I would like to perform fairly complex filtering on Marionette Collections.
Is there way to search for models with a DB like querys like the MongoDB API?
Example:
MarionetteCollection.find(
{
type: 'product',
$or: [ { qty: { $gt: 100 } }, { price: { $lt: 9.95 } } ],
$and [ { active: true} ],
$sortby{'name'},
$order {'asc'}
});
Maybe an extension to Marionette.js?
There is nothing in Marionette to help you here and Marionette doesn't make any changes/additions to the regular Backbone.Collection.
You could take a look at backbone-query. It appears to do what you are wanting.
Backbone has a simple implementation of what you are asking. Collection.where() && Collection.findWhere() can take an object and will find the model based on your object. But it doesn't more complex matchings like, greater than, less than, etc.
MarionetteCollection.find(
{
type: 'product',
qty: 55,
active: true
});
Related
I have one collection who include some value coming from sensor. My collection look like this.
const MainSchema: Schema = new Schema(
{
deviceId: {
type: mongoose.Types.ObjectId,
required: true,
ref: 'Device',
},
sensorId: {
type: mongoose.Types.ObjectId,
default: null,
ref: 'Sensor',
},
value: {
type: Number,
},
date: {
type: Date,
},
},
{
versionKey: false,
}
);
I want to get data from this collection with my endpoint. This collection should has more 300.000 documents. I want to get data from this collection with sensor data. (like name and desc. to "Sensor")
My Sensor Collection:
const Sensor: Schema = new Schema(
{
name: {
type: String,
required: true,
min: 3,
},
description: {
type: String,
default: null,
},
type: {
type: String,
},
},
{
timestamps: true,
versionKey: false,
}
);
I use 2 method for get data from MainSchema. First approach is look like this (Include aggregate):
startDate, endDate and _sensorId are passed by parameter for this functions.
const data= await MainSchema.aggregate([
{
$lookup: {
from: 'Sensor',
localField: 'sensorId',
foreignField: '_id',
as: 'sensorDetail',
},
},
{
$unwind: '$sensorDetail',
},
{
$match: {
$and: [
{ sensorId: new Types.ObjectId(_sensorId) },
{
date: {
$gte: new Date(startDate),
$lt: new Date(endDate),
},
},
],
},
},
{
$project: {
sensorDetail: {
name: 1,
description: 1,
},
value: 1,
date: 1,
},
},
{
$sort: {
_id: 1,
},
},
]);
Second approach look like this (Include find and populate):
const data= await MainSchema.find({
sensorId: _sensorId,
date: {
$gte: new Date(startDate),
$lte: new Date(endDate),
},
})
.lean()
.sort({ date: 1 })
.populate('sensorId', { name: 1, description: 1});
Execution time for same data set:
First approach: 25 - 30 second
Second approach: 11 - 15 second
So how can i get this data more faster. Which one is best practise?
And how can i do extras for improve the query speed?
Overall #NeNaD's answer touches on a lot of the important points. What I'm going to say in this one should be considered in addition to that other information.
Index
Just to clarify, the ideal index here would be a compound index of { sensorId: 1, date: 1 }. This index follows the ESR Guidance for index key ordering and will provide the most efficient retrieval of the data according to the query predicates specified in the $match stage.
If the index: true annotation in Mongoose creates two separate single field indexes, then you should go manually create this index in the collection. MongoDB will only use one of those indexes to execute this query which will not be as efficient as using the compound index described above.
Also regarding the existing approach, what is the purpose of the trailing $sort?
If the application (a chart in this situation) does not need sorted results then you should remove this stage entirely. If the client does need sorted results then you should:
Move the $sort stage earlier in the pipeline (behind the $match), and
Test if including the sort field in the index improves performance.
As written, the $sort is currently a blocking operation which is going to prevent any results from being returned to the client until they are all processed. If you move the $sort stage up and can change it to sort on date (which probably makes sense for sensor data) the it should automatically use the compound index that we mentioned earlier to provide the sort in a non-blocking manner.
