I'm pretty new to Mongoose and MongoDB in general so I'm having a difficult time figuring out if something like this is possible:
Item = new Schema({
id: Schema.ObjectId,
dateCreated: { type: Date, default: Date.now },
title: { type: String, default: 'No Title' },
description: { type: String, default: 'No Description' },
tags: [ { type: Schema.ObjectId, ref: 'ItemTag' }]
});
ItemTag = new Schema({
id: Schema.ObjectId,
tagId: { type: Schema.ObjectId, ref: 'Tag' },
tagName: { type: String }
});
var query = Models.Item.find({});
query
.desc('dateCreated')
.populate('tags')
.where('tags.tagName').in(['funny', 'politics'])
.run(function(err, docs){
// docs is always empty
});
Is there a better way do this?
Edit
Apologies for any confusion. What I'm trying to do is get all Items that contain either the funny tag or politics tag.
Edit
Document without where clause:
[{
_id: 4fe90264e5caa33f04000012,
dislikes: 0,
likes: 0,
source: '/uploads/loldog.jpg',
comments: [],
tags: [{
itemId: 4fe90264e5caa33f04000012,
tagName: 'movies',
tagId: 4fe64219007e20e644000007,
_id: 4fe90270e5caa33f04000015,
dateCreated: Tue, 26 Jun 2012 00:29:36 GMT,
rating: 0,
dislikes: 0,
likes: 0
},
{
itemId: 4fe90264e5caa33f04000012,
tagName: 'funny',
tagId: 4fe64219007e20e644000002,
_id: 4fe90270e5caa33f04000017,
dateCreated: Tue, 26 Jun 2012 00:29:36 GMT,
rating: 0,
dislikes: 0,
likes: 0
}],
viewCount: 0,
rating: 0,
type: 'image',
description: null,
title: 'dogggg',
dateCreated: Tue, 26 Jun 2012 00:29:24 GMT
}, ... ]
With the where clause, I get an empty array.
With a modern MongoDB greater than 3.2 you can use $lookup as an alternate to .populate() in most cases. This also has the advantage of actually doing the join "on the server" as opposed to what .populate() does which is actually "multiple queries" to "emulate" a join.
So .populate() is not really a "join" in the sense of how a relational database does it. The $lookup operator on the other hand, actually does the work on the server, and is more or less analogous to a "LEFT JOIN":
Item.aggregate(
[
{ "$lookup": {
"from": ItemTags.collection.name,
"localField": "tags",
"foreignField": "_id",
"as": "tags"
}},
{ "$unwind": "$tags" },
{ "$match": { "tags.tagName": { "$in": [ "funny", "politics" ] } } },
{ "$group": {
"_id": "$_id",
"dateCreated": { "$first": "$dateCreated" },
"title": { "$first": "$title" },
"description": { "$first": "$description" },
"tags": { "$push": "$tags" }
}}
],
function(err, result) {
// "tags" is now filtered by condition and "joined"
}
)
N.B. The .collection.name here actually evaluates to the "string" that is the actual name of the MongoDB collection as assigned to the model. Since mongoose "pluralizes" collection names by default and $lookup needs the actual MongoDB collection name as an argument ( since it's a server operation ), then this is a handy trick to use in mongoose code, as opposed to "hard coding" the collection name directly.
Whilst we could also use $filter on arrays to remove the unwanted items, this is actually the most efficient form due to Aggregation Pipeline Optimization for the special condition of as $lookup followed by both an $unwind and a $match condition.
This actually results in the three pipeline stages being rolled into one:
{ "$lookup" : {
"from" : "itemtags",
"as" : "tags",
"localField" : "tags",
"foreignField" : "_id",
"unwinding" : {
"preserveNullAndEmptyArrays" : false
},
"matching" : {
"tagName" : {
"$in" : [
"funny",
"politics"
]
}
}
}}
This is highly optimal as the actual operation "filters the collection to join first", then it returns the results and "unwinds" the array. Both methods are employed so the results do not break the BSON limit of 16MB, which is a constraint that the client does not have.
The only problem is that it seems "counter-intuitive" in some ways, particularly when you want the results in an array, but that is what the $group is for here, as it reconstructs to the original document form.
It's also unfortunate that we simply cannot at this time actually write $lookup in the same eventual syntax the server uses. IMHO, this is an oversight to be corrected. But for now, simply using the sequence will work and is the most viable option with the best performance and scalability.
