apache

Apache Camel: integrating systems with Java

Standard

Hi, dear readers! Welcome to my blog. On this post, we will talk about Apache Camel, a robust solution for deploying system integrations across various technologies, such as REST, WS, JMS, JDBC, AWS Products, LDAP, SMTP, Elasticsearch etc.

So, let’s get start!

Origin

Apache Camel was created on Apache Service Mix. Apache Service Mix was a project powered by the Spring Framework and implemented following the JBI specification. The Java Business Integration specification specifies a plug and play platform for systems integrations, following the EIP (Enterprise Integration Patterns) patterns.

Terminology

Exchange

Exchanges – or MEPs(Message Exchange Patterns) – are like frames where we transport our data across the integrations on Camel. A Exchange can have 2 messages inside, one representing the input and another one representing the output of a integration.

The output message on Camel is optional, since we could have a integration that doesn’t have a response. Also, a Exchange can have properties, represented as key-value entries, that can be used as data that will be used across the whole route (we will see more about routes very soon)

Message

Messages are the data itself that is transferred inside a Camel route. A Message can have a body, which is the data itself and headers, which are, like properties on a Exchange, key-value entries that can be used along the processing.

One important aspect to keep in mind, however, is that along a Camel route our Messages are changed – when we convert the body with a Type converter, for instance – and when this happens, we lose all our headers. So, Message headers must be seen as ephemeral data, that will not be used through the whole route. For that type of data, it is better to use Exchange properties.

The Message body can de made of several types of data, such as binaries, JSON, etc.

Camel context

The Camel context is the runtime container where Camel runs it. It initializes type converters, routes, endpoints, EIPs etc.

A Camel context has 3 possible status: started, suspended and stopped. When started, the context will serve the routes processing as normal.

When on suspended status, the Camel context will stop the processing – after the Exchanges already on processing are completed – , but keep all the caches, resources etc still loaded. A suspended context can be restarted.

Finally, there’s the stop status. When stopped, the context will stop the processing like the suspended status, but also will release all the resources caches etc, making a complete shutdown. As with the suspended status, Camel will also guarantee that all the Exchanges being processing will be finished before the shutdown.

Route

Routes on Camel are the heart of the processing. It consists of a flow, that start on a endpoint, pass through a stream of processors/convertors and finishes on another endpoint. it is possible to chain routes by calling another route as the final endpoint of a previous route.

A route can also use other features, such as EIPs, asynchronous and parallel processing.

Channel

When Camel executes a route, the controller in which it executes the route is called Channel.

A Channel is responsible for chaining the processors execution, passing the Exchange from one to another, alongside monitoring the route execution. It also allow us to implement interceptors to run any logic on some route’s events, such as when a Exchange is going to a specific Endpoint.

Processor

Processors are the primary extension points on Camel. By creating classes that extend the org.apache.camel.Processor interface, we create programming units that we can use to include our own code on a Camel route, inside a convenient execute method.

Component

A Component act like a factory to instantiate Endpoints for our use. We don’t directly use a Component, we reference instead by defining a Endpoint URI, that makes Camel infer about the Component that it needs to be using in order to create the Endpoint.

Camel provides dozens of Components, from file to JMS, AWS Connections to their products and so on.

Registry

In order to utilize beans from IoC systems, such as OSGi, Spring and JNDI, Camel supplies us with a Bean Registry. The Registry’s mission is to supply the beans referred on Camel routes with the ones create on his associated context, such as a OSGi container, a Spring context etc

Type converter

Type converters, as the name implies, are used in order to convert the body of a message from one type to another. The uses for a converter are varied, ranging from converting a binary format to a String to converting XML to JSON.

We can create our own Type Converter by extending the org.apache.camel.TypeConverter  interface. After creating our own Converter by extending the interface, we need to register it on the Type’s Converter Registry.

