Posts Tagged Spring

Spring Music Revisited: Java-Spring-MongoDB Web App with Docker 1.12

Build, test, deploy, and monitor a multi-container, MongoDB-backed, Java Spring web application, using the new Docker 1.12.

Spring Music Infrastructure

Introduction

** This post and associated project code were updated 9/3/2016 to use Tomcat 8.5.4 with OpenJDK 8.**

This post and the post’s example project represent an update to a previous post, Build and Deploy a Java-Spring-MongoDB Application using Docker. This new post incorporates many improvements made in Docker 1.12, including the use of the new Docker Compose v2 YAML format. The post’s project was also updated to use Filebeat with ELK, as opposed to Logspout, which was used previously.

In this post, we will demonstrate how to build, test, deploy, and manage a Java Spring web application, hosted on Apache Tomcat, load-balanced by NGINX, monitored by ELK with Filebeat, and all containerized with Docker.

We will use a sample Java Spring application, Spring Music, available on GitHub from Cloud Foundry. The Spring Music sample record album collection application was originally designed to demonstrate the use of database services on Cloud Foundry, using the Spring Framework. Instead of Cloud Foundry, we will host the Spring Music application locally, using Docker on VirtualBox, and optionally on AWS.

All files necessary to build this project are stored on the docker_v2 branch of the garystafford/spring-music-docker repository on GitHub. The Spring Music source code is stored on the springmusic_v2 branch of the garystafford/spring-music repository, also on GitHub.

Spring Music Application

Application Architecture

The Java Spring Music application stack contains the following technologies: JavaSpring Framework, AngularJS, Bootstrap, jQueryNGINXApache TomcatMongoDB, the ELK Stack, and Filebeat. Testing frameworks include the Spring MVC Test Framework, Mockito, Hamcrest, and JUnit.

A few changes were made to the original Spring Music application to make it work for this demonstration, including:

  • Move from Java 1.7 to 1.8 (including newer Tomcat version)
  • Add unit tests for Continuous Integration demonstration purposes
  • Modify MongoDB configuration class to work with non-local, containerized MongoDB instances
  • Add Gradle warNoStatic task to build WAR without static assets
  • Add Gradle zipStatic task to ZIP up the application’s static assets for deployment to NGINX
  • Add Gradle zipGetVersion task with a versioning scheme for build artifacts
  • Add context.xml file and MANIFEST.MF file to the WAR file
  • Add Log4j RollingFileAppender appender to send log entries to Filebeat
  • Update versions of several dependencies, including Gradle, Spring, and Tomcat

We will use the following technologies to build, publish, deploy, and host the Java Spring Music application: GradlegitGitHubTravis CIOracle VirtualBoxDockerDocker ComposeDocker MachineDocker Hub, and optionally, Amazon Web Services (AWS).

NGINX
To increase performance, the Spring Music web application’s static content will be hosted by NGINX. The application’s WAR file will be hosted by Apache Tomcat 8.5.4. Requests for non-static content will be proxied through NGINX on the front-end, to a set of three load-balanced Tomcat instances on the back-end. To further increase application performance, NGINX will also be configured for browser caching of the static content. In many enterprise environments, the use of a Java EE application server, like Tomcat, is still not uncommon.

Reverse proxying and caching are configured thought NGINX’s default.conf file, in the server configuration section:

The three Tomcat instances will be manually configured for load-balancing using NGINX’s default round-robin load-balancing algorithm. This is configured through the default.conf file, in the upstream configuration section:

Client requests are received through port 80 on the NGINX server. NGINX redirects requests, which are not for non-static assets, to one of the three Tomcat instances on port 8080.

MongoDB
The Spring Music application was designed to work with a number of data stores, including MySQL, Postgres, Oracle, MongoDB, Redis, and H2, an in-memory Java SQL database. Given the choice of both SQL and NoSQL databases, we will select MongoDB.

The Spring Music application, hosted by Tomcat, will store and modify record album data in a single instance of MongoDB. MongoDB will be populated with a collection of album data from a JSON file, when the Spring Music application first creates the MongoDB database instance.

ELK
Lastly, the ELK Stack with Filebeat, will aggregate NGINX, Tomcat, and Java Log4j log entries, providing debugging and analytics to our demonstration. A similar method for aggregating logs, using Logspout instead of Filebeat, can be found in this previous post.

Kibana 4 Web Console

Continuous Integration

In this post’s example, two build artifacts, a WAR file for the application and ZIP file for the static web content, are built automatically by Travis CI, whenever source code changes are pushed to the springmusic_v2 branch of the garystafford/spring-music repository on GitHub.

Travis CI Output

Following a successful build and a small number of unit tests, Travis CI pushes the build artifacts to the build-artifacts branch on the same GitHub project. The build-artifacts branch acts as a pseudo binary repository for the project, much like JFrog’s Artifactory. These artifacts are used later by Docker to build the project’s immutable Docker images and containers.

Build Artifact Repository

Build Notifications
Travis CI pushes build notifications to a Slack channel, which eliminates the need to actively monitor Travis CI.

Travis CI Slack Notifications

Automation Scripting
The .travis.yaml file, custom gradle.build Gradle tasks, and the deploy_travisci.sh script handles the Travis CI automation described, above.

Travis CI .travis.yaml file:

Custom gradle.build tasks:

The deploy.sh file:

You can easily replicate the project’s continuous integration automation using your choice of toolchains. GitHub or BitBucket are good choices for distributed version control. For continuous integration and deployment, I recommend Travis CI, Semaphore, Codeship, or Jenkins. Couple those with a good persistent chat application, such as Glider Labs’ Slack or Atlassian’s HipChat.

Building the Docker Environment

Make sure VirtualBox, Docker, Docker Compose, and Docker Machine, are installed and running. At the time of this post, I have the following versions of software installed on my Mac:

  • Mac OS X 10.11.6
  • VirtualBox 5.0.26
  • Docker 1.12.1
  • Docker Compose 1.8.0
  • Docker Machine 0.8.1

To build the project’s VirtualBox VM, Docker images, and Docker containers, execute the build script, using the following command: sh ./build_project.sh. A build script is useful when working with CI/CD automation tools, such as Jenkins CI or ThoughtWorks go. However, to understand the build process, I suggest first running the individual commands, locally.

Deploying to AWS
By simply changing the Docker Machine driver to AWS EC2 from VirtualBox, and providing your AWS credentials, the springmusic environment may also be built on AWS.

Build Process
Docker Machine provisions a single VirtualBox springmusic VM on which host the project’s containers. VirtualBox provides a quick and easy solution that can be run locally for initial development and testing of the application.

Next, the script creates a Docker data volume and project-specific Docker bridge network.

Next, using the project’s individual Dockerfiles, Docker Compose pulls base Docker images from Docker Hub for NGINX, Tomcat, ELK, and MongoDB. Project-specific immutable Docker images are then built for NGINX, Tomcat, and MongoDB. While constructing the project-specific Docker images for NGINX and Tomcat, the latest Spring Music build artifacts are pulled and installed into the corresponding Docker images.

