Fault Tolerance

Currently Mars supports two levels of fault tolerance: process-level and worker-level. Scheduler-level support is not implemented now.

Process-level Fault Tolerance

Mars uses multiple processes in its worker. When a worker process fails, for instance, gets killed because of out of memory, and the process is not Process 0 where control actors run, Mars worker will mark relevant tasks as failed, start another process, restore actors on it and the retry mechanism will restart the failed task.

Worker-level Fault Tolerance


New in version 0.2.0a2

As Mars uses execution graphs to schedule tasks, when some workers fail, scheduler will find lost chunks and work out affected operands. After that the spotted operands are rescheduled.

Failure Notification and Processing

When a worker fails to respond, actors both in other workers and schedulers will detect it and send a feedback to ResourceActor, where changes in worker list is accepted and broadcast into all the sessions. When SessionActors accept the message, they will collect keys of missing data and relay all collected information to running GraphActors, where fail-over decisions are actually made.

The failure notification procedure is illustrated in the graph below.


When accepting a fail-over call, a GraphActor will first try reassigning states of affected operands, reassigning initial workers for initial operands, and then send updates to corresponding OperandActors to rerun these operands.

Reassigning States

When some workers fail, data stored in these workers are lost. Therefore we need to change the states of these operands in order to run them again. As is stated in operand states, data generated by an operand only exist when operands are in FINISHED state, we perform a two-pass scanning procedure to calculate new assignments for operands:

  1. Scan all FINISHED operands whose data are lost and mark them and their successors as affected;

  2. Scan the graph from bottom to top, starting from affected operands;

  3. For every affected operand, we scan its predecessors. If the data of the predecessor is lost or in a state without data or generating data, for instance, FREED or UNSCHEDULED, the predecessor will be marked as affected;

  4. Mark current operand as READY when no predecessors are affected, otherwise it will be marked as UNSCHEDULED;

  5. When there are no affected operands to be scanned, stop, otherwise go to Step 2.

Reassigning Initial Workers

When workers fail, some of initial operands assigned in graph preparation step may not have workers to execute on. What’s more, change in number of workers may lead to imbalance in worker assignments. We solve this problem by applying an adaptive worker reassigning strategy. The algorithm framework is similar with the original one except that we do not visit initial operands which are executed, and the stop criterion of visiting operands in Step 3 is now limited to the average number of operands per worker minus the number of operands already executed or determined to run in the candidate worker.