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How Applications are Executed on a Spark Cluster

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Before you begin your journey as a Spark programmer, you should have a solid understanding of the Spark application architecture and how applications are executed on a Spark cluster. This chapter closely examines the components of a Spark application, looks at how these components work together, and looks at how Spark applications run on Standalone and YARN clusters.

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  • It is not the beauty of a building you should look at; it’s the construction of the foundation that will stand the test of time.

    David Allan Coe, American songwriter

In This Chapter:

  • Detailed overview of the Spark application and cluster components

  • Spark resource schedulers and Cluster Managers

  • How Spark applications are scheduled on YARN clusters

  • Spark deployment modes

Before you begin your journey as a Spark programmer, you should have a solid understanding of the Spark application architecture and how applications are executed on a Spark cluster. This chapter closely examines the components of a Spark application, looks at how these components work together, and looks at how Spark applications run on Standalone and YARN clusters.

Anatomy of a Spark Application

A Spark application contains several components, all of which exist whether you’re running Spark on a single machine or across a cluster of hundreds or thousands of nodes.

Each component has a specific role in executing a Spark program. Some of these roles, such as the client components, are passive during execution; other roles are active in the execution of the program, including components executing computation functions.

The components of a Spark application are the Driver, the Master, the Cluster Manager, and the Executor(s), which run on worker nodes, or Workers. Figure 3.1 shows all the Spark components in the context of a Spark Standalone application. You will learn more about each component and its function in more detail later in this chapter.

Figure 3.1

Figure 3.1 Spark Standalone cluster application components.

All Spark components, including the Driver, Master, and Executor processes, run in Java virtual machines (JVMs). A JVM is a cross-platform runtime engine that can execute instructions compiled into Java bytecode. Scala, which Spark is written in, compiles into bytecode and runs on JVMs.

It is important to distinguish between Spark’s runtime application components and the locations and node types on which they run. These components run in different places using different deployment modes, so don’t think of these components in physical node or instance terms. For instance, when running Spark on YARN, there would be several variations of Figure 3.1. However, all the components pictured are still involved in the application and have the same roles.

Spark Driver

The life of a Spark application starts and finishes with the Spark Driver. The Driver is the process that clients use to submit applications in Spark. The Driver is also responsible for planning and coordinating the execution of the Spark program and returning status and/or results (data) to the client. The Driver can physically reside on a client or on a node in the cluster, as you will see later.


The Spark Driver is responsible for creating the SparkSession. The SparkSession object represents a connection to a Spark cluster. The SparkSession is instantiated at the beginning of a Spark application, including the interactive shells, and is used for the entirety of the program.

Prior to Spark 2.0, entry points for Spark applications included the SparkContext, used for Spark core applications; the SQLContext and HiveContext, used with Spark SQL applications; and the StreamingContext, used for Spark Streaming applications. The SparkSession object introduced in Spark 2.0 combines all these objects into a single entry point that can be used for all Spark applications.

Through its SparkContext and SparkConf child objects, the SparkSession object contains all the runtime configuration properties set by the user, including configuration properties such as the Master, application name, number of Executors, and more. Figure 3.2 shows the SparkSession object and some of its configuration properties within a pyspark shell.

Figure 3.2

Figure 3.2 SparkSession properties.

Listing 3.1 demonstrates how to create a SparkSession within a non-interactive Spark application, such as a program submitted using spark-submit.

Listing 3.1 Creating a SparkSession

from pyspark.sql import SparkSession
spark = SparkSession.builder     .master("spark://sparkmaster:7077")     .appName("My Spark Application")     .config("spark.submit.deployMode", "client")     .getOrCreate()
numlines = spark.sparkContext.textFile("file:///opt/spark/licenses")     .count()
print("The total number of lines is " + str(numlines))

Application Planning

One of the main functions of the Driver is to plan the application. The Driver takes the application processing input and plans the execution of the program. The Driver takes all the requested transformations (data manipulation operations) and actions (requests for output or prompts to execute programs) and creates a directed acyclic graph (DAG) of nodes, each representing a transformational or computational step.

A Spark application DAG consists of tasks and stages. A task is the smallest unit of schedulable work in a Spark program. A stage is a set of tasks that can be run together. Stages are dependent upon one another; in other words, there are stage dependencies.

