Workflow tool to launch Spark jobs on AWS EMR
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.
pip install sparksteps
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
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.
Apache License 2.0
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