Is it possible to modify Mongo document entry by id?
in the example below I want to change checked property to true where identifier is priner.settings.id4
{
"identifier": "priner.settings.id4",
"Question caption": "Check ink flow",
"checked": "false"
}
kinda like in SQL
update table set checked = true where identifier = 'priner.settings.id4'
the document itself is huge so I am trying to avoid appending the entire path to the setting like
InspectionContent-> inkLevels -> PrinterInspection -> questions
Is there an easy way to achieve this?
sample document:
{
"InspectionContent": {
"InkLevels": {
"PrinterInspection": {
"questions": [
{
"identifier": "priner.settings.id1",
"Question caption": "Check link level",
"checked": "false"
},
{
"identifier": "priner.settings.id2",
"Question caption": "Check ink cond",
"checked": "false"
},
{
"identifier": "priner.settings.id3",
"Question caption": "Check ink color",
"checked": "false"
},
{
"identifier": "priner.settings.id4",
"Question caption": "Check ink flow",
"checked": "false"
}
]
}
}
}
}
you need arrayfilters to update nested array elements.
db.collection.update(
{},
{
$set: { "InspectionContent.InkLevels.PrinterInspection.questions.$[x].checked": "true" }
},
{
multi: true,
arrayFilters: [
{ "x.identifier": { $eq: "priner.settings.id4" } }
]
}
)
Related
The problem I am facing is that I want to develop an autocomplete search bar using Mean Stack like the one in this site, but when I type, for example, 'ag' it's not returning the right location that should be 'Aguascalientes'.
I have two different search indexes set up and a different query for each.
First Index:
{
"mappings": {
"dynamic": false,
"fields": {
"name": {
"foldDiacritics": false,
"maxGrams": 7,
"minGrams": 3,
"tokenization": "edgeGram",
"type": "autocomplete"
},
"searchName": {
"foldDiacritics": false,
"maxGrams": 7,
"minGrams": 3,
"tokenization": "edgeGram",
"type": "autocomplete"
}
}
}
}
First Query:
[
{
$search: {
index: "autocomplete2",
compound: {
must: [
{
text: {
query: search,
path: "searchName",
fuzzy: {
maxEdits: 2,
},
},
},
],
},
},
},
{
$limit: 10,
},
]
The first ones are not returning any document at all. But the second example is:
{
"mappings": {
"dynamic": false,
"fields": {
"name": {
"analyzer": "lucene.standard",
"type": "string"
},
"searchName": {
"analyzer": "lucene.standard",
"type": "string"
}
}
}
}
Query:
[
{
$search: {
index: 'default',
compound: {
must: [
{
text: {
query: search,
path: 'name',
fuzzy: {
maxEdits: 1,
},
},
},
{
text: {
query: search,
path: 'searchName',
fuzzy: {
maxEdits: 1,
},
},
},
],
},
},
},
{
$limit: 5,
},
]
The second example is only returning documents if the search term 'aguascalient' but is not returning any document if the search term is shorter like the site. Maybe it has something to do with the fuzzy edits but if I set it up to greater than 2 I get an error.
Also the order is not right, it returns first the CITY and second the STATE but I need the STATE first because the search term is more similar than the city. Let me explain, search field for STATE is only 'Aguascalientes' but search field cities is 'Aguascalientes Aguascalientes' so I don't know why is not working properly. Maybe in that case I should give weights accordingly but I'm not sure if it's the right approach to solve this.
My data structure:
{
"_id": "638d0ffc34ad076c6bd12cb6",
"depth": 2,
"label": "CITY",
"location_id": "V1-C-247",
"name": "Aguascalientes",
"parent": "Aguascalientes",
"fullName": "Aguascalientes, Aguascalientes",
"parentId": "V1-B-61",
"searchName": "Aguascalientes Aguascalientes",
}
{
"_id": "638d0ffc34ad076c6bd12cb6",
"depth": 1,
"label": "STATE",
"location_id": "V1-C-248",
"name": "Aguascalientes",
"parent": null,
"fullName": "Aguascalientes",
"parentId": null,
"searchName": "Aguascalientes",
}
For the first index + query setup:
First, you are indexing the name field but are not searching on it. I will remove it from the code snippets for readability, but you can add it back to your index definition if you find you need to search on it.
