> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-fbfa8bee.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# البدء السريع لـ DataStore

> ابدأ مع DataStore - التثبيت، والترحيل بسطر واحد من pandas، والاستخدام الأساسي

شغّل DataStore وابدأ استخدامه خلال دقائق. يغطي هذا الدليل التثبيت، والترحيل من pandas، وأنماط الاستخدام الأساسية.

<div id="installation">
  ## التثبيت
</div>

ثبّت chDB باستخدام pip:

```bash theme={null}
pip install "chdb>=4.0"
```

أما التبعيات الاختيارية:

```bash theme={null}
# 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"
```

<div id="verify">
  ### التحقق من التثبيت
</div>

```python theme={null}
import chdb
print(chdb.__version__)  # Should print 4.x.x or higher

from chdb import datastore as pd
print("DataStore ready!")
```

<div id="migration">
  ## الترحيل بسطر واحد من Pandas
</div>

أسهل طريقة لبدء استخدام DataStore هي تعديل سطر import:

```python theme={null}
# Before (pandas)
import pandas as pd

# After (DataStore)
from chdb import datastore as pd
```

هذا كل شيء! سيعمل كود pandas الحالي لديك الآن باستخدام DataStore ويستفيد من تحسين SQL.

<div id="migration-example">
  ### مثال على الترحيل
</div>

```python theme={null}
from pathlib import Path
Path("employees.csv").write_text("""\
name,age,city,salary,department,dept_id,status,email
Alice,28,NYC,75000,Engineering,1,active,alice@company.com
Bob,35,LA,85000,Engineering,1,active,bob@company.com
Charlie,52,NYC,95000,Product,2,active,charlie@company.com
Diana,32,SF,70000,Design,3,active,diana@company.com
Eve,23,LA,48000,Product,2,inactive,eve@company.com
""")

# 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!
```

<div id="basic-usage">
  ## الاستخدام الأساسي
</div>

<div id="creating">
  ### إنشاء DataStore
</div>

```python theme={null}
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")
```

<div id="filtering">
  ### تصفية البيانات
</div>

```python theme={null}
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)
```

<div id="selecting">
  ### اختيار الأعمدة
</div>

```python theme={null}
# Pandas style
subset = ds[['name', 'age']]

# SQL style
subset = ds.select('name', 'age')
```

<div id="sorting">
  ### الفرز
</div>

```python theme={null}
# Pandas style
sorted_ds = ds.sort_values('salary', ascending=False)

# SQL style
sorted_ds = ds.sort('salary', ascending=False)
```

<div id="groupby">
  ### التجميع والتلخيص
</div>

```python theme={null}
from pathlib import Path
Path("sales.csv").write_text("""\
region,product,category,amount,quantity,price,date,order_id
East,Widget,Electronics,5200,10,120,2024-01-15,1001
West,Gadget,Electronics,800,5,160,2024-02-20,1002
East,Gizmo,Home,6500,3,100,2024-03-10,1003
North,Widget,Electronics,4500,6,150,2024-06-18,1004
West,Gadget,Electronics,2000,8,250,2024-09-14,1005
""")

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'
})
```

<div id="joining">
  ### ضمّ DataStores
</div>

```python theme={null}
from pathlib import Path
Path("departments.csv").write_text("""\
dept_id,department_name
1,Engineering
2,Product
3,Design
""")

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')
```

<div id="results">
  ## الحصول على النتائج
</div>

يعتمد DataStore على التقييم الكسول — فلا تُنفَّذ العمليات إلا عند الحاجة إلى النتائج.

<div id="execution-triggers">
  ### بدء التنفيذ
</div>

```python theme={null}
# 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()
```

<div id="view-sql">
  ### عرض استعلام SQL المُولَّد
</div>

```python title="Query" theme={null}
# See what SQL DataStore will execute
query = ds.filter(ds['age'] > 25).groupby('city').agg({'salary': 'mean'})
print(query.to_sql())
```

```sql title="Response" theme={null}
SELECT city, AVG(salary) AS mean
FROM file('data.csv', 'CSVWithNames')
WHERE age > 25
GROUP BY city
```

<div id="data-sources">
  ## التعامل مع مصادر بيانات مختلفة
</div>

<div id="local-files">
  ### الملفات المحلية
</div>

```python theme={null}
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")
```

<div id="cloud-storage">
  ### التخزين السحابي
</div>

```python theme={null}
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")
```

<div id="databases">
  ### قواعد البيانات
</div>

```python theme={null}
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")
```

<div id="accessors">
  ## عمليات سلسلة نصية وDateTime
</div>

<div id="string-ops">
  ### عمليات على السلاسل النصية
</div>

```python theme={null}
# 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')
```

<div id="datetime-ops">
  ### عمليات على DateTime
</div>

```python theme={null}
# 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
```

<div id="extensions">
  ### إضافات ClickHouse
</div>

```python theme={null}
# 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()
```

<div id="best-practices">
  ## أفضل الممارسات
</div>

<div id="use-parquet-for-large-files">
  ### 1. استخدم Parquet للملفات الكبيرة
</div>

```python theme={null}
# 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")
```

<div id="filter-early">
  ### 2. طبّق التصفية مبكرًا
</div>

```python theme={null}
# 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()
```

<div id="select-only-needed-columns">
  ### 3. اختر الأعمدة اللازمة فقط
</div>

```python theme={null}
# 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)
```

<div id="use-sql-for-complex-operations">
  ### 4. استخدم SQL لإجراء العمليات المعقدة
</div>

```python theme={null}
# 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
""")
```

<div id="next-steps">
  ## الخطوات التالية
</div>

* تعرّف إلى جميع [Factory Methods](/ar/products/chdb/datastore/factory-methods) لإنشاء DataStore
* استكشف [Query Building](/ar/products/chdb/datastore/query-building) لإجراء عمليات بأسلوب SQL
* اطّلع على [Accessors](/ar/products/chdb/datastore/accessors) للسلاسل النصية وdatetime والمزيد
* اقرأ [Performance Guide](/ar/products/chdb/guides/pandas-performance) للتعرّف على نصائح تحسين الأداء