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
Related
here's my case: I'm trying to make a query on a table (table name HCI.LocCatApp) using a value sent by API as KeyConditionExpression, and I'm storing the results (which must be numbers not strings) in an array, and I want to use each value from this array as a FilterExpression to scan another table (table name HCI.Category) .. So what I need is to loop on the array values, take each of them as FilterExpression and perform the scan operation. I'm currently trying to use IN but I'm not sure if it's even supported or not.
And keep in mind that the array is being filled during the runtime. And the callback can be performed only once.
here's my code:
'use strict'
var AWS = require('aws-sdk');
var mydocumentClient = new AWS.DynamoDB.DocumentClient();
exports.handler = function (event, context, callback) {
var params = {
TableName: 'HCI.LocCatApp',
KeyConditionExpression : 'LocID = :lid',
ExpressionAttributeValues: {
":lid": event.LocID
},
ProjectionExpression: 'CatID'
};
var catIDs = [];
var catIDsObject = {};
var index = 0;
mydocumentClient.query(params, function (err, data){
if (err) {
callback(err, null);
}else{
data.Items.forEach(function(item){catIDs.push(item.CatID)});
//callback(null, catIDs);
}
})
catIDs.forEach(function(value){
index ++;
var catIDsKey = ":catID"+index;
catIDsObject[catIDsKey] = value;
})
var params2 = {
TableName: 'HCI.Category',
FilterExpression : "CatID IN (:cIDs)",
ExpressionAttributeValues : {
':cIDs' : catIDs
}
};
mydocumentClient.scan(params2, function (err, data){
if (err) {
callback(err, null);
}else{
callback(null, data);
}
})
}
For some reason, the current code runs successfully but it doesn't find any matches, even if I fill in the values manually in the array, there's still no results, the IN operation doesn't seem to work.
And many thanks in advance
In your code catIds is an array of IDs (strings probably).
When you pass it to FilterExpression, you are assuming that it will be converted to a) string b) to a string in correct format.
FilterExpression : "CatID IN (:cIDs)",
ExpressionAttributeValues : {
':cIDs' : catIDs
}
I cannot try this myself at the moment, but I'm assuming this is where the query fails. IN operator expects a comma separated list of values to compare to, in parenthesis. So, after the array is inserted to query, it should be like this
FilterExpression : "CatID IN (cat1, cat2, cat2)",
But most probably it contains extra set of [ and ], and maybe even the array to string conversion causes it to something like [Object object] etc.
One solution would be to use Array.join to concatenate all the elements from the array to single string before passing it to FilterExperession. Something like this
FilterExpression : "CatID IN (:cIDs)",
ExpressionAttributeValues : {
':cIDs' : catIDs.join()
}
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"}}}
}},
)
I want to sort embedded records in my content. Initially I had separated the embedded record as an ArrayController and did sorting on it - it was pretty straight forward, but now I am told that I should use just embedded records without ArrayController. I followed http://www.javascriptkit.com/javatutors/arraysort2.shtml to sort the array objects and the content is getting sorted but the view is not getting updated accordingly. My function looks like :
setSort: function (sort) {
var sortedContent = this.get('content.analyticsRunParameters');
sortedContent.sort(function(a, b){
var colA=a.get(sort).toLowerCase(), colB=b.get(sort).toLowerCase();
if (colA < colB) //sort string ascending
return -1;
if (colA > colB)
return 1;
return 0; //default return value (no sorting)
});
this.set('content.analyticsRunParameters',sortedContent);
console.log(sortedContent);//is sorted
console.log(this.get('content.analyticsRunParameters'));//is sorted
}
Is there a way to update the view when my content is sorted? Or using ArrayController the only way around? Thanks.