There are two problems with the this index + query setup if you want to return results with a query for "ag". You have searchName defined as a field mapping of type autocomplete, but you also need to use the autocomplete operator in your query:
[
{
$search: {
index: "autocomplete2",
compound: {
must: [
{
autocomplete: {
query: search,
path: "searchName",
},
},
],
},
},
},
{
$limit: 10,
},
]
Second, in your index definition field mapping for searchName, you have minGram set to 3 and maxGram set to 7. Based on the documentation for the autocomplete field mapping, this means that your data will be tokenized into sequences of character lengths between 3 to 7, using the selected tokenization strategy. Since you have selected edgeGram, the tokens generated by the text "Aguascalientes" will be tokenized starting from the left edge, resulting in tokens "agu", "agua", "aguas", "aguasc", "aguasca". Since the search term "ag" does not match any of the tokens, nothing is returned. So, you must change the minGram to 2 to get the token "ag":
{
"mappings": {
"dynamic": false,
"fields": {
"searchName": {
"foldDiacritics": false,
"maxGrams": 7,
"minGrams": 2,
"tokenization": "edgeGram",
"type": "autocomplete"
}
}
}
}
Finally, if you want the document with an exact match to return over a partial match, ie. "Aguascalientes" should return before "Aguascalientes Aguascalientes", you need to implement exact matching. Here is a MongoDB blog post outlining a few options.
One option that I tried: In the index, use a keyword analyzer on the "searchName" field typed as a string data type. In the query, use the text operator nested in a should clause so that exact matches will return higher than other results.
Index:
{
"mappings": {
"dynamic": false,
"fields": {
"searchName": [
{
"foldDiacritics": false,
"maxGrams": 7,
"type": "autocomplete"
},
{
"analyzer": "lucene.keyword",
"searchAnalyzer": "lucene.keyword",
"type": "string"
}
]
}
}
}
Query:
[
{
$search: {
compound: {
must: [
{
autocomplete: {
query: search,
path: "searchName"
}
}
],
should:[
{
text: {
query: search,
path: "searchName"
}
}
],
},
},
},
]
I am using Postgres DB 13.5. From pgdocs -
The technical difference between a jsonb_ops and a jsonb_path_ops GIN
index is that the former creates independent index items for each key
and value in the data, while the latter creates index items only for
each value in the data. Basically, each jsonb_path_ops index item
is a hash of the value and the key(s) leading to it; for example to
index {"foo": {"bar": "baz"}}
Understanding the above in detail is important for me coz my jdata (document) is big with many keys and nested objects. Consider my json data that is stored as jsonb in a column named jdata looks like below -
{
"supplier": {
"id": "3c67b6eb-3b0d-492d-8736-66df107b83b3",
"customer": {
"type": "pro",
"name": "John George",
"address": [
{
"add-id": "098ad4df-2a90-4fda-8f92-dbe8d7196732",
"addressActive": true,
"street": "abc street",
"zip": 94044,
"staying-since": "long long",
"accessibility": {
"traffic": "heavy/congested",
"bestwaytoreach": {
"weekdays": {
"bart/metro/calltrain": true,
"price": {
"off-peak-hours": "affordable",
"peak-hours": "high"
},
"journey-time": "super-fast"
}
},
"weekends": {
"byroad": {
"ok": true,
"distance": "long",
"has-tolls": {
"true": true,
"toll-price": "relatively-high"
},
"journey-speed": "fast"
}
}
}
},
{
"add-id": "ddd1d2a0-9050-4bcf-a3ad-2e608d65e468",
"addressActive": true,
"street": "xyz street",
"zip": 10001,
"staying-since": "moved recently",
"accessibility": {
"traffic": "heavy/congested",
"bestwaytoreach": {
"weekdays": {
"subway": true,
"price": {
"off-peak-hours": "affordable",
"peak-hours": "high"
},
"journey-speed": "super-fast"
}
},
"weekends": {
"byroad": {
"ok": true,
"distance": "moderate",
"tolls": {
"has-tolls": true,
"toll-price": "relatively-high"
},
"journey-time": "super-fast"
}
}
}
}
],
"firstName": "John",
"lastName": "CRAWFORD",
"emailAddresses": {
"personal": [
"johnreplies#jg.com",
"ursjohn#jg.com",
"1234#jg.com"
],
"official": [
{
"repies-in": "1 day",
"email": "jg#jg.com"
},
{
"check's regularly": true,
"repies-in": "1 Hour",
"email": "jg-watching#jg.com"
}
]
},
"cities": [
"NYC",
"LA",
"SF",
"DC"
],
"splCustFlag": null,
"isPerson": true,
"isEntity": false,
"allowEmailSolicit": "Y",
"allowPhoneSolicit": "Y",
"taxPayer": true,
"suffix": null,
"title": null,
"birthDate": "05/10/1993",
"loyaltyPrograms": null,
"phoneNumbers-summary": [
1234567890,
1234567899,
1234567898,
1234567897
],
"phoneNumbers": [
{
"description": null,
"extension": null,
"number": 1234567890,
"countryCode": null,
"type": "Business"
},
{
"description": null,
"extension": null,
"number": 1234567899,
"countryCode": null,
"type": "Home"
}
],
"data-privacy": {
"required": true,
"laws": [
"CCPA",
"GDPR"
]
}
}
}
}
Now if I create GIN jsonb_ops index for jdata column - I want to clarify what all keys and values will be part of index.