Stage Ordering
Ordering of aggregation stages is important, both for semantic purposes as well as for performance reasons. The database itself will attempt to do various things (such as reordering stages) to improve performance so long as it does not logically change the result set in any way. Some of these optimizations are described here. As these are version specific anyway, you can always take a look at the explain plan to get a better indication of what specific changes the database has applied. The fact that performance did not improve when you manually moved the $match to the beginning (which is generally a best practice) could suggest that the database was able to automatically do that on your behalf.
Schema
I'm a little curious about the schema itself. Is there any reason that there are two separate collections here?
My guess is that this is mostly a play at 'normalization' to help reduce data duplication. That is mostly fine, unless you find yourself constantly performing $lookups like this for most of your read operations. You could certainly consider testing what performance (and storage) looks like if you combine them.
Also for this particular operation, would it make sense to just issue two separate queries, one to get the measurements and one to get the sensor data (a single time)? The aggregation matches on sensorId and the value of that field is what is then used to match against the _id field from the other collection. Unless I'm doing the logic wrong, this should be the same data for each of the source documents.
Time Series Collections
Somewhat related to schema, have you looked into using Time Series Collections? I don't know what your specific goals or pain points are, but it seems that you may be working with IoT data. Time Series collections are purpose-built to help handle use cases like that. Might be worth looking into as they may help you achieve your goals with less hassle or overhead.
Frist step
Create index for sensorId and date properties in the collection. You can do it by specifying index: true in your model:
const MainSchema: Schema = new Schema(
{
deviceId: { type: mongoose.Types.ObjectId, required: true, ref: 'Device' },
sensorId: { type: mongoose.Types.ObjectId, default: null, ref: 'Sensor', index: true },
value: { type: Number },
date: { type: Date, index: true },
},
{
versionKey: false,
}
);
Second step
Aggregation queries can take leverage of indexes only if your $match stage is the first stage in the pipeline, so you should change the order of the items in your aggregation query:
const data= await MainSchema.aggregate([
{
$match: {
{ sensorId: new Types.ObjectId(_sensorId) },
{
date: {
$gte: new Date(startDate),
$lt: new Date(endDate),
},
},
},
},
{
$lookup: {
from: 'Sensor',
localField: 'sensorId',
foreignField: '_id',
as: 'sensorDetail',
},
},
{
$unwind: '$sensorDetail',
},
{
$project: {
sensorDetail: {
name: 1,
description: 1,
},
value: 1,
date: 1,
},
},
{
$sort: {
_id: 1,
},
},
]);
I am trying to find out if there any any es6 (or external library) ways to handle deep object comparison and parsing in JavaScript.
Take the following example, where I have a property history, which is an array, embedded within a property services, which is also an array:
{
_id: 4d39fe8b23dac43194a7f571,
name: {
first: "Jane",
last: "Smith"
}
services: [
{
service: "typeOne",
history: [
{ _id: 121,
completed: true,
title: "rookie"
},
{ _id: 122,
completed: false,
title: "novice"
}
]
},
{
service: "typeTwo",
history: [
{ _id: 135,
completed: true,
title: "rookie"
},
{ _id: 136,
completed: false,
title: "novice"
}
]
}
]
}
Now, say a new element is pushed onto the "history" array within the second "services" element, where (service : "typeTwo") -- on the "services" array. I need to identify that's happened, and pull out the entire parent element, because I also need to know what "service" within the "services" array had a new "history" element added.
Is there a way I can scan this entire object and not only determine when something's changed, but actually be able to pull out the section I need reference to? I'm open to either a native JS or JS library option here.
You can check for duplicates like this:
function isEqual(firstObject, secondObject) {
function _equals(firstObject, secondObject) {
let clone = {...{}, ...firstObject}, cloneStr = JSON.stringify(clone);
return cloneStr === JSON.stringify({...clone, ...secondObject});
}
return _equals(firstObject, secondObject) && _equals(secondObject, firstObject);
}
https://jsfiddle.net/b1puL04w/
If you considering libraries has stated, then lodash has _.isEqual which does perform a deep comparison between two values to determine if they are equal.
I have used it extensively for deep comparison in the past.
I want to sort a sequelize query by a nested attribute of a JSONB object.
