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6973475 ded8838 6973475 bb35191 6973475 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | """Lightweight DAG execution engine — inspired by Apache Airflow concepts."""
from __future__ import annotations
import time
import uuid
import threading
from datetime import datetime
from typing import Callable
# ── Shared execution state ─────────────────────────────────────────────────────
pipeline_executions: dict = {}
_lock = threading.Lock()
class Task:
"""A single unit of work in a DAG."""
def __init__(self, task_id: str, name: str, description: str,
func: Callable, upstream: list[str] | None = None,
icon: str = "⚙️", layer: int = 0):
self.task_id = task_id
self.name = name
self.description = description
self.func = func
self.upstream = upstream or [] # list of task_ids this depends on
self.icon = icon
self.layer = layer # visual column in the DAG
class DAG:
"""A directed acyclic graph of Tasks."""
def __init__(self, dag_id: str, name: str, description: str):
self.dag_id = dag_id
self.name = name
self.description = description
self.tasks: dict[str, Task] = {}
def add_task(self, task: Task):
self.tasks[task.task_id] = task
def topological_order(self) -> list[str]:
"""Kahn's algorithm — returns task_ids in execution order."""
in_degree = {tid: 0 for tid in self.tasks}
for task in self.tasks.values():
for up in task.upstream:
in_degree[task.task_id] += 1
queue = [tid for tid, deg in in_degree.items() if deg == 0]
order = []
while queue:
# Sort for determinism
queue.sort(key=lambda t: (self.tasks[t].layer, t))
tid = queue.pop(0)
order.append(tid)
for task in self.tasks.values():
if tid in task.upstream:
in_degree[task.task_id] -= 1
if in_degree[task.task_id] == 0:
queue.append(task.task_id)
return order
def to_dict(self) -> dict:
"""Serialise DAG structure for the frontend."""
return {
"dag_id": self.dag_id,
"name": self.name,
"description": self.description,
"tasks": {
tid: {
"task_id": t.task_id,
"name": t.name,
"description": t.description,
"upstream": t.upstream,
"icon": t.icon,
"layer": t.layer,
}
for tid, t in self.tasks.items()
},
}
# ── Execution engine ──────────────────────────────────────────────────────────
def _run_dag(exec_id: str, dag: DAG, context: dict):
"""Execute a DAG in a background thread."""
try:
order = dag.topological_order()
total = len(order)
task_results: dict = {}
def _upd(**kw):
with _lock:
pipeline_executions[exec_id].update(kw)
def _upd_task(tid: str, **kw):
with _lock:
pipeline_executions[exec_id]["task_states"][tid].update(kw)
def _push_log(msg: str):
with _lock:
ts = datetime.utcnow().strftime('%H:%M:%S')
pipeline_executions[exec_id]["logs"].append(f"[{ts}] {msg}")
context = {**context, "_log": _push_log}
_upd(status="running", progress=0)
for step_idx, tid in enumerate(order):
task = dag.tasks[tid]
_upd_task(tid, status="running",
started_at=datetime.utcnow().isoformat())
log_line = f"[{datetime.utcnow().strftime('%H:%M:%S')}] ▶ {task.name}"
with _lock:
pipeline_executions[exec_id]["logs"].append(log_line)
try:
result = task.func(context, task_results)
task_results[tid] = result
_upd_task(tid, status="success",
finished_at=datetime.utcnow().isoformat(),
result=str(result)[:200] if result is not None else "OK")
ok_line = f"[{datetime.utcnow().strftime('%H:%M:%S')}] ✔ {task.name} — OK"
with _lock:
pipeline_executions[exec_id]["logs"].append(ok_line)
except Exception as exc:
_upd_task(tid, status="failed",
finished_at=datetime.utcnow().isoformat(),
error=str(exc))
err_line = f"[{datetime.utcnow().strftime('%H:%M:%S')}] ✖ {task.name} — {exc}"
with _lock:
pipeline_executions[exec_id]["logs"].append(err_line)
# Continue with remaining tasks (soft failure)
progress = int(100 * (step_idx + 1) / total)
_upd(progress=progress)
time.sleep(0.1) # small delay so the UI can animate
_upd(status="completed", progress=100,
completed_at=datetime.utcnow().isoformat())
except Exception as exc:
with _lock:
pipeline_executions[exec_id]["status"] = "failed"
pipeline_executions[exec_id]["error"] = str(exc)
def execute_dag(dag: DAG, context: dict | None = None) -> str:
"""Start DAG execution in a background thread; return exec_id."""
exec_id = str(uuid.uuid4())[:8]
task_states = {
tid: {"status": "pending", "started_at": None,
"finished_at": None, "result": None, "error": None}
for tid in dag.tasks
}
with _lock:
pipeline_executions[exec_id] = {
"exec_id": exec_id,
"dag_id": dag.dag_id,
"dag_name": dag.name,
"status": "queued",
"progress": 0,
"task_states": task_states,
"logs": [f"[{datetime.utcnow().strftime('%H:%M:%S')}] DAG '{dag.name}' queued"],
"created_at": datetime.utcnow().isoformat(),
}
t = threading.Thread(target=_run_dag, args=(exec_id, dag, context or {}), daemon=True)
t.start()
return exec_id
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