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Monday, March 2, 2015

QBit Java Microservice Lib Working With Service Workers Sharded and Pooled

QBit Microservice Lib Working With Workers Sharded and Pooled


This tutorial continues where the callbacks tutorial leaves off seeQBit Microservice Lib Working With CallBacks before you tackle this one.

Imagining an app - CPU Bound and IO Bound

Let's recall the app from the first example on callbacks. Remember this app will be a recommendation engine. Think of Cupid.com or DatesRUs.com or iTunes match or NetFlix suggestions or Amazon.com "Customers who bought this also bought these other fine products".
Now we are not building a real recommendation engine although QBit has been used for similar things.
The trick with an example is to keep the concepts clear enough without getting too much clutter with a real world implementation so it can be followed.
RecommendationService is our CPU intensive service. will be the recommendation engine. We are going to shard CPU instances. Any user data would get pushed into its shard. We would replicate non-user data, and shard user data to live alongside the rules to operate on users.
It our hypothetical example RecommendationService is very CPU intensive. Now we can run on X CPUs without duplicating all of the user data which is a lot of data. It has to iterate through products and user data and pick a match. It is a classic N+1. In our example, we do all of this real time based on the latest user activity, to the last second. What page did that just view? What product did they just bookmark? What product did they buy? What product did their friends buy? What product are people in their same demographic buying now. This is real time analytics. This does not mean that there is not machine learning or Hadoop batch jobs churning data some where. But the churned data is mixed with the data science, pre-chewed caches and counters, and up to the second user activity to make a decision based on data in memory and historic data (blended).RecommendationService is the tip of the ice-burg, but it is brute force, exhaustive and fast.
UserDataService is our heavy IO service. As much as we would like to, we do know which RecommendationService node a user might land on we can't if we are truly elastic. Are they using an XBox on the west coast, an iPhone in Hawaii, when/how will they hit our recommendation engine who knows. UserDataService manages editing, backup, and syncing user data. UserDataService keeps most users in-memory and also manages replicating and storing user data. Once users get loaded into the system, we can keep them in-memory for a while. We will just sync any changes to the user back to the UserDataService. Since UserDataService is IO bound, we will create a pool of them.
This is what I want you to think about when I am talking about UserDataService andRecommendationService.
UserDataService IO bound. RecommendationService CPU bound.
In this tutorial we will cover Sharded Workers and Workers pools.

Creating a pool of IO Workers

To create a pool of IO workers, we will useio.advantageous.qbit.service.dispatchers.ServiceWorkers.workers method to create a pooled ServiceWorkers instance.
Then we will create service queues that wrap our UserDataService and add those queues to our userDataServiceWorkers.

Creating a worker pool of services

    private static UserDataServiceClient createUserDataServiceClientProxy(
            final ServiceBundle serviceBundle,
            final int numWorkers) {
        final ServiceWorkers userDataServiceWorkers = workers();

        for (int index =0; index < numWorkers; index++) {
            ServiceQueue userDataService = serviceBuilder()
                    .setServiceObject(new UserDataService())
                    .build();
            userDataService.startCallBackHandler();
            userDataServiceWorkers.addService(userDataService);
        }

        userDataServiceWorkers.start();



        serviceBundle.addServiceConsumer("workers", userDataServiceWorkers);

        return serviceBundle.createLocalProxy(UserDataServiceClient.class, "workers");
    }
Now we have a pool of IO workers. Every call to the UserDataServiceClient will go to a different service queue instance which will go to a different userDataService. The calls are round robin.
That takes care of our heavy IO. We can create just the right amount of workers which will talk to our backend database or key/value store. Next we need to create our CPU intensive service, our recommendation engine so that we can utilize all of our CPU cores when evaluating user data. Instead of copying user data to each shard, each shard will have a portion of the users.
This is very much like message driven beans except that you have more methods than just onMessage and you get the benefits of a high-speed queue system.

Creating a CPU intensive shards to maximize CPU intensive services and use all of your cores

In this example, we will use a canned shard rule which will shard on the hashcode of the first argument to each method. We would want that first argument to be something like userName or some other object that would give us a nice consistent hashCode to use to divvy up users so we can execute the CPU intensive rules right next to the actual user data that we have in memory. We use the methodio.advantageous.qbit.service.dispatchers.ServiceWorkers.shardOnFirstArgumentWorkers, and there are many such methods on the ServiceWorkers class. You can also create your own ShardRules and pass that to the ServiceWorkers.shardedWorkersmethod.
Other than that the code looks pretty similar to what we did with the IO bound workers. We pass the service queue client proxy userDataServiceClient from the last creation method as an argument to this one so that this recommendationService can calluserDataServiceClient as needed.

Creating a sharded set of services

    private static RecommendationServiceClient createRecommendationServiceClientProxy(
            final ServiceBundle serviceBundle,
            final UserDataServiceClient userDataServiceClient,
            int numWorkers) {


        final ServiceWorkers recommendationShardedWorkers = shardOnFirstArgumentWorkers();

        for (int index = 0; index < numWorkers; index++) {
            RecommendationService recommendationServiceImpl =
                    new RecommendationService(userDataServiceClient);

            ServiceQueue serviceQueue = serviceBuilder()
                    .setServiceObject(recommendationServiceImpl)
                    .build();
            serviceQueue.startCallBackHandler();
            recommendationShardedWorkers.addService(serviceQueue);
        }

        recommendationShardedWorkers.start();

        serviceBundle.addServiceConsumer("recommendation", recommendationShardedWorkers);

        return serviceBundle.createLocalProxy(RecommendationServiceClient.class, "recommendation");
    }
Each time the service queue client proxy is called, i.e., RecommendationServiceClient it will select on the N RecommendationService service queues to handle the method call. If we could only handle 20,000 recommendation lists per second for users, then with 5 CPU cores, we can approach 100,000 recommendation lists per second.

Putting it altogether

The complete example with the changes for worker pools and sharded pools

package io.advantageous.qbit.example;

import io.advantageous.qbit.QBit;
import io.advantageous.qbit.service.ServiceBundle;
import io.advantageous.qbit.service.ServiceQueue;
import io.advantageous.qbit.service.dispatchers.ServiceWorkers;
import org.boon.core.Sys;

import static io.advantageous.qbit.service.ServiceBundleBuilder.serviceBundleBuilder;
import static io.advantageous.qbit.service.ServiceProxyUtils.flushServiceProxy;

import java.util.List;

import static io.advantageous.qbit.service.ServiceBuilder.serviceBuilder;
import static io.advantageous.qbit.service.dispatchers.ServiceWorkers.shardOnFirstArgumentWorkers;
import static io.advantageous.qbit.service.dispatchers.ServiceWorkers.workers;
import static org.boon.Lists.list;

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

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


        QBit.factory().systemEventManager();


        final ServiceBundle serviceBundle = serviceBundleBuilder()
                .setAddress("/root").build();


        serviceBundle.start();

        final UserDataServiceClient userDataServiceClient =
                createUserDataServiceClientProxy(serviceBundle, 8);


        final RecommendationServiceClient recommendationServiceClient =
                createRecommendationServiceClientProxy(serviceBundle,
                        userDataServiceClient, 4);




        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);

    }

    private static RecommendationServiceClient createRecommendationServiceClientProxy(
            final ServiceBundle serviceBundle,
            final UserDataServiceClient userDataServiceClient,
            int numWorkers) {


        final ServiceWorkers recommendationShardedWorkers = shardOnFirstArgumentWorkers();

        for (int index = 0; index < numWorkers; index++) {
            RecommendationService recommendationServiceImpl =
                    new RecommendationService(userDataServiceClient);

            ServiceQueue serviceQueue = serviceBuilder()
                    .setServiceObject(recommendationServiceImpl)
                    .build();
            serviceQueue.startCallBackHandler();
            recommendationShardedWorkers.addService(serviceQueue);
        }

        recommendationShardedWorkers.start();

        serviceBundle.addServiceConsumer("recomendation", recommendationShardedWorkers);

        return serviceBundle.createLocalProxy(RecommendationServiceClient.class, "recomendation");
    }

    private static UserDataServiceClient createUserDataServiceClientProxy(
            final ServiceBundle serviceBundle,
            final int numWorkers) {
        final ServiceWorkers userDataServiceWorkers = workers();

        for (int index =0; index < numWorkers; index++) {
            ServiceQueue userDataService = serviceBuilder()
                    .setServiceObject(new UserDataService())
                    .build();
            userDataService.startCallBackHandler();
            userDataServiceWorkers.addService(userDataService);
        }

        userDataServiceWorkers.start();



        serviceBundle.addServiceConsumer("workers", userDataServiceWorkers);

        return serviceBundle.createLocalProxy(UserDataServiceClient.class, "workers");
    }
}

Sunday, February 22, 2015

Guide to QBit Microservice Lib - Revision 3

qbit - The Microservice Lib for Java - JSON, REST, WebSocket, Speed! Build Status

Got a question? Ask here: QBit Google Group.
Everything is a queue. You have a choice. You can embrace it and control it. You can optimize for it. Or you can hide behind abstractions. QBit opens you up to peeking into what is going on, and allows you to pull some levers without selling your soul.
QBit is a library not a framework. You can mix and match QBit with Spring, Guice, etc.
QBit is FAST!
QBit the microservice framework for java

Status

Lot's of progress. More people are helping out. QBit now works with Vertx (standalone or embedded), Jetty (standalone) or just plain Java Servlets.

License

Apache 2

Java Microservice Lib

QBit has inproc services, REST microservices and WebSocket microservices as well as an in-proc service event bus (which can be per module or per app). It supports workers and in-memory services.
Before we describe more, here are two sample services:

Todo Service

@RequestMapping("/todo-service")
public class TodoService {

    @RequestMapping("/todo/count")
    public int size() {...

    @RequestMapping("/todo/")
    public List<TodoItem> list() {...

Adder Service using URI params

    @RequestMapping("/adder-service")
    public class AdderService {

        @RequestMapping("/add/{0}/{1}")
        public int add(@PathVariable int a, @PathVariable int b) {...
    }

QBit philosophy:

At the end of the day QBit is a simple library not a framework. Your app is not a QBit app but a Java app that uses the QBit lib. QBit allows you to work with Java UTIL concurrent, and does not endeavor to hide it from you. Just trying to take the sting out of it.

