Each element in collection has following format:
{
"Name": "Some Name",
"Description": "Some description",
"Tags": ["java", "code", "some tag"]
}
I have created index on field "Tags" as follows:
db.Establishments.ensureIndex({ Tags: 1 });
Now I want to make query to find out all the tags that begins with "ja" for example (for auto-complete suggestion).
Instead of querying collection is there a way to query index directly, or efficient query which involves operation on index only?
I suppose that you actually want to query the tag attribute and return distinct values for your autocompletion feature, right?
This is quite easy using the distinct method:
db.Establishments.distinct( 'Tags' )
See http://docs.mongodb.org/manual/reference/method/db.collection.distinct/ for more info on distinct queries
As to your question about index queries: you can't ask an index directly - the index serves query optimization as such. Using distinct on an indexed attribute will be quick.
To query the distinct method, execute:
db.Establishments.distinct( 'Tags', { 'Tags': /^ja/ } )
Related
I'm new with mongo
Entity:
{
"sender": {
"id": <unique key inside type>,
"type": <enum value>,
},
"recipient": {
"id": <unique key inside type>,
"type": <enum value>,
},
...
}
I need to create effective seach by query "find entities where sender or recipient equal to user from collection" with paging
foreach member in memberIHaveAccessTo:
condition ||= member == recipient || member == sender
I have read some about mongo indexes. Probably my problem can be solve by storing addional field "members" which will be array contains sender and recipient and then create index on this array
Is it possible to build such an index with monga?
Is mongo good choise to create indexes like?
Some thoughts about the issues raised in the question about querying and the application of indexes on the queried fields.
(i) The $or and two indexes:
I need to create effective search by query "find entities where sender
or recipient equal to user from collection...
Your query is going to be like this:
db.test.find( { $or: [ { "sender.id": "someid" }, { "recipient.id": "someid" } ] } )
With indexes defined on "sender.id" and "recipient.id", two individual indexes, the query with the $or operator will use both the indexes.
From the docs ($or Clauses and Indexes):
When evaluating the clauses in the $or expression, MongoDB either
performs a collection scan or, if all the clauses are supported by
indexes, MongoDB performs index scans.
Running the query with an explain() and examining the query plan shows that indexes are used for both the conditions.
(ii) Index on members array:
Probably my problem can be solve by storing addtional field "members"
which will be array contains sender and recipient and then create
index on this array...
With the members array field, the query will be like this:
db.test.find( { members_array: "someid" } )
When an index is defined on members_array field, the query will use the index; the generated query plan shows the index usage. Note that an index defined on an array field is referred as Multikey Index.
I've read the MongoDB documentation on getting the indexes within a collection, and have also searched SO and Google for my question. I want to get the actual indexed values.
Or maybe my understanding of how MongoDB indexes is incorrect. If I've been indexing a field called text that contains paragraphs, am I right in thinking that what gets indexed is each word in the paragraph?
Either case I want to retrieve the values that were indexed, which db.collection.getIndexes() doesn't seem to be returning.
Well yes and no, in summary.
Indexes work on the "values" of the fields they are supplied to index, and are much like a "card index" in that there is a point of reference to look at to find the location of something that matches that term.
What "you" seem to be asking about here is "text indexes". This is a special index format in MongoDB and other databases as well that looks at the "text" content of a field and breaks down every "word" in that content into a value in that "index".
Typically we do:
db.collection.createIndex({ "text": "text" })
Where the "field name" here is "text" as you asked, but more importantly the type of index here is "text".
This allows you to then insert data like this:
db.collection.insert({ "text": "The quick brown fox jumped over the lazy dog" })
And then search like this, using the $text operator:
db.collection.find({ "$text": { "$search": "brown fox" } })
Which will return and "rank" in order the terms you gave in your query depending how they matched the given "text" of your field in the index on your collection.
Note that a "text" index and it's query does not interact on a specific field. But the index itself can be made over multiple fields. The query and the constraints on the "index" itself are that there can "only be one" text index present on any given collection otherwise errors will occur.
As per mongodb's docs:
"db.collection.getIndexes() returns an array of documents that hold index information for the collection. Index information includes the keys and options used to create the index. For information on the keys and index options, see db.collection.createIndex()."
