Advanced update using mongodb [duplicate] - c++

In MongoDB, is it possible to update the value of a field using the value from another field? The equivalent SQL would be something like:
UPDATE Person SET Name = FirstName + ' ' + LastName
And the MongoDB pseudo-code would be:
db.person.update( {}, { $set : { name : firstName + ' ' + lastName } );

The best way to do this is in version 4.2+ which allows using the aggregation pipeline in the update document and the updateOne, updateMany, or update(deprecated in most if not all languages drivers) collection methods.
MongoDB 4.2+
Version 4.2 also introduced the $set pipeline stage operator, which is an alias for $addFields. I will use $set here as it maps with what we are trying to achieve.
db.collection.<update method>(
{},
[
{"$set": {"name": { "$concat": ["$firstName", " ", "$lastName"]}}}
]
)
Note that square brackets in the second argument to the method specify an aggregation pipeline instead of a plain update document because using a simple document will not work correctly.
MongoDB 3.4+
In 3.4+, you can use $addFields and the $out aggregation pipeline operators.
db.collection.aggregate(
[
{ "$addFields": {
"name": { "$concat": [ "$firstName", " ", "$lastName" ] }
}},
{ "$out": <output collection name> }
]
)
Note that this does not update your collection but instead replaces the existing collection or creates a new one. Also, for update operations that require "typecasting", you will need client-side processing, and depending on the operation, you may need to use the find() method instead of the .aggreate() method.
MongoDB 3.2 and 3.0
The way we do this is by $projecting our documents and using the $concat string aggregation operator to return the concatenated string.
You then iterate the cursor and use the $set update operator to add the new field to your documents using bulk operations for maximum efficiency.
Aggregation query:
var cursor = db.collection.aggregate([
{ "$project": {
"name": { "$concat": [ "$firstName", " ", "$lastName" ] }
}}
])
MongoDB 3.2 or newer
You need to use the bulkWrite method.
var requests = [];
cursor.forEach(document => {
requests.push( {
'updateOne': {
'filter': { '_id': document._id },
'update': { '$set': { 'name': document.name } }
}
});
if (requests.length === 500) {
//Execute per 500 operations and re-init
db.collection.bulkWrite(requests);
requests = [];
}
});
if(requests.length > 0) {
db.collection.bulkWrite(requests);
}
MongoDB 2.6 and 3.0
From this version, you need to use the now deprecated Bulk API and its associated methods.
var bulk = db.collection.initializeUnorderedBulkOp();
var count = 0;
cursor.snapshot().forEach(function(document) {
bulk.find({ '_id': document._id }).updateOne( {
'$set': { 'name': document.name }
});
count++;
if(count%500 === 0) {
// Excecute per 500 operations and re-init
bulk.execute();
bulk = db.collection.initializeUnorderedBulkOp();
}
})
// clean up queues
if(count > 0) {
bulk.execute();
}
MongoDB 2.4
cursor["result"].forEach(function(document) {
db.collection.update(
{ "_id": document._id },
{ "$set": { "name": document.name } }
);
})

You should iterate through. For your specific case:
db.person.find().snapshot().forEach(
function (elem) {
db.person.update(
{
_id: elem._id
},
{
$set: {
name: elem.firstname + ' ' + elem.lastname
}
}
);
}
);

Apparently there is a way to do this efficiently since MongoDB 3.4, see styvane's answer.
Obsolete answer below
You cannot refer to the document itself in an update (yet). You'll need to iterate through the documents and update each document using a function. See this answer for an example, or this one for server-side eval().

For a database with high activity, you may run into issues where your updates affect actively changing records and for this reason I recommend using snapshot()
db.person.find().snapshot().forEach( function (hombre) {
hombre.name = hombre.firstName + ' ' + hombre.lastName;
db.person.save(hombre);
});
http://docs.mongodb.org/manual/reference/method/cursor.snapshot/

Starting Mongo 4.2, db.collection.update() can accept an aggregation pipeline, finally allowing the update/creation of a field based on another field:
// { firstName: "Hello", lastName: "World" }
db.collection.updateMany(
{},
[{ $set: { name: { $concat: [ "$firstName", " ", "$lastName" ] } } }]
)
// { "firstName" : "Hello", "lastName" : "World", "name" : "Hello World" }
The first part {} is the match query, filtering which documents to update (in our case all documents).
The second part [{ $set: { name: { ... } }] is the update aggregation pipeline (note the squared brackets signifying the use of an aggregation pipeline). $set is a new aggregation operator and an alias of $addFields.

