DataStore 快速入门
几分钟内即可开始使用 DataStore。本指南涵盖安装、从 pandas 迁移以及基本使用模式。
安装
使用 pip 安装 chDB：
pip install "chdb>=4.0"
可选依赖：
# For pandas DataFrame support
pip install "chdb[pandas]>=4.0"
# For PyArrow support
pip install "chdb[arrow]>=4.0"
# All optional dependencies
pip install "chdb[all]>=4.0"
验证安装
import chdb
print(chdb.__version__) # Should print 4.x.x or higher
from chdb import datastore as pd
print("DataStore ready!")
一行代码从 Pandas 迁移
开始使用 DataStore 的最简单方式是把你的 import 语句改成：
# Before (pandas)
import pandas as pd
# After (DataStore)
from chdb import datastore as pd
就这样，你现有的 pandas 代码将改用 DataStore，并自动受益于 SQL 优化。
迁移示例
# Original pandas code
import pandas as pd
df = pd.read_csv("employees.csv")
result = (df[df['salary'] > 50000]
.groupby('department')['salary']
.agg(['mean', 'count'])
.sort_values('mean', ascending=False))
print(result)
# DataStore version - just change the import!
from chdb import datastore as pd
df = pd.read_csv("employees.csv")
result = (df[df['salary'] > 50000]
.groupby('department')['salary']
.agg(['mean', 'count'])
.sort_values('mean', ascending=False))
print(result) # Same result, faster execution!
基本用法
创建 DataStore 实例
from chdb import datastore as pd
# From a dictionary
ds = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie'],
'age': [25, 30, 35],
'city': ['NYC', 'LA', 'NYC']
})
# From a pandas DataFrame
import pandas
pdf = pandas.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
ds = pd.DataFrame(pdf)
# From a CSV file
ds = pd.read_csv("data.csv")
# From a Parquet file (recommended for large datasets)
ds = pd.read_parquet("data.parquet")
过滤数据
from chdb import datastore as pd
ds = pd.read_csv("employees.csv")
# Single condition
senior = ds[ds['age'] > 30]
# Multiple conditions (AND)
senior_nyc = ds[(ds['age'] > 30) & (ds['city'] == 'NYC')]
# Multiple conditions (OR)
young_or_senior = ds[(ds['age'] < 25) | (ds['age'] > 50)]
# Using filter method (SQL-style)
result = ds.filter(ds['salary'] > 50000)
选择列
# Pandas style
subset = ds[['name', 'age']]
# SQL style
subset = ds.select('name', 'age')
排序
# Pandas style
sorted_ds = ds.sort_values('salary', ascending=False)
# SQL style
sorted_ds = ds.sort('salary', ascending=False)
分组与聚合
from chdb import datastore as pd
ds = pd.read_csv("sales.csv")
# Group by single column
by_region = ds.groupby('region')['amount'].sum()
# Group by multiple columns
by_region_product = ds.groupby(['region', 'product']).agg({
'amount': ['sum', 'mean'],
'quantity': 'sum'
})
# Multiple aggregations
summary = ds.groupby('category').agg({
'price': ['min', 'max', 'mean'],
'quantity': 'sum'
})
关联 DataStores
from chdb import datastore as pd
employees = pd.read_csv("employees.csv")
departments = pd.read_csv("departments.csv")
# Inner join
result = employees.join(departments, on='dept_id', how='inner')
# Left join
result = employees.join(departments, on='dept_id', how='left')
# Using merge (pandas style)
result = pd.merge(employees, departments, on='dept_id')
获取结果
DataStore 使用惰性求值策略——只有在你真正需要结果时，相关操作才会被执行。
触发执行
# Automatic triggers
print(ds) # Displaying results
len(ds) # Getting row count
ds.columns # Accessing properties
list(ds) # Converting to list
# Explicit conversion
df = ds.to_df() # Convert to pandas DataFrame
df = ds.to_pandas() # Same as to_df()
查看生成的 SQL 语句
# See what SQL DataStore will execute
query = ds.filter(ds['age'] > 25).groupby('city').agg({'salary': 'mean'})
print(query.to_sql())
输出结果：
SELECT city, AVG(salary) AS mean
FROM file('data.csv', 'CSVWithNames')
WHERE age > 25
GROUP BY city
处理不同数据源
本地文件
from chdb import datastore as pd
# CSV
ds = pd.read_csv("data.csv")
# Parquet (best performance)
ds = pd.read_parquet("data.parquet")
# JSON
ds = pd.read_json("data.json")
Cloud 存储
from chdb.datastore import DataStore
# S3 (anonymous)
ds = DataStore.uri("s3://bucket/data.parquet?nosign=true")
# S3 (with credentials)
ds = DataStore.from_s3(
"s3://bucket/data.parquet",
access_key_id="KEY",
secret_access_key="SECRET"
)
# HTTP/HTTPS
ds = DataStore.uri("https://example.com/data.csv")
数据库
from chdb.datastore import DataStore
# MySQL
ds = DataStore.from_mysql(
host="localhost",
database="mydb",
table="users",
user="root",
password="pass"
)
# PostgreSQL
ds = DataStore.from_postgresql(
host="localhost",
database="mydb",
table="users",
user="postgres",
password="pass"
)
# Using URI
ds = DataStore.uri("mysql://user:pass@localhost:3306/mydb/users")
字符串与 DateTime 操作
字符串操作
# All pandas .str methods work
ds['name_upper'] = ds['name'].str.upper()
ds['name_len'] = ds['name'].str.len()
ds['has_a'] = ds['name'].str.contains('a')
DateTime 运算
# All pandas .dt methods work
ds['year'] = ds['date'].dt.year
ds['month'] = ds['date'].dt.month
ds['day_of_week'] = ds['date'].dt.dayofweek
ClickHouse 扩展功能
# URL parsing (not available in pandas!)
ds['domain'] = ds['url'].url.domain()
# JSON extraction
ds['user_name'] = ds['json_data'].json.get_string('name')
# IP address operations
ds['is_ipv4'] = ds['ip_addr'].ip.is_ipv4_string()
最佳实践
1. 大文件请使用 Parquet 格式
# CSV - slower, reads entire file
ds = pd.read_csv("large_data.csv")
# Parquet - faster, columnar format, reads only needed columns
ds = pd.read_parquet("large_data.parquet")
2. 尽早进行过滤
# Good - filter first, then aggregate
result = (ds
.filter(ds['date'] >= '2024-01-01')
.groupby('category')['amount'].sum()
)
# Less optimal - aggregate first
result = ds.groupby('category')['amount'].sum()
3. 仅选择所需的列
# Good - select specific columns
result = ds.select('name', 'age', 'city').filter(ds['age'] > 25)
# Less optimal - work with all columns
result = ds.filter(ds['age'] > 25)
4. 使用 SQL 处理复杂操作
# For complex queries, use SQL directly
ds = DataStore()
result = ds.sql("""
SELECT category,
SUM(amount) as total,
COUNT(*) as count,
AVG(amount) as avg
FROM file('sales.csv', 'CSVWithNames')
WHERE date >= '2024-01-01'
GROUP BY category
HAVING total > 10000
ORDER BY total DESC
LIMIT 10
""")
