Import/Export from SQL Databases

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Send and receive data from PostgreSQL, SQLite, MySQL, TigerData, or Snowflake for use in external applications. What’s in this recipe:

Export entire tables or filtered queries to any SQL database
Importing exported data back into Pixeltable
Select specific columns for export
Handle existing tables with replace or append options
Connect to cloud PostgreSQL services (e.g. TigerData)

Problem

You have processed data in your pipeline—cleaned text, generated embeddings, extracted metadata—and need to send it to a SQL database for use by other applications or teams.

Solution

You use export_sql() & import_sql() to transfer tables or queries to and from any SQL database. The functions automatically map Pixeltable types to appropriate SQL types for each database dialect.

Setup

%pip install -qU pixeltable snowflake-sqlalchemy

Create sample data

import pixeltable as pxt
import tempfile
from pathlib import Path
from pixeltable.io.sql import export_sql, import_sql

# Create a fresh directory
pxt.drop_dir('sql_export_demo', force=True)
pxt.create_dir('sql_export_demo')

Created directory ‘sql_export_demo’.
<pixeltable.catalog.dir.Dir at 0x17fec0790>

# Create a table with product data
products = pxt.create_table(
    'sql_export_demo/products',
    {
        'name': pxt.String,
        'price': pxt.Float,
        'in_stock': pxt.Bool,
        'metadata': pxt.Json,
    },
)

Created table ‘products’.

# Insert sample products
products.insert(
    [
        {
            'name': 'Wireless Mouse',
            'price': 29.99,
            'in_stock': True,
            'metadata': {'category': 'electronics', 'rating': 4.5},
        },
        {
            'name': 'USB-C Hub',
            'price': 49.99,
            'in_stock': False,
            'metadata': {'category': 'electronics', 'rating': 4.2},
        },
        {
            'name': 'Mechanical Keyboard',
            'price': 89.99,
            'in_stock': True,
            'metadata': {'category': 'electronics', 'rating': 4.8},
        },
        {
            'name': 'Monitor Stand',
            'price': 39.99,
            'in_stock': True,
            'metadata': {'category': 'accessories', 'rating': 4.0},
        },
        {
            'name': 'Webcam',
            'price': 59.99,
            'in_stock': False,
            'metadata': {'category': 'electronics', 'rating': 3.9},
        },
    ]
)

Inserted 5 rows with 0 errors in 0.01 s (501.18 rows/s)
5 rows inserted.

# View the data
products.collect()

Export an entire table

You pass a table and a SQLAlchemy connection string to export all rows and columns.

# Create a SQLite database for this demo
db_path = Path(tempfile.mkdtemp()) / 'products.db'
connection_string = f'sqlite:///{db_path}'

# Export the full table
export_sql(products, 'products', db_connect_str=connection_string)

# Verify the export with SQLAlchemy
import sqlalchemy as sql

engine = sql.create_engine(connection_string)
with engine.connect() as conn:
    result = conn.execute(sql.text('SELECT * FROM products')).fetchall()

result

[(‘Wireless Mouse’, 29.99, 1, ’{“rating”: 4.5, “category”: “electronics”}’),
 (‘USB-C Hub’, 49.99, 0, ’{“rating”: 4.2, “category”: “electronics”}’),
 (‘Mechanical Keyboard’, 89.99, 1, ’{“rating”: 4.8, “category”: “electronics”}’),
 (‘Monitor Stand’, 39.99, 1, ’{“rating”: 4.0, “category”: “accessories”}’),
 (‘Webcam’, 59.99, 0, ’{“rating”: 3.9, “category”: “electronics”}’)]

Export a filtered query

You can export any query result—filter rows, select specific columns, or apply transformations before export.