Docker Compose builds and deploys (6) containers onto the VirtualBox VM: (1) NGINX, (3) Tomcat, (1) MongoDB, and (1) ELK.

The NGINX Dockerfile:

The Tomcat Dockerfile:

Docker Compose v2 YAML
This post was recently updated for Docker 1.12, and to use Docker Compose v2 YAML file format. The post’s docker-compose.yml takes advantage of improvements in Docker 1.12 and Docker Compose v2 YAML. Improvements to the YAML file include eliminating the need to link containers and expose ports, and the addition of named networks and volumes.

The Results

Spring Music Infrastructure

Below are the results of building the project.

Testing the Application

Below are partial results of the curl test, hitting the NGINX endpoint. Note the different IP addresses in the Upstream-Address field between requests. This test proves NGINX’s round-robin load-balancing is working across the three Tomcat application instances: music_app_1, music_app_2, and music_app_3.

Also, note the sharp decrease in the Request-Time between the first three requests and subsequent three requests. The Upstream-Response-Time to the Tomcat instances doesn’t change, yet the total Request-Time is much shorter, due to caching of the application’s static assets by NGINX.

Spring Music Application Links

Assuming the springmusic VM is running at 192.168.99.100, the following links can be used to access various project endpoints. Note the (3) Tomcat instances each map to randomly exposed ports. These ports are not required by NGINX, which maps to port 8080 for each instance. The port is only required if you want access to the Tomcat Web Console. The port, shown below, 32771, is merely used as an example.

* The Tomcat user name is admin and the password is t0mcat53rv3r.

Helpful Links

TODOs

  • Automate the Docker image build and publish processes
  • Automate the Docker container build and deploy processes
  • Automate post-deployment verification testing of project infrastructure
  • Add Docker Swarm multi-host capabilities with overlay networking
  • Update Spring Music with latest CF project revisions
  • Include scripting example to stand-up project on AWS
  • Add Consul and Consul Template for NGINX configuration

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Diving Deeper into ‘Getting Started with Spring Cloud’

Spring_Cloud_Config_2

Explore the integration of Spring Cloud and Spring Cloud Netflix tooling, through a deep dive into Pivotal’s ‘Getting Started with Spring Cloud’ presentation.

Introduction

Keeping current with software development and DevOps trends can often make us feel we are, as the overused analogy describes, drinking from a firehose, often several hoses at once. Recently joining a large client engagement, I found it necessary to supplement my knowledge of cloud-native solutions, built with the support of Spring Cloud and Spring Cloud Netflix technologies. One of my favorite sources of information on these subjects is presentations by people like Josh Long, Dr. Dave Syer, and Cornelia Davis of Pivotal Labs, and Jon Schneider and Taylor Wicksell of Netflix.

One presentation, in particular, Getting Started with Spring Cloud, by Long and Syer, provides an excellent end-to-end technical overview of the latest Spring and Netflix technologies. Josh Long’s fast-paced, eighty-minute presentation, available on YouTube, was given at SpringOne2GX 2015 with co-presenter, Dr. Dave Syer, founder of Spring Cloud, Spring Boot, and Spring Batch.

As the presenters of Getting Started with Spring Cloud admit, the purpose of the presentation was to get people excited about Spring Cloud and Netflix technologies, not to provide a deep dive into each technology. However, I believe the presentation’s Reservation Service example provides an excellent learning opportunity. In the following post, we will examine the technologies, components, code, and configuration presented in Getting Started with Spring Cloud. The goal of the post is to provide a greater understanding of the Spring Cloud and Spring Cloud Netflix technologies.

System Overview

Technologies

The presentation’s example introduces a dizzying array of technologies, which include:

Spring Boot
Stand-alone, production-grade Spring-based applications

Spring Data REST / Spring HATEOAS
Spring-based applications following HATEOAS principles

Spring Cloud Config
Centralized external configuration management, backed by Git

Netflix Eureka
REST-based service discovery and registration for failover and load-balancing

Netflix Ribbon
IPC library with built-in client-side software load-balancers

Netflix Zuul
Dynamic routing, monitoring, resiliency, security, and more

Netflix Hystrix
Latency and fault tolerance for distributed system

Netflix Hystrix Dashboard
Web-based UI for monitoring Hystrix

Spring Cloud Stream
Messaging microservices, backed by Redis

Spring Data Redis
Configuration and access to Redis from a Spring app, using Jedis

Spring Cloud Sleuth
Distributed tracing solution for Spring Cloud, sends traces via Thrift to the Zipkin collector service

Twitter Zipkin
Distributed tracing system, backed by Apache Cassandra

H2
In-memory Java SQL database, embedded and server modes

Docker
Package applications with dependencies into standardized Linux containers

System Components

Several components and component sub-systems comprise the presentation’s overall Reservation Service example. Each component implements a combination of the technologies mentioned above. Below is a high-level architectural diagram of the presentation’s example. It includes a few additional features, added as part of this post.

Overall Reservation System Diagram

Individual system components include:

Spring Cloud Config Server
Stand-alone Spring Boot application provides centralized external configuration to multiple Reservation system components

Spring Cloud Config Git Repo
Git repository containing multiple Reservation system components configuration files, served by Spring Cloud Config Server

H2 Java SQL Database Server (New)
This post substitutes the original example’s use of H2’s embedded version with a TCP Server instance, shared by Reservation Service instances

Reservation Service
Multi load-balanced instances of stand-alone Spring Boot application, backed by H2 database

Reservation Client
Stand-alone Spring Boot application (aka edge service or client-side proxy), forwards client-side load-balanced requests to the Reservation Service, using Eureka, Zuul, and Ribbon

Reservation Data Seeder (New)
Stand-alone Spring Boot application, seeds H2 with initial data, instead of the Reservation Service

Eureka Service
Stand-alone Spring Boot application provides service discovery and registration for failover and load-balancing

Hystrix Dashboard
Stand-alone Spring Boot application provides web-based Hystrix UI for monitoring system performance and Hystrix circuit-breakers

Zipkin
Zipkin Collector, Query, and Web, and Cassandra database, receives, correlates, and displays traces from Spring Cloud Sleuth

Redis
In-memory data structure store, acting as message broker/transport for Spring Cloud Stream

Github

All the code for this post is available on Github, split between two repositories. The first repository, spring-cloud-demo, contains the source code for all of the components listed above, except the Spring Cloud Config Git Repo. To function correctly, the configuration files, consumed by the Spring Cloud Config Server, needs to be placed into a separate repository, spring-cloud-demo-config-repo.

The first repository contains a git submodule , docker-zipkin. If you are not familiar with submodules, you may want to take a moment to read the git documentation. The submodule contains a dockerized version of Twitter’s OpenZipkin, docker-zipkin. To  clone the two repositories, use the following commands. The --recursive option is required to include the docker-zipkin submodule in the project.

