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「搜索API」和統計組

2016-11-23 67 views
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在Search APIs有一個叫統計組爲部分：「搜索API」和統計組

一個搜索可以用統計組相關聯，它保持每組統計彙總。它可以稍後使用索引統計API特別檢索。例如，下面是搜索的身體要求，即與兩個不同的組請求關聯的：

{ 
    "query" : { 
     "match_all" : {} 
    }, 
    "stats" : ["group1", "group2"] 
}

我的問題是，什麼是統計組，我們如何創建它們，以及它們在哪裏使用？

編輯1：

看來這些組涉及_stats。正如@evanv所說，在Index stats下有更多的解釋。但是，該文件不解釋如何創建組。另外，我找不到使用_search API的方法。我cound，然而，使用使用search得到的東西_stats下：

GET /_stats/search?groups=search,indexing

所以我的問題依然存在：

  • 我怎麼使用這跟_search API？
  • 我該如何理解這些羣組中報告的數字？如何創建組？如果這是有道理的！

編輯2：

看來你通過在你的操作stats參數創建這些組。舉例來說，如果我提交這個查詢5次：

GET /twitter/tweet/_search 
{ 
    "query": { 
    "match_all": { 

    } 
    }, 
    "stats": [ 
    "makes_no_sense" 
    ] 
}

它將創建，如果它簡化版，已經存在一個新的羣體，被稱爲「makes_no_sense」，acossiates操作該組，然後當我得到的該指數的統計：

GET /_stats/search?groups=makes_no_sense

響應將包括makes_no_sense爲search下一個組，如：

{ 
    "_shards": { 
    "total": 43, 
    "successful": 22, 
    "failed": 0 
    }, 
    "_all": { 
    "primaries": { 
     "search": { 
     "open_contexts": 0, 
     "query_total": 37983, 
     "query_time_in_millis": 2695, 
     "query_current": 0, 
     "fetch_total": 37796, 
     "fetch_time_in_millis": 1472, 
     "fetch_current": 0, 
     "scroll_total": 5, 
     "scroll_time_in_millis": 266, 
     "scroll_current": 0, 
     "suggest_total": 0, 
     "suggest_time_in_millis": 0, 
     "suggest_current": 0, 
     "groups": { 
      "makes_no_sense": { 
      "query_total": 5, 
      "query_time_in_millis": 0, 
      "query_current": 0, 
      "fetch_total": 5, 
      "fetch_time_in_millis": 0, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
      } 
     } 
     } 
    }, 
    "total": { 
     "search": { 
     "open_contexts": 0, 
     "query_total": 37983, 
     "query_time_in_millis": 2695, 
     "query_current": 0, 
     "fetch_total": 37796, 
     "fetch_time_in_millis": 1472, 
     "fetch_current": 0, 
     "scroll_total": 5, 
     "scroll_time_in_millis": 266, 
     "scroll_current": 0, 
     "suggest_total": 0, 
     "suggest_time_in_millis": 0, 
     "suggest_current": 0, 
     "groups": { 
      "makes_no_sense": { 
      "query_total": 5, 
      "query_time_in_millis": 0, 
      "query_current": 0, 
      "fetch_total": 5, 
      "fetch_time_in_millis": 0, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
      } 
     } 
     } 
    } 
    }, 
    "indices": { 
    "bank": { 
     "primaries": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 180, 
      "query_time_in_millis": 369, 
      "query_current": 0, 
      "fetch_total": 71, 
      "fetch_time_in_millis": 35, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     }, 
     "total": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 180, 
      "query_time_in_millis": 369, 
      "query_current": 0, 
      "fetch_total": 71, 
      "fetch_time_in_millis": 35, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     } 
    }, 
    "twitter": { 
     "primaries": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 19, 
      "query_time_in_millis": 1, 
      "query_current": 0, 
      "fetch_total": 19, 
      "fetch_time_in_millis": 0, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0, 
      "groups": { 
      "makes_no_sense": { 
       "query_total": 5, 
       "query_time_in_millis": 0, 
       "query_current": 0, 
       "fetch_total": 5, 
       "fetch_time_in_millis": 0, 
       "fetch_current": 0, 
       "scroll_total": 0, 
       "scroll_time_in_millis": 0, 
       "scroll_current": 0, 
       "suggest_total": 0, 
       "suggest_time_in_millis": 0, 
       "suggest_current": 0 
      } 
      } 
     } 
     }, 
     "total": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 19, 
      "query_time_in_millis": 1, 
      "query_current": 0, 
      "fetch_total": 19, 
      "fetch_time_in_millis": 0, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0, 
      "groups": { 
      "makes_no_sense": { 
       "query_total": 5, 
       "query_time_in_millis": 0, 
       "query_current": 0, 
       "fetch_total": 5, 
       "fetch_time_in_millis": 0, 
       "fetch_current": 0, 
       "scroll_total": 0, 
       "scroll_time_in_millis": 0, 
       "scroll_current": 0, 
       "suggest_total": 0, 
       "suggest_time_in_millis": 0, 
       "suggest_current": 0 
      } 
      } 
     } 
     } 
    }, 
    "test": { 
     "primaries": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 45, 
      "query_time_in_millis": 6, 
      "query_current": 0, 
      "fetch_total": 10, 
      "fetch_time_in_millis": 1, 
      "fetch_current": 0, 
      "scroll_total": 5, 
      "scroll_time_in_millis": 266, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     }, 
     "total": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 45, 
      "query_time_in_millis": 6, 
      "query_current": 0, 
      "fetch_total": 10, 
      "fetch_time_in_millis": 1, 
      "fetch_current": 0, 
      "scroll_total": 5, 
      "scroll_time_in_millis": 266, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     } 
    }, 
    ".kibana": { 
     "primaries": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 37689, 
      "query_time_in_millis": 2303, 
      "query_current": 0, 
      "fetch_total": 37688, 
      "fetch_time_in_millis": 1386, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     }, 
     "total": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 37689, 
      "query_time_in_millis": 2303, 
      "query_current": 0, 
      "fetch_total": 37688, 
      "fetch_time_in_millis": 1386, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     } 
    }, 
    "blogs": { 
     "primaries": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 40, 
      "query_time_in_millis": 11, 
      "query_current": 0, 
      "fetch_total": 6, 
      "fetch_time_in_millis": 1, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     }, 
     "total": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 40, 
      "query_time_in_millis": 11, 
      "query_current": 0, 
      "fetch_total": 6, 
      "fetch_time_in_millis": 1, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     } 
    }, 
    "customer": { 
     "primaries": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 10, 
      "query_time_in_millis": 5, 
      "query_current": 0, 
      "fetch_total": 2, 
      "fetch_time_in_millis": 49, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     }, 
     "total": { 
     "search": { 
      "open_contexts": 0, 
      "query_total": 10, 
      "query_time_in_millis": 5, 
      "query_current": 0, 
      "fetch_total": 2, 
      "fetch_time_in_millis": 49, 
      "fetch_current": 0, 
      "scroll_total": 0, 
      "scroll_time_in_millis": 0, 
      "scroll_current": 0, 
      "suggest_total": 0, 
      "suggest_time_in_millis": 0, 
      "suggest_current": 0 
     } 
     } 
    } 
    } 
}

現在我的問題是：

  • 我該如何在創建/更新/刪除等操作中使用/創建那些組？

來源

2016-11-23 adrin

回答

0

它們是在每個索引級別上維護的計數器和元數據的混合。如果你有一個索引「foo」，並且你去了localhost:9200/foo/_stats?pretty&human，你會看到一堆有關索引有多大的信息，索引有多少搜索請求，有多少個請求，有多少數據被緩存該指數等要創建一個統計組，你可以簡單地包括

"stats" : ["stat_1", "stat_2", .... "stat_n"]
在您的要求

。

而當您訪問localhost:9200/foo/_stats?pretty&human時，您會看到您定義的統計信息組的統計信息。

您可以瞭解更多關於存儲在此處的指標的信息：https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-stats.html

來源

2016-11-23 21:13:39 evanv

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