# Executor

Executors act as a job execution abstraction layer to adapt the graph service to different platforms on which jobs can be run (e.g. local processes, containers, kubernetes, cloud container services, databricks/EMR, etc).

## Capabilities

- stdout/stderr capture
- producing job BEL events
- parsing missing upstream partition deps
- heartbeating - allows the graph to determine what jobs are still live
- job re-entrance

## Job Lifecycle

```mermaid
stateDiagram-v2
    [*] --> Buffering
    Buffering --> Queued : collecting other wants
    Queued --> Running : scheduled
    Running --> Running : heartbeat
    Running --> Failure
    Buffering --> Canceled
    Queued --> Canceled
    Running --> Canceled
    Canceled --> [*] : will not retry
    Running --> MissingDeps
    Running --> Success
    MissingDeps --> [*] : await deps to rerun
    Failure --> [*] : retry according \n to policy
    Success --> [*]
```

At each state transition the executor emits a BEL event to the graph

### Buffering
For jobs that buffer - non buffering jobs emit `Buffering` but immediately move to `Queued`. Signified by BEL event with buffering start timestamp and other relevant details for when job can be queued. 

### Queued
Job run will be launched as soon as the constraints allow (pool slots/etc).

### Running
The job run is active, as indicated by continual heartbeating. In this state, the executor will capture logs to disk.

### MissingDeps
Job run has emitted the `__DATABUILD_ERROR__::{...}` line in stdout, executor will emit a missing deps event.

### Canceled
Job run explicitly canceled, emits canceled event along with details.

### Success
The job run has succeeded, executor emits events with written partitions.

### Failure
The job run has failed. The run will be retried according to the