I have been looking for a way to relate a collection that has an array of objects, each object belongs to a different user, I would not like to force my backend by making it extract manually through maps or reduce the ids that belong to each user, I would like to know if is there a way to use populate so that only matching ids are added to my list
So far I have not been able to find information about it, I hope someone can help me
Related
I want to read data from firebase, in the ref ("locations/{{someLocation}}/logs/{{someDevice}}")
but I do not know if exists a correct form to do, because I need to use ".on" to hear constantly all the devices changes, the problem is that I need to read all devices in logs in all locations, but I do not want another information, I just need information in logs or in the specific device, I know that I could reach that invoking one callback ".on" for each device, but I want a cleaner form to make it and in the documentation, there is no help for this.
maybe I could invoke a method like that
firebase.database("locations/{eachLocation}/logs/{eachDevice}").ref().on...
because I do not want all the devices, the problem is that I do not know if there is a form to make it thank you.
It sounds like you've nested your data too much. The Firebase documentation has explicit sections on avoiding nesting data and flattening data structures with hints on how to prevent this.
At first glance you'll need at least two top-level lists: locations and locationLogs. Under each you have the same keys as you have now, but the logs are now under /locationLogs/$key instead of under /locations/$key/logs. With that change you can get the logs for a location without getting the other data for that location.
If you don't know the location key, but don't want to get the logs for all locations, it sounds like you've nested another level too deep. Firebase queries work on a flat list of nodes, and can't search across multiple levels of unknown keys. If you want to search across all logs across all locations, you will need to keep a flat list of all logs. You can then tie each log back to its location, by adding the location ID to each log.
So that might lead to a structure of:
logs: {
"adjustableLight....1": {
locationId: "DTZB35",
date: 156...,"
...
}
}
What you're trying to do isn't supported by Realtime Database. There are no wildcard queries or placeholders. You must be able to build the full path to the node whose data you want to get.
Consider changing the structure of your data so that you can more easily find the nodes you want. It is common in nosql type databases to duplicate data into structures that are easier to query for a particular use case.
plnkr
I am trying to traverse through a collection, and update each document respectively.
My UserProfile collection consists of multiple JSON objects of userProfiles. As you can see, each profile has a lot of the same information. The only difference is the personal information. (This is just a test case of hard coded objects. The real data will be in an SQL DB managed by a sysadmin).
What I am trying to do is write a function (replaceTopics) that will take in an array of topics and replace each topic that matches in the collection. So if the system admin makes a change to a topic/s, he will send me the topic/s and I will be checking each document in my userProfile collection to see if that document has the matching topic (by matching topicIDs), if so, I need to replace that entire topic with the editedTopic.
I have tried this but with no luck. You can take a look at my function.
I am working on a project in Meteor which uses ElasticSearch as a search engine. I need the search feature on the site to allow 'stacking' searches. So, for instance, one can search for a file that a user in a certain 'group' uploaded by 'stacking' the user's name, followed by the group name and ending with the file name or some content in the file.
Now, on the MongoDB database the group, user, and files would be stored in separate collections and be related to each other through Ids. However, ElasticSearch uses a distributed datastore where everything is 'flat'. This makes it necessary to denormalize data/do application-side joins/etc. (https://www.elastic.co/guide/en/elasticsearch/guide/current/relations.html).
My question is: which method would be the best...
Denormalize data, use nests, etc.
--> So, when rivering data to the elasticsearch datastore, I would make copies of the data and replace every parent element with a new one which has the data added to it.
FOR EX. If someone comments on let's say a post in a group. The server would have to add to the general list of comments + find the post object, append the comment to it, and re-add the post object to the database + update the group object which contains the post object which should contain the comment + do the same for a user object (since I want to be able to stack searches on groups, users, etc.).
Basically When ever something is added or deleted, I'd have to update every object in the database that relates to it.
Run multiple elastic search queries (https://www.elastic.co/guide/en/elasticsearch/guide/current/application-joins.html) to retrieve the data I want.
Just perform search queries on each de-centralized collection, and use javascript on the server-side to compare the arrays and produce the search results.
** Note: this is for scaling up to a relatively mid-level load/usage. So around hundreds-thousands of instances of data to search through. Although, if this can work larger scale (millions), that would be great!
Please correct me if my understanding of anything is wrong, and thank you for reading through all this!
well, i am creating a network that allows users creating posts and like them.
Asking on stackoverflow i've understood how to structure my database:
A collection which includes a document for each post.
A collection which includes a document for each like, in each of these documents there is a reference to post is referenced to.
When i want to get ALL likes about a post i can query the like collection looking for the reference to that post.
And till here i am ok. But assuming i'll have millions documents in like collection, i wondered how could i query and search among them in not too long time.
And i was advised of ensureIndex, in this case, i have to ensureindex of the field which contains reference to a post.
But when do i have to create this index? is enough to create it once (for example when i set up my database) and it will be as default in mongodb or do i have to do it during application life-time? thank you
But assuming i'll have millions documents in like collection, i wondered how could i query and search among them in not too long time.
I assume you would most likely want to do a count on the likes as an example?
You can't, instead you use optimizations to combat this. A count on millions of rows might get a bit slow.
A typical scenario are counters in SQL techs that you use to amend the parent row with a sum figure of its children.
Same applies to MongoDB.
You would aggregate important data to the top.
If you require to actually query the likes to show some who have liked it then you limit those likes. Google+ and other networks tend to limit the amount of likes they show to about 1,000.
And i was advised of ensureIndex,
Adding indexes to a database does help with actually searching for documents.
But when do i have to create this index? is enough to create it once
Yes, MongoDB will manage the index itself. You only need to ensure it once.
I have two riak buckets , "client_user" which contains compose_key as key like clientid-userid and another bucket "account_user" which contains userid as key and users' information. I am trying to make one mapreduce query to find out all users info base on one client. I search out on internet, I can't find any answer. Anyone can help me with a javascript mapreduce query on riak? really appreciate it.
If the key is a composite such as clientId_userId, there is no way to query it individually to find the users associated with that client. You can list all keys; Write a javascript map phase to split the key based on a delimiter; Feed the results of this operation to a SetMemberFilter based on client ID; Use another map reduce operation on the account_user bucket with this SetMemberFilter.
Your data is not modelled correctly for Riak. Had the user info just contained a secondary index for the client ID, this would have been a simple call to that bucket. I'm afraid there is no easy answer. You can still retrieve the information using the series of steps I outlined in my answer, but it would be inefficient. There is no other way.