performance improvements, charts improvements, minor UI improvements
This commit is contained in:
Антон
Binary file not shown.
@@ -243,11 +243,51 @@ class OpenVPNAPI:
|
||||
pass
|
||||
|
||||
try:
|
||||
# 4. Формирование запроса
|
||||
# В агрегированных таблицах нет полей rate_mbps, возвращаем 0
|
||||
# 4. Request Formation
|
||||
is_aggregated = target_table != 'usage_history'
|
||||
|
||||
if is_aggregated:
|
||||
# High resolution handling for 1h and 3h ranges
|
||||
is_high_res = False
|
||||
interval = 0
|
||||
points_count = 0
|
||||
|
||||
if target_table == 'usage_history':
|
||||
if duration_hours <= 1.1:
|
||||
is_high_res = True
|
||||
interval = 30
|
||||
points_count = 120
|
||||
elif duration_hours <= 3.1:
|
||||
is_high_res = True
|
||||
interval = 60
|
||||
points_count = 180
|
||||
elif duration_hours <= 6.1:
|
||||
is_high_res = True
|
||||
interval = 120 # 2 minutes
|
||||
points_count = 180
|
||||
elif duration_hours <= 12.1:
|
||||
is_high_res = True
|
||||
interval = 300 # 5 minutes
|
||||
points_count = 144
|
||||
elif duration_hours <= 24.1:
|
||||
is_high_res = True
|
||||
interval = 900 # 15 minutes
|
||||
points_count = 96
|
||||
|
||||
if is_high_res:
|
||||
query = f'''
|
||||
SELECT
|
||||
datetime((strftime('%s', uh.timestamp) / {interval}) * {interval}, 'unixepoch') as timestamp,
|
||||
SUM(uh.bytes_received) as bytes_received,
|
||||
SUM(uh.bytes_sent) as bytes_sent,
|
||||
MAX(uh.bytes_received_rate_mbps) as bytes_received_rate_mbps,
|
||||
MAX(uh.bytes_sent_rate_mbps) as bytes_sent_rate_mbps
|
||||
FROM usage_history uh
|
||||
JOIN clients c ON uh.client_id = c.id
|
||||
WHERE c.common_name = ? AND uh.timestamp BETWEEN ? AND ?
|
||||
GROUP BY datetime((strftime('%s', uh.timestamp) / {interval}) * {interval}, 'unixepoch')
|
||||
ORDER BY timestamp ASC
|
||||
'''
|
||||
elif is_aggregated:
|
||||
query = f'''
|
||||
SELECT
|
||||
t.timestamp,
|
||||
@@ -280,13 +320,44 @@ class OpenVPNAPI:
|
||||
cursor.execute(query, (common_name, s_str, e_str))
|
||||
|
||||
columns = [column[0] for column in cursor.description]
|
||||
data = [dict(zip(columns, row)) for row in cursor.fetchall()]
|
||||
db_data_list = [dict(zip(columns, row)) for row in cursor.fetchall()]
|
||||
|
||||
final_data = db_data_list
|
||||
|
||||
if is_high_res:
|
||||
# Zero-filling
|
||||
final_data = []
|
||||
db_data_map = {row['timestamp']: row for row in db_data_list}
|
||||
|
||||
# Align to nearest interval
|
||||
ts_end = end_date.timestamp()
|
||||
ts_aligned = ts_end - (ts_end % interval)
|
||||
aligned_end = datetime.utcfromtimestamp(ts_aligned)
|
||||
|
||||
# Generate points
|
||||
start_generated = aligned_end - timedelta(seconds=points_count * interval)
|
||||
|
||||
current = start_generated
|
||||
for _ in range(points_count):
|
||||
current += timedelta(seconds=interval)
|
||||
ts_str = current.strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
if ts_str in db_data_map:
|
||||
final_data.append(db_data_map[ts_str])
|
||||
else:
|
||||
final_data.append({
|
||||
'timestamp': ts_str,
|
||||
