While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs. Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown. Author: Sean Owen <sowen@cloudera.com> Closes #653 from srowen/SPARK-1727 and squashes the following commits: 6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count 8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output) 99966a9 [Sean Owen] Update issue tracker URL in docs 23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak) 8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs
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layout | title |
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global | Cluster Mode Overview |
This document gives a short overview of how Spark runs on clusters, to make it easier to understand the components involved.
Components
Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object in your main program (called the driver program). Specifically, to run on a cluster, the SparkContext can connect to several types of cluster managers (either Spark's own standalone cluster manager or Mesos/YARN), which allocate resources across applications. Once connected, Spark acquires executors on nodes in the cluster, which are processes that run computations and store data for your application. Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to the executors. Finally, SparkContext sends tasks for the executors to run.
There are several useful things to note about this architecture:
- Each application gets its own executor processes, which stay up for the duration of the whole application and run tasks in multiple threads. This has the benefit of isolating applications from each other, on both the scheduling side (each driver schedules its own tasks) and executor side (tasks from different applications run in different JVMs). However, it also means that data cannot be shared across different Spark applications (instances of SparkContext) without writing it to an external storage system.
- Spark is agnostic to the underlying cluster manager. As long as it can acquire executor processes, and these communicate with each other, it is relatively easy to run it even on a cluster manager that also supports other applications (e.g. Mesos/YARN).
- Because the driver schedules tasks on the cluster, it should be run close to the worker nodes, preferably on the same local area network. If you'd like to send requests to the cluster remotely, it's better to open an RPC to the driver and have it submit operations from nearby than to run a driver far away from the worker nodes.
Cluster Manager Types
The system currently supports three cluster managers:
- Standalone -- a simple cluster manager included with Spark that makes it easy to set up a cluster.
- Apache Mesos -- a general cluster manager that can also run Hadoop MapReduce and service applications.
- Hadoop YARN -- the resource manager in Hadoop 2.
In addition, Spark's EC2 launch scripts make it easy to launch a standalone cluster on Amazon EC2.
Bundling and Launching Applications
Bundling Your Application's Dependencies
If your code depends on other projects, you will need to package them alongside
your application in order to distribute the code to a Spark cluster. To do this,
to create an assembly jar (or "uber" jar) containing your code and its dependencies. Both
sbt and
Maven
have assembly plugins. When creating assembly jars, list Spark and Hadoop
as provided
dependencies; these need not be bundled since they are provided by
the cluster manager at runtime. Once you have an assembled jar you can call the bin/spark-submit
script as shown here while passing your jar.
For Python, you can use the pyFiles
argument of SparkContext
or its addPyFile
method to add .py
, .zip
or .egg
files to be distributed.
Launching Applications with ./bin/spark-submit
Once a user application is bundled, it can be launched using the spark-submit
script located in
the bin directory. This script takes care of setting up the classpath with Spark and its
dependencies, and can support different cluster managers and deploy modes that Spark supports.
It's usage is
./bin/spark-submit --class path.to.your.Class [options] <app jar> [app options]
When calling spark-submit
, [app options]
will be passed along to your application's
main class. To enumerate all options available to spark-submit
run it with
the --help
flag. Here are a few examples of common options:
{% highlight bash %}
Run application locally
./bin/spark-submit
--class my.main.ClassName
--master local[8]
my-app.jar
Run on a Spark standalone cluster
./bin/spark-submit
--class my.main.ClassName
--master spark://mycluster:7077
--executor-memory 20G
--total-executor-cores 100
my-app.jar
Run on a YARN cluster
HADOOP_CONF_DIR=XX /bin/spark-submit
--class my.main.ClassName
--master yarn-cluster \ # can also be yarn-client
for client mode
--executor-memory 20G
--num-executors 50
my-app.jar
{% endhighlight %}
Loading Configurations from a File
The spark-submit
script can load default SparkConf
values from a properties file and pass them
onto your application. By default it will read configuration options from
conf/spark-defaults.conf
. Any values specified in the file will be passed on to the
application when run. They can obviate the need for certain flags to spark-submit
: for
instance, if spark.master
property is set, you can safely omit the
--master
flag from spark-submit
. In general, configuration values explicitly set on a
SparkConf
take the highest precedence, then flags passed to spark-submit
, then values
in the defaults file.
If you are ever unclear where configuration options are coming from. fine-grained debugging
information can be printed by adding the --verbose
option to ./spark-submit
.
Advanced Dependency Management
When using ./bin/spark-submit
the app jar along with any jars included with the --jars
option
will be automatically transferred to the cluster. --jars
can also be used to distribute .egg and .zip
libraries for Python to executors. Spark uses the following URL scheme to allow different
strategies for disseminating jars:
- file: - Absolute paths and
file:/
URIs are served by the driver's HTTP file server, and every executor pulls the file from the driver HTTP server. - hdfs:, http:, https:, ftp: - these pull down files and JARs from the URI as expected
- local: - a URI starting with local:/ is expected to exist as a local file on each worker node. This means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker, or shared via NFS, GlusterFS, etc.
Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes.
This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup
is handled automatically, and with Spark standalone, automatic cleanup can be configured with the
spark.worker.cleanup.appDataTtl
property.
Monitoring
Each driver program has a web UI, typically on port 4040, that displays information about running
tasks, executors, and storage usage. Simply go to http://<driver-node>:4040
in a web browser to
access this UI. The monitoring guide also describes other monitoring options.
Job Scheduling
Spark gives control over resource allocation both across applications (at the level of the cluster manager) and within applications (if multiple computations are happening on the same SparkContext). The job scheduling overview describes this in more detail.
Glossary
The following table summarizes terms you'll see used to refer to cluster concepts:
Term | Meaning |
---|---|
Application | User program built on Spark. Consists of a driver program and executors on the cluster. |
Application jar | A jar containing the user's Spark application. In some cases users will want to create an "uber jar" containing their application along with its dependencies. The user's jar should never include Hadoop or Spark libraries, however, these will be added at runtime. |
Driver program | The process running the main() function of the application and creating the SparkContext |
Cluster manager | An external service for acquiring resources on the cluster (e.g. standalone manager, Mesos, YARN) |
Deploy mode | Distinguishes where the driver process runs. In "cluster" mode, the framework launches the driver inside of the cluster. In "client" mode, the submitter launches the driver outside of the cluster. |
Worker node | Any node that can run application code in the cluster |
Executor | A process launched for an application on a worker node, that runs tasks and keeps data in memory or disk storage across them. Each application has its own executors. |
Task | A unit of work that will be sent to one executor |
Job | A parallel computation consisting of multiple tasks that gets spawned in response to a Spark action
(e.g. save , collect ); you'll see this term used in the driver's logs. |
Stage | Each job gets divided into smaller sets of tasks called stages that depend on each other (similar to the map and reduce stages in MapReduce); you'll see this term used in the driver's logs. |