Does it work

We have used techniques in Boon and QBit with great success in high-end, high-performance, high-scalable apps. We helped clients handle 10x the load with 1/10th the servers of their competitors using techniques in QBit. QBit is us being sick of hand tuning queue access and threads.

Boon and QBit humility policy

Ideas for Boon and QBit often come from all over the web. We make mistakes. Point them out. As a developer of Boon and QBit, we are fellow travelers. If you have an idea or technique you want to share, we listen.

Inspiration

A big inspiration for Boon/QBit was Akka, Go Channels, Active Objects, Apartment Model Threading, Actor, and the Mechnical Sympathy papers.
"I have read the AKKA in Action Book. It was inspiring, but not the only inspiration for QBit.". "I have written apps where I promised a lot of performance and the techniques from QBit is how I got it."
  • Rick Hightower
QBit has ideas that are similar to many frameworks. We are all reading the same papers. QBit got inspiration from the LMAX disruptor papers and this blog post about link transfer queue versus disruptor. We had some theories about queues that blog post insprired us to try them out. Some of these theories are deployed at some of the biggest middleware backends and whose name brands are known around the world. And thus QBit was born.
QBit also took an lot of inspiration by the great work done by Tim Fox on Vertx. The first project using something that could actually be called QBit (albeit early QBit) was using Vertx on an web/mobile microserivce for an app that could potentially have 80 million users. It was this experience with Vertx and early QBit that led to QBit development and evolution. QBit is built on the shoulders of giants.

Does QBit compete with...

Spring Disruptor: No. You could use QBit to write plugins for Spring Disruptor I suppose, but QBit does not compete with Spring Disruptor. Spring Boot/Spring MVC: No. We use the same annotations but QBit is geared for high-speed in-memory microservices. It is more like Akka than Spring Boot. QBit has a subset of the features of Spring MVC geared only for microservices, i.e., WebSocket RPC, REST, JSON marshaling, etc. Akka: No. Well Maybe. Akka has similar concepts but they take a different approach. QBit is more focused on Java, and microservices (REST, JSON, WebSocket) than Akka. LMAX Disruptor: No. In fact, we can use disruptor as on of the queues that QBit uses underneath the covers.
(Early benchmarks have been removed. They were here. QBit got a lot faster. Benchmarking QBit is a moving target at the moment. Links and reports will be created.)
Code Examples

Basic Queue example (REST style services is further down)

     BasicQueue<Integer> queue =  BasicQueue.create(Integer.class, 1000);

    //Sending threads

     SendQueue<Integer> sendQueue = queue.sendQueue();
     for (int index = 0; index < amount; index++) {
           sendQueue.send(index);
     }
     sendQueue.flushSends();
     ...
     sendQueue.sendAndFlush(code);
     //other methods for sendQueue, writeBatch, writeMany


     //Recieving Threads
     ReceiveQueue<Integer> receiveQueue = queue.receiveQueue();
     Integer item = receiveQueue.take(); 
     //other methods poll(), pollWait(), readBatch(), readBatch(count)

What is QBit again?

QBit is a queuing library for microservices. It is similar to many other projects like Akka, Spring Reactor, etc. QBit is just a library not a platform. QBit has libraries to put a service behind a queue. You can use QBit queues directly or you can create a service. QBit services can be exposed by WebSocket, HTTP, HTTP pipeline, and other types of remoting. A service in QBit is a Java class whose methods are executed behind service queues. QBit implements apartment model threading and is similar to the Actor model or a better description would be Active Objects. QBit does not use a disruptor. It uses regular Java Queues. QBit can do north of 100 million ping pong calls per second which is an amazing speed (seen as high as 200M). QBit also supports calling services via REST, and WebSocket. QBit is microservices in the pure Web sense: JSON, HTTP, WebSocket, etc. QBit uses micro batching to push messages through the pipe (queue, IO, etc.) faster to reduce thread hand-off.

