major refactor
This commit is contained in:
260
core/task/executor.py
Normal file
260
core/task/executor.py
Normal file
@@ -0,0 +1,260 @@
|
||||
"""
|
||||
Executor abstraction for job processing.
|
||||
|
||||
Supports different backends:
|
||||
- LocalExecutor: FFmpeg via Celery (default)
|
||||
- LambdaExecutor: AWS Lambda (future)
|
||||
"""
|
||||
|
||||
import os
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Callable, Dict, Optional
|
||||
|
||||
from core.ffmpeg.transcode import TranscodeConfig, transcode
|
||||
|
||||
# Configuration from environment
|
||||
MPR_EXECUTOR = os.environ.get("MPR_EXECUTOR", "local")
|
||||
|
||||
|
||||
class Executor(ABC):
|
||||
"""Abstract base class for job executors."""
|
||||
|
||||
@abstractmethod
|
||||
def run(
|
||||
self,
|
||||
job_id: str,
|
||||
source_path: str,
|
||||
output_path: str,
|
||||
preset: Optional[Dict[str, Any]] = None,
|
||||
trim_start: Optional[float] = None,
|
||||
trim_end: Optional[float] = None,
|
||||
duration: Optional[float] = None,
|
||||
progress_callback: Optional[Callable[[int, Dict[str, Any]], None]] = None,
|
||||
) -> bool:
|
||||
"""
|
||||
Execute a transcode/trim job.
|
||||
|
||||
Args:
|
||||
job_id: Unique job identifier
|
||||
source_path: Path to source file
|
||||
output_path: Path for output file
|
||||
preset: Transcode preset dict (optional, None = trim only)
|
||||
trim_start: Trim start time in seconds (optional)
|
||||
trim_end: Trim end time in seconds (optional)
|
||||
duration: Source duration in seconds (for progress calculation)
|
||||
progress_callback: Called with (percent, details_dict)
|
||||
|
||||
Returns:
|
||||
True if successful
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class LocalExecutor(Executor):
|
||||
"""Execute jobs locally using FFmpeg."""
|
||||
|
||||
def run(
|
||||
self,
|
||||
job_id: str,
|
||||
source_path: str,
|
||||
output_path: str,
|
||||
preset: Optional[Dict[str, Any]] = None,
|
||||
trim_start: Optional[float] = None,
|
||||
trim_end: Optional[float] = None,
|
||||
duration: Optional[float] = None,
|
||||
progress_callback: Optional[Callable[[int, Dict[str, Any]], None]] = None,
|
||||
) -> bool:
|
||||
"""Execute job using local FFmpeg."""
|
||||
|
||||
# Build config from preset or use stream copy for trim-only
|
||||
if preset:
|
||||
config = TranscodeConfig(
|
||||
input_path=source_path,
|
||||
output_path=output_path,
|
||||
video_codec=preset.get("video_codec", "libx264"),
|
||||
video_bitrate=preset.get("video_bitrate"),
|
||||
video_crf=preset.get("video_crf"),
|
||||
video_preset=preset.get("video_preset"),
|
||||
resolution=preset.get("resolution"),
|
||||
framerate=preset.get("framerate"),
|
||||
audio_codec=preset.get("audio_codec", "aac"),
|
||||
audio_bitrate=preset.get("audio_bitrate"),
|
||||
audio_channels=preset.get("audio_channels"),
|
||||
audio_samplerate=preset.get("audio_samplerate"),
|
||||
container=preset.get("container", "mp4"),
|
||||
extra_args=preset.get("extra_args", []),
|
||||
trim_start=trim_start,
|
||||
trim_end=trim_end,
|
||||
)
|
||||
else:
|
||||
# Trim-only: stream copy
|
||||
config = TranscodeConfig(
|
||||
input_path=source_path,
|
||||
output_path=output_path,
|
||||
video_codec="copy",
|
||||
audio_codec="copy",
|
||||
trim_start=trim_start,
|
||||
trim_end=trim_end,
|
||||
)
|
||||
|
||||
# Wrapper to convert float percent to int
|
||||
def wrapped_callback(percent: float, details: Dict[str, Any]) -> None:
|
||||
if progress_callback:
|
||||
progress_callback(int(percent), details)
|
||||
|
||||
return transcode(
|
||||
config,
|
||||
duration=duration,
|
||||
progress_callback=wrapped_callback if progress_callback else None,
|
||||
)
|
||||
|
||||
|
||||
class LambdaExecutor(Executor):
|
||||
"""Execute jobs via AWS Step Functions + Lambda."""
