> ## 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.

# DataStore Query building

> Build SQL-style queries with DataStore using fluent method chaining

DataStore provides SQL-style query building methods that compile to optimized SQL queries. All operations are lazy until results are needed.

<h2 id="overview">
  Query Methods Overview
</h2>

| Method             | SQL Equivalent  | Description       |
| ------------------ | --------------- | ----------------- |
| `select(*cols)`    | `SELECT cols`   | Select columns    |
| `filter(cond)`     | `WHERE cond`    | Filter rows       |
| `where(cond)`      | `WHERE cond`    | Alias for filter  |
| `sort(*cols)`      | `ORDER BY cols` | Sort rows         |
| `orderby(*cols)`   | `ORDER BY cols` | Alias for sort    |
| `limit(n)`         | `LIMIT n`       | Limit rows        |
| `offset(n)`        | `OFFSET n`      | Skip rows         |
| `distinct()`       | `DISTINCT`      | Remove duplicates |
| `groupby(*cols)`   | `GROUP BY cols` | Group rows        |
| `having(cond)`     | `HAVING cond`   | Filter groups     |
| `join(right, ...)` | `JOIN`          | Join DataStores   |
| `union(other)`     | `UNION`         | Combine results   |

***

<h2 id="selection">
  Selection
</h2>

<h3 id="select">
  `select`
</h3>

Select specific columns from the DataStore.

```python theme={null}
select(*fields: Union[str, Expression]) -> DataStore
```

**Examples:**

```python theme={null}
from chdb.datastore import DataStore
from pathlib import Path
Path("employees.csv").write_text("""\
name,age,city,salary,department,dept_id,status,email,manager_id,bonus
Alice,28,NYC,75000,Engineering,1,active,alice@company.com,3,5000
Bob,35,LA,85000,Engineering,1,active,bob@company.com,3,
Charlie,52,NYC,95000,Product,2,active,charlie@company.com,,10000
Diana,32,SF,70000,Design,3,active,diana@company.com,3,3000
Eve,23,LA,48000,Product,2,inactive,eve@company.com,2,
""")

ds = DataStore.from_file("employees.csv")

# Select by column names
result = ds.select('name', 'age', 'salary')

# Select all columns
result = ds.select('*')

# Select with expressions
result = ds.select(
    'name',
    (ds['salary'] * 12).as_('annual_salary'),
    ds['age'].as_('employee_age')
)

# Equivalent pandas style
result = ds[['name', 'age', 'salary']]
```

***

<h2 id="filtering">
  Filtering
</h2>

<h3 id="filter">
  `filter` / `where`
</h3>

Filter rows based on conditions. Both methods are equivalent.

```python theme={null}
filter(condition) -> DataStore
where(condition) -> DataStore  # alias
```

**Examples:**

```python theme={null}
ds = DataStore.from_file("employees.csv")

# Single condition
result = ds.filter(ds['age'] > 30)
result = ds.where(ds['salary'] >= 50000)

# Multiple conditions (AND)
result = ds.filter((ds['age'] > 30) & (ds['department'] == 'Engineering'))

# Multiple conditions (OR)
result = ds.filter((ds['city'] == 'NYC') | (ds['city'] == 'LA'))

# NOT condition
result = ds.filter(~(ds['status'] == 'inactive'))

# String conditions
result = ds.filter(ds['name'].str.contains('John'))
result = ds.filter(ds['email'].str.endswith('@company.com'))

# NULL checks
result = ds.filter(ds['manager_id'].notnull())
result = ds.filter(ds['bonus'].isnull())

# IN condition
result = ds.filter(ds['department'].isin(['Engineering', 'Product', 'Design']))

# BETWEEN condition
result = ds.filter(ds['salary'].between(50000, 100000))

# Chained filters (AND)
result = (ds
    .filter(ds['age'] > 25)
    .filter(ds['salary'] > 50000)
    .filter(ds['city'] == 'NYC')
)
```

<h3 id="pandas-filtering">
  Pandas-Style Filtering
</h3>

```python theme={null}
# Boolean indexing (equivalent to filter)
result = ds[ds['age'] > 30]
result = ds[(ds['age'] > 30) & (ds['salary'] > 50000)]

# Query method
result = ds.query('age > 30 and salary > 50000')
```

***

<h2 id="sorting">
  Sorting
</h2>

<h3 id="sort">
  `sort` / `orderby`
</h3>

Sort rows by one or more columns.

```python theme={null}
sort(*fields, ascending=True) -> DataStore
orderby(*fields, ascending=True) -> DataStore  # alias
```

