I have a JSON string, which needs to be parsed in order to retrieve particular values.Here is an example I am working with;
{
"assignable_type": "SHIPMENT",
"rule": {
"rules": [
{
"meta_data": {},
"rules": [
{
"op": "IN",
"target": "CLIENT_FID",
"type": "ARRAY_VALUE_ASSERTION",
"values": [
"flx::core:client:dbid/64171",
"flx::core:client:dbid/76049",
"flx::core:client:dbid/34040",
"flx::core:client:dbid/61806"
]
}
],
"type": "AND"
}
],
"type": "OR"
},
"type": "USER_DEFINED"
}
The goal is to get the values when "target":"CLIENT_FID".
Expected Output for this JSON file should be ;
["flx::core:client:dbid/64171",
"flx::core:client:dbid/76049",
"flx::core:client:dbid/34040",
"flx::core:client:dbid/61806"]
Here, as we can see rules is a list of dictionaries, and we can have nested lists as seen in the example.
Similarly, we have other JSON file of following type;
{
"assignable_type": "SHIPMENT",
"rule": {
"rules": [
{
"meta_data": {},
"rules": [
{
"op": "IN",
"target": "PORT_OF_ENTRY_FID",
"type": "ARRAY_VALUE_ASSERTION",
"values": [
"flx::core:port:dbid/566788",
"flx::core:port:dbid/566931",
"flx::core:port:dbid/561482"
]
}
],
"type": "AND"
},
{
"meta_data": {},
"rules": [
{
"op": "IN",
"target": "PORT_OF_LOADING_FID",
"type": "ARRAY_VALUE_ASSERTION",
"values": [
"flx::core:port:dbid/561465"
]
},
{
"op": "IN",
"target": "SHIPMENT_MODE",
"type": "ARRAY_VALUE_ASSERTION",
"values": [
0
]
},
{
"op": "IN",
"target": "CLIENT_FID",
"type": "ARRAY_VALUE_ASSERTION",
"values": [
"flx::core:client:dbid/28169"
]
}
],
"type": "AND"
}
],
"type": "OR"
},
"type": "USER_DEFINED"
}
For the second example ,
Expected Output shd be;
["flx::core:client:dbid/28169"]
As. seen, we may need to read the values at different depths in the file. In order to address this issue, I used following code;
/* first convert the string to a JSON object in cte1 */
with cte1 as (
select to_json(json_string) as json_rep,
parse_json(json_extract_path_text(json_rep, 'rule.rules')) as list_elem
from table 1),
cte2 as (select split_array,
json_extract_path_text(split_array, 'target') as target_client
from (
select json_rep,
list_elem,
t.value as split_array,
typeof(split_array) as obj_type,
index
from cte1,
table(flatten(cte1.list_elem, recursive=>true)) as t) temp /* use recursive feature */
where split_array ilike '%"target":"client_fid"%' /* filter for those rows containing this string */
and obj_type='OBJECT')
select
split_array,
json_extract_path_text(split_array, 'values') as client_values
from cte2
where target_client='CLIENT_FID'; /* filter the rows where we have the dictionary containing client fid */
In order to address the issue of varying depth at which client_fid is found we're recursing while flattening the string into rows. The output which is obtained for both of above inputs is provided below,
For the first String we get the actual output in variable client_values as
["flx::core:client:dbid/64171",
"flx::core:client:dbid/76049",
"flx::core:client:dbid/34040",
"flx::core:client:dbid/61806"]
Similarly, for the second string we get the actual output as
["flx::core:client:dbid/28169"]
As seen the code seems to be working in getting the correct output, but the way I filtered in the final query for target_client='CLIENT_FID'; it seems to be a very hacky way. Hence is it possible to get a better approach to resolve the issue of retrieving client fid values though the depth can vary in the given input.
Help is appreciated.
