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feat(statistic): 优化内存使用，添加分批查询和统计处理上限

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feat(typo_generator): 实现单例模式以复用拼音字典和字频数据
feat(query): 添加分批迭代获取结果的功能，优化内存使用
This commit is contained in:
Windpicker-owo
2025-12-02 12:45:10 +08:00
parent 8f4b846630
commit bcdd987e4c
4 changed files with 483 additions and 226 deletions
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545
src/chat/utils/statistic.py
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@@ -4,6 +4,7 @@ from datetime import datetime, timedelta
from typing import Any
from src.common.database.compatibility import db_get, db_query
from src.common.database.api.query import QueryBuilder
from src.common.database.core.models import LLMUsage, Messages, OnlineTime
from src.common.logger import get_logger
from src.manager.async_task_manager import AsyncTask
@@ -11,6 +12,11 @@ from src.manager.local_store_manager import local_storage
logger = get_logger("maibot_statistic")
# 统计查询的批次大小
STAT_BATCH_SIZE = 2000
# 内存优化：单次统计最大处理记录数（防止极端情况）
STAT_MAX_RECORDS = 100000
# 彻底异步化：删除原同步包装器 _sync_db_get，所有数据库访问统一使用 await db_get。
@@ -314,85 +320,100 @@ class StatisticOutputTask(AsyncTask):
}
# 以最早的时间戳为起始时间获取记录
# 🔧 内存优化：使用分批查询代替全量加载
query_start_time = collect_period[-1][1]
records = (
await db_get(
model_class=LLMUsage,
filters={"timestamp": {"$gte": query_start_time}},
order_by="-timestamp",
)
or []
query_builder = (
QueryBuilder(LLMUsage)
.no_cache()
.filter(timestamp__gte=query_start_time)
.order_by("-timestamp")
)
for record_idx, record in enumerate(records, 1):
if not isinstance(record, dict):
continue
record_timestamp = record.get("timestamp")
if isinstance(record_timestamp, str):
record_timestamp = datetime.fromisoformat(record_timestamp)
if not record_timestamp:
continue
for period_idx, (_, period_start) in enumerate(collect_period):
if record_timestamp >= period_start:
for period_key, _ in collect_period[period_idx:]:
stats[period_key][TOTAL_REQ_CNT] += 1
request_type = record.get("request_type") or "unknown"
user_id = record.get("user_id") or "unknown"
model_name = record.get("model_name") or "unknown"
provider_name = record.get("model_api_provider") or "unknown"
# 提取模块名：如果请求类型包含"."，取第一个"."之前的部分
module_name = request_type.split(".")[0] if "." in request_type else request_type
stats[period_key][REQ_CNT_BY_TYPE][request_type] += 1
stats[period_key][REQ_CNT_BY_USER][user_id] += 1
stats[period_key][REQ_CNT_BY_MODEL][model_name] += 1
stats[period_key][REQ_CNT_BY_MODULE][module_name] += 1
stats[period_key][REQ_CNT_BY_PROVIDER][provider_name] += 1
prompt_tokens = record.get("prompt_tokens") or 0
completion_tokens = record.get("completion_tokens") or 0
total_tokens = prompt_tokens + completion_tokens
stats[period_key][IN_TOK_BY_TYPE][request_type] += prompt_tokens
stats[period_key][IN_TOK_BY_USER][user_id] += prompt_tokens
stats[period_key][IN_TOK_BY_MODEL][model_name] += prompt_tokens
stats[period_key][IN_TOK_BY_MODULE][module_name] += prompt_tokens
stats[period_key][OUT_TOK_BY_TYPE][request_type] += completion_tokens
stats[period_key][OUT_TOK_BY_USER][user_id] += completion_tokens
stats[period_key][OUT_TOK_BY_MODEL][model_name] += completion_tokens
stats[period_key][OUT_TOK_BY_MODULE][module_name] += completion_tokens
stats[period_key][TOTAL_TOK_BY_TYPE][request_type] += total_tokens
stats[period_key][TOTAL_TOK_BY_USER][user_id] += total_tokens
stats[period_key][TOTAL_TOK_BY_MODEL][model_name] += total_tokens
stats[period_key][TOTAL_TOK_BY_MODULE][module_name] += total_tokens
stats[period_key][TOTAL_TOK_BY_PROVIDER][provider_name] += total_tokens
cost = record.get("cost") or 0.0
stats[period_key][TOTAL_COST] += cost
stats[period_key][COST_BY_TYPE][request_type] += cost
stats[period_key][COST_BY_USER][user_id] += cost
stats[period_key][COST_BY_MODEL][model_name] += cost
stats[period_key][COST_BY_MODULE][module_name] += cost
stats[period_key][COST_BY_PROVIDER][provider_name] += cost
# 收集time_cost数据
time_cost = record.get("time_cost") or 0.0
if time_cost > 0: # 只记录有效的time_cost