I found the solution in another post here : Ember.ArrayProxy changes not triggering handlebars #each update
I don't know if this is a best solution either. It seems like I should be just able to sort the array objects without the help of array proxy. I had to make minor modification in the transform I was using to return ArrayProxy instead of normal array of objects like :
AS.AnalyticsRunParameterTransform = DS.Transform.extend({
//return array of ember objects
deserialize: function (serialized) {
var objects = [];
for (var key in serialized) {
objects.push(Ember.Object.create({
"name": serialized[key].name,
"description": serialized[key].description,
"default": serialized[key]["default"],
"value": serialized[key].value,
"category": serialized[key].category
}));
}
//return objects;
return Ember.ArrayProxy.create({ content: objects });
},
//return JSON object
serialize: function (deserialized) {
var analyticsTemplate = {}, object;
for (var i = 0, len = deserialized.length; i < len; i++) {
object = deserialized[i];
analyticsTemplate[object.get('name')] = {"name": object.get('name'),
"description": object.get('description'),
"default": object.get('default'),
"value": object.get('value'),
"category": object.get('category')};
}
return analyticsTemplate;
}
});
Alright SO users. I am trying to learn and use CouchDB. I have the StackExchange data export loaded as document per row from the XML file, so the documents in couch look basically like this:
//This is a representation of a question:
{
"Id" : "1",
"PostTypeId" : "1",
"Body" : "..."
}
//This is a representation of an answer
{
"Id" : "1234",
"ParentId" : "1",
"PostTypeId" : "2"
"Body" : "..."
}
(Please ignore the fact that the import of these documents basically treated all the attributes as text, I understand that using real numbers, bools, etc. could yield better space/processing efficiency.)
What I'd like to do is to map this into a single aggregate document:
Here's my map:
function(doc) {
if(doc.PostTypeId === "2"){
emit(doc.ParentId, doc);
}
else{
emit(doc.Id, doc);
}
}
And here's the reduce:
function(keys, values, rereduce){
var retval = {question: null, answers : []};
if(rereduce){
for(var i in values){
var current = values[i];
retval.answers = retval.answers.concat(current.answers);
if(retval.question === null && current.question !== null){
retval.question = current.question;
}
}
}
else{
for(var i in values){
var current = values[i];
if(current.PostTypeId === "2"){
retval.push(current);
}
else{
retval.question = current;
}
}
}
return retval;
}
Theoretically, this would yield a document like this:
{
"question" : {...},
"answers" : [answer1, answer2, answer3]
}
But instead I am getting the standard "does not reduce fast enough" error.
Am I using Map-Reduce incorrectly, is there a well-established pattern for how to accomplish this in CouchDb?
(Please also note that I would like a response with the complete documents, where the question is the "parent" and the answers are the "children", not just the Ids.)
So, the "right" way to accomplish what I'm trying to do above is to add a "list" as part of my design document. (and the end I am trying to achieve appears to be referred to as "collating documents").
At any rate, you can configure your map however you like, and combine it with an a "list" in the same function.
To solve the above question, I eliminated my reduce (only have a map function), and then added a function like the following:
{
"_id": "_design/posts",
"_rev": "11-8103b7f3bd2552a19704710058113b32",
"language": "javascript",
"views": {
"by_question_id": {
"map": "function(doc) {
if(doc.PostTypeId === \"2\"){
emit(doc.ParentId, doc);
}
else{
emit(doc.Id, doc);
}
}"
}
},
"lists": {
"aggregated": "function(head, req){
start({\"headers\": {\"Content-Type\": \"text/json\"}});
var currentRow = null;
var currentObj = null;
var retval = [];
while(currentRow = getRow()){
if(currentObj === null || currentRow.key !== currentObj.key){
currentObj = {key: currentRow.key, question : null, answers : []};
retval.push(currentObj);
}
if(currentRow.value.PostTypeId === \"2\"){
currentObj.answers.push(currentRow.value);
}
else{
currentObj.question = currentRow.value;
}
}
send(toJSON(retval));
}"
}
}
So, after you have some elements loaded up, you can access them like so:
http://localhost:5984/<db>/_design/posts/_list/aggregated/by_question_id?<standard view limiters>
I hope this saves people some time.
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.