我有一个收藏产品，里面有~7.000.000本书，共有~40GB mongodb 3.4数据库。以下是一本书籍文档的示例：
{
"_id" : ObjectId("597f17d22be7925d9a056e82"),
"ean13" : "9783891491904",
"price" : NumberInt(2100),
"name" : "My cool title",
"author_name" : "Doe, John",
"warengruppe" : "HC",
"book_category_key" : "728",
"keywords": ["fairy tale", "magic", "fantasy"]
...
}
现在我想对产品系列进行一些文本搜索：
db.products.find({
$text : {
$search: '"harry" "potter" "3" lsxger'
}
}, {
score: {
"$meta": "textScore"
},
ean13: 1,
name: 1,
author_name: 1,
price: 1,
images: 1,
warengruppe: 1
}).sort({
score: {
"$meta": "textScore"
},
name: 1
}).limit(9);
这是解释的结果：
{
"queryPlanner" : {
"plannerVersion" : NumberInt(1),
"namespace" : "mydb.products",
"indexFilterSet" : false,
"parsedQuery" : {
"$text" : {
"$search" : "\"harry\" \"potter\" \"3\" lsxger",
"$language" : "german",
"$caseSensitive" : false,
"$diacriticSensitive" : false
}
},
"winningPlan" : {
"stage" : "PROJECTION",
"transformBy" : {
"score" : {
"$meta" : "textScore"
},
"ean13" : 1.0,
"name" : 1.0,
"author_name" : 1.0,
"price" : 1.0,
"images" : 1.0,
"warengruppe" : 1.0
},
"inputStage" : {
"stage" : "SORT",
"sortPattern" : {
"score" : {
"$meta" : "textScore"
},
"name" : 1.0
},
"limitAmount" : NumberInt(9),
"inputStage" : {
"stage" : "SORT_KEY_GENERATOR",
"inputStage" : {
"stage" : "TEXT",
"indexPrefix" : {
},
"indexName" : "fulltextsearch",
"parsedTextQuery" : {
"terms" : [
"3",
"harry",
"lsxger",
"pott"
],
"negatedTerms" : [
],
"phrases" : [
"harry",
"potter",
"3"
],
"negatedPhrases" : [
]
},
"textIndexVersion" : NumberInt(3),
"inputStage" : {
"stage" : "TEXT_MATCH",
"inputStage" : {
"stage" : "TEXT_OR",
"inputStages" : [
{
"stage" : "IXSCAN",
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
}
},
{
"stage" : "IXSCAN",
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
}
},
{
"stage" : "IXSCAN",
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
}
},
{
"stage" : "IXSCAN",
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
}
}
]
}
}
}
}
}
},
"rejectedPlans" : [
]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : NumberInt(9),
"executionTimeMillis" : NumberInt(15441),
"totalKeysExamined" : NumberInt(1206999),
"totalDocsExamined" : NumberInt(1195069),
"executionStages" : {
"stage" : "PROJECTION",
"nReturned" : NumberInt(9),
"executionTimeMillisEstimate" : NumberInt(15294),
"works" : NumberInt(2402085),
"advanced" : NumberInt(9),
"needTime" : NumberInt(2402075),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"transformBy" : {
"score" : {
"$meta" : "textScore"
},
"ean13" : 1.0,
"name" : 1.0,
"author_name" : 1.0,
"price" : 1.0,
"images" : 1.0,
"warengruppe" : 1.0
},
"inputStage" : {
"stage" : "SORT",
"nReturned" : NumberInt(9),
"executionTimeMillisEstimate" : NumberInt(15234),
"works" : NumberInt(2402085),
"advanced" : NumberInt(9),
"needTime" : NumberInt(2402075),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"sortPattern" : {
"score" : {
"$meta" : "textScore"
},
"name" : 1.0
},
"memUsage" : NumberInt(22949),
"memLimit" : NumberInt(33554432),
"limitAmount" : NumberInt(9),
"inputStage" : {
"stage" : "SORT_KEY_GENERATOR",
"nReturned" : NumberInt(455),
"executionTimeMillisEstimate" : NumberInt(15074),
"works" : NumberInt(2402075),
"advanced" : NumberInt(455),
"needTime" : NumberInt(2401619),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"inputStage" : {
"stage" : "TEXT",
"nReturned" : NumberInt(455),
"executionTimeMillisEstimate" : NumberInt(15024),
"works" : NumberInt(2402074),
"advanced" : NumberInt(455),
"needTime" : NumberInt(2401618),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"indexPrefix" : {
},
"indexName" : "fulltextsearch",
"parsedTextQuery" : {
"terms" : [
"3",
"harry",
"lsxger",
"pott"
],
"negatedTerms" : [
],
"phrases" : [
"harry",
"potter",
"3"
],
"negatedPhrases" : [
]
},
"textIndexVersion" : NumberInt(3),
"inputStage" : {
"stage" : "TEXT_MATCH",
"nReturned" : NumberInt(455),
"executionTimeMillisEstimate" : NumberInt(14974),
"works" : NumberInt(2402074),
"advanced" : NumberInt(455),
