I am trying to build an app where I just have these 3 models:
topic (has just a title (max 100 chars.))
comment (has text (may be very long), author_id, topic_id, createdDate)
author (has just a username)
Actually a very simple db structure. A Topic may have many comments, which are created by authors. And an author may have many comments.
I am still trying to figure out the best way of designing the database structure (documents). First I though to put everything to its own schema like above. 3 Documents. But since this is a nosql db, I should actually try to eliminate the needs for a join. And now I am really thinking of putting everything to a single document, which also sounds crazy.
These are my actually queries from ui:
Homepage query: Listing all the topics, which have received the most comments today (will run very often)
Auto suggestion list for search field: Listing all the topics, whose title contains string "X"
Main page of a topic query: Listing all the comments of a topic, with their authors' username.
Since most of my queries need data from at least 2 documents, should I really just use them all together in a single document like this:
Comment (text, username, topic_title, createdDate)
This way I will not need any join, but also save i.e. the title of topics multiple times.. in every comment..
I just could not decide.
I appreciate any help.
You can do the second design you suggested but it all comes down to how you want to use the data. I assume you’re going to be using it for a website.
If you want the comments to be clickable, in such that clicking on the topic name will redirect to the topic’s page or clicking the username will redirect to the user’s page where you can see all his comments, i suggest you keep them as IDs. Since you can later use .populate(“field1 field2”) and you can select the fields you would like to get from that ID.
Alternatively you can store both the topic_name and username and their IDs in the same document to reduce queries, but you would end up storing more redundant data.
Revised design:
The three queries (in the question post) are likely to be like this (pseudo-code):
select all topics from comments, where date is today, group by topic and count comments, order by count (desc)
select topics from comments, where topic matches search, group by topic.
select all from comments, where topic matches topic_param, order by comment_date (desc).
So, as you had intended (in your question post) it is likely there will be one main collection, comments.
comments:
date
author
text
topic
The user and topic collections with one field each, are optional, to maintain uniqueness.
Note the group-by queries will be aggregation queries, for example, the main query will be like this:
db.comments.aggregate( [
{ $match: { date: ISODate("2019-11-15") } },
{ $group: { _id: "$topic", count: { $sum: 1 } } },
{ $sort: { count: -1 } }
] )
This will give you all the topics names, today and with highest counted topics first.
You could also take a bit different approach. Storing information redundant is not a bad thing in all cases.
1. Homepage query: Listing all the topics, which have received the most comments today (will run very often)
You could implement this as two extra fields in your Topic entity. One describing the last date a comment was added and the second to count the amount of comments added that day. By doing so you do not need to join but can write a query that only looks at the Topic collection.
You could also store these statistics independently of the other data and update it when required. Think of this as having a document that describes your database its current state (at least those parts relevant to you).
This might give you a time penalty on storing information but it improves reading times.
2. Auto suggestion list for search field: Listing all the topics, whose title contains string "X"
Far as I understand this one you only need the topic title. Meaning you can query the database once and retrieve all titles. If the collection grows so big this becomes slow you could trigger a refresh of the retrieval query that only returns a subset (a user is not likely to go through 100 possible topics).
3. Main page of a topic query: Listing all the comments of a topic, with their authors' username.
This is actually the tricky one. If this is really what it is you want to do then you are most likely best off storing all data in one document. However I would ask you: what is the problem making more than one query? I doubt you will be showing all comments at once when there are thousands (as you say). Instead of storing each in a separate document or throwing all in one document, you could also bucket them and retrieve only the 20 most recent ones (if you would create buckets of size 20). Read more about the bucket pattern here and update the ones shown when required.
You said:
"Since most of my queries need data from at least 2 documents, should I really just use them all together in a single document like this..."
I"ll make an argument from a 'domain driven design' point of view.
Given that all your data exists within the same bounded context (business domain). Then it is acceptable to encapsulate it all within the same document!
Related
I've searched and didn't find any exact answer to this common problem.
I would like to show to users new/unread posts in a way that they get for example list of topics with unreaded posts.