You first have to create the index on the collection, using the createIndex() method:
db.records.createIndex( { userid: 1 } )
Queries on the userid field are supported by the index:
Example:
db.records.find( { userid: 2 } )
db.records.find( { userid: { $gt: 10 } } )
Indexes help you avoid scanning the whole document. They basically are references or pointers to specific parts of your collection.
The docs explain it better:
http://docs.mongodb.org/manual/tutorial/create-an-index/
I have different types of data that would be difficult to model and scale with a relational database (e.g., a product type)
I'm interested in using Mongodb to solve this problem.
I am referencing the documentation at mongodb's website:
http://docs.mongodb.org/manual/tutorial/model-referenced-one-to-many-relationships-between-documents/
For the data type that I am storing, I need to also maintain a relational list of id's where this particular product is available (e.g., store location id's).
In their example regarding "one-to-many relationships with embedded documents", they have the following:
{
name: "O'Reilly Media",
founded: 1980,
location: "CA",
books: [12346789, 234567890, ...]
}
I am currently importing the data with a spreadsheet, and want to use a batchInsert.
To avoid duplicates, I assume that:
1) I need to do an ensure index on the ID, and ignore errors on the insert?
2) Do I then need to loop through all the ID's to insert a new related ID to the books?
Your question could possibly be defined a little better, but let's consider the case that you have rows in a spreadsheet or other source that are all de-normalized in some way. So in a JSON representation the rows would be something like this:
{
"publisher": "O'Reilly Media",
"founded": 1980,
"location": "CA",
"book": 12346789
},
{
"publisher": "O'Reilly Media",
"founded": 1980,
"location": "CA",
"book": 234567890
}
So in order to get those sort of row results into the structure you wanted, one way to do this would be using the "upsert" functionality of the .update() method:
So assuming you have some way of looping the input values and they are identified with some structure then an analog to this would be something like:
books.forEach(function(book) {
db.publishers.update(
{
"name": book.publisher
},
{
"$setOnInsert": {
"founded": book.founded,
"location": book.location,
},
"$addToSet": { "books": book.book }
},
{ "upsert": true }
);
})
This essentially simplified the code so that MongoDB is doing all of the data collection work for you. So where the "name" of the publisher is considered to be unique, what the statement does is first search for a document in the collection that matches the query condition given, as the "name".
In the case where that document is not found, then a new document is inserted. So either the database or driver will take care of creating the new _id value for this document and your "condition" is also automatically inserted to the new document since it was an implied value that should exist.
The usage of the $setOnInsert operator is to say that those fields will only be set when a new document is created. The final part uses $addToSet in order to "push" the book values that have not already been found into the "books" array (or set).
The reason for the separation is for when a document is actually found to exist with the specified "publisher" name. In this case, all of the fields under the $setOnInsert will be ignored as they should already be in the document. So only the $addToSet operation is processed and sent to the server in order to add the new entry to the "books" array (set) and where it does not already exist.
So that would be simplified logic compared to aggregating the new records in code before sending a new insert operation. However it is not very "batch" like as you are still performing some operation to the server for each row.
This is fixed in MongoDB version 2.6 and above as there is now the ability to do "batch" updates. So with a similar analog:
var batch = [];
books.forEach(function(book) {
batch.push({
"q": { "name": book.publisher },
"u": {
"$setOnInsert": {
"founded": book.founded,
"location": book.location,
},
"$addToSet": { "books": book.book }
},
"upsert": true
});
if ( ( batch.length % 500 ) == 0 ) {
db.runCommand( "update", "updates": batch );
batch = [];
}
});
db.runCommand( "update", "updates": batch );
So what is doing in setting up all of the constructed update statements into a single call to the server with a sensible size of operations sent in the batch, in this case once every 500 items processed. The actual limit is the BSON document maximum of 16MB so this can be altered appropriate to your data.
If your MongoDB version is lower than 2.6 then you either use the first form or do something similar to the second form using the existing batch insert functionality. But if you choose to insert then you need to do all the pre-aggregation work within your code.
All of the methods are of course supported with the PHP driver, so it is just a matter of adapting this to your actual code and which course you want to take.
Is it possible to index some documents of the collection "only if" one of the fields to be indexed has a particular value?