In a process scheduling sense, DAGs are not unique to Spark. For instance, they are used in other Big Data ecosystem projects, such as Tez, Drill, and Presto for scheduling. DAGs are fundamental to Spark, so it is worth being familiar with the concept.

Application Orchestration

The Driver also coordinates the running of stages and tasks defined in the DAG. Key driver activities involved in the scheduling and running of tasks include the following:

  • Keeping track of available resources to execute tasks

  • Scheduling tasks to run “close” to the data where possible (the concept of data locality)

Other Functions

In addition to planning and orchestrating the execution of a Spark program, the Driver is also responsible for returning the results from an application. These could be return codes or data in the case of an action that requests data to be returned to the client (for example, an interactive query).

The Driver also serves the application UI on port 4040, as shown in Figure 3.3. This UI is created automatically; it is independent of the code submitted or how it was submitted (that is, interactive using pyspark or non-interactive using spark-submit).

Figure 3.3

Figure 3.3 Spark application UI.

If subsequent applications launch on the same host, successive ports are used for the application UI (for example, 4041, 4042, and so on).

Spark Workers and Executors

Spark Executors are the processes on which Spark DAG tasks run. Executors reserve CPU and memory resources on slave nodes, or Workers, in a Spark cluster. An Executor is dedicated to a specific Spark application and terminated when the application completes. A Spark program normally consists of many Executors, often working in parallel.

Typically, a Worker node—which hosts the Executor process—has a finite or fixed number of Executors allocated at any point in time. Therefore, a cluster—being a known number of nodes—has a finite number of Executors available to run at any given time. If an application requires Executors in excess of the physical capacity of the cluster, they are scheduled to start as other Executors complete and release their resources.

As mentioned earlier in this chapter, JVMs host Spark Executors. The JVM for an Executor is allocated a heap, which is a dedicated memory space in which to store and manage objects. The amount of memory committed to the JVM heap for an Executor is set by the property spark.executor.memory or as the --executor-memory argument to the pysparkspark-shell, or spark-submit commands.

Executors store output data from tasks in memory or on disk. It is important to note that Workers and Executors are aware only of the tasks allocated to them, whereas the Driver is responsible for understanding the complete set of tasks and the respective dependencies that comprise an application.

By using the Spark application UI on port 404x of the Driver host, you can inspect Executors for the application, as shown in Figure 3.4.

Figure 3.4

Figure 3.4 Executors tab in the Spark application UI.

For Spark Standalone cluster deployments, a worker node exposes a user interface on port 8081, as shown in Figure 3.5.

Figure 3.5

Figure 3.5 Spark Worker UI.

The Spark Master and Cluster Manager

The Spark Driver plans and coordinates the set of tasks required to run a Spark application. The tasks themselves run in Executors, which are hosted on Worker nodes.

The Master and the Cluster Manager are the central processes that monitor, reserve, and allocate the distributed cluster resources (or containers, in the case of YARN or Mesos) on which the Executors run. The Master and the Cluster Manager can be separate processes, or they can combine into one process, as is the case when running Spark in Standalone mode.

Spark Master

The Spark Master is the process that requests resources in the cluster and makes them available to the Spark Driver. In all deployment modes, the Master negotiates resources or containers with Worker nodes or slave nodes and tracks their status and monitors their progress.

When running Spark in Standalone mode, the Spark Master process serves a web UI on port 8080 on the Master host, as shown in Figure 3.6.

Figure 3.6

Figure 3.6 Spark Master UI.

Cluster Manager

The Cluster Manager is the process responsible for monitoring the Worker nodes and reserving resources on these nodes upon request by the Master. The Master then makes these cluster resources available to the Driver in the form of Executors.

As discussed earlier, the Cluster Manager can be separate from the Master process. This is the case when running Spark on Mesos or YARN. In the case of Spark running in Standalone mode, the Master process also performs the functions of the Cluster Manager. Effectively, it acts as its own Cluster Manager.

A good example of the Cluster Manager function is the YARN ResourceManager process for Spark applications running on Hadoop clusters. The ResourceManager schedules, allocates, and monitors the health of containers running on YARN NodeManagers. Spark applications then use these containers to host Executor processes, as well as the Master process if the application is running in cluster mode; we will look at this shortly.

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