from sqlite_utils.db import Index, Database import collections import datetime import pathlib import pytest import json try: import pandas as pd except ImportError: pd = None def test_create_table(fresh_db): assert [] == fresh_db.table_names() table = fresh_db.create_table( "test_table", { "text_col": str, "float_col": float, "int_col": int, "bool_col": bool, "bytes_col": bytes, "datetime_col": datetime.datetime, }, ) assert ["test_table"] == fresh_db.table_names() assert [ {"name": "text_col", "type": "TEXT"}, {"name": "float_col", "type": "FLOAT"}, {"name": "int_col", "type": "INTEGER"}, {"name": "bool_col", "type": "INTEGER"}, {"name": "bytes_col", "type": "BLOB"}, {"name": "datetime_col", "type": "TEXT"}, ] == [{"name": col.name, "type": col.type} for col in table.columns] assert ( "CREATE TABLE [test_table] (\n" " [text_col] TEXT,\n" " [float_col] FLOAT,\n" " [int_col] INTEGER,\n" " [bool_col] INTEGER,\n" " [bytes_col] BLOB,\n" " [datetime_col] TEXT\n" ")" ) == table.schema @pytest.mark.parametrize( "example,expected_columns", ( ( {"name": "Ravi", "age": 63}, [{"name": "name", "type": "TEXT"}, {"name": "age", "type": "INTEGER"}], ), ( {"create": "Reserved word", "table": "Another"}, [{"name": "create", "type": "TEXT"}, {"name": "table", "type": "TEXT"}], ), ), ) def test_create_table_from_example(fresh_db, example, expected_columns): fresh_db["people"].insert(example) assert ["people"] == fresh_db.table_names() assert expected_columns == [ {"name": col.name, "type": col.type} for col in fresh_db["people"].columns ] def test_create_table_column_order(fresh_db): fresh_db["table"].insert( collections.OrderedDict( ( ("zzz", "third"), ("abc", "first"), ("ccc", "second"), ("bbb", "second-to-last"), ("aaa", "last"), ) ), column_order=("abc", "ccc", "zzz"), ) assert [ {"name": "abc", "type": "TEXT"}, {"name": "ccc", "type": "TEXT"}, {"name": "zzz", "type": "TEXT"}, {"name": "bbb", "type": "TEXT"}, {"name": "aaa", "type": "TEXT"}, ] == [{"name": col.name, "type": col.type} for col in fresh_db["table"].columns] def test_create_table_works_for_m2m_with_only_foreign_keys(fresh_db): fresh_db["one"].insert({"id": 1}, pk="id") fresh_db["two"].insert({"id": 1}, pk="id") fresh_db["m2m"].insert( {"one_id": 1, "two_id": 1}, foreign_keys=( ("one_id", "INTEGER", "one", "id"), ("two_id", "INTEGER", "two", "id"), ), ) assert [ {"name": "one_id", "type": "INTEGER"}, {"name": "two_id", "type": "INTEGER"}, ] == [{"name": col.name, "type": col.type} for col in fresh_db["m2m"].columns] assert sorted( [ {"column": "one_id", "other_table": "one", "other_column": "id"}, {"column": "two_id", "other_table": "two", "other_column": "id"}, ], key=lambda s: repr(s), ) == sorted( [ { "column": fk.column, "other_table": fk.other_table, "other_column": fk.other_column, } for fk in fresh_db["m2m"].foreign_keys ], key=lambda s: repr(s), ) @pytest.mark.parametrize( "columns,index_name,expected_index", ( ( ["is_good_dog"], None, Index( seq=0, name="idx_dogs_is_good_dog", unique=0, origin="c", partial=0, columns=["is_good_dog"], ), ), ( ["is_good_dog", "age"], None, Index( seq=0, name="idx_dogs_is_good_dog_age", unique=0, origin="c", partial=0, columns=["is_good_dog", "age"], ), ), ( ["age"], "age_index", Index( seq=0, name="age_index", unique=0, origin="c", partial=0, columns=["age"], ), ), ), ) def