I have imported the above json data into it's own collection on mongoDB database. I'm trying to aggregate the values (ie 40, 30, 30) and SUM and AVG them as they reside in the inner most embedded document. I'm having a problem doing this when I try using dot notation and can not get any vaules. I feel the unique timestamps (ie 1567544426000, 1567541464000, 1567541475000) are a problem. Is this json file formatted correctly for aggregation and how would I do so. Thanks for any help or if you can even point me in the right direction where I can find out how to do SUM, AVG etc to the data.
I've tried use NoSQLBooster and Query ASsist for MongoDB
{
"Barcode": "97-1908-577-1032-BE1-332",
"IP": "192.162.656.111",
"VFD": {
"CurrentPV": {
"Type": "Speed",
"Data": {
"1567544426000": 40,
"1567541464000": 30
"1567541475000": 30
}
},
"CurrentSP": {
"Type": "Speed",
"Data": {
"1567544426000": 55,
"1567541464000": 5
"1567541488000": 10
}
},
"Program_Running": {
"Type": "Active",
"Data": {
"1567544426000": 1,
"1567541464000": 0
"1567541475000": 3
}
}
},
"Equipment": "PieceOfEquipment",
"Location": "Garage",
"RunEnd": "NA",
"RunStart": 1533541438
}
I can't seem to reach the values even when I use dot notation down to the "Data" branch object (ie Equipment.VFD.CurrentPV.Data) but no result sets are returned.
We can convert the VFD.CurrentPV.Data into an array of key-value pairs using $objectToArray and then perform SUM and AVG on the values itself.
The following query can get us the expected output:
db.collection.aggregate([
{
$addFields:{
"data":{
$objectToArray: "$VFD.CurrentPV.Data"
}
}
},
{
$project:{
"sum":{
$sum:"$data.v"
},
"avg":{
$avg:"$data.v"
}
}
}
]).pretty()
Data set:
{
"_id" : ObjectId("5d830f3afb35a835fbd8638e"),
"Barcode" : "97-1908-577-1032-BE1-332",
"IP" : "192.162.656.111",
"VFD" : {
"CurrentPV" : {
"Type" : "Speed",
"Data" : {
"1567544426000" : 40,
"1567541464000" : 30,
"1567541475000" : 30
}
},
"CurrentSP" : {
"Type" : "Speed",
"Data" : {
"1567544426000" : 55,
"1567541464000" : 5,
"1567541488000" : 10
}
},
"Program_Running" : {
"Type" : "Active",
"Data" : {
"1567544426000" : 1,
"1567541464000" : 0,
"1567541475000" : 3
}
}
},
"Equipment" : "PieceOfEquipment",
"Location" : "Garage",
"RunEnd" : "NA",
"RunStart" : 1533541438
}
Output:
{
"_id" : ObjectId("5d830f3afb35a835fbd8638e"),
"sum" : 100,
"avg" : 33.333333333333336
}
Suppose you have the following documents in my collection:
{
"_id":ObjectId("562e7c594c12942f08fe4192"),
"shapes":[
{
"shape":"square",
"color":"blue"
},
{
"shape":"circle",
"color":"red"
}
]
},
{
"_id":ObjectId("562e7c594c12942f08fe4193"),
"shapes":[
{
"shape":"square",
"color":"black"
},
{
"shape":"circle",
"color":"green"
}
]
}
Do query:
db.test.find({"shapes.color": "red"}, {"shapes.color": 1})
Or
db.test.find({shapes: {"$elemMatch": {color: "red"}}}, {"shapes.color": 1})
Returns matched document (Document 1), but always with ALL array items in shapes:
{ "shapes":
[
{"shape": "square", "color": "blue"},
{"shape": "circle", "color": "red"}
]
}
However, I'd like to get the document (Document 1) only with the array that contains color=red:
{ "shapes":
[
{"shape": "circle", "color": "red"}
]
}
How can I do this?