Regarding this answer, the snapshot function is deprecated in version 3.6, according to this update. So, on version 3.6 and above, it is possible to perform the operation this way:
db.person.find().forEach(
function (elem) {
db.person.update(
{
_id: elem._id
},
{
$set: {
name: elem.firstname + ' ' + elem.lastname
}
}
);
}
);

I tried the above solution but I found it unsuitable for large amounts of data. I then discovered the stream feature:
MongoClient.connect("...", function(err, db){
var c = db.collection('yourCollection');
var s = c.find({/* your query */}).stream();
s.on('data', function(doc){
c.update({_id: doc._id}, {$set: {name : doc.firstName + ' ' + doc.lastName}}, function(err, result) { /* result == true? */} }
});
s.on('end', function(){
// stream can end before all your updates do if you have a lot
})
})

update() method takes aggregation pipeline as parameter like
db.collection_name.update(
{
// Query
},
[
// Aggregation pipeline
{ "$set": { "id": "$_id" } }
],
{
// Options
"multi": true // false when a single doc has to be updated
}
)
The field can be set or unset with existing values using the aggregation pipeline.
Note: use $ with field name to specify the field which has to be read.

Here's what we came up with for copying one field to another for ~150_000 records. It took about 6 minutes, but is still significantly less resource intensive than it would have been to instantiate and iterate over the same number of ruby objects.
js_query = %({
$or : [
{
'settings.mobile_notifications' : { $exists : false },
'settings.mobile_admin_notifications' : { $exists : false }
}
]
})
js_for_each = %(function(user) {
if (!user.settings.hasOwnProperty('mobile_notifications')) {
user.settings.mobile_notifications = user.settings.email_notifications;
}
if (!user.settings.hasOwnProperty('mobile_admin_notifications')) {
user.settings.mobile_admin_notifications = user.settings.email_admin_notifications;
}
db.users.save(user);
})
js = "db.users.find(#{js_query}).forEach(#{js_for_each});"
Mongoid::Sessions.default.command('$eval' => js)

With MongoDB version 4.2+, updates are more flexible as it allows the use of aggregation pipeline in its update, updateOne and updateMany. You can now transform your documents using the aggregation operators then update without the need to explicity state the $set command (instead we use $replaceRoot: {newRoot: "$$ROOT"})
Here we use the aggregate query to extract the timestamp from MongoDB's ObjectID "_id" field and update the documents (I am not an expert in SQL but I think SQL does not provide any auto generated ObjectID that has timestamp to it, you would have to automatically create that date)
var collection = "person"
agg_query = [
{
"$addFields" : {
"_last_updated" : {
"$toDate" : "$_id"
}
}
},
{
$replaceRoot: {
newRoot: "$$ROOT"
}
}
]
db.getCollection(collection).updateMany({}, agg_query, {upsert: true})

(I would have posted this as a comment, but couldn't)
For anyone who lands here trying to update one field using another in the document with the c# driver...
I could not figure out how to use any of the UpdateXXX methods and their associated overloads since they take an UpdateDefinition as an argument.
// we want to set Prop1 to Prop2
class Foo { public string Prop1 { get; set; } public string Prop2 { get; set;} }
void Test()
{
var update = new UpdateDefinitionBuilder<Foo>();
update.Set(x => x.Prop1, <new value; no way to get a hold of the object that I can find>)
}
As a workaround, I found that you can use the RunCommand method on an IMongoDatabase (https://docs.mongodb.com/manual/reference/command/update/#dbcmd.update).
var command = new BsonDocument
{
{ "update", "CollectionToUpdate" },
{ "updates", new BsonArray
{
new BsonDocument
{
// Any filter; here the check is if Prop1 does not exist
{ "q", new BsonDocument{ ["Prop1"] = new BsonDocument("$exists", false) }},
// set it to the value of Prop2
{ "u", new BsonArray { new BsonDocument { ["$set"] = new BsonDocument("Prop1", "$Prop2") }}},
{ "multi", true }
}
}
}
};
database.RunCommand<BsonDocument>(command);