QBit lingo

QBit is a Java microservice lib supporting REST, JSON and WebSocket. It is written in Java but we could one day write a version in Rust or Go or C# (but that would require a large payday).
Service POJO (plain old Java object) behind a queue that can receive method calls via proxy calls or events (May have one thread managing events, method calls, and responses or two one for method calls and events and the other for responses so response handlers do not block service. One is faster unless responses block). Services can use Spring MVC style REST annotations to expose themselves to the outside world via REST and WebSocket.
ServiceBundle Many POJOs behind one response queue and many receive queues. There may be one thread for all responses or not. They also can be one receive queue.
Queue A thread managing a queue. It supports batching. It has events for empty, reachedLimit, startedBatch, idle. You can listen to these events from services that sit behind a queue. You don't have to use Services. You can use Queue's direct. In QBit, you have sender queues and receivers queues. They are separated to support micro-batching.
ServiceServer ServiceBundle that is exposed to REST and WebSocket communication.
EventBus EventBus is a way to send a lot of messages to services that may be loosely coupled.
ClientProxy ClientProxy is a way to invoke service through async interface, service can be inproc (same process) or remoted over WebSocket.
Non-blocking QBit is a non-blocking lib. You use CallBacks via Java 8 Lambdas. You can also send event messages and get replies. Messaging is built into the system so you can easily coordinate complex tasks. QBit takes an object-oriented approach to service development so services look like normal Java services that you already write, but the services live behind a queue/thread. This is not a new concept. Microsoft did this with DCOM/COM and called it active objects. Akka does it with actors and called them strongly typed Actors. The important concepts is that you get the speed of reactive and actor style messaging but you develop in a natural OOP approach. QBit is not the first. QBit is not the only.
Speed QBit is VERY fast. There is a of course a lot of room for improvement. But already 200M+ TPS inproc ping pong, 10M-20M+ TPS event bus, 500K TPS RPC calls over WebSocket/JSON, etc. More work needs to be done to improve speed, but now it is fast enough where we are focusing more on usability. The JSON support uses Boon by default which is up to 4x faster than other JSON parsers for the REST/JSON, WebSocket/JSON use case.

CURLable REST services example

Talk is cheap. Let's look at some code. You can get a detailed walk through in the Wiki. We have a lot of documentation already.
We will create a service that is exposed through REST/JSON.
To query the size of the todo list:
curl localhost:8080/services/todo-service/todo/count
To add a new TODO item.
curl -X POST -H "Content-Type: application/json" -d \
'{"name":"xyz","description":"xyz"}' \
http://localhost:8080/services/todo-service/todo 
To get a list of TODO items
curl http://localhost:8080/services/todo-service/todo/
The TODO example will use and track Todo items.

Todo item POJO sans getter

package io.advantageous.qbit.examples;

import java.util.Date;


public class TodoItem {


    private final String description;
    private final String name;
    private final Date due;
The TodoService uses Spring MVC style annotations.

Todo Service

@RequestMapping("/todo-service")
public class TodoService {


    private List<TodoItem> todoItemList = new ArrayList<>();


    @RequestMapping("/todo/count")
    public int size() {

        return todoItemList.size();
    }

    @RequestMapping("/todo/")
    public List<TodoItem> list() {

        return todoItemList;
    }

    @RequestMapping(value = "/todo", method = RequestMethod.POST)
    public void add(TodoItem item) {

        todoItemList.add(item);
    }

}

Side note Why Spring style annotations?

Why did we pick Spring style annotations? 1) Spring is not a standard and neither is QBit. 2) We found the Spring annotations to be less verbose. 3) More people use Spring than Java EE. We wrote QBit for people to use. We could easily support JAX-RS style annotations, and we probably will. Since QBit focuses on JSON, we do not need all of the complexity of JAX-RS or even all the features of the Spring MVC annotations. Also we can literally use the actual Spring annotations. QBit and Boon use a non-type safe mechanism for annotations which means they are not tied to a particular lib. You can define your own. We hate vendor tie-in even if it is an open source vendor.
Now just start it up.
    public static void main(String... args) {
        ServiceServer server = new ServiceServerBuilder().build();
        server.initServices(new TodoService());
        server.start();
    }
That is it. There is also out of the box WebSocket support with client side proxy generation so you can call into services at the rate of millions of calls per second.

Using URI Params for QBit microservice

    @RequestMapping("/adder-service")
    public class AdderService {


        @RequestMapping("/add/{0}/{1}")
        public int add(@PathVariable int a, @PathVariable int b) {

            return a + b;
        }
    }

WebSocket

You can always invoke QBit services via a WebSocket proxy. The advantage of a WebSocket proxy is it allows you execute 1M RPC+ a second (1 million remote calls every second).

Using a microservice remotely with WebSocket

       /* Start QBit client for WebSocket calls. */
        final Client client = clientBuilder()
                   .setPort(7000).setRequestBatchSize(1).build();


       /* Create a proxy to the service. */
        final AdderServiceClientInterface adderService =
                client.createProxy(AdderServiceClientInterface.class, 
                "adder-service");

        client.start();



       /* Call the service */
        adderService.add(System.out::println, 1, 2);
The output is 3.
3
The above uses a WebSocket proxy interface to call the service async.
    interface AdderServiceClientInterface {

        void add(Callback<Integer> callback, int a, int b);
    }

REST call with URI params

The last client example uses WebSocket. You could also just use REST, and actually use the URI params that we setup. REST is nice but it is going to be slower than WebSocket support.
QBit ships with a nice little HTTP client. We can use it.
You can use it to send async calls and WebSocket messages with the HTTP client.
Here we will use the http client to invoke our remote method:

Using a microservice remotely with REST QBit microservice client

        HttpClient httpClient = httpClientBuilder()
                .setHost("localhost")
                .setPort(7000).build();

        httpClient.start();
        String results = httpClient
                   .get("/services/adder-service/add/2/2").body();
        System.out.println(results);
The output is 4.
4

Accessing The URI Param example with CURL

You can also access the service from curl.
$ curl http://localhost:7000/services/adder-service/add/2/2
See this full example here: QBit microservice getting started tutorial.