If user decide and open any of that topics then automatically it's marked as read and will not show inside that list again when he click on unread posts. Plus the possibility to mark all as read.
I was thinking about maybe showing unread posts only from last 30 days, so data would not be so big.
The obvious and best solution would be making this inside objects embedded inside arrays, every array would have userid and timestamp of last view of specific topic, then i would just compare timestamp of the last post in thread to timestamp of last view of that thread by the user.
Only 2-3 queries then would be needed to show results to the user.
So for example it would look like this:
{
_id: uniqueObjectid,
id: topic_id,
topic: topic of the thread,
last_update: timestamp of last reply to that topic,
reads: [
{id: userid, last_view: timestamp of last view on this topic by user},
...
]
}
I would delete all threads from this collection that have last_update field older than 30 days.
Showing then unread posts for users would be very easy, just compare last_update with last_view for certain userid.
But it's not a good solution, from what i've read the way arrays are implemented in mongodb make that solution very slow. Imagine having last view of some topic for 1000 users, it means 1000 indexed array elements.
So it can't be done in arrays.
Here Asya from MongoDB describes why big embedded arrays should not be used link
I am having difficulties to think of any other efficient way of solving this issue.
I don't understand one thing about Cassandra. Say, I have similar website to Facebook, where people can share, like, comment, upload images and so on.
Now, let's say, I want to get all of the things my friends did:
Username1 liked you comment
username 2 updated his profile picture
And so on.
So after a lot of reading, I guess I would need to do is create new Column Family for each single thing, for example: user_likes user_comments, user_shares. Basically, anything you can think off, and even after I do that, I would still need to create secondary indexes for most of the columns just so I could search for data? And even so how would I know which users are my friends? Would I need to first get all of my friends id's and then search through all of those Column Families for each user id?
EDIT
Ok so i did some more reading and now i understand things a little bit better, but i still can't really figure out how to structure my tables, so i will set a bounty and i want to get a clear example of how my tables should look like if i want to store and retrieve data in this kind of order:
All
Likes
Comments
Favourites
Downloads
Shares
Messages
So let's say i want to retrieve ten last uploaded files of all my friends or the people i follow, this is how it would look like:
John uploaded song AC/DC - Back in Black 10 mins ago
And every thing like comments and shares would be similar to that...
Now probably the biggest challenge would be to retrieve 10 last things of all categories together, so the list would be a mix of all the things...
Now i don't need an answer with a fully detailed tables, i just need some really clear example of how would i structure and retrieve data like i would do in mysql with joins
With sql, you structure your tables to normalize your data, and use indexes and joins to query. With cassandra, you can't do that, so you structure your tables to serve your queries, which requires denormalization.
You want to query items which your friends uploaded, one way to do this is t have a single table per user, and write to this table whenever a friend of that user uploads something.
friendUploads { #columm family
userid { #column
timestamp-upload-id : null #key : no value
}
}
as an example,
friendUploads {
userA {
12313-upload5 : null
12512-upload6 : null
13512-upload8 : null
}
}
friendUploads {
userB {
11313-upload3 : null
12512-upload6 : null
}
}
Note that upload 6 is duplicated to two different columns, as whoever did upload6 is a friend of both User A and user B.
Now to query the friends upload display of a friend, do a getSlice with a limit of 10 on the userid column. This will return you the first 10 items, sorted by key.
To put newest items first, use a reverse comparator that sorts larger timestamps before smaller timestamps.
The drawback to this code is that when User A uploads a song, you have to do N writes to update the friendUploads columns, where N is the number of people who are friends of user A.
For the value associated with each timestamp-upload-id key, you can store enough information to display the results (probably in a json blob), or you can store nothing, and fetch the upload information using the uploadid.
To avoid duplicating writes, you can use a structure like,
userUploads { #columm family
userid { #column
timestamp-upload-id : null #key : no value
}
}
This stores the uploads for a particular user. Now when want to display the uploads of User B's friends, you have to do N queries, one for each friend of User B, and merge the result in your application. This is slower to query, but faster to write.