MongoDB 4.2+ Golang
result, err := collection.UpdateMany(ctx, bson.M{},
mongo.Pipeline{
bson.D{{"$set",
bson.M{"name": bson.M{"$concat": []string{"$lastName", " ", "$firstName"}}}
}},
)

Related

Modelling Complex Types for DynamoDB in Kotlin

I have a DynamoDB table that I need to read/write to. I am trying to create a model for reading and writing from DynamoDB with Kotlin. But I keep encountering com.amazonaws.services.dynamodbv2.datamodeling.DynamoDBMappingException: MyModelDB[myMap]; could not unconvert attribute when I run dynamoDBMapper.scanPage(...). Some times myMap will be MyListOfMaps instead, but I guess it's from iterating the keys of a Map.
My code is below:
#DynamoDBTable(tableName = "") // Non-issue, I am assigning the table name in the DynamoDBMapper
data class MyModelDB(
#DynamoDBHashKey(attributeName = "id")
var id: String,
#DynamoDBAttribute(attributeName = "myMap")
var myMap: MyMap,
#DynamoDBAttribute(attributeName = "MyListOfMapItems")
var myListOfMapItems: List<MyMapItem>,
) {
constructor() : this(id = "", myMap = MyMap(), myListOfMaps = mutableListOf())
#DynamoDBDocument
class MyMap {
#get:DynamoDBAttribute(attributeName = "myMapAttr")
var myMapAttr: MyMapAttr = MyMapAttr()
#DynamoDBDocument
class MyMapAttr {
#get:DynamoDBAttribute(attributeName = "stringValue")
var stringValue: String = ""
}
}
#DynamoDBDocument
class MyMapItem {
#get:DynamoDBAttribute(attributeName = "myMapItemAttr")
var myMapItemAttr: String = ""
}
}
I am using the com.amazonaws:aws-java-sdk-dynamodb:1.11.500 package and my dynamoDBMapper is initialised with DynamoDBMapperConfig.Builder().build() (along with some other configurations).
My question is what am I doing wrong and why? I have also seen that some Java implementations use DynamoDBTypeConverter. Is it better and I should be using that instead?
Any examples would be appreciated!
A couple comments here. First, you are not using the AWS SDK for Kotlin. You are using another SDK and simply writing Kotlin code. Using this SDK, you are not getting full benefits of Kotlin such as support of Coroutines.
The AWS SDK for Kotlin (which does offer full support of Kotlin features) was just released as DEV Preview this week. See the DEV Guide:
Setting up the AWS SDK for Kotlin
However this SDK does not support this mapping as of now. To place items into an Amazon DynamoDB table using the AWS SDK for Kotlin, you need to use:
mutableMapOf<String, AttributeValue>
Full example here.
To map Java Objects to a DynamoDB table, you should look at using the DynamoDbEnhancedClient that is part of AWS SDK for Java V2. See this topic in the AWS SDK for Java V2 Developer Guide:
Mapping items in DynamoDB tables
You can find other example of using the Enhanced Client in the AWS Github repo.
Ok, I eventually got this working thanks to some help. I edited the question slightly after getting a better understanding. Here is how my data class eventually turned out. For Java users, Kotlin compiles to Java, so if you can figure out how the conversion works, the idea should be the same for your use too.
data class MyModelDB(
#DynamoDBHashKey(attributeName = "id")
var id: String = "",
#DynamoDBAttribute(attributeName = "myMap")
#DynamoDBTypeConverted(converter = MapConverter::class)
var myMap: Map<String, AttributeValue> = mutableMapOf(),
#DynamoDBAttribute(attributeName = "myList")
#DynamoDBTypeConverted(converter = ListConverter::class)
var myList: List<AttributeItem> = mutableListOf(),
) {
constructor() : this(id = "", myMap = MyMap(), myList = mutableListOf())
}
class MapConverter : DynamoDBTypeConverter<AttributeValue, Map<String,AttributeValue>> {
override fun convert(map: Map<String,AttributeValue>>): AttributeValue {
return AttributeValue().withM(map)
}
override fun unconvert(itemMap: AttributeValue?): Map<String,AttributeValue>>? {
return itemMap?.m
}
}
class ListConverter : DynamoDBTypeConverter<AttributeValue, List<AttributeValue>> {
override fun convert(list: List<AttributeValue>): AttributeValue {
return AttributeValue().withL(list)
}
override fun unconvert(itemList: AttributeValue?): List<AttributeValue>? {
return itemList?.l
}
}
This would at least let me use my custom converters to get my data out of DynamoDB. I would go on to define a separate data container class for use within my own application, and I created a method to serialize and unserialize between these 2 data objects. This is more of a preference for how you would like to handle the data, but this is it for me.
// For reading and writing to DynamoDB
class MyModelDB {
...
fun toMyModel(): MyModel {
...
}
}
// For use in my application
class MyModel {
var id: String = ""
var myMap: CustomObject = CustomObject()
var myList<CustomObject2> = mutableListOf()
fun toMyModelDB():MyModelDB {
...
}
}
Finally, we come to the implementation of the 2 toMyModel.*() methods. Let's start with input, this is what my columns looked like:
myMap:
{
"key1": {
"M": {
"subKey1": {
"S": "some"
},
"subKey2": {
"S": "string"
}
}
},
"key2": {
"M": {
"subKey1": {
"S": "other"
},
"subKey2": {
"S": "string"
}
}
}
}
myList:
[
{
"M": {
"key1": {
"S": "some"
},
"key2": {
"S": "string"
}
}
},
{
"M": {
"key1": {
"S": "some string"
},
"key3": {
"M": {
"key4": {
"S": "some string"
}
}
}
}
}
]
The trick then is to use com.amazonaws.services.dynamodbv2.model.AttributeValue to convert each field in the JSON. So if I wanted to access the value of subKey2 in key1 field of myMap, I would do something like this:
myModelDB.myMap["key1"]
?.m // Null check and get the value of key1, a map
?.get("subKey2") // Get the AttributeValue associated with the "subKey2" key
?.s // Get the value of "subKey2" as a String
The same applies to myList:
myModelDB.myList.foreach {
it?.m // Null check and get the map at the current index
?.get("key1") // Get the AttributeValue associated with the "key1"
...
}
Edit: Doubt this will be much of an issue, but I also updated my DynamoDB dependency to com.amazonaws:aws-java-sdk-dynamodb:1.12.126