**Examples:**

```python theme={null}
ds = DataStore.from_file("employees.csv")

# Single column ascending
result = ds.sort('name')

# Single column descending
result = ds.sort('salary', ascending=False)

# Multiple columns
result = ds.sort('department', 'salary')

# Mixed order (use list for ascending parameter)
result = ds.sort('department', 'salary', ascending=[True, False])

# Pandas style
result = ds.sort_values('salary', ascending=False)
result = ds.sort_values(['department', 'salary'], ascending=[True, False])
```

***

<h2 id="limiting">
  Limiting and Pagination
</h2>

<h3 id="limit">
  `limit`
</h3>

Limit the number of rows returned.

```python theme={null}
limit(n: int) -> DataStore
```

<h3 id="offset">
  `offset`
</h3>

Skip the first n rows.

```python theme={null}
offset(n: int) -> DataStore
```

**Examples:**

```python theme={null}
ds = DataStore.from_file("employees.csv")

# First 10 rows
result = ds.limit(10)

# Skip first 100, take next 50
result = ds.offset(100).limit(50)

# Pandas style
result = ds.head(10)
result = ds.tail(10)
result = ds.iloc[100:150]
```

***

<h2 id="distinct">
  Distinct
</h2>

<h3 id="distinct-method">
  `distinct`
</h3>

Remove duplicate rows.

```python theme={null}
distinct(subset=None, keep='first') -> DataStore
```

**Examples:**

```python theme={null}
from pathlib import Path
Path("events.csv").write_text("""\
user_id,event_type,timestamp
1,click,2024-01-15 10:30:00
2,view,2024-01-15 11:00:00
1,purchase,2024-01-15 11:30:00
3,click,2024-01-16 09:00:00
2,click,2024-01-16 10:00:00
""")

ds = DataStore.from_file("events.csv")

# Remove all duplicate rows
result = ds.distinct()

# Remove duplicates based on specific columns
result = ds.distinct(subset=['user_id', 'event_type'])

# Pandas style
result = ds.drop_duplicates()
result = ds.drop_duplicates(subset=['user_id'])
```

***

<h2 id="grouping">
  Grouping
</h2>

<h3 id="groupby">
  `groupby`
</h3>

Group rows by one or more columns. Returns a `LazyGroupBy` object.

```python theme={null}
groupby(*fields, sort=True, as_index=True, dropna=True) -> LazyGroupBy
```

**Examples:**

```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
""")

ds = DataStore.from_file("sales.csv")

# Group by single column
by_region = ds.groupby('region')

# Group by multiple columns
by_region_product = ds.groupby('region', 'product')

# Aggregation after groupby
result = ds.groupby('region')['amount'].sum()
result = ds.groupby('region').agg({'amount': 'sum', 'quantity': 'mean'})

# Multiple aggregations
result = ds.groupby('category').agg({
    'price': ['min', 'max', 'mean'],
    'quantity': 'sum'
})

# Named aggregation
result = ds.groupby('region').agg(
    total_amount=('amount', 'sum'),
    avg_quantity=('quantity', 'mean'),
    order_count=('order_id', 'count')
)
```

<h3 id="having">
  `having`
</h3>

Filter groups after aggregation.

```python theme={null}
having(condition: Union[Condition, str]) -> DataStore
```

**Examples:**

```python theme={null}
# Filter groups with total > 10000
result = (ds
    .groupby('region')
    .agg({'amount': 'sum'})
    .having(ds['sum'] > 10000)
)

# Using SQL-style having
result = (ds
    .select('region', 'SUM(amount) as total')
    .groupby('region')
    .having('total > 10000')
)
```

***

<h2 id="joining">
  Joining
</h2>

<h3 id="join">
  `join`
</h3>

Join two DataStores.

```python theme={null}
join(right, on=None, how='inner', left_on=None, right_on=None) -> DataStore
```

**Parameters:**

| Parameter  | Type      | Default    | Description                                  |
| ---------- | --------- | ---------- | -------------------------------------------- |
| `right`    | DataStore | *required* | Right DataStore to join                      |
| `on`       | str/list  | `None`     | Columns to join on                           |
| `how`      | str       | `'inner'`  | Join type: 'inner', 'left', 'right', 'outer' |
| `left_on`  | str/list  | `None`     | Left join columns                            |
| `right_on` | str/list  | `None`     | Right join columns                           |

**Examples:**

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

employees = DataStore.from_file("employees.csv")
departments = DataStore.from_file("departments.csv")

# Inner join on single column
result = employees.join(departments, on='dept_id')

# Left join
result = employees.join(departments, on='dept_id', how='left')

# Join on different column names
result = employees.join(
    departments,
    left_on='department_id',
    right_on='id',
    how='inner'
)

# Pandas style merge
from chdb import datastore as pd
result = pd.merge(employees, departments, on='dept_id')
result = pd.merge(employees, departments, left_on='department_id', right_on='id')
```

<h3 id="union">
  `union`
</h3>

Combine results from two DataStores.