Configuration

To try out the post’s Reservation system example, you need to configure at least one property. The Spring Cloud Config Server needs to know the location of the Spring Cloud Config Repository, which is the second GitHub repository you cloned, spring-cloud-demo-config-repo. From the root of the spring-cloud-demo repo, edit the Spring Cloud Config Server application.properties file, located in config-server/src/main/resources/application.properties. Change the following property’s value to your local path to the spring-cloud-demo-config-repo repository:

Startup

There are a few ways you could run the multiple components that make up the post’s example. I suggest running one component per terminal window, in the foreground. In this way, you can monitor the output from the bootstrap and startup processes of the system’s components. Furthermore, you can continue to monitor the system’s components once they are up and running, and receiving traffic. Yes, that is twelve terminal windows…

ReservationServices.png

There is a required startup order for the components. For example, Spring Cloud Config Server needs to start before the other components that rely on it for configuration. Netflix’s Eureka needs to start before the Reservation Client and ReservationServices, so they can register with Eureka on startup. Similarly, Zipkin needs to be started in its Docker container before the Reservation Client and Services, so Spring Cloud Sleuth can start sending traces. Redis needs to be started in its Docker container before Spring Cloud Stream tries to create the message queue. All instances of the Reservation Service needs to start before the Reservation Client. Once every component is started, the Reservation Data Seeder needs to be run once to create initial data in H2. For best results, follow the instructions below. Let each component start completely, before starting the next component.

Docker

Both Zipkin and Redis run in Docker containers. Redis runs in a single container. Zipkin’s four separate components run in four separate containers. Be advised, Zipkin seems to have trouble successfully starting all four of its components on a consistent basis. I believe it’s a race condition caused by Docker Compose simultaneously starting the four Docker containers, ignoring a proper startup order. More than half of the time, I have to stop Zipkin and rerun the docker command to get Zipkin to start without any errors.

If you’ve followed the instructions above, you should see the following Docker images and Docker containers installed and running in your local environment.

Components

Spring Cloud Config Server

At the center of the Reservation system is Spring Cloud Config. Configuration, typically found in the application.properties file, for the Reservation Services, Reservation Client, Reservation Data Seeder, Eureka Service, and Hystix Dashboard, has been externalized with Spring Cloud Config.

Spring_Cloud_Config_2

Each component has a bootstrap.properties file, which modifies its startup behavior during the bootstrap phase of an application context. Each bootstrap.properties file contains the component’s name and the address of the Spring Cloud Config Server. Components retrieve their configuration from the Spring Cloud Config Server at runtime. Below, is an example of the Reservation Client’s bootstrap.properties file.

Spring Cloud Config Git Repo

In the presentation, as in this post, the Spring Cloud Config Server is backed by a locally cloned Git repository, the Spring Cloud Config Git Repo. The Spring Cloud Config Server’s application.properties file contains the address of the Git repository. Each properties file within the Git repository corresponds to a system component. Below, is an example of the reservation-client.properties file, from the Spring Cloud Config Git Repo.

As shown in the original presentation, the configuration files can be viewed using HTTP endpoints of the Spring Cloud Config Server. To view the Reservation Service’s configuration stored in the Spring Cloud Config Git Repo, issue an HTTP GET request to http://localhost:8888/reservation-service/master. The master URI refers to the Git repo branch in which the configuration resides. This will return the configuration, in the response body, as JSON:

SpringCloudConfig

In a real Production environment, the Spring Cloud Config Server would be backed by a highly-available Git Server or GitHub repository.

Reservation Service

The Reservation Service is the core component in the presentation’s example. The Reservation Service is a stand-alone Spring Boot application. By implementing Spring Data REST and Spring HATEOAS, Spring automatically creates REST representations from the Reservation JPA Entity class of the Reservation Service. There is no need to write a Spring Rest Controller and explicitly code each endpoint.

HATEOAS

Spring HATEOAS allows us to interact with the Reservation Entity, using HTTP methods, such as GET and POST. These endpoints, along with all addressable endpoints, are displayed in the terminal output when a Spring Boot application starts. For example, we can use an HTTP GET request to call the reservations/{id} endpoint, such as:

The Reservation Service also makes use of the Spring RepositoryRestResource annotation. By annotating the RepositoryReservation Interface, which extends JpaRepository, we can customize export mapping and relative paths of the Reservation JPA Entity class. As shown below, the RepositoryReservation Interface contains the findByReservationName method signature, annotated with /by-name endpoint, which accepts the rn input parameter.

Calling the findByReservationName method, we can search for a particular reservation by using an HTTP GET request to call the reservations/search/by-name?rn={reservationName} endpoint.

Spring Screengrab 04

Reservation Client

Querying the Reservation Service directly is possible, however, is not the recommended. Instead, the presentation suggests using the Reservation Client as a proxy to the Reservation Service. The presentation offers three examples of using the Reservation Client as a proxy.

The first demonstration of the Reservation Client uses the /message endpoint on the Reservation Client to return a string from the Reservation Service. The message example has been modified to include two new endpoints on the Reservation Client. The first endpoint, /reservations/client-message, returns a message directly from the Reservation Client. The second endpoint, /reservations/service-message, returns a message indirectly from the Reservation Service. To retrieve the message from the Reservation Service, the Reservation Client sends a request to the endpoint Reservation Service’s /message endpoint.

To retrieve both messages, send separate HTTP GET requests to each endpoint:

Spring Screengrab 02

The second demonstration of the Reservation Client uses a Data Transfer Object (DTO). Calling the Reservation Client’s reservations/names endpoint, invokes the getReservationNames method. This method, in turn, calls the Reservation Service’s /reservations endpoint. The response object returned from the Reservation Service, a JSON array of reservation records, is deserialized and mapped to the Reservation Client’s Reservation DTO. Finally, the method returns a collection of strings, representing just the names from the reservations.

To retrieve the collection of reservation names, an HTTP GET request is sent to the /reservations/names endpoint:

Spring Screengrab 05

Spring Cloud Stream

One of the more interesting technologies in the presentation is Spring’s Spring Cloud Stream. The Spring website describes Spring Cloud Stream as a project that allows users  to develop and run messaging microservices using Spring Integration. In other words, it provides native Spring messaging capabilities, backed by a choice of message buses, including Redis, RabbitMQ, and Apache Kafka, to Spring Boot applications.

A detailed explanation of Spring Cloud Stream would take an entire post. The best technical demonstration I have found is the presentation, Message Driven Microservices in the Cloud, by speakers Dr. David Syer and Dr. Mark Pollack, given in January 2016, also at SpringOne2GX 2015.

Diagram_03

In the presentation, a new reservation is submitted via an HTTP POST to the acceptNewReservations method of the Reservation Client. The method, in turn, builds (aka produces) a message, containing the new reservation, and publishes that message to the queue.reservation queue.