stats[period_key][TIME_COST_BY_TYPE][request_type].append(time_cost)
stats[period_key][TIME_COST_BY_USER][user_id].append(time_cost)
stats[period_key][TIME_COST_BY_MODEL][model_name].append(time_cost)
stats[period_key][TIME_COST_BY_MODULE][module_name].append(time_cost)
stats[period_key][TIME_COST_BY_PROVIDER][provider_name].append(time_cost)
total_processed = 0
async for batch in query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
for record in batch:
if total_processed >= STAT_MAX_RECORDS:
logger.warning(f"统计处理记录数达到上限 {STAT_MAX_RECORDS}，跳过剩余记录")
break
if not isinstance(record, dict):
continue
await StatisticOutputTask._yield_control(record_idx)
record_timestamp = record.get("timestamp")
if isinstance(record_timestamp, str):
record_timestamp = datetime.fromisoformat(record_timestamp)
if not record_timestamp:
continue
for period_idx, (_, period_start) in enumerate(collect_period):
if record_timestamp >= period_start:
for period_key, _ in collect_period[period_idx:]:
stats[period_key][TOTAL_REQ_CNT] += 1
request_type = record.get("request_type") or "unknown"
user_id = record.get("user_id") or "unknown"
model_name = record.get("model_name") or "unknown"
provider_name = record.get("model_api_provider") or "unknown"
# 提取模块名：如果请求类型包含"."，取第一个"."之前的部分
module_name = request_type.split(".")[0] if "." in request_type else request_type
stats[period_key][REQ_CNT_BY_TYPE][request_type] += 1
stats[period_key][REQ_CNT_BY_USER][user_id] += 1
stats[period_key][REQ_CNT_BY_MODEL][model_name] += 1
stats[period_key][REQ_CNT_BY_MODULE][module_name] += 1
stats[period_key][REQ_CNT_BY_PROVIDER][provider_name] += 1
prompt_tokens = record.get("prompt_tokens") or 0
completion_tokens = record.get("completion_tokens") or 0
total_tokens = prompt_tokens + completion_tokens
stats[period_key][IN_TOK_BY_TYPE][request_type] += prompt_tokens
stats[period_key][IN_TOK_BY_USER][user_id] += prompt_tokens
stats[period_key][IN_TOK_BY_MODEL][model_name] += prompt_tokens
stats[period_key][IN_TOK_BY_MODULE][module_name] += prompt_tokens
stats[period_key][OUT_TOK_BY_TYPE][request_type] += completion_tokens
stats[period_key][OUT_TOK_BY_USER][user_id] += completion_tokens
stats[period_key][OUT_TOK_BY_MODEL][model_name] += completion_tokens
stats[period_key][OUT_TOK_BY_MODULE][module_name] += completion_tokens
stats[period_key][TOTAL_TOK_BY_TYPE][request_type] += total_tokens
stats[period_key][TOTAL_TOK_BY_USER][user_id] += total_tokens
stats[period_key][TOTAL_TOK_BY_MODEL][model_name] += total_tokens
stats[period_key][TOTAL_TOK_BY_MODULE][module_name] += total_tokens
stats[period_key][TOTAL_TOK_BY_PROVIDER][provider_name] += total_tokens
cost = record.get("cost") or 0.0
stats[period_key][TOTAL_COST] += cost
stats[period_key][COST_BY_TYPE][request_type] += cost
stats[period_key][COST_BY_USER][user_id] += cost
stats[period_key][COST_BY_MODEL][model_name] += cost
stats[period_key][COST_BY_MODULE][module_name] += cost
stats[period_key][COST_BY_PROVIDER][provider_name] += cost
# 收集time_cost数据
time_cost = record.get("time_cost") or 0.0
if time_cost > 0: # 只记录有效的time_cost
stats[period_key][TIME_COST_BY_TYPE][request_type].append(time_cost)
stats[period_key][TIME_COST_BY_USER][user_id].append(time_cost)
stats[period_key][TIME_COST_BY_MODEL][model_name].append(time_cost)
stats[period_key][TIME_COST_BY_MODULE][module_name].append(time_cost)
stats[period_key][TIME_COST_BY_PROVIDER][provider_name].append(time_cost)
break
total_processed += 1
if total_processed % 500 == 0:
await StatisticOutputTask._yield_control(total_processed, interval=1)
# 检查是否达到上限
if total_processed >= STAT_MAX_RECORDS:
break
# 每批处理完后让出控制权
await asyncio.sleep(0)
# -- 计算派生指标 --
for period_key, period_stats in stats.items():
# 计算模型相关指标
@@ -591,45 +612,47 @@ class StatisticOutputTask(AsyncTask):
}
query_start_time = collect_period[-1][1]
records = (
await db_get(
model_class=OnlineTime,
filters={"end_timestamp": {"$gte": query_start_time}},
order_by="-end_timestamp",
)
or []
# 🔧 内存优化：使用分批查询
query_builder = (
QueryBuilder(OnlineTime)
.no_cache()