MongoDB 2.2's new $elemMatch projection operator provides another way to alter the returned document to contain only the first matched shapes element:
db.test.find(
{"shapes.color": "red"},
{_id: 0, shapes: {$elemMatch: {color: "red"}}});
Returns:
{"shapes" : [{"shape": "circle", "color": "red"}]}
In 2.2 you can also do this using the $ projection operator, where the $ in a projection object field name represents the index of the field's first matching array element from the query. The following returns the same results as above:
db.test.find({"shapes.color": "red"}, {_id: 0, 'shapes.$': 1});
MongoDB 3.2 Update
Starting with the 3.2 release, you can use the new $filter aggregation operator to filter an array during projection, which has the benefit of including all matches, instead of just the first one.
db.test.aggregate([
// Get just the docs that contain a shapes element where color is 'red'
{$match: {'shapes.color': 'red'}},
{$project: {
shapes: {$filter: {
input: '$shapes',
as: 'shape',
cond: {$eq: ['$$shape.color', 'red']}
}},
_id: 0
}}
])
Results:
[
{
"shapes" : [
{
"shape" : "circle",
"color" : "red"
}
]
}
]
The new Aggregation Framework in MongoDB 2.2+ provides an alternative to Map/Reduce. The $unwind operator can be used to separate your shapes array into a stream of documents that can be matched:
db.test.aggregate(
// Start with a $match pipeline which can take advantage of an index and limit documents processed
{ $match : {
"shapes.color": "red"
}},
{ $unwind : "$shapes" },
{ $match : {
"shapes.color": "red"
}}
)
Results in:
{
"result" : [
{
"_id" : ObjectId("504425059b7c9fa7ec92beec"),
"shapes" : {
"shape" : "circle",
"color" : "red"
}
}
],
"ok" : 1
}
Caution: This answer provides a solution that was relevant at that time, before the new features of MongoDB 2.2 and up were introduced. See the other answers if you are using a more recent version of MongoDB.
The field selector parameter is limited to complete properties. It cannot be used to select part of an array, only the entire array. I tried using the $ positional operator, but that didn't work.
The easiest way is to just filter the shapes in the client.
If you really need the correct output directly from MongoDB, you can use a map-reduce to filter the shapes.
function map() {
filteredShapes = [];
this.shapes.forEach(function (s) {
if (s.color === "red") {
filteredShapes.push(s);
}
});
emit(this._id, { shapes: filteredShapes });
}
function reduce(key, values) {
return values[0];
}
res = db.test.mapReduce(map, reduce, { query: { "shapes.color": "red" } })
db[res.result].find()
Another interesing way is to use $redact, which is one of the new aggregation features of MongoDB 2.6. If you are using 2.6, you don't need an $unwind which might cause you performance problems if you have large arrays.
db.test.aggregate([
{ $match: {
shapes: { $elemMatch: {color: "red"} }
}},
{ $redact : {
$cond: {
if: { $or : [{ $eq: ["$color","red"] }, { $not : "$color" }]},
then: "$$DESCEND",
else: "$$PRUNE"
}
}}]);
$redact "restricts the contents of the documents based on information stored in the documents themselves". So it will run only inside of the document. It basically scans your document top to the bottom, and checks if it matches with your if condition which is in $cond, if there is match it will either keep the content($$DESCEND) or remove($$PRUNE).
In the example above, first $match returns the whole shapes array, and $redact strips it down to the expected result.
Note that {$not:"$color"} is necessary, because it will scan the top document as well, and if $redact does not find a color field on the top level this will return false that might strip the whole document which we don't want.