The queue.reservation queue is located in Redis, which is running inside a Docker container. To view the messages being published to the queue in real-time, use the redis-cli, with the monitor command, from within the Redis Docker container. Below is an example of tests messages pushed (LPUSH) to the reservations queue from the Reservation Client.

The published messages are consumed by subscribers to the reservation queue. In this example, the consumer is the Reservation Service. The Reservation Service’s acceptNewReservation method processes the message and saves the new reservation to the H2 database. In Spring Cloud Stream terms, the Reservation Client is the Sink.

Netflix Eureka

Netflix’s Eureka, in combination with Netflix’s Zuul and Ribbon, provide the ability to scale the Reservation Service horizontally, and to load balance those instances. By using the @EnableEurekaClient annotation on the Reservation Client and Reservation Services, each instance will automatically register with Eureka on startup, as shown in the Eureka Web UI, below.

Diagram9

The names of the registered instances are in three parts: the address of the host on which the instance is running, followed by the value of the spring.application.name property of the instance’s bootstrap.properties file, and finally, the port number the instance is running on. Eureka displays each instance’s status, along with additional AWS information, if you are running on AWS, as Netflix does.

Diagram_07

According to Spring in their informative post, Spring Cloud, service discovery is one of the key tenets of a microservice based architecture. Trying to hand-configure each client, or to rely on convention over configuration, can be difficult to do and is brittle. Eureka is the Netflix Service Discovery Server and Client. A client (Spring Boot application), registers with Eureka, providing metadata about itself. Eureka then receives heartbeat messages from each instance. If the heartbeat fails over a configurable timetable, the instance is normally removed from the registry.

The Reservation Client application is also annotated with @EnableZuulProxy. Adding this annotation pulls in Spring Cloud’s embedded Zuul proxy. Again, according to Spring, the proxy is used by front-end applications to proxy calls to one or more back-end services, avoiding the need to manage CORS and authentication concerns independently for all the backends. In the presentation and this post, the front end is the Reservation Client and the back end is the Reservation Service.

In the code snippet below from the ReservationApiGatewayRestController, note the URL of the endpoint requested in the getReservationNames method. Instead of directly calling http://localhost:8000/reservations, the method calls http://reservation-service/reservations. The reservation-service segment of the URL is the registered name of the service in Eureka and contained in the Reservation Service’s bootstrap.properties file.

In the following abridged output from the Reservation Client, you can clearly see the interaction of Zuul, Ribbon, Eureka, and Spring Cloud Config. Note the Client application has successfully registering itself with Eureka, along with the Reservation Client’s status. Also, note Zuul mapping the Reservation Service’s URL path.

Load Balancing

One shortcoming of the original presentation was true load balancing. With only a single instance of the Reservation Service in the original presentation, there is nothing to load balance; it’s more of a reverse proxy example. To demonstrate load balancing, we need to spin up additional instances of the Reservation Service. Following the post’s component start-up instructions, we should have three instances of the Reservation Service running, on ports 8000, 8001, and 8002, each in separate terminal windows.

ReservationServices.png

To confirm the three instances of the Reservation Service were successfully registered with Eureka, review the output from the Eureka Server terminal window. The output should show three instances of the Reservation Service registering on startup, in addition to the Reservation Client.

Viewing Eureka’s web console, we should observe three members in the pool of Reservation Services.

Diagram9b

Lastly, looking at the terminal output of the Reservation Client, we should see three instances of the Reservation Service being returned by Ribbon (aka the DynamicServerListLoadBalancer).

Requesting

Requesting http://localhost:8050/reservations/names, Ribbon forwards the request to one of the three Reservation Service instances registered with Eureka. By default, Ribbon uses a round-robin load-balancing strategy to select an instance from the pool of available Reservation Services.

H2 Server

The original presentation’s Reservation Service used an embedded instance of H2. To scale out the Reservation Service, we need a common database for multiple instances to share. Otherwise, queries would return different results, specific to the particular instance of Reservation Service chosen by the load-balancer. To solve this, the original presentation’s embedded version of H2 has been replaced with the TCP Server client/server version of H2.

Reservation Service Instances

Thanks to more Spring magic, the only change we need to make to the original presentation’s code is a few additional properties added to the Reservation Service’s reservation-service.properties file. This changes H2 from the embedded version to the TCP Server version.

Reservation Data Seeder

In the original presentation, the Reservation Service created several sample reservation records in its embedded H2 database on startup. Since we now have multiple instances of the Reservation Service running, the sample data creation task has been moved from the Reservation Service to the new Reservation Data Seeder. The Reservation Service only now validates the H2 database schema on startup. The Reservation Data Seeder now updates the schema based on its entities. This also means the seed data will be persisted across restarts of the Reservation Service, unlike in the original configuration.

Running the Reservation Data Seeder once will create several reservation records into the H2 database. To confirm the H2 Server is running and the initial reservation records were created by the Reservation Data Seeder, point your web browser to the H2 login page at http://192.168.99.1:6889. and log in using the credentials in the reservation-service.properties file.

H2_grab1

The H2 Console should contain the RESERVATION table, which holds the reservation sample records.

H2_grab2

Spring Cloud Sleuth and Twitter’s Zipkin

According to the project description, “Spring Cloud Sleuth implements a distributed tracing solution for Spring Cloud. All your interactions with external systems should be instrumented automatically. You can capture data simply in logs, or by sending it to a remote collector service.” In our case, that remote collector service is Zipkin.

Zipkin describes itself as, “a distributed tracing system. It helps gather timing data needed to troubleshoot latency problems in microservice architectures. It manages both the collection and lookup of this data through a Collector and a Query service.” Zipkin provides critical insights into how microservices perform in a distributed system.

Zipkin_Diagram

In the presentation, as in this post, the Reservation Client’s main ReservationClientApplication class contains the alwaysSampler bean, which returns a new instance of org.springframework.cloud.sleuth.sampler.AlwaysSampler. As long as Spring Cloud Sleuth is on the classpath and you have added alwaysSampler bean, the Reservation Client will automatically generate trace data.

Sending a request to the Reservation Client’s service/message endpoint (http://localhost:8050/reservations/service-message,), will generate a trace, composed of spans. in this case, the spans are individual segments of the HTTP request/response lifecycle. Traces are sent by Sleuth to Zipkin, to be collected. According to Spring, if spring-cloud-sleuth-zipkin is available, then the application will generate and collect Zipkin-compatible traces using Brave). By default, it sends them via Apache Thrift to a Zipkin collector service on port 9410.

Zipkin’s web-browser interface, running on port 8080, allows us to view traces and drill down into individual spans.

Zipkin_UI

Zipkin contains fine-grain details about each span within a trace, as shown below.

Zipkin_UI_Popup

Correlation IDs

Note the x-trace-id and x-span-id in the request header, shown below. Sleuth injects the trace and span IDs to the SLF4J MDC (Simple Logging Facade for Java – Mapped Diagnostic Context). According to Spring, IDs provides the ability to extract all the logs from a given trace or span in a log aggregator. The use of correlation IDs and log aggregation are essential for monitoring and supporting a microservice architecture.