AdonisJS exists Validator

I'm following the official [documentation] (https://legacy.adonisjs.com/docs/4.0/validator) && indicative, but I couldn't find anything to help me.
I want to validate if the given param exists on database.
So I tried:
app/Validators/myValidator
const { rule } = use('Validator')
get rules () {
return {
userId: 'required|integer|exists:MyDatabase.users,userId', <-- this is what isn't working
date: [
rule('date'),
rule('dateFormat', 'YYYY-MM-DD')
]
}
}
// Getting the data to be validated
get data () {
const params = this.ctx.params
const { userId, date } = params
return Object.assign({}, { userId }, { date })
}
It gives me the following error:
{
"error": {
"message": "select * from `MyDatabase`.`users`.`userId` where `undefined` = '2' limit 1 - ER_PARSE_ERROR: You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near '.`userId` where `undefined` = '2' limit 1' at line 1",
"name": "ErrorValidator",
"status": 40
}
}
How should I properly indicate that I want to compare MyDatabase.users.userid to the given parameter?
After a few hard try/error I stumbled upon the solution.
Just need to follow what is inside hooks.js and pass the values separated by comma, like so:
get rules () {
return {
userId: 'required|integer|exists:MyDatabase,MyTable,columntoCompare',
}
}

lookback API where filter with multiple conditions

When using loopback API, is 'AND' operator redundant in 'where' filter with multiple conditions?
For example, I tested the following two queries and they return the same result:
<model>.find({ where: { <condition1>, <condition2> } });
<model>.find({ where: { and: [<condition1>, <condtion2>] } });
To be more specific, suppose this is the table content:
name value
---- -----
a 1
b 2
When I execute 'find()' using two different 'where' filters, I get the first record in both cases:
{ where: { name: 'a', value: 1 } }
{ where: { and: [ { name: 'a'}, { value: 1 } ] } }
I've read through the API documents, but didn't find what logical operator is used when there are multiple conditions.
If 'AND' is redundant as shown in my test, I prefer not using it. But I just want to make sure if this is true in general, or if it just happens to work with postgreSQL which I'm using.
This is a valid query which could only be accomplished with an and statement.
{
"where": {
"or": [
{"and": [{"classification": "adn"}, {"series": "2"}]},
{"series": "3"}
]
}
}
EDIT: https://github.com/strongloop/loopback-filters/blob/master/index.js
function matchesFilter(obj, filter) {
var where = filter.where;
var pass = true;
var keys = Object.keys(where);
keys.forEach(function(key) {
if (key === 'and' || key === 'or') {
if (Array.isArray(where[key])) {
if (key === 'and') {
pass = where[key].every(function(cond) {
return applyFilter({where: cond})(obj);
});
return pass;
}
if (key === 'or') {
pass = where[key].some(function(cond) {
return applyFilter({where: cond})(obj);
});
return pass;
}
}
}
if (!test(where[key], getValue(obj, key))) {
pass = false;
}
});
return pass;
}
It iterates through the keys of the where object where looking for failure, so it acts like an implicit and statement in your case.
EDIT 2: https://github.com/strongloop/loopback-datasource-juggler/blob/cc60ef8202092ae4ed564fc7bd5aac0dd4119e57/test/relations.test.js
The loopback datasource juggler contains tests which use the implicit and format
{PictureLink.findOne({where: {pictureId: anotherPicture.id, imageableType: 'Article'}},
{pictureId: anotherPicture.id, imageableId: article.id, imageableType: 'Article',}
But I just want to make sure if this is true in general, or if it just happens to work with postgreSQL which I'm using.
Is it true in general? No.
It appears that this is handled for PostgreSQL and MySQL (and probably other SQL databases) in SQLConnector. So, it is possible connectors not using SQLConnector (e.g MongoDB) don't support this. However, given the many examples I've seen online, I would say it's safe to assume other connectors have implemented it this way, too.

How Do I Make a Faster Riak MapReduce Query?

