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README.md

Estimate Pi Using Spark

This example demonstrates integration of Spark with Pachyderm by launching a Spark job on an existing cluster from within a Pachyderm Job. The job uses configuration info that is versioned within Pachyderm, and stores it's reduced result back into a Pachyderm output repo, maintaining full provenance and version history within Pachyderm, while taking advantage of Spark for computation.

The example assumes that you have:

Note: if deploying on Minikube, you'll need to increase the default memory allocation to accomodate the deploy of a Spark cluster. When running minikube start, append --memory 4096.

Set up Spark Cluster

The simpelst way to run this example is by deploying a Spark cluster into the same Kubernetes cluster on which Pachyderm is running. We'll do so with Helm. (Note: if you already have an external Spark cluster running, you can skip this section. Be sure to read the note about connecting to an existing Spark cluster)

Install Helm

If you don't already have the Helm client installed, you can do so by following the instructions here (or, for the bold, by running curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get | bash.)

Set up Helm/Tiller

In order to use Helm with your Kubernetes cluster, you'll need to install Tiller:

kubectl create serviceaccount --namespace kube-system tiller
kubectl create clusterrolebinding tiller-cluster-rule --clusterrole=cluster-admin --serviceaccount=kube-system:tiller
helm init --service-account tiller --upgrade

Tiller will take about a minute to initialize and enter Running status. You can check it's status by running: kubectl get pod -n kube-system -l name=tiller

Install Spark

Finally, once Tiller is Running, use Helm to install Spark:

helm install --name spark stable/spark

This will again take several minutes to pull the relevant Docker images and start running. You can check the status with kubectl get pod -l release=spark

Deploy Pachyderm Pipeline

Once your Spark cluster is running, you're ready to deploy the Pachyderm pipeline:

# create a repo to hold configuration data that acts as input to the pipeline
pachctl create repo estimate_pi_config

# create the actual processing pipeline
pachctl create pipeline -f estimate_pi_pipeline.json

# kick off a job with 1000 samples
echo 1000 | pachctl put file estimate_pi_config@master:num_samples

# check job status
pachctl list job --pipeline estimate_pi

# once job has completed, retrieve the results
pachctl get file estimate_pi@master:pi_estimate

Connecting to an existing Spark cluster

By default, this example makes use of Kubernetes' service discovery to connect your Pachyderm pipeline code to your Spark cluster. If you wish to connect to a different Spark cluster, you can do so by adding the --master flag to the list of arguments provided to cmd in the pipeline spec: append "--master" and "spark://$MYSPARK_MASTER_SERVICE_HOST:$MYSPARK_MASTER_SERVICE_PORT" to the cmd array.

To test a manually-specified connection, deploy a Spark cluster into a different name in Kubernetes:

helm install --name my-custom-spark stable/spark
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