I'm running some queries to a mongodb 2.4.9 server that populate a datatable on a webpage. The user needs to be able to do a substring search across multiple fields, sort the data on various columns, and flip through the results in pages. I have to check multiple fields for matches since the user could be searching for anything related to the documents. There are about 300,000 documents in the collection so the database is relatively small.
I have indexes created for the created_by, requester, desc.name, metaprogram.id, program.id, and arr.programid fields. I've also created indexes [("created", 1), ("created_by", 1), ("requester", 1)] and [("created_by", 1), ("requester", 1)] at the suggestion of Dex.
It's also worth mentioning that documents might not have all of the fields that are being searched for here. Some documents might have a metaprogram.id but not the other ID fields for example.
An example of a query I might run is
{
"$query" : {
"$and" : [
{
"created_by" : {"$ne" : "automation"},
"requester" : {"$in" : ["Broadway", "Spec", "Falcon"] }
},
{
"$or" : [
{"requester" : /month/i },
{"created_by" : /month/i },
{"desc.name" : /month/i },
{"metaprogram.id" : {"$in" : [708, 2314, 709 ] } },
{"program.id" : {"$in" : [708, 2314, 709 ] } },
{"arr.programid" : {"$in" : [708, 2314, 709 ] } }
]
}
]
},
"$orderby" : {
"created" : 1
}
}
with differing orderby, limit, and skip values as well.
Queries on average take 500-1500ms to complete.
I've looked into how to make it faster, but haven't been able to come up with anything. Some of the text searching stuff looks handy but as far as I know each collection only supports at most one text index and it doesn't support pagination (skips). I'm sure that prefix searching instead of regex substring matches would be faster as well but I need substring matching.
Is there anything you can think of to improve the speed of a query like this?
It's quite hard to optimize a query when it's unpredictable.
Analyze how the system is being used and place indexes on the most popular fields.
Use .explain() to make sure the indexes are being used.
Also limit the results returned to a value of 50 or 100. The user doesn't need to see everything at once.
Try upgrading mongodb to see if there's a performance improvement.
Side note:
You might want to consider using ElasticSearch as a search engine instead of Mongodb. ElasticSearch would store the searchable fields and return the Mongodb Ids for matched results. ElasticSearch is a magnitude faster as a search engine than Mongodb.
More info:
How to find queries not using indexes or slow in mongodb
Range query for MongoDB pagination
http://www.elasticsearch.org/overview/
Related
I have a mongodb collection with about 100.000 documents.
Each document has an array with about ~ 100 elements. Is an array of strings like this:
features: [
"0_Toyota",
"29776_Grey",
"101037_Hybrid",
"240473_Iron Gray",
"46290_Aluminium,Magnesium",
"2787_14",
"9350_1920 x 1080",
"36303_Y",
"310870_N",
"57721_Y"
...
Making queries like this, are very fast. But sometimes gets very slow, including an specific extra condition inside $and. I have no idea why this happens. When gets slow, it takes more than 40 seconds. Always happens with the same extra condition. It is very possible that it happens with other conditions.
db.products.find({
$and:[
{
"features" : {
"$eq" : "36303_N"
}
},
{
"features" : {
"$eq" : "91135_IPS"
}
},
{
"features" : {
"$eq" : "9350_1366 x 768"
}
},
{
"features" : {
"$eq" : "178874_Y"
}
},
{
"features" : {
"$eq" : "43547_Y"
}
}
...
I'm running the same mongodb in my unix laptop and on a linux server instance.
Also trying indexing the field "features" with the same results.
use $all in your mongo query with your data helps you to query for an array
first create index on features
use this query may helps to you
db.products.find( { features: { $all: ["36303_N", "91135_IPS","others..."] } } )
by the way ,
if your query is very slow ,get the slow operation from your mongod log
show your mongodb version .
any writing when query (write will blocking read in some version)
I have realized that order inside $all matters. I change the order of elements by its number of documents that exists inside the collection, ascending. Making the query more selective.
Before, the query takes ~ 40 seconds to execute, now, with elements ordered, it takes ~ 22 seconds.
Still many seconds anyway.
This is the case: A webshop in which I want to configure which items should be listed in the sjop based on a set of parameters.
I want this to be configurable, because that allows me to experiment with different parameters also change their values easily.
I have a Product collection that I want to query based on multiple parameters.
A couple of these are found here:
within product:
"delivery" : {
"maximum_delivery_days" : 30,
"average_delivery_days" : 10,
"source" : 1,
"filling_rate" : 85,
"stock" : 0
}
but also other parameters exist.
An example of such query to decide whether or not to include a product could be:
"$or" : [
{
"delivery.stock" : 1
},
{
"$or" : [
{
"$and" : [
{
"delivery.maximum_delivery_days" : {
"$lt" : 60
}
},
{
"delivery.filling_rate" : {
"$gt" : 90
}
}
]
},
{
"$and" : [
{
"delivery.maximum_delivery_days" : {
"$lt" : 40
}
},
{
"delivery.filling_rate" : {
"$gt" : 80
}
}
]
},
{
"$and" : [
{
"delivery.delivery_days" : {
"$lt" : 25
}
},
{
"delivery.filling_rate" : {
"$gt" : 70
}
}
]
}
]
}
]
Now to make this configurable, I need to be able to handle boolean logic, parameters and values.