.filter(end_timestamp__gte=query_start_time)
.order_by("-end_timestamp")
)
for record_idx, record in enumerate(records, 1):
if not isinstance(record, dict):
continue
async for batch in query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
for record in batch:
if not isinstance(record, dict):
continue
record_end_timestamp = record.get("end_timestamp")
if isinstance(record_end_timestamp, str):
record_end_timestamp = datetime.fromisoformat(record_end_timestamp)
record_end_timestamp = record.get("end_timestamp")
if isinstance(record_end_timestamp, str):
record_end_timestamp = datetime.fromisoformat(record_end_timestamp)
record_start_timestamp = record.get("start_timestamp")
if isinstance(record_start_timestamp, str):
record_start_timestamp = datetime.fromisoformat(record_start_timestamp)
record_start_timestamp = record.get("start_timestamp")
if isinstance(record_start_timestamp, str):
record_start_timestamp = datetime.fromisoformat(record_start_timestamp)
if not record_end_timestamp or not record_start_timestamp:
continue
if not record_end_timestamp or not record_start_timestamp:
continue
for boundary_idx, (_, period_boundary_start) in enumerate(collect_period):
if record_end_timestamp >= period_boundary_start:
# Calculate effective end time for this record in relation to 'now'
effective_end_time = min(record_end_timestamp, now)
for boundary_idx, (_, period_boundary_start) in enumerate(collect_period):
if record_end_timestamp >= period_boundary_start:
# Calculate effective end time for this record in relation to 'now'
effective_end_time = min(record_end_timestamp, now)
for period_key, current_period_start_time in collect_period[boundary_idx:]:
# Determine the portion of the record that falls within this specific statistical period
overlap_start = max(record_start_timestamp, current_period_start_time)
overlap_end = effective_end_time # Already capped by 'now' and record's own end
for period_key, current_period_start_time in collect_period[boundary_idx:]:
# Determine the portion of the record that falls within this specific statistical period
overlap_start = max(record_start_timestamp, current_period_start_time)
overlap_end = effective_end_time # Already capped by 'now' and record's own end
if overlap_end > overlap_start:
stats[period_key][ONLINE_TIME] += (overlap_end - overlap_start).total_seconds()
break
if overlap_end > overlap_start:
stats[period_key][ONLINE_TIME] += (overlap_end - overlap_start).total_seconds()
break
# 每批处理完后让出控制权
await asyncio.sleep(0)
await StatisticOutputTask._yield_control(record_idx)
return stats
async def _collect_message_count_for_period(self, collect_period: list[tuple[str, datetime]]) -> dict[str, Any]:
@@ -652,57 +675,70 @@ class StatisticOutputTask(AsyncTask):
}
query_start_timestamp = collect_period[-1][1].timestamp() # Messages.time is a DoubleField (timestamp)
records = (
await db_get(
model_class=Messages,
filters={"time": {"$gte": query_start_timestamp}},
order_by="-time",
)
or []
# 🔧 内存优化：使用分批查询
query_builder = (
QueryBuilder(Messages)
.no_cache()
.filter(time__gte=query_start_timestamp)
.order_by("-time")
)
for message_idx, message in enumerate(records, 1):
if not isinstance(message, dict):
continue
message_time_ts = message.get("time") # This is a float timestamp
if not message_time_ts:
continue
chat_id = None
chat_name = None
# Logic based on SQLAlchemy model structure, aiming to replicate original intent
if message.get("chat_info_group_id"):
chat_id = f"g{message['chat_info_group_id']}"
chat_name = message.get("chat_info_group_name") or f"群{message['chat_info_group_id']}"
elif message.get("user_id"): # Fallback to sender's info for chat_id if not a group_info based chat
# This uses the message SENDER's ID as per original logic's fallback
chat_id = f"u{message['user_id']}" # SENDER's user_id
chat_name = message.get("user_nickname") # SENDER's nickname
else:
# If neither group_id nor sender_id is available for chat identification
logger.warning(f"Message (PK: {message.get('id', 'N/A')}) lacks group_id and user_id for chat stats.")
