Heroku Button deploy of an Apache Spark cluster.
requires: a private space with dns-discovery enabled, NOTE dont use the button if you have a non dns-discovery space, it will absolutely not work.
If you use this cluster for real work, please protect it by adding a domain and ssl cert.
You should probably right-click 'open link in new window' on the button so you can follow the readme here. As long as this repo is private you will need to oauth your github account to the heroku dashboard.
Note: The rest of this readme assumes you have set your app name as
$app in your shell, like so
Once your button deploy completes, you can tail the logs and see the master come online and the workers connect to it.
heroku logs -t -a $app
once you see the master online and the workers registered, you can verify the workers do work by:
heroku run bin/spark-shell -a $app sc.parallelize(1 to 1000000).reduce(_ + _)
Note that this will take about 2 minutes to start up. If you ask a herokai to enable the
dyno-run-inside feature on your app,
you can then scale up a console process and use run:inside to get instant consoles.
heroku scale console=1 -a $app # wait for console.1 to be up heroku run:inside console.1 bin/spark-shell -a $app sc.parallelize(1 to 1000000).reduce(_ + _)
ou can view the spark master by running the
heroku open -a $app command, assuming you set the
when creating the app. If not, or to change it you can run
heroku config:set SPACE_PROXY_DEFAULT_BACKEND=1.master.$app.app.localspace:8080 -a app heroku logs -t -a $app #wait for master to restart heroku open -a $app
The default basic auth credentials are
spark:space, and can be changed
by updating the
SPACE_PROXY_BASIC_AUTH config var, which by default is set to nginx PLAIN format,
to add more worker nodes to your spark cluster, simply scale the worker processes.
heroku scale worker=5 -a $app
to make use of more worker nodes by default, set the config var:
SPARK_DEFAULT_CORES: default 4; see Resource Scheduling
to change the memory allocated to Spark, set the config vars:
SPARK_EXECUTOR_MEMORY: default 12g; see Application Properties
SPARK_DRIVER_MEMORY: default 2g; see Application Properties
individual jobs may be tuned for memory & core utilization with
spark-submit options, examples:
There is an nginx server that can proxy to any dyno in the space. The server will default you to proxying to the master.1 spark process.
As of Spark 2.1.0, a new configuration supports improved UI functionality when served through a this kind of reverse proxy. To enable the reverse proxy behavior, set the fully-qualified hostname that clients use to access Spark Master UI:
# Point to your custom domain/SSL endpoint for the Spark in Space deployment: heroku config:set SPARK_REVERSE_PROXY_URL=https://spark-in-space.example.com # …or if you're using the plain HTTP Space app URLs: heroku config:set SPARK_REVERSE_PROXY_URL=http://<your-spark-app>.herokuapp.com
To set a cookie so that you proxy to the spark master, hit the following url
you will be redirected to the root web ui of the backend you have selected.
You will see proxied version of the spark master UI. You can hit the
/set-backend path with other in-space hostname:port combos
to be able to see workers and driver program ui.
job.1in the app
LOG_LEVEL environment variable controls the log4j log level for spark, it defaults to INFO, but you may set it to any
valid level. INFO provides a lot of logging so you can see things are working properly, but can slow things down.
A better setting for real use is
heroku config:set LOG_LEVEL=WARN -a $app.
You can use s3 as an hdfs compatible filesystem by installing the
bucketeer addon, using the
--as SPARK_S3 option.
This is provided by the button deploy.
If you do this, bucketeer will set
SPARK_S3_AWS_SECRET_ACCESS_KEY config vars.
This will be detected and cause the writing of proper defaults to spark-defaults.conf. You can then use s3n:// or s3a:// urls in spark.
If you are deploying this app manually or dont need S3 HDFS, you can skip or remove the bucketeer adddon and the spark cluster should still function, just without S3 access.
To try out the S3 functionality, you can do the following.
heroku run:inside console.1 bash -a your-spark-app ./bin/spark-shell val bucket = sys.env("SPARK_S3_BUCKET_NAME") val file = s"s3a://$bucket/test-object-file" val ints = sc.makeRDD(1 to 10000) ints.saveAsObjectFile(file) ### lots of spark output :q ./bin/spark-shell val bucket = sys.env("SPARK_S3_BUCKET_NAME") val file = s"s3a://$bucket/test-object-file" val theInts = sc.objectFile[Int](file) theInts.reduce(_ + _) ### lots of spark output res0: Int = 50005000
If you set the
SPARK_MASTERS config var to a number greater than 1, then workers, spark-submit and spark-shell will use spark master urls that point at
the number of masters you specify.
For example, If you want 3 masters, you should
heroku scale master=3 -a $app, then
heroku config:set SPARK_MASTERS=3 -a $app, and the master url will be
High availability spark masters require zookeeper.
If there is a
SPARK_ZK_ZOOKEEPER_URL set, then the spark processes will be configured to use zookeeper for recovery.
SPARK_ZK_ZOOKEEPER_URL should be of the following form for a 3 node cluster
This repo, when used as a buildpack, can support:
To use a specific version, place a file named
.spark.version in the root of your project, containing the Spark version number, e.g.
Copy the snippet above into CLI.