# -*- coding: utf-8 -*- 
import redis
from constant import redis_ip, redis_db, redis_pw, logger, redis_zset_clean_date, redis_zset_name, \
   move_file_lm

pool = redis.ConnectionPool(host=redis_ip, db=redis_db, password=redis_pw)
# pool = redis.ConnectionPool(db=6, password=redis_pw)
rr = redis.Redis(connection_pool=pool)  # # 客戶端StrictRedis=Redis 合併了
 
pipe = rr.pipeline(transaction=True)  # 批量操作, 遇到非同步容易出錯, 可以不使用, 直接用 rr 連線物件,一次一個新增


def distinct_md5(date, eml_md5, eml_path, redis_fail_path):
　　# 
   # pipe.zadd(redis_zset_name, eml_md5, date)
   # pipe.zadd(redis_zset_name, {date: eml_md5}, nx=True)  # nx 只插入,存在的值不會修改其分數
   # pipe.zadd(redis_zset_name, {date: eml_md5})
 
   pipe.zadd(redis_zset_name, {eml_md5: date})
   return pipe.execute()[0]  # 1 新增, 0 沒新增

   # pipe.zcard('eml_md5_zset')  # 檢視總個數


def del_redis_md5(n, m):  # 按照分數值進行刪除
   try:
      pipe.zremrangebyscore(redis_zset_name, n, m)
   except Exception as e:
      logger.warning("刪除Redis快取中md5值失敗: {},".format(e))
 
      return
   pipe.execute()


#  return
# return pipe.execute()[0]


def clean_redis_zset(md5_score):
   if md5_score <= redis_zset_clean_date:
      md5_score += 31
   md5_clean_score = md5_score - redis_zset_clean_date
   del_redis_md5(md5_clean_score - 0.5, md5_clean_score + 0.5)  # 清理Redis上的md5  閉區間
# print(distinct_md5(redis_zset_name, "qwe", 2, "sdfsd", "sdfsdf"))
#print(distinct_md5(27, "qweqwe","sdfsd", "sdfsdf"))
# print(del_redis_md5(redis_zset_name, 1, 10