How do I get a value from nested array of object?
Here is my API looks like:
{
"id": 7,
"code": "ABC123",
"name": "Abu Bakar Enterprise",
"speCompanyDetails": [
{
"id": 1,
"speCompanyId": 7,
"registrationType": "2",
"registrationNo": "12345678",
"registrationYear": 2005,
"annualIncome": 100000,
}
]
}
My objective is I want to get value for code and name but at the same time I also want to fetch value from speCompanyDetails.annualIncome
Currently my query is similar like this:
const SUPPLIER_INFO_QUERY = gql`
query SupplierInfoQuery($mainSuppId: String!) {
supplierInfo(id: $mainSuppId)
#rest(path: "services/supplier/api/spe-companies/{args.id}", method: "GET", type: "SupplierInfo") {
id
name
code
speCompanyDetails
}
}
`;
But the value for anuualIncome is undefined.
const SUPPLIER_INFO_QUERY = gql
query SupplierInfoQuery($mainSuppId: String!) {
supplierInfo(id: $mainSuppId)
#rest(path: "services/supplier/api/spe-companies/{args.id}", method: "GET", type: "SupplierInfo") {
id
name
code
speCompanyDetails{ annualIncome }
}
}
;
I have a json object that I load:
QJsonObject json = CommonToolkit::Types::LoadJson(config);
Here is a partial of the json file content:
{
"config": {
"macos": {
"screen": {
"main": {
"height": 0,
"left": 0,
"top": 0,
"width": 0
}
},
"windows: {
}
What I do is check what os i'm running in. Using the following variable to store that os:
QString osPath;
In my test osPath = "macos"
So I want to update the applications geometry
QJsonObject jparam{
{ "height", value.height() },
{ "left", value.left() },
{ "width", value.width() },
{ "top", value.top() }
};
My problem is when I try to set jon with jparam using osPath:
json["config"][osPath]["screen"]["main"] = jparam;
The error I'm getting is:
/Users/adviner/Projects/Notes/src/Notes/data/config.cpp:87: error: type 'QJsonValueRef' does not provide a subscript operator
json["config"][osPath]["screen"]["main"] = jparam;
~~~~~~~~~~~~~~^~~~~~~
Editing a Json in Qt is not a simple task, in this case when using json["config"] you get a QJsonValueRef, this class is a helper to get other types of elements like QJsonObject, QJsonArray, int, QString, etc. by what to get the next element must be used toObject() but this value is a copy, so if you modify it, the initial element will not be modified, you have to reassign it.
QJsonObject json = CommonToolkit::Types::LoadJson(config);
QString osPath = "macos";
QJsonObject jparam{
{ "height", value.height() },
{ "left", value.left() },
{ "width", value.width() },
{ "top", value.top() }
};
// get objects
QJsonObject config_obj = json["config"].toObject();
QJsonObject osPath_obj = config_obj[osPath].toObject();
QJsonObject screen_obj = osPath_obj["screen"].toObject();
// modify param
screen_obj["main"] = jparam;
// set objects
osPath_obj["screen"] = screen_obj;
config_obj[osPath] = osPath_obj;
json["config"] = config_obj;
I want to store multiple dictionary into an array so that the final results looks like so
(
{
id: 12,
task : completed
},
{
id: 15,
task : error
},
{
id: 17,
task : pending
},
)
I tried with code below but it does not give me what I want Please can someone help me out. Thanks
var FinalTaskData = [[String:AnyObject]]()
for i in 0..<taskObj.count{
let dict = ["id":taskObj[i].id!,"task":taskObj[i].task!] as [String : AnyObject]
FinalTaskData.append(dict)
}
And this gives me the output of
(
{
id = 190;
},
{
task = "Task To Be Edited";
},
{
id = 191;
},
{
task = "Also To Be Edited";
}
)
Which is not what I want. Thanks
I have a MongoDB collection of documents of the form
{
"id": 42,
"title": "candy can",
"description": "canada candy canteen",
"brand": "cannister candid",
"manufacturer": "candle canvas"
}
I need to implement auto-complete feature based on the input search term by matching in the fields except id. For example, if the input term is can, then I should return all matching words in the document as
{ hints: ["candy", "can", "canada", "canteen", ...]
I looked at this question but it didn't help. I also tried searching how to do regex search in multiple fields and extract matching tokens, or extracting matching tokens in a MongoDB text search but couldn't find any help.
tl;dr
There is no easy solution for what you want, since normal queries can't modify the fields they return. There is a solution (using the below mapReduce inline instead of doing an output to a collection), but except for very small databases, it is not possible to do this in realtime.
The problem
As written, a normal query can't really modify the fields it returns. But there are other problems. If you want to do a regex search in halfway decent time, you would have to index all fields, which would need a disproportional amount of RAM for that feature. If you wouldn't index all fields, a regex search would cause a collection scan, which means that every document would have to be loaded from disk, which would take too much time for autocompletion to be convenient. Furthermore, multiple simultaneous users requesting autocompletion would create considerable load on the backend.