Working with WebSocket, HttpClient etc.

QBit has a library for working with and writing async microservices that is lightweight and fun to use.

WebSocket server and client.

Create an HTTP server

        /* Create an HTTP server. */
        HttpServer httpServer = httpServerBuilder()
                .setPort(8080).build();

Setup server WebSocket support

        /* Setup WebSocket Server support. */
        httpServer.setWebSocketOnOpenConsumer(webSocket -> {
            webSocket.setTextMessageConsumer(message -> {
                webSocket.sendText("ECHO " + message);
            });
        });

Start the server

        /* Start the server. */
        httpServer.start();

Setup the WebSocket client

        /** CLIENT. */

        /* Setup an httpClient. */
        HttpClient httpClient = httpClientBuilder()
                .setHost("localhost").setPort(8080).build();
        httpClient.start();

Client WebSocket

        /* Setup the client websocket. */
        WebSocket webSocket = httpClient
                .createWebSocket("/websocket/rocket");

        /* Setup the text consumer. */
        webSocket.setTextMessageConsumer(message -> {
            System.out.println(message);
        });
        webSocket.openAndWait();

        /* Send some messages. */
        webSocket.sendText("Hi mom");
        webSocket.sendText("Hello World!");

Output


ECHO Hi mom
ECHO Hello World!

Now stop the server and client. Pretty easy eh?

High-speed HTTP client and server done microservice style

Starting up an HTTP server
        /* Create an HTTP server. */
        HttpServer httpServer = httpServerBuilder()
                .setPort(8080).build();

        /* Setting up a request Consumer with Java 8 Lambda expression. */
        httpServer.setHttpRequestConsumer(httpRequest -> {

            Map<String, Object> results = new HashMap<>();
            results.put("method", httpRequest.getMethod());
            results.put("uri", httpRequest.getUri());
            results.put("body", httpRequest.getBodyAsString());
            results.put("headers", httpRequest.getHeaders());
            results.put("params", httpRequest.getParams());
            httpRequest.getReceiver()
                .response(200, "application/json", Boon.toJson(results));
        });


        /* Start the server. */
        httpServer.start();

The focus is on ease of use and using Java 8 Lambdas for callbacks so the code is tight and small.

Using HTTP Client lib

Now, let's try out our HTTP client.
Starting up an HTTP client
        /* Setup an httpClient. */
        HttpClient httpClient = httpClientBuilder()
                  .setHost("localhost").setPort(8080).build();
        httpClient.start();
You just pass the URL, the port and then call start.

Synchronous HTTP calls

Now you can start sending HTTP requests.
No Param HTTP GET
        /* Send no param get. */
        HttpResponse httpResponse = httpClient.get( "/hello/mom" );
        puts( httpResponse );
An HTTP response just contains the results from the server.
No Param HTTP Response
public interface HttpResponse {

    MultiMap<String, String> headers();

    int code();

    String contentType();

    String body();

}
There are helper methods for sync HTTP GET calls.
Helper methods for GET
        /* Send one param get. */
        httpResponse = httpClient.getWith1Param("/hello/singleParam", 
                                        "hi", "mom");
        puts("single param", httpResponse );


        /* Send two param get. */
        httpResponse = httpClient.getWith2Params("/hello/twoParams",
                "hi", "mom", "hello", "dad");
        puts("two params", httpResponse );

...

        /* Send five param get. */
        httpResponse = httpClient.getWith5Params("/hello/5params",
                "hi", "mom",
                "hello", "dad",
                "greetings", "kids",
                "yo", "pets",
                "hola", "neighbors");
        puts("5 params", httpResponse );

The puts method is a helper method it does System.out.println more or less by the way.
The first five params are covered. Beyond five, you have to use the HttpBuilder.
        /* Send six params with get. */

        final HttpRequest httpRequest = httpRequestBuilder()
                .addParam("hi", "mom")
                .addParam("hello", "dad")
                .addParam("greetings", "kids")
                .addParam("yo", "pets")
                .addParam("hola", "pets")
                .addParam("salutations", "all").build();

        httpResponse = httpClient.sendRequestAndWait(httpRequest);
        puts("6 params", httpResponse );

Http Async HTTP Client

There are async calls for GET as well.

Async calls for HTTP GET using Java 8 lambda

        /* Using Async support with lambda. */
        httpClient.getAsync("/hi/async", (code, contentType, body) -> {
            puts("Async text with lambda", body);
        });

        Sys.sleep(100);


        /* Using Async support with lambda. */
        httpClient.getAsyncWith1Param("/hi/async", "hi", "mom", (code, contentType, body) -> {
            puts("Async text with lambda 1 param\n", body);
        });

        Sys.sleep(100);



        /* Using Async support with lambda. */
        httpClient.getAsyncWith2Params("/hi/async",
                "p1", "v1",
                "p2", "v2",
                (code, contentType, body) -> {
                    puts("Async text with lambda 2 params\n", body);
                });

        Sys.sleep(100);


