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clustered collection after the query is removed Copy CodeThe code is as follows: Db.userInfo.distinct ("name");Will filter out the same data in nameEquivalent: Select Distict name from UserInfo;3. Check the record of age = 22 Copy CodeThe code is as follows: Db.userInfo.find ({"Age": 22});Equivalent to: SELECT * from userInfo where age = 22;4. Check the records of age > 22 Copy CodeThe code is as follows: Db.userInfo.find ({age: {$gt: 22}});Equivalent to: SELECT * from UserInfo where >22;5. Che
nameEquivalent: Select Distict name from UserInfo;3. Check the record of age = 22Db.userInfo.find ({"Age": 22});Equivalent to: SELECT * from userInfo where age = 22;4. Check the records of age > 22Db.userInfo.find ({age: {$gt: 22}});Equivalent to: SELECT * from UserInfo where >22;5. Check the records of age Db.userInfo.find ({age: {$lt: 22}});Equivalent to: SELECT * from UserInfo where 6. Check the records of age >= 25Db.userInfo.find ({age: {$gte: 25}});Equivalent to: SELECT * from UserInfo wh
Distict name from UserInfo;3. Check the record of age = 22Db.userInfo.find ({"Age": 22});Equivalent to: SELECT * from userInfo where age = 22;4. Check the records of age > 22Db.userInfo.find ({age: {$gt: 22}});Equivalent to: SELECT * from UserInfo where >22;5. Check the records of age Db.userInfo.find ({age: {$lt: 22}});Equivalent to: SELECT * from UserInfo where 6. Check the records of age >= 25Db.userInfo.find ({age: {$gte: 25}});Equivalent to: SELECT * from UserInfo where age >= 25;7. Check
age The code is as follows:Db.userInfo.find ({age: {$lte: 25}});8. Query age >= 23 and age The code is as follows:Db.userInfo.find ({age: {$gte: $lte: 26}});9. Query the data containing MONGO in nameThe code is as follows:Db.userInfo.find ({name:/mongo/});Equal to percent[Code]select * from UserInfo where name like '%mongo% ';10, query the name in the beginning of MONGOThe code is as follows:Db.userInfo.fi
query is removedDb.userInfo.distinct ("name");Will filter out the same data in nameEquivalent: Select Distict name from UserInfo;3. Check the record of age = 22Db.userInfo.find ({"Age": 22});Equivalent to: SELECT * from userInfo where age = 22;4. Check the records of age > 22Db.userInfo.find ({age: {$gt: 22}});Equivalent to: SELECT * from UserInfo where age > 22;5. Check the records of age Db.userInfo.find ({age: {$lt: 22}});Equivalent to: SELECT * from UserInfo where age 6. Check the records o
, which shows 50 records per page.2. Duplicate data for a column in the current clustered collection after the query is removedDb.userInfo.distinct ("name");Will filter out the same data in nameEquivalent: Select Distict name from UserInfo;3. Check the record of age = 22Db.userInfo.find ({"Age": 22});Equivalent to: SELECT * from userInfo where age = 22;4. Check the records of age > 22Db.userInfo.find ({age: {$gt: 22}});Equivalent to: SELECT * from UserInfo where >22;5. Check the records of age D
follows:Db.userInfo.find ({age: {$lte: 25}});8. Query age >= 23 and age The code is as follows:Db.userInfo.find ({age: {$gte: $lte: 26}});9. Query the data containing MONGO in name The code is as follows:Db.userInfo.find ({name:/mongo/});Equal to percent[Code]select * from UserInfo where name like '%mongo% ';10, query the name in the beginning of MONGO The code is as follows:Db.userInfo.find ({name:/^mong
({age: {$lt: 22}});Equivalent to: SELECT * from UserInfo where 6. Check the records of age >= 25 Copy CodeThe code is as follows: Db.userInfo.find ({age: {$gte: 25}});Equivalent to: SELECT * from UserInfo where age >= 25;7. Check the records of age Copy CodeThe code is as follows: Db.userInfo.find ({age: {$lte: 25}});8. Query age >= 23 and age Copy CodeThe code is as follows: Db.userInfo.find ({age: {$gte: $lte
({age: {$gte: 25}});Equivalent to: SELECT * from UserInfo where age >= 25;7. Check the records of age Copy CodeThe code is as follows: Db.userInfo.find ({age: {$lte: 25}});8. Query age >= 23 and age Copy CodeThe code is as follows: Db.userInfo.find ({age: {$gte: $lte: 26}});9. Query the data containing MONGO in nameCopy CodeThe code is as follows: Db.userInfo.find ({name:/mongo/});Equal to percent[Code]sel
dbquery.shellbatchsize= 50, which shows 50 records per page. 2. Duplicate data for a column in the current clustered collection after the query is removed Db.userInfo.distinct ("name"); Will filter out the same data in name Equivalent: Select Distict name from UserInfo; 3. Check the record of age = 22 Db.userInfo.find ({"Age": 22}); Equivalent to: SELECT * from userInfo where age = 22; 4. Check the records of age > 22 Db.userInfo.find ({age: {$gt: 22}}); Equivalent to: SELECT * from UserInfo