For example - "staying-since" is a key nested at below path and it's part of "address" array too. But it's still a key, thought nested deep in the document. So will it be part of the index.
{
"supplier": {
"customer": {
"address": [
{
"staying-since": "long long" ...
And similarly "long long" is a value of a deeply nested key. Will it also be indexed.
And if GIN jsonb_path_ops index is created for jdata column --
Will a hash of "long long" value along with the path that leads to it will also be indexed.
hash(
"supplier": {
"customer": {
"address":[{"staying-since": "long long"}]
}
}
)
will the above also gets index.
I am aware about the operators that are supported by the GIN index types and am aware about the usage of these operators -
jsonb_ops ? ?& ?| #> #? ##
jsonb_path_ops #> #? ##
I have a collection with the following documents (for example):
{
"_id": {
"$oid": "61acefe999e03b9324czzzzz"
},
"matchId": {
"$oid": "61a392cc54e3752cc71zzzzz"
},
"logs": [
{
"actionType": "CREATE",
"data": {
"talent": {
"talentId": "qq",
"talentVersion": "2.10",
"firstName": "Joelle",
"lastName": "Doe",
"socialLinks": [
{
"type": "FACEBOOK",
"url": "https://www.facebook.com"
},
{
"type": "LINKEDIN",
"url": "https://www.linkedin.com"
}
],
"webResults": [
{
"type": "VIDEO",
"date": "2021-11-28T14:31:40.728Z",
"link": "http://placeimg.com/640/480",
"title": "Et necessitatibus",
"platform": "Repellendus"
}
]
},
"createdBy": "DEVELOPER"
}
},
{
"actionType": "UPDATE",
"data": {
"talent": {
"firstName": "Joelle new",
"webResults": [
{
"type": "VIDEO",
"date": "2021-11-28T14:31:40.728Z",
"link": "http://placeimg.com/640/480",
"title": "Et necessitatibus",
"platform": "Repellendus"
}
]
}
}
}
]
},
{
"_id": {
"$oid": "61acefe999e03b9324caaaaa"
},
"matchId": {
"$oid": "61a392cc54e3752cc71zzzzz"
},
"logs": [....]
}
a brief breakdown: I have many objects like this one in the collection. they are a kind of an audit log for actions takes on other documents, 'Match(es)'. for example CREATE + the data, UPDATE + the data, etc.
As you can see, logs field of the document is an array of objects, each describing one of these actions.
data for each action may or may not contain specific fields, that in turn can also be an array of objects: socialLinks and webResults.
I'm trying to remove sensitive data from all of these documents with specified Match ids.
For each document, I want to go over the logs array field, and change the value of specific fields only if they exist, for example: change firstName to *****, same for lastName, if those appear. also, go over the socialLinks array if exists, and for each element inside it, if a field url exists, change it to ***** as well.
What I've tried so far are many minor variations for this query:
$set: {
'logs.$[].data.talent.socialLinks.$[].url': '*****',
'logs.$[].data.talent.webResults.$[].link': '*****',
'logs.$[].data.talent.webResults.$[].title': '*****',
'logs.$[].data.talent.firstName': '*****',
'logs.$[].data.talent.lastName': '*****',
},
and some play around with this kind of aggregation query:
[{
$set: {
'talent.socialLinks.$[el].url': {
$cond: [{ $ne: ['el.url', null] },'*****', undefined],
},
},
}]
resulting in errors like: message: "The path 'logs.0.data.talent.socialLinks' must exist in the document in order to apply array updates.",
But I just cant get it to work... :(
Would love an explanation on how to exactly achieve this kind of set-only-if-exists behaviour.
A working example would also be much appreciated, thx.
Would suggest using $\[<indentifier>\] (filtered positional operator) and arrayFilters to update the nested document(s) in the array field.
