Need help with microservices?

These days I’m focussed on microservices.

I provide consulting services and training classes that help you get started with microservices.

I am the founder of Eventuate, Inc, a startup that is building a microservices application platform.

Contact me if you want to learn more or need help with developing microservice-based applications.

Here is some of my latest articles and presentations:

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Need to install MongoDB, RabbitMQ, or MySQL? Use Docker to simplify dev and test

Almost every interesting application uses at least one infrastructure service such as a database or a message broker. For example, if you tried to build and/or run the Spring Boot-based user registration service you would have discovered that it needs both MongoDB and RabbitMQ.

One option, of course, is to install both of those services on your machine. Unfortunately, installing a service is not always easy. Also, different projects might need different incompatible versions. Moreover, I’m not a fan of cluttering my machine with random services. Fortunately, there is a great way to solve this problem: Docker. You install Docker and use it to run the services that you need as containers.

Docker only directly runs on Linux so if you are using Mac OSX or Windows the first step is to install Boot2Docker (Mac OSX, Window). Boot2Docker installs the Docker command line locally but runs the Docker daemon in a Virtual Box VM.  As a result, the user experience is very similar to running Docker directly on Linux.

The following command runs a VM containing the Docker daemon:

$ boot2docker up

You then need to set some environment variables in order for the Docker command line to know the host and port of the Docker daemon.

$ boot2docker shellinit
Writing /Users/c/.boot2docker/certs/boot2docker-vm/ca.pem
Writing /Users/c/.boot2docker/certs/boot2docker-vm/cert.pem
Writing /Users/c/.boot2docker/certs/boot2docker-vm/key.pem
    export DOCKER_HOST=tcp://
    export DOCKER_CERT_PATH=/Users/c/.boot2docker/certs/boot2docker-vm
    export DOCKER_TLS_VERIFY=1

You can set those variables by simply running this command:

$ $(boot2docker shellinit)

Once you have done that you are ready to start using Docker from you Mac or Windows machine. For example, docker ps shows the running Docker containers.

$ docker ps
CONTAINER ID        IMAGE                             COMMAND                CREATED             STATUS              PORTS                                                                                        NAMES
6fcf0779a5e1        dockerfile/mongodb:latest         "mongod"               8 days ago          Up 8 days           28017/tcp,>27017/tcp                                                          mongodb                 

So what about running MongoDB and RabbitMQ? One of the great features of the Docker ecosystem is, which is a website where the community shares Docker images. In particular, you can find images for infrastructure services such as RabbitMQ and MongoDB. You can, for example, run MongoDB with the following command:

docker run -d -p 27017:27017 --name mongodb dockerfile/mongodb

This command will, if necessary, download the dockerfile/mongodb image from Docker Hub and launch the container running MongoDB listening on port 27017. Similarly, you can run RabbitMQ using this command:

docker run -d -p 5672:5672 -p 15672:15672 --name rabbitmq dockerfile/rabbitmq

These containers are, of course, running in the Virtual Box VM so you need to connect to them using the appropriate IP address. Fortunately, boot2docker makes that easy to configure:

export DOCKER_HOST_IP=$(boot2docker ip)
export SPRING_DATA_MONGODB_URI=mongodb://${DOCKER_HOST_IP}/userregistration

The “boot2docker ip” command outputs the IP address that you use to connect to the containers.

Now that you have setup the MongoDB and RabbitMQ, you can now run the User Registration Service:

docker run -d -p 8080:8080 -e SPRING_DATA_MONGODB_URI -e SPRING_RABBITMQ_HOST --name sb_rest_svc
Posted in docker, microservices, mongodb, rabbitmq | Tagged , , | Leave a comment

The world is an amazing place – the photos on my business cards

I like to order business cards from since they let you put photos on the back.

Here are the photos from the January 2015 order.









IMG_2864 - Version 2





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Example application for my #qconsf presentation about building microservices with event sourcing and CQRS

My presentation Building microservices with event sourcing and CQRS describes the challenges of functionally decomposing a domain model in a microservices based application. It shows that  a great way to solve those problems is with an event-driven architecture that is based on event sourcing and CQRS.