prompt for the primary keySave ({_id:1, "name": "N2"}) will change N1 to N2.Same point:If there is no primary key in the new data, a record is added.Data already exists: {_id:1, ' name ': ' N1 '}, when the insert operation is performed again,Insert ({"name": "N2"}) inserted data because there is no primary key, so it will add a piece of dataSave ({"name": "N2"}) adds a single piece of data.CheckComparison operation: =,!=,>,#1, select * from db1.user where id = 3Db.user.find ({"_id": 3})#2, sele
How GSM is calculated:A: This is GSM, whether the LTE should be different.ASU conversion to dbm:dbm=-113+ (2*asu)All* Get the GSM Signal strength, valid values is (0-31) as defined in TS* 27.007 8.5if (ASU else if (ASU >=) level = Signal_strength_great (4);else if (ASU >= 8) level = Signal_strength_good (3);else if (ASU >= 5) level = Signal_strength_moderate (2);else level = Signal_strength_poor (1);Lte:The LTE
PhpSmarty character comparison code. Equal eq, not equal to ne, neq, gt greater than, lt less than, gte, ge Greater than or equal to, lte, le less than or equal to, not, mod modulo. Is [not] whether divby can be divisible by a certain number. is [not] eq is equal, Ne, neq are not equal, Gt>, Lt is less, Gte and ge are greater than or equal, Less than or equal to lte and le, Not Non. mod modulo. Is [not] can
, the index is used in ascending order (that is, the number 1), when using a point query to find {age:21}, the assumption is still 100,000 data, may be 21 of a lot of people, so will find more than one piece of data. Then sort ({' username ':-1}) sorts the data in reverse order, which is the intention. But let's not forget to index ' username ': 1 is ascending (from small to large), if you want to reverse the order as long as the data from the last index, and then iterate to get the desired resu
We often see the form in the HTML of the page like [if LTE IE 9] ... The code for [ENDIF] represents a statement that restricts certain browser versions to execute, so what are the rules for these judgment statements? Please see below: Project Example Description ! [If! IE] The NOT operator. This was placed immediately in front of the feature, operator, or subexpression to reverse the Boolean meaning of
I. Setting method:A. Ensure the connectivity 4G signal of the computer's region is good;B. For the first use, you must first maintain the computer's Wi-Fi network status to ensure that the computer can access the Internet through wifi. (for the first time, you only need to link the Wi-Fi network and then you can directly use the LTE network)C. Click the Start menu in the lower-left corner of the screen, and click the "Lenovo Internet acce
configuration is successful and can be connected to GitHub;You have to be successful, or you can't do it later.Configure Local Users and mailboxesUser Name Mailbox role: We need to set a username and mailbox, which is used to upload a local repository to GitHub and display code uploader on GitHub;Use the command:git config--global user.email "registered mailbox"//Set up a mailboxThe GIT client has been installed and the GitHub configuration is complete, and now the code can be transferred from
, through the s1u protocol and eNodeB to communicate:1. Responsible for routing and transmitting the use of the information;2. act as the anchor point for the user Plane and eNodeB when changing hands ;3. As a 3GPP anchor point between LTE and other 3GPP wireless communication systems;4. Responsible for idle mode mobile phone downlink data path termination and when its downlink data arrived at the call to find the mobile phone left idle mode for data
methods (such as middleware design), good data model design can achieve high system scalability at a low cost.The scalability of data models mainly includes the following three factors:1. Whether to meet the existing business needs;2. Is it easy to cope with possible changes in data requirements in the future and maintain the stability of the data model;3. Is it easy to cope with possible changes to business needs in the future and maintain the flexibility of the data model;4. Efficient or not.
Learn about GSM's overall network architecture today.Before understanding the GSM network architecture, let's take a look at its system composition:MS: Mobile user or Mobile station, ≈td/lte inside the UEBSS: Base station subsystem, containing BTS,BSC; a part of the enodeb of Nodeb≈lte in BTS≈TD; BSC≈TD Rnc≈lte;SS: Switching subsystem: MSC,VLR,HLR,AUC,EIR, these
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