```python theme={null}
union(other, all=False) -> DataStore
```

**Examples:**

```python theme={null}
from pathlib import Path
Path("sales_2023.csv").write_text("""\
region,product,amount,date
East,Widget,1200,2023-06-15
West,Gadget,800,2023-09-20
North,Gizmo,600,2023-11-10
""")
Path("sales_2024.csv").write_text("""\
region,product,amount,date
East,Widget,1500,2024-03-10
North,Gizmo,900,2024-07-22
West,Gadget,1100,2024-05-05
""")

ds1 = DataStore.from_file("sales_2023.csv")
ds2 = DataStore.from_file("sales_2024.csv")

# UNION (removes duplicates)
result = ds1.union(ds2)

# UNION ALL (keeps duplicates)
result = ds1.union(ds2, all=True)

# Pandas style
from chdb import datastore as pd
result = pd.concat([ds1, ds2])
```

***

<h2 id="conditional">
  Conditional Expressions
</h2>

<h3 id="when">
  `when`
</h3>

Create CASE WHEN expressions.

```python theme={null}
when(condition, value) -> CaseWhenBuilder
```

**Examples:**

```python theme={null}
ds = DataStore.from_file("employees.csv")

# Simple case-when
result = ds.select(
    'name',
    ds.when(ds['salary'] > 100000, 'High')
      .when(ds['salary'] > 50000, 'Medium')
      .otherwise('Low')
      .as_('salary_tier')
)

# With column assignment
ds['salary_tier'] = (
    ds.when(ds['salary'] > 100000, 'High')
      .when(ds['salary'] > 50000, 'Medium')
      .otherwise('Low')
)
```

***

<h2 id="raw-sql">
  Raw SQL
</h2>

<h3 id="run-sql">
  `run_sql` / `sql`
</h3>

Execute raw SQL queries.

```python theme={null}
run_sql(query: str) -> DataStore
sql(query: str) -> DataStore  # alias
```

**Examples:**

```python theme={null}
from chdb.datastore import DataStore

# Execute raw SQL
result = DataStore().sql("""
    SELECT 
        department,
        COUNT(*) as count,
        AVG(salary) as avg_salary
    FROM file('employees.csv', 'CSVWithNames')
    WHERE status = 'active'
    GROUP BY department
    HAVING count > 5
    ORDER BY avg_salary DESC
    LIMIT 10
""")

# SQL on existing DataStore
ds = DataStore.from_file("employees.csv")
result = ds.sql("SELECT * FROM __table__ WHERE age > 30")
```

<h3 id="to-sql">
  `to_sql`
</h3>

View the generated SQL without executing.

```python theme={null}
to_sql(**kwargs) -> str
```

**Examples:**

```python theme={null}
ds = DataStore.from_file("employees.csv")

query = (ds
    .filter(ds['age'] > 30)
    .groupby('department')
    .agg({'salary': 'mean'})
    .sort('mean', ascending=False)
)

print(query.to_sql())
# Output:
# SELECT department, AVG(salary) AS mean
# FROM file('employees.csv', 'CSVWithNames')
# WHERE age > 30
# GROUP BY department
# ORDER BY mean DESC
```

***

<h2 id="chaining">
  Method Chaining
</h2>

All query methods support fluent chaining:

```python theme={null}
from chdb.datastore import DataStore

ds = DataStore.from_file("sales.csv")

result = (ds
    .select('region', 'product', 'amount', 'date')
    .filter(ds['date'] >= '2024-01-01')
    .filter(ds['amount'] > 100)
    .groupby('region', 'product')
    .agg({
        'amount': ['sum', 'mean'],
        'date': 'count'
    })
    .having(ds['sum'] > 10000)
    .sort('sum', ascending=False)
    .limit(20)
)

# View SQL
print(result.to_sql())

# Execute
df = result.to_df()
```

***

<h2 id="aliasing">
  Aliasing
</h2>

<h3 id="as">
  `as_`
</h3>

Set an alias for a column or subquery.

```python theme={null}
as_(alias: str) -> DataStore
```

**Examples:**

```python theme={null}
# Column alias
result = ds.select(
    ds['name'].as_('employee_name'),
    (ds['salary'] * 12).as_('annual_salary')
)

# Subquery alias
subquery = ds.filter(ds['age'] > 30).as_('senior_employees')
```