The presentation uses  a simple banking application, which transfers money between accounts, as an example. The application has the architecture shown in the following diagram:

Banking application architecture

The application consists of four modules that communicate only via events and can be deployed either together in a monolithic application or separately as microservices.

The four modules are as follows:

  • Accounts – the business logic for Accounts
  • Money Transfers – the business logic for Money Transfers
  • Account View Updater – updates a denormalized view of Accounts and Money Transfers in MongoDB
  • Account View Reader – queries the denormalized view

You can find the source code and documentation for the example application in this github repository. There are currently two versions of the application: Java+Spring Boot and Scala+Spring Boot. I plan to publish a Scala/Play version shortly. The application uses a simple, embedded event store.

Watch the presentation (slides+video), and take a look at the source code and documentation. It would be great to get feedback on the programming model.

Posted in cqrs, event-driven architecture, microservices | Tagged , , | 4 Comments

Microservices are to services, as organic food is to food

On Thanksgiving morning, I listened to a fascinating interview with Jacques Pepin, the celebrated French chef and PBS personality. Part of the interview touched on the topic of organic food. He joked that his parents were organic farmers even though at the time they were simply called farmers. That’s because the term ‘organic food’ didn’t exist until 1939. Before then all farming was organic. It wasn’t until the rise of industrialized agriculture, which uses pesticides, synthetic fertilizers, etc, that we needed a term to describe food grown without pesticides.

In many ways the organic food vs. food distinction mirrors the difference between microservices and service-oriented architecture (SOA). The term SOA has very negative connotations because it is strongly associated with commercialization and the baggage of WS* and ESBs. Consequently, in same way as we need to distinguish between organic food and food, we need to distinguish between microservices and SOA. It’s a shame that we need to do this because the word ‘microservices’  over-emphasizes service size.

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Deploying Spring Boot-based microservices with Docker – part 3

In part 1 and part 2 of this series, we looked at the benefits of the microservice architecture and showed how Spring Boot simplifies the development of microservices. We also built a couple of simple Spring Boot-based services for user registration: a web application and a RESTful backend service (github repo). In this article, we will look at how to deploy Spring Boot-based services with an exciting new technology called Docker.

Microservice deployment options

One of the neat things about Spring Boot is that it builds a self-contained, executable JAR file. We don’t need to install and correctly configure an application server. All that’s required is the appropriate version of Java. This means that you just need to tell whoever is responsible for deploying the application in production (or a QA environment) the JAR file along with the following information:

  1. What version of Java to install.
  2. What external configuration parameters to specify. For example, the coordinates of infrastructure services, such as MongoDB and RabbitMQ, and of other application services.
  3. The command for running the application.

It’s straightforward to deploy an individual service. But deploying all of an application’s services is likely to be much more complicated. Different services might use different Java versions. For example, the Restful web service developed in part 1 used Java 7 where as the web application build in part 2 uses Java 8. Some services might be Play or Tomcat-based web applications. Services might not even be written in Java and instead use, for example, Ruby or NodeJS. The details of how each service is configured and run depend very much on the framework and language. Services might even have conflicting requirements. As a result, whoever is responsible to deploying an application in test and production environments has to deal with a lot of complexity.

Overview of Docker

The journey of services from development to production mirrors how cargo was shipped prior to the invention of intermodal shipping containers. At the origin, cargo was manually loaded one piece at a time onto the truck or train that carried it to the port. At the port of origin the cargo was unloaded onto the dock. Longshoremen then carefully loaded the tens of thousands of individual pieces of cargo one at time – boxes of bananas, ingots, coffee, etc. – onto the ship in a way that optimized space while simultaneously ensuring that the ship was stable. All of these steps then happened in reverse at the destination. Not only was shipping a time consuming and labor intensive process but repeated handling often broke fragile goods.