'bytes_received': 0,
|
||||
'bytes_sent': 0,
|
||||
'bytes_received_rate_mbps': 0,
|
||||
'bytes_sent_rate_mbps': 0
|
||||
})
|
||||
|
||||
return {
|
||||
'data': data,
|
||||
'data': final_data,
|
||||
'meta': {
|
||||
'resolution_used': target_table,
|
||||
'record_count': len(data),
|
||||
'resolution_used': target_table + ('_hires' if is_high_res else ''),
|
||||
'record_count': len(final_data),
|
||||
'start': s_str,
|
||||
'end': e_str
|
||||
}
|
||||
@@ -342,64 +413,105 @@ class OpenVPNAPI:
|
||||
'traffic_distribution': {'rx': 0, 'tx': 0}
|
||||
}
|
||||
|
||||
# 1. Определяем таблицу и временную метку
|
||||
target_table = 'usage_history'
|
||||
# 1. Configuration
|
||||
hours = 24
|
||||
interval_seconds = 900 # 15 min default
|
||||
target_table = 'usage_history'
|
||||
|
||||
if range_arg == '7d':
|
||||
target_table = 'stats_hourly'
|
||||
hours = 168 # 7 * 24
|
||||
hours = 168
|
||||
interval_seconds = 6300 # 105 min -> 96 points
|
||||
target_table = 'stats_hourly'
|
||||
elif range_arg == '30d':
|
||||
target_table = 'stats_6h' # или stats_daily
|
||||
hours = 720 # 30 * 24
|
||||
|
||||
hours = 720
|
||||
interval_seconds = 27000 # 450 min -> 96 points
|
||||
target_table = 'stats_hourly' # Fallback to hourly/raw as needed
|
||||
|
||||
# Fallback logic for table existence
|
||||
try:
|
||||
# Проверка наличия таблицы
|
||||
try:
|
||||
cursor.execute(f"SELECT name FROM sqlite_master WHERE type='table' AND name='{target_table}'")
|
||||
if not cursor.fetchone():
|
||||
target_table = 'usage_history'
|
||||
except:
|
||||
pass
|
||||
cursor.execute(f"SELECT name FROM sqlite_master WHERE type='table' AND name='{target_table}'")
|
||||
if not cursor.fetchone():
|
||||
target_table = 'usage_history' # Fallback to raw if aggregated missing
|
||||
except:
|
||||
target_table = 'usage_history'
|
||||
|
||||
# 2. Глобальная история (График)
|
||||
# Для агрегированных таблиц поля rate могут отсутствовать, заменяем нулями
|
||||
try:
|
||||
# 2. Global History (Chart)
|
||||
if target_table == 'usage_history':
|
||||
rate_cols = "SUM(bytes_received_rate_mbps) as total_rx_rate, SUM(bytes_sent_rate_mbps) as total_tx_rate,"
|
||||
else:
|
||||
rate_cols = "0 as total_rx_rate, 0 as total_tx_rate,"
|
||||
|
||||
# Aggregation Query
|
||||
# Group by interval_seconds
|
||||
query_hist = f'''
|
||||
SELECT
|
||||
timestamp,
|
||||
SUM(bytes_received) as total_rx,
|
||||
SUM(bytes_sent) as total_tx,
|
||||
{rate_cols}
|
||||
COUNT(DISTINCT client_id) as active_count
|
||||
FROM {target_table}
|
||||
WHERE timestamp >= datetime('now', '-{hours} hours')
|
||||
GROUP BY timestamp
|
||||
datetime((strftime('%s', timestamp) / {interval_seconds}) * {interval_seconds}, 'unixepoch') as timestamp,
|
||||
SUM(total_rx) as total_rx,
|
||||
SUM(total_tx) as total_tx,
|
||||
MAX(total_rx_rate) as total_rx_rate,
|
||||
MAX(total_tx_rate) as total_tx_rate,
|
||||
MAX(active_count) as active_count
|
||||
FROM (
|
||||
SELECT
|
||||
timestamp,
|
||||
SUM(bytes_received) as total_rx,
|
||||
SUM(bytes_sent) as total_tx,
|
||||
{rate_cols}