How can we make our MapReduce Queries Faster?
We have built an application using a five node Riak DB cluster.
Our data model is composed of three buckets: matches, leagues, and teams.
Matches contains links to leagues and teams:
Model
var match = {
id: matchId,
leagueId: meta.leagueId,
homeTeamId: meta.homeTeamId,
awayTeamId: meta.awayTeamId,
startTime: m.match.startTime,
firstHalfStartTime: m.match.firstHalfStartTime,
secondHalfStartTime: m.match.secondHalfStartTime,
score: {
goals: {
a: 1*safeGet(m.match, 'score.goals.a'),
b: 1*safeGet(m.match, 'score.goals.b')
},
corners: {
a: 1*safeGet(m.match, 'score.corners.a'),
b: 1*safeGet(m.match, 'score.corners.b')
}
}
};
var options = {
index: {
leagueId: match.leagueId,
teamId: [match.homeTeamId, match.awayTeamId],
startTime: match.startTime || match.firstHalfStartTime || match.secondHalfStartTime
},
links: [
{ bucket: 'leagues', key: match.leagueId, tag: 'league' },
{ bucket: 'teams', key: match.homeTeamId, tag: 'home' },
{ bucket: 'teams', key: match.awayTeamId, tag: 'away' }
]
};
match.model = 'match';
modelCache.save('matches', match.id, match, options, callback);
Queries
We write a query that returns results from several buckets, one way is to query each bucket separately. The other way is to use links to combine results from a single query.
Two versions of the query we tried both take over a second, no matter how small our bucket size.
The first version uses two map phases, which we modeled after this post (Practical Map-Reduce: Forwarding and Collecting).
#!/bin/bash
curl -X POST \
-H "content-type: application/json" \
-d #- \
http://localhost:8091/mapred \
<<EOF
{
"inputs":{
"bucket":"matches",
"index":"startTime_bin",
"start":"2012-10-22T23:00:00",
"end":"2012-10-24T23:35:00"
},
"query": [
{"map":{"language": "javascript", "source":"
function(value, keydata, arg){
var match = Riak.mapValuesJson(value)[0];
var links = value.values[0].metadata.Links;
var result = links.map(function(l) {
return [l[0], l[1], match];
});
return result;
}
"}
},
{"map":{"language": "javascript", "source": "
function(value, keydata, arg) {
var doc = Riak.mapValuesJson(value)[0];
return [doc, keydata];
}
"}
},
{"reduce":{
"language": "javascript",
"source":"
function(values) {
var merged = {};
values.forEach(function(v) {
if(!merged[v.id]) {
merged[v.id] = v;
}
});
var results = [];
for(key in merged) {
results.push(merged[key]);
}
return results;
}
"
}
}
]
}
EOF
In the second version we do four separate Map-Reduce queries to get the objects from the three buckets:
async.series([
//First get all matches
function(callback) {
db.mapreduce
.add(inputs)
.map(function (val, key, arg) {
var data = Riak.mapValuesJson(val)[0];
if(arg.leagueId && arg.leagueId != data.leagueId) {
return [];
}
var d = new Date();
var date = data.startTime || data.firstHalfStartTime || data.secondHalfStartTime;
d.setFullYear(date.substring(0, 4));
d.setMonth(date.substring(5, 7) - 1);
d.setDate(date.substring(8, 10));
d.setHours(date.substring(11, 13));
d.setMinutes(date.substring(14, 16));
d.setSeconds(date.substring(17, 19));
d.setMilliseconds(0);
startTimestamp = d.getTime();
var short = {
id: data.id,
l: data.leagueId,
h: data.homeTeamId,
a: data.awayTeamId,
t: startTimestamp,
s: data.score,
c: startTimestamp
};
return [short];
}, {leagueId: query.leagueId, page: query.page}).reduce(function (val, key) {
return val;
}).run(function (err, matches) {
matches.forEach(function(match) {
result.match[match.id] = match; //Should maybe filter this
leagueIds.push(match.l);
teamIds.push(match.h);
teamIds.push(match.a);
});
callback();
});
},
//Then get all leagues, teams and lines in parallel