Zipkin_UI_Popup2

Hystix and Hystrix Dashboard

The last major technology highlighted in the presentation is Netflix’s Hystrix. According to Netflix, “Hystrix is a latency and fault tolerance library designed to isolate points of access to remote systems, services, and 3rd party libraries, stop cascading failure and enable resilience in complex distributed systems where failure is inevitable.” Hystrix is essential, it protects applications from cascading dependency failures, an issue common to complex distributed architectures, with multiple dependency chains. According to Netflix, Hystrix uses multiple isolation techniques, such as bulkhead, swimlane, and circuit breaker patterns, to limit the impact of any one dependency on the entire system.

The presentation demonstrates one of the simpler capabilities of Hystrix, fallback. The getReservationNames method is decorated with the @HystrixCommand annotation. This annotation contains the fallbackMethod. According to Netflix, a graceful degradation of a method is provided by adding a fallback method. Hystrix will call to obtain a default value or values, in case the main command fails. In the presentation’s example, the Reservation Service, a direct dependency of the Reservation Client, has failed. The Reservation Service failure causes the failure of the Reservation Client.

In the presentation’s example, the Reservation Service, a direct dependency of the Reservation Client, has failed. The Reservation Service failure causes the failure of the Reservation Client’s getReservationNames method to return a collection of reservation names. Hystrix redirects the application to the getReservationNameFallback method. Instead of returning a collection of reservation names, the getReservationNameFallback returns an empty collection, as opposed to an error message to the client.

A more relevant example  involves Netflix movie recommendation service. In the event a failure of the recommendation service’s method to return a collection of personalized list of movie recommendations to a customer, Hystrix fallbacks to a method that returns a generic list of the most popular movies to the customer. Netflix has determined that, in the event of a failure of their recommendation service, falling back to a generic list of movies is better than returning no movies at all.

The Hystrix Dashboard is a tool, available with Hystrix, to visualize the current state of Hystrix instrumented methods. Although visually simplistic, the dashboard effectively presents the health of calls to external systems, which are wrapped in a HystrixCommand or HystrixObservableCommand.

Hystrix_Stream_Diagram

The Hystrix dashboard is a visual representation of the Hystrix Stream. This stream is a live feed of data sent by the Hystrix instrumented application, in this case, the Reservation Client. For a single Hystrix application, such as the Reservation Client, the feed requested from the application’s hystrix.stream endpoint is http://localhost:8050/hystrix.stream. The dashboard consumes the stream resource’s response and visualizes it in the browser using JavaScript, jQuery, and d3.

In the post, as in the presentation, hitting the Reservation Client with a volume of requests, we observe normal activity in Hystrix Dashboard. All three instances of the Reservation Service are running and returning the collection of reservations from H2, to the Reservation Client.

Hystrix_success

If all three instances of the Reservation Service fail or the maximum latency is exceeded, the Reservation Client falls back to returning an empty collection in the response body. In the example below, 15 requests, representing 100% of the current traffic, to the getReservationNames method failed and subsequently fell back to return an empty collection. Hystrix succeeded in helping the application gracefully fall back to an alternate response.

Hystrix_failures

Conclusion

It’s easy to see how Spring Cloud and Netflix’s technologies are easily combined to create a performant, horizontally scalable, reliable system. With the addition of a few missing components, such metrics monitoring and log aggregation, this example could easily be scaled up to support a production-grade microservices-based, enterprise software platform.

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Build and Deploy a Java-Spring-MongoDB Application using Docker

Build a multi-container, MongoDB-backed, Java Spring web application, and deploy to a test environment using Docker.

Spring Music Diagram

Introduction
Application Architecture
Spring Music Environment
Building the Environment
Spring Music Application Links
Helpful Links

Introduction

In this post, we will demonstrate how to build, deploy, and host a multi-tier Java application using Docker. For the demonstration, we will use a sample Java Spring application, available on GitHub from Cloud Foundry. Cloud Foundry’s Spring Music sample record album collection application was originally designed to demonstrate the use of database services on Cloud Foundry and Spring Framework. Instead of Cloud Foundry, we will host the Spring Music application using Docker with VirtualBox and optionally, AWS.

All files required to build this post’s demonstration are located in the master branch of this GitHub repository. Instructions to clone the repository are below. The Java Spring Music application’s source code, used in this post’s demonstration, is located in the master branch of this GitHub repository.

Spring Music

A few changes were necessary to the original Spring Music application to make it work for the this demonstration. At a high-level, the changes included:

  • Modify MongoDB configuration class to work with non-local MongoDB instances
  • Add Gradle warNoStatic task to build WAR file without the static assets, which will be host separately in NGINX
  • Create new Gradle task, zipStatic, to ZIP up the application’s static assets for deployment to NGINX
  • Add versioning scheme for build artifacts
  • Add context.xml file and MANIFEST.MF file to the WAR file
  • Add log4j syslog appender to send log entries to Logstash
  • Update versions of several dependencies, including Gradle to 2.6

Application Architecture

The Java Spring Music application stack contains the following technologies:

The Spring Music web application’s static content will be hosted by NGINX for increased performance. The application’s WAR file will be hosted by Apache Tomcat. Requests for non-static content will be proxied through a single instance of NGINX on the front-end, to one of two load-balanced Tomcat instances on the back-end. NGINX will also be configured to allow for browser caching of the static content, to further increase application performance. Reverse proxying and caching are configured thought NGINX’s default.conf file’s server configuration section:

server {
  listen        80;
  server_name   localhost;

  location ~* \/assets\/(css|images|js|template)\/* {
    root          /usr/share/nginx/;
    expires       max;
    add_header    Pragma public;
    add_header    Cache-Control "public, must-revalidate, proxy-revalidate";
    add_header    Vary Accept-Encoding;
    access_log    off;
  }

The two Tomcat instances will be configured on NGINX, in a load-balancing pool, using NGINX’s default round-robin load-balancing algorithm. This is configured through NGINX’s default.conf file’s upstream configuration section:

upstream backend {
  server app01:8080;
  server app02:8080;
}

The Spring Music application can be run with MySQL, Postgres, Oracle, MongoDB, Redis, or H2, an in-memory Java SQL database. Given the choice of both SQL and NoSQL databases available for use with the Spring Music application, we will select MongoDB.

The Spring Music application, hosted by Tomcat, will store and modify record album data in a single instance of MongoDB. MongoDB will be populated with a collection of album data when the Spring Music application first creates the MongoDB database instance.

Lastly, the ELK Stack with Logspout, will aggregate both Docker and Java Log4j log entries, providing debugging and analytics to our demonstration. I’ve used the same method for Docker and Java Log4j log entries, as detailed in this previous post.