Let me explain with an example:
The collection "posts" has millions of documents, ALL defined as follows:
{
"network": "network_1",
"blogname": "blogname_1",
"post_id": 1234,
"post_slug": "abcdefg"
}
Let's assume that the distribution of the post is equally split on network_1 and network_2
My application OFTEN select the type of query based on the value of "network" (although sometimes I need the data from both networks):
For example:
www.test.it/network_1/blog_1/**postid**/1234/
-> db.posts.find ({network: "network_1" blogname "blog_1", post_id: 1234})
www.test.it/network_2/blog_4/**slug**/aaaa/
-> db.posts.find ({network: "network_2" blogname "blog_4" post_slug: "yyyy"})
I could create two separate indexes (network / blogname / post_id and network / blogname / post_slug) but I would get a huge waste of RAM, since 50% of the data in the index will never be used.
Is there a way to create an index "filtered"?
Example:
(Note the WHERE parameter)
db.posts.ensureIndex ({network: 1 blogname: 1, post_id: 1}, {where: {network: "network_1"}})
db.posts.ensureIndex ({network: 1 blogname: 1, post_slug: 1}, {where: {network: "network_2"}})
Indeed it's possible in MongoDB 3.2+ They call it partialFilterExpression where you can set a condition based on which index will be created.
Example
db.users.createIndex({ "userId": 1, "project": 1 },
{ unique: true, partialFilterExpression:{
userId: { $exists: true, $gt : { $type : 10 } } } })
Please see Partial Index documentation
As of MongoDB v3.2, partial indexes are supported. Documentation: https://docs.mongodb.org/manual/core/index-partial/
It's possible, but it requires a workaround which creates redundancy in your documents, requires you to rewrite your find-queries and limits find-queries to exact matches.
MongoDB supports sparse indexes which only index the documents where the given field exists. You can use this feature to only index a part of the collection by adding this field only to those documents you want to index.
The bad news is that sparse indexes can only include a single field. But the good news is, that this field can also contain an object with multiple fields, so you can still store all the data you want to search for in this field.
To do this, add a new field to the included documents which includes an object with the fields you search for:
{
"network": "network_1",
"blogname": "blogname_1",
"post_id": 1234,
"post_slug": "abcdefg"
"network_1_index_key": {
"blogname": "blogname_1",
"post_id": 1234
}
}
Your ensureIndex command would index the field network_1_index_key:
db.posts.ensureIndex( { network_1_index_key: 1 }, { sparse: true } )
A find-query which is supposed to use this index, must now query for the exact object of the field network_1_index_key:
db.posts.find ({
network_1_index_key: {
blogname: "blogname_1",
post_id: 1234
}
})
Doing this would likely only make sense when the documents you want to index are a very small part of the collection. When its about half, I would just create a regular index and live with it because the larger document-size could mitigate the gains from the reduced index size.
You can try create index on all field (network / blogname / post_id / post_slug)
Suppose I have following collection :
{ _id" : ObjectId("4f1d8132595bb0e4830d15cc"),
"Data" : "[
{ "id1": "100002997235643", "from": {"name": "Joannah" ,"id": "100002997235643"} , "label" : "test" } ,
{ "id1": "100002997235644", "from": {"name": "Jon" ,"id": "100002997235644"} , "label" : "test1" }
]" ,
"stat" : "true"
}
How can I retrieve id1 , name , id ,label or any other element?
I am able to get _id field , DATA (complete array) but not the inner elements in DATA.
You cannot query for embedded structures. You always query for top level documents. If you want to query for individual elements from your array you will have to make those element top level documents (so, put them in their own collection) and maintain an array of _ids in this document.
That said, unless the array becomes very large it's almost always more efficient to simply grab your entire document and find the appropriate element in your app.
I don't think you can do that. It is explained here.
If you want to access specific fields, then following MongoDB Documentation,
you could add a flag parameter to your query, but you should redesign your documents for this to be useful:
Field Selection
In addition to the query expression, MongoDB queries can take some additional arguments. For example, it's possible to request only certain fields be returned. If we just wanted the social security numbers of users with the last name of 'Smith,' then from the shell we could issue this query:
// retrieve ssn field for documents where last_name == 'Smith':
db.users.find({last_name: 'Smith'}, {'ssn': 1});
// retrieve all fields *except* the thumbnail field, for all documents:
db.users.find({}, {thumbnail:0});