Mongolike
Mongolike is an experimental MongoDB clone being built on top of PLV8 and Postgres.
Implemented (so far)
- create_collection()
- drop_collection()
- save()
- find()
- runCommand() (Map/Reduce)
- ensureIndex()
- removeIndex()
- getIndexes()
Installing
Install PLV8
Visit http://code.google.com/p/plv8js/wiki/PLV8 and follow the build instructions.
Install Mongolike
The Easy Way
The easy way to install is to use node.js.
$ npm install -g mongolike
$ mongolike-install -d yourdb
The Slightly Less Easy Way
$ for file in sql/*.sql; do psql yourdb < $file; done
Running Tests
Mongolike includes a test suite and a test runner.
$ test/test_runner.js -d yourdb
Additional tests can be added to test/tests.sql.
Using
All commands must be prefixed by SELECT, and are modified slightly to work in the Postgres environment.
create_collection(collection)
Create a collection.
Example:
SELECT create_collection('test');
drop_collection(collection)
Drop a collection.
Example:
SELECT drop_collection('test');
save(collection, object)
Save an object into a collection.
Example:
SELECT save('test', '{ "foo": "bar" }');
find(collection /*, terms, limit, skip */)
Find an object, with optional terms, limit, and skip.
Example:
SELECT find('test', '{ "type": { "$in": [ "food", "snacks" ] } }');
runCommand(command)
Run a command on the Database. Currently only mapReduce is supported.
NOTE The JSON object cannot have carriage returns, the example below does for readability.
Example:
SELECT runCommand('{
"map": "function MapCode() {
emit(this.Country, {
\"data\": [
{
\"city\": this.City,
\"lat\": this.Latitude,
\"lon\": this.Longitude
}
]
});
}",
"reduce": "function ReduceCode(key, values) {
var reduced = {
\"data\": [ ]
};
for (var i in values) {
var inter = values[i];
for (var j in inter.data) {
reduced.data.push(inter.data[j]);
}
}
return reduced;
}",
"mapreduce": "cities",
"finalize": "function Finalize(key, reduced) {
if (reduced.data.length == 1) {
return {
\"message\" : \"This Country contains only 1 City\"
};
}
var min_dist = 999999999999;
var city1 = { \"name\": \"\" };
var city2 = { \"name\": \"\" };
var c1;
var c2;
var d;
for (var i in reduced.data) {
for (var j in reduced.data) {
if (i >= j) continue;
c1 = reduced.data[i];
c2 = reduced.data[j];
d = Math.sqrt((c1.lat-c2.lat)*(c1.lat-c2.lat)+(c1.lon-c2.lon)*(c1.lon-c2.lon));
if (d < min_dist && d > 0) {
min_dist = d;
city1 = c1;
city2 = c2;
}
}
}
return {
\"city1\": city1.city,
\"city2\": city2.city,
\"dist\": min_dist
};
}" }');
ensureIndex(collection, terms /*, type */)
Creates a new index on a collection.
Example:
SELECT ensureIndex('test', '{ "foo", "bar" }', '{ "unique": true }');
removeIndex(collection, name)
Removes an index from a collection by name.
Example:
SELECT removeIndex('test', 'idx_col_woo_foo');
removeIndex(collection, terms)
Removes an index from a collection by terms.
NOTE in order to remove an index with terms you MUST cast the query due to how Postgres handles JSON.
Example:
SELECT removeIndex('test', '{ "foo", "bar" }'::json);
getIndexes(collection)
Retrieves all indexes for a given collection.
Example:
SELECT getIndexes('test');
Importing the Data
I have included a modest amount of data for testing and benchmarking, both for Postgres and for MongoDB (1,706,873 rows).
Importing into Postgres:
$ psql yourdb < data/cities.sql
This will create the collection and save() all of the data.
Importing into MongoDB
$ mongoimport --collection cities --type csv --headerline --file data/cities.csv --db yourdb
Follow along at http://legitimatesounding.com/blog/