Kibana Spring Music

Spring Music Environment

To build, deploy, and host the Java Spring Music application, we will use the following technologies:

All files necessary to build this project are stored in the garystafford/spring-music-docker repository on GitHub. The Spring Music source code and build artifacts are stored in a seperate garystafford/spring-music repository, also on GitHub.

Build artifacts are automatically built by Travis CI when changes are checked into the garystafford/spring-music repository on GitHub. Travis CI then overwrites the build artifacts back to a build artifact branch of that same project. The build artifact branch acts as a pseudo binary repository for the project. The .travis.yaml file, gradle.build file, and deploy.sh script handles these functions.

.travis.yaml file:

language: java
jdk: oraclejdk7
before_install:
- chmod +x gradlew
before_deploy:
- chmod ugo+x deploy.sh
script:
- bash ./gradlew clean warNoStatic warCopy zipGetVersion zipStatic
- bash ./deploy.sh
env:
  global:
  - GH_REF: github.com/garystafford/spring-music.git
  - secure: <secure hash here>

gradle.build file snippet:

// new Gradle build tasks

task warNoStatic(type: War) {
  // omit the version from the war file name
  version = ''
  exclude '**/assets/**'
  manifest {
    attributes 
      'Manifest-Version': '1.0', 
      'Created-By': currentJvm, 
      'Gradle-Version': GradleVersion.current().getVersion(), 
      'Implementation-Title': archivesBaseName + '.war', 
      'Implementation-Version': artifact_version, 
      'Implementation-Vendor': 'Gary A. Stafford'
  }
}

task warCopy(type: Copy) {
  from 'build/libs'
  into 'build/distributions'
  include '**/*.war'
}

task zipGetVersion (type: Task) {
  ext.versionfile = 
    new File("${projectDir}/src/main/webapp/assets/buildinfo.properties")
  versionfile.text = 'build.version=' + artifact_version
}

task zipStatic(type: Zip) {
  from 'src/main/webapp/assets'
  appendix = 'static'
  version = ''
}

deploy.sh file:

#!/bin/bash

# reference: https://gist.github.com/domenic/ec8b0fc8ab45f39403dd

set -e # exit with nonzero exit code if anything fails

# go to the distributions directory and create a *new* Git repo
cd build/distributions && git init

# inside this git repo we'll pretend to be a new user
git config user.name "travis-ci"
git config user.email "auto-deploy@travis-ci.com"

# The first and only commit to this new Git repo contains all the
# files present with the commit message.
git add .
git commit -m "Deploy Travis CI build #${TRAVIS_BUILD_NUMBER} artifacts to GitHub"

# Force push from the current repo's master branch to the remote
# repo's build-artifacts branch. (All previous history on the gh-pages branch
# will be lost, since we are overwriting it.) We redirect any output to
# /dev/null to hide any sensitive credential data that might otherwise be exposed. Environment variables pre-configured on Travis CI.
git push --force --quiet "https://${GH_TOKEN}@${GH_REF}" master:build-artifacts > /dev/null 2>&1

Base Docker images, such as NGINX, Tomcat, and MongoDB, used to build the project’s images and subsequently the containers, are all pulled from Docker Hub.

This NGINX and Tomcat Dockerfiles pull the latest build artifacts down to build the project-specific versions of the NGINX and Tomcat Docker images used for this project. For example, the NGINX Dockerfile looks like:

# NGINX image with build artifact

FROM nginx:latest

MAINTAINER Gary A. Stafford <garystafford@rochester.rr.com>

ENV REFRESHED_AT 2015-09-20
ENV GITHUB_REPO https://github.com/garystafford/spring-music/raw/build-artifacts
ENV STATIC_FILE spring-music-static.zip

RUN apt-get update -y && 
  apt-get install wget unzip nano -y && 
  wget -O /tmp/${STATIC_FILE} ${GITHUB_REPO}/${STATIC_FILE} && 
  unzip /tmp/${STATIC_FILE} -d /usr/share/nginx/assets/

COPY default.conf /etc/nginx/conf.d/default.conf

Docker Machine builds a single VirtualBox VM. After building the VM, Docker Compose then builds and deploys (1) NGINX container, (2) load-balanced Tomcat containers, (1) MongoDB container, (1) ELK container, and (1) Logspout container, onto the VM. Docker Machine’s VirtualBox driver provides a basic solution that can be run locally for testing and development. The docker-compose.yml for the project is as follows:

proxy:
  build: nginx/
  ports: "80:80"
  links:
   - app01
   - app02
  hostname: "proxy"

app01:
  build: tomcat/
  expose: "8080"
  ports: "8180:8080"
  links:
   - nosqldb
   - elk
  hostname: "app01"

app02:
  build: tomcat/
  expose: "8080"
  ports: "8280:8080"
  links:
   - nosqldb
   - elk
  hostname: "app01"

nosqldb:
  build: mongo/
  hostname: "nosqldb"
  volumes: "/opt/mongodb:/data/db"

elk:
  build: elk/
  ports:
   - "8081:80"
   - "8082:9200"
  expose: "5000/upd"

logspout:
  build: logspout/
  volumes: "/var/run/docker.sock:/tmp/docker.sock"
  links: elk
  ports: "8083:80"
  environment: ROUTE_URIS=logstash://elk:5000

Building the Environment

Before continuing, ensure you have nothing running on ports 80, 8080, 8081, 8082, and 8083. Also, make sure VirtualBox, Docker, Docker Compose, Docker Machine, VirtualBox, cURL, and git are all pre-installed and running.

docker --version && 
docker-compose --version && 
docker-machine --version && 
echo "VirtualBox $(vboxmanage --version)" && 
curl --version && git --version

All of the below commands may be executed with the following single command (sh ./build_project.sh). This is useful for working with Jenkins CI, ThoughtWorks go, or similar CI tools. However, I suggest building the project step-by-step, as shown below, to better understand the process.

# clone project
git clone -b master 
  --single-branch https://github.com/garystafford/spring-music-docker.git && 
cd spring-music-docker

# build VM
docker-machine create --driver virtualbox springmusic --debug

# create directory to store mongo data on host
docker-machine ssh springmusic mkdir /opt/mongodb

# set new environment
docker-machine env springmusic && 
eval "$(docker-machine env springmusic)"

# build images and containers
docker-compose -f docker-compose.yml -p music up -d

# wait for container apps to start
sleep 15

# run quick test of project
for i in {1..10}
do
  curl -I --url $(docker-machine ip springmusic)
done

By simply changing the driver to AWS EC2 and providing your AWS credentials, the same environment can be built on AWS within a single EC2 instance. The ‘springmusic’ environment has been fully tested both locally with VirtualBox, as well as on AWS.