function(callback) {
async.parallel([
//Leagues
function(callback) {
db.getMany('leagues', leagueIds, function(err, leagues) {
if (err) { callback(err); return; }
leagues.forEach(function(league) {
visibleLeagueIds[league.id] = true;
result.league[league.id] = {
r: league.regionId,
n: league.name,
s: league.name
};
});
callback();
});
},
//Teams
function(callback) {
db.getMany('teams', teamIds, function(err, teams) {
if (err) { callback(err); return; }
teams.forEach(function(team) {
result.team[team.id] = {
n: team.name,
h: team.name,
s: team.stats
};
});
callback();
});
}
], callback);
}
], function(err) {
if (err) { callback(err); return; }
_.each(regionModel.getAll(), function(region) {
result.region[region.id] = {
id: region.id,
c: 'https://d1goqbu19rcwi8.cloudfront.net/icons/silk-flags/' + region.icon + '.png',
n: region.name
};
});
var response = {
success: true,
result: {
modelRecords: result,
paging: {
page: query.page,
pageSize: 50,
total: result.match.length
},
time: moment().diff(a)/1000.00,
visibleLeagueIds: visibleLeagueIds
}
};
callback(null, JSON.stringify(response, null, '\t'));
});
How do we make these queries faster?
Additional info:
We are using riak-js and node.js to run our queries.
One way to make it at least a bit faster would be to deploy the JavaScript mapreduce functions to the server instead of passing them through as part of the job. (see description of js_source_dir parameter here). This is usually recommended if you have a JavaScript functions that you run repeatedly.
As there is some overhead associated with running JavaScript mapreduce functions compared to native ones implemented in Erlang, using non-JavaScript functions where possible may also help.
The two map phase functions in your first query appear to be designed to work around the limitation that a normal linking phase (which I believe is more efficient) does not pass on the record being processed (the matches record). The first function includes all the links and passes on the match data as additional data in JSON form, while the second passes on the data of the match as well as the linked record in JSON form.
I have written a simple Erlang function that includes all links as well as the ID of the record passed in. This could be used together with the native Erlang function riak_kv_mapreduce:map_object_value to replace the two map phase functions in your first example, removing some of the JavaScript usage. As in the existing solution, I would expect you to receive a number of duplicates as several matches may link to the same league/team.
-module(riak_mapreduce_example).
-export([map_link/3]).
%% #spec map_link(riak_object:riak_object(), term(), term()) ->
%% [{{Bucket :: binary(), Key :: binary()}, Props :: term()}]
%% #doc map phase function for adding linked records to result set
map_link({error, notfound}, _, _) ->
[];
map_link(RiakObject, Props, _) ->
Bucket = riak_object:bucket(RiakObject),
Key = riak_object:key(RiakObject),
Meta = riak_object:get_metadata(RiakObject),
Current = [{{Bucket, Key}, Props}],
Links = case dict:find(<<"Links">>, Meta) of
{ok, List} ->
[{{B, K}, Props} || {{B, K}, _Tag} <- List];
error ->
[]
end,
lists:append([Current, Links]).
The results of these can either be sent back to the client for aggregation or passed into a reduce phase function as in the example you provided.
The example function would need to be compiled and installed on all nodes, and may require a restart.
Another way to improve performance (that very well may not be an option for you) would perhaps be alter the data model in order to avoid having to use mapreduce queries for performance critical queries altogether.