continue
if not chat_id: # Should not happen if above logic is correct
continue
# Update name_mapping
if chat_name:
if chat_id in self.name_mapping:
if chat_name != self.name_mapping[chat_id][0] and message_time_ts > self.name_mapping[chat_id][1]:
self.name_mapping[chat_id] = (chat_name, message_time_ts)
else:
self.name_mapping[chat_id] = (chat_name, message_time_ts)
for period_idx, (_, period_start_dt) in enumerate(collect_period):
if message_time_ts >= period_start_dt.timestamp():
for period_key, _ in collect_period[period_idx:]:
stats[period_key][TOTAL_MSG_CNT] += 1
stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1
total_processed = 0
async for batch in query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
for message in batch:
if total_processed >= STAT_MAX_RECORDS:
logger.warning(f"消息统计处理记录数达到上限 {STAT_MAX_RECORDS}，跳过剩余记录")
break
if not isinstance(message, dict):
continue
message_time_ts = message.get("time") # This is a float timestamp
await StatisticOutputTask._yield_control(message_idx)
if not message_time_ts:
continue
chat_id = None
chat_name = None
# Logic based on SQLAlchemy model structure, aiming to replicate original intent
if message.get("chat_info_group_id"):
chat_id = f"g{message['chat_info_group_id']}"
chat_name = message.get("chat_info_group_name") or f"群{message['chat_info_group_id']}"
elif message.get("user_id"): # Fallback to sender's info for chat_id if not a group_info based chat
# This uses the message SENDER's ID as per original logic's fallback
chat_id = f"u{message['user_id']}" # SENDER's user_id
chat_name = message.get("user_nickname") # SENDER's nickname
else:
# If neither group_id nor sender_id is available for chat identification
continue
if not chat_id: # Should not happen if above logic is correct
continue
# Update name_mapping
if chat_name:
if chat_id in self.name_mapping:
if chat_name != self.name_mapping[chat_id][0] and message_time_ts > self.name_mapping[chat_id][1]:
self.name_mapping[chat_id] = (chat_name, message_time_ts)
else:
self.name_mapping[chat_id] = (chat_name, message_time_ts)
for period_idx, (_, period_start_dt) in enumerate(collect_period):
if message_time_ts >= period_start_dt.timestamp():
for period_key, _ in collect_period[period_idx:]:
stats[period_key][TOTAL_MSG_CNT] += 1
stats[period_key][MSG_CNT_BY_CHAT][chat_id] += 1
break
total_processed += 1
if total_processed % 500 == 0:
await StatisticOutputTask._yield_control(total_processed, interval=1)
# 检查是否达到上限
if total_processed >= STAT_MAX_RECORDS:
break
# 每批处理完后让出控制权
await asyncio.sleep(0)
return stats
@@ -755,8 +791,39 @@ class StatisticOutputTask(AsyncTask):
current_dict = stat["all_time"][key]
for sub_key, sub_val in val.items():
if sub_key in current_dict:
