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https://github.com/mozilla/emr-bootstrap-spark
AWS bootstrap scripts for Mozilla's flavoured Spark setup.
https://github.com/mozilla/emr-bootstrap-spark
aws jupyter spark zeppelin
Last synced: 2 days ago
JSON representation
AWS bootstrap scripts for Mozilla's flavoured Spark setup.
- Host: GitHub
- URL: https://github.com/mozilla/emr-bootstrap-spark
- Owner: mozilla
- Archived: true
- Created: 2015-03-14T08:01:33.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2020-02-13T21:05:30.000Z (over 4 years ago)
- Last Synced: 2024-09-19T02:27:38.342Z (8 days ago)
- Topics: aws, jupyter, spark, zeppelin
- Language: Python
- Homepage:
- Size: 910 KB
- Stars: 46
- Watchers: 31
- Forks: 23
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
emr-bootstrap-spark
===================
This package contains the AWS bootstrap scripts for Mozilla's flavoured Spark setup.
The deployed scripts in S3 are referenced by
[ATMO clusters](https://github.com/mozilla/telemetry-analysis-service) and
[Airflow jobs](https://github.com/mozilla/telemetry-airflow).
## Interactive job
```bash
export SPARK_PROFILE=telemetry-spark-cloudformation-TelemetrySparkInstanceProfile-1SATUBVEXG7E3
export SPARK_BUCKET=telemetry-spark-emr-2
export KEY_NAME=20161025-dataops-dev
aws emr create-cluster \
--region us-west-2 \
--name SparkCluster \
--instance-type c3.4xlarge \
--instance-count 1 \
--service-role EMR_DefaultRole \
--ec2-attributes KeyName=${KEY_NAME},InstanceProfile=${SPARK_PROFILE} \
--release-label emr-5.2.1 \
--applications Name=Spark Name=Hive Name=Zeppelin \
--bootstrap-actions Path=s3://${SPARK_BUCKET}/bootstrap/telemetry.sh \
--configurations https://s3-us-west-2.amazonaws.com/${SPARK_BUCKET}/configuration/configuration.json \
--steps Type=CUSTOM_JAR,Name=CustomJAR,ActionOnFailure=TERMINATE_JOB_FLOW,Jar=s3://us-west-2.elasticmapreduce/libs/script-runner/script-runner.jar,Args=\["s3://${SPARK_BUCKET}/steps/zeppelin/zeppelin.sh"\]
```
## Batch job
```bash
# Also export the vars from the 'interactive' section above.
export DATA_BUCKET=telemetry-public-analysis-2 # Or use the private bucket.
export CODE_BUCKET=telemetry-analysis-code-2
aws emr create-cluster \
--region us-west-2 \
--name SparkCluster \
--instance-type c3.4xlarge \
--instance-count 1 \
--service-role EMR_DefaultRole \
--ec2-attributes KeyName=${KEY_NAME},InstanceProfile=${SPARK_PROFILE} \
--release-label emr-5.2.1 \
--applications Name=Spark Name=Hive \
--bootstrap-actions Path=s3://${SPARK_BUCKET}/bootstrap/telemetry.sh \
--configurations https://s3-us-west-2.amazonaws.com/${SPARK_BUCKET}/configuration/configuration.json \
--auto-terminate \
--steps Type=CUSTOM_JAR,Name=CustomJAR,ActionOnFailure=TERMINATE_JOB_FLOW,Jar=s3://us-west-2.elasticmapreduce/libs/script-runner/script-runner.jar,Args=\["s3://${SPARK_BUCKET}/steps/batch.sh","--job-name","foo","--notebook","s3://${CODE_BUCKET}/jobs/foo/Telemetry Hello World.ipynb","--data-bucket","${DATA_BUCKET}"\]
```
## Deploy to AWS via ansible
To deploy to the staging location:
```bash
ansible-playbook ansible/deploy.yml -e '@ansible/envs/stage.yml' -i ansible/inventory
```
Once deployed, you can see the effects in action by launching a cluster via
[ATMO stage](https://atmo.stage.mozaws.net/).
To deploy for production clusters:
```bash
ansible-playbook ansible/deploy.yml -e '@ansible/envs/production.yml' -i ansible/inventory
```
The Spark Jupyter notebook configuration is hosted at `https://s3-us-west-2.amazonaws.com/telemetry-spark-emr-2/credentials/jupyter_notebook_config.py`. At the moment, this is only needed for the GitHub Gist export option in the Jupyter notebook. The credentials it contains are managed under the [Mozilla GitHub account](https://github.com/mozilla/) by :whd. This file **should not be made public**.
## Contributing to `emr-bootstrap-spark`
You may set up a development environment to test and verify modifications applied to this repository.
### Install prerequisite packages
```
pip install ansible boto boto3
```
### Create and bootstrap the development environment
* Define a new ansible environment in `env/dev-.yml`
* Set `spark_emr_bucket` to a unique bucket e.g. `telemetry-spark-emr-2-dev-`
* Set `stack_name` to a unique name e.g. `telemetry-spark-cloudformation-dev-`
* Recursively copy assets from `staging` to `dev`
* `aws s3 cp --recursive s3://telemetry-spark-emr-2-stage s3://telemetry-spark-emr-2-dev-`
* Deploy to AWS using `ansible-playbook` on the new environment
* Launch a new instance using the appropriate `SPARK_PROFILE` and `SPARK_BUCKET` keys
* Set `SPARK_PROFILE` to the cloudformation instance profile
* This can be found as an output on the cloudformation dashboard
* Alternatively:
```
aws cloudformation describe-stacks --stack-name telemetry-spark-cloudformation-dev- |
jq '.Stacks[0].Outputs[0].OutputValue'
```
* Set `SPARK_BUCKET` to `spark_emr_bucket` value in `env/dev-.yml`