lazybear-polars 0.3.0

Polars-like lazy access to a database.

LazyBear

LazyBear is a polars-flavored lazy SQL query builder on top of sqlalchemy. It lets you compose familiar transformations like select, filter, with_columns, join, group_by().agg(...), and sort/order_by against a SQL database, and then materialize results to polars or arrow (or stream them in batches).

The purpose of this library is to provide lazy, polars-like access to a single sql database server, providing multi-site (and multi-server) stable code while pushing most memory-intensive operations to the remote server.

• Familiar, chainable API similar to polars
• Backed by sqlalchemy for broad database support
• Zero data is loaded until you call collect()/to_arrow()/writers
• Convenient I/O helpers for CSV and Parquet (using polars)

Full API documentation:

• Markdown
• Github Pages

Installation

• LazyBear targets Python 3.11+.
• Required dependencies:
  • sqlalchemy
  • polars
• Optional:
  • pyarrow for to_arrow()

Install from PyPI:

pip install lazybear-polars

Install from source using pip:

# after git clone
pip install -e .
# straight from repo
pip install git+https://github.com/kpwhri/lazybear.git@master

Usage

Quickstart

Below is an end-to-end walkthrough using an in-memory sqlite database. The same api should work for other databases supported by sqlalchemy.

Create Playgruond

First, let's setup the backend play data:

import sqlalchemy as sa
import polars as pl

# create a sqlalchemy engine
eng = sa.create_engine('sqlite:///:memory:')

# prepare some tables for the demo
meta = sa.MetaData()
users = sa.Table(
    'users', meta,
    sa.Column('id', sa.Integer, primary_key=True),
    sa.Column('name', sa.String),
    sa.Column('age', sa.Integer),
)
orders = sa.Table(
    'orders', meta,
    sa.Column('id', sa.Integer, primary_key=True),
    sa.Column('user_id', sa.Integer),
    sa.Column('amount', sa.Float),
)
meta.create_all(eng)
with eng.begin() as conn:
    conn.execute(users.insert(), [
        {'id': 1, 'name': 'Ahti', 'age': 30},
        {'id': 2, 'name': 'Kalma', 'age': 28},
        {'id': 3, 'name': 'Tellervo', 'age': 41},
        {'id': 4, 'name': 'Ukko', 'age': 41},
    ])
    conn.execute(orders.insert(), [
        {'id': 10, 'user_id': 1, 'amount': 12.5},
        {'id': 11, 'user_id': 1, 'amount': 7.5},
        {'id': 12, 'user_id': 2, 'amount': 99.0},
    ])

Implemented SQL Operations

Now, let's see what we can do:

from lazybear import scan_table, scan_sql_query, col

# scan tables lazily
lf_users = scan_table('users', eng)  # columns exposed lowercase by default
lf_orders = scan_table('orders', eng)

# basic select / filter / collect → returns a polars DataFrame
out_df = (
    lf_users
    .filter(col('age') > 30)
    .select('id', 'name', 'age')
    .collect()
)
print(out_df)

# with_columns — add or replace columns
with_cols_df = (
    lf_users
    .with_columns(('age2', col('age') * 2), decade=col('age') / 10 * 10)
    .select('id', 'age2', 'decade')
    .collect()
)
print(with_cols_df)

# order and limit (keeps ordering stable even across subqueries)
ordered_df = lf_users.order_by('-age', 'name').limit(2).collect()
print(ordered_df)

# polars-style sort api
sorted_df = lf_users.sort(by='age', descending=True).collect()
print(sorted_df)

# joins (left columns keep names; right overlaps get suffixed with `_y` by default)
joined_df = (
    lf_users
    .join(lf_orders, on={'id': 'user_id'}, how='left')
    .select('id', 'name', 'age', ('amount_y', col('amount')))
    .collect()
)
print(joined_df)

# joins (left columns keep names; right overlaps get suffixed with `_y` by default)
joined_df = (
    lf_users
    .join(lf_orders, on={'id': 'user_id'}, how='left')
    .select('id', 'name', 'age', ('amount_y', col('amount')))
    .collect()
)
print(joined_df)

# when chaining multiple joins that overlap on the same column names,
# provide a different suffixes=... value for later joins to avoid label collisions.
chained_join_df = (
    lf_users
    .join(lf_orders, on={'id': 'user_id'}, how='left')
    .join(lf_orders, on={'id': 'user_id'}, how='left', suffixes=('_x2', '_y2'))
    .select('id', 'name', 'amount', 'amount_y2')
    .order_by('id', 'amount', 'amount_y2')
    .collect()
)
print(chained_join_df)

# group_by + aggregations
agg_df = (
    lf_users
    .group_by('age')
    .agg(n=('id', 'count'), min_id=('id', 'min'))
    .collect()
)
print(agg_df)

# expressions: membership, null checks, and string helpers
expr_df = (
    lf_users
    .filter(
        (col('name').str.contains('k', literal=True)) |
        (col('name').startswith('A')) |
        (col('age').is_in([28, 41]))
    )
    .collect()
)
print(expr_df)

Uploading Temp Tables (Beta)

You can use a local polars DataFrame in lazy sql operations by creating a temporary table. This is useful for joining local data with database tables.

Be aware that the table will be inserted when collect is called. After the collection is complete, a best effort attempt to delete the temp table will be completed.

import polars as pl
from lazybear import scan_df, scan_table, col

# local polars dataframe
df_local = pl.DataFrame({
    'user_id': [1, 3],
    'status': ['active', 'inactive']
})

# create a temp table frame
lf_temp = scan_df(df_local, eng, table_name='user_status')

# join with a database table
lf_users = scan_table('users', eng)
result = (
    lf_users
    .join(lf_temp, on={'id': 'user_id'})
    .select('name', 'status')
    .collect()
)

Limitations & Behavior:

• Temp tables are created and data is inserted only when collect() (or another execution method) is called.
• Best-effort cleanup (DROP TABLE) is performed after the data is fetched.
• Currently offers beta support for sqlite, PostgreSQL, SQL Server (MSSQL), Oracle, and Teradata. A warning is issued for other dialects.
• For certain dialects, will attempt bulk insert to speed up processing

Exporting Data

Finally, we'll

# iterating rows
for row in lf_users.order_by('id').iter_rows():
    # row is a tuple by default
    print(row)
for row in lf_users.order_by('id').iter_rows(named=True):
    # named=True yields dicts
    print(row)

# streaming in batches
for batch_df in lf_users.order_by('id').collect_batches(chunk_size=2):
    print('Batch:', batch_df)

# scan a raw SQL query (must be SELECT)
q_df = (
    scan_sql_query('select name, age from users where age > 30', eng)
    .filter(col('age') < 60)
    .select('name')
    .collect()
)
print(q_df)

# materialize to arrow (requires optional pyarrow)
arrow_tbl = lf_users.to_arrow()

# explain shows the composed SQL (with literal binds where possible)
print(lf_users.filter(col('age') > 30).explain())

I/O helpers

• CSV

# single file
lf_users.order_by('id').write_csv('users.csv', include_header=True)

# append in chunks to one file
lf_users.order_by('id').write_csv('users_chunked.csv', chunk_size=2, include_header=True)

• Parquet

# single file
lf_users.collect().write_parquet('users.parquet')

# chunked parts with a common prefix like users-00000.parquet, users-00001.parquet, ...
lf_users.order_by('id').write_parquet('users.parquet', chunk_size=2)

Notes:

• write_csv/write_parquet use polars under the hood. For chunked Parquet, files are created with a numeric suffix.
• to_arrow() requires pyarrow to be installed.

Properties

• columns: Returns a list of column names in the frame.
• engine: Returns the SQLAlchemy Engine that the frame is bound to.

Materialization & Execution

• collect(limit=None, infer_schema_length=200): Materializes the lazy query into a polars DataFrame.
• to_arrow(limit=None): Materializes the query into a pyarrow.Table.
• collect_batches(chunk_size=10_000): Returns an iterator of polars DataFrame chunks.
• iter_rows(named=False, chunk_size=10_000): Returns an iterator of row tuples (or dicts if named=True).
• explain(): Returns the rendered SQL string for the query.

I/O Helpers

• write_parquet(file, chunk_size=None, start_index=0, **kwargs): Writes the result to one or more Parquet files.
• write_csv(file, chunk_size=None, **kwargs): Writes the result to a CSV file.

Advanced

• immutability: every transform (select, filter, with_columns, join, sort, limit, group_by) returns a new LazyBearFrame.
• to_select() returns the current SQLAlchemy Select if you need to interop with SQLAlchemy APIs directly.
• join column naming: overlapping right-side columns are suffixed with _y by default; If you chain multiple joins that would reuse the same labels, pass custom suffixes on later joins to keep names unique.
• case sensitivity: scan_table(..., lowercase=True) exposes columns as lowercase labels by default. Set lowercase=False to preserve database-reflected casing.
• explain() returns the rendered SQL string; if supported, literal binds are inlined.

Database-specific result cleaning

lazybear applies a small database-specific cleaning step after SQL results are materialized into a polars DataFrame.

Currently, this is used for Teradata connections, including connections using the teradatasql driver. Teradata character datatypes may return values padded with trailing whitespace. When lazybear detects a Teradata connection, trailing whitespace is stripped from string columns in collected results.

This applies to result materialization methods such as:

• collect()
• collect_batches()
• iter_rows(), through collect_batches()

For example, a Teradata value like:

python
"ABC   "

is returned as:

python
"ABC"

This cleaning is intentionally implemented through a dispatch layer so that future database-specific cleanup behavior can be added in one place. For example, other dialects could eventually normalize driver-specific string padding, timestamp quirks, binary values, or other result-formatting issues before returning a polars DataFrame.

Notes:

• The Teradata cleanup only affects string columns.
• It strips trailing whitespace, not leading whitespace.
• Non-Teradata connections currently return results unchanged by this cleaning step.

Minimal example

import sqlalchemy as sa
from lazybear import scan_table, col

engine = sa.create_engine('sqlite:///:memory:')
# ... create a table `users` with columns id, name, age ...
lf = scan_table('users', engine)
df = lf.filter(col('age') >= 30).select('id', 'name').collect()
print(df)

License

This project is licensed under the MIT License. See LICENSE.txt or https://kpwhri.mit-license.org for the full text.