Endpoint

A Endpoint is the entity responsible for communicating a Camel Route in or out of his execution process. It comprises several types of sources and destinations as mentioned before, such as SQS, files, Relational Databases and so on. A Endpoint is instantiated and configured by providing a URI to a Camel Route, following the pattern below:

component:option?option1=value1&option2=value2

We can create our own Components by extending the org.apache.camel.Endpoint interface. When extending the interface, we need to override 3 methods, where we supply the logic to create a polling consumer Endpoint, a passive consumer Endpoint and a producer Endpoint.

Lab

So, without further delay, let’s start our lab! On this lab, we will create a route that polls access files from a access log style file, sends the logs to a SQS and backups the file on a S3.

Setup

The setup for our lab is pretty simple: It is a Spring Boot application, configured to work with Camel. Our Gradle.build file is as follows:

apply plugin: 'java'
apply plugin: 'eclipse'
apply plugin: 'org.springframework.boot'
apply plugin: 'maven'
apply plugin: 'idea'

jar {
    baseName = 'apache-camel-handson'
    version = '1.0'
}

project.ext {
    springBootVersion = '1.5.4.RELEASE'
    camelVersion = '2.18.3'

}

sourceCompatibility = 1.8
targetCompatibility = 1.8

repositories {
    mavenLocal()
    mavenCentral()
}

bootRun {
    systemProperties = System.properties
}


dependencies {

    compile group: 'org.apache.camel', name: 'camel-spring-boot-starter', version: camelVersion
    compile group: 'org.apache.camel', name: 'camel-commands-spring-boot', version: camelVersion
    compile group: 'org.apache.camel',name: 'camel-aws', version: camelVersion
    compile group: 'org.apache.camel',name: 'camel-mail', version: camelVersion
    compile group: 'org.springframework.boot', name: 'spring-boot-autoconfigure', version: springBootVersion
    

}
group 'com.alexandreesl.handson'
version '1.0'

buildscript {
    repositories {
        mavenLocal()
        maven {
            url "https://plugins.gradle.org/m2/"
        }
        mavenCentral()
    }
    dependencies {
        classpath("org.springframework.boot:spring-boot-gradle-plugin:1.5.4.RELEASE")
    }
}

And the Java main file is a simple Java Spring Boot Application file, as follows:

package com.alexandreesl.handson;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.EnableAutoConfiguration;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.ComponentScan;

/**
 * Created by alexandrelourenco on 28/06/17.
 */

@ComponentScan(basePackages = {"com.alexandreesl.handson"})
@SpringBootApplication
@EnableAutoConfiguration
public class ApacheCamelHandsonApp {

    public static void main(String[] args) {
        SpringApplication.run(ApacheCamelHandsonApp.class, args);
    }

}

We also configure a configuration class, where we will register a type converter that we will create on the next section:

package com.alexandreesl.handson.configuration;

import org.apache.camel.CamelContext;
import org.apache.camel.spring.boot.CamelContextConfiguration;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class CamelConfiguration {


    @Bean
    public CamelContextConfiguration camelContextConfiguration() {

        return new CamelContextConfiguration() {

            @Override
            public void beforeApplicationStart(CamelContext camelContext) {
               

            }

            @Override
            public void afterApplicationStart(CamelContext camelContext) {

            }

        };

    }

}

We also create a configuration which will create a AmazonS3Client and AmazonSQSClient, that will be used by the AWS-S3 and AWS-SQS Camel endpoints:

package com.alexandreesl.handson.configuration;

import com.amazonaws.auth.BasicAWSCredentials;
import com.amazonaws.internal.StaticCredentialsProvider;
import com.amazonaws.regions.Regions;
import com.amazonaws.services.s3.AmazonS3Client;
import com.amazonaws.services.sqs.AmazonSQSClient;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.env.Environment;

@Configuration
public class AWSConfiguration {


    @Autowired
    private Environment environment;

    @Bean(name = "s3Client")
    public AmazonS3Client s3Client() {
        return new AmazonS3Client(staticCredentialsProvider()).withRegion(Regions.fromName("us-east-1"));
    }

    @Bean(name = "sqsClient")
    public AmazonSQSClient sqsClient() {
        return new AmazonSQSClient(staticCredentialsProvider()).withRegion(Regions.fromName("us-east-1"));
    }

    @Bean
    public StaticCredentialsProvider staticCredentialsProvider() {
        return new StaticCredentialsProvider(new BasicAWSCredentials("<access key>", "<secret access key>"));
    }

}

PS: this lab assumes that the reader is familiar with AWS and already has a account. For the lab, a bucket called “apache-camel-handson” and a SQS called “MyInputQueue” were created.