Results
Resulting Docker images and containers:

gstafford@gstafford-X555LA:$ docker images
REPOSITORY            TAG                 IMAGE ID            CREATED              VIRTUAL SIZE
music_proxy           latest              46af4c1ffee0        52 seconds ago       144.5 MB
music_logspout        latest              fe64597ab0c4        About a minute ago   24.36 MB
music_app02           latest              d935211139f6        2 minutes ago        370.1 MB
music_app01           latest              d935211139f6        2 minutes ago        370.1 MB
music_elk             latest              b03731595114        2 minutes ago        1.05 GB
gliderlabs/logspout   master              40a52d6ca462        14 hours ago         14.75 MB
willdurand/elk        latest              04cd7334eb5d        9 days ago           1.05 GB
tomcat                latest              6fe1972e6b08        10 days ago          347.7 MB
mongo                 latest              5c9464760d54        10 days ago          260.8 MB
nginx                 latest              cd3cf76a61ee        10 days ago          132.9 MB

gstafford@gstafford-X555LA:$ docker ps -a
CONTAINER ID        IMAGE               COMMAND                  CREATED              STATUS              PORTS                                                  NAMES
facb6eddfb96        music_proxy         "nginx -g 'daemon off"   46 seconds ago       Up 46 seconds       0.0.0.0:80->80/tcp, 443/tcp                            music_proxy_1
abf9bb0821e8        music_app01         "catalina.sh run"        About a minute ago   Up About a minute   0.0.0.0:8180->8080/tcp                                 music_app01_1
e4c43ed84bed        music_logspout      "/bin/logspout"          About a minute ago   Up About a minute   8000/tcp, 0.0.0.0:8083->80/tcp                         music_logspout_1
eca9a3cec52f        music_app02         "catalina.sh run"        2 minutes ago        Up 2 minutes        0.0.0.0:8280->8080/tcp                                 music_app02_1
b7a7fd54575f        mongo:latest        "/entrypoint.sh mongo"   2 minutes ago        Up 2 minutes        27017/tcp                                              music_nosqldb_1
cbfe43800f3e        music_elk           "/usr/bin/supervisord"   2 minutes ago        Up 2 minutes        5000/0, 0.0.0.0:8081->80/tcp, 0.0.0.0:8082->9200/tcp   music_elk_1

Partial result of the curl test, calling NGINX. Note the two different upstream addresses for Tomcat. Also, note the sharp decrease in request times, due to caching.

HTTP/1.1 200 OK
Server: nginx/1.9.4
Date: Mon, 07 Sep 2015 17:56:11 GMT
Content-Type: text/html;charset=ISO-8859-1
Content-Length: 2090
Connection: keep-alive
Accept-Ranges: bytes
ETag: W/"2090-1441648256000"
Last-Modified: Mon, 07 Sep 2015 17:50:56 GMT
Content-Language: en
Request-Time: 0.521
Upstream-Address: 172.17.0.121:8080
Upstream-Response-Time: 1441648570.774

HTTP/1.1 200 OK
Server: nginx/1.9.4
Date: Mon, 07 Sep 2015 17:56:11 GMT
Content-Type: text/html;charset=ISO-8859-1
Content-Length: 2090
Connection: keep-alive
Accept-Ranges: bytes
ETag: W/"2090-1441648256000"
Last-Modified: Mon, 07 Sep 2015 17:50:56 GMT
Content-Language: en
Request-Time: 0.326
Upstream-Address: 172.17.0.123:8080
Upstream-Response-Time: 1441648571.506

HTTP/1.1 200 OK
Server: nginx/1.9.4
Date: Mon, 07 Sep 2015 17:56:12 GMT
Content-Type: text/html;charset=ISO-8859-1
Content-Length: 2090
Connection: keep-alive
Accept-Ranges: bytes
ETag: W/"2090-1441648256000"
Last-Modified: Mon, 07 Sep 2015 17:50:56 GMT
Content-Language: en
Request-Time: 0.006
Upstream-Address: 172.17.0.121:8080
Upstream-Response-Time: 1441648572.050

HTTP/1.1 200 OK
Server: nginx/1.9.4
Date: Mon, 07 Sep 2015 17:56:12 GMT
Content-Type: text/html;charset=ISO-8859-1
Content-Length: 2090
Connection: keep-alive
Accept-Ranges: bytes
ETag: W/"2090-1441648256000"
Last-Modified: Mon, 07 Sep 2015 17:50:56 GMT
Content-Language: en
Request-Time: 0.006
Upstream-Address: 172.17.0.123:8080
Upstream-Response-Time: 1441648572.266

Assuming springmusic VM is running at 192.168.99.100:

* The Tomcat user name is admin and the password is t0mcat53rv3r.

Helpful Links

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Spring Integration with Eclipse Using Maven

Integrate the Spring Framework into your next Eclipse-based project using Apache Maven. Learn how to install, configure, and integrate these three leading Java development tools. All source code for this post is available on GitHub.

 

Introduction

Although there is a growing adoption of Java EE 6 and CDI in recent years, Spring is still a well-entrenched, open-source framework for professional Java development. According to GoPivotal’s website, “The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications. Spring focuses on the ‘plumbing’ of enterprise applications so that teams can focus on application-level business logic, without unnecessary ties to specific deployment environments.”

Similar to Spring in terms of wide-spread adoption, Eclipse is leading Java IDE, competing with Oracle’s NetBeans and JetBrain’s IntelliJ. The use of Spring within Eclipse is very common. In the following post, I will demonstrate the ease of integrating Spring with Eclipse, using Maven.

Maven is a marketed as a project management tool, centralizing a project’s build, reporting and documentation. Conveniently, Maven is tightly integrated with Eclipse. We will use Maven for one of its best known features, dependency management. Maven will take care of downloading and managing the required Spring artifacts into our Eclipse-based project.

Note there are alternatives to integrating Spring into Eclipse, using Maven. You can download and add the Spring artifacts yourself, or go full-bore with GoPivotal’s Spring Tool Suite (STS). According to their website, STS is an Eclipse-based development environment, customized for developing Spring applications.

The steps covered in this post are as follows:

  1. Download and install Maven
  2. Download and install the Eclipse IDE
  3. Linking the installed version of Maven to Eclipse
  4. Creating a new sample Maven Project
  5. Adding Spring dependencies to the project
  6. Demonstrate a simple example of Spring Beans and ApplicationContext
  7. Modify the project to allow execution from an external command prompt

Installing Maven

Installing Maven is simple process, requiring minimal configuration:

  1. Download the latest version of Maven from the Apache Maven Project website. At the time of this post, Maven is at version 3.1.1.
  2. Assuming you are Windows, unzip the ‘apache-maven-3.1.1’ folder and place in your ‘Program Files’ directory.
  3. Add the path to Maven’s bin directory to your system’s ‘PATH’ Environmental Variable.
Adding Maven bin Directory to PATH Environmental Variable

Adding Maven bin Directory to PATH Environmental Variable

We can test our Maven installation by opening a new Command Prompt and issuing the ‘mvn -version’ command. The command should display the installed version of Maven, Maven home directory, and other required variables, like your machine’s current version of Java and its location. To learn other Maven commands, try ‘mvn -help’.