Addendum - MongoDB 3.6 and upwards
Though the pattern shown here is fairly optimized due to how the other stages get rolled into the $lookup, it does have one failing in that the "LEFT JOIN" which is normally inherent to both $lookup and the actions of populate() is negated by the "optimal" usage of $unwind here which does not preserve empty arrays. You can add the preserveNullAndEmptyArrays option, but this negates the "optimized" sequence described above and essentially leaves all three stages intact which would normally be combined in the optimization.
MongoDB 3.6 expands with a "more expressive" form of $lookup allowing a "sub-pipeline" expression. Which not only meets the goal of retaining the "LEFT JOIN" but still allows an optimal query to reduce results returned and with a much simplified syntax:
Item.aggregate([
{ "$lookup": {
"from": ItemTags.collection.name,
"let": { "tags": "$tags" },
"pipeline": [
{ "$match": {
"tags": { "$in": [ "politics", "funny" ] },
"$expr": { "$in": [ "$_id", "$$tags" ] }
}}
]
}}
])
The $expr used in order to match the declared "local" value with the "foreign" value is actually what MongoDB does "internally" now with the original $lookup syntax. By expressing in this form we can tailor the initial $match expression within the "sub-pipeline" ourselves.
In fact, as a true "aggregation pipeline" you can do just about anything you can do with an aggregation pipeline within this "sub-pipeline" expression, including "nesting" the levels of $lookup to other related collections.
Further usage is a bit beyond the scope of what the question here asks, but in relation to even "nested population" then the new usage pattern of $lookup allows this to be much the same, and a "lot" more powerful in it's full usage.
Working Example
The following gives an example using a static method on the model. Once that static method is implemented the call simply becomes:
Item.lookup(
{
path: 'tags',
query: { 'tags.tagName' : { '$in': [ 'funny', 'politics' ] } }
},
callback
)
Or enhancing to be a bit more modern even becomes:
let results = await Item.lookup({
path: 'tags',
query: { 'tagName' : { '$in': [ 'funny', 'politics' ] } }
})
Making it very similar to .populate() in structure, but it's actually doing the join on the server instead. For completeness, the usage here casts the returned data back to mongoose document instances at according to both the parent and child cases.
It's fairly trivial and easy to adapt or just use as is for most common cases.
N.B The use of async here is just for brevity of running the enclosed example. The actual implementation is free of this dependency.
const async = require('async'),
mongoose = require('mongoose'),
Schema = mongoose.Schema;
mongoose.Promise = global.Promise;
mongoose.set('debug', true);
mongoose.connect('mongodb://localhost/looktest');
const itemTagSchema = new Schema({
tagName: String
});
const itemSchema = new Schema({
dateCreated: { type: Date, default: Date.now },
title: String,
description: String,
tags: [{ type: Schema.Types.ObjectId, ref: 'ItemTag' }]
});
itemSchema.statics.lookup = function(opt,callback) {
let rel =
mongoose.model(this.schema.path(opt.path).caster.options.ref);
let group = { "$group": { } };
this.schema.eachPath(p =>
group.$group[p] = (p === "_id") ? "$_id" :
(p === opt.path) ? { "$push": `$${p}` } : { "$first": `$${p}` });
let pipeline = [
{ "$lookup": {
"from": rel.collection.name,
"as": opt.path,
"localField": opt.path,
"foreignField": "_id"
}},
{ "$unwind": `$${opt.path}` },
{ "$match": opt.query },
group
];
this.aggregate(pipeline,(err,result) => {
if (err) callback(err);
result = result.map(m => {
m[opt.path] = m[opt.path].map(r => rel(r));
return this(m);
});
callback(err,result);
});
}
const Item = mongoose.model('Item', itemSchema);
const ItemTag = mongoose.model('ItemTag', itemTagSchema);
function log(body) {
console.log(JSON.stringify(body, undefined, 2))
}
async.series(
[
// Clean data
(callback) => async.each(mongoose.models,(model,callback) =>
model.remove({},callback),callback),
// Create tags and items
(callback) =>
async.waterfall(
[
(callback) =>
ItemTag.create([{ "tagName": "movies" }, { "tagName": "funny" }],
callback),
(tags, callback) =>
Item.create({ "title": "Something","description": "An item",