Configuring the route

Now that we have our Camel environment set up, let’s begin creating our route. First, we create a type converter called “StringToAccessLogDTOConverter” with the following code:

package com.alexandreesl.handson.converters;

import com.alexandreesl.handson.dto.AccessLogDTO;
import org.apache.camel.Converter;
import org.apache.camel.TypeConverters;

import java.util.StringTokenizer;

/**
 * Created by alexandrelourenco on 30/06/17.
 */

public class StringToAccessLogDTOConverter implements TypeConverters {

    @Converter
    public AccessLogDTO convert(String row) {

        AccessLogDTO dto = new AccessLogDTO();

        StringTokenizer tokens = new StringTokenizer(row);

        dto.setIp(tokens.nextToken());
        dto.setUrl(tokens.nextToken());
        dto.setHttpMethod(tokens.nextToken());
        dto.setDuration(Long.parseLong(tokens.nextToken()));

        return dto;

    }

}

Next, we change our Camel configuration, registering the converter:

package com.alexandreesl.handson.configuration;

import com.alexandreesl.handson.converters.StringToAccessLogDTOConverter;
import org.apache.camel.CamelContext;
import org.apache.camel.spring.boot.CamelContextConfiguration;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class CamelConfiguration {


    @Bean
    public CamelContextConfiguration camelContextConfiguration() {

        return new CamelContextConfiguration() {

            @Override
            public void beforeApplicationStart(CamelContext camelContext) {

                camelContext.getTypeConverterRegistry().addTypeConverters(new StringToAccessLogDTOConverter());

            }

            @Override
            public void afterApplicationStart(CamelContext camelContext) {

            }

        };

    }

}

Our converter reads a String and converts to a DTO, with the following attributes:

package com.alexandreesl.handson.dto;

/**
 * Created by alexandrelourenco on 30/06/17.
 */
public class AccessLogDTO {

    private String ip;

    private String url;

    private String httpMethod;

    private long duration;

    public String getIp() {
        return ip;
    }

    public void setIp(String ip) {
        this.ip = ip;
    }

    public String getUrl() {
        return url;
    }

    public void setUrl(String url) {
        this.url = url;
    }

    public String getHttpMethod() {
        return httpMethod;
    }

    public void setHttpMethod(String httpMethod) {
        this.httpMethod = httpMethod;
    }

    public long getDuration() {
        return duration;
    }

    public void setDuration(long duration) {
        this.duration = duration;
    }

    @Override
    public String toString() {

        StringBuffer buffer = new StringBuffer();
        buffer.append("[");
        buffer.append(ip);
        buffer.append(",");
        buffer.append(url);
        buffer.append(",");
        buffer.append(httpMethod);
        buffer.append(",");
        buffer.append(duration);
        buffer.append("]");

        return buffer.toString();

    }
}

Finally, we have our route, defined on our RouteBuilder:

package com.alexandreesl.handson.routes;

import com.alexandreesl.handson.dto.AccessLogDTO;
import org.apache.camel.LoggingLevel;
import org.apache.camel.spring.SpringRouteBuilder;
import org.springframework.context.annotation.Configuration;

/**
 * Created by alexandrelourenco on 30/06/17.
 */

@Configuration
public class MyFirstCamelRoute extends SpringRouteBuilder {


    @Override
    public void configure() throws Exception {

        from("file:/Users/alexandrelourenco/Documents/apachecamelhandson?delay=1000&charset=utf-8&delete=true")
                .setHeader("CamelAwsS3Key", header("CamelFileName"))
                .to("aws-s3:arn:aws:s3:::apache-camel-handson?amazonS3Client=#s3Client")
                .convertBodyTo(String.class)
                .split().tokenize("\n")
                    .convertBodyTo(AccessLogDTO.class)
                    .log(LoggingLevel.INFO, "${body}")
                    .to("aws-sqs://MyInputQueue?amazonSQSClient=#sqsClient");

    }
}

On the route above, we define a file endpoint that will poll for files on a folder, each 1 second and remove the file if the processing is completed successfully. Then we send the file to Amazon using S3 as a backup storage.

