This document briefly describes how Flink schedules jobs and how it represents and tracks job status on the JobManager.
Execution resources in Flink are defined through Task Slots. Each TaskManager will have one or more task slots, each of which can run one pipeline of parallel tasks. A pipeline consists of multiple successive tasks, such as the n-th parallel instance of a MapFunction together with the n-th parallel instance of a ReduceFunction. Note that Flink often executes successive tasks concurrently: For Streaming programs, that happens in any case, but also for batch programs, it happens frequently.
The figure below illustrates that. Consider a program with a data source, a MapFunction, and a ReduceFunction. The source and MapFunction are executed with a parallelism of 4, while the ReduceFunction is executed with a parallism of 3. A pipeline consists of the sequence Source - Map - Reduce. On a cluster with 2 TaskManagers with 3 slots each, the program will be executed as described below.
During job execution, the JobManager keeps track of distributed tasks, decides when to schedule the next task (or set of tasks), and reacts to finished tasks or execution failures.
The JobManager receives the JobGraph, which is a representation of the data flow consisting of operators (JobVertex) and intermediate results (IntermediateDataSet). Each operator has properies, like the parallelism and the code that it executes. In addition, the JobGraph has a set of attached libraries, that are neccessary to execute the code of the operators.
The JobManager transforms the JobGraph into an ExecutionGraph. The ExecutionGraph is a parallel version of the JobGraph: For each JobVertex, it contains an ExecutionVertex per parallel subtask. An operator with a parallelism of 100 will have one JobVertex and 100 ExecutionVertices. The ExecutionVertex tracks the state of execution of a particular subtask. All ExecutionVertices from one JobVertex are held in an ExecutionJobVertex, which tracks the status of the operator as a whole. Besides the vertices, the ExecutionGraph also contains the IntermediateResult and the IntermediateResultPartition. The former tracks the state of the IntermediateDataSet, the latter the state of each of its partitions.
During its execution, each parallel task goes through multiple stages, from created to finished or failed. The diagram below illustrates the states and possible transitions between them. A task may be executed multiple times (for example in the course of failure recovery). For that reason, the execution of an ExecutionVertex is tracked in an Execution. Each ExecutionVertex has a current Execution, and prior Executions.