"tags": tags },callback)
],
callback
),
// Query with our static
(callback) =>
Item.lookup(
{
path: 'tags',
query: { 'tags.tagName' : { '$in': [ 'funny', 'politics' ] } }
},
callback
)
],
(err,results) => {
if (err) throw err;
let result = results.pop();
log(result);
mongoose.disconnect();
}
)
Or a little more modern for Node 8.x and above with async/await and no additional dependencies:
const { Schema } = mongoose = require('mongoose');
const uri = 'mongodb://localhost/looktest';
mongoose.Promise = global.Promise;
mongoose.set('debug', true);
const itemTagSchema = new Schema({
tagName: String
});
const itemSchema = new Schema({
dateCreated: { type: Date, default: Date.now },
title: String,
description: String,
tags: [{ type: Schema.Types.ObjectId, ref: 'ItemTag' }]
});
itemSchema.statics.lookup = function(opt) {
let rel =
mongoose.model(this.schema.path(opt.path).caster.options.ref);
let group = { "$group": { } };
this.schema.eachPath(p =>
group.$group[p] = (p === "_id") ? "$_id" :
(p === opt.path) ? { "$push": `$${p}` } : { "$first": `$${p}` });
let pipeline = [
{ "$lookup": {
"from": rel.collection.name,
"as": opt.path,
"localField": opt.path,
"foreignField": "_id"
}},
{ "$unwind": `$${opt.path}` },
{ "$match": opt.query },
group
];
return this.aggregate(pipeline).exec().then(r => r.map(m =>
this({ ...m, [opt.path]: m[opt.path].map(r => rel(r)) })
));
}
const Item = mongoose.model('Item', itemSchema);
const ItemTag = mongoose.model('ItemTag', itemTagSchema);
const log = body => console.log(JSON.stringify(body, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri);
// Clean data
await Promise.all(Object.entries(conn.models).map(([k,m]) => m.remove()));
// Create tags and items
const tags = await ItemTag.create(
["movies", "funny"].map(tagName =>({ tagName }))
);
const item = await Item.create({
"title": "Something",
"description": "An item",
tags
});
// Query with our static
const result = (await Item.lookup({
path: 'tags',
query: { 'tags.tagName' : { '$in': [ 'funny', 'politics' ] } }
})).pop();
log(result);
mongoose.disconnect();
} catch (e) {
console.error(e);
} finally {
process.exit()
}
})()
And from MongoDB 3.6 and upward, even without the $unwind and $group building:
const { Schema, Types: { ObjectId } } = mongoose = require('mongoose');
const uri = 'mongodb://localhost/looktest';
mongoose.Promise = global.Promise;
mongoose.set('debug', true);
const itemTagSchema = new Schema({
tagName: String
});
const itemSchema = new Schema({
title: String,
description: String,
tags: [{ type: Schema.Types.ObjectId, ref: 'ItemTag' }]
},{ timestamps: true });
itemSchema.statics.lookup = function({ path, query }) {
let rel =
mongoose.model(this.schema.path(path).caster.options.ref);
// MongoDB 3.6 and up $lookup with sub-pipeline
let pipeline = [
{ "$lookup": {
"from": rel.collection.name,
"as": path,
"let": { [path]: `$${path}` },
"pipeline": [
{ "$match": {
...query,
"$expr": { "$in": [ "$_id", `$$${path}` ] }
}}
]
}}
];
return this.aggregate(pipeline).exec().then(r => r.map(m =>
this({ ...m, [path]: m[path].map(r => rel(r)) })
));
};
const Item = mongoose.model('Item', itemSchema);
const ItemTag = mongoose.model('ItemTag', itemTagSchema);
const log = body => console.log(JSON.stringify(body, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri);
// Clean data
await Promise.all(Object.entries(conn.models).map(([k,m]) => m.remove()));
// Create tags and items
const tags = await ItemTag.insertMany(
["movies", "funny"].map(tagName => ({ tagName }))
);
const item = await Item.create({
"title": "Something",
"description": "An item",
tags
});
// Query with our static
let result = (await Item.lookup({
path: 'tags',
query: { 'tagName': { '$in': [ 'funny', 'politics' ] } }
})).pop();
log(result);
await mongoose.disconnect();
} catch(e) {
console.error(e)
} finally {
process.exit()
}
})()
what you are asking for isn't directly supported but can be achieved by adding another filter step after the query returns.
first, .populate( 'tags', null, { tagName: { $in: ['funny', 'politics'] } } ) is definitely what you need to do to filter the tags documents. then, after the query returns you'll need to manually filter out documents that don't have any tags docs that matched the populate criteria. something like:
query....