|
||||
COUNT(DISTINCT client_id) as active_count
|
||||
FROM {target_table}
|
||||
WHERE timestamp >= datetime('now', '-{hours} hours')
|
||||
GROUP BY timestamp
|
||||
) sub
|
||||
GROUP BY datetime((strftime('%s', timestamp) / {interval_seconds}) * {interval_seconds}, 'unixepoch')
|
||||
ORDER BY timestamp ASC
|
||||
'''
|
||||
|
||||
cursor.execute(query_hist)
|
||||
rows = cursor.fetchall()
|
||||
if rows:
|
||||
columns = [col[0] for col in cursor.description]
|
||||
analytics['global_history_24h'] = [dict(zip(columns, row)) for row in rows]
|
||||
|
||||
# Максимум клиентов
|
||||
max_clients = 0
|
||||
for row in analytics['global_history_24h']:
|
||||
if row['active_count'] > max_clients:
|
||||
max_clients = row['active_count']
|
||||
analytics['max_concurrent_24h'] = max_clients
|
||||
|
||||
# 3. Топ-3 самых активных клиентов (за выбранный период)
|
||||
# Внимание: для топа всегда берем данные, но запрос может быть тяжелым на usage_history за месяц.
|
||||
# Лучше использовать агрегаты, если период большой.
|
||||
columns = [col[0] for col in cursor.description]
|
||||
db_data = {row[0]: dict(zip(columns, row)) for row in rows}
|
||||
|
||||
# Используем ту же таблицу, что и для истории, чтобы согласовать данные
|
||||
# Post-processing: Zero Fill
|
||||
analytics['global_history_24h'] = []
|
||||
|
||||
now = datetime.utcnow()
|
||||
# Round down to nearest interval
|
||||
ts_now = now.timestamp()
|
||||
ts_aligned = ts_now - (ts_now % interval_seconds)
|
||||
now_aligned = datetime.utcfromtimestamp(ts_aligned)
|
||||
|
||||
# We want exactly 96 points ending at now_aligned
|
||||
# Start time = now_aligned - (96 * interval)
|
||||
start_time = now_aligned - timedelta(seconds=96 * interval_seconds)
|
||||
|
||||
current = start_time
|
||||
# Generate exactly 96 points
|
||||
for _ in range(96):
|
||||
current += timedelta(seconds=interval_seconds)
|
||||
ts_str = current.strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
if ts_str in db_data:
|
||||
analytics['global_history_24h'].append(db_data[ts_str])
|
||||
else:
|
||||
analytics['global_history_24h'].append({
|
||||
'timestamp': ts_str,
|
||||
'total_rx': 0,
|
||||
'total_tx': 0,
|
||||
'total_rx_rate': 0,
|
||||
'total_tx_rate': 0,
|
||||
'active_count': 0
|
||||
})
|
||||
|
||||
# Max Clients metric
|
||||
max_clients = 0
|
||||
for row in analytics['global_history_24h']:
|
||||
if row.get('active_count', 0) > max_clients:
|
||||
max_clients = row['active_count']
|
||||
analytics['max_concurrent_24h'] = max_clients
|
||||
|
||||
# 3. Top Clients & 4. Traffic Distribution (Keep existing logic)
|
||||
# Use same target table
|
||||
query_top = f'''
|
||||
SELECT
|
||||
c.common_name,
|
||||
@@ -417,7 +529,6 @@ class OpenVPNAPI:
|
||||
top_cols = [col[0] for col in cursor.description]
|
||||
analytics['top_clients_24h'] = [dict(zip(top_cols, row)) for row in cursor.fetchall()]
|
||||
|
||||
# 4. Распределение трафика
|
||||
query_dist = f'''
|
||||
SELECT
|
||||
SUM(bytes_received) as rx,
|
||||
|
||||
Reference in New Issue
Block a user