdb", "_type" : "movie", "_id" : "1370", "_score" : 11.262714, "_source" : { "overview" : "Combat has taken its toll on Rambo, but he's finally begun to find inner peace in a monastery. When Rambo's friend and mentor Col. Trautman asks for his help on a top secret mission to Afghanistan, Rambo declines but must reconsider when Trautman is captured.", "title" : "Rambo III" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "7555", "_score" : 11.2569065, "_source" : { "overview" : "When governments fail to act on behalf of captive missionaries, ex-Green Beret John James Rambo sets aside his peaceful existence along the Salween River in a war-torn region of Thailand to take action. Although he's still haunted by violent memories of his time as a U.S. soldier during the Vietnam War, Rambo can hardly turn his back on the aid workers who so desperately need his help.", "title" : "Rambo" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "1368", "_score" : 10.558305, "_source" : { "overview" : "When former Green Beret John Rambo is harassed by local law enforcement and arrested for vagrancy, the Vietnam vet snaps, runs for the hills and rat-a-tat-tats his way into the action-movie hall of fame. Hounded by a relentless sheriff, Rambo employs heavy-handed guerilla tactics to shake the cops off his tail.", "title" : "First Blood" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "1369", "_score" : 10.558305, "_source" : { "overview" : "Col. Troutman recruits ex-Green Beret John Rambo for a highly secret and dangerous mission. Teamed with Co Bao, Rambo goes deep into Vietnam to rescue POWs. Deserted by his own team, he's left in a hostile jungle to fight for his life, avenge the death of a woman and bring corrupt officials to justice.", "title" : "Rambo: First Blood Part II" } }, { "_index" : "tmdb", "_type" : "movie", "_id" : "13258", "_score" : 6.4600153, "_source" : { "overview" : """Will Proudfoot (Bill Milner) is looking for an escape from his family's stifling home life when he encounters Lee Carter (Will Poulter), the school bully. Armed with a video camera and a copy of "Rambo: First Blood", Lee plans to make cinematic history by filming his own action-packed video epic. Together, these two newfound friends-turned-budding-filmmakers quickly discover that their imaginative ― and sometimes mishap-filled ― cinematic adventure has begun to take on a life of its own!""", "title" : "Son of Rambow" } } ] } }

根据您认为模型的执行情况,调整判断列表和功能。然后,重复步骤 2—8 以随着时间的推移改进排名结果。

学习排名 API

使用学习排名操作以编程方式使用功能集和模型。

创建存储

创建隐藏的 .ltrstore 索引,用于存储元数据信息(如功能集和模型)。

PUT _ltr

删除存储

删除隐藏的 .ltrstore 索引并重置插件。

DELETE _ltr

创建功能集

创建功能集。

POST _ltr/_featureset/<name_of_features>

删除功能集

删除功能集。

DELETE _ltr/_featureset/<name_of_feature_set>

获取功能集

检索功能集。

GET _ltr/_featureset/<name_of_feature_set>

创建模型

创建模型。

POST _ltr/_featureset/<name_of_feature_set>/_createmodel

删除模型

删除模型。

DELETE _ltr/_model/<name_of_model>

获取模型

检索模型。

GET _ltr/_model/<name_of_model>

获取统计信息

提供有关插件操作方式的信息。

GET _ltr/_model/<name_of_model>

您还可以按节点和/或集群进行筛选:

GET _ltr/nodeID,nodeID,/stats/stat,stat { "_nodes" : { "total" : 1, "successful" : 1, "failed" : 0 }, "cluster_name" : "873043598401:ltr-77", "stores" : { ".ltrstore" : { "model_count" : 1, "featureset_count" : 1, "feature_count" : 2, "status" : "green" } }, "status" : "green", "nodes" : { "DjelK-_ZSfyzstO5dhGGQA" : { "cache" : { "feature" : { "eviction_count" : 0, "miss_count" : 0, "entry_count" : 0, "memory_usage_in_bytes" : 0, "hit_count" : 0 }, "featureset" : { "eviction_count" : 2, "miss_count" : 2, "entry_count" : 0, "memory_usage_in_bytes" : 0, "hit_count" : 0 }, "model" : { "eviction_count" : 2, "miss_count" : 3, "entry_count" : 1, "memory_usage_in_bytes" : 3204, "hit_count" : 1 } }, "request_total_count" : 6, "request_error_count" : 0 } } }

统计数据在两个级别(节点和集群)提供,如下表所示:

节点级统计
字段名称 描述
request_total_count 排名请求的总计数。
request_error_count 不成功请求的总计数。
缓存 所有缓存(功能、功能集、模型)的统计数据。当用户查询插件并且模型已加载到内存中时,会发生缓存命中。
cache.eviction_count 缓存移出次数。
cache.hit_count 缓存命中次数。
cache.miss_count 缓存丢失次数。当用户查询插件并且模型尚未加载到内存中时,会发生缓存丢失。
cache.entry_count 缓存中的条目数。
cache.memory_usage_in_bytes 字节中使用的总内存。
cache.cache_capacity_reached 指示是否达到缓存限制。
集群级统计数据
字段名称 描述
存储 指示功能集和模型元数据的存储位置。(原定设置为 ".ltrstore"。否则,它的前缀为 ".ltrstore_",并带有用户提供的名称)。
stores.status 索引状态。
stores.feature_sets 功能集数。
stores.features_count 功能数。
stores.model_count 型号数。
status 基于功能存储索引状态(红色、黄色或绿色)和断路器状态(打开或关闭)的插件状态。
cache.cache_capacity_reached 指示是否达到缓存限制。

获取缓存统计信息

返回有关缓存和内存使用情况的统计信息。

GET _ltr/_cachestats { "_nodes": { "total": 2, "successful": 2, "failed": 0 }, "cluster_name": "opensearch-cluster", "all": { "total": { "ram": 612, "count": 1 }, "features": { "ram": 0, "count": 0 }, "featuresets": { "ram": 612, "count": 1 }, "models": { "ram": 0, "count": 0 } }, "stores": { ".ltrstore": { "total": { "ram": 612, "count": 1 }, "features": { "ram": 0, "count": 0 }, "featuresets": { "ram": 612, "count": 1 }, "models": { "ram": 0, "count": 0 } } }, "nodes": { "ejF6uutERF20wOFNOXB61A": { "name": "opensearch1", "hostname": "172.18.0.4", "stats": { "total": { "ram": 612, "count": 1 }, "features": { "ram": 0, "count": 0 }, "featuresets": { "ram": 612, "count": 1 }, "models": { "ram": 0, "count": 0 } } }, "Z2RZNWRLSveVcz2c6lHf5A": { "name": "opensearch2", "hostname": "172.18.0.2", "stats": { ... } } } }

清除缓存

清除插件缓存。使用此选项可刷新模型。

POST _ltr/_clearcache