How do you sort results of a _View_ by value in the in Couchbase?

So from what I understand in Couchbase is that one can sort keys* by using
descending=true
but in my case I want to sort by values instead. Consider the Twitter data in json format, my question is What it the most popular user mentioned?
Each tweet has the structure of:
{
"text": "",
"entities" : {
"hashtags" : [ ... ],
"user_mentions" : [ ...],
"urls" : [ ... ]
}
So having used MongoDB before I reused the Map function and modified it slightly to be usable in Couchbase as follows:
function (doc, meta) {
if (!doc.entities) { return; }
doc.entities.user_mentions.forEach(
function(mention) {
if (mention.screen_name !== undefined) {
emit(mention.screen_name, null);
}
}
)
}
And then I used the reduce function _count to count all the screen_name occurrences. Now my problem is How do I sort by the count values, rather than the key?
Thanks
The short answer is you cannot sort by value the result of you view. You can only sort by key.
Some work around will be to either:
analyze the data before inserting them into Couchbase and create a counter for the values you are interested by (mentions in your case)
use the view you have to sort on the application size if the size of the view is acceptable for a client side sort.
The following JS code calls a view, sorts the result, and prints the 10 hottest subjects (hashtags):
var http = require('http');
var options = {
host: '127.0.0.1',
port: 8092,
path: '/social/_design/dev_tags/_view/tags?full_set=true&connection_timeout=60000&group=true',
method: 'GET'
}
http.request(
options,
function(res) {
var buf = new Buffer(0);
res.on('data', function(data) {
buf += data;
});
res.on('end', function() {
var tweets = JSON.parse(buf);
var rows = tweets.rows;
rows.sort( function (a,b){ return b.value - a.value }
);
for ( var i = 0; i < 10; i++ ) {
console.log( rows[i] );
}
});
}
).end();
In the same time I am looking at other options to achieve this
I solved this by using a compound key.
function (doc, meta) {
emit([doc.constraint,doc.yoursortvalue]);
}
url elements:
&startkey=["jim",5]&endkey=["jim",10]&descending=true