Containerization dramatically changed the ship industry (see the excellent book The Box for more details). All non-bulk cargo is now packed into standard shipping containers, which can be carried by truck, trains and ships. Crane operators now rapidly load and unload containers regardless of their contents. The contents of the container are never touched in transit. In other words, the shipping container encapsulates it’s contents. It has become the standardized API of cargo.

One way of containerizing a service is to package it as a VM machine image. For example, your continuous integration server can run a VM building tool such as Packer to create a VM image that can be deployed where needed. The virtual machine’s standardized API becomes the service’s management API. Starting and stopping the VM starts and stops the service regardless of the technology used to implement it. The contents of the VM are never touched. The service-as-a-VM approach is popular way of packaging and deploying services. For example, the Netflix video streaming service consists of many services each packaged as an AMI and deployed on Amazon EC2.

There are, however, various downsides to the service-as-a-VM approach. It assumes that your application runs in a virtualized environment, which is not always the case. Also, virtual machines are a heavyweight technology. It’s not practical to deploy more than a couple of VMs on a developer laptop. Building and booting an VM machine is usually I/O intensive and consequently slow. Also, some IaaS offerings such AWS EC2, don’t let you specify arbitrary VM sizes and so it’s likely that a given service doesn’t fully utilize its VM. Furthermore, the cost of deploying many small services as individual VMs on a public IaaS such as Amazon EC2 can rapidly add up.

Introduction to Docker

Docker is a new way to containerize applications that is becomingly increasingly popular. It allows you to package a microservice in a standardized portable format that’s independent of the technology used to implement the service. At runtime it provides a high degree of isolation between different services. However, unlike virtual machines, Docker containers are extremely lightweight and as a result can be built and started extremely quickly. A container can typically be built in just a few seconds and starting a container simply consists of starting the service’s process(es).

Docker runs on a variety of platforms. It runs natively on Linux. You can also run Docker on Windows and Mac OSX using Boot2Docker, which runs the Docker daemon in a VirtualBox VM. Some clouds also have added extra support for Docker. For example, not only can you run Docker inside your EC2 instances but you can also use Elastic Beanstalk to run Docker containers. Amazon also recently announced the Amazon EC2 Container Service, which is a hosted Docker container management service.  Google Cloud also has support for Docker.

The two main Docker concepts are image, which is a portable application packaging format, and container, which is a running image and consists of one or more sandboxed processes. Let’s first look at how images work.

About Docker images

A Docker image is read-only file system image of an operating system and an application. It’s analogous to an AWS EC2 AMI. An image is self-contained and will run on any Docker installation. You can create an image from scratch but normally an image is created by starting a container from existing base image, installing applications by executing the same kinds of commands you would use when configuring a regular machine, such as apt-get install –y and then saving the container as a new image. For example, to create an image containing a Spring Boot based application, you could start from a vanilla Ubuntu image, install the JDK and then install the executable JAR.

In many ways, building a Docker image is similar to building an AMI. However, while an AMI is a blob of bits, a Docker image has a layered structure that dramatically reduces the amount of time needed to build and deploy a Docker image. An image consists of a sequence of layers. When building an image, each command that changes the file system (e.g. apt-get install) create a new layer that references it’s parent layer.

This layered structure has two important benefits. First it enables of sharing of layers between images, which means that Docker does not need to move an entire image over the network. Only those layers that don’t exist on the destination machine need to be copied, which usually results in a dramatic speedup. Another important benefit of the layered structure is that Docker aggressively caches layers when building an image. When re-executing a command against an input layer Docker tries to skip executing the command and instead reuses the already built output layer. As a result, building an image is usually extremely fast.

About Docker containers

A Docker container is a running image consisting of one or more sandboxed processes. Docker isolates a container’s processes using a variety of mechanisms including relatively mature OS-level virtualization mechanisms such as control groups and namespaces. Each process group has its own root file-system. Process groups can be assigned resource limits, e.g. CPU and memory limits. In the same way that a hypervisor divides up the hardware amongst virtual machines, this mechanism divides up the OS between process groups. Each Docker container is a process group.