.exec(function(err, docs){
docs = docs.filter(function(doc){
return doc.tags.length;
})
// do stuff with docs
});
Try replacing
.populate('tags').where('tags.tagName').in(['funny', 'politics'])
by
.populate( 'tags', null, { tagName: { $in: ['funny', 'politics'] } } )
Update: Please take a look at the comments - this answer does not correctly match to the question, but maybe it answers other questions of users which came across (I think that because of the upvotes) so I will not delete this "answer":
First: I know this question is really outdated, but I searched for exactly this problem and this SO post was the Google entry #1. So I implemented the docs.filter version (accepted answer) but as I read in the mongoose v4.6.0 docs we can now simply use:
Item.find({}).populate({
path: 'tags',
match: { tagName: { $in: ['funny', 'politics'] }}
}).exec((err, items) => {
console.log(items.tags)
// contains only tags where tagName is 'funny' or 'politics'
})
Hope this helps future search machine users.
After having the same problem myself recently, I've come up with the following solution:
First, find all ItemTags where tagName is either 'funny' or 'politics' and return an array of ItemTag _ids.
Then, find Items which contain all ItemTag _ids in the tags array
ItemTag
.find({ tagName : { $in : ['funny','politics'] } })
.lean()
.distinct('_id')
.exec((err, itemTagIds) => {
if (err) { console.error(err); }
Item.find({ tag: { $all: itemTagIds} }, (err, items) => {
console.log(items); // Items filtered by tagName
});
});
#aaronheckmann 's answer worked for me but I had to replace return doc.tags.length; to return doc.tags != null; because that field contain null if it doesn't match with the conditions written inside populate.
So the final code:
query....
.exec(function(err, docs){
docs = docs.filter(function(doc){
return doc.tags != null;
})
// do stuff with docs
});
I am trying to setup a filter that is similar to a defect view within a Trend chart. The filter in the defect view is:
(State < Closed) AND (Severity <= Major) AND (Tags !contains Not a Stop Ship)
I cannot seem to get the Tags find to work correctly. Any suggestions?
this.myTrendChart = Ext.create('Rally.ui.chart.Chart', {
storeType: 'Rally.data.lookback.SnapshotStore',
storeConfig: {
find: {
_TypeHierarchy: "Defect",
State: {
$lt: "Closed"
},
Severity: {
$lte: "Major"
},
Tags: {
$ne: "Not a Stop Ship"
},
_ProjectHierarchy: ProjectOid
},
hydrate: ["Priority"],
fetch: ["_ValidFrom", "_ValidTo", "ObjectID", "Priority"]
},
calculatorType: 'My.TrendCalc',
calculatorConfig: {},
chartConfig: {
chart: {
zoomType: 'x',
type: 'line'
},
title: {
text: 'Defects over Time'
},
xAxis: {
type: 'datetime',
minTickInterval: 3
},
yAxis: {
title: {
text: 'Number of Defects'
}
}
}
});
Based on reviewing the JSON messages, I figured out the tag needed to be the ObjectId. Once I found this, I replaced "Not a Stop Ship" with the ObjectId value and the filter worked correctly.
I want to create a chart of how many tasks are in a given Schedule State during the length of the sprint. Is it possible to call WsapiDataStore on each day?
What you are looking for is a lookback Snapshot Store , using the Lookback API - this allows you to specify a date or a point in time that you want to query by.
A typical use looks like this:
Ext.create('Rally.data.lookback.SnapshotStore', {
pageSize : 10000,
fetch : ['fetch'],
filters : [{
property : '__At',
value : 'current'
},{
property : '_ItemHierarchy',
value : 'HierarchicalRequirement'
}]
}).load({
callback : function(records) {
Ext.Array.each(records, function(record) {
// do something with each record
});
}
});
WsapiDataStore is not intended for historic data. You need to use Rally.data.lookback.SnapshotStore which retrieves data from the Lookback API.
Lookback API allows to see what any work item or collection of work items looked like in the past. This is different from using WS API directly (or via WsapiDataStore) which can provide you with the current state of objects, but does not have historical data.
LBAPI documentation is available here
As far as Rally release object's attributes see WS API object model here. But it is not clear from your comment what you mean by data for the entire release. If you are interested in getting back user stories assigned to a specific release then your query object should be hierarchical requirement and not release, and you may filter by release.