"needTime" : NumberInt(2401618),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"docsRejected" : NumberInt(1194614),
"inputStage" : {
"stage" : "TEXT_OR",
"nReturned" : NumberInt(1195069),
"executionTimeMillisEstimate" : NumberInt(4500),
"works" : NumberInt(2402074),
"advanced" : NumberInt(1195069),
"needTime" : NumberInt(1207004),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"docsExamined" : NumberInt(1195069),
"inputStages" : [
{
"stage" : "IXSCAN",
"nReturned" : NumberInt(59101),
"executionTimeMillisEstimate" : NumberInt(131),
"works" : NumberInt(59102),
"advanced" : NumberInt(59101),
"needTime" : NumberInt(0),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
},
"keysExamined" : NumberInt(59101),
"seeks" : NumberInt(1),
"dupsTested" : NumberInt(59101),
"dupsDropped" : NumberInt(0),
"seenInvalidated" : NumberInt(0)
},
{
"stage" : "IXSCAN",
"nReturned" : NumberInt(9512),
"executionTimeMillisEstimate" : NumberInt(0),
"works" : NumberInt(9513),
"advanced" : NumberInt(9512),
"needTime" : NumberInt(0),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
},
"keysExamined" : NumberInt(9512),
"seeks" : NumberInt(1),
"dupsTested" : NumberInt(9512),
"dupsDropped" : NumberInt(0),
"seenInvalidated" : NumberInt(0)
},
{
"stage" : "IXSCAN",
"nReturned" : NumberInt(1134940),
"executionTimeMillisEstimate" : NumberInt(1381),
"works" : NumberInt(1134941),
"advanced" : NumberInt(1134940),
"needTime" : NumberInt(0),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
},
"keysExamined" : NumberInt(1134940),
"seeks" : NumberInt(1),
"dupsTested" : NumberInt(1134940),
"dupsDropped" : NumberInt(0),
"seenInvalidated" : NumberInt(0)
},
{
"stage" : "IXSCAN",
"nReturned" : NumberInt(3446),
"executionTimeMillisEstimate" : NumberInt(0),
"works" : NumberInt(3447),
"advanced" : NumberInt(3446),
"needTime" : NumberInt(0),
"needYield" : NumberInt(0),
"saveState" : NumberInt(18814),
"restoreState" : NumberInt(18814),
"isEOF" : NumberInt(1),
"invalidates" : NumberInt(0),
"keyPattern" : {
"_fts" : "text",
"_ftsx" : NumberInt(1)
},
"indexName" : "fulltextsearch",
"isMultiKey" : true,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : NumberInt(2),
"direction" : "backward",
"indexBounds" : {
},
"keysExamined" : NumberInt(3446),
"seeks" : NumberInt(1),
"dupsTested" : NumberInt(3446),
"dupsDropped" : NumberInt(0),
"seenInvalidated" : NumberInt(0)
}
]
}
}
}
}
}
},
"allPlansExecution" : [
]
},
"serverInfo" : {
"host" : "lvps83-169-23-14.dedicated.hosteurope.de",
"port" : NumberInt(27017),
"version" : "3.4.4",
"gitVersion" : "888390515874a9debd1b6c5d36559ca86b44babd"
},
"ok" : 1.0
}
这需要大约25秒或更长时间。我已经为book_category_key，ean13，author_name，name和fulltextsearch设置了一些indizes：
{
"v" : 2,
"name" : "fulltextsearch",
"ns" : "mydb.products",
"background" : true,
"weights" : {
"author_name" : 5,
"ean13" : 10,
"isbn" : 10,
"keywords" : 2,
"languages.search" : 8,
"mainsubject.name" : 3,
"name" : 10
},
"default_language" : "german",
"language_override" : "language_x",
"textIndexVersion" : 3
}
如何提高速度或在哪里寻找更多信息？
答案 0 :(得分：3)
搜索花了大约15秒。
需要4.5秒来进行TEXT_OR搜索
"stage" : "TEXT_OR",
"nReturned" : NumberInt(1195069),
"executionTimeMillisEstimate" : NumberInt(4500),
剩余的10个人需要执行比赛
"stage" : "TEXT_MATCH",
"nReturned" : NumberInt(455),
"executionTimeMillisEstimate" : NumberInt(14974), //this includes the 4.5
text_or匹配表示必须检查1.2 Mio文件。这有一些含义：
从磁盘加载文件（如果它们尚未在内存中）需要一段时间。由于您的总内存小于集合大小（40GB）+索引（9GB），因此必须交换某些数据的可能性很大（您是否检查了连续搜索是否更快？）。 有两种选择：1。减小索引大小（仅包括部分字段），2。添加更多内存。尽管如此，获取文档只占总执行时间的1/3。
主要问题（2/3）是约1.2 Mio文档的文本匹配，显然需要一段时间。所以你必须考虑减少文件数量的方法（见下文）
可能有几种策略可以解决这个问题：
您应该考虑使用附加标准的复合索引来限制总数（即仅在书籍类别中搜索：“728”......无论这意味着什么）（另请参阅此处Limit the Number of Entries Scanned）
将索引限制为仅包含实际文本（名称，关键字，作者）的字段，并为其他类型使用专用索引（isbn，ean）。您的应用程序可以对用户输入进行有根据的猜测（根据格式测试可能 ean或isb，并直接查找/正则表达式查找）。 这可能会有所帮助，因为'3'很可能会碰到几个完全不相关的isbns或eans 。
使用AND而不是OR来连接搜索词（"\"harry potter 3\""）可能会加快这个过程，尽管它会改变搜索的语义。
监控并分析常见搜索模式的实际用户搜索行为。因此，您可以优化实际使用模式（即添加一个具有常见搜索项的附加数组，并在数组字段上进行精确搜索，可以在几秒钟后使用全文搜索结果进行细化）