Better you can query in matching array element using $slice is it helpful to returning the significant object in an array.
db.test.find({"shapes.color" : "blue"}, {"shapes.$" : 1})
$slice is helpful when you know the index of the element, but sometimes you want
whichever array element matched your criteria. You can return the matching element
with the $ operator.
db.getCollection('aj').find({"shapes.color":"red"},{"shapes.$":1})
OUTPUTS
{
"shapes" : [
{
"shape" : "circle",
"color" : "red"
}
]
}
The syntax for find in mongodb is
db.<collection name>.find(query, projection);
and the second query that you have written, that is
db.test.find(
{shapes: {"$elemMatch": {color: "red"}}},
{"shapes.color":1})
in this you have used the $elemMatch operator in query part, whereas if you use this operator in the projection part then you will get the desired result. You can write down your query as
db.users.find(
{"shapes.color":"red"},
{_id:0, shapes: {$elemMatch : {color: "red"}}})
This will give you the desired result.
Thanks to JohnnyHK.
Here I just want to add some more complex usage.
// Document
{
"_id" : 1
"shapes" : [
{"shape" : "square", "color" : "red"},
{"shape" : "circle", "color" : "green"}
]
}
{
"_id" : 2
"shapes" : [
{"shape" : "square", "color" : "red"},
{"shape" : "circle", "color" : "green"}
]
}
// The Query
db.contents.find({
"_id" : ObjectId(1),
"shapes.color":"red"
},{
"_id": 0,
"shapes" :{
"$elemMatch":{
"color" : "red"
}
}
})
//And the Result
{"shapes":[
{
"shape" : "square",
"color" : "red"
}
]}
You just need to run query
db.test.find(
{"shapes.color": "red"},
{shapes: {$elemMatch: {color: "red"}}});
output of this query is
{
"_id" : ObjectId("562e7c594c12942f08fe4192"),
"shapes" : [
{"shape" : "circle", "color" : "red"}
]
}
as you expected it'll gives the exact field from array that matches color:'red'.
Along with $project it will be more appropriate other wise matching elements will be clubbed together with other elements in document.
db.test.aggregate(
{ "$unwind" : "$shapes" },
{ "$match" : { "shapes.color": "red" } },
{
"$project": {
"_id":1,
"item":1
}
}
)
Likewise you can find for the multiple
db.getCollection('localData').aggregate([
// Get just the docs that contain a shapes element where color is 'red'
{$match: {'shapes.color': {$in : ['red','yellow'] } }},
{$project: {
shapes: {$filter: {
input: '$shapes',
as: 'shape',
cond: {$in: ['$$shape.color', ['red', 'yellow']]}
}}
}}
])
db.test.find( {"shapes.color": "red"}, {_id: 0})
Use aggregation function and $project to get specific object field in document
db.getCollection('geolocations').aggregate([ { $project : { geolocation : 1} } ])
result:
{
"_id" : ObjectId("5e3ee15968879c0d5942464b"),
"geolocation" : [
{
"_id" : ObjectId("5e3ee3ee68879c0d5942465e"),
"latitude" : 12.9718313,
"longitude" : 77.593551,
"country" : "India",
"city" : "Chennai",
"zipcode" : "560001",
"streetName" : "Sidney Road",
"countryCode" : "in",
"ip" : "116.75.115.248",
"date" : ISODate("2020-02-08T16:38:06.584Z")
}
]
}
Although the question was asked 9.6 years ago, this has been of immense help to numerous people, me being one of them. Thank you everyone for all your queries, hints and answers. Picking up from one of the answers here.. I found that the following method can also be used to project other fields in the parent document.This may be helpful to someone.
For the following document, the need was to find out if an employee (emp #7839) has his leave history set for the year 2020. Leave history is implemented as an embedded document within the parent Employee document.
