[Detailed Tutorial] Building a simple recommendation engine with QBit (CallBack Blocking)

fadihub edited this page Jun 8, 2015 · 11 revisions

##overview

To really grasp QBit, one must grasp the concepts of a CallBack and queues. A CallBack is a way to get an async response in QBit from a microservice. You call a service method and it calls you back.

Building a simple recommendation engine with QBit - CallBack Blocking

This wiki will walk you through the process of building a simple recommendation engine with QBit, in this example things are going to be very simple, and you will notice that we are blocking on loadUser. This is bad for most apps, but we meant to show it here so that we can explain how to fix it in the next example.

What you will build

You will build a simple recommendation engine with QBit; that will give a set of recommendations to users. When you run it you will get the following:

Recommendations for: Bob
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}
Recommendations for: Joe
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}
Recommendations for: Scott
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}
Recommendations for: William
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}

How to complete this guide

In order to complete this example successfully you will need the following installed on your machine:

Now that your machine is all ready let's get started:

https://github.com/fadihub/worker-callback-blocking.git

Once this is done you can test the service, let's first explain the process:

This is just the User object or the Domain object.

User.java Listing

~/src/main/java/io.advantageous.qbit.example.recommendationengine/User

package io.advantageous.qbit.example.recommendationengine;

/* Domain object. */
public class User {

    private final String userName;


    public User(String userName){
        this.userName = userName;

    }

    public String getUserName() {
        return userName;
    }

}

In a real scenario RecommendationService is CPU bound intensive; it has to iterate through products and user data and pick a match. This is all done in real time based on the latest user activity, to the last second. What page did the users just view? What product did they just bookmark? What product did they buy? etc... This is real time analytics. As you can see in the following code if per-say loadUser has to look in a local cache, and if the user is not found, look in an off-heap cache and if not found it must ask for the user from the UserService which must check its caches and perhaps fallback to loading the user data from a database or from another services. In other words, loadUser can potentially block on IO. this is very bad for most apps and shouldn't happen. When you block, you are blocking the thread that is handling all of the messages, events, method calls for this service. This will be fixed in a future example. In this example RecommendationService is very simple; it is just a POJO, simply loads a user and matches it with a set of recommendations.

RecommendationService.java Listing

~/src/main/java/io.advantageous.qbit.example.recommendationengine/RecommendationService

package io.advantageous.qbit.example.recommendationengine;




import io.advantageous.boon.Lists;
import io.advantageous.boon.cache.SimpleLRUCache;
import java.util.List;

public class RecommendationService {


    private final SimpleLRUCache<String, User> users =
            new SimpleLRUCache<>(10_000);

    public List<Recommendation> recommend(final String userName) {
        System.out.println("recommend called");
        User user = users.get(userName);
        if (user == null) {
            user = loadUser(userName);
        }
        return runRulesEngineAgainstUser(user);
    }

    private User loadUser(String userName) {
        return new User("bob"); //stubbed out... next example will use UserService
    }

    private List<Recommendation> runRulesEngineAgainstUser(final User user) {

        return Lists.list(new Recommendation("Take a walk"), new Recommendation("Read a book"),
                new Recommendation("Love more, complain less"));
    }


}

A RecommendationService has an interface, where others can call methods. Calling methods on the client interface enqueues those method calls onto the ServiceQueue for the service. You create a client interface from a ServiceQueue as follows:

        ServiceQueue recommendationServiceQueue = ...

        /** Create the client interface from the recommendationServiceQueue.
        RecommendationServiceClient recommendationServiceClient =
                recommendationServiceQueue.createProxy(RecommendationServiceClient.class);

The ServiceQueue manages threads/queues for a Service implementation so the service can be thread safe and fast.

RecommendationServiceClient.java Listing

~/src/main/java/io.advantageous.qbit.example.recommendationengine/RecommendationServiceClient

package io.advantageous.qbit.example.recommendationengine;

import io.advantageous.qbit.reactive.Callback;

import java.util.List;

/**
 * @author  rhightower
 * on 2/20/15.
 */
public interface RecommendationServiceClient {


    void recommend(final Callback<List<Recommendation>> recommendationsCallback,
                   final String userName);
}

The Callback is created as follows:

Callback<List<Recommendation>> callback = new Callback<List<Recommendation>>() {
            @Override
            public void accept(List<Recommendation> recommendations) {
                System.out.println("recommendations " + recommendations);
            }

            @Override
            public void onError(Throwable error) {
                error.printStackTrace();
            }
        };

This is how it is done using pre Java 8 style of inner classes. Now when we call the RecommendationServiceClient if the callback succeeds we get a an accept call, and if it fails we get an onError call. We call it on one thread, and it calls us back on another thread when it finishes.

creating Callback with a Lambda 8 expression is as follows:

recommendationServiceClient.recommend(recommendations -> 
            System.out.println("recommendations " + recommendations), "Rick");

Here is the Recommendation object or Domain object.

