4.1.0

Features

Decimal Data Type Support

This release adds support for ingesting data into QuestDB's native
DECIMAL(precision, scale) column type introduced in QuestDB 9.2.0.
Use decimals when you need exact-precision values (e.g. prices, balances)
without floating-point rounding issues.

Decimal values can be sent using Python decimal.Decimal objects in both
row() and dataframe() methods. When sending dataframes, you can also use
PyArrow decimal types for better performance.

Sending decimals requires protocol version 3. When using HTTP, this protocol
version is auto-negotiated. For TCP connections, you must explicitly specify
protocol_version=3 in the configuration string:

# HTTP - protocol version 3 is auto-negotiated
conf = 'http::addr=localhost:9000;'

# TCP - must specify protocol_version=3 explicitly
conf = 'tcp::addr=localhost:9009;protocol_version=3;'

Important

Server Requirement: This feature requires QuestDB server version 9.2.0 or higher.

Unlike other column types, DECIMAL columns must be created in advance via SQL
before ingesting data. Auto-creation is not supported for DECIMAL columns.

For details on creating DECIMAL columns and working with this data type, see the
QuestDB Decimal documentation.

Usage with Python Decimal objects:

from decimal import Decimal
from questdb.ingress import Sender, TimestampNanos

# First, create the table with DECIMAL column via SQL:
# CREATE TABLE trades (
#     symbol SYMBOL,
#     price DECIMAL(18,4),
#     amount DECIMAL(18,8),
#     timestamp TIMESTAMP
# ) TIMESTAMP(timestamp);

conf = 'http::addr=localhost:9000;'
with Sender.from_conf(conf) as sender:
    sender.row(
        'trades',
        symbols={'symbol': 'ETH-USD'},
        columns={
            'price': Decimal('2615.5400'),
            'amount': Decimal('0.00044000')},
        at=TimestampNanos.now())

Usage with Python Decimal objects in Pandas dataframes:

import pandas as pd
from decimal import Decimal

# Create DataFrame with Python Decimal objects
df = pd.DataFrame({
    'symbol': ['ETH-USD', 'BTC-USD'],
    'price': [Decimal('2615.5400'), Decimal('43210.1234')],
    'volume': [Decimal('1234.56789012'), Decimal('98.76543210')]
})

with Sender.from_conf(conf) as sender:
    sender.dataframe(
        df, 
        table_name='trades',
        symbols='symbol',
        at=TimestampNanos.now())

Usage with PyArrow Decimal types in Pandas dataframes:

import pandas as pd
import pyarrow as pa
from decimal import Decimal

# Create DataFrame with Arrow decimal types
df = pd.DataFrame({
    'prices': pd.array(
        [Decimal('-99999.99'), Decimal('-678.00')],
        dtype=pd.ArrowDtype(pa.decimal128(18, 2))
    )
})

with Sender.from_conf(conf) as sender:
    sender.dataframe(df, table_name='prices', at=TimestampNanos.now())

Additional Arrow Data Type Support

Added support for additional PyArrow column types commonly encountered when
deserializing from Parquet files or converting Polars dataframes to Pandas:

• int16, int32, float32 (float), bool
• string, large_string (including as symbol types)
• timestamp[us] with timezone support for microsecond-precision timestamps

Microsecond Timestamp Precision

Microsecond-precision timestamp columns (datetime64[us] in NumPy and
timestamp[us] in PyArrow) are now fully supported. When using
protocol_version 2 or higher, microsecond timestamps are sent with full
precision, including for the designated timestamp column.

Bug fixes

• Updated type hints to allow None as a valid column value in
  Sender.row(). This brings the type annotations in line with the actual
  behavior, where None values have always been supported to represent NULL
  values.

Python Version Support

• Added support for Python 3.14 on all platforms and 3.14t (free threaded) on
  all platforms except Windows.
• Dropped support for Python 3.9 (end of life).

Full Changelog: v4.0.0...v4.1.0