test_create_index(fresh_db, columns, index_name, expected_index): dogs = fresh_db["dogs"] dogs.insert({"name": "Cleo", "twitter": "cleopaws", "age": 3, "is_good_dog": True}) assert [] == dogs.indexes dogs.create_index(columns, index_name) assert expected_index == dogs.indexes[0] @pytest.mark.parametrize( "data_structure", ( ["list with one item"], ["list with", "two items"], {"dictionary": "simple"}, {"dictionary": {"nested": "complex"}}, [{"list": "of"}, {"two": "dicts"}], ), ) def test_insert_dictionaries_and_lists_as_json(fresh_db, data_structure): fresh_db["test"].insert({"id": 1, "data": data_structure}, pk="id") row = fresh_db.conn.execute("select id, data from test").fetchone() assert row[0] == 1 assert data_structure == json.loads(row[1]) def test_insert_thousands_using_generator(fresh_db): fresh_db["test"].insert_all( {"i": i, "word": "word_{}".format(i)} for i in range(10000) ) assert [{"name": "i", "type": "INTEGER"}, {"name": "word", "type": "TEXT"}] == [ {"name": col.name, "type": col.type} for col in fresh_db["test"].columns ] assert 10000 == fresh_db["test"].count def test_insert_thousands_ignores_extra_columns_after_first_100(fresh_db): fresh_db["test"].insert_all( [{"i": i, "word": "word_{}".format(i)} for i in range(100)] + [{"i": 101, "extra": "This extra column should cause an exception"}] ) rows = fresh_db.execute_returning_dicts("select * from test where i = 101") assert [{"i": 101, "word": None}] == rows def test_create_view(fresh_db): fresh_db["data"].insert({"foo": "foo", "bar": "bar"}) fresh_db.create_view("bar", "select bar from data") rows = fresh_db.conn.execute("select * from bar").fetchall() assert [("bar",)] == rows def test_vacuum(fresh_db): fresh_db["data"].insert({"foo": "foo", "bar": "bar"}) fresh_db.vacuum() def test_works_with_pathlib_path(tmpdir): path = pathlib.Path(tmpdir / "test.db") db = Database(path) db["demo"].insert_all([{"foo": 1}]) assert 1 == db["demo"].count @pytest.mark.skipif(pd is None, reason="pandas and numpy are not installed") def test_create_table_numpy(fresh_db): import numpy as np df = pd.DataFrame({"col 1": range(3), "col 2": range(3)}) fresh_db["pandas"].insert_all(df.to_dict(orient="records")) assert [ {"col 1": 0, "col 2": 0}, {"col 1": 1, "col 2": 1}, {"col 1": 2, "col 2": 2}, ] == list(fresh_db["pandas"].rows) # Now try all the different types df = pd.DataFrame( { "np.int8": [-8], "np.int16": [-16], "np.int32": [-32], "np.int64": [-64], "np.uint8": [8], "np.uint16": [16], "np.uint32": [32], "np.uint64": [64], "np.float16": [16.5], "np.float32": [32.5], "np.float64": [64.5], } ) df = df.astype( { "np.int8": "int8", "np.int16": "int16", "np.int32": "int32", "np.int64": "int64", "np.uint8": "uint8", "np.uint16": "uint16", "np.uint32": "uint32", "np.uint64": "uint64", "np.float16": "float16", "np.float32": "float32", "np.float64": "float64", } ) assert [ "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64", "float16", "float32", "float64", ] == [str(t) for t in df.dtypes] fresh_db["types"].insert_all(df.to_dict(orient="records")) assert [ { "np.float16": 16.5, "np.float32": 32.5, "np.float64": 64.5, "np.int16": -16, "np.int32": -32, "np.int64": -64, "np.int8": -8, "np.uint16": 16, "np.uint32": 32, "np.uint64": 64, "np.uint8": 8, } ] == list(fresh_db["types"].rows)