The solution
The problem is quite similar to one I have already answered: We need to extract every word out of multiple fields, remove the stop words and save the remaining words together with a link to the respective document(s) the word was found in a collection. Now, for getting an autocompletion list, we simply query the indexed word list.
Step 1: Use a map/reduce job to extract the words
db.yourCollection.mapReduce(
// Map function
function() {
// We need to save this in a local var as per scoping problems
var document = this;
// You need to expand this according to your needs
var stopwords = ["the","this","and","or"];
for(var prop in document) {
// We are only interested in strings and explicitly not in _id
if(prop === "_id" || typeof document[prop] !== 'string') {
continue
}
(document[prop]).split(" ").forEach(
function(word){
// You might want to adjust this to your needs
var cleaned = word.replace(/[;,.]/g,"")
if(
// We neither want stopwords...
stopwords.indexOf(cleaned) > -1 ||
// ...nor string which would evaluate to numbers
!(isNaN(parseInt(cleaned))) ||
!(isNaN(parseFloat(cleaned)))
) {
return
}
emit(cleaned,document._id)
}
)
}
},
// Reduce function
function(k,v){
// Kind of ugly, but works.
// Improvements more than welcome!
var values = { 'documents': []};
v.forEach(
function(vs){
if(values.documents.indexOf(vs)>-1){
return
}
values.documents.push(vs)
}
)
return values
},
{
// We need this for two reasons...
finalize:
function(key,reducedValue){
// First, we ensure that each resulting document
// has the documents field in order to unify access
var finalValue = {documents:[]}
// Second, we ensure that each document is unique in said field
if(reducedValue.documents) {
// We filter the existing documents array
finalValue.documents = reducedValue.documents.filter(
function(item,pos,self){
// The default return value
var loc = -1;
for(var i=0;i<self.length;i++){
// We have to do it this way since indexOf only works with primitives
if(self[i].valueOf() === item.valueOf()){
// We have found the value of the current item...
loc = i;
//... so we are done for now
break
}
}
// If the location we found equals the position of item, they are equal
// If it isn't equal, we have a duplicate
return loc === pos;
}
);
} else {
finalValue.documents.push(reducedValue)
}
// We have sanitized our data, now we can return it
return finalValue
},
// Our result are written to a collection called "words"
out: "words"
}
)
Running this mapReduce against your example would result in db.words look like this:
{ "_id" : "can", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "canada", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "candid", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "candle", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "candy", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "cannister", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "canteen", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "canvas", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
Note that the individual words are the _id of the documents. The _id field is indexed automatically by MongoDB. Since indices are tried to be kept in RAM, we can do a few tricks to both speed up autocompletion and reduce the load put to the server.
Step 2: Query for autocompletion
For autocompletion, we only need the words, without the links to the documents.
Since the words are indexed, we use a covered query – a query answered only from the index, which usually resides in RAM.
To stick with your example, we would use the following query to get the candidates for autocompletion:
db.words.find({_id:/^can/},{_id:1})
which gives us the result
{ "_id" : "can" }
{ "_id" : "canada" }
{ "_id" : "candid" }
{ "_id" : "candle" }
{ "_id" : "candy" }
{ "_id" : "cannister" }
{ "_id" : "canteen" }
{ "_id" : "canvas" }
Using the .explain() method, we can verify that this query uses only the index.
{
"cursor" : "BtreeCursor _id_",
"isMultiKey" : false,
"n" : 8,
"nscannedObjects" : 0,
"nscanned" : 8,
"nscannedObjectsAllPlans" : 0,
"nscannedAllPlans" : 8,
"scanAndOrder" : false,
"indexOnly" : true,
"nYields" : 0,
"nChunkSkips" : 0,
"millis" : 0,
"indexBounds" : {
"_id" : [
[
"can",
"cao"
],
[
/^can/,
/^can/
]
]
},
"server" : "32a63f87666f:27017",
"filterSet" : false
}
Note the indexOnly:true field.
Step 3: Query the actual document
Albeit we will have to do two queries to get the actual document, since we speed up the overall process, the user experience should be well enough.
Step 3.1: Get the document of the words collection
When the user selects a choice of the autocompletion, we have to query the complete document of words in order to find the documents where the word chosen for autocompletion originated from.
db.words.find({_id:"canteen"})
which would result in a document like this:
{ "_id" : "canteen", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
Step 3.2: Get the actual document
With that document, we can now either show a page with search results or, like in this case, redirect to the actual document which you can get by:
db.yourCollection.find({_id:ObjectId("553e435f20e6afc4b8aa0efb")})
Notes
While this approach may seem complicated at first (well, the mapReduce is a bit), it is actual pretty easy conceptually. Basically, you are trading real time results (which you won't have anyway unless you spend a lot of RAM) for speed. Imho, that's a good deal. In order to make the rather costly mapReduce phase more efficient, implementing Incremental mapReduce could be an approach – improving my admittedly hacked mapReduce might well be another.
