Running Spark on Mesos

Spark can run on hardware clusters managed by Apache Mesos.

The advantages of deploying Spark with Mesos include:

How it Works

In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master instance. When using Mesos, the Mesos master replaces the Spark master as the cluster manager.

Spark cluster components

Now when a driver creates a job and starts issuing tasks for scheduling, Mesos determines what machines handle what tasks. Because it takes into account other frameworks when scheduling these many short-lived tasks, multiple frameworks can coexist on the same cluster without resorting to a static partitioning of resources.

To get started, follow the steps below to install Mesos and deploy Spark jobs via Mesos.

Installing Mesos

Spark 1.2.0 is designed for use with Mesos 0.18.1 and does not require any special patches of Mesos.

If you already have a Mesos cluster running, you can skip this Mesos installation step.

Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other frameworks. You can install Mesos either from source or using prebuilt packages.

From Source

To install Apache Mesos from source, follow these steps:

  1. Download a Mesos release from a mirror
  2. Follow the Mesos Getting Started page for compiling and installing Mesos

Note: If you want to run Mesos without installing it into the default paths on your system (e.g., if you lack administrative privileges to install it), pass the --prefix option to configure to tell it where to install. For example, pass --prefix=/home/me/mesos. By default the prefix is /usr/local.

Third-Party Packages

The Apache Mesos project only publishes source releases, not binary packages. But other third party projects publish binary releases that may be helpful in setting Mesos up.

One of those is Mesosphere. To install Mesos using the binary releases provided by Mesosphere:

  1. Download Mesos installation package from downloads page
  2. Follow their instructions for installation and configuration

The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master failover, but Mesos can be run without ZooKeeper using a single master as well.


To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at port :5050 Confirm that all expected machines are present in the slaves tab.

Connecting Spark to Mesos

To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and a Spark driver program configured to connect to Mesos.

Uploading Spark Package

When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark binary package for running the Spark Mesos executor backend. The Spark package can be hosted at any Hadoop-accessible URI, including HTTP via http://, Amazon Simple Storage Service via s3n://, or HDFS via hdfs://.

To use a precompiled package:

  1. Download a Spark binary package from the Spark download page
  2. Upload to hdfs/http/s3

To host on HDFS, use the Hadoop fs put command: hadoop fs -put spark-1.2.0.tar.gz /path/to/spark-1.2.0.tar.gz

Or if you are using a custom-compiled version of Spark, you will need to create a package using the script included in a Spark source tarball/checkout.

  1. Download and build Spark using the instructions here
  2. Create a binary package using --tgz.
  3. Upload archive to http/s3/hdfs

Using a Mesos Master URL

The Master URLs for Mesos are in the form mesos://host:5050 for a single-master Mesos cluster, or mesos://zk://host:2181 for a multi-master Mesos cluster using ZooKeeper.

The driver also needs some configuration in to interact properly with Mesos:

  1. In set some environment variables:
    • export MESOS_NATIVE_LIBRARY=<path to>. This path is typically <prefix>/lib/ where the prefix is /usr/local by default. See Mesos installation instructions above. On Mac OS X, the library is called libmesos.dylib instead of
    • export SPARK_EXECUTOR_URI=<URL of spark-1.2.0.tar.gz uploaded above>.
  2. Also set spark.executor.uri to <URL of spark-1.2.0.tar.gz>.

Now when starting a Spark application against the cluster, pass a mesos:// URL as the master when creating a SparkContext. For example:

val conf = new SparkConf()
  .setAppName("My app")
  .set("spark.executor.uri", "<path to spark-1.2.0.tar.gz uploaded above>")
val sc = new SparkContext(conf)

(You can also use spark-submit and configure spark.executor.uri in the conf/spark-defaults.conf file. Note that spark-submit currently only supports deploying the Spark driver in client mode for Mesos.)

When running a shell, the spark.executor.uri parameter is inherited from SPARK_EXECUTOR_URI, so it does not need to be redundantly passed in as a system property.

./bin/spark-shell --master mesos://host:5050

Mesos Run Modes

Spark can run over Mesos in two modes: “fine-grained” (default) and “coarse-grained”.

In “fine-grained” mode (default), each Spark task runs as a separate Mesos task. This allows multiple instances of Spark (and other frameworks) to share machines at a very fine granularity, where each application gets more or fewer machines as it ramps up and down, but it comes with an additional overhead in launching each task. This mode may be inappropriate for low-latency requirements like interactive queries or serving web requests.

The “coarse-grained” mode will instead launch only one long-running Spark task on each Mesos machine, and dynamically schedule its own “mini-tasks” within it. The benefit is much lower startup overhead, but at the cost of reserving the Mesos resources for the complete duration of the application.

To run in coarse-grained mode, set the spark.mesos.coarse property in your SparkConf:

conf.set("spark.mesos.coarse", "true")

In addition, for coarse-grained mode, you can control the maximum number of resources Spark will acquire. By default, it will acquire all cores in the cluster (that get offered by Mesos), which only makes sense if you run just one application at a time. You can cap the maximum number of cores using conf.set("spark.cores.max", "10") (for example).

Known issues

Running Alongside Hadoop

You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a separate service on the machines. To access Hadoop data from Spark, a full hdfs:// URL is required (typically hdfs://<namenode>:9000/path, but you can find the right URL on your Hadoop Namenode web UI).

In addition, it is possible to also run Hadoop MapReduce on Mesos for better resource isolation and sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each node. Please refer to Hadoop on Mesos.

In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos.


See the configuration page for information on Spark configurations. The following configs are specific for Spark on Mesos.

Spark Properties

Property NameDefaultMeaning
spark.mesos.coarse false Set the run mode for Spark on Mesos. For more information about the run mode, refer to #Mesos Run Mode section above.
spark.mesos.extra.cores 0 Set the extra amount of cpus to request per task. This setting is only used for Mesos coarse grain mode. The total amount of cores requested per task is the number of cores in the offer plus the extra cores configured. Note that total amount of cores the executor will request in total will not exceed the spark.cores.max setting.
spark.mesos.executor.home SPARK_HOME The location where the mesos executor will look for Spark binaries to execute, and uses the SPARK_HOME setting on default. This variable is only used when no spark.executor.uri is provided, and assumes Spark is installed on the specified location on each slave.
spark.mesos.executor.memoryOverhead 384 The amount of memory that Mesos executor will request for the task to account for the overhead of running the executor itself. The final total amount of memory allocated is the maximum value between executor memory plus memoryOverhead, and overhead fraction (1.07) plus the executor memory.

Troubleshooting and Debugging

A few places to look during debugging:

And common pitfalls: