一、简介
这里简单介绍一下各个工具的使用场景，一般用mysql，redis，mongodb做存储层，hadoop,spark做大数据分析。
-
mysql适合结构化数据，类似excel表格一样定义严格的数据，用于数据量中，速度一般支持事务处理场合
-
redis适合缓存内存对象，如缓存队列，用于数据量小，速度快不支持事务处理高并发场合
-
mongodb，适合半结构化数据，如文本信息，用于数据量大，速度较快不支持事务处理场合
-
hadoop是个生态系统，上面有大数据分析很多组件，适合事后大数据分析任务
-
spark类似hadoop，偏向于内存计算，流计算，适合实时半实时大数据分析任务
移动互联网及物联网让数据呈指数增长，NoSql大数据新起后，数据存储领域发展很快，似乎方向都是向大数据，内存计算，分布式框架，平台化发展，出现不少新的方法，普通应用TB，GB级别达不到PB级别的数据存储，用mongodb，mysql就够了，hadoop，spark这类是航母一般多是大规模应用场景,多用于事后分析统计用，如电商的推荐系统分析系统。IAO
看标题，这里是不是跑题了呢，显然不是，了解一下mongodb在存储中的位置还是非常有必要的，explain 和 hint 一看就知道是从mysql借鉴过来的（猜的），实际就是检测查询语句的性能和使用强制索引
二、explain
先写入测试数据
db.test.insertMany([
{ "_id" : 1, "a" : "f1", b: "food", c: 500 },
{ "_id" : 2, "a" : "f2", b: "food", c: 100 },
{ "_id" : 3, "a" : "p1", b: "paper", c: 200 },
{ "_id" : 4, "a" : "p2", b: "paper", c: 150 },
{ "_id" : 5, "a" : "f3", b: "food", c: 300 },
{ "_id" : 6, "a" : "t1", b: "toys", c: 500 },
{ "_id" : 7, "a" : "a1", b: "apparel", c: 250 },
{ "_id" : 8, "a" : "a2", b: "apparel", c: 400 },
{ "_id" : 9, "a" : "t2", b: "toys", c: 50 },
{ "_id" : 10, "a" : "f4", b: "food", c: 75 }]);
写入成功返回值
{
"acknowledged" : true,
"insertedIds" : [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
}
开始查询
> db.test.find();
{ "_id" : 1, "a" : "f1", "b" : "food", "c" : 500 }
{ "_id" : 2, "a" : "f2", "b" : "food", "c" : 100 }
{ "_id" : 3, "a" : "p1", "b" : "paper", "c" : 200 }
{ "_id" : 4, "a" : "p2", "b" : "paper", "c" : 150 }
{ "_id" : 5, "a" : "f3", "b" : "food", "c" : 300 }
{ "_id" : 6, "a" : "t1", "b" : "toys", "c" : 500 }
{ "_id" : 7, "a" : "a1", "b" : "apparel", "c" : 250 }
{ "_id" : 8, "a" : "a2", "b" : "apparel", "c" : 400 }
{ "_id" : 9, "a" : "t2", "b" : "toys", "c" : 50 }
{ "_id" : 10, "a" : "f4", "b" : "food", "c" : 75 }
> db.test.find().count();
10
> db.test.find({ c: { $gte: 100, $lte: 200 }}).count()
3
> db.test.find({ c: { $gte: 100, $lte: 200 }}).explain("executionStats")
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.test",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"c" : {
"$lte" : 200
}
},
{
"c" : {
"$gte" : 100
}
}
]
},
"winningPlan" : {
"stage" : "COLLSCAN",
"filter" : {
"$and" : [
{
"c" : {
"$lte" : 200
}
},
{
"c" : {
"$gte" : 100
}
}
]
},
"direction" : "forward"
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 3,
"executionTimeMillis" : 0,
"totalKeysExamined" : 0,
"totalDocsExamined" : 10,
"executionStages" : {
"stage" : "COLLSCAN",
"filter" : {
"$and" : [
{
"c" : {
"$lte" : 200
}
},
{
"c" : {
"$gte" : 100
}
}
]
},
"nReturned" : 3,
"executionTimeMillisEstimate" : 0,
"works" : 12,
"advanced" : 3,
"needTime" : 8,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"direction" : "forward",
"docsExamined" : 10
}
},
"serverInfo" : {
"host" : "iZbp1g11g0cdeeq9ht9fhjZ",
"port" : 27017,
"version" : "3.4.12",
"gitVersion" : "bfde702b19c1baad532ed183a871c12630c1bbba"
},
"ok" : 1
}
看一下几个关键词
"stage" : "COLLSCAN",
"nReturned" : 3,
"totalDocsExamined" : 10,
全部扫描，不走索引，这里只是演示，所以数据量比较少，如果数据量多起来这样查询将会很慢，甚至会卡死
COLLSCAN
这个是什么意思呢？ 如果你仔细一看，应该知道就是CollectionScan，就是所谓的“集合扫描”，对不对，看到集合扫描是不是就可以直接map到数据库中的table scan/heap scan呢？？？ 是的，这个就是所谓的性能最烂最无奈的由来。
nReturned
这个很简单，就是所谓的numReturned，就是说最后返回的num个数，从图中可以看到，就是最终返回了三条。。。
docsExamined
那这个是什么意思呢？？就是documentsExamined，检查了10个documents。。。而从返回上面的nReturned。
创建索引并查询
> db.test.createIndex({ c:1})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}
> db.test.find({ c: { $gte: 100, $lte: 200 }}).explain("executionStats")
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.test",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"c" : {
"$lte" : 200
}
},
{
"c" : {
"$gte" : 100
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"c" : 1
},
"indexName" : "c_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"c" : [
"[100.0, 200.0]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 3,
"executionTimeMillis" : 0,
"totalKeysExamined" : 3,
"totalDocsExamined" : 3,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 3,
"executionTimeMillisEstimate" : 0,
"works" : 4,
"advanced" : 3,
"needTime" : 0,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 3,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 3,
"executionTimeMillisEstimate" : 0,
"works" : 4,
"advanced" : 3,
"needTime" : 0,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"c" : 1
},
"indexName" : "c_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"c" : [
"[100.0, 200.0]"
]
},
"keysExamined" : 3,
"seeks" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
},
"serverInfo" : {
"host" : "iZbp1g11g0cdeeq9ht9fhjZ",
"port" : 27017,
"version" : "3.4.12",
"gitVersion" : "bfde702b19c1baad532ed183a871c12630c1bbba"
},
"ok" : 1
}
再看看上面几个关键词
"stage" : "IXSCAN"
"totalDocsExamined" : 3,
瞬间就少了，这样查询时间也会大大减少
三、hint
这时一个很好玩的一个东西，就是用来force mongodb to excute special index，对吧，为了方便演示，我们做两组复合索引，比如这次我们在c和b上构建一下：
创建索引
> db.test.createIndex({ c:1,b:1})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 2,
"numIndexesAfter" : 3,
"ok" : 1
}
> db.test.createIndex({ b:1,c:1})
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 3,
"numIndexesAfter" : 4,
"ok" : 1
}
hint查询
> db.test.find({ c: { $gte: 100, $lte: 200 },b:"food"}).hint({c:1,b:1}).explain("executionStats")
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.test",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"b" : {
"$eq" : "food"
}
},
{
"c" : {
"$lte" : 200
}
},
{
"c" : {
"$gte" : 100
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"c" : 1,
"b" : 1
},
"indexName" : "c_1_b_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"c" : [
"[100.0, 200.0]"
],
"b" : [
"[\"food\", \"food\"]"
]
}
}
},
"rejectedPlans" : [ ]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1,
"executionTimeMillis" : 0,
"totalKeysExamined" : 3,
"totalDocsExamined" : 1,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1,
"executionTimeMillisEstimate" : 10,
"works" : 3,
"advanced" : 1,
"needTime" : 1,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 1,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1,
"executionTimeMillisEstimate" : 10,
"works" : 3,
"advanced" : 1,
"needTime" : 1,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"c" : 1,
"b" : 1
},
"indexName" : "c_1_b_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"c" : [
"[100.0, 200.0]"
],
"b" : [
"[\"food\", \"food\"]"
]
},
"keysExamined" : 3,
"seeks" : 2,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
},
"serverInfo" : {
"host" : "iZbp1g11g0cdeeq9ht9fhjZ",
"port" : 27017,
"version" : "3.4.12",
"gitVersion" : "bfde702b19c1baad532ed183a871c12630c1bbba"
},
"ok" : 1
}
正常查询
> db.test.find({ c: { $gte: 100, $lte: 200 },b:"food"}).explain("executionStats")
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "test.test",
"indexFilterSet" : false,
"parsedQuery" : {
"$and" : [
{
"b" : {
"$eq" : "food"
}
},
{
"c" : {
"$lte" : 200
}
},
{
"c" : {
"$gte" : 100
}
}
]
},
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"b" : 1,
"c" : 1
},
"indexName" : "b_1_c_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"b" : [
"[\"food\", \"food\"]"
],
"c" : [
"[100.0, 200.0]"
]
}
}
},
"rejectedPlans" : [
{
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"c" : 1,
"b" : 1
},
"indexName" : "c_1_b_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"c" : [
"[100.0, 200.0]"
],
"b" : [
"[\"food\", \"food\"]"
]
}
}
},
{
"stage" : "FETCH",
"filter" : {
"b" : {
"$eq" : "food"
}
},
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {
"c" : 1
},
"indexName" : "c_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"c" : [
"[100.0, 200.0]"
]
}
}
}
]
},
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1,
"executionTimeMillis" : 0,
"totalKeysExamined" : 1,
"totalDocsExamined" : 1,
"executionStages" : {
"stage" : "FETCH",
"nReturned" : 1,
"executionTimeMillisEstimate" : 0,
"works" : 3,
"advanced" : 1,
"needTime" : 0,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"docsExamined" : 1,
"alreadyHasObj" : 0,
"inputStage" : {
"stage" : "IXSCAN",
"nReturned" : 1,
"executionTimeMillisEstimate" : 0,
"works" : 2,
"advanced" : 1,
"needTime" : 0,
"needYield" : 0,
"saveState" : 0,
"restoreState" : 0,
"isEOF" : 1,
"invalidates" : 0,
"keyPattern" : {
"b" : 1,
"c" : 1
},
"indexName" : "b_1_c_1",
"isMultiKey" : false,
"isUnique" : false,
"isSparse" : false,
"isPartial" : false,
"indexVersion" : 1,
"direction" : "forward",
"indexBounds" : {
"b" : [
"[\"food\", \"food\"]"
],
"c" : [
"[100.0, 200.0]"
]
},
"keysExamined" : 1,
"seeks" : 1,
"dupsTested" : 0,
"dupsDropped" : 0,
"seenInvalidated" : 0
}
}
},
"serverInfo" : {
"host" : "iZbp1g11g0cdeeq9ht9fhjZ",
"port" : 27017,
"version" : "3.4.12",
"gitVersion" : "bfde702b19c1baad532ed183a871c12630c1bbba"
},
"ok" : 1
}
主要对比的还是：
"totalKeysExamined" : 3,
"totalDocsExamined" : 1,
和
"totalKeysExamined" : 1,
"totalDocsExamined" : 1,
是不是比较有意思，有时候monogdb并不会，走你想要的索引，当你创建多个联合索引的时候，情况就比较明显了
