I need to query where descr like 'xxx' or short_descr like 'xxx'
I know how to do it using:
{"where": {
"or": [
{"description": {"like": "xxx"}},
{"short_description": {"like": "xxx"}}
}
}
}
but need to add query params in REST syntax.
I'm trying:
params['filter[where][or]'] = JSON.stringify([
{ "description": { "like": "xxx" } },
{ "short_description": { "like": "xxx" } }
])
with The or operator has invalid clauses result.
Here is an example (I used 'desc' instead of 'description'):
http://localhost:3000/api/cats?filter[where][or][0][desc][like]=foo&filter[where][or][1][short_desc][like]=goo
So the important parts are this:
First, you need to give an index to each part of the OR clause. Note the first one is 0, then 1.
Secondly - um... I thought I had more, but that's pretty much it.
More information on WHERE filters: https://docs.strongloop.com/display/LB/Where+filter
I have movie database with different fields. the Genre field contains a comma separated string like :
{genre: 'Action, Adventure, Sci-Fi'}
I know I can use regular expression to find the matches. I also tried:
{'genre': {'$in': genre}}
the problem is the running time. it take lot of time to return a query result. the database has about 300K documents and I have done normal indexing over 'genre' field.
Would say use Map-Reduce to create a separate collection that stores the genre as an array with values coming from the split comma separated string, which you can then run the Map-Reduce job and administer queries on the output collection.
For example, I've created some sample documents to the foo collection:
db.foo.insert([
{genre: 'Action, Adventure, Sci-Fi'},
{genre: 'Thriller, Romantic'},
{genre: 'Comedy, Action'}
])
The following map/reduce operation will then produce the collection from which you can apply performant queries:
map = function() {
var array = this.genre.split(/\s*,\s*/);
emit(this._id, array);
}
reduce = function(key, values) {
return values;
}
result = db.runCommand({
"mapreduce" : "foo",
"map" : map,
"reduce" : reduce,
"out" : "foo_result"
});
Querying would be straightforward, leveraging the queries with an multi-key index on the value field:
db.foo_result.createIndex({"value": 1});
var genre = ['Action', 'Adventure'];
db.foo_result.find({'value': {'$in': genre}})
Output:
/* 0 */
{
"_id" : ObjectId("55842af93cab061ff5c618ce"),
"value" : [
"Action",
"Adventure",
"Sci-Fi"
]
}
/* 1 */
{
"_id" : ObjectId("55842af93cab061ff5c618d0"),
"value" : [
"Comedy",
"Action"
]
}
Well you cannot really do this efficiently so I'm glad you used the tag "performance" on your question.
If you want to do this with the "comma separated" data in a string in place you need to do this:
Either with a regex in general if it suits:
db.collection.find({ "genre": { "$regex": "Sci-Fi" } })
But not really efficient.
Or by JavaScript evaluation via $where:
db.collection.find(function() {
return (
this.genre.split(",")
.map(function(el) {
return el.replace(/^\s+/,"")
})
.indexOf("Sci-Fi") != -1;
)
})
Not really efficient and probably equal to above.
Or better yet and something that can use an index, the separate to an array and use a basic query:
{
"genre": [ "Action", "Adventure", "Sci-Fi" ]
}
With an index:
db.collection.ensureIndex({ "genre": 1 })
Then query:
db.collection.find({ "genre": "Sci-Fi" })
Which is when you do it that way it's that simple. And really efficient.
You make the choice.
I have a MongoDB collection of documents of the form
{
"id": 42,
"title": "candy can",
"description": "canada candy canteen",
"brand": "cannister candid",
"manufacturer": "candle canvas"
}
I need to implement auto-complete feature based on the input search term by matching in the fields except id. For example, if the input term is can, then I should return all matching words in the document as
{ hints: ["candy", "can", "canada", "canteen", ...]
I looked at this question but it didn't help. I also tried searching how to do regex search in multiple fields and extract matching tokens, or extracting matching tokens in a MongoDB text search but couldn't find any help.
tl;dr
There is no easy solution for what you want, since normal queries can't modify the fields they return. There is a solution (using the below mapReduce inline instead of doing an output to a collection), but except for very small databases, it is not possible to do this in realtime.
The problem
As written, a normal query can't really modify the fields it returns. But there are other problems. If you want to do a regex search in halfway decent time, you would have to index all fields, which would need a disproportional amount of RAM for that feature. If you wouldn't index all fields, a regex search would cause a collection scan, which means that every document would have to be loaded from disk, which would take too much time for autocompletion to be convenient. Furthermore, multiple simultaneous users requesting autocompletion would create considerable load on the backend.
The solution
The problem is quite similar to one I have already answered: We need to extract every word out of multiple fields, remove the stop words and save the remaining words together with a link to the respective document(s) the word was found in a collection. Now, for getting an autocompletion list, we simply query the indexed word list.
