Run a Wallaroo Application in Docker
In this section, we're going to run an example Wallaroo application in Docker. By the time you are finished, you'll have validated that your Docker environment is set up and working correctly. If you haven't already completed the Docker setup instructions, please do so before continuing.
There are a few Wallaroo support applications that you'll be interacting with for the first time:
- Our Metrics UI allows you to monitor the performance and health of your applications.
- Data receiver is designed to capture TCP output from Wallaroo applications.
- Machida or Machida3, our program for running Wallaroo Python applications, for Python 2.7 and 3.5+ respectively..
You're going to set up our "Alerts" example application. We will use an internal generator source to generate simulated inputs into the system. Data receiver will receive the output, and our Metrics UI will be running so you can observe the overall performance.
The Metrics UI process will be run in the background. The other two processes (data_receiver and Wallaroo) will run in the foreground. We recommend that you run each process in a separate terminal.
NOTE: If you haven't set up Docker to run without root, you will need to use
sudo with your Docker commands.
Let's get started!
Since Wallaroo is a distributed application, its components need to run separately, and concurrently, so that they may connect to one another to form the application cluster. For this example, you will need 5 separate terminal shells to start the docker container, run the metrics UI, run a sink, run the Alerts application, and eventually, to send a cluster shutdown command.
Shell 1: Start the Wallaroo Docker container for Machida
docker run --rm -it --privileged -p 4000:4000 \ -v /tmp/wallaroo-docker/wallaroo-0.6.0/wallaroo-src:/src/wallaroo \ -v /tmp/wallaroo-docker/wallaroo-0.6.0/python-virtualenv-machida:/src/python-virtualenv \ --name wally \ wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.6.0
docker run --rm -it --privileged -p 4000:4000 ` -v c:/wallaroo-docker/wallaroo--0.6.0/wallaroo-src:/src/wallaroo ` -v c:/wallaroo-docker/wallaroo-0.6.0/python-virtualenv-machida:/src/python-virtualenv ` --name wally ` wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.6.0
docker run --rm -it --privileged -p 4000:4000 ^ -v c:/wallaroo-docker/wallaroo-0.6.0/wallaroo-src:/src/wallaroo ^ -v c:/wallaroo-docker/wallaroo-0.6.0/python-virtualenv-machida:/src/python-virtualenv ^ --name wally ^ wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.6.0
Shell 1: Start the Wallaroo Docker container for Machida3
docker run --rm -it --privileged -p 4000:4000 \ -v /tmp/wallaroo-docker/wallaroo-0.6.0/wallaroo-src:/src/wallaroo \ -v /tmp/wallaroo-docker/wallaroo-0.6.0/python-virtualenv-machida3:/src/python-virtualenv \ --name wally \ wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.6.0 -p python3
docker run --rm -it --privileged -p 4000:4000 ` -v c:/wallaroo-docker/wallaroo--0.6.0/wallaroo-src:/src/wallaroo ` -v c:/wallaroo-docker/wallaroo-0.6.0/python-virtualenv-machida3:/src/python-virtualenv ` --name wally ` wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.6.0 -p python3
docker run --rm -it --privileged -p 4000:4000 ^ -v c:/wallaroo-docker/wallaroo-0.6.0/wallaroo-src:/src/wallaroo ^ -v c:/wallaroo-docker/wallaroo-0.6.0/python-virtualenv-machida3:/src/python-virtualenv ^ --name wally ^ wallaroo-labs-docker-wallaroolabs.bintray.io/release/wallaroo:0.6.0 -p python3
Breaking down the Docker command
docker run: The Docker command to start a new container.
--rm: Automatically clean up the container and remove the file system on exit.
-it: Allows us to work with interactive processes by allocating a tty for the container.
--privileged: Gives the container access to the hosts' devices. This allows certain system calls to be used by Wallaroo, specifically
set_mempolicy. This setting is optional, but by excluding it there will be a performance degradation in Wallaroo's processing capabilities.
-p 4000:4000: Maps the default port for HTTP requests for the Metrics UI from the container to the host. This makes it possible to call up the Metrics UI from a browser on the host.