Last but not least, this way is a rather ugly hack altogether. You might want to dig into elasticsearch or lucene. Those products imho are much, much more suited for what you want.
Thanks to #Markus solution, I came up with something similar with aggregations instead. Knowing that map-reduce are flagged as deprecated for later versions.
const { MongoDBNamespace, Collection } = require('mongodb')
//.replace(/(\b(\w{1,3})\b(\W|$))/g,'').split(/\s+/).join(' ')
const routine = `function (text) {
const stopwords = ['the', 'this', 'and', 'or', 'id']
text = text.replace(new RegExp('\\b(' + stopwords.join('|') + ')\\b', 'g'), '')
text = text.replace(/[;,.]/g, ' ').trim()
return text.toLowerCase()
}`
// If the pipeline includes the $out operator, aggregate() returns an empty cursor.
const agg = [
{
$match: {
a: true,
d: false,
},
},
{
$project: {
title: 1,
desc: 1,
},
},
{
$replaceWith: {
_id: '$_id',
text: {
$concat: ['$title', ' ', '$desc'],
},
},
},
{
$addFields: {
cleaned: {
$function: {
body: routine,
args: ['$text'],
lang: 'js',
},
},
},
},
{
$replaceWith: {
_id: '$_id',
text: {
$trim: {
input: '$cleaned',
},
},
},
},
{
$project: {
words: {
$split: ['$text', ' '],
},
qt: {
$const: 1,
},
},
},
{
$unwind: {
path: '$words',
includeArrayIndex: 'id',
preserveNullAndEmptyArrays: true,
},
},
{
$group: {
_id: '$words',
docs: {
$addToSet: '$_id',
},
weight: {
$sum: '$qt',
},
},
},
{
$sort: {
weight: -1,
},
},
{
$limit: 100,
},
{
$out: {
db: 'listings_db',
coll: 'words',
},
},
]
// Closure for db instance only
/**
*
* #param { MongoDBNamespace } db
*/
module.exports = function (db) {
/** #type { Collection } */
let collection
/**
* Runs the aggregation pipeline
* #return {Promise}
*/
this.refreshKeywords = async function () {
collection = db.collection('listing')
// .toArray() to trigger the aggregation
// it returns an empty curson so it's fine
return await collection.aggregate(agg).toArray()
}
}
Please check for very minimal changes for your convenience.
I have some documents in my Couchbase with the following template:
{
"id": 102750,
"status": 5,
"updatedAt": "2014-09-10T10:50:39.297Z",
"points1": 1,
"points2": -3,
"user1": {
"id": 26522,
...
},
"user2": {
"id": 38383,
...
},
....
}
What I want to do is to group the documents on the user and sum the points for each user and then show the top 100 users in the last week. I have been circling around but I haven't come with any solution.
I have started with the following map function:
function (doc, meta) {
if (doc.user1 && doc.user2) {
emit(doc.user1.id, doc.points1);
emit(doc.user2.id, doc.points2);
}
}
and then tried the sum to reduce the results but clearly I was wrong because I wasn't able to sort on the points and I couldn't also include the date parameter
you need to see my exemple I was able to group by date and show the values with reduce. but calculate the sum I did it in my program.
see the response How can I groupBy and change content of the value in couchbase?
I have solved this issue by the help of a server side script.
What I have done is I changed my map function to be like this:
function (doc, meta) {
if (doc.user1 && doc.user2) {
emit(dateToArray(doc.createdAt), { 'userId': doc.user1.id, 'points': doc.points1});
emit(dateToArray(doc.createdAt), { 'userId': doc.user2.id, 'points': doc.points2});
}
}
And in the script I query the view with the desired parameters and then I group and sort them then send the top 100 users.
I am using Node JS so my script is like this: (the results are what I read from couchbase view)
function filterResults(results) {
debug('filtering ' + results.length + ' entries..');
// get the values
var values = _.pluck(results, 'value');
var groupedUsers = {};
// grouping users and sum their points in the games
// groupedUsers will be like the follwoing:
// {
// '443322': 33,
// '667788': 55,
// ...
// }
for (var val in values) {
var userId = values[val].userId;
var points = values[val].points;
if (_.has(groupedUsers, userId)) {
groupedUsers[userId] += points;
}
else
groupedUsers[userId] = points;
}
// changing the groupedUsers to array form so it can be sorted by points:
// [['443322', 33], ['667788', 55], ...]
var topUsers = _.pairs(groupedUsers);
// sort descending
topUsers.sort(function(a, b) {
return b[1] - a[1];
});
debug('Number of users: ' + topUsers.length + '. Returning top 100 users');
return _.first(topUsers, 100);
}