I have a model like that:
sequelize.define('product', {
available: { type: Sequelize.BOOLEAN },
price: { type: Sequelize.JSONB },
...
}
where price is kind of this:
price = {
EUR: 1.2,
CHF: 1.3,
}
I want to sort a findAll query by price.EUR. I hoped that this works:
product.findAll({ where: { available: true }, order: [['price.EUR', 'ASC']] }
But I get an unsorted array.
The underlying DB is postgres.
A solution is to sort the result programmatically in javascript (array._prototype.sort()). But I wonder if it is possible with a sequelize query.
try this:
roduct.findAll({ where: { available: true }, order: [[sequelize.literal('price->>\'EUR\''), 'ASC']] }
what's the best way to sort the following documents in a collection:
{"topic":"11.Topic","text":"a.Text"}
{"topic":"2.Topic","text":"a.Text"}
{"topic":"1.Topic","text":"a.Text"}
I am using the following
find.(topic:req.body.topic).(sort({topic:1}))
but is not working (because the fields are strings and not numbers so I get):
{"topic":"1.Topic","text":"a.Text"},
{"topic":"11.Topic","text":"a.Text"},
{"topic":"2.Topic","text":"a.Text"}
but i'd like to get:
{"topic":"1.Topic","text":"a.Text"},
{"topic":"2.Topic","text":"a.Text"},
{"topic":"11.Topic","text":"a.Text"}
I read another post here that this will require complex sorting which mongoose doesn't have. So perhaps there is no real solution with this architecture?
Your help is greatly appreciated
i will suggest you make your topic filed as type : Number, and create another field topic_text.
Your Schema would look like:
var documentSchema = new mongoose.Schema({
topic : Number,
topic_text : String,
text : String
});
Normal document would look something like this:
{document1:[{"topic":11,"topic_text" : "Topic" ,"text":"a.Text"},
{"topic":2,"topic_text" : "Topic","text":"a.Text"},
{"topic":1,"topic_text" : "Topic","text":"a.Text"}]}
Thus, you will be able to use .sort({topic : 1}) ,and get the result you want.
while using topic value, append topic_text to it.
find(topic:req.body.topic).sort({topic:1}).exec(function(err,result)
{
var topic = result[0].topic + result[0].topic_text;//use index i to extract the value from result array.
})
If you do not want (or maybe do not even can) change the shape of your documents to include a numeric field for the topic number then you can achieve your desired sorting with the aggregation framework.
The following pipeline essentially splits the topic strings like '11.Topic' by the dot '.' and then prefixes the first part of the resulting array with a fixed number of leading zeros so that sorting by those strings will result in 'emulated' numeric sorting.
Note however that this pipeline uses $split and $strLenBytes operators which are pretty new so you may have to update your mongoDB instance - I used version 3.3.10.
db.getCollection('yourCollection').aggregate([
{
$project: {
topic: 1,
text: 1,
tmp: {
$let: {
vars: {
numStr: { $arrayElemAt: [{ $split: ["$topic", "."] }, 0] }
},
in: {
topicNumStr: "$$numStr",
topicNumStrLen: { $strLenBytes: "$$numStr" }
}
}
}
}
},
{
$project: {
topic: 1,
text: 1,
topicNumber: { $substr: [{ $concat: ["_0000", "$tmp.topicNumStr"] }, "$tmp.topicNumStrLen", 5] },
}
},
{
$sort: { topicNumber: 1 }
},
{
$project: {
topic: 1,
text: 1
}
}
])
How can I adopt my ext js models to hold data containing objects.
Data example:
fruit {
apples {
quantity: '10',
color: 'red
}
pears {
color:'yellow',
taste: 'very bad'
}
How would then my model look like? I only know how to put data on one level:
Ext.define('app.mode.Fruits', {
extend: 'Ext.data.Model',
fields: [
{
name: 'apples'
},
{
name: 'pears'
},
],
Where can I put the other properties? I am working in Sencha Architect.
Take a read through the Ext.data.Field API docs. You'll see a variety of ways in which to configure data on your model.
I think what you're specifically asking for is the mapping config.