|
||||
|
||||
def __init__(self):
|
||||
import boto3
|
||||
|
||||
region = os.environ.get("AWS_REGION", "us-east-1")
|
||||
self.sfn = boto3.client("stepfunctions", region_name=region)
|
||||
self.state_machine_arn = os.environ["STEP_FUNCTION_ARN"]
|
||||
self.callback_url = os.environ.get("CALLBACK_URL", "")
|
||||
self.callback_api_key = os.environ.get("CALLBACK_API_KEY", "")
|
||||
|
||||
def run(
|
||||
self,
|
||||
job_id: str,
|
||||
source_path: str,
|
||||
output_path: str,
|
||||
preset: Optional[Dict[str, Any]] = None,
|
||||
trim_start: Optional[float] = None,
|
||||
trim_end: Optional[float] = None,
|
||||
duration: Optional[float] = None,
|
||||
progress_callback: Optional[Callable[[int, Dict[str, Any]], None]] = None,
|
||||
) -> bool:
|
||||
"""Start a Step Functions execution for this job."""
|
||||
import json
|
||||
|
||||
payload = {
|
||||
"job_id": job_id,
|
||||
"source_key": source_path,
|
||||
"output_key": output_path,
|
||||
"preset": preset,
|
||||
"trim_start": trim_start,
|
||||
"trim_end": trim_end,
|
||||
"duration": duration,
|
||||
"callback_url": self.callback_url,
|
||||
"api_key": self.callback_api_key,
|
||||
}
|
||||
|
||||
response = self.sfn.start_execution(
|
||||
stateMachineArn=self.state_machine_arn,
|
||||
name=f"mpr-{job_id}",
|
||||
input=json.dumps(payload),
|
||||
)
|
||||
|
||||
# Store execution ARN on the job
|
||||
execution_arn = response["executionArn"]
|
||||
try:
|
||||
from core.db import update_job_fields
|
||||
update_job_fields(job_id, execution_arn=execution_arn)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return True
|
||||
|
||||
|
||||
class GCPExecutor(Executor):
|
||||
"""Execute jobs via Google Cloud Run Jobs."""
|
||||
|
||||
def __init__(self):
|
||||
from google.cloud import run_v2
|
||||
|
||||
self.client = run_v2.JobsClient()
|
||||
self.project_id = os.environ["GCP_PROJECT_ID"]
|
||||
self.region = os.environ.get("GCP_REGION", "us-central1")
|
||||
self.job_name = os.environ["CLOUD_RUN_JOB"]
|
||||
self.callback_url = os.environ.get("CALLBACK_URL", "")
|
||||
self.callback_api_key = os.environ.get("CALLBACK_API_KEY", "")
|
||||
|
||||
def run(
|
||||
self,
|
||||
job_id: str,
|
||||
source_path: str,
|
||||
output_path: str,
|
||||
preset: Optional[Dict[str, Any]] = None,
|
||||
trim_start: Optional[float] = None,
|
||||
trim_end: Optional[float] = None,
|
||||
duration: Optional[float] = None,
|
||||
progress_callback: Optional[Callable[[int, Dict[str, Any]], None]] = None,
|
||||
) -> bool:
|
||||
"""Trigger a Cloud Run Job execution for this job."""
|
||||
import json
|
||||
|
||||
from google.cloud import run_v2
|
||||
|
||||
payload = {
|
||||
"job_id": job_id,
|
||||
"source_key": source_path,
|
||||
"output_key": output_path,
|
||||
"preset": preset,
|
||||
"trim_start": trim_start,
|
||||
"trim_end": trim_end,
|
||||
"duration": duration,
|
||||
"callback_url": self.callback_url,
|
||||
"api_key": self.callback_api_key,
|
||||
}
|
||||
|
||||
job_path = (
|
||||
f"projects/{self.project_id}/locations/{self.region}/jobs/{self.job_name}"
|
||||
)
|
||||
|
||||
request = run_v2.RunJobRequest(
|
||||
name=job_path,
|
||||
overrides=run_v2.RunJobRequest.Overrides(
|
||||
container_overrides=[
|
||||
run_v2.RunJobRequest.Overrides.ContainerOverride(
|
||||
env=[
|
||||
run_v2.EnvVar(
|
||||
name="MPR_JOB_PAYLOAD", value=json.dumps(payload)
|
||||
)
|
||||
]
|
||||
)
|
||||
]
|
||||
),
|
||||
)
|
||||
|
||||
operation = self.client.run_job(request=request)
|
||||
execution_name = operation.metadata.name
|
||||
|
||||
try:
|
||||
from core.db import update_job_fields
|
||||
|
||||
update_job_fields(job_id, execution_arn=execution_name)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return True
|
||||
|
||||
|
||||
# Executor registry
|
||||
_executors: Dict[str, type] = {
|
||||
"local": LocalExecutor,
|
||||
"lambda": LambdaExecutor,
|
||||
"gcp": GCPExecutor,
|
||||
}
|
||||
|
||||
_executor_instance: Optional[Executor] = None
|
||||
|
||||
|
||||
def get_executor() -> Executor:
|
||||
"""Get the configured executor instance."""
|
||||
global _executor_instance
|
||||
|
||||
if _executor_instance is None:
|
||||
executor_type = MPR_EXECUTOR.lower()
|
||||
if executor_type not in _executors:
|
||||
raise ValueError(f"Unknown executor type: {executor_type}")
|
||||
_executor_instance = _executors[executor_type]()
|
||||
|
||||
return _executor_instance
|
||||
Reference in New Issue
Block a user