Most likely, if users can have thousands of friends, you would use the first scheme, and do more writes rather than more queries, as you can do the writes in the background after the user uploads, but the queries have to happen while the user is waiting.
As an example of denormalization, look at how many writes twitter rainbird does when a single click occurs. Each write is used to support a single query.
In some regards, you "can" treat noSQL as a relational store. In others, you can denormalize to make things faster. For instance, PlayOrm's #OneToMany stored the many like so
user1 -> friend.user23, friend.user25, friend.user56, friend.user87
This is the wide row approach so when you find your user, you have all the foreign keys to his friends. Each row can be different lengths. You may also have a reverse reference stored as well so the user might have references to the people that marked him as a friend but he did not mark them back(let's call it buddy) so you might have
user1 -> friend.user23, friend.user25, buddy.user29, buddy.user37
Notice that if designed right, you may NOT need to "search" for the data. That said, with PlayOrm, you can still do Scalable SQL and do joins(you just have to figure out how to partition your tables so it can scale to trillions of rows).
A row can have millions of columns in it or it could have just 10. We are actually in the process of updating alot of the documentation in PlayOrm and the noSQL patterns this month so if you keep an eye on that, you can also learn more about general noSQL there as well.
Dean
Think of each DB query as of request to the service running on another machine. Your goal is to minimize number of these requests (because each request requires network roundtrip).
Here comes the main difference from RDBMS paradigm: In SQL you would typically use joins and secondary indexes. In cassandra joins aren't possible, since related data would reside on different servers. Things like materialized views are used in cassandra for the same purpose (to fetch all related data with single query).
I'd recommend to read this article:
http://maxgrinev.com/2010/07/12/do-you-really-need-sql-to-do-it-all-in-cassandra/
And to look into twissandra sample project https://github.com/twissandra/twissandra
This is nice collection of optimization technics for the kind of projects you described.
I'm working on a Rails app that implements some social network features as relationships, following, etc. So far everything was fine until I came across with a problem on many to many relations. As you know mongo lacks of joins, so the recommended workaround is to store the relation as an array of ids on both related documents. OK, it's a bit redundant but it should work, let's say:
field :followers, type: Array, default: []
field :following, type: Array, default: []
def follow!(who)
self.followers << who.id
who.following << self.id
self.save
who.save
end
That works pretty well, but this is one of those cases where we would need a transaction, uh, but mongo doesn't support transactions. What if the id is added to the 'followed' followers list but not to the 'follower' following list? I mean, if the first document is modified properly but the second for some reason can't be updated.
Maybe I'm too pessimistic, but there isn't a better solution?
I would recommend storing relationships only in one direction, storing the users someone follows in their user document as "following". Then if you need to query for all followers of user U1, you can query for {users.following : "U1"} Since you can have a multi-key index on an array, this query will be fast if you index this field.
The other reason to go in that direction only is a single user has a practical limit to how many different users they may be following. But the number of followers that a really popular user may have could be close to the total number of users in your system. You want to avoid creating an array in a document that could be that large.
I'm writing a basic event registration web application and I'm wondering whether MongoDB would be a good choice for the datastore and if so, how to model my domain. The app will be very small, so performance and scalability is not a concern, however when I started to think out the model in RDBMS third-normal terms it sounded quite complicated for what it is and from the bits and pieces I'm picking up about Mongo, sounded like a typical use-case. Is it?
The Application
The app allows creation of events, and for attendees to sign up to those events, giving their name, date of birth, etc. Easy, two tables with n:n join. The tricky part is that the organisers wish to be able to ask attendees of certain events for information particular to that event, for example on one event there might be a question about their accomodation preference. I narrowed it down to two types of question: those that require to select from certain options (will be an HTML select list) and questions which allow free-text answers. By the way it's a Rails app in case that matters.
Traditional RDBMS
In an RDBMS I would need perhaps a table for Constrained Question (where answers are from a list), a table for Answer Options, a table for Free Text Question and Free Text Answers; and to appropriately link this all up to the event and the atendee via a Signup. If you think about it the links between the tables are rather complicated!