Recommendtion.java Listing

~/src/main/java/io.advantageous.qbit.example.recommendationengine/Recommendation

package io.advantageous.qbit.example.recommendationengine;


/* Domain object. */
public class Recommendation {

    private final String recommendation;

    public Recommendation(String recommendation) {
        this.recommendation = recommendation;
    }


    @Override
    public String toString() {
        return "Recommendation{" +
                "recommendation='" + recommendation + '\'' +
                '}';
    }
}

Here is our PrototypeMain to run our program. First we create the service:

 /* Create the service. */
        RecommendationService recommendationServiceImpl =
                new RecommendationService();

Then wrap the service in a queue that manages threads so the service can be thread safe and fast:

 /* Wrap the service in a queue. */
        ServiceQueue recommendationServiceQueue = serviceBuilder()
                .setServiceObject(recommendationServiceImpl)
                .build().startServiceQueue().startCallBackHandler();

Create a proxy interface:

/* Create a proxy interface for the service. */
        RecommendationServiceClient recommendationServiceClient =
                recommendationServiceQueue.createProxy(RecommendationServiceClient.class);

Call the service with the proxy:

 /* Call the service with the proxy. */
        recommendationServiceClient.recommend(out::println, "Rick");

then finally flush the call:

/* Flush the call. This can be automated, but is a core concept. */
        flushServiceProxy(recommendationServiceClient);
        Sys.sleep(1000);

Periodically, you need to tell QBit to flush what you have done so far. Every time you tell it to flush, you are sending all of the methods you called from the last time you flushed. There are ways to get QBit to auto-flush, which will be covered later. When you use a QBit queue, you can specify a batch size. The bigger the batch the less time doing thread hand-off and less synchronizing queue internals across multiple threads. When the queue gets requests beyond this amount, it will batch them. You can also periodically, flush the methods when you feel its appropriate like after a related unit of work. This is microbatching and QBit supports it at its core. Therefore periodically (unless you setup auto flush or a batch size of 1), you need to flush your method call queue.

Also in this example we used a Lambda expression to make Callbacks call the service:

  List<String> userNames = list("Bob", "Joe", "Scott", "William");

        userNames.forEach( userName->
                        recommendationServiceClient.recommend(recommendations -> {
                            System.out.println("Recommendations for: " + userName);
                            recommendations.forEach(recommendation->
                                    System.out.println("\t" + recommendation));
                        }, userName)
        );



        flushServiceProxy(recommendationServiceClient);
        Sys.sleep(1000);

PrototypeMain.java Listing

~/src/main/java/io.advantageous.qbit.example.recommendationengine/PrototypeMain

package io.advantageous.qbit.example.recommendationengine;

import io.advantageous.boon.core.Sys;
import io.advantageous.qbit.service.ServiceQueue;

import java.util.List;

import static io.advantageous.boon.core.Lists.list;
import static io.advantageous.qbit.service.ServiceBuilder.serviceBuilder;
import static io.advantageous.qbit.service.ServiceProxyUtils.flushServiceProxy;
import static java.lang.System.out;

/**
 * Created by rhightower on 2/20/15.
 */
public class PrototypeMain {

    public static void main(String... args) {


        /* Create the service. */
        RecommendationService recommendationServiceImpl =
                new RecommendationService();

        /* Wrap the service in a queue. */
        ServiceQueue recommendationServiceQueue = serviceBuilder()
                .setServiceObject(recommendationServiceImpl)
                .build().startServiceQueue().startCallBackHandler();

        /* Create a proxy interface for the service. */
        RecommendationServiceClient recommendationServiceClient =
                recommendationServiceQueue.createProxy(RecommendationServiceClient.class);


        /* Call the service with the proxy. */
        recommendationServiceClient.recommend(out::println, "Rick");

        /* Flush the call. This can be automated, but is a core concept. */
        flushServiceProxy(recommendationServiceClient);
        Sys.sleep(1000);



        /* Lambdas gone wild. */

        List<String> userNames = list("Bob", "Joe", "Scott", "William");

        userNames.forEach( userName->
                        recommendationServiceClient.recommend(recommendations -> {
                            System.out.println("Recommendations for: " + userName);
                            recommendations.forEach(recommendation->
                                    System.out.println("\t" + recommendation));
                        }, userName)
        );



        flushServiceProxy(recommendationServiceClient);
        Sys.sleep(1000);

    }
}

Here is the build file.

build.gradle Listing

apply plugin: 'idea'
apply plugin: 'java'
apply plugin: 'application'


sourceCompatibility = 1.8
version = '1.0'

repositories {
    mavenLocal()
    mavenCentral()
}

sourceCompatibility = JavaVersion.VERSION_1_8
targetCompatibility = JavaVersion.VERSION_1_8


mainClassName = "io.advantageous.qbit.example.recommendationengine.PrototypeMain"

dependencies {
    compile group: 'io.advantageous.qbit', name: 'qbit-jetty', version: '0.7.2'
    compile group: 'javax.inject', name: 'javax.inject', version: '1'
    compile('org.springframework.boot:spring-boot-starter-web:1.2.1.RELEASE') {
        exclude module: 'spring-boot-starter-tomcat'
    }
    compile 'org.eclipse.jetty:jetty-webapp:9.+'
    compile 'org.eclipse.jetty:jetty-jsp:9.+'

    testCompile "junit:junit:4.11"
    testCompile "org.slf4j:slf4j-simple:[1.7,1.8)"
}

Test The Service

With your terminal cd worker-callback-blocking

then gradle clean build and finally gradle run you should get the following:

Recommendations for: Bob
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}
Recommendations for: Joe
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}
Recommendations for: Scott
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}
Recommendations for: William
	Recommendation{recommendation='Take a walk'}
	Recommendation{recommendation='Read a book'}
	Recommendation{recommendation='Love more, complain less'}

Summary

This was a brief introduction to Callbacks and how to use them with QBit. You have just built and tested a simple recommendation engine with QBit, see you in the next tutorial!

Tutorials

__

Docs

Getting Started

Basics

Concepts

REST

Callbacks and Reactor

Event Bus

Advanced

Integration

QBit case studies

QBit 2 Roadmap

-- Related Projects

Kafka training, Kafka consulting, Cassandra training, Cassandra consulting, Spark training, Spark consulting

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