In arrayFilters, with $exists to check the existence of the certain document which matches the condition and to be updated.
db.collection.update({},
{
$set: {
"logs.$[a].data.talent.socialLinks.$[].url": "*****",
"logs.$[b].data.talent.webResults.$[].link": "*****",
"logs.$[b].data.talent.webResults.$[].title": "*****",
"logs.$[c].data.talent.firstName": "*****",
"logs.$[d].data.talent.lastName": "*****",
}
},
{
arrayFilters: [
{
"a.data.talent.socialLinks": {
$exists: true
}
},
{
"b.data.talent.webResults": {
$exists: true
}
},
{
"c.data.talent.firstName": {
$exists: true
}
},
{
"d.data.talent.lastName": {
$exists: true
}
}
]
})
Sample Mongo Playground
I'm using graphQL to query a MongoDB database in React, using Strapi as my CMS. I'm using Apollo to handle the GraphQL queries. I'm able to get my objects by passing an ID argument, but I want to be able to pass different arguments like a name.
This works:
{
course(id: "5eb4821d20c80654609a2e0c") {
name
description
modules {
title
}
}
}
This doesn't work, giving the error "Unknown argument \"name\" on field \"course\" of type \"Query\"
{
course(name: "course1") {
name
description
modules {
title
}
}
}
From what I've read, I need to define a custom query, but I'm not sure how to do this.
The model for Course looks like this currently:
"kind": "collectionType",
"collectionName": "courses",
"info": {
"name": "Course"
},
"options": {
"increments": true,
"timestamps": true
},
"attributes": {
"name": {
"type": "string",
"unique": true
},
"description": {
"type": "richtext"
},
"banner": {
"collection": "file",
"via": "related",
"allowedTypes": [
"images",
"files",
"videos"
],
"plugin": "upload",
"required": false
},
"published": {
"type": "date"
},
"modules": {
"collection": "module"
},
"title": {
"type": "string"
}
}
}
and the
Any help would be appreciated.
Referring to Strapi GraphQL Query API
You can use where with the query courses to filter your fields. You will get a list of courses instead of one course
This should work:
{
courses(where: { name: "course1" }) {
name
description
modules {
title
}
}
}
I'm trying to do a sample application with firebase and I don't have quite understand how I should retrieve nested-flattered data.
Let's suppose I have a db like this
{
"users": {
"user-1": {
"email": "email1",
"matches": [
"match-1",
"match-2"
]
},
"user-2": {
"email": "email2",
"matches": [
"match-1",
"match-2"
]
},
"user-3": {
"email": "email3",
"matches": [
"match-2"
]
}
},
"matches": {
"match-1": {
"name": "Match 1",
"users": [
"user-1",
"user-2"
]
},
"match-2": {
"name": "Match 2",
"users": [
"user-1",
"user-2",
"user-3"
]
}
}
}
and I want to get all the match of the user-1.
What I'm doing now is observe users.user-1.matches to get the matches list and then observe every match so the final flow is:
observe users.user-1.matches
observe matches.match-1
observe matches.match-2
...
The question is: how can I optimize this flow? (like making something like the sql join)
My idea is to get something like this
{
"users": {
"user-1": {
"email": "email1",
"matches": {
"match-1": {
"name": "Match 1",
"users": [
"user-1",
"user-2"
]
},
"match-2": {
"name": "Match 2",
"users": [
"user-1",
"user-2",
"user-3"
]
}
}
}
}
}
So I can observer the whole structure at once.
I'm using firebase on iOS with swift but feel free to reply in every language you like.
Thanks.
The answer linked in the comment is an answer, but let's simplify. Let's create a users node to store the users and a matches node to track the matches they played in.
Then we'll do a query to retrieve which matches user-1 played in:
users
user-1
name: "some name"
email: "somename#thing.com"
user-2
name: "another name"
email: "anothername#thing.com"
user-3
name: "cool name"
email: "coolname#thing.com"
and then the matches node
matches
match-1
name: "Match 1"
users
user-1: true
user-2: true
match-2
name: "Match 2"
users
user-1: true
user-2: true
user-3: true
match-3
name: "Match 3"
users
user-2: true
user-3: true
As you can see, we've structured the data to directly address our query
matchesRef.queryOrdered(byChild: "users/user-1").queryEqual(toValue: true)
.observe(.value, with: { snapshot in
print(snapshot)
});
and the snapshot will contain the nodes match-1 and match-2 but not match-3