...
        /* Using Async support with lambda. */
        httpClient.getAsyncWith5Params("/hi/async",
                "p1", "v1",
                "p2", "v2",
                "p3", "v3",
                "p4", "v4",
                "p5", "v5",
                (code, contentType, body) -> {
                    puts("Async text with lambda 5 params\n", body);
                });

        Sys.sleep(100);

InProc QBit services

QBit allows for services behind queues to be run in-proc as well.
        /* POJO service. */
        final TodoManager todoManagerImpl = new TodoManager();

        /*
        Create the service which manages async calls to todoManagerImpl.
         */
        final Service service = serviceBuilder()
                .setServiceObject(todoManagerImpl)
                .build().start();


        /* Create Asynchronous proxy over Synchronous service. */
        final TodoManagerClientInterface todoManager = 
              service.createProxy(TodoManagerClientInterface.class);

        service.startCallBackHandler();


        System.out.println("This is an async call");
        /* Asynchronous method call. */
        todoManager.add(new Todo("Call Mom", "Give Mom a call"));


        AtomicInteger countTracker = new AtomicInteger(); 
        //Hold count from async call to service... for testing and showing it is an async callback

        System.out.println("This is an async call to count");

        todoManager.count(count -> {
            System.out.println("This lambda expression is the callback " + count);

            countTracker.set(count);
        });


        todoManager.clientProxyFlush(); //Flush all methods. It batches calls.

        Sys.sleep(100);

        System.out.printf("This is the count back from the server %d\n", countTracker.get());

QBit Event Bus

QBit also has a service event bus. This example is a an employee benefits services example.
We have two channels.
public static final String NEW_HIRE_CHANNEL = "com.mycompnay.employee.new";

public static final String PAYROLL_ADJUSTMENT_CHANNEL = "com.mycompnay.employee.payroll";
An employee object looks like this:
public static class Employee {
       final String firstName;
       final int employeeId;
This example has three services: EmployeeHiringService, BenefitsService, and PayrollService.
These services are inproc services. QBit supports WebSocket, HTTP and REST remote services as well, but for now, let's focus on inproc services. If you understand inproc then you will understand remote.
The EmployeeHiringService actually fires off the events to other two services.
public class EmployeeHiringService {


    public void hireEmployee(final Employee employee) {

           int salary = 100;
           System.out.printf("Hired employee %s\n", employee);

           //Does stuff to hire employee

           //Sends events
           final EventManager eventManager = 
                               serviceContext().eventManager();
           eventManager.send(NEW_HIRE_CHANNEL, employee);

           eventManager.sendArray(PAYROLL_ADJUSTMENT_CHANNEL, 
                                     employee, salary);


    }

   }
Notice that we call sendArray so we can send the employee and their salary. The listener for PAYROLL_ADJUSTMENT_CHANNEL will have to handle both an employee and an int that represents the new employees salary. You can also use event bus proxies so you do not have to call into the event bus at all.
The BenefitsService listens for new employees being hired so it can enroll them into the benefits system.
public static class BenefitsService {

       @OnEvent(NEW_HIRE_CHANNEL)
       public void enroll(final Employee employee) {

           System.out.printf("Employee enrolled into benefits system employee %s %d\n",
                   employee.getFirstName(), employee.getEmployeeId());

       }
Daddy needs to get paid.
    public static class PayrollService {

        @OnEvent(PAYROLL_ADJUSTMENT_CHANNEL)
        public void addEmployeeToPayroll(final Employee employee, int salary) {

            System.out.printf("Employee added to payroll  %s %d %d\n",
                    employee.getFirstName(), employee.getEmployeeId(), salary);

        }

    }
The employee is the employee object from the EmployeeHiringService.
so you can get your benefits, and paid!
Find more details here:

Private event bus and event bus proxies

You can define your own interface to the event bus and you can use your own event buses with QBit. Each module in your service can have its own internal event bus.

Queue Callbacks

To really grasp QBit, one must grasp the concepts of a CallBack.
A CallBack is a way to get an async response in QBit.
You call a service method and it calls you back.
Client proxies can have callbacks:

Queue Callbacks - RecommendationService client interface

public interface RecommendationServiceClient {


    void recommend(final Callback<List<Recommendation>> recommendationsCallback,
                          final String userName);
}
Callbacks are Java 8 Consumers with some optional extra error handling.

Queue Callbacks - Callback

public interface Callback <T> extends java.util.function.Consumer<T> {
    default void onError(java.lang.Throwable error) { /* compiled code */ }
}
Services that can block should use callbacks. Thus if loadUser blocked in the following example, it should really use a callback instead of returning a value.
public class RecommendationService {

Queue Callbacks - Simple minded implementation of RecommendationService

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

    public List<Recommendation> recommend(final String userName) {
        User user = users.get(userName);
        if (user == null) {
            user = loadUser(userName);
        }
        return runRulesEngineAgainstUser(user);
    }
Let's pretend 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 other services. In other words, loadUser can potentially block on IO.

Queue Callbacks - The first rule of Queue Club - don't block

Our client does not block, but our service does. Going back to our RecommendationService. If we get a lot of cache hits for user loads, perhaps the block will not be that long, but it will be there and every time we have to fault in a user, the whole system is gummed up. What we want to be able to do is if we can't handle the recommendation request, we go ahead and make an async call to the UserDataService. When that async callback comes back, then we handle that request. In the mean time, we handle recommendation lists requests as quickly as we can. We never block.
So let's revisit the service. The first thing we are going to do is make the service method take a callback. Before we do that, let's set down some rules.

