Data Types

The Python client uses PyArrow types for schema definitions:

PyArrow Type	Fluss Type	Python Type
pa.bool_()	Boolean	bool
pa.int8() / int16() / int32() / int64()	TinyInt / SmallInt / Int / BigInt	int
pa.float32() / float64()	Float / Double	float
pa.string()	String	str
pa.binary()	Bytes	bytes
pa.date32()	Date	datetime.date
pa.time32("ms")	Time	datetime.time
pa.timestamp("us")	Timestamp (NTZ)	datetime.datetime
pa.timestamp("us", tz="UTC")	TimestampLTZ	datetime.datetime
pa.decimal128(precision, scale)	Decimal	decimal.Decimal

All Python native types (date, time, datetime, Decimal) work when appending rows via dicts.

Writing Data

Rows can be dicts, lists, or tuples:

from datetime import date, time, datetime
from decimal import Decimal

row = {
    "user_id": 1,
    "name": "Alice",
    "active": True,
    "score": 95.5,
    "balance": Decimal("1234.56"),
    "birth_date": date(1990, 3, 15),
    "login_time": time(9, 30, 0),
    "created_at": datetime(2024, 1, 1, 0, 0, 0),
    "nickname": None,  # null value
}
handle = writer.append(row)

Lists and tuples must have values in column order:

row = [1, "Alice", True, 95.5, Decimal("1234.56"), date(1990, 3, 15), time(9, 30, 0), datetime(2024, 1, 1), None]
handle = writer.append(row)

Reading Data

records = scanner.poll(timeout_ms=1000)
for record in records:
    row = record.row  # dict[str, Any]
    print(row["user_id"])     # int
    print(row["name"])        # str
    print(row["balance"])     # decimal.Decimal
    print(row["birth_date"])  # datetime.date
    print(row["created_at"])  # datetime.datetime

    if row["nickname"] is None:
        print("nickname is null")