So, I got the idea, since such query itself is JSON, to store it in Mongo and have my Java app retrieve it.
Next thing is using it in the filter (e.g. find, or whatever) and work on the corresponding selection of products.
The advantage of this approach is that I can actually analyse the data and the effectiveness of the query outside of my program.
I would store it by name in the database. E.g.
{
"name": "query1",
"query": { the thing printed above starting with "$or"... }
}
using:
db.queries.insert({
"name" : "query1",
"query": { the thing printed above starting with "$or"... }
})
Which results in:
2016-03-27T14:43:37.265+0200 E QUERY Error: field names cannot start with $ [$or]
at Error (<anonymous>)
at DBCollection._validateForStorage (src/mongo/shell/collection.js:161:19)
at DBCollection._validateForStorage (src/mongo/shell/collection.js:165:18)
at insert (src/mongo/shell/bulk_api.js:646:20)
at DBCollection.insert (src/mongo/shell/collection.js:243:18)
at (shell):1:12 at src/mongo/shell/collection.js:161
But I CAN STORE it using Robomongo, but not always. Obviously I am doing something wrong. But I have NO IDEA what it is.
If it fails, and I create a brand new collection and try again, it succeeds. Weird stuff that goes beyond what I can comprehend.
But when I try updating values in the "query", changes are not going through. Never. Not even sometimes.
I can however create a new object and discard the previous one. So, the workaround is there.
db.queries.update(
{"name": "query1"},
{"$set": {
... update goes here ...
}
}
)
doing this results in:
WriteResult({
"nMatched" : 0,
"nUpserted" : 0,
"nModified" : 0,
"writeError" : {
"code" : 52,
"errmsg" : "The dollar ($) prefixed field '$or' in 'action.$or' is not valid for storage."
}
})
seems pretty close to the other message above.
Needles to say, I am pretty clueless about what is going on here, so I hope some of the wizzards here are able to shed some light on the matter
I think the error message contains the important info you need to consider:
QUERY Error: field names cannot start with $
Since you are trying to store a query (or part of one) in a document, you'll end up with attribute names that contain mongo operator keywords (such as $or, $ne, $gt). The mongo documentation actually references this exact scenario - emphasis added
Field names cannot contain dots (i.e. .) or null characters, and they must not start with a dollar sign (i.e. $)...
I wouldn't trust 3rd party applications such as Robomongo in these instances. I suggest debugging/testing this issue directly in the mongo shell.
My suggestion would be to store an escaped version of the query in your document as to not interfere with reserved operator keywords. You can use the available JSON.stringify(my_obj); to encode your partial query into a string and then parse/decode it when you choose to retrieve it later on: JSON.parse(escaped_query_string_from_db)
Your approach of storing the query as a JSON object in MongoDB is not viable.
You could potentially store your query logic and fields in MongoDB, but you have to have an external app build the query with the proper MongoDB syntax.
MongoDB queries contain operators, and some of those have special characters in them.
There are rules for mongoDB filed names. These rules do not allow for special characters.
Look here: https://docs.mongodb.org/manual/reference/limits/#Restrictions-on-Field-Names
The probable reason you can sometimes successfully create the doc using Robomongo is because Robomongo is transforming your query into a string and properly escaping the special characters as it sends it to MongoDB.
This also explains why your attempt to update them never works. You tried to create a document, but instead created something that is a string object, so your update conditions are probably not retrieving any docs.
I see two problems with your approach.
In following query
db.queries.insert({
"name" : "query1",
"query": { the thing printed above starting with "$or"... }
})
a valid JSON expects key, value pair. here in "query" you are storing an object without a key. You have two options. either store query as text or create another key inside curly braces.
Second problem is, you are storing query values without wrapping in quotes. All string values must be wrapped in quotes.
so your final document should appear as
db.queries.insert({
"name" : "query1",
"query": 'the thing printed above starting with "$or"... '
})
Now try, it should work.
Obviously my attempt to store a query in mongo the way I did was foolish as became clear from the answers from both #bigdatakid and #lix. So what I finally did was this: I altered the naming of the fields to comply to the mongo requirements.
E.g. instead of $or I used _$or etc. and instead of using a . inside the name I used a #. Both of which I am replacing in my Java code.
This way I can still easily try and test the queries outside of my program. In my Java program I just change the names and use the query. Using just 2 lines of code. It simply works now. Thanks guys for the suggestions you made.
String documentAsString = query.toJson().replaceAll("_\\$", "\\$").replaceAll("#", ".");
Object q = JSON.parse(documentAsString);
Is there a way to match a value with every array and sub document inside the document in mongodb collection and return the document
{
"_id" : "2000001956",
"trimline1" : "abc",
"trimline2" : "xyz",
"subtitle" : "www",
"image" : {
"large" : 0,
"small" : 0,
"tiled" : 0,
"cropped" : false
},
"Kytrr" : {
"count" : 0,
"assigned" : 0
}
}
for eg if in the above document I am searching for xyz or "ab" or "xy" or "z" or "0" this document should be returned.