Mongo
Would this be simpler to model in Mongo? I thought that perhaps besides the Attendee and Event collections, there could be a Question collection which has its allowed answers embedded, if there are no answers then it's free text. A Signup collection that relates an Attendee to an Event and references the id of the relevant Question, and embeds the text of the answer? If the text of an answer option ever changes it might get complicated... but I guess that's the tradeoff of Mongo.
Is this a good use case for Mongo on should I stick with Postgres? Can you suggest a (or improve my) schema?
Mongodb is a awesome tool for this job. You can pretty much utilize the embedded collection here to maximize the performance.
Your current schema is perfectly fine. By tweaking this a little bit with embedded collections , it will be a blast.
For instance, instead of keeping the Question collection seperately, you can have this inside Attendee. This will let you store all relevant info about the attendee in single place.
- Attendee
- Info
- Event_id
- Questions {
-Question id
- Answers [ {
- answer id 1
- or answer text
},{
- answer id 2
- or answer text
}],
}
Also you can cache the frequently used data about the attendees inside the Event collection.
This will be immensely useful for quickly displaying home page data.
For example, you may need to display the users who are attending the event and their count in the event home page. To do that you need to first query the event and query the Attendees.
But i suggest you to do store the attendee_id/name inside the Event as an array, which looks
Event :
- Info
- attendees {
attendee_id : 'xx'
name : 'Fletch'
}
So you can populate the event home page with a single db call to Event collection. Because you can get a minimum info to be displayed about the user and total user count for an event will be retrieved here itself. You can query the attendee when you need to display more info about the user like his question/answers.
Hope this helps
I'm designing an application that processes RSS feeds using MongoDB. Currently my collections are as follows:
Entry
fields: content, feed_id, title, publish_date, url
Feed
fields: description, title, url
User
fields: email_address
subscriptions (embedded collection; fields: feed_id, tags)
A user can subscribe to feeds which are linked from the embedded subscription collection. From the subscriptions I can get a list of all the feeds a user should see and also the corresponding entries.
How should I store entry status information (isRead, isStarred, etc.) that is specific to a user? When a user views an entry I need to record isRead = 1. Two common queries I need to be able to perform are:
Find all entries for a specific feed where isRead = 0 or no status exists currently
For a specific user, mark all entries prior to a publish date with isRead = 1 (this could be hundreds or even thousands of records so it must be efficient)
Hmm, this is a tricky one!
It makes sense to me to store a record for entries that are unread, and delete them when they're read. I'm basing this on the assumption that there will be more read posts than unread for each individual user, so you might as well not have documents for all of those already-read entries sitting around in your DB forever. It also makes it easier to not have to worry about the 16MB document size limit if you're not having to drag around years of history with you everywhere.
For starred entries, I would simply add an array of Entry ObjectIds to User. No need to make these subscription-specific; it'll be much easier to pull a list of items a User has starred that way.
For unread entries, it's a little more complex. I'd still add it as an array, but to satisfy your requirement of being able to quickly mark as-read entries before a specific date, I would denormalize and save the publish-date alongside the Entry ObjectId, in a new 'UnreadEntry' document.
User
fields: email_address, starred_entries[]
subscriptions (embedded collection; fields: feed_id, tags, unread_entries[])
UnreadEntry
fields: id is Entry ObjectId, publish_date
You need to be conscious of the document limit, but 16MB is one hell of a lot of unread entries/feeds, so be realistic about whether that's a limit you really need to worry about. (If it is, it should be fairly straightforward to break out User.subscriptions to its own document.)
Both of your queries now become fairly easy to write:
All entries for a specific feed that are unread:
user.subscriptions.find(feedID).unread_entries
Mark all entries prior to a publish date read:
user.subscriptions.find(feedID).unread_entries.where(publish_date.lte => my_date).delete_all
And, of course, if you simply need to mark all entries in a feed as read, that's very easy:
user.subscriptions.find(feedID).unread_entries.delete_all