in SQL world I could do something to the effect of:
SELECT name FROM table WHERE UPPER(name) = UPPER('Smith');
and this would match a search for "Smith", "SMITH", "SmiTH", etc... because it forces the query and the value to be the same case.
However, MongoDB doesn't seem to have this capability without using a RegEx, which won't use indexes and would be slow for a large amount of data.
Is there a way to convert a stored value to a particular case before doing a search against it in MongoDB?
I've come across the $toUpper aggregate, but I can't figure out how that would be used in this particular case.
If there's not way to convert stored values before searching, is it possible to have MongoDB convert a value when it's created in Mongo? So when I add a document to the collection it would force the "name" attribute to a particular case? Something like a callback in the Rails world.
It looks like there's the ability to create stored JS for MongoDB as well, similar to a Stored Procedure. Would that be a feasible solution as well?
Mostly looking for a push in the right direction; I can figure out the particular code once I know what I'm looking for, but so far I'm not even sure if my desired functionality is doable.
You have to normalize your data before storing them. There is no support for performing normalization as part of a query at runtime.
The simplest thing to do is probably to save both a case-normalized (i.e. all-uppercase) and display version of the field you want to search by. Suppose you are storing users and want to do a case-insensitive search on last name. You might store:
{
_id: ObjectId(...),
first_name: "Dan",
last_name: "Crosta",
last_name_upper: "CROSTA"
}
You can then create an index on last_name_upper, and query like:
> db.users.find({last_name_upper: "CROSTA"})
Related
Using mgo, it seems that best practice is to set object ids to be bson.ObjectId.
This is not very convenient, as the result is that instead of a plain string id the id is stored as binary in the DB. Googling this seems to yield tons of questions like "how do I get a string out of the bson id?", and indeed in golang there is the Hex() method of the ObjectId to allow you to get the string.
The bson becomes even more annoying to work with when exporting data from mongo to another DB platform (this is the case when dealing with big data that is collected and you want to merge it with some properties from the back office mongo DB), this means a lot of pain (you need to transform the binary ObjectId to a string in order to join with the id in different platforms that do not use bson representation).
My question is: what are the benefits of using bson.ObjectId vs string id? Will I lose anything significant if I store my mongo entities with a plain string id?
As was already mentioned in the comments, storing the ObjectId as a hex string would double the space needed for it and in case you want to extract one of its values, you'd first need to construct an ObjectId from that string.
But you have a misconception. There is absolutely no need to use an ObjectId for the mandatory _id field. Quite often, I advice against that. Here is why.
Take the simple example of a book, relations and some other considerations set aside for simplicty:
{
_id: ObjectId("56b0d36c23da2af0363abe37"),
isbn: "978-3453056657",
title: "Neuromancer",
author: "William Gibson",
language: "German"
}
Now, what use would have the ObjectId here? Actually none. It would be an index with hardly any use, since you would never search your book databases by an artificial key like that. It holds no semantic value. It would be a unique ID for an object which already has a globally unique ID – the ISBN.
So we simplify our book document like this:
{
_id: "978-3453056657",
title: "Neuromancer",
author: "William Gibson",
language: "German"
}
We have reduced the size of the document, make use of a preexisting globally unique ID and do not have a basically unused index.
Back to your basic question wether you loose something by not using ObjectIds: Quite often, not using the ObjectId is the better choice. But if you use it, use the binary form.
In e-commerce application I have documents like this:
{ category:'A', ..., price:122,
attr:{ width:6, height:4, hasLCD:true, lcdType:'some text', ..., a36:null }
}
I.e. every product has many attributes of various simple types.
Now I want to filter products by dynamic queries containing top level fields plus some attributes. For example:
find({category:'A', price:{$lt:200}, ...,
'attr.height':{$lt:6}, 'attr.hasLCD':true, 'attr.lcdType':{$in:[...]}, ...})
And I'd like this to perform fast.
Trying to index on all possible 'attr.*' variants gives me an error (too many compound keys). I also suspect that if I index it that way and then omit one of attrs in query index won't work.
Trying to index on 'attr' as a whole does not help either.
What is the proper way to model this under MongoDB?
Update
I have tried this approach (also mentioned here). I.e. store attributes as array of key-value pairs:
attr2: [ {tag:'lcgType', value:'some text'}, ...
And index it like this:
ensureIndex({ 'attr2.tag':1, 'attr2.value':1 })
And query like this:
find({attr2:{$all:[
{$elemMatch:{tag:'bestseller',value:true}},
{$elemMatch:{tag:'weight',value:{$lte:100}}}
]}})
Now explain() says that it is using "BtreeCursor attr2.tag_1_attr2.value_1" but still "nscanned" : 31607 and the whole execution time have actually increased (compared to non-indexed scenario).
Something is wrong here.
Sub-question
What if I select some (less than 31) most frequently queried attributes and try to index on those. If I put all of them in single compound index:
ensureIndex({'attr.a1':1, 'attr.a2':1, ...})
According to the docs this index won't be used for queries missing attr.a1 attribute.
How to define index in this case?
If you really have to allow a lot of filters, combinations and possibly even sorts, MongoDB is not a good fit because it uses only one index per query. The number of indexes then grows way too fast, because compound keys are somewhat inflexible (that should answer the subquestion) and becomes a performance hog.
Use a search database like ElasticSearch, SolR, etc. instead that comes with the features you need. You can the use a $in on the ids that the search server returned if you want to keep the base information in MongoDB (it's usually a good idea to have the search database simply replicate the information of the primary data store so you don't need to sync changes two-way, which would be a nightmare)
I am using mongoose with node.js for this.
My current Schema is this:
var linkSchema = new Schema({
text: String,
tags: array,
body: String,
user: String
})
My use-case is this: There are a list of users and each user has a list of links associated with it. Users and links are different Schemas of course. Thus, how does one get that sort of one to one relationship done using mongo-db.