Here is an app that builds a chart using a Release dropdown. Based on the selection in the dropdown the chart is refreshed (it is destroyed and added):
Ext.define('CustomApp', {
extend: 'Rally.app.TimeboxScopedApp',
componentCls: 'app',
scopeType: 'release',
comboboxConfig: {
fieldLabel: 'Select a Release:',
labelWidth: 100,
width: 300
},
addContent: function() {
this._makeStore();
},
onScopeChange: function() {
this._makeStore();
},
_makeStore: function() {
Ext.create('Rally.data.WsapiDataStore', {
model: 'UserStory',
autoLoad: true,
filters: [this.getContext().getTimeboxScope().getQueryFilter()],
listeners: {
load: this._onDataLoaded,
scope: this
}
});
},
_onDataLoaded: function(store, data) {
var records = [];
var scheduleStateGroups = ["Defined","In-Progress","Completed","Accepted"]
// State count variables
var definedCount = 0;
var inProgressCount = 0;
var completedCount = 0;
var acceptedCount = 0;
// Loop through returned data and group/count by ScheduleState
Ext.Array.each(data, function(record) {
//Perform custom actions with the data here
//Calculations, etc.
scheduleState = record.get('ScheduleState');
switch(scheduleState)
{
case "Defined":
definedCount++;
break;
case "In-Progress":
inProgressCount++;
break;
case "Completed":
completedCount++;
break;
case "Accepted":
acceptedCount++;
}
});
if (this.down('#myChart')) {
this.remove('myChart');
}
this.add(
{
xtype: 'rallychart',
height: 400,
itemId: 'myChart',
chartConfig: {
chart: {
},
title: {
text: 'User Story Schedule State Counts',
align: 'center'
},
xField : 'ScheduleState',
xAxis: [
{
//categories: scheduleStateGroups,
title: {
text: 'ScheduleState'
}
}
],
yAxis: {
title: {
text: 'Count'
}
},
plotOptions : {
column: {
color: '#F00'
},
series : {
animation : {
duration : 2000,
easing : 'swing'
}
}
}
},
chartData: {
categories: scheduleStateGroups,
series: [
{
type: 'column',
data: [definedCount, inProgressCount, completedCount, acceptedCount]
}
]
}
}
);
this.down('#myChart')._unmask();
}
});
Not sure if this is possible. In my example I am using json as the source but this could be any size. In my example on fiddle I would use this data in a shared fashion by only binding two columns ProductFamily (xAxis) and Value (yAxis) but I would like to be able to group the columns by using an aggregate.
In this example without the grouping it shows multiple columns for FamilyA. Can this be grouped into ONE column and the values aggregated regardless of the amount of data?
So the result will show one column for FamilyA of Value 4850 + 4860 = 9710 etc.?
A problem with all examples online is that there is always the correct amount of data for each category. Not sure if this makes sense?
http://jsfiddle.net/jqIndy/ZPUr4/3/
//Sample data (see fiddle for complete sample)
[{
"Client":"",
"Date":"2011-06-01",
"ProductNumber":"5K190",
"ProductName":"CABLE USB",
"ProductFamily":"FamilyC",
"Status":"OPEN",
"Units":5000,
"Value":5150.0,
"ShippedToDestination":"CHINA"
}]
var productDataSource = new kendo.data.DataSource({
data: dr,
//group: {
// field: "ProductFamily",
//},
sort: {
field: "ProductFamily",
dir: "asc"
},
//aggregate: [
// { field: "Value", aggregate: "sum" }
//],
//schema: {
// model: {
// fields: {
// ProductFamily: { type: "string" },
// Value: { type: "number" },
// }
// }
//}
})
$("#product-family-chart").kendoChart({
dataSource: productDataSource,
//autoBind: false,
title: {
text: "Product Family (past 12 months)"
},
seriesDefaults: {
overlay: {
gradient: "none"
},
markers: {
visible: false
},
majorTickSize: 0,
opacity: .8
},
series: [{
type: "column",
field: "Value"
}],
valueAxis: {
line: {
visible: false
},
labels: {
format: "${0}",
skip: 2,
step: 2,
color: "#727f8e"
}
},
categoryAxis: {
field: "ProductFamily"
},
legend: {
visible: false
},
tooltip: {
visible: true,
format: "Value: ${0:N0}"
}
});
The Kendo UI Chart does not support binding to group aggregates. At least not yet.
My suggestion is to:
Move the aggregate definition, so it's calculated per group:
group: {
field: "ProductFamily",
aggregates: [ {
field: "Value",
aggregate: "sum"
}]
}
Extract the aggregated values in the change handler:
var view = products.view();
var families = $.map(view, function(v) {
return v.value;
});
var values = $.map(view, function(v) {
return v.aggregates.Value.sum;
});
Bind the groups and categories manually:
series: [ {
type: "column",
data: values
}],
categoryAxis: {
categories: families
}
Working demo can be found here: http://jsbin.com/ofuduy/5/edit
I hope this helps.