Next, we split the file using a splitter, that generates a string for each line of the file. For each line we convert the line to a DTO, log the data and finally we send the data to a SQS.

Now that we have our code done, let’s run it!

Running

First, we start our Camel route. To do this, we simply run the main Spring Boot class, as we would do with any common Java program.

After firing up Spring Boot, we would receive on our console the output that the route was successful started:

. ____ _ __ _ _
 /\\ / ___'_ __ _ _(_)_ __ __ _ \ \ \ \
( ( )\___ | '_ | '_| | '_ \/ _` | \ \ \ \
 \\/ ___)| |_)| | | | | || (_| | ) ) ) )
 ' |____| .__|_| |_|_| |_\__, | / / / /
 =========|_|==============|___/=/_/_/_/
 :: Spring Boot :: (v1.5.4.RELEASE)

2017-07-01 12:52:02.224 INFO 3042 --- [ main] c.a.handson.ApacheCamelHandsonApp : Starting ApacheCamelHandsonApp on Alexandres-MacBook-Pro.local with PID 3042 (/Users/alexandrelourenco/Applications/git/apache-camel-handson/build/classes/main started by alexandrelourenco in /Users/alexandrelourenco/Applications/git/apache-camel-handson)
2017-07-01 12:52:02.228 INFO 3042 --- [ main] c.a.handson.ApacheCamelHandsonApp : No active profile set, falling back to default profiles: default
2017-07-01 12:52:02.415 INFO 3042 --- [ main] s.c.a.AnnotationConfigApplicationContext : Refreshing org.springframework.context.annotation.AnnotationConfigApplicationContext@475e586c: startup date [Sat Jul 01 12:52:02 BRT 2017]; root of context hierarchy
2017-07-01 12:52:03.325 INFO 3042 --- [ main] trationDelegate$BeanPostProcessorChecker : Bean 'org.apache.camel.spring.boot.CamelAutoConfiguration' of type [org.apache.camel.spring.boot.CamelAutoConfiguration$$EnhancerBySpringCGLIB$$72a2a9b] is not eligible for getting processed by all BeanPostProcessors (for example: not eligible for auto-proxying)
2017-07-01 12:52:09.520 INFO 3042 --- [ main] o.a.c.i.converter.DefaultTypeConverter : Loaded 192 type converters
2017-07-01 12:52:09.612 INFO 3042 --- [ main] roperties$SimpleAuthenticationProperties :