-v /tmp/wallaroo-docker/wallaroo-0.6.0/wallaroo-src:/src/wallaroo: Mounts a host directory as a data volume within the container. The first time you run this, an empty directory needs to be used in order for the Docker container to copy the Wallaroo source code to your host. If an empty directory is not used, we are assuming it is prepopulated with the Wallaroo source code from this point forward. This allows you to open and modify the Wallaroo source code with the editor of your choice on your host. The Wallaroo source code will persist on your machine after the container is stopped or deleted. This setting is optional, but without it you would need to use an editor within the container to view or modify the Wallaroo source code.
-v /tmp/wallaroo-docker/wallaroo-0.6.0/python-virtualenv-machida(machida3):/src/python-virtualenv: Mounts a host directory as a data volume within the container. The first time this is run for the provided directory, this command will setup a persistent Python virtual environment using virtualenv for the container on your host. Thus, if you need to install any python modules using
easy_installthey will persist after the container is stopped or deleted. This setting is optional, but without it, you will not have a persistent
virtualenvfor the container. We ask you to mount to
machida3to avoid having conflicting
--name wally: The name for the container. This setting is optional but makes it easier to reference the container in later commands.
-p python3: This is a required argument if you will be running
machida3. This argument allows us to set the python interpreter for the
python3instead of the default
python2. If you plan to run
machida, this argument is not needed.
Starting new shells
For each Shell you're expected to setup, you'd have to run the following to enter the Wallaroo Docker container:
Enter the Wallaroo Docker container:
docker exec -it wally env-setup
docker exec -it wally env-setup -p python3
This command will start a new Bash shell within the container, which will run the
env-setup script to ensure our persistent Python
virtualenv is set up.
Shell 2: Start the Metrics UI
To start the Metrics UI run:
You can verify it started up correctly by visiting http://localhost:4000.
If you need to restart the UI, run:
When it's time to stop the UI, run:
If you need to start the UI after stopping it, run:
Shell 3: Run Data Receiver
We'll use Data Receiver to listen for data from our Wallaroo application.
data_receiver --listen 127.0.0.1:5555 --no-write --ponythreads=1 --ponynoblock
Data Receiver will start up and receive data without creating any output. By default, it prints received data to standard out, but we are giving it the
--no-write flag which results in no output.
Shell 4: Run the "Alerts" Application
First, we'll need to get to the python Alerts example directory with the following command:
Now that we are in the proper directory, and the Metrics UI and Data receiver are up and running, we can run the application itself by executing the following command (remember to use the
machida3 executable instead of
machida if you are using Python 3.X):
machida --application-module alerts \ --out 127.0.0.1:5555 --metrics 127.0.0.1:5001 --control 127.0.0.1:6000 \ --data 127.0.0.1:6001 --name worker-name --external 127.0.0.1:5050 \ --cluster-initializer --ponythreads=1 --ponynoblock
This tells the "Alerts" application that it should write outgoing data to port
5555, and send metrics data to port
Check Out Some Metrics
Once the application has successfully initialized, the internal test generator source will begin simulating inputs into the system. If you visit the Metrics UI, the landing page should show you that the "Alerts" application has successfully connected.
If your landing page resembles the one above, the "Alerts" application has successfully connected to the Metrics UI.
Now, let's have a look at some metrics. By clicking on the "Alerts" link, you'll be taken to the "Application Dashboard" page. On this page you should see metric stats for the following:
- a single pipeline:
- a single worker:
- a single computation:
check transaction total
You'll see the metric stats update as data continues to be processed in our application.
You can then click into one of the elements within a category to get to a detailed metrics page for that element. If we were to click into the
check transaction total computation, we'll be taken to this page:
Feel free to click around and get a feel for how the Metrics UI is set up and how it is used to monitor a running Wallaroo application. If you'd like a deeper dive into the Metrics UI, have a look at our Monitoring Metrics with the Monitoring Hub section.
Shell 5: Cluster Shutdown
You can shut down the cluster with this command at any time:
You can shut down Data Receiver by pressing Ctrl-c from its shell.
You can shut down the Metrics UI with the following command:
To shut down the Wallaroo container, use the
docker stop command in a shell on your host:
docker stop wally
This command will also terminate any active sessions you may have left open to the docker container.
For tips on editing existing Wallaroo example code or installing Python modules within Docker, have a look at our Tips for using Wallaroo in Docker section.