I actually have to achieve this at the back end using C# driver but a mongo query would also help greatly.
Please advice.
Thanks
You could probably do this using '$where'
db.mycollection({$where:"JSON.stringify(this).indexOf('xyz')!=-1"})
I'm converting the whole record to a big string and then searching to see if your element is in the resulting string. Probably won't work if your xyz is in the fieldnames!
You can make it iterate through the fields to make a big string and then search it though.
This isn't the most elegant way and will involve a full tablescan. It will be faster if you look through the individual fields!
While Malcolm's answer above would work, when your collection gets large or you have high traffic, you'll see this fall over pretty quickly. This is because of 2 things. First, dropping down to javascript is a big deal and second, this will always be a full table scan because $where can't use an index.
MongoDB 2.6 introduced text indexing which is on by default (it was in beta in 2.4). With it, you can have a full text index on all the fields in the document. The documentation gives the following example where a text index is created for every field and names the index "TextIndex".
db.collection.ensureIndex(
{ "$**": "text" },
{ name: "TextIndex" }
)
I'm new to NoSQL databases and I'm stuck with a fairly basic query.
I have a collection of tweets in a MongoDB database, which I'm querying through both the Mongo shell and pyMongo. The documents are similar to:
{ loc : { lng : 40, lat : 3 },
timestamp : 124125512,
userid = 55 }
I need to find all pairs of users with events close to each other with less than 4 hours of difference. The most naive way would be:
db.tweets.find().forEach(function(tweet)
{
found = db.tweets.find({ "timestamp": { "$gt" : tweet['timestamp'] - 60*60*4,
"$lt" : tweet['timestamp'] + 60*60*4},
"loc" : {"$near" : [ tweet['loc']['lng'],
tweet['loc']['lat'] ],
"$maxDistance" : 500 }
});
//... extract the users from those tweets...
}
Which of course is extremely slow (it can contain as many as a few million tweets).
I haven't been able to express this query using neither aggregation nor MapReduce. How would you do it? What is the most NoSQL-y, efficient and clear way of making this kind of query?
EDIT: I've kind of given up. I've been convinced by a friend that it is not going to worth it using Mongo for this. I can leverage that time restriction to avoid iterating over the whole collection and do it in a simple, more traditional iterative script. Since it is not such a huge dataset as to not fit in RAM, it's going to be faster.
Use $near in conjuction with $maxDistance is the most recommended way
db.collectionName.find({loc: {$near: [50, 50], $maxDistance: 5}});
For performance issues you can try creating index as mentioned below:
To create a geospatial index for GeoJSON-formatted data, use the ensureIndex() method and set the value of the location field for your collection to 2dsphere.
db.points.ensureIndex( { loc : "2dsphere" } );
For more information:
Index creation
Build a 2dsphere index
Geospatial indexes and queries
I'm considering bundling time-sequence data together in session documents. Inside each session, there would be an array of events. Each event would have a timestamp. I know that I can create a multikey index on the timestamp of those events, but I'm curious what mechanism MongoDB uses to prevent the same document from showing up twice in one query.
To clarify, imagine a collection of sessions with the following documents:
{
_id: 'A',
events: [
{time: '10:00'},
{time: '15:00'}
]
}
{
_id: 'B',
events: [
{time: '12:00'}
]
}
If I add a multikey index with db.sessions.ensureIndex({'events.time' : 1}), I would expect the b-tree of that index to look like this:
'10:00' => 'A'
'12:00' => 'B'
'15:00' => 'A'
If I query the collection with {'events.time': {$gte: '10:00'}}, MongoDB scans the b-tree and returns:
{ "_id" : "A", "events" : [ { "time" : "10:00" }, { "time" : "15:00" } ] }
{ "_id" : "B", "events" : [ { "time" : "12:00" } ] }
How does Mongo prevent document A from showing up a second time as the third result in the cursor? For small index scans, it could just keep track of which documents had already been seen, but what happens if the index is enormous? Is there ever a case where the same document would show up more than once in a singe cursor?
My assumption is that it would not. Mongo could look at the document it is scanning and detect that it already would have matched earlier in the scan by inspecting earlier entries in the indexed array. However, I cannot find any mention of this behavior in the MongoDB documentation, and it is important to actually know what to expect.
(NOTE: I do know that it is possible for a document to show up in a single query more than once if the document is modified while the cursor is being scanned. That shouldn't pose a problem for queries on time-sequence data where timestamps are never edited. Even if a new event is added to a session during a scan, if Mongo uses something like the detection mechanism I mentioned above, it should be able to omit the moved document from query results.)
I cannot find any mention of this behavior in the MongoDB
documentation, and it is important to actually know what to expect.
Internals of implementation are seldom mentioned in the documentation, and after all, what you describe is the expected behavior.
There is code to deduplicate a result set and there are tests to make sure that it's working correctly. After all, a multi-key index isn't the primary use case for such functionality - if you have an $or clause in your query, the results must be de-duplicated as well.