Using default password for shell access: b738eab1-6577-4f9b-9a98-2f12eae59828




2017-07-01 12:52:15.463 WARN 3042 --- [ main] tarterDeprecatedWarningAutoConfiguration : spring-boot-starter-remote-shell is deprecated as of Spring Boot 1.5 and will be removed in Spring Boot 2.0
2017-07-01 12:52:15.511 INFO 3042 --- [ main] o.s.j.e.a.AnnotationMBeanExporter : Registering beans for JMX exposure on startup
2017-07-01 12:52:15.519 INFO 3042 --- [ main] o.s.c.support.DefaultLifecycleProcessor : Starting beans in phase 0
2017-07-01 12:52:15.615 INFO 3042 --- [ main] o.a.camel.spring.boot.RoutesCollector : Loading additional Camel XML routes from: classpath:camel/*.xml
2017-07-01 12:52:15.615 INFO 3042 --- [ main] o.a.camel.spring.boot.RoutesCollector : Loading additional Camel XML rests from: classpath:camel-rest/*.xml
2017-07-01 12:52:15.616 INFO 3042 --- [ main] o.a.camel.spring.SpringCamelContext : Apache Camel 2.18.3 (CamelContext: camel-1) is starting
2017-07-01 12:52:15.618 INFO 3042 --- [ main] o.a.c.m.ManagedManagementStrategy : JMX is enabled
2017-07-01 12:52:25.695 INFO 3042 --- [ main] o.a.c.i.DefaultRuntimeEndpointRegistry : Runtime endpoint registry is in extended mode gathering usage statistics of all incoming and outgoing endpoints (cache limit: 1000)
2017-07-01 12:52:25.810 INFO 3042 --- [ main] o.a.camel.spring.SpringCamelContext : StreamCaching is not in use. If using streams then its recommended to enable stream caching. See more details at http://camel.apache.org/stream-caching.html
2017-07-01 12:52:27.853 INFO 3042 --- [ main] o.a.camel.spring.SpringCamelContext : Route: route1 started and consuming from: file:///Users/alexandrelourenco/Documents/apachecamelhandson?charset=utf-8&delay=1000&delete=true
2017-07-01 12:52:27.854 INFO 3042 --- [ main] o.a.camel.spring.SpringCamelContext : Total 1 routes, of which 1 are started.
2017-07-01 12:52:27.854 INFO 3042 --- [ main] o.a.camel.spring.SpringCamelContext : Apache Camel 2.18.3 (CamelContext: camel-1) started in 12.238 seconds
2017-07-01 12:52:27.858 INFO 3042 --- [ main] c.a.handson.ApacheCamelHandsonApp : Started ApacheCamelHandsonApp in 36.001 seconds (JVM running for 36.523)

PS: Don’t forget it to replace the access key and secret with your own!

Now, to test it, we place a file on the polling folder. For testing, we create a file like the following:

10.12.64.3 /api/v1/test1 POST 123
10.12.67.3 /api/v1/test2 PATCH 125
10.15.64.3 /api/v1/test3 GET 166
10.120.64.23 /api/v1/test1 POST 100

We put a file with the content on the folder and after 1 second, the file is gone! Where did it go?

If we check the Amazon S3 bucket interface, we will see that the file was created on the storage:

 

Screen Shot 2017-07-01 at 13.12.39

And if we check the Amazon SQS interface, we will see 4 messages on the queue, proving that our integration is a success:

Screen Shot 2017-07-01 at 13.38.12

If we check the messages, we will see that Camel correctly parsed the information from the file, as we can see on the example bellow:

[10.12.64.3,/api/v1/test1,POST,123]

Implementing Error Handling

On Camel, we can implement logic designed for handling errors. These are done by defining routes as well, which inputs are the exceptions fired by the routes.

On our lab, let’s implement a error handling. First, we add a option on the file endpoint that makes the file to be moved to a .error folder when a error occurs, and then we send a email to ourselves to alert of the failure. we can do this by changing the route as follows:

package com.alexandreesl.handson.routes;

import com.alexandreesl.handson.dto.AccessLogDTO;
import org.apache.camel.LoggingLevel;
import org.apache.camel.spring.SpringRouteBuilder;
import org.springframework.context.annotation.Configuration;

/**
 * Created by alexandrelourenco on 30/06/17.
 */

@Configuration
public class MyFirstCamelRoute extends SpringRouteBuilder {


    @Override
    public void configure() throws Exception {

        onException(Exception.class)
                .handled(false)
                .log(LoggingLevel.ERROR, "An Error processing the file!")
                .to("smtps://smtp.gmail.com:465?password=xxxxxxxxxxxxxxxx&username=alexandreesl@gmail.com&subject=A error has occurred!");

        from("file:/Users/alexandrelourenco/Documents/apachecamelhandson?delay=1000&charset=utf-8&delete=true&moveFailed=.error")
                .setHeader("CamelAwsS3Key", header("CamelFileName"))
                .to("aws-s3:arn:aws:s3:::apache-camel-handson?amazonS3Client=#s3Client")
                .convertBodyTo(String.class)
                .split().tokenize("\n")
                    .convertBodyTo(AccessLogDTO.class)
                    .log(LoggingLevel.INFO, "${body}")
                    .to("aws-sqs://MyInputQueue?amazonSQSClient=#sqsClient");