# For lists (like TIME_COST), this extends. For numbers, this adds.
current_dict[sub_key] += sub_val
current_val = current_dict[sub_key]
# 🔧 内存优化：处理压缩格式的 TIME_COST 数据
if isinstance(sub_val, dict) and "sum" in sub_val and "count" in sub_val:
# 压缩格式合并
if isinstance(current_val, dict) and "sum" in current_val:
# 两边都是压缩格式
current_dict[sub_key] = {
"sum": current_val["sum"] + sub_val["sum"],
"count": current_val["count"] + sub_val["count"],
"sum_sq": current_val.get("sum_sq", 0) + sub_val.get("sum_sq", 0),
}
elif isinstance(current_val, list):
# 当前是列表，历史是压缩格式：先压缩当前再合并
curr_sum = sum(current_val) if current_val else 0
curr_count = len(current_val)
curr_sum_sq = sum(v * v for v in current_val) if current_val else 0
current_dict[sub_key] = {
"sum": curr_sum + sub_val["sum"],
"count": curr_count + sub_val["count"],
"sum_sq": curr_sum_sq + sub_val.get("sum_sq", 0),
}
else:
# 未知情况，保留历史值
current_dict[sub_key] = sub_val
elif isinstance(sub_val, list):
# 列表格式：extend（兼容旧数据，但新版不会产生这种情况）
if isinstance(current_val, list):
current_dict[sub_key] = current_val + sub_val
else:
current_dict[sub_key] = sub_val
else:
# 数值类型：直接相加
current_dict[sub_key] += sub_val
else:
current_dict[sub_key] = sub_val
else:
@@ -764,8 +831,10 @@ class StatisticOutputTask(AsyncTask):
stat["all_time"][key] += val
# 更新上次完整统计数据的时间戳
# 🔧 内存优化：在保存前压缩 TIME_COST 列表为聚合数据，避免无限增长
compressed_stat_data = self._compress_time_cost_lists(stat["all_time"])
# 将所有defaultdict转换为普通dict以避免类型冲突
clean_stat_data = self._convert_defaultdict_to_dict(stat["all_time"])
clean_stat_data = self._convert_defaultdict_to_dict(compressed_stat_data)
local_storage["last_full_statistics"] = {
"name_mapping": self.name_mapping,
"stat_data": clean_stat_data,
@@ -774,6 +843,54 @@ class StatisticOutputTask(AsyncTask):
return stat
def _compress_time_cost_lists(self, data: dict[str, Any]) -> dict[str, Any]:
"""🔧 内存优化：将 TIME_COST_BY_* 的 list 压缩为聚合数据
原始格式: {"model_a": [1.2, 2.3, 3.4, ...]} (可能无限增长)
压缩格式: {"model_a": {"sum": 6.9, "count": 3, "sum_sq": 18.29}}
这样合并时只需要累加 sum/count/sum_sq，不会无限增长。
avg = sum / count
std = sqrt(sum_sq / count - (sum / count)^2)
"""
# TIME_COST 相关的 key 前缀
time_cost_keys = [
TIME_COST_BY_TYPE, TIME_COST_BY_USER, TIME_COST_BY_MODEL,
TIME_COST_BY_MODULE, TIME_COST_BY_PROVIDER
]
result = dict(data) # 浅拷贝
for key in time_cost_keys:
if key not in result:
continue
original = result[key]
if not isinstance(original, dict):
continue
compressed = {}
for sub_key, values in original.items():
if isinstance(values, list):
# 原始列表格式，需要压缩
if values:
total = sum(values)
count = len(values)
sum_sq = sum(v * v for v in values)
compressed[sub_key] = {"sum": total, "count": count, "sum_sq": sum_sq}
else:
compressed[sub_key] = {"sum": 0.0, "count": 0, "sum_sq": 0.0}
elif isinstance(values, dict) and "sum" in values and "count" in values:
# 已经是压缩格式，直接保留
compressed[sub_key] = values
else:
# 未知格式，保留原值
compressed[sub_key] = values
result[key] = compressed
return result
def _convert_defaultdict_to_dict(self, data):
# sourcery skip: dict-comprehension, extract-duplicate-method, inline-immediately-returned-variable, merge-duplicate-blocks
"""递归转换defaultdict为普通dict"""
@@ -884,70 +1001,70 @@ class StatisticOutputTask(AsyncTask):
time_labels = [t.strftime("%H:%M") for t in time_points]
interval_seconds = interval_minutes * 60
# 单次查询 LLMUsage
llm_records = (
await db_get(
model_class=LLMUsage,
filters={"timestamp": {"$gte": start_time}},
order_by="-timestamp",
)
or []
# 🔧 内存优化：使用分批查询 LLMUsage
llm_query_builder = (
QueryBuilder(LLMUsage)
.no_cache()
.filter(timestamp__gte=start_time)
.order_by("-timestamp")
)
