A lambda architecture implementation for mongodb with simple API for providing mongo's aggregation pipepline reports. Written in javascript designed as an npm module.

version: 2.3.x

The data model is based on an stream processing / event sourcing model. All data points are treated as immutable facts that are then aggregated into batches at regular intervals. This provides safety against dev mistakes when creating new reports, as raw data can be past processed at any point in the future. On top of this speed collections are created that temporarily store data until a batch aggregation has been produced, after this the raw data is cleared from the speed collection. This keeps the speed collections small in size, so quick to query for near realtime results.

var ML = require('mongo-lambda');

var lambda = new ML.Lambda({
    url: 'mongodb:https://localhost:27017/hits',
    masterColl: "hits",
    ttl: 60*60 // 1 hour
});

lambda.reports([{
    name: "hitCount",
    agg: [{ $group: {_id: null, count: { $sum: 1 }}}],
    cron: "*/5 * * * * *",
    timezone: "EST",
    startCron: true // default
}]);

lambda.start(function() {
    setInterval(function() {
        // Drip data
        lambda.insert({ua: "iphone"}, function(err, results) {
            // Get batches and live data
            Async.parallel({
                batches: Async.apply(lambda.batches, 'report4'),
                onTheFly: Async.apply(lambda.speedAgg, 'report4')
            }, function(err, results){
                var total = 0;

                results.batches.forEach(function(batch) {
                    if (batch.data.length > 0) {
                        total = total + batch.data[0].count;
                    }

                })

                if(results.onTheFly.length > 0) {
                    total = total + results.onTheFly[0].count;
                }

                console.log('---------------------');
                console.log('TOTAL COUNT: '+total)
                console.log('---------------------\n');
            });
        });
    }, 1000);
});

• Inserting data into master and and each reports speed collection. All data is timestamped by adding a _ts field.
• Generating batch reports. Will run a mongo aggregation pipeline batch at scheduled cron, using supplied agg array.
• Scrub data from speed collection when bactch report is produced.

Returns lambda instance. Object that has the methods listed underneath. It is configured with the following object:

• url: Mongodb url connection string.
• masterColl: Name of master collection.
• ttl: OPTIONAL The time-to-live for your master collection. Data in speed collections will also expire after this time. If field is absent data will never expire.

Will insert array of reports into system and start new cron job to run using their supplied agg. A report has the following structure:

• name: Name of report, used to refer to later.
• agg: Mongo aggregation pipeline array.
• cron: Cron string that defines schedule of when aggregations are run. See here for allowed cron strings.
• timezone: The timezone of the cron job.
• startCron: OPTIONAL Whether to start the cron (defaults to true), useful if you want to have separate instances for inserting and getting data.
• lateWindow: OPTIONAL Defines the how late data can arrive before batch is computed, example wait 5 mins before the last hour's report is calculated.

Starts Lambda instance, initialises cron jobs and mongodb. NOTE! This function must be called before you can insert or get data, ie before you can call any of the methods below.

Will insert data into batch and speed layer's mongo collection. Accepts data object or array of objects, just like mongodb's native insert. All data points are timestamped with _ts field unless already timestamped.

Get's batches of data produced by cron job. Callback has following signature: function(err, batches).

Get's a speed aggregation on speed collection, ie data that has not yet been batched. Callback has following signature: function(err, speedAgg).

Will re-run batch reports according to specified dates. Batches will start from earliest specified date in incrementing chunks until the lastest specified date.