    }
}

Then, we restart the route and feed up a file like the following, that will cause a parse exception:

10.12.64.3 /api/v1/test1 POST 123
10.12.67.3 /api/v1/test2 PATCH 125
10.15.64.3 /api/v1/test3 GET 166
10.120.64.23 /api/v1/test1 POST 10a

After the processing, we can see the console and watch how the error was handled:

2017-07-01 14:18:48.695  INFO 3230 --- [           main] o.a.camel.spring.SpringCamelContext      : Apache Camel 2.18.3 (CamelContext: camel-1) started in 11.899 seconds2017-07-01 14:18:48.695  INFO 3230 --- [           main] o.a.camel.spring.SpringCamelContext      : Apache Camel 2.18.3 (CamelContext: camel-1) started in 11.899 seconds2017-07-01 14:18:48.699  INFO 3230 --- [           main] c.a.handson.ApacheCamelHandsonApp        : Started ApacheCamelHandsonApp in 35.612 seconds (JVM running for 36.052)2017-07-01 14:18:52.737  WARN 3230 --- [checamelhandson] c.amazonaws.services.s3.AmazonS3Client   : No content length specified for stream data.  Stream contents will be buffered in memory and could result in out of memory errors.2017-07-01 14:18:53.105  INFO 3230 --- [checamelhandson] route1                                   : [10.12.64.3,/api/v1/test1,POST,123]2017-07-01 14:18:53.294  INFO 3230 --- [checamelhandson] route1                                   : [10.12.67.3,/api/v1/test2,PATCH,125]2017-07-01 14:18:53.504  INFO 3230 --- [checamelhandson] route1                                   : [10.15.64.3,/api/v1/test3,GET,166]2017-07-01 14:18:53.682 ERROR 3230 --- [checamelhandson] route1                                   : An Error processing the file!2017-07-01 14:19:02.058 ERROR 3230 --- [checamelhandson] o.a.camel.processor.DefaultErrorHandler  : Failed delivery for (MessageId: ID-Alexandres-MacBook-Pro-local-52251-1498929510223-0-9 on ExchangeId: ID-Alexandres-MacBook-Pro-local-52251-1498929510223-0-10). Exhausted after delivery attempt: 1 caught: org.apache.camel.InvalidPayloadException: No body available of type: com.alexandreesl.handson.dto.AccessLogDTO but has value: 10.120.64.23 /api/v1/test1 POST 10a of type: java.lang.String on: Message[ID-Alexandres-MacBook-Pro-local-52251-1498929510223-0-9]. Caused by: Error during type conversion from type: java.lang.String to the required type: com.alexandreesl.handson.dto.AccessLogDTO with value 10.120.64.23 /api/v1/test1 POST 10a due java.lang.NumberFormatException: For input string: "10a". Exchange[ID-Alexandres-MacBook-Pro-local-52251-1498929510223-0-10]. Caused by: [org.apache.camel.TypeConversionException - Error during type conversion from type: java.lang.String to the required type: com.alexandreesl.handson.dto.AccessLogDTO with value 10.120.64.23 /api/v1/test1 POST 10a due java.lang.NumberFormatException: For input string: "10a"]. Processed by failure processor: FatalFallbackErrorHandler[Pipeline[[Channel[Log(route1)[An Error processing the file!]], Channel[sendTo(smtps://smtp.gmail.com:465?password=xxxxxx&subject=A+error+has+occurred%21&username=alexandreesl%40gmail.com)]]]]
Message History---------------------------------------------------------------------------------------------------------------------------------------RouteId              ProcessorId          Processor                                                                        Elapsed (ms)[route1            ] [route1            ] [file:///Users/alexandrelourenco/Documents/apachecamelhandson?charset=utf-8&del] [      9323][route1            ] [convertBodyTo2    ] [convertBodyTo[com.alexandreesl.handson.dto.AccessLogDTO]                      ] [      8370][route1            ] [log1              ] [log                                                                           ] [         1][route1            ] [to1               ] [smtps://smtp.gmail.com:xxxxxx@gmail.com&subject=A error ha                    ] [      8366]
Stacktrace---------------------------------------------------------------------------------------------------------------------------------------
org.apache.camel.InvalidPayloadException: No body available of type: com.alexandreesl.handson.dto.AccessLogDTO but has value: 10.120.64.23 /api/v1/test1 POST 10a of type: java.lang.String on: Message[ID-Alexandres-MacBook-Pro-local-52251-1498929510223-0-9]. Caused by: Error during type conversion from type: java.lang.String to the required type: com.alexandreesl.handson.dto.AccessLogDTO with value 10.120.64.23 /api/v1/test1 POST 10a due java.lang.NumberFormatException: For input string: "10a". Exchange[ID-Alexandres-MacBook-Pro-local-52251-1498929510223-0-10]. Caused by: [org.apache.camel.TypeConversionException - Error during type conversion from type: java.lang.String to the required type: com.alexandreesl.handson.dto.AccessLogDTO with value 10.120.64.23 /api/v1/test1 POST 10a due java.lang.NumberFormatException: For input string: "10a"] at org.apache.camel.impl.MessageSupport.getMandatoryBody(MessageSupport.java:107) ~[camel-core-2.18.3.jar:2.18.3] at org.apache.camel.processor.ConvertBodyProcessor.process(ConvertBodyProcessor.java:91) ~[camel-core-2.18.3.jar:2.18.3] at org.apache.camel.management.InstrumentationProcessor.process(InstrumentationProcessor.java:77) [camel-core-2.18.3.jar:2.18.3] at org.apache.camel.processor.RedeliveryErrorHandler.process(RedeliveryErrorHandler.java:542) [camel-core-2.18.3.jar:2.18.3] at org.apache.camel.processor.CamelInternalProcessor.process(CamelInternalProcessor.java:197) [camel-core-2.18.3.jar:2.18.3]

