Source code for airflow.providers.edge.models.edge_logs
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from __future__ import annotations
from datetime import datetime
from sqlalchemy import (
Column,
Integer,
Text,
text,
)
from sqlalchemy.dialects.mysql import MEDIUMTEXT
from airflow.models.base import Base, StringID
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.sqlalchemy import UtcDateTime
[docs]class EdgeLogsModel(Base, LoggingMixin):
"""
Temporary collected logs from a Edge Worker while job runs on remote site.
As the Edge Worker in most cases has a local file system and the web UI no access
to read files from remote site, Edge Workers will send incremental chunks of logs
of running jobs to the central site. As log storage backends in most cloud cases can not
append logs, the table is used as buffer to receive. Upon task completion logs can be
flushed to task log handler.
Log data therefore is collected in chunks and is only temporary.
"""
[docs] __tablename__ = "edge_logs"
[docs] dag_id = Column(StringID(), primary_key=True, nullable=False)
[docs] task_id = Column(StringID(), primary_key=True, nullable=False)
[docs] run_id = Column(StringID(), primary_key=True, nullable=False)
[docs] map_index = Column(Integer, primary_key=True, nullable=False, server_default=text("-1"))
[docs] try_number = Column(Integer, primary_key=True, default=0)
[docs] log_chunk_time = Column(UtcDateTime, primary_key=True, nullable=False)
[docs] log_chunk_data = Column(Text().with_variant(MEDIUMTEXT(), "mysql"), nullable=False)
def __init__(
self,
dag_id: str,
task_id: str,
run_id: str,
map_index: int,
try_number: int,
log_chunk_time: datetime,
log_chunk_data: str,
):
self.dag_id = dag_id
self.task_id = task_id
self.run_id = run_id
self.map_index = map_index
self.try_number = try_number
self.log_chunk_time = log_chunk_time
self.log_chunk_data = log_chunk_data
super().__init__()