The first rule of queue club don't block.

The second rule of queue club if you are not ready, use a callback and continue handling stuff you are ready for

Queue Callbacks - Adding a CallBack to the RecommendationService inproc microservice

public class RecommendationService {


    public void recommend(final Callback<List<Recommendation>> recommendationsCallback,
                          final String userName) {
Now we are taking a callback and we can decide when we want to handle this recommendation generation request. We can do it right away if there user data we need is in-memory or we can delay it.

If the user is found, call the callback right away for RecommendationService inproc microservice

    public void recommend(final Callback<List<Recommendation>> recommendationsCallback,
                          final String userName) {

        /** Look for user in user cache. */
        User user = users.get(userName);

        /** If the user not found, load the user from the user service. */
        if (user == null) {
             ...
        } else {
             /* Call the callback now because we can handle the callback now. */
            recommendationsCallback.accept(runRulesEngineAgainstUser(user));
        }

    }
Notice, if the user is found in the cache, we run our recommendation rules in-memory and call the callback right awayrecommendationsCallback.accept(runRulesEngineAgainstUser(user)).
The interesting part is what do we do if don't have the user loaded.

If the user was not found, load him from the user microservice, but still don't block

    public void recommend(final Callback<List<Recommendation>> recommendationsCallback,
                          final String userName) {


        /** Look for user in users cache. */
        User user = users.get(userName);

        /** If the user not found, load the user from the user service. */
        if (user == null) {

            /* Load user using Callback. */
            userDataService.loadUser(new Callback<User>() {
                @Override
                public void accept(final User loadedUser) {
                        handleLoadFromUserDataService(loadedUser,
                                recommendationsCallback);
                }
            }, userName);

        }
        ...
Here we use a CallBack to load the user, and when the user is loaded, we call handleLoadFromUserDataService which adds some management about handling the callback so we can still handle this call, just not now.

Lambda version of last example

    public void recommend(final Callback<List<Recommendation>> recommendationsCallback,
                          final String userName) {


        /** Look for user in users cache. */
        User user = users.get(userName);

        /** If the user not found, load the user from the user service. */
        if (user == null) {

            /* Load user using lambda expression. */
         userDataService.loadUser(
                    loadedUser -> {
                        handleLoadFromUserDataService(loadedUser, 
                        recommendationsCallback);
                    }, userName);

        }
        ...
Using lambdas like this makes the code more readable and terse, but remember don't deeply nest lambda expressions or you will create a code maintenance nightmare. Use them judiciously.

Queue Callbacks - Doing something later

What we want is to handle the request for recommendations after the user service system loads the user from its store.

Handling UserServiceData callback methods once we get them.

public class RecommendationService {


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

    private UserDataServiceClient userDataService;


    private BlockingQueue<Runnable> callbacks = 
               new ArrayBlockingQueue<Runnable>(10_000);


    ...

    public void recommend(final Callback<List<Recommendation>> recommendationsCallback,
                          final String userName) {

        ...

    }

    /** Handle defered recommendations based on user loads. */
    private void handleLoadFromUserDataService(final User loadedUser,
                                               final Callback<List<Recommendation>> recommendationsCallback) {

        /** Add a runnable to the callbacks queue. */
        callbacks.add(new Runnable() {
            @Override
            public void run() {
              List<Recommendation> recommendations = runRulesEngineAgainstUser(loadedUser);
              recommendationsCallback.accept(recommendations);
            }
        });
    }


handleLoadFromUserDataService rewritten using Lambda

public class RecommendationService {

...

    /** Handle defered recommendations based on user loads. */
    private void handleLoadFromUserDataService(final User loadedUser,
                                               final Callback<List<Recommendation>> recommendationsCallback) {

        /** Add a runnable to the callbacks list. */
        callbacks.add(() -> {
            List<Recommendation> recommendations = runRulesEngineAgainstUser(loadedUser);
            recommendationsCallback.accept(recommendations);
        });

    }
The important part there is that every time we get a callback call from UserDataService, we then perform our CPU intensive recommendation rules and callback our caller. Well not exactly, what we do is enqueue an runnable onto our callbacks queue, and later we will iterate through those but when?

Queue Callbacks Handling callbacks when our receive queue is empty, a new batch started or we hit a batch limit

The RecommendationService can be notified when its queue is empty, it has started a new batch and when it has reached a batch limit. These are all good times to handle callbacks from the UserDataService.

Draining our callback queue

    @QueueCallback({
            QueueCallbackType.EMPTY,
            QueueCallbackType.START_BATCH,
            QueueCallbackType.LIMIT})
    private void handleCallbacks() {

        flushServiceProxy(userDataService);
        Runnable runnable = callbacks.poll();

        while (runnable != null) {
            runnable.run();
            runnable = callbacks.poll();
        }
    }
It is important to remember when handling callbacks from another microservice that you want to handle callbacks from the other service before you handle more incomming requests from you clients. Essentially you have clients that have been waiting (async waiting but still), and these clients might represent an open TCP/IP connection like an HTTP call so it is best to close them out before handling more requests and like we said they were already waiting around with an open connection for users to load form the user service.
To learn more about CallBacks, plesae read QBit Java MicroService Lib CallBack fundamentals.