If we look to the folder, we will see that a .error folder was created and the file was moved to the folder:

Screen Shot 2017-07-01 at 14.25.18

And if we check the mailbox, we will see that we received the failure alert:

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Conclusion

And so we conclude our tour through Apache Camel. With a easy-to-use architecture and dozens of components, it is a highly pluggable and robust option on integration developing. Thank you for following me on this post, until next time.

Big Data – part 4

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Welcome, dear reader, to another post in our series on Big Data. If the reader has not read the previous posts in this series, the links can be found at the end of this post, or in the menus, “Big Data” section. In this post, we will cover a technology that has gained quite popularity in the world of Big Data: The Apache Spark.

Origin

Created in Berkeley University by AMPLab, the goal of Spark is to provide a computer model, according to the official website, up to 100x faster than a conventional mapreduce Hadoop job. But how it hopes to achieve this performance improvement?

Architecture

Such gain is based on this one point in Hadoop mapreduce model. During the execution of a Hadoop job, we have 3 times when the data is “stored” in the processing:

  • At initial processing, before the map step;
  • In the midst of processing, when the data filtered by the map phase is being stored for later stages of sort and reduce;
  • At the end of the processing, when the final result is delivered;

In Hadoop, on these three aforementioned moments, we have an IO disk consumption, because the data is stored on disk, rather than kept in memory, including the intermediate step between the steps of map and reduce. In a production environment of Big Data, it is common to have iterative jobs, running several times on a given body of data, using the result of the previous run as input for the next run. It is precisely in this scenario that the Spark has its biggest gain: keeping the data in memory, the access / write of the data becomes much faster, thus ensuring the announced earnings. From this seemingly simple change, the Spark project, which allows constructing jobs following the BSP model (Bulk Synchronous Parallel), was born keeping as much as possible of the data in memory within a run, thus ensuring a fast and scalable computational model. In the picture below we can see the architecture of the Spark and its subprojects, which we will discuss below

Complementary modules

From the Spark initial project, 4 subprojects were born, that complement his use. All these modules are already part of the default installation of Spark and they are:

Spark SQL: Similar to what is the Hive for Hadoop, Spark SQL brings a language similar to SQL for data query on a Spark installation;

Spark Streaming: Spark streaming allows the build of streaming style applications, where the data can be read / written during the processing, instead of the traditional model, where results from a process can only be delivered at the end of a execution;

MLlib: Equivalent to Apache Mahout, allows the construction of machine learning processes. Machine learning is a field within computer science, where using of statistical and logical rules, programs can “learn” and draw your own conclusions from a mass of data provided as input, simulating a human reasoning;

GraphX: The Spark GraphX allows processing to be built in the Graph format, allowing the resolution of problems through algorithms like Pert, BFS and DFS.

