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Workflow tool to launch Spark jobs on AWS EMR

Project description

Build Status Documentation Status

SparkSteps allows you to configure your EMR cluster and upload your spark script and its dependencies via AWS S3. All you need to do is define an S3 bucket.

Install

pip install sparksteps

CLI Options

Prompt parameters:
  app               main spark script for submit spark (required)
  app-args:         arguments passed to main spark script
  aws-region:       AWS region name
  bid-price:        specify bid price for task
  bootstrap-action: include a bootstrap script (s3 path)
  cluster-id:       job flow id of existing cluster to submit to
  debug:            allow debugging of cluster
  defaults:         spark-defaults configuration of the form key1=val1 key=val2
  dynamic-pricing:  allow sparksteps to determine best bid price for task nodes
  ec2-key:          name of the Amazon EC2 key pair
  ec2-subnet-id:    Amazon VPC subnet id
  help (-h):        argparse help
  keep-alive:       Keep EMR cluster alive when no steps
  master:           instance type of of master host (default='m4.large')
  name:             specify cluster name
  num-core:         number of core nodes
  num-task:         number of task nodes
  release-label:    EMR release label
  s3-bucket:        name of s3 bucket to upload spark file (required)
  s3-dist-cp:       s3-dist-cp step after spark job is done
  slave:            instance type of of slave hosts
  submit-args:      arguments passed to spark-submit
  sparksteps-conf:  use sparksteps Spark conf
  tags:             EMR cluster tags of the form "key1=value1 key2=value2"
  uploads:          files to upload to /home/hadoop/ in master instance

Example

AWS_S3_BUCKET = <insert-s3-bucket>
cd sparksteps/
sparksteps examples/episodes.py \
  --s3-bucket $AWS_S3_BUCKET \
  --aws-region us-east-1 \
  --release-label emr-4.7.0 \
  --uploads examples/lib examples/episodes.avro \
  --submit-args="--deploy-mode client --jars /home/hadoop/lib/spark-avro_2.10-2.0.2-custom.jar" \
  --app-args="--input /home/hadoop/episodes.avro" \
  --tags Application="Spark Steps" \
  --debug

The above example creates an EMR cluster of 1 node with default instance type m4.large, uploads the pyspark script episodes.py and its dependencies to the specified S3 bucket and copies the file from S3 to the cluster. Each operation is defined as an EMR “step” that you can monitor in EMR. The final step is to run the spark application with submit args that includes a custom spark-avro package and app args “–input”.

Run Spark Job on Existing Cluster

You can use the option --cluster-id to specify a cluster to upload and run the Spark job. This is especially helpful for debugging.

Dynamic Pricing (alpha)

Use CLI option --dynamic-pricing to allow sparksteps to dynamically determine best bid price for EMR task notes.

Currently the algorithm looks back at spot history over the last 12 hours and calculates min(50% * on_demand_price, max_spot_price) to determine bid price. That said, if the current spot price is over 80% of the on-demand cost, then on-demand instances are used to be conservative.

Note: code depends on ec2instances for getting demand price.

Testing

make test

License

Apache License 2.0

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