Workers - pools and shards

public class ServiceWorkers {

    public static RoundRobinServiceDispatcher workers() {...

    public static ShardedMethodDispatcher shardedWorkers(final ShardRule shardRule) {...
You can compose sharded workers (for in-memory, thread safe, CPU intensive services), or workers for IO or talking to foreign services or foreign buses.
Here is an example that uses a worker pool with three service workers in it:
Let's say you have a service that does something:
    //Your POJO
    public  class MultiWorker {

        void doSomeWork(...) {
           ...
        }

    }
Now this does some sort of IO and you want to have a bank of these running not just one so you can do IO in parallel. After some performance testing, you found out that three is the magic number.
You want to use your API for accessing this service:
    public  interface MultiWorkerClient {
        void doSomeWork(...);
    }
Now let's create a bank of these and use it.
First create the QBit services which add the thread/queue/microbatch.
        /* Create a service builder. */
        final ServiceBuilder serviceBuilder = serviceBuilder();

        /* Create some qbit services. */
        final Service service1 = serviceBuilder.setServiceObject(new MultiWorker()).build();
        final Service service2 = serviceBuilder.setServiceObject(new MultiWorker()).build();
        final Service service3 = serviceBuilder.setServiceObject(new MultiWorker()).build();
Now add them to a ServiceWorkers object.
        ServiceWorkers dispatcher;
        dispatcher = workers(); //Create a round robin service dispatcher
        dispatcher.addServices(service1, service2, service3);
        dispatcher.start(); // start up the workers
You can add services, POJOs and method consumers, method dispatchers to a service bundle. The service bundle is an integration point into QBit.
Let's add our new Service workers. ServiceWorkers is a ServiceMethodDispatcher.
        /* Add the dispatcher to a service bundle. */
        bundle = serviceBundleBuilder().setAddress("/root").build();
        bundle.addServiceConsumer("/workers", dispatcher);
        bundle.start();
We are probably going to add a helper method to the service bundle so most of this can happen in a single call.
Now you can start using your workers.
        /* Start using the workers. */
        final MultiWorkerClient worker = bundle.createLocalProxy(MultiWorkerClient.class, "/workers");
Now you could use Spring or Guice to configure the builders and the service bundle. But you can just do it like the above which is good for testing and understanding QBit internals.
QBit also supports the concept of sharded services which is good for sharding resources like CPU (run a rules engine on each CPU core for a user recommendation engine).
QBit does not know how to shard your services, you have to give it a hint. You do this through a shard rule.
public interface ShardRule {
    int shard(String methodName, Object[] args, int numWorkers);
}
We worked on an app where the first argument to the services was the username, and then we used that to shard calls to a CPU intensive in-memory rules engine. This technique works. :)
The ServiceWorkers class has a method for creating a sharded worker pool.
    public static ShardedMethodDispatcher shardedWorkers(final ShardRule shardRule) {
        ...
    }
To use you just pass a shard key when you create the service workers.
        dispatcher = shardedWorkers((methodName, methodArgs, numWorkers) -> {
            String userName = methodArgs[0].toString();
            int shardKey =  userName.hashCode() % numWorkers;
            return shardKey;
        });
Then add your services to the ServiceWorkers composition.
        int workerCount = Runtime.getRuntime().availableProcessors();

        for (int index = 0; index < workerCount; index++) {
            final Service service = serviceBuilder
                    .setServiceObject(new ContentRulesEngine()).build();
            dispatcher.addServices(service);

        }
Then add it to the service bundle as before.
        dispatcher.start();

        bundle = serviceBundleBuilder().setAddress("/root").build();

        bundle.addServiceConsumer("/workers", dispatcher);
        bundle.start();
Then just use it:
        final MultiWorkerClient worker = bundle.createLocalProxy(MultiWorkerClient.class, "/workers");

        for (int index = 0; index < 100; index++) {
            String userName = "rickhigh" + index;
            worker.pickSuggestions(userName);
        }

Built in shard rules

public class ServiceWorkers {
...
    public static ShardedMethodDispatcher shardOnFirstArgumentWorkers() {
       ...
    }

...

    public static ShardedMethodDispatcher shardOnFifthArgumentWorkers() {
         ...
    }


    public static ShardedMethodDispatcher shardOnBeanPath(final String beanPath) {
        ...
    }
The shardOnBeanPath allows you to create a complex bean path navigation call and use its property to shard on.
     /* shard on 2nd arg which is an employee
       Use the employees department's id property. */
     dispatcher = shardOnBeanPath("[1].department.id"); 

     /* Same as above. */
     dispatcher = shardOnBeanPath("1/department/id"); 
You can find a lot more in the wiki. Also follow the commits. We have been busy beavers. QBit the microservice lib for Java - JSON, REST, WebSocket.
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