Neither exit codes and status nor signals are Spark specific but part of the way processes work on Unix-like systems.

Exit status and exit code

Exit status and exit codes are different names for the same thing. An exit status is a number between 0 and 255 which indicates the outcome of a process after it terminated. Exit status 0 usually indicates success. The meaning of the other codes is program dependent and should be described in the program's documentation. There are some established standard codes, though. See this answer for a comprehensive list.

Exit codes used by Spark

In the Spark sources I found the followingexit codes. Their descriptions are taken from log statements and comments in the code and from my understanding of the code where the exit status appeared.

Spark SQL CLI Driver in Hive Thrift Server

• 3: if an UnsupportedEncodingException occurred when setting up stdout and stderr streams.

Spark/Yarn

• 10: if an uncaught exception occurred
• 11: if more than spark.yarn.scheduler.reporterThread.maxFailures executor failures occurred
• 12: if the reporter thread failed with an exception
• 13: if the program terminated before the user had initialized the spark context or if the spark context did not initialize before a timeout.
• 14: This is declared as EXIT_SECURITY but never used
• 15: if a user class threw an exception
• 16: if the shutdown hook called before final status was reported. A comment in the source code explains the expected behaviour of user applications:

  The default state of ApplicationMaster is failed if it is invoked by shut down hook. This behavior is different compared to 1.x version. If user application is exited ahead of time by calling System.exit(N), here mark this application as failed with EXIT_EARLY. For a good shutdown, user shouldn't call System.exit(0) to terminate the application.

Executors

• 50: The default uncaught exception handler was reached
• 51: The default uncaught exception handler was called and an exception was encountered while logging the exception
• 52: The default uncaught exception handler was reached, and the uncaught exception was an OutOfMemoryError
• 53: DiskStore failed to create local temporary directory after many attempts (bad spark.local.dir?)
• 54: ExternalBlockStore failed to initialize after many attempts
• 55: ExternalBlockStore failed to create a local temporary directory after many attempts

• 56: Executor is unable to send heartbeats to the driver more than "spark.executor.heartbeat.maxFailures" times.

• 101: Returned by spark-submit if the child main class was not found. In client mode (command line option --deploy-mode client) the child main class is the user submitted application class (--class CLASS). In cluster mode (--deploy-mode cluster) the child main class is the cluster manager specific submission/client class.

Exit codes greater than 128

These exit codes most likely result from a program shutdown triggered bya Unix signal. The signal number can be calculated by substracting 128 from the exit code. This is explained in more details in this blog post (which was originally linked in this question). There is also a good answer explaining JVM-generated exit codes. Spark works with this assumption as explained in a comment in ExecutorExitCodes.scala

Other exit codes

Apart from the exit codes listed above there are number of System.exit() calls in the Spark sources setting 1 or -1 as exit code. As far as I an tell -1 seems to be used to indicate missing or incorrect command line parameters while 1 indicates all other errors.

Signals

Signals are a kind of events which allow to send system messages to a process. These messages are used to ask a process to reload its configuration (SIGHUP) or to terminate itself (SIGKILL), for instance. A list of standard signals can be found in the signal(7) man page in section Standard Signals.

As explained by Rick Moritz in the comments below (thank you!), the most likely sources of signals in a Spark setup are

• the cluster resource manager when the container size exceeded, the job finished, a dynamic scale-down was made, or a job was aborted by the user
• the operating system: as part of a controlled system shut down or if some resource limit was hit (out of memory, over hard quota, no space left on disk etc.)
• a local user who killed a job

I hope this makes it a bit clearer what these messages by spark might mean.