ai_v/venv/Lib/site-packages/apscheduler/jobstores/redis.py

161 lines
5.4 KiB
Python
Raw Normal View History

feat(api): 实现图像生成及后台同步功能 - 新增图像生成接口,支持试用、积分和自定义API Key模式 - 实现生成图片结果异步上传至MinIO存储,带重试机制 - 优化积分预扣除和异常退还逻辑,保障用户积分准确 - 添加获取生成历史记录接口,支持时间范围和分页 - 提供本地字典配置接口,支持模型、比例、提示模板和尺寸 - 实现图片批量上传接口,支持S3兼容对象存储 feat(admin): 增加管理员角色管理与权限分配接口 - 实现角色列表查询、角色创建、更新及删除功能 - 增加权限列表查询接口 - 实现用户角色分配接口,便于统一管理用户权限 - 增加系统字典增删查改接口,支持分类过滤和排序 - 权限控制全面覆盖管理接口,保证安全访问 feat(auth): 完善用户登录注册及权限相关接口与页面 - 实现手机号验证码发送及校验功能,保障注册安全 - 支持手机号注册、登录及退出接口,集成日志记录 - 增加修改密码功能,验证原密码后更新 - 提供动态导航菜单接口,基于权限展示不同菜单 - 实现管理界面路由及日志、角色、字典管理页面访问权限控制 - 添加系统日志查询接口,支持关键词和等级筛选 feat(app): 初始化Flask应用并配置蓝图与数据库 - 创建应用程序工厂,加载配置,初始化数据库和Redis客户端 - 注册认证、API及管理员蓝图,整合路由 - 根路由渲染主页模板 - 应用上下文中自动创建数据库表,保证运行环境准备完毕 feat(database): 提供数据库创建与迁移支持脚本 - 新增数据库创建脚本,支持自动检测是否已存在 - 添加数据库表初始化脚本,支持创建和删除所有表 - 实现RBAC权限初始化,包含基础权限和角色创建 - 新增字段手动修复脚本,添加用户API Key和积分字段 - 强制迁移脚本支持清理连接和修复表结构,初始化默认数据及角色分配 feat(config): 新增系统配置参数 - 配置数据库、Redis、Session和MinIO相关参数 - 添加AI接口地址及试用Key配置 - 集成阿里云短信服务配置及开发模式相关参数 feat(extensions): 初始化数据库、Redis和MinIO客户端 - 创建全局SQLAlchemy数据库实例和Redis客户端 - 配置基于boto3的MinIO兼容S3客户端 chore(logs): 添加示例系统日志文件 - 记录用户请求、验证码发送成功与失败的日志信息
2026-01-12 00:53:31 +08:00
import pickle
from datetime import datetime, timezone
from apscheduler.job import Job
from apscheduler.jobstores.base import BaseJobStore, ConflictingIdError, JobLookupError
from apscheduler.util import datetime_to_utc_timestamp, utc_timestamp_to_datetime
try:
from redis import Redis
except ImportError as exc: # pragma: nocover
raise ImportError("RedisJobStore requires redis installed") from exc
class RedisJobStore(BaseJobStore):
"""
Stores jobs in a Redis database. Any leftover keyword arguments are directly passed to redis's
:class:`~redis.StrictRedis`.
Plugin alias: ``redis``
:param int db: the database number to store jobs in
:param str jobs_key: key to store jobs in
:param str run_times_key: key to store the jobs' run times in
:param int pickle_protocol: pickle protocol level to use (for serialization), defaults to the
highest available
"""
def __init__(
self,
db=0,
jobs_key="apscheduler.jobs",
run_times_key="apscheduler.run_times",
pickle_protocol=pickle.HIGHEST_PROTOCOL,
**connect_args,
):
super().__init__()
if db is None:
raise ValueError('The "db" parameter must not be empty')
if not jobs_key:
raise ValueError('The "jobs_key" parameter must not be empty')
if not run_times_key:
raise ValueError('The "run_times_key" parameter must not be empty')
self.pickle_protocol = pickle_protocol
self.jobs_key = jobs_key
self.run_times_key = run_times_key
self.redis = Redis(db=int(db), **connect_args)
def lookup_job(self, job_id):
job_state = self.redis.hget(self.jobs_key, job_id)
return self._reconstitute_job(job_state) if job_state else None
def get_due_jobs(self, now):
timestamp = datetime_to_utc_timestamp(now)
job_ids = self.redis.zrangebyscore(self.run_times_key, 0, timestamp)
if job_ids:
job_states = self.redis.hmget(self.jobs_key, *job_ids)
return self._reconstitute_jobs(zip(job_ids, job_states))
return []
def get_next_run_time(self):
next_run_time = self.redis.zrange(self.run_times_key, 0, 0, withscores=True)
if next_run_time:
return utc_timestamp_to_datetime(next_run_time[0][1])
def get_all_jobs(self):
job_states = self.redis.hgetall(self.jobs_key)
jobs = self._reconstitute_jobs(job_states.items())
paused_sort_key = datetime(9999, 12, 31, tzinfo=timezone.utc)
return sorted(jobs, key=lambda job: job.next_run_time or paused_sort_key)
def add_job(self, job):
if self.redis.hexists(self.jobs_key, job.id):
raise ConflictingIdError(job.id)
with self.redis.pipeline() as pipe:
pipe.multi()
pipe.hset(
self.jobs_key,
job.id,
pickle.dumps(job.__getstate__(), self.pickle_protocol),
)
if job.next_run_time:
pipe.zadd(
self.run_times_key,
{job.id: datetime_to_utc_timestamp(job.next_run_time)},
)
pipe.execute()
def update_job(self, job):
if not self.redis.hexists(self.jobs_key, job.id):
raise JobLookupError(job.id)
with self.redis.pipeline() as pipe:
pipe.hset(
self.jobs_key,
job.id,
pickle.dumps(job.__getstate__(), self.pickle_protocol),
)
if job.next_run_time:
pipe.zadd(
self.run_times_key,
{job.id: datetime_to_utc_timestamp(job.next_run_time)},
)
else:
pipe.zrem(self.run_times_key, job.id)
pipe.execute()
def remove_job(self, job_id):
if not self.redis.hexists(self.jobs_key, job_id):
raise JobLookupError(job_id)
with self.redis.pipeline() as pipe:
pipe.hdel(self.jobs_key, job_id)
pipe.zrem(self.run_times_key, job_id)
pipe.execute()
def remove_all_jobs(self):
with self.redis.pipeline() as pipe:
pipe.delete(self.jobs_key)
pipe.delete(self.run_times_key)
pipe.execute()
def shutdown(self):
self.redis.connection_pool.disconnect()
def _reconstitute_job(self, job_state):
job_state = pickle.loads(job_state)
job = Job.__new__(Job)
job.__setstate__(job_state)
job._scheduler = self._scheduler
job._jobstore_alias = self._alias
return job
def _reconstitute_jobs(self, job_states):
jobs = []
failed_job_ids = []
for job_id, job_state in job_states:
try:
jobs.append(self._reconstitute_job(job_state))
except BaseException:
self._logger.exception(
'Unable to restore job "%s" -- removing it', job_id
)
failed_job_ids.append(job_id)
# Remove all the jobs we failed to restore
if failed_job_ids:
with self.redis.pipeline() as pipe:
pipe.hdel(self.jobs_key, *failed_job_ids)
pipe.zrem(self.run_times_key, *failed_job_ids)
pipe.execute()
return jobs
def __repr__(self):
return f"<{self.__class__.__name__}>"