Spark local

Spark local is one of the available runtime environments in Apache Spark. It is the only available runtime with no need for a proper cluster manager (and hence many call it a pseudo-cluster, however such concept do exist in Spark and is a bit different).

Spark local is used for the following master URLs (as specified using SparkConf.setMaster method or spark.master configuration property):

  • local (with exactly 1 CPU core)

  • local[n] (with exactly n CPU cores)

  • local[*] (with the total number of CPU cores that is the number of available CPU cores on the local machine)

  • local[n, m] (with exactly n CPU cores and m retries when a task fails)

  • local[*, m] (with the total number of CPU cores that is the number of available CPU cores on the local machine)

Internally, Spark local uses LocalSchedulerBackend as the SchedulerBackend and ExecutorBackend.

spark local architecture.png
Figure 1. Architecture of Spark local

In this non-distributed multi-threaded runtime environment, Spark spawns all the main execution components - the driver and an executor - in the same single JVM.

The default parallelism is the number of threads as specified in the master URL. This is the only mode where a driver is used for execution (as it acts both as the driver and the only executor).

The local mode is very convenient for testing, debugging or demonstration purposes as it requires no earlier setup to launch Spark applications.

This mode of operation is also called Spark in-process or (less commonly) a local version of Spark.

SparkContext.isLocal returns true when Spark runs in local mode.

scala> sc.isLocal
res0: Boolean = true

Spark shell defaults to local mode with local[*] as the the master URL.

scala> sc.master
res0: String = local[*]

Tasks are not re-executed on failure in local mode (unless local-with-retries master URL is used).

The task scheduler in local mode works with LocalSchedulerBackend task scheduler backend.

Master URL

You can run Spark in local mode using local, local[n] or the most general local[*] for the master URL.

The URL says how many threads can be used in total:

  • local uses 1 thread only.

  • local[n] uses n threads.

  • local[*] uses as many threads as the number of processors available to the Java virtual machine (it uses Runtime.getRuntime.availableProcessors() to know the number).

Note
What happens when there are less cores than n in local[n] master URL? "Breaks" scheduling as Spark assumes more CPU cores available to execute tasks.
  • local[N, maxFailures] (called local-with-retries) with N being * or the number of threads to use (as explained above) and maxFailures being the value of spark.task.maxFailures configuration property.

Task Submission a.k.a. reviveOffers

taskscheduler submitTasks local mode.png
Figure 2. TaskSchedulerImpl.submitTasks in local mode

When ReviveOffers or StatusUpdate messages are received, LocalEndpoint places an offer to TaskSchedulerImpl (using TaskSchedulerImpl.resourceOffers).

If there is one or more tasks that match the offer, they are launched (using executor.launchTask method).

The number of tasks to be launched is controlled by the number of threads as specified in master URL. The executor uses threads to spawn the tasks.

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