Question

Mongodb 3.2，安装在centos 6上，有足够的RAM和磁盘。我有一个包含以下结构的10K文档的集合：

{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "proddb.product_trees" }
{ "v" : 1, "key" : { "positiveAspects.id" : 1.0, "positiveAspects.score" : 1.0 }, "name" : "positiveAspects.id_1_positiveAspects.score_1", "ns" : "proddb.product_trees" }
{ "v" : 1, "key" : { "categoryId" : 1.0, "score" : 1.0 }, "name" : "categoryId_1_score_1", "ns" : "proddb.product_trees" }
{ "v" : 1, "key" : { "rank" : -1.0 }, "name" : "rank_-1", "ns" : "proddb.product_trees" }
{ "v" : 1, "key" : { "positiveAspects.rank" : -1.0 }, "name" : "positiveAspects.rank_-1", "ns" : "proddb.product_trees" }

对于每个文档，嵌套数组positiveAspects只有几百个元素。

collectoin有以下索引：

{  
  aggregate:"product_trees",
  pipeline:[  
  {  
     $match:{  
        categoryId:"803",
        score:{  
           $gte:8.0
        }
     }
  },
  {  
     $unwind:"$positiveAspects"
  },
  {  
     $match:{  
        positiveAspects.id:30030525,
        positiveAspects.score:{  
           $gte:9.0
        }
     }
  },
  {  
     $sort:{  
        positiveAspects.rank:-1
     }
  },
  {  
     $project:{  
        _id:0,
        score:1,
        id:1,
        name:1,
        positiveAspects:1
     }
  },
  {  
     $limit:10
  }
 ]
}

我想运行以下聚合，大约需要40秒：

2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Beginning planning...
=============================
Options = NO_BLOCKING_SORT INDEX_INTERSECTION
Canonical query:
ns=proddb.product_treesTree: $and
    categoryId == "803"
    score $gte 8.0
Sort: {}
Proj: {}
=============================
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Index 0 is kp: { _id: 1 } unique name: '_id_' io: { v: 1, key: { _id: 1 }, name: "_id_", ns: "proddb.product_trees" }
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Index 1 is kp: { positiveAspects.id: 1.0, positiveAspects.score: 1.0 } multikey name: 'positiveAspects.id_1_positiveAspects.score_1' io: { v: 1, key: { positiveAspects.id: 1.0, positiveAspects.score: 1.0 }, name: "positiveAspects.id_1_positiveAspects.score_1", ns: "proddb.product_trees" }
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Index 2 is kp: { categoryId: 1.0, score: 1.0 } name: 'categoryId_1_score_1' io: { v: 1, key: { categoryId: 1.0, score: 1.0 }, name: "categoryId_1_score_1", ns: "proddb.product_trees" }
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Index 3 is kp: { rank: -1.0 } name: 'rank_-1' io: { v: 1, key: { rank: -1.0 }, name: "rank_-1", ns: "proddb.product_trees" }
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Index 4 is kp: { positiveAspects.rank: -1.0 } multikey name: 'positiveAspects.rank_-1' io: { v: 1, key: { positiveAspects.rank: -1.0 }, name: "positiveAspects.rank_-1", ns: "proddb.product_trees" }
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Predicate over field 'score'
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Predicate over field 'categoryId'
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Relevant index 0 is kp: { categoryId: 1.0, score: 1.0 } name: 'categoryId_1_score_1' io: { v: 1, key: { categoryId: 1.0, score: 1.0 }, name: "categoryId_1_score_1", ns: "proddb.product_trees" }
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Rated tree:
$and
    categoryId == "803"  || First: 0 notFirst: full path: categoryId
    score $gte 8.0  || First: notFirst: 0 full path: score
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Tagging memoID 1
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Enumerator: memo just before moving:
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] About to build solntree from tagged tree:
$and
    categoryId == "803"  || Selected Index #0 pos 0
    score $gte 8.0  || Selected Index #0 pos 1
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Planner: adding solution:
FETCH
---fetched = 1
---sortedByDiskLoc = 0
---getSort = [{ categoryId: 1 }, { categoryId: 1, score: 1 }, { score: 1 }, ]
---Child:
------IXSCAN
---------keyPattern = { categoryId: 1.0, score: 1.0 }
---------direction = 1
---------bounds = field #0['categoryId']: ["803", "803"], field #1['score']: [8.0, inf.0]
---------fetched = 0
---------sortedByDiskLoc = 0
---------getSort = [{ categoryId: 1 }, { categoryId: 1, score: 1 }, { score: 1 }, ]
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Planner: outputted 1 indexed solutions.
2016-06-01T16:10:49.140-0500 D QUERY    [conn47] Only one plan is available; it will be run but will not be cached. query: { categoryId: "803", score: { $gte: 8.0 } } sort: {} projection: {}, planSummary: IXSCAN { categoryId: 1.0, score: 1.0 }
2016-06-01T16:11:27.170-0500 I COMMAND  [conn47] command proddb.product_trees command: aggregate { aggregate: "product_trees", pipeline: [ { $match: { categoryId: "803", score: { $gte: 8.0 } } }, { $unwind: "$positiveAspects" }, { $match: { positiveAspects.id: 30030525, positiveAspects.score: { $gte: 9.0 } } }, { $sort: { positiveAspects.rank: -1 } }, { $project: { _id: 0, score: 1, id: 1, name: 1, positiveAspects: 1 } }, { $limit: 10 } ], cursor: {} } keyUpdates:0 writeConflicts:0 numYields:226 reslen:7459 locks:{ Global: { acquireCount: { r: 906 } }, Database: { acquireCount: { r: 453 } }, Collection: { acquireCount: { r: 453 } } } protocol:op_query 38030ms

通过以下说明：

$sort

取出$sort，查询在2秒内运行。

你能解释为什么{{1}}导致这样的性能下降，考虑它有可用的索引吗？我错过了一个索引可以做些什么来修复？

谢谢！

Mongodb aggregarion - sort使查询非常慢

Answer 1

这是因为$sort在聚合框架的早期阶段没有使用时没有使用索引。要利用索引，$ sort或$ match必须用作第一阶段。

请参阅Pipeline Operators and Indexes

Answer 2

这是因为$ unwind stage.Your查询性能很慢，因为在$ unwind阶段之后查询没有考虑索引。检查一下，解释。你会知道。这是因为$ unwind之后整个文档会改变并且存储在RAM中用于索引目的变得不同。

Mongodb聚合 - 排序使查询非常慢

2 个答案: