MongoDB queries, indexes, and aggregations

Initializing the MongoDB database

> Use dengswitched to DB deng> db.createcollection ("Jingdong")            #无參数 {"OK":1}> show Collectionsjingdongsystem.indexes> userdoc1= ({"user_id": 1, "name": "Cloud", "state": "Active", "actor": "User", " E-Mail ":" [email protected] "," Vm_num ": 2," time ": [{" Date ":" 2014-08-12 "," Hour ":" 10:53 PM "}]})        > userdoc2= ({" USER_ID ": 2," "Name": "Testadmin", "state": "Active", "actor": "Admin", "e-mail": "[email protected]", "Vm_num": 2, "time": [{ "Date": "2014-08-11", "Hour": "06:34 AM"}]})    > doc= ({"Name": "Peter", "position": "Teacher"}) > Db.jingdong.insert (Userdoc1) writeresult ({"ninserted": 1}) > Db.jingdong.insert (USERDOC2) writeresult ({" Ninserted ": 1}" > Db.jingdong.insert (Doc1) writeresult ({"ninserted": 1})

Query statements

Db.jingdong.find () #相当于select * from Jingdong;

Conditional operator

The conditional operators in MongoDB are: (>) greater than-\ $gt #greate (<) less than-\ $lt #low (>=) greater than or equal to-\ $gte #equal (<=) less than equals-\ $lte

Examples:

> Db.jingdong.find ({user_id:{$gt: 1}}) > Db.jingdong.find ({user_id:{$lte: 2, $GT: 1}})

#type的值双精度型-1 String-2 Object-3 array-42 binary Data-5 object ID-7 Boolean type-8 data-9 null-10 regular table -11js code-13 symbol-14 A scoped JS code-1532-bit integer-16 timestamp-1764-bit integer number -18min Key-255max Key-127db.jingdong.find ({"name": {$type: 2}}) #查找name是字符串的文档记录

Limit and Skip

MongoDB sort-sort ()

As with the sort in sqlite there are ascending and descending, in ascending with 1, descending with-1 means db.jingdong.find (). Sort ({"Time": 1})

Index-Ensureindex ()

Indexes can often greatly improve the efficiency of queries, assuming there is no index, MongoDB must scan every file in the collection and select those records that meet the criteria of the query when reading the data. Such a scan full set of query efficiency is very low, especially when processing a large amount of data. Queries can take up to dozens of seconds or even minutes. There is no doubt that the performance of the site is very deadly.

An index is a special data structure. The index is stored in a collection of data that is easy to traverse through the read. An index is a structure that sorts the values of a document or multiple documents in a database collection.

Db. Collection_name.ensureindex ({key:1|-1}) > Db.shiyanlou.ensureIndex ({"NAME": 1}) #1代表升序-1 for descending > Db.shiyanlou.ensureIndex ({"user_id": 1, "name": 1},{background:1})

The Ensureindex parameter table is as follows:

number of references	type	Descriptive narrative
background	boolean	indexing either to block other database operations, default to False
unique	boolean	Index established is unique, default false
name	string	The name of the index. If not specified. The system proactively generates
dropdups	boolean	When creating a unique index. Whether to delete the repeated records. Default Flase
sparse	boolean	The index is not enabled for field data that does not exist on the document. Default False
expireafterseconds	integer	Set the lifetime of the collection, in seconds
v	index version	version of index
weights	document	index weight value, range 1 to 99999
default-language	string	default English
Language_override	String	The default value is language

Polymerization-aggregate ()

Db. Collection_name.aggregate ({$match: {x:1},{limit:num}, $group: {_id: $age}})

$match: Query. Same as find; $limit: Limit the number of results displayed; $skip: ignores the number of results. $sort: Sort, $group: combines the results according to a given expression.

> db.jingdong.aggregate ([{$group: {_id: "$name", user:{$sum: "$user _id"}}])

name	Descriptive narrative
$sum	calculate sum
$avg	calculate average
\ $min and $max	calculate minimum and maximum values
$push	inserting values into an array in the result document
$addToSet	Insert values into an array in the resulting document, but do not create a copy
$first	Get the first document data based on the sort of resource document
$last	Get the last document data based on the ordering of the resource documents

Pipeline MongoDB's aggregation pipeline passes the MongoDB document to the next pipeline processing after a pipeline has finished processing.

Pipeline operations can be repeated.

Expression: Processes the input document and outputs it.

An expression is stateless. Only the documents that can be used to calculate the current aggregated pipeline cannot process other documents. Several actions that are frequently used in the aggregation framework:

$project: Changes the structure of the input document. Can be used to rename, add, or delete fields, as well as to create calculation results and nested documents. $match: Used to filter data and only output documents that match the criteria. $match a standard query operation using MONGODB. $limit: Used to limit the number of documents returned by the MongoDB aggregation pipeline. $skip: Skips the specified number of documents in the aggregation pipeline and returns the remaining documents. $unwind: Splits one of the array type fields in a document into multiple bars, each of which includes a value in the array.
$group: Groups The documents in the collection to be used for statistical results. $sort: Sorts the input documents after the output. $geoNear: Outputs an ordered document that is close to a geographic location.

> db.shiyanlou.aggregate ([{$match: {user_id:{$gt: 0, $lte: 2}}},{$group: {_id: "user", count:{$sum: 1}}]) {"_id": " User "," Count ": 2}

MongoDB queries, indexes, and aggregations