I want to write data into a specific location in the database. Let's say, I have a couple of users in the database. Each of them has their own personal information, including their e-mails. I want to find the user based on the e-mail, that's to say by using his e-mail (but I don't know exactly whose e-mail it is, but whoever it is do something with that user's information). To be more visible, here is my database sample.
Now, while working on one of my javascript files, when the user let's say name1 changes his name, I update my object in javascript and want to replace the whole object under ID "-LEp2F2fSDUt94SRU0cx". To cut short, I want to write this updated object in the path ("Users/-LEp2F2fSDUt94SRU0cx") without doing it by hand and just "knowing" the e-mail. So the logic is "Go find the user with the e-mail "name1#yahoo.com" and replace the whole object with his new updated object". I tried to use orderByChild("Email").equalTo("name1#yahoo.com").set(updated_object), but this syntax does not work I guess. Hopefully I could explain myself.
The first part is the query, that is separate from the post to update. This part is the query to get the value:
ref.child('users').orderByChild("Email").equalTo("name1#yahoo.com")
To update, you need to do something like this once you have the user id from the query result:
ref.child('users').child(userId).child("Email").update(newValue);
firebase.database.Query
A Query sorts and filters the data at a Database location so only a
subset of the child data is included. This can be used to order a
collection of data by some attribute (for example, height of
dinosaurs) as well as to restrict a large list of items (for example,
chat messages) down to a number suitable for synchronizing to the
client. Queries are created by chaining together one or more of the
filter methods defined here.
// Find all dinosaurs whose height is exactly 25 meters.
var ref = firebase.database().ref("dinosaurs");
ref.orderByChild("height").equalTo(25).on("child_added", function(snapshot) {
console.log(snapshot.key);
});
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.
I have a node.js API that is responsible for 3 things:
Registering a buyer
Getting a buyer with ID
Finding the matching buyer's offer based on some criteria
Details here
Since I'm new to Redis, I started the implementation like this:
JSON.stringify the buyer and store it with SET
Store all buyer's offers as ordered set (this is for the third endpoint, which requires the offer with the highest value) - this set contains string that represents the name of a hash
Then, that hash stores strings that represent the names of sets that have certain values and a location which the user will be redirected to after these conditions have been fulfilled (buyer1_devices, buyer1_hours, etc.)
Now, here is the problem:
I need to get GET /route working. As described on GitHub page that I have provided, I have 3 parameters: a timestamp, devices, and states. I have to browse through all the sets and get the appropriate location to redirect a user to. The location is stored in a hash, but I have to browse through all the sets. Since this is probably a bad implementation, where did it all go wrong and to go about implementing this?
Note that this is a redis problem, not a node one. I need instructions on how to go about implementing this in Redis, and then I will be ready to code it in Node.
Thank you in advance
The first rule of Redis: store the data just like you want to read it.
To answer the /route query you need "filteration" on two attributes of from the buyers' offers - state and device. There is more than one way to skin that cat, so here's one: use many Sorted Sets for the offers.
Each such offers Sorted Set key name could look like this: <device>:<state> (so the example offered in the git will be added to the key desktop:CA).
To query, use the route's arguments to compose your key's name, then proceed regularly to find the highest-scored offer and resolve the buyer's details in the Hash.
Now go get that job!
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!
I have a MongoDB JavaScript function saved in db.system.js, and I want to use it to both query and produce output data.
I'm able to query the results of the function using the $where clause like so:
db.records.find(
{$where: "formatEmail(this.email.toString(), 'format type') == 'xxx'"},
{email:1})
but I'm unable to use this to return a formatted value for the projected "email" field, like so:
db.records.find({}, {"formatEmail(this.email.toString(), 'format type')": 1})
Is there any way to do this while preserving the ability to simply use a pre-built function?
UPDATE:
Thank you all for your prompt participation.
Let me explain why I need to do this in MongoDB and it's not a matter of client logic at the wrong layer.. What I am really trying to do is use the function for a shard bucketing value. Email was just one example, but in reality, what I have is a hash function that returns a mod value.
I'm aware of Mongo having the ability to shard based on a hashed value, but from what I gather, it produces a highly random value that can burden the re-balancing of shards with unnecessary load. So I want to control it like so func(_id, mod), which would return a value from 0 to say 1000 (depending on the mod value).
Also, I guess I would also like to use the output of the function in some sort of grouping scenario, and I guess Map Reduce does come to mind.. I was just hoping to avoid writing overly complex M/R for something so simple.. also, I don't really know how to do Map Reduce .. lol.
So, I gather that from your answers, there is no way to return any formatted value back from mongo (without map/reduce), is that right?
I think you are mixing your "layers" of functionality here -- the database stores and retrieves data, thats all. What you need to do is:
* get that data and store the cursor in a variable
* loop through your cursor, and for every record you go through
* format and output your record as you see fit.
This is somewhat similar to what you have described in your question, but its not part of MongoDB and you have to provide the "formatEmail" function in your "application layer"
Hope it helps
As #alernerdev has already mentioned, this is generally not done at a database layer. However, sometimes storing a pre-formatted version in your database is the way to go. Here's some instances where you may wish to store extra data:
If you need to lookup data in a particular format. For example, I have a "username" and a "usernameLowercase" fields for my primary user collection. The lowercase'd one is indexed, and is the one I use for username lookup. The mixed-case one is used for displaying the username.
If you need to report a large amount of data in a particular format. 100,000 email addresses all formatted in a particular way? Probably best to just store them in that format in the db.
If your translation from one format to another is computationally expensive. Doubly so if you're processing lots of records.
In this case, if all you're doing is looking up or retrieving an email in a specific format, I'd recommend adding a field for it and then indexing it. That way you won't need to do actual document retrieval for the lookup or the display. Super fast. Disk storage space for something the size of an email address is super cheap!