db.employees.find( {"leave_history.calendar_year": 2020},
{leave_history: {$elemMatch: {calendar_year: 2020}},empno:true,ename:true}).pretty()
{
"_id" : ObjectId("5e907ad23997181dde06e8fc"),
"empno" : 7839,
"ename" : "KING",
"mgrno" : 0,
"hiredate" : "1990-05-09",
"sal" : 100000,
"deptno" : {
"_id" : ObjectId("5e9065f53997181dde06e8f8")
},
"username" : "none",
"password" : "none",
"is_admin" : "N",
"is_approver" : "Y",
"is_manager" : "Y",
"user_role" : "AP",
"admin_approval_received" : "Y",
"active" : "Y",
"created_date" : "2020-04-10",
"updated_date" : "2020-04-10",
"application_usage_log" : [
{
"logged_in_as" : "AP",
"log_in_date" : "2020-04-10"
},
{
"logged_in_as" : "EM",
"log_in_date" : ISODate("2020-04-16T07:28:11.959Z")
}
],
"leave_history" : [
{
"calendar_year" : 2020,
"pl_used" : 0,
"cl_used" : 0,
"sl_used" : 0
},
{
"calendar_year" : 2021,
"pl_used" : 0,
"cl_used" : 0,
"sl_used" : 0
}
]
}
if you want to do filter, set and find at the same time.
let post = await Post.findOneAndUpdate(
{
_id: req.params.id,
tasks: {
$elemMatch: {
id: req.params.jobId,
date,
},
},
},
{
$set: {
'jobs.$[i].performer': performer,
'jobs.$[i].status': status,
'jobs.$[i].type': type,
},
},
{
arrayFilters: [
{
'i.id': req.params.jobId,
},
],
new: true,
}
);
This answer does not fully answer the question but it's related and I'm writing it down because someone decided to close another question marking this one as duplicate (which is not).
In my case I only wanted to filter the array elements but still return the full elements of the array. All previous answers (including the solution given in the question) gave me headaches when applying them to my particular case because:
I needed my solution to be able to return multiple results of the subarray elements.
Using $unwind + $match + $group resulted in losing root documents without matching array elements, which I didn't want to in my case because in fact I was only looking to filter out unwanted elements.
Using $project > $filter resulted in loosing the rest of the fields or the root documents or forced me to specify all of them in the projection as well which was not desirable.
So at the end I fixed all of this problems with an $addFields > $filter like this:
db.test.aggregate([
{ $match: { 'shapes.color': 'red' } },
{ $addFields: { 'shapes': { $filter: {
input: '$shapes',
as: 'shape',
cond: { $eq: ['$$shape.color', 'red'] }
} } } },
])
Explanation:
First match documents with a red coloured shape.
For those documents, add a field called shapes, which in this case will replace the original field called the same way.
To calculate the new value of shapes, $filter the elements of the original $shapes array, temporarily naming each of the array elements as shape so that later we can check if the $$shape.color is red.
Now the new shapes array only contains the desired elements.
for more details refer =
mongo db official referance
suppose you have document like this (you can have multiple document too) -
{
"_id": {
"$oid": "63b5cfbfbcc3196a2a23c44b"
},
"results": [
{
"yearOfRelease": "2022",
"imagePath": "https://upload.wikimedia.org/wikipedia/en/d/d4/The_Kashmir_Files_poster.jpg",
"title": "The Kashmir Files",
"overview": "Krishna endeavours to uncover the reason behind his parents' brutal killings in Kashmir. He is shocked to uncover a web of lies and conspiracies in connection with the massive genocide.",
"originalLanguage": "hi",
"imdbRating": "8.3",
"isbookMark": null,
"originCountry": "india",
"productionHouse": [
"Zee Studios"
],
"_id": {
"$oid": "63b5cfbfbcc3196a2a23c44c"
}
},
{
"yearOfRelease": "2022",
"imagePath": "https://upload.wikimedia.org/wikipedia/en/a/a9/Black_Adam_%28film%29_poster.jpg",
"title": "Black Adam",
"overview": "In ancient Kahndaq, Teth Adam was bestowed the almighty powers of the gods. After using these powers for vengeance, he was imprisoned, becoming Black Adam. Nearly 5,000 years have passed, and Black Adam has gone from man to myth to legend. Now free, his unique form of justice, born out of rage, is challenged by modern-day heroes who form the Justice Society: Hawkman, Dr. Fate, Atom Smasher and Cyclone",
"originalLanguage": "en",
"imdbRating": "8.3",
"isbookMark": null,
"originCountry": "United States of America",
"productionHouse": [
"DC Comics"
],
"_id": {
"$oid": "63b5cfbfbcc3196a2a23c44d"
}
},
{
"yearOfRelease": "2022",
"imagePath": "https://upload.wikimedia.org/wikipedia/en/0/09/The_Sea_Beast_film_poster.png",
"title": "The Sea Beast",
"overview": "A young girl stows away on the ship of a legendary sea monster hunter, turning his life upside down as they venture into uncharted waters.",
"originalLanguage": "en",
"imdbRating": "7.1",
"isbookMark": null,
"originCountry": "United States Canada",
"productionHouse": [
"Netflix Animation"
],
"_id": {
"$oid": "63b5cfbfbcc3196a2a23c44e"
}
},
{
"yearOfRelease": "2021",
"imagePath": "https://upload.wikimedia.org/wikipedia/en/7/7d/Hum_Do_Hamare_Do_poster.jpg",
"title": "Hum Do Hamare Do",
"overview": "Dhruv, who grew up an orphan, is in love with a woman who wishes to marry someone with a family. In order to fulfil his lover's wish, he hires two older individuals to pose as his parents.",
"originalLanguage": "hi",
"imdbRating": "6.0",
"isbookMark": null,
"originCountry": "india",
"productionHouse": [
"Maddock Films"
],
"_id": {
"$oid": "63b5cfbfbcc3196a2a23c44f"
}
},
{
"yearOfRelease": "2021",
"imagePath": "https://upload.wikimedia.org/wikipedia/en/7/74/Shang-Chi_and_the_Legend_of_the_Ten_Rings_poster.jpeg",
"title": "Shang-Chi and the Legend of the Ten Rings",
"overview": "Shang-Chi, a martial artist, lives a quiet life after he leaves his father and the shadowy Ten Rings organisation behind. Years later, he is forced to confront his past when the Ten Rings attack him.",
"originalLanguage": "en",
"imdbRating": "7.4",
"isbookMark": null,
"originCountry": "United States of America",
"productionHouse": [
"Marvel Entertainment"
],
"_id": {
"$oid": "63b5cfbfbcc3196a2a23c450"
}
}
],
"__v": 0
}
=======
mongo db query by aggregate command -
mongomodels.movieMainPageSchema.aggregate(
[
{
$project: {
_id:0, // to supress id
results: {
$filter: {
input: "$results",
as: "result",
cond: { $eq: [ "$$result.yearOfRelease", "2022" ] }
}
}
}
}
]
)
For the new version of MongoDB, it's slightly different.
For db.collection.find you can use the second parameter of find with the key being projection
db.collection.find({}, {projection: {name: 1, email: 0}});
You can also use the .project() method.
However, it is not a native MongoDB method, it's a method provided by most MongoDB driver like Mongoose, MongoDB Node.js driver etc.
db.collection.find({}).project({name: 1, email: 0});
And if you want to use findOne, it's the same that with find
db.collection.findOne({}, {projection: {name: 1, email: 0}});
But findOne doesn't have a .project() method.
I'm trying to add a custom analyzer to my index while also mapping that analyzer to a property on a type. Here is my JSON object for doing this:
{ "settings" : {
"analysis" : {
"analyzer" : {
"test_analyzer" : {
"type" : "custom",
"tokenizer": "standard",
"filter" : ["lowercase", "asciifolding"],
"char_filter": ["html_strip"]
}
}
}
},
"mappings" : {
"test" : {
"properties" : {
"checkanalyzer" : {
"type" : "string",
"analyzer" : "test_analyzer"
}
}
}
}
}
I know this analyzer works because I've tested it using /wp2/_analyze?analyzer=test_analyzer -d '<p>Testing analyzer.</p>' and also it shows up as the analyzer for the checkanalyzer property when I check /wp2/test/_mapping. However, if I add a document like {"checkanalyzer": "<p>The tags should not show up</p>"}, the HTML tags don't get stripped out when I retrieve the document using the _search endpoint. Am I misunderstanding how the mapping works or is there something wrong with my JSON object? I'm dynamically creating the wp2 index and also the test type when I make this call to Elasticsearch, not sure if that matters.
The html doesn't get removed from the source, it gets removed from the terms generated by that source. You can see this if you use a terms aggregation:
POST /test_index/_search
{
"aggs": {
"checkanalyzer_field_terms": {
"terms": {
"field": "checkanalyzer"
}
}
}
}
{
"took": 77,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test_index",
"_type": "test",
"_id": "1",
"_score": 1,
"_source": {
"checkanalyzer": "<p>The tags should not show up</p>"
}
}
]
},
"aggregations": {
"checkanalyzer_field_terms": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "not",
"doc_count": 1
},
{
"key": "should",
"doc_count": 1
},
{
"key": "show",
"doc_count": 1
},
{
"key": "tags",
"doc_count": 1
},
{
"key": "the",
"doc_count": 1
},
{
"key": "up",
"doc_count": 1
}
]
}
}
}
Here's some code I used to test it:
http://sense.qbox.io/gist/2971767aa0f5949510fa0669dad6729bbcdf8570
Now if you want to completely strip out the html prior to indexing and storing the content as is, you can use the mapper attachment plugin - in which when you define the mapping, you can categorize the content_type to be "html."
The mapper attachment is useful for many things especially if you are handling multiple document types, but most notably - I believe just using this for the purpose of stripping out the html tags is sufficient enough (which you cannot do with the html_strip char filter).
Just a forewarning though - NONE of the html tags will be stored. So if you do need those tags somehow, I would suggest defining another field to store the original content. Another note: You cannot specify multifields for mapper attachment documents, so you would need to store that outside of the mapper attachment document. See my working example below.
You'll need to result in this mapping:
{
"html5-es" : {
"aliases" : { },
"mappings" : {
"document" : {
"properties" : {
"delete" : {
"type" : "boolean"
},
"file" : {
"type" : "attachment",
"fields" : {
"content" : {
"type" : "string",
"store" : true,
"term_vector" : "with_positions_offsets",
"analyzer" : "autocomplete"
},
"author" : {
"type" : "string",
"store" : true,
"term_vector" : "with_positions_offsets"
},
"title" : {
"type" : "string",
"store" : true,
"term_vector" : "with_positions_offsets",
"analyzer" : "autocomplete"
},
"name" : {
"type" : "string"
},
"date" : {
"type" : "date",
"format" : "strict_date_optional_time||epoch_millis"
},
"keywords" : {
"type" : "string"
},
"content_type" : {
"type" : "string"
},
"content_length" : {
"type" : "integer"
},
"language" : {
"type" : "string"
}
}
},
"hash_id" : {
"type" : "string"
},
"path" : {
"type" : "string"
},
"raw_content" : {
"type" : "string",
"store" : true,
"term_vector" : "with_positions_offsets",
"analyzer" : "raw"
},
"title" : {
"type" : "string"
}
}
}
},
"settings" : { //insert your own settings here },
"warmers" : { }
}
}
Such that in NEST, I will assemble the content as such:
Attachment attachment = new Attachment();
attachment.Content = Convert.ToBase64String(File.ReadAllBytes("path/to/document"));
attachment.ContentType = "html";
Document document = new Document();
document.File = attachment;
document.RawContent = InsertRawContentFromString(originalText);
I have tested this in Sense - results are as follows:
"file": {
"_content": "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",
"_content_length": 0,
"_content_type": "html",
"_date": "0001-01-01T00:00:00",
"_title": "Topic10"
},
"delete": false,
"raw_content": "<h1>Topic10</h1><p>Delete this text and replace it with your own content. Check your mailbox.</p><p> </p><p>asdf</p><p> </p><p>10</p><p> </p><p>Lavender.</p><p> </p><p>10/6 12:03</p><p> </p><p>5 09</p><p> </p><p>11 47</p><p> </p><p>Halloween is in October.</p><p> </p><p>jog</p>"
},
"highlight": {
"file.content": [
"\n <em>Topic10</em>\n\n Delete this text and replace it with your own content. Check your mailbox.\n\n \n\n asdf\n\n \n\n 10\n\n \n\n Lavender.\n\n \n\n 10/6 12:03\n\n \n\n 5 09\n\n \n\n 11 47\n\n \n\n Halloween is in October.\n\n \n\n jog\n\n "
]
}
I have a collection called 'cupcakes' that was loaded from a GeoJSON file using mongoimport. The GeoJSON can be found here, and is a known valid JSON file. This part seems to have gone to plan, and doing:
use cupcakes
db.cupcakes.find({ })
This pulls back all the features as expected. However, I am trying to do a query that will return only the features that have gluten free set to "no".
I have tried the instructions as per the documentation around querying arrays of documents.
db.cupcakes.find({features: { $elemMatch: { "properties.gluten free":"no"}}}).pretty()
But as far as I can tell this appears to be returning all of the features as opposed to just those that are gluten free establishments.
Essentially I am wanting to pull back all the features where "properties.gluten free" of the features is set to "no" (or "yes" for that matter).
A sample output might be:
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [
-122.70069837570189,
45.53570881624427
]
},
"properties": {
"name": "Le Cookie Monkey",
"address": "1902 NW 24th Avenue",
"website": "http://www.lecookiemonkey.com/",
"gluten free": "no",
"open1": "Tuesday - Friday, 9am - 3pm",
"open2": "Saturday, 9am - 2pm"
}
},
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [
-122.6375323534012,
45.5219957171348
]
},
"properties": {
"name": "Crema Coffee + Bakery",
"address": "2728 SE Ankeny Street",
"website": "http://www.cremabakery.com/",
"gluten free": "no",
"open1": "Monday - Sunday, 7am - 6pm"
}
},
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [
-122.60960519313811,
45.472775425296014
]
},
"properties": {
"name": "Mehri's Bakery & Deli",
"address": "6923 SE 52nd Avenue",
"website": "http://www.mehris.com/",
"gluten free": "no",
"open1": "Monday - Friday, 7am - 7pm",
"open2": "Saturday, 8am - 5pm",
"open3": "Sunday, 8am - 2pm"
}
},
... and so on until all the features where "gluten free" : "no" are returned.
What am I missing here? Thanks in advance.
You forgot to add the positional $ operator in your projection which limits the contents of the features array from the query results to contain only the first element matching the query document as follows:
db.cupcakes.find(
{ "features.properties.gluten free": "no" },
{ "features.$": 1, "_id": 0 }
);
Sample Output:
/* 0 */
{
"features" : [
{
"type" : "Feature",
"geometry" : {
"type" : "Point",
"coordinates" : [
-122.653357386589,
45.51208367658516
]
},
"properties" : {
"name" : "Hungry Heart Cupcakes",
"address" : "1212 SE Hawthorne Boulevard",
"website" : "http://www.hungryheartcupcakes.com",
"gluten free" : "no",
"open1" : "Monday - Sunday, 11am - 9pm"
}
}
]
}
To pull back all the features where gluten free is equal to no, the $redact pipeline operator in the aggregation framework should work for you in this case. This will recursively descend through the document structure and do some actions based on an evaluation of specified conditions at each level. The map() method will then produce the final array of objects as per expectation. The concept can be a bit tricky to grasp but the following example demonstrates this:
db.cupcakes.aggregate([
{
"$match": { "features.properties.gluten free" : "no" }
},
{
"$redact": {
"$cond": {
"if": {
"$eq": [ "$gluten free", "yes" ]
},
"then": "$$PRUNE",
"else": "$$DESCEND"
}
}
}
]).map(function(doc){ return doc.features; });