DataStore 日志

DataStore 使用 Python 标准日志模块。本文指南介绍如何配置日志以便进行调试。

快速入门

from chdb import datastore as pd
from chdb.datastore.config import config

# Enable debug logging
config.enable_debug()

# Now all operations will log details
ds = pd.read_csv("data.csv")
result = ds.filter(ds['age'] > 25).to_df()

日志级别

级别	数值	描述
DEBUG	10	用于调试的详细信息
INFO	20	常规运行信息
WARNING	30	警告消息（默认）
ERROR	40	错误消息
CRITICAL	50	严重故障

设置日志级别

import logging
from chdb.datastore.config import config

# Using standard logging levels
config.set_log_level(logging.DEBUG)
config.set_log_level(logging.INFO)
config.set_log_level(logging.WARNING)  # Default
config.set_log_level(logging.ERROR)

# Using quick preset
config.enable_debug()  # Sets DEBUG level + verbose format

日志格式

简洁格式（默认）

config.set_log_format("simple")

输出：

DEBUG - Executing SQL query
DEBUG - Cache miss for key abc123

详细日志格式

config.set_log_format("verbose")

输出：

2024-01-15 10:30:45.123 DEBUG datastore.core - Executing SQL query
2024-01-15 10:30:45.456 DEBUG datastore.cache - Cache miss for key abc123

---

会记录哪些数据

DEBUG 级别

• 生成的 SQL 查询
• 执行引擎的选择
• 缓存操作（命中/未命中）
• 操作耗时
• 数据源信息

DEBUG - Creating DataStore from file 'data.csv'
DEBUG - SQL: SELECT * FROM file('data.csv', 'CSVWithNames') WHERE age > 25
DEBUG - Using engine: chdb
DEBUG - Execution time: 0.089s
DEBUG - Cache: Storing result (key: abc123)

INFO 级别

• 重要操作的完成
• 配置变更
• 数据源连接情况

INFO - Loaded 1,000,000 rows from data.csv
INFO - Execution engine set to: chdb
INFO - Connected to MySQL: localhost:3306/mydb

WARNING 级别

• 已弃用功能的使用
• 性能警告
• 非关键问题

WARNING - Large result set (>1M rows) may cause memory issues
WARNING - Cache TTL exceeded, re-executing query
WARNING - Column 'date' has mixed types, using string

ERROR 级别

• 查询执行失败
• 连接错误
• 数据转换错误

ERROR - Failed to execute SQL: syntax error near 'FORM'
ERROR - Connection to MySQL failed: timeout
ERROR - Cannot convert column 'price' to float

---

自定义日志配置

使用 Python 日志功能

import logging

# Configure root logger
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('datastore.log'),
        logging.StreamHandler()
    ]
)

# Get DataStore logger
ds_logger = logging.getLogger('chdb.datastore')
ds_logger.setLevel(logging.DEBUG)

日志输出到文件

import logging

# Create file handler
file_handler = logging.FileHandler('datastore_debug.log')
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(logging.Formatter(
    '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
))

# Add to DataStore logger
ds_logger = logging.getLogger('chdb.datastore')
ds_logger.addHandler(file_handler)

抑制日志输出

import logging

# Suppress all DataStore logs
logging.getLogger('chdb.datastore').setLevel(logging.CRITICAL)

# Or using config
config.set_log_level(logging.CRITICAL)

---

调试场景

调试 SQL 语句生成

config.enable_debug()

ds = pd.read_csv("data.csv")
result = ds.filter(ds['age'] > 25).groupby('city').sum()

日志输出：

DEBUG - Creating DataStore from file 'data.csv'
DEBUG - Building filter: age > 25
DEBUG - Building groupby: city
DEBUG - Building aggregation: sum
DEBUG - Generated SQL:
        SELECT city, SUM(*) 
        FROM file('data.csv', 'CSVWithNames')
        WHERE age > 25
        GROUP BY city

选择调试引擎

config.enable_debug()

result = ds.filter(ds['x'] > 10).apply(custom_func)

日志输出：

DEBUG - filter: selecting engine (eligible: chdb, pandas)
DEBUG - filter: using chdb (SQL-compatible)
DEBUG - apply: selecting engine (eligible: pandas)
DEBUG - apply: using pandas (custom function)

调试缓存操作

config.enable_debug()

# First execution
result1 = ds.filter(ds['age'] > 25).to_df()
# DEBUG - Cache miss for query hash abc123
# DEBUG - Executing query...
# DEBUG - Caching result (key: abc123, size: 1.2MB)

# Second execution (same query)
result2 = ds.filter(ds['age'] > 25).to_df()
# DEBUG - Cache hit for query hash abc123
# DEBUG - Returning cached result

排查性能问题

config.enable_debug()
config.enable_profiling()

# Logs will show timing for each operation
result = (ds
    .filter(ds['amount'] > 100)
    .groupby('region')
    .agg({'amount': 'sum'})
    .to_df()
)

日志输出：

DEBUG - filter: 0.002ms
DEBUG - groupby: 0.001ms
DEBUG - agg: 0.003ms
DEBUG - SQL generation: 0.012ms
DEBUG - SQL execution: 89.456ms  <- Main time spent here
DEBUG - Result conversion: 2.345ms

---

生产环境配置

推荐配置

import logging
from chdb.datastore.config import config

# Production: minimal logging
config.set_log_level(logging.WARNING)
config.set_log_format("simple")
config.set_profiling_enabled(False)

日志轮转

import logging
from logging.handlers import RotatingFileHandler

# Create rotating file handler
handler = RotatingFileHandler(
    'datastore.log',
    maxBytes=10*1024*1024,  # 10MB
    backupCount=5
)
handler.setLevel(logging.WARNING)

# Add to DataStore logger
logging.getLogger('chdb.datastore').addHandler(handler)

---

环境变量

你还可以通过环境变量配置日志：

# Set log level
export CHDB_LOG_LEVEL=DEBUG

# Set log format
export CHDB_LOG_FORMAT=verbose

import os
import logging

# Read from environment
log_level = os.environ.get('CHDB_LOG_LEVEL', 'WARNING')
config.set_log_level(getattr(logging, log_level))

---

摘要

任务	命令
启用调试	config.enable_debug()
设置级别	config.set_log_level(logging.DEBUG)
设置格式	config.set_log_format("verbose")
输出到文件	使用 Python logging 处理器
抑制日志	config.set_log_level(logging.CRITICAL)