Docker also isolates the networking portion of each container. When Docker is installed, it creates a virtual interface called docker0 on the host and sets up subnet. Each container is given it’s own virtual interface called eth0 (within the container’s namespace), which is assigned an available IP address from the Docker subnet. This means, for example, that a Spring Boot application running in a container listens on port 8080 of the virtual interface that’s specific to its container. Later on we will look how you can enable a service to be accessed from outside its container by setting up a port mapping that associates a host port with a container port.

It’s important to remember that even though an image contains an entire OS a Docker container often only consists of the application’s processes. You often don’t need to start any of the typical OS processes such as initd. For example, a Docker container that runs a Spring Boot application might only start Java. As a result, a Docker container has a minimal runtime overhead and its startup time is the startup time of your application.

Now that we have looked at basic Docker concepts let’s look at using Docker to package Spring Boot applications.

Spring Boot and Docker

Let’s now build a Docker image that runs the Spring Boot application. Because Spring Boot packages the application as a self-contained executable JAR, we just need to build an image containing that JAR file and Java. One option is to take a vanilla Ubuntu Docker image, install Java and install the JAR. Fortunately, we can skip the first step because it’s already been done. One of the great features of the Docker ecosystem is, which is a website where the community shares Docker images. There are a huge number of images available including dockerfile/java, which provides Java images for Oracle and OpenJDK versions 6, 7, and 8.

Once we have identified a suitable base image the next step is to build a new image that runs the Spring Boot application. You could build an image manually by launching the base image and entering shell commands in pretty much the same way that you would configure a regular OS. However, it’s much better to automate image creation. To do that we need to create a Dockerfile, which is a text file containing series of commands that tell Docker how to build an image. Once we have written a Dockerfile, we can then repeatedly build an image by running docker build.

Here is the Dockerfile (see github repo) that builds an image that runs the application

FROM dockerfile/java:oracle-java7
CMD java -jar spring-boot-restful-service.jar
ADD build/spring-boot-restful-service.jar /data/spring-boot-restful-service.jar

As you can see, the Dockerfile is very simple. It consists of the following instructions:

  • FROM – the FROM instruction specifies the starting image, which in this example is the Java 7 image mentioned above. The first time you build this image, Docker will download the Java 7 image from the central Docker registry
  • MAINTAINER – this instruction simply specifies the author
  • EXPOSE – this instruction tells Docker that this server process will listen on port 8080
  • CMD – the CMD instruction specifies the command to run (by default) when the container is started, i.e. the Spring Boot application.
  • ADD – this instruction copies the JAR file to the specified location in the image. Note that working directory for the dockerfile/java image is /data so that’s why we are putting the JAR file there.

Here is the shell script that builds the image:

rm -fr build
mkdir build
cp ../build/libs/spring-boot-restful-service.jar build
docker build -t sb_rest_svc .

This script builds the image using the docker build command. The –t argument specifies the name give to the new image. The “.” argument tells Docker to build the image using the current working directory as what is called the context of the build. The context defines the set of files that are uploaded to the Docker daemon and used to build the image. At the root of the context is the Dockerfile, which contains commands such as ADD that reference the other files in the context. We could specify the Gradle project root as the context and upload the entire project to the Docker daemon. But since the only files needed to build the image are the Dockerfile and the JAR it’s much more efficient to copy the JAR file to a docker/build subdirectory.

Now that have packaged the application as a Docker image we need to run it. To do that we use the docker run command:

docker run –d –p 8080:8080 --name sb_rest_svc sb_rest_svc

The arguments to the run command are as follows:

  • -d – tells Docker to run the service as a daemon
  • -p – specifies the port mapping for the container. In this particular case, it specifies that the container’s port 8080 should be mapped to port 8080 on the host. In other words, clients can access this service via http://host:8080
  • –name – specifies the name of the newly created container
  • sb_rest_svc – the name of the image to run

If we execute this command, the service starts up but the Spring application context initialization fails because it doesn’t know how connect to MongoDB and RabbitMQ.