for record_idx, record in enumerate(llm_records, 1):
if not isinstance(record, dict) or not record.get("timestamp"):
continue
record_time = record["timestamp"]
if isinstance(record_time, str):
try:
record_time = datetime.fromisoformat(record_time)
except Exception:
async for batch in llm_query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
for record in batch:
if not isinstance(record, dict) or not record.get("timestamp"):
continue
time_diff = (record_time - start_time).total_seconds()
idx = int(time_diff // interval_seconds)
if 0 <= idx < len(time_points):
cost = record.get("cost") or 0.0
total_cost_data[idx] += cost
model_name = record.get("model_name") or "unknown"
if model_name not in cost_by_model:
cost_by_model[model_name] = [0.0] * len(time_points)
cost_by_model[model_name][idx] += cost
request_type = record.get("request_type") or "unknown"
module_name = request_type.split(".")[0] if "." in request_type else request_type
if module_name not in cost_by_module:
cost_by_module[module_name] = [0.0] * len(time_points)
cost_by_module[module_name][idx] += cost
record_time = record["timestamp"]
if isinstance(record_time, str):
try:
record_time = datetime.fromisoformat(record_time)
except Exception:
continue
time_diff = (record_time - start_time).total_seconds()
idx = int(time_diff // interval_seconds)
if 0 <= idx < len(time_points):
cost = record.get("cost") or 0.0
total_cost_data[idx] += cost
model_name = record.get("model_name") or "unknown"
if model_name not in cost_by_model:
cost_by_model[model_name] = [0.0] * len(time_points)
cost_by_model[model_name][idx] += cost
request_type = record.get("request_type") or "unknown"
module_name = request_type.split(".")[0] if "." in request_type else request_type
if module_name not in cost_by_module:
cost_by_module[module_name] = [0.0] * len(time_points)
cost_by_module[module_name][idx] += cost
await asyncio.sleep(0)
await StatisticOutputTask._yield_control(record_idx)
# 单次查询 Messages
msg_records = (
await db_get(
model_class=Messages,
filters={"time": {"$gte": start_time.timestamp()}},
order_by="-time",
)
or []
# 🔧 内存优化：使用分批查询 Messages
msg_query_builder = (
QueryBuilder(Messages)
.no_cache()
.filter(time__gte=start_time.timestamp())
.order_by("-time")
)
for msg_idx, msg in enumerate(msg_records, 1):
if not isinstance(msg, dict) or not msg.get("time"):
continue
msg_ts = msg["time"]
time_diff = msg_ts - start_time.timestamp()
idx = int(time_diff // interval_seconds)
if 0 <= idx < len(time_points):
chat_id = None
if msg.get("chat_info_group_id"):
chat_id = f"g{msg['chat_info_group_id']}"
elif msg.get("user_id"):
chat_id = f"u{msg['user_id']}"
async for batch in msg_query_builder.iter_batches(batch_size=STAT_BATCH_SIZE, as_dict=True):
for msg in batch:
if not isinstance(msg, dict) or not msg.get("time"):
continue
msg_ts = msg["time"]
time_diff = msg_ts - start_time.timestamp()
idx = int(time_diff // interval_seconds)
if 0 <= idx < len(time_points):
chat_id = None
if msg.get("chat_info_group_id"):
chat_id = f"g{msg['chat_info_group_id']}"
elif msg.get("user_id"):
chat_id = f"u{msg['user_id']}"
if chat_id:
chat_name = self.name_mapping.get(chat_id, (chat_id, 0))[0]
if chat_name not in message_by_chat:
message_by_chat[chat_name] = [0] * len(time_points)
message_by_chat[chat_name][idx] += 1
await StatisticOutputTask._yield_control(msg_idx)
if chat_id:
chat_name = self.name_mapping.get(chat_id, (chat_id, 0))[0]
if chat_name not in message_by_chat:
message_by_chat[chat_name] = [0] * len(time_points)
message_by_chat[chat_name][idx] += 1
await asyncio.sleep(0)
return {
"time_labels": time_labels,