Step 1: Use a map/reduce job to extract the words
db.yourCollection.mapReduce(
// Map function
function() {
// We need to save this in a local var as per scoping problems
var document = this;
// You need to expand this according to your needs
var stopwords = ["the","this","and","or"];
for(var prop in document) {
// We are only interested in strings and explicitly not in _id
if(prop === "_id" || typeof document[prop] !== 'string') {
continue
}
(document[prop]).split(" ").forEach(
function(word){
// You might want to adjust this to your needs
var cleaned = word.replace(/[;,.]/g,"")
if(
// We neither want stopwords...
stopwords.indexOf(cleaned) > -1 ||
// ...nor string which would evaluate to numbers
!(isNaN(parseInt(cleaned))) ||
!(isNaN(parseFloat(cleaned)))
) {
return
}
emit(cleaned,document._id)
}
)
}
},
// Reduce function
function(k,v){
// Kind of ugly, but works.
// Improvements more than welcome!
var values = { 'documents': []};
v.forEach(
function(vs){
if(values.documents.indexOf(vs)>-1){
return
}
values.documents.push(vs)
}
)
return values
},
{
// We need this for two reasons...
finalize:
function(key,reducedValue){
// First, we ensure that each resulting document
// has the documents field in order to unify access
var finalValue = {documents:[]}
// Second, we ensure that each document is unique in said field
if(reducedValue.documents) {
// We filter the existing documents array
finalValue.documents = reducedValue.documents.filter(
function(item,pos,self){
// The default return value
var loc = -1;
for(var i=0;i<self.length;i++){
// We have to do it this way since indexOf only works with primitives
if(self[i].valueOf() === item.valueOf()){
// We have found the value of the current item...
loc = i;
//... so we are done for now
break
}
}
// If the location we found equals the position of item, they are equal
// If it isn't equal, we have a duplicate
return loc === pos;
}
);
} else {
finalValue.documents.push(reducedValue)
}
// We have sanitized our data, now we can return it
return finalValue
},
// Our result are written to a collection called "words"
out: "words"
}
)
Running this mapReduce against your example would result in db.words look like this:
{ "_id" : "can", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "canada", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "candid", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "candle", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "candy", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "cannister", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "canteen", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
{ "_id" : "canvas", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
Note that the individual words are the _id of the documents. The _id field is indexed automatically by MongoDB. Since indices are tried to be kept in RAM, we can do a few tricks to both speed up autocompletion and reduce the load put to the server.
Step 2: Query for autocompletion
For autocompletion, we only need the words, without the links to the documents.
Since the words are indexed, we use a covered query – a query answered only from the index, which usually resides in RAM.
To stick with your example, we would use the following query to get the candidates for autocompletion:
db.words.find({_id:/^can/},{_id:1})
which gives us the result
{ "_id" : "can" }
{ "_id" : "canada" }
{ "_id" : "candid" }
{ "_id" : "candle" }
{ "_id" : "candy" }
{ "_id" : "cannister" }
{ "_id" : "canteen" }
{ "_id" : "canvas" }
Using the .explain() method, we can verify that this query uses only the index.
{
"cursor" : "BtreeCursor _id_",
"isMultiKey" : false,
"n" : 8,
"nscannedObjects" : 0,
"nscanned" : 8,
"nscannedObjectsAllPlans" : 0,
"nscannedAllPlans" : 8,
"scanAndOrder" : false,
"indexOnly" : true,
"nYields" : 0,
"nChunkSkips" : 0,
"millis" : 0,
"indexBounds" : {
"_id" : [
[
"can",
"cao"
],
[
/^can/,
/^can/
]
]
},
"server" : "32a63f87666f:27017",
"filterSet" : false
}
Note the indexOnly:true field.
Step 3: Query the actual document
Albeit we will have to do two queries to get the actual document, since we speed up the overall process, the user experience should be well enough.
Step 3.1: Get the document of the words collection
When the user selects a choice of the autocompletion, we have to query the complete document of words in order to find the documents where the word chosen for autocompletion originated from.
db.words.find({_id:"canteen"})
which would result in a document like this:
{ "_id" : "canteen", "value" : { "documents" : [ ObjectId("553e435f20e6afc4b8aa0efb") ] } }
Step 3.2: Get the actual document
With that document, we can now either show a page with search results or, like in this case, redirect to the actual document which you can get by:
db.yourCollection.find({_id:ObjectId("553e435f20e6afc4b8aa0efb")})
Notes
While this approach may seem complicated at first (well, the mapReduce is a bit), it is actual pretty easy conceptually. Basically, you are trading real time results (which you won't have anyway unless you spend a lot of RAM) for speed. Imho, that's a good deal. In order to make the rather costly mapReduce phase more efficient, implementing Incremental mapReduce could be an approach – improving my admittedly hacked mapReduce might well be another.