Fortunately, this problem is easy to fix because of how Spring Boot and Docker support environment variables. One of the nice features of Spring Boot is that it let’s you specify configuration properties using OS environment variables. Specifically, for this service we need to supply values for SPRING_DATA_MONGODB_URI, which specifies the MongoDB database, and SPRING_RABBITMQ_HOST, which specifies the RabbitMQ host.

This mechanism works extremely well with Docker because you can use the docker run command’s –e option to specify values for a container’s environment variables. For example, let’s suppose that RabbitMQ and Mongo are running on a machine with an IP address of You can then run the container with the following command:

docker run -d -p 8080:8080 -e SPRING_DATA_MONGODB_URI=mongodb:// 
--name sb_rest_svc

This command starts the service, which connects to MongoDB and RabbitMQ. You can examine the output of the process using the docker log command:

docker logs sb_rest_svc

This command outputs the stdout/stderr of the service.


In this article, we saw how containerization is an excellent way to simplify deployment. Rather than giving operations your application and detailed deployment instructions, you simply package your application as a container. Docker is an extremely lightweight and efficient container technology. You can package your services as Docker containers and run them unchanged in any Docker environment: development, test and production. Later articles will look at various aspects of deploying Docker-based applications including a Jenkins-based deployment pipeline that builds and tests Docker images. We will also look at using Docker to simplify the setup of test environments.

Next steps

Posted in architecture, microservices, spring boot | Tagged , , | 5 Comments

Upgrading my microservices-based application to the new version of Docker using Packer and Chef

For some time now I’ve been running Docker-based dev and prod environments on Amazon EC2. Even though Docker is pretty new it works well and it’s a good platform for deploying microservice based applications. Each microservice is packaged as a Docker container, which provides a fast and convenient packaging and deployment mechanism regardless of which language and framework the microservice uses.

Over the weekend I finally upgraded my  EC2 instances to use the latest version of Docker. I’d been meaning to do this for a while but there was a big obstacle: the EC2 instance running the Jenkins-based deployment pipeline was handcrafted :-(. I should have known better. It’s just that this particular server was created when I was getting started with Docker and so I had done a lot of configuration via the command line. Oops!

Upgrading to Docker consisted of creating some Chef cookbooks and a series of Packer templates that built AMIs using those cookbooks. I wrote three different Packer templates for building three different AMIs.

  1. The first Packer template built a base AMI containing the newest Docker, Chef Solo and some other utilities.
  2. The second  Packer template for extended the base AMI to run a private Docker registry backed by S3. Originally, the registry was part of the base AMI but it took a few iterations to get cookbook for configuring the registry to work. To avoid wasting time repeatedly reinstalling Docker etc.  I moved the registry into it’s own Packer template. In particular, it seems that a Docker container that’s running when an AMI is created still has the old network configuration in an instance created from that AMI. The solution was to remove the container before creating the AMI and then restart it when the instance boots.
  3. The third Packer template extended AMI #2 to run Docker containers for various development tools: Jenkins, etc. This template configured the AMI using some Chef cookbooks that were created by reverse engineering the configuration I had done manually.

Since Jenkins home was on an EBS volume, it was trivial to shutdown the old CI server and attach the EBS volume to the newly created CI server. I then kicked off the deployment pipelines for all of the microservices in order to push their containers to the new S3-backed private registry.

Rebuilding prod servers starting from the Docker registry AMI was easy since I’d already written Chef cookbooks to do that. Moving the applications over simply consisted of pulling the containers from the private registry and launching them.

Now that I’ve automated AMI creation future upgrades should be trivial.

Write 100 times “I must not configure EC2 instances by hand”:

I must not configure EC2 instances by hand

I must not configure EC2 instances by hand

I must not configure EC2 instances by hand


Posted in architecture, deployment, docker, microservices | Tagged , , , , , , | Leave a comment