Should I make a User Schema and embed linkSchema in it? Or the other way around?
Another doubt regarding that. Tags would always be an array of strings which I can use to browse through links later. Should it be an array data type or is there a better way to represent it?
If it's 1:1 then nest one document inside the other. Which way around depends on the queries, but you could easily do both if you need to.
For tags, you can index an array field and use that for searching/filtering documents and from the information you've given that sounds reasonable IMHO.
If you had a fixed set of tags it would make sense to represent those as a nested object with named fields perhaps, depending on queries. Don't forget you not only can create nested documents in Mongo but you can also search on sub-fields and even use entire nested documents as searchable/indexable fields. For instance, you could have a username like this;
email: "joe#somewhere.com"
as a string, and you could also do;
email: {
user: "joe",
domain: "somewhere.com"
}
you could index email in both cases and use either for matching. In the latter case though you could also search on domain or user only without resorting to RegEx style queries. You could also store both variants, so there's lots of flexibile options in Mongo.
Going back to tags, I think your array of strings is a fine model given what you've described, but if you were doing more complex bulk aggregation, it wouldn't be crazy to store a document for every tag with the same document contents, since that's essentially what you'd have to do for every query during aggregation.
I have collections with huge amount of Documents on which I need to do custom search with various different queries.
Each Document have boolean property. Let's call it "isInTop".
I need to show Documents which have this property first in all queries.
Yes. I can easy do sort in this field like:
.sort( { isInTop: -1 } );
And create proper index with field "isInTop" as last field in it. But this will be work slowly, as indexes in mongo works best with unique fields.
So is there is solution to show Documents with field "isInTop" on top of each query?
I see two solutions here.
First: set Documents wich need to be in top the _id from "future". As you know, ObjectId contains timestamp. So I can create ObjectId with timestamp from future and use natural order
Second: create separate collection for Ducuments wich need to be in top. And do queries in it first.
Is there is any other solutions for this problem? Which will work fater?
UPDATE
I have done this issue with sorting on custom field which represent rank.
Using the _id field trick you mention has the problem that at some point in time you will reach the special time, and you can't change the _id field (without inserting a new document and removing the old one).
Creating a special collection which just holds the ones you care about is probably the best option. It gives you the ability to logically (and to some extent, physically) separate the documents.
Newly introduced in mongodb there is also support for a "sparse" index which may fulfill your needs as well. You could only set the "isInTop" field when you want it to be special, and then create a sparse index on it which would not have the problems you would normally have with a single indexed boolean field (in btrees).
My question may be not very good formulated because I haven't worked with MongoDB yet, so I'd want to know one thing.
I have an object (record/document/anything else) in my database - in global scope.
And have a really huge array of other objects in this object.
So, what about speed of search in global scope vs search "inside" object? Is it possible to index all "inner" records?
Thanks beforehand.
So, like this
users: {
..
user_maria:
{
age: "18",
best_comments :
{
goodnight:"23rr",
sleeptired:"dsf3"
..
}
}
user_ben:
{
age: "18",
best_comments :
{
one:"23rr",
two:"dsf3"
..
}
}
So, how can I make it fast to find user_maria->best_comments->goodnight (index context of collections "best_comment") ?
First of all, your example schema is very questionable. If you want to embed comments (which is a big if), you'd want to store them in an array for appropriate indexing. Also, post your schema in JSON format so we don't have to parse the whole name/value thing :
db.users {
name:"maria",
age: 18,
best_comments: [
{
title: "goodnight",
comment: "23rr"
},
{
title: "sleeptired",
comment: "dsf3"
}
]
}
With that schema in mind you can put an index on name and best_comments.title for example like so :
db.users.ensureIndex({name:1, 'best_comments.title:1})
Then, when you want the query you mentioned, simply do
db.users.find({name:"maria", 'best_comments.title':"first"})
And the database will hit the index and will return this document very fast.
Now, all that said. Your schema is very questionable. You mention you want to query specific comments but that requires either comments being in a seperate collection or you filtering the comments array app-side. Additionally having huge, ever growing embedded arrays in documents can become a problem. Documents have a 16mb limit and if document increase in size all the time mongo will have to continuously move them on disk.
My advice :
Put comments in a seperate collection
Either do document per comment or make comment bucket documents (say,
100 comments per document)
Read up on Mongo/NoSQL schema design. You always query for root documents so if you end up needing a small part of a large embedded structure you need to reexamine your schema or you'll be pumping huge documents over the connection and require app-side filtering.
I'm not sure I understand your question but it sounds like you have one record with many attributes.
record = {'attr1':1, 'attr2':2, etc.}
You can create an index on any single attribute or any combination of attributes. Also, you can create any number of indices on a single collection (MongoDB collection == MySQL table), whether or not each record in the collection has the attributes being indexed on.
edit: I don't know what you mean by 'global scope' within MongoDB. To insert any data, you must define a database and collection to insert that data into.
Database 'Example':
Collection 'table1':
records: {a:1,b:1,c:1}
{a:1,b:2,d:1}
{a:1,c:1,d:1}
indices:
ensureIndex({a:ascending, d:ascending}) <- this will index on a, then by d; the fact that record 1 doesn't have an attribute 'd' doesn't matter, and this will increase query performance
edit 2:
Well first of all, in your table here, you are assigning multiple values to the attribute "name" and "value". MongoDB will ignore/overwrite the original instantiations of them, so only the final ones will be included in the collection.
I think you need to reconsider your schema here. You're trying to use it as a series of key value pairs, and it is not specifically suited for this (if you really want key value pairs, check out Redis).
Check out: http://www.jonathanhui.com/mongodb-query