Scaling a Sales Recommendation Engine With Apache Spark and MongoDB
Scaling a Sales Recommendation Engine With Apache Spark and MongoDB
Last time, we covered building a sales recommendation engine with Apache Spark. To go one step further, I have replaced the file system layer with MongoDB.
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In my last post on building a sales recommendation engine with Apache Spark, we built a standalone version of spark-mllib's ALS demonstration based using local CSV files.
To go one step further, I have replaced the file system layer with MongoDB. MongoDB provides a spark-mongo connector that wraps the standard Java/Scala connector with Spark's interpolatable data format/APIs.
Getting Started
Apart from the spark core APIs, you need the following dependency to connect to MongoDB server.
<dependency>
<groupId>org.mongodb.spark</groupId>
<artifactId>mongo-spark-connector_2.11</artifactId>
<version>2.2.1</version>
</dependency>Preparing the Datasets
In the current scenario, i.e MongoDB, instead of creating files, we need same data in JSON format in collections.
Sales orders:
| UserId | UserName | ProductId | ProductName | Rate | Quantity | Amount |
| 1 | User 1 | 1 | Product 1 | 10 | 5 | 50 |
| 1 | User 1 | 2 | Product 2 | 20 | 10 | 200 |
| 1 | User 1 | 3 | Product 3 | 10 | 15 | 150 |
| 2 | User 2 | 1 | Product 1 | 10 | 5 | 50 |
| 2 | User 2 | 2 | Product 2 | 20 | 20 | 400 |
| 2 | User 2 | 4 | Product 4 | 10 | 15 | 150 |
Sales leads:
| UserId | UserName | ProductId | ProductName |
| 1 | User 1 | 4 | Product 4 |
| 1 | User 1 | 5 | Product 5 |
| 2 | User 2 | 3 | Product 3 |
| 2 | User 2 | 6 | Product 6 |
We need to predict/recommend the most relevant product for both the users based on their past order history. Here, we can see that both User 1 and User 2 ordered Product 1 and Product 2; also, they ordered one item separately.
Now, we predict their rating for alternate products and one new product.
Implementation
Step 1
Our first step is making a database connection using MongoDB specific properties.
SparkConf conf = new SparkConf().//
setAppName("rnd").//
setMaster("local").//
set("spark.mongodb.input.uri", "mongodb://127.0.0.1:27017/sparkdb.myCollection").//
set("spark.mongodb.output.uri", "mongodb://127.0.0.1:27017/sparkdb.myCollection");Step 2
Now you can read training model via JavaMongoRDD API and convert it to Rating format using JavaRDD API.
private static JavaMongoRDD < Document > getJavaMongoRDD(JavaSparkContext jsc, String collName) {
Map < String, String > readOverrides = new HashMap < String, String > ();
readOverrides.put("collection", collName);
readOverrides.put("readPreference.name", "secondaryPreferred");
ReadConfig readConfig = ReadConfig.create(jsc).withOptions(readOverrides);
JavaMongoRDD < Document > mongoRDD = MongoSpark.load(jsc, readConfig);
return mongoRDD;
}
JavaMongoRDD < Document > salesOrdersRDD = getJavaMongoRDD(jsc, "SalesOrders");
// Map file to Ratings(user,item,rating) tuples
JavaRDD < Rating > ratings = salesOrdersRDD.map(new Function < Document, Rating > () {
public Rating call(Document d) {
return new Rating(d.getInteger("userCode"), d.getInteger("productCode"), ((Number) d.get("amount")).doubleValue());
}
});Step 3
The next step is to train the matrix factorization model using the ALS algorithm.
MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations); Step 4
Now, we load the sales lead file and convert it to tuple format.
JavaMongoRDD < Document > salesLeadsRDD = getJavaMongoRDD(jsc, "SalesLeads");
// Create user-item tuples from ratings
JavaRDD < Tuple2 < Object, Object >> userProducts = salesLeadsRDD.map(new Function < Document, Tuple2 < Object, Object >> () {
public Tuple2 < Object, Object > call(Document d) {
return new Tuple2 < Object, Object > (d.getInteger("userCode"), d.getInteger("productCode"));
}
});Step 5
Finally, we can predict the future rating using a simple API.
// Predict the ratings of the products not rated by user
JavaRDD<Rating> recomondations = model.predict(userProducts.rdd()).toJavaRDD().distinct();Step 6
Optionally, you can sort the output using a basic pipeline operation:
// Sort the recommendations by rating in descending order
recomondations = recomondations.sortBy(new Function<Rating, Double>() {
@Override
public Double call(Rating v1) throws Exception {
return v1.rating();
}
}, false, 1);Step 7
Now, you can display your result using the basic JavaRDD API.
// Print the recommendations .
recomondations.foreach(new VoidFunction<Rating>() {
@Override
public void call(Rating rating) throws Exception {
String str = "User : " + rating.user() + // " Product : " + rating.product() + // " Rating : " + rating.rating();
System.out.println(str);
}
});Output
User : 2 Product : 3 Rating : 54.54927015541634
User : 1 Product : 4 Rating : 49.93948224984236Conclusion
The above output recommends that User 2 would like to buy Product 3 and User 1 would go for Product 4.
This also recommends that there is no recommendation for new products, as they do not match any similarity criteria in past.
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