Last but not least, this way is a rather ugly hack altogether. You might want to dig into elasticsearch or lucene. Those products imho are much, much more suited for what you want.
Thanks to #Markus solution, I came up with something similar with aggregations instead. Knowing that map-reduce are flagged as deprecated for later versions.
const { MongoDBNamespace, Collection } = require('mongodb')
//.replace(/(\b(\w{1,3})\b(\W|$))/g,'').split(/\s+/).join(' ')
const routine = `function (text) {
const stopwords = ['the', 'this', 'and', 'or', 'id']
text = text.replace(new RegExp('\\b(' + stopwords.join('|') + ')\\b', 'g'), '')
text = text.replace(/[;,.]/g, ' ').trim()
return text.toLowerCase()
}`
// If the pipeline includes the $out operator, aggregate() returns an empty cursor.
const agg = [
{
$match: {
a: true,
d: false,
},
},
{
$project: {
title: 1,
desc: 1,
},
},
{
$replaceWith: {
_id: '$_id',
text: {
$concat: ['$title', ' ', '$desc'],
},
},
},
{
$addFields: {
cleaned: {
$function: {
body: routine,
args: ['$text'],
lang: 'js',
},
},
},
},
{
$replaceWith: {
_id: '$_id',
text: {
$trim: {
input: '$cleaned',
},
},
},
},
{
$project: {
words: {
$split: ['$text', ' '],
},
qt: {
$const: 1,
},
},
},
{
$unwind: {
path: '$words',
includeArrayIndex: 'id',
preserveNullAndEmptyArrays: true,
},
},
{
$group: {
_id: '$words',
docs: {
$addToSet: '$_id',
},
weight: {
$sum: '$qt',
},
},
},
{
$sort: {
weight: -1,
},
},
{
$limit: 100,
},
{
$out: {
db: 'listings_db',
coll: 'words',
},
},
]
// Closure for db instance only
/**
*
* #param { MongoDBNamespace } db
*/
module.exports = function (db) {
/** #type { Collection } */
let collection
/**
* Runs the aggregation pipeline
* #return {Promise}
*/
this.refreshKeywords = async function () {
collection = db.collection('listing')
// .toArray() to trigger the aggregation
// it returns an empty curson so it's fine
return await collection.aggregate(agg).toArray()
}
}
Please check for very minimal changes for your convenience.
I can't figure out how to sort query results based on the "best" match.
Here's a simple example, I have a "zone" collection containing a list of city/zipcode couples.
If I search several words through the regex using the "and" keyword like that :
"db.zones.find({$or : [ {ville: /ROQUE/}, {ville: /ANTHERON/}] })"
Results won't be ordered by "best match".
What other solutions do I have for that ?
You could try to use http://docs.mongodb.org/manual/reference/operator/query/text/#match-any-of-the-search-terms
db.zones.ensureIndex( { 'ville' : 'text' } ,{ score: {$meta:'textScore'}})
db.zones.find(
{ $text: { $search: "ROQUE ANTHERON"}},
{ score: { $meta: "textScore" } }
).sort( { score: { $meta: "textScore" } } )
Result:
{
"_id" : ObjectId("547c2473371ea419f07b954c"),
"ville" : "ANTHERON",
"score" : 1.1
}
{
"_id" : ObjectId("547c246f371ea419f07b954b"),
"ville" : "ROQUE",
"score" : 1
}
From documentation
If the search string is a space-delimited string, $text operator
performs a logical OR search on each term and returns documents that
contains any of the terms.
You have to use mongodb 2.6
I ended up using ElasticSearch search engine do this query :
#zones = Zone.es.search(
body: {
query: {
bool: {
should: [
{match: {city: search}},
{match: {zipcode: search.to_i}}
]
}
},
size: limit
})
Where search is a search param sent by view.
ElasticSearch with Selectize plugin
I am looking to find all duplicates in my collection by flagging duplicates based on the date. The following was my attempt but I am not sure how to use cmdResult within update. Any clues?
//filter duplicates
bson::bo cmdResult;
bool ok = c.runCommand(dbcol, BSON("distinct" << "date"), cmdResult);
c.update(dbcol,Query("date"<<cmdResult<<NOT<<"_id"), BSON("$set"<<BSON("noise"<<"true")), false, true);
The "distinct" command will return you a list of all unique "date" values there are in the collection. But what you need is a list of "date" values that occur more than once.
You can get this list using the aggregate command, by grouping by "date" and counting the entries, then matching for counts > 1:
aggregate([
{ $group: { "_id": "$name", count: {$sum:1} } },
{ $match: { $gt: [ count, 1 ] } }
])
You would then update your collection (multi:true) by querying for "date" IN that list, setting the "noise" field:
update( {"name": {$in: [<list>]} },{$set: {"noise": true} }, true, false )
For help on aggregation, see http://docs.mongodb.org/manual/reference/aggregation/