mirror of
https://github.com/lancedb/lancedb.git
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cbb56e25ab
* to_df() is now async, added `to_df_blocking` to convenience * add remote lancedb client to public lancedb * make lancedb connection class understand url scheme `lancedb+<connection_type>://<host>:<port>`.
144 lines
3.8 KiB
Python
144 lines
3.8 KiB
Python
# Copyright 2023 LanceDB Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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from pathlib import Path
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import pandas as pd
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import pyarrow as pa
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import pytest
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from lancedb.table import LanceTable
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class MockDB:
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def __init__(self, uri: Path):
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self.uri = uri
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@functools.cached_property
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def is_managed_remote(self) -> bool:
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return False
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@pytest.fixture
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def db(tmp_path) -> MockDB:
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return MockDB(tmp_path)
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def test_basic(db):
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ds = LanceTable.create(
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db,
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"test",
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data=[
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{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
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{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
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],
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).to_lance()
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table = LanceTable(db, "test")
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assert table.name == "test"
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assert table.schema == ds.schema
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assert table.to_lance().to_table() == ds.to_table()
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def test_create_table(db):
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schema = pa.schema(
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[
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pa.field("vector", pa.list_(pa.float32(), 2)),
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pa.field("item", pa.string()),
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pa.field("price", pa.float32()),
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]
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)
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expected = pa.Table.from_arrays(
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[
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pa.FixedSizeListArray.from_arrays(pa.array([3.1, 4.1, 5.9, 26.5]), 2),
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pa.array(["foo", "bar"]),
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pa.array([10.0, 20.0]),
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],
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schema=schema,
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)
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data = [
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[
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{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
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{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
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]
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]
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df = pd.DataFrame(data[0])
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data.append(df)
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data.append(pa.Table.from_pandas(df, schema=schema))
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for i, d in enumerate(data):
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tbl = (
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LanceTable.create(db, f"test_{i}", data=d, schema=schema)
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.to_lance()
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.to_table()
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)
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assert expected == tbl
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def test_add(db):
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table = LanceTable.create(
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db,
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"test",
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data=[
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{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
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{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
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],
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)
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# table = LanceTable(db, "test")
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assert len(table) == 2
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count = table.add([{"vector": [6.3, 100.5], "item": "new", "price": 30.0}])
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assert count == 3
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expected = pa.Table.from_arrays(
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[
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pa.FixedSizeListArray.from_arrays(
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pa.array([3.1, 4.1, 5.9, 26.5, 6.3, 100.5]), 2
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),
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pa.array(["foo", "bar", "new"]),
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pa.array([10.0, 20.0, 30.0]),
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],
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schema=pa.schema(
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[
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pa.field("vector", pa.list_(pa.float32(), 2)),
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pa.field("item", pa.string()),
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pa.field("price", pa.float64()),
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]
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),
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)
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assert expected == table.to_arrow()
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def test_versioning(db):
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table = LanceTable.create(
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db,
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"test",
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data=[
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{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
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{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
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],
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)
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assert len(table.list_versions()) == 1
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assert table.version == 1
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table.add([{"vector": [6.3, 100.5], "item": "new", "price": 30.0}])
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assert len(table.list_versions()) == 2
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assert table.version == 2
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assert len(table) == 3
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table.checkout(1)
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assert table.version == 1
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assert len(table) == 2
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