Spark & Hadoop

The reader may be wondering at this point: may I use Spark or Hadoop in my Big Data project? Like everything in the world of technology, this is no simple answer. Several factors may influence this decision, not only technical, but also business, such as the absence, to date, of major players that provide distributions with commercial support, unlike Hadoop that already has commercial distributions of weight as Cloudera and Hortonworks. Due to his complementary nature – Spark integrates with most of the components that make up Hadoop – however, it is possible that Spark could go for a complementary technology over than a competing platform. An example of this is the distribution of Cloudera itself, which provides a Hadoop distribution that also has a Spark distribution. Thus, we have as an increasingly scenario, the combination of the two technologies, rather than using only one of the two. After all, why should we use only 1, if we can enjoy the best that each has to offer us?

Conclusion

And so we come to the conclusion of another chapter of our series. In the next and last post in our series, we will examine some cases of the use of Big Data in the world, in order that we see in practice all the benefits that the Big Data can offer us. Until next time.
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Big Data – part 2

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This is the second part of a series of posts on Big Data.On this post, let’s talk about the two most popular distributed  processing models of Big Data, the mapreduce, and the BSP (Bulk Synchronous Parallel). A process model is a kind of algorithm upon which to develop software.

Mapreduce model

Modelo map reduce

In the figure above, we can see the mapreduce model. This model is widely used in the market today, especially in companies that use Hadoop as her main Big Data technology. The model consists of two well-defined steps, called map and reduce:

  • In the step known as Map, hundreds – or even thousands – of parallel processes, called “threads”, perform a type of task called mapping, where a large mass of data is divided into pieces, and each performs a filtering process within a respective piece, creating a mass of values in the key-value format. At the end of this phase, there is a group phase, where the values for the same key are grouped to form data in the format key: {value1, value2, value3 …. valueN};
  • In the step known as reduce, the data generated by the map phase is again divided into pieces and passed to hundreds or even thousands of processes that perform processing on the received data bits and generate as a key-value output, which is the final output of the processing that is finally grouped into a mass of results;

In a future post, we’ll take a hands-on hadoop, where we can see an example of this processing model in practice with the WordCount.

BSP Model (Bulk Synchronous Parallel)

bsp

Although widespread, the mapreduce model is not without its drawbacks. When we talk about the model being applied in the context of Hadoop, for example, all of the cluster steps and mounting of the final mass with the results is done through files on the file system of Hadoop, HDFS, which generates an overhead in performance when it has to perform the same processing in a iterative manner.Another problem is that for graph algorithms such as DFS, BFS or Pert, MapReduce model is not satisfactory. For these scenarios, there is the BSP.

In the BSP algorithm, we have the concept of supersteps. A superstep consists of a unit of generic programming, which through a global communication component, makes thousands of parallel processing on a mass of data and sends it to a “meeting” called synchronization barrier. At this point, the data are grouped, and passed on to the next superstep chain. In this model, it is simpler to construct iterative workloads, since the same logic can be re-executed in a flow of supersteps. Another advantage pointed out by proponents of this model is that it has a simpler learning curve for developers coming from the procedural world.

Speaking in terms of platforms, Hadoop has the Apache Hama as implementation of this model. The main competitor of Hadoop, Spark, come with this feature natively.

Conclusion

And so we conclude another part of our series on Big Data. To date, these are the main models used by the Big Data platforms. As a technology booming, it is natural that in the future we could have more models emerging and gaining their adoption shares. In the next parts of our series, we’ll talk about the two most known implementations of Big Data to date: Hadoop and Spark. U.

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