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https://github.com/nafeen/mongo-commands
MongoDB commands cheatsheet. Contains map-reduce, aggregate etc.
https://github.com/nafeen/mongo-commands
mongodb nosql
Last synced: about 2 months ago
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MongoDB commands cheatsheet. Contains map-reduce, aggregate etc.
- Host: GitHub
- URL: https://github.com/nafeen/mongo-commands
- Owner: nafeen
- Created: 2016-09-24T11:15:36.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-10-10T08:08:32.000Z (about 7 years ago)
- Last Synced: 2024-05-18T17:45:13.388Z (7 months ago)
- Topics: mongodb, nosql
- Homepage:
- Size: 3.91 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
========================================================================
MongoDB Commands
========================================================================
### How to easily run multi-line queries using 'VIM'
_type in Mongo shell_
EDITOR = 'vim'
edit query
_inside VIM editor, copy-paste query/function_
_type ':x' to save and exit VIM editor_
db.sampleCollection.aggregate(query)
========================================================================
### Find ALL records
db.collectionName.find({}).pretty()
### Filter ALL records based on field value(s)
db.collectionName.find({}, {
fieldName1 : value1,
fieldName2 : value2
}).pretty()
### Find ONE record
db.collectionName.findOne()
### Find DISTINCT field values
db.sampleCollection.distinct("fieldName")
### Find DISTINCT nested field values
db.sampleCollection.distinct("fieldName.nestedField")
========================================================================
### Find ALL records BETWEEN two dates
begin_date_range = "2016-09-20T00:00:00Z";
end_date_range = "2016-09-21T00:00:00Z";
db.collectionName.find(
{
"fieldName":
{
"$gte":begin_date_range,
"$lt":end_date_range
}
}).pretty();
========================================================================
### Count of documents between TWO dates using ObjectId
var objIdMin = ObjectId(Math.floor((new Date('2016/1/7'))/1000).toString(16) + "0000000000000000");
var objIdMax = ObjectId(Math.floor((new Date('2016/8/25'))/1000).toString(16) + "0000000000000000");
db.collectionName.aggregate([
{
$match : {
"_id" : {
$gt: objIdMin,
$lt: objIdMax
}
}
},
{
$group : {
_id : null,
count: { $sum: 1 }
}
}
])
========================================================================
### Find all fields in a collection
_using map-reduce_
mr = db.runCommand({
"mapreduce" : "collectionName",
"map" : function() {
for (var key in this) { emit(key, null); }
},
"reduce" : function(key, stuff) { return null; },
"out": "fieldName" + "_keys"
});
db[mr.result].distinct("fieldName");
========================================================================
### Count distinct of all occurances of a particular field
db.collectionName.aggregate([
{
$match: {
keywords: { $not: {$size: 0} }
}
},
{
$unwind: "$fieldName"
},
{
$group: {
_id: {$toLower: '$fieldName'},
count: { $sum: 1 }
}
},
{
$match: {
count: { $gte: 2 }
}
},
{ $sort : { count : -1} },
{ $limit : 100 }
]);
========================================================================
### Finding aggregate of metrics after grouping a field
_analogous to SQL GROUP BY_
db.collectionName.aggregate([{
$group: {
"_id" : "$fieldToGroupUnder",
aggregateField1: {
$sum: "$fieldName1"
},
aggregateField2: {
$sum: "$fieldName2.subFieldName"
},
count: { $sum: 1 }
}
}]).pretty();
========================================================================
### Sort + Group using aggregate
db.collectionName.aggregate(
{
$sort: {
"fieldName" : -1
}
},
{
$group: {
fieldName: {
$first: "value"
}
}
}
);
========================================================================
### Join + Filter + Select
db.collectionName1.aggregate([
{
$match : {
"fieldToFilter" : "filterValue" // filter
}
},
{
$lookup : { // Left-outer Join
from : "collectionName2", // joining collection
localField : "fieldFromCollection1", // joining field
foreignField : "fieldFromCollection2", // joining field
as : "joinFieldLabel" // label for result
}
},
{
$project : { // FILTER result set
"_id" : 0,
"filterValue" : 1,
"fieldName1" : 1,
"fieldName2.subFieldName" : 1
}
}
])
========================================================================
### Projecting all data and some custom fields with same query
db.collectionName.aggregate([
{
$project: {
data : "$$ROOT", // this prints out all fields
customField: {
$concat : [ "$fieldName1", "-", "$fieldName2" ]
}
}
},
{
$match : {
fieldName2 : "fieldValue"
}
}
]).pretty();
========================================================================