# Export only in-stock products
export_sql(
    products.where(products.in_stock == True),
    'in_stock_products',
    db_connect_str=connection_string,
)

# Verify filtered export
with engine.connect() as conn:
    result = conn.execute(
        sql.text('SELECT name, price FROM in_stock_products')
    ).fetchall()

result

[(‘Wireless Mouse’, 29.99),
 (‘Mechanical Keyboard’, 89.99),
 (‘Monitor Stand’, 39.99)]

Import a table & query

You can use import_sql() to import an existing SQL table or query into Pixeltable. pxt.Table schema is automatically inferred from the schema of the existing SQL table, and can be overridden using schema_overrides.

# Re-import the exported products
sql_tbl = sql.Table('products', sql.MetaData(), autoload_with=engine)

with engine.connect() as conn:
    import_sql(
        selectable=sql_tbl,
        conn=conn,
        tbl_name='sql_export_demo/imported_products',
    )

reimported_tbl = pxt.get_table('sql_export_demo/imported_products')

reimported_tbl.collect()

Created table ‘imported_products’.
Inserted 5 rows with 0 errors in 0.01 s (779.78 rows/s)

# Re-import the exported products selecting only name and price, and overriding price type to Int (lossy cast)
with engine.connect() as conn:
    import_sql(
        selectable=sql.select(sql_tbl.c.name, sql_tbl.c.price),
        conn=conn,
        tbl_name='sql_export_demo/imported_products_lossy',
        schema_overrides={'price': pxt.Int},
    )

reimported_tbl = pxt.get_table('sql_export_demo/imported_products_lossy')
reimported_tbl.collect()

Created table ‘imported_products_lossy’.
Inserted 5 rows with 0 errors in 0.01 s (867.33 rows/s)

Export specific columns

You select only the columns you need before exporting. You can also rename columns in the output.

# Export only name and price columns
export_sql(
    products.select(products.name, products.price),
    'price_list',
    db_connect_str=connection_string,
)

# Export with renamed columns
export_sql(
    products.select(
        product_name=products.name, unit_price=products.price
    ),
    'renamed_columns',
    db_connect_str=connection_string,
)

# Verify column selection
inspector = sql.inspect(engine)
columns = [col['name'] for col in inspector.get_columns('price_list')]
columns

[‘name’, ‘price’]

Handle existing tables

You control what happens when the target table already exists using the if_exists parameter:

# Append new data to existing table
export_sql(
    products.where(products.price > 50),
    'products',
    db_connect_str=connection_string,
    if_exists='insert',
)

# Check row count after insert
with engine.connect() as conn:
    result = conn.execute(
        sql.text('SELECT COUNT(*) FROM products')
    ).fetchone()

f'Total rows after insert: {result[0]}'

‘Total rows after insert: 7’

# Replace with fresh data
export_sql(
    products.select(products.name, products.price),
    'products',
    db_connect_str=connection_string,
    if_exists='replace',
)

# Check that table was replaced
inspector = sql.inspect(engine)
columns = [col['name'] for col in inspector.get_columns('products')]

with engine.connect() as conn:
    row_count = conn.execute(
        sql.text('SELECT COUNT(*) FROM products')
    ).fetchone()[0]

f'Columns: {columns}, Row count: {row_count}'

“Columns: [‘name’, ‘price’], Row count: 5”

Export to cloud PostgreSQL (TigerData)

You can export directly to cloud-hosted PostgreSQL databases like TigerData (Timescale Cloud). Get your credentials from the TigerData dashboard after creating a service.

import getpass
import os

# Skip interactive sections in CI environments
SKIP_CLOUD_TESTS = os.environ.get('CI') or os.environ.get(
    'GITHUB_ACTIONS'
)

if not SKIP_CLOUD_TESTS:
    # Enter your TigerData credentials interactively
    tigerdata_host = input(
        'TigerData host (e.g., abc123.tsdb.cloud.timescale.com): '
    )
    tigerdata_port = input('TigerData port (e.g., 38963): ')
    tigerdata_user = input('TigerData username (e.g., tsdbadmin): ')
    tigerdata_password = getpass.getpass('TigerData password: ')
    tigerdata_dbname = input('TigerData database name (e.g., tsdb): ')