Checking Maven is Installed Correctly

Checking Maven is Installed Correctly

Installing Eclipse IDE

Installing Eclipse is even easier:

  1. Download the latest version of Eclipse from The Eclipse Foundation website. There are several versions of Eclipse available. I chose ‘Eclipse IDE for Java EE Developers’, currently Kepler Service Release 1.
  2. Similar to Maven, unzip the ‘eclipse’ folder and place in your ‘Program Files’ directory.
  3. For ease of access, I recommend pinning the main eclispe.exe file to your Start Menu.
Downloading Eclipse IDE for Java EE Developers

Downloading Eclipse IDE for Java EE Developers

Linking Maven to Eclipse

The latest version of Eclipse comes pre-loaded with the ‘M2E – Maven Integration for Eclipse’ plug-in. There is no additional software installs required to use Maven from within Eclipse. Eclipse also includes an embedded runtime version of Maven (currently 3.04). According to the Eclipse website wiki, the M2E plug-in uses the embedded runtime version of Maven when running Maven builder, importing projects and updating project configuration.

Latest Version of Eclipse Kepler SR1 with M2E Installed

Latest Version of Eclipse Kepler SR1 with M2E Installed

Although Eclipse contains an embedded version of Maven, we can configure M2E to use our own external Maven installation when launching Maven using Run as… -> M2 Maven actions. To configure Maven to use the version of Maven we just installed:

  1. Go to Windows -> Preferences -> Maven -> Installations window. Note the embedded version of Maven is the only one listed and active.
  2. Click Add… and select the Maven folder we installed in your Program Files directory. Click OK.
  3. Check the box for new installation we just added instead of the embedded version. Click OK.
Adding Installed Version of Maven to Eclipse

Adding Installed Version of Maven to Eclipse

Adding Installed Version of Maven to Eclipse

Adding Installed Version of Maven to Eclipse

Adding Installed Version of Maven to Eclipse

Adding Installed Version of Maven to Eclipse

Sample Maven Project

To show how to integrate Spring into a project using Maven, we will create a Maven Project in Eclipse using the Maven Quickstart Archetype template. The basic project will show the use of Spring Beans and an ApplicationContext IoC container. On a scale of 1 to 10, with 10 being the most complex Spring example, this project is barely a 1! However, it will demonstrate that Spring is working in Eclipse, with minimal effort thanks to Maven.

To create the project:

  1. File -> New Project -> Other…
  2. Search on ‘maven’ in the Wizards text box and select ‘Maven Project’.
  3. Select the Maven Quickstart Archetype.
  4. Specify the Archetype parameters.
Creating a New Maven Project - Using Wizard

Creating a New Maven Project – Using Wizard

Creating a New Maven Project - Project Location

Creating a New Maven Project – Project Location

Creating a New Maven Project - Choosing Archetype

Creating a New Maven Project – Choosing Archetype

Creating a New Maven Project - Archetype Parameters

Creating a New Maven Project – Archetype Parameters

Spring Dependencies

Once the Maven Quickstart project is created, we will add the required Spring dependencies using Maven:

  1. Open the Maven Project Object Model (POM) file and select the Dependencies tab.
  2. Use the The Central Repository website to find the Dependency Information for spring-core and Spring-context artifacts (jar files).
  3. Add… both Spring Dependencies to the pom.xml file.
  4. Right-click on the project and click Maven -> Update Project…
Adding Spring Dependencies to pom.xml - Dependencies Tab

Adding Spring Dependencies to pom.xml – Dependencies Tab

Adding Spring Dependencies to pom.xml - Artifact Details for spring-core

Adding Spring Dependencies to pom.xml – Artifact Details for spring-core

Adding Spring Dependencies to pom.xml - Adding spring-context

Adding Spring Dependencies to pom.xml – Adding spring-context

Adding Spring Dependencies to pom.xml - Spring Dependencies Added

Adding Spring Dependencies to pom.xml – Spring Dependencies Added

Adding Spring Dependencies to pom.xml - Dependencies Added to Project

Adding Spring Dependencies to pom.xml – Dependencies Added to Project

We now have a Maven-managed Eclipse project with our Spring dependencies included. Note the root of the file paths to the jar files in the Maven Dependencies project folder is the location of our Maven Repository. This is where all the dependent artifacts (jar files) are stored. In my case, the root is ‘C:\Users\{user}\.m2\repository’. The repository location is stored in Eclipse’s Maven User Setting’s Preferences (see below).

Project Object Model File (pom.xml):

Location of Local Maven Repository

Local Maven Repository Location

Sample Code

Next add the supplied Code to the project. We will add two new java classes and a Spring configuration file. We will replace the contents of main App class with our sample code. Steps are as follows:

  1. Add the supplied Vehicle.java and MaintainVehicle.java class files to the project, in the same classpath as the App.java class.
  2. Add the supplied Beans.xml Spring configuration file to the project at the ‘src/main/java’ folder.
  3. Open the App.java class file and replace the contents with the supplied App.java class file.

The sample Spring application is based on vehicles. There are three Spring Beans defined in the xml-based Spring configuration file, representing three different vehicles. The main App class uses an ApplicationContext IoC Container to instantiate three Vehicle POJOs from the Spring Beans defined in the Beans.xml Spring configuration. The main class then instantiates an instance of the MaintainVehicle class, passes in the Vehicle objects and calls MaintainVehicle’s two methods.

Location of New Files in Project Explorer

Location of New Files in Project Explorer

Spring Configuration File (Beans.xml):

Main Method Class (App.java)

Running the Application

If successful, the application will output a series of messages to the Console. The first few messages in red are Spring-related messages, signifying Spring is working. The next messages in black are output by the application. The messages show that the three Spring Beans are successfully instantiated and passed to the MaintainVehicle object, where it’s methods were called. If the application would only buy me that Silver Mercedes!

Successful Console Output of Java Application

Successful Console Output of Java Application

Running the Application from a Command Prompt

All the source code for this project is available on GitHub. Note the pom.xml contains a some extra configuration information not shown above. The extra configuration information is not necessary for running the application from within Eclipse. However, if you want to run the application from an external Command Prompt, you will need the added configuration. This extra configuration ensures that the project is correctly packaged into a jar file, with all the necessary dependencies to run. Extra configuration includes an additional logging dependency, a resource reference to the Spring configuration file, one additional property, and three maven plug-in references for compiling and packaging the jar.

To run the java application from an external Command Prompt:

  1. Open a new Command Prompt
  2. Change current directory to the project’s root directory (local GitHub repository in my case)
  3. Run a ‘mvn compile’ command
  4. Run a ‘mvn package’ command (downloads dependencies and creates jar)
  5. Change the current directory to the project’s target sub-directory
  6. Run a ‘dir’ command. You should see the project’s jar file
  7. Run a ‘java -jar {name-of-jar-file.jar}’ command.

You should see the same messages output in Eclipse, earlier.

Running Application from External Command Prompt

Running Application from External Command Prompt

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