    # Build the connection string (use postgresql+psycopg:// for SQLAlchemy compatibility)
    tigerdata_connection = f'postgresql+psycopg://{tigerdata_user}:{tigerdata_password}@{tigerdata_host}:{tigerdata_port}/{tigerdata_dbname}?sslmode=require'
else:
    print('Skipping TigerData section (running in CI)')

if not SKIP_CLOUD_TESTS:
    # Export to TigerData
    export_sql(
        products,
        'pixeltable_products',
        db_connect_str=tigerdata_connection,
        if_exists='replace',
    )

if not SKIP_CLOUD_TESTS:
    # Verify the export in TigerData
    tigerdata_engine = sql.create_engine(tigerdata_connection)
    with tigerdata_engine.connect() as conn:
        result = conn.execute(
            sql.text('SELECT * FROM pixeltable_products')
        ).fetchall()
    result

[(‘Wireless Mouse’, 29.99, True, {‘rating’: 4.5, ‘category’: ‘electronics’}),
 (‘USB-C Hub’, 49.99, False, {‘rating’: 4.2, ‘category’: ‘electronics’}),
 (‘Mechanical Keyboard’, 89.99, True, {‘rating’: 4.8, ‘category’: ‘electronics’}),
 (‘Monitor Stand’, 39.99, True, {‘rating’: 4.0, ‘category’: ‘accessories’}),
 (‘Webcam’, 59.99, False, {‘rating’: 3.9, ‘category’: ‘electronics’})]

Export to Snowflake

You can export directly to Snowflake data warehouses. Get your account identifier from the Snowflake web interface under Admin → Accounts.

if not SKIP_CLOUD_TESTS:
    # Enter your Snowflake credentials interactively
    snowflake_account = input(
        'Snowflake account identifier (e.g., WEZMMGC-AIB20064): '
    )
    snowflake_user = input('Snowflake username: ')
    snowflake_password = getpass.getpass('Snowflake password: ')
    snowflake_warehouse = input(
        'Snowflake warehouse (e.g., COMPUTE_WH): '
    )
    snowflake_database = input('Snowflake database: ')
    snowflake_schema = input('Snowflake schema (e.g., PUBLIC): ')

    # Build the connection string
    snowflake_connection = f'snowflake://{snowflake_user}:{snowflake_password}@{snowflake_account}/{snowflake_database}/{snowflake_schema}?warehouse={snowflake_warehouse}'
else:
    print('Skipping Snowflake section (running in CI)')

if not SKIP_CLOUD_TESTS:
    # Export to Snowflake (without JSON column)
    export_sql(
        products.select(products.name, products.price, products.in_stock),
        'PIXELTABLE_PRODUCTS',
        db_connect_str=snowflake_connection,
        if_exists='replace',
    )

if not SKIP_CLOUD_TESTS:
    # Verify the export in Snowflake
    snowflake_engine = sql.create_engine(snowflake_connection)
    with snowflake_engine.connect() as conn:
        result = conn.execute(
            sql.text('SELECT * FROM PIXELTABLE_PRODUCTS')
        ).fetchall()
    result

[(‘Wireless Mouse’, 29.99, True, None),
 (‘USB-C Hub’, 49.99, False, None),
 (‘Mechanical Keyboard’, 89.99, True, None),
 (‘Monitor Stand’, 39.99, True, None),
 (‘Webcam’, 59.99, False, None)]

Exporting media data

For tables containing media types (pxt.Image, pxt.Video, pxt.Audio), you have two options:

Extract metadata before export - Select only the columns you need (paths, embeddings, extracted text, etc.) and export those to SQL.
Use Pixeltable destinations - For syncing media files to cloud storage, use Pixeltable’s built-in destination support with providers like Tigris.

Example: Export image metadata to SQL

# Create a table with images
images = pxt.create_table(
    'sql_export_demo/images', {'image': pxt.Image, 'label': pxt.String}
)

# Add computed columns for metadata
images.add_computed_column(width=images.image.width)
images.add_computed_column(height=images.image.height)
images.add_computed_column(mode=images.image.mode)

Created table ‘images’.
Added 0 column values with 0 errors in 0.01 s
Added 0 column values with 0 errors in 0.01 s
Added 0 column values with 0 errors in 0.01 s
No rows affected.

# Insert sample images
base_url = 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images'
images.insert(
    [
        {'image': f'{base_url}/000000000036.jpg', 'label': 'cat'},
        {'image': f'{base_url}/000000000090.jpg', 'label': 'scene'},
    ]
)

Inserted 2 rows with 0 errors in 0.08 s (26.28 rows/s)
2 rows inserted.

# Export metadata (not the image itself) to SQL
export_sql(
    images.select(images.label, images.width, images.height, images.mode),
    'image_metadata',
    db_connect_str=connection_string,  # or tigerdata_connection for cloud
)

# Verify the metadata export
with engine.connect() as conn:
    result = conn.execute(
        sql.text('SELECT * FROM image_metadata')
    ).fetchall()

result

[(‘cat’, 481, 640, ‘RGB’), (‘scene’, 640, 429, ‘RGB’)]

Round-trip media via fileurl

Media columns can’t be written to SQL directly, but their location can. Select images.image.fileurl to export each image’s URL (here, the original GitHub links) as a plain string column.

# Export the image locations (fileurl), not the image bytes
export_sql(
    images.select(images.label, image=images.image.fileurl),
    'image_locations',
    db_connect_str=connection_string,
    if_exists='replace',
)

with engine.connect() as conn:
    locations = conn.execute(
        sql.text('SELECT label, image FROM image_locations')
    ).fetchall()

locations

[(‘cat’, ‘https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000036.jpg&#x27;),
 (‘scene’, ‘https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images/000000000090.jpg&#x27;)\]

Import the locations back as images

Reflect the SQL table and import it, using schema_overrides to promote the location column back to pxt.Image. Pixeltable rehydrates each URL into a real image, so expressions like .width work again.

src = sql.Table('image_locations', sql.MetaData(), autoload_with=engine)

# schema_overrides promotes the location strings back into a pxt.Image column on import
restored = import_sql(
    src,
    engine,
    'sql_export_demo/restored_images',
    schema_overrides={'image': pxt.Image},
)

restored.select(
    restored.label, restored.image, width=restored.image.width
).collect()

Created table ‘restored_images’.
Inserted 2 rows with 0 errors in 0.01 s (142.39 rows/s)

Explanation

Connection strings: The function uses SQLAlchemy connection strings. Common formats:

Type mapping: Pixeltable types map to SQL types automatically:

Unsupported types: Media types like pxt.Image, pxt.Video, and pxt.Audio cannot be exported directly. Extract the data you need (paths, embeddings, metadata) before export.

Welcome to Pixeltable

Tools

Core Concepts

How-To

Problem

Solution

Setup

Create sample data

Export an entire table

Export a filtered query

Import a table & query

Export specific columns

Handle existing tables

Export to cloud PostgreSQL (TigerData)

Export to Snowflake

Exporting media data

Round-trip media via fileurl

Import the locations back as images

Explanation

See also

​Problem

​Solution

​Setup

​Create sample data

​Export an entire table

​Export a filtered query

​Import a table & query

​Export specific columns

​Handle existing tables

​Export to cloud PostgreSQL (TigerData)

​Export to Snowflake

​Exporting media data

​Round-trip media via fileurl

​Import the locations back as images

​Explanation

​See also

Problem

Solution

Setup

Create sample data

Export an entire table

Export a filtered query

Import a table & query

Export specific columns

Handle existing tables

Export to cloud PostgreSQL (TigerData)

Export to Snowflake

Exporting media data

Round-trip media via fileurl

Import the locations back as images

Explanation

See also