| # Licensed to the Apache Software Foundation (ASF) under one | |
| # or more contributor license agreements. See the NOTICE file | |
| # distributed with this work for additional information | |
| # regarding copyright ownership. The ASF licenses this file | |
| # to you under the Apache License, Version 2.0 (the | |
| # "License"); you may not use this file except in compliance | |
| # with the License. You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, | |
| # software distributed under the License is distributed on an | |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | |
| # KIND, either express or implied. See the License for the | |
| # specific language governing permissions and limitations | |
| # under the License. | |
| # | |
| import math | |
| import pandas as pd | |
| from tzlocal import get_localzone_name | |
| from iotdb.table_session import TableSession, TableSessionConfig | |
| from iotdb.utils.IoTDBConstants import TSDataType | |
| from iotdb.utils.Tablet import Tablet, ColumnType | |
| from datetime import date | |
| from iotdb.utils.rpc_utils import convert_to_timestamp | |
| from .iotdb_container import IoTDBContainer | |
| # Test query data | |
| def test_query_data(): | |
| with IoTDBContainer("iotdb:dev") as db: | |
| db: IoTDBContainer | |
| config = TableSessionConfig( | |
| node_urls=[f"{db.get_container_host_ip()}:{db.get_exposed_port(6667)}"] | |
| ) | |
| session = TableSession(config) | |
| # Preparation before testing | |
| session.execute_non_query_statement( | |
| "create database test_insert_relational_tablet_tablet" | |
| ) | |
| session.execute_non_query_statement("use test_insert_relational_tablet_tablet") | |
| session.execute_non_query_statement( | |
| "create table table_b(" | |
| "tag1 STRING TAG, tag2 STRING TAG, tag3 STRING TAG, " | |
| "attr1 string attribute, attr2 string attribute, attr3 string attribute," | |
| "BOOLEAN BOOLEAN FIELD, INT32 INT32 FIELD, INT64 INT64 FIELD, FLOAT FLOAT FIELD, DOUBLE DOUBLE FIELD," | |
| "TEXT TEXT FIELD, TIMESTAMP TIMESTAMP FIELD, DATE DATE FIELD, BLOB BLOB FIELD, STRING STRING FIELD)" | |
| ) | |
| # 1、General scenario | |
| expect = 10 | |
| table_name = "table_b" | |
| column_names = [ | |
| "tag1", | |
| "tag2", | |
| "tag3", | |
| "attr1", | |
| "attr2", | |
| "attr3", | |
| "BOOLEAN", | |
| "INT32", | |
| "INT64", | |
| "FLOAT", | |
| "DOUBLE", | |
| "TEXT", | |
| "TIMESTAMP", | |
| "DATE", | |
| "BLOB", | |
| "STRING", | |
| ] | |
| data_types = [ | |
| TSDataType.STRING, | |
| TSDataType.STRING, | |
| TSDataType.STRING, | |
| TSDataType.STRING, | |
| TSDataType.STRING, | |
| TSDataType.STRING, | |
| TSDataType.BOOLEAN, | |
| TSDataType.INT32, | |
| TSDataType.INT64, | |
| TSDataType.FLOAT, | |
| TSDataType.DOUBLE, | |
| TSDataType.TEXT, | |
| TSDataType.TIMESTAMP, | |
| TSDataType.DATE, | |
| TSDataType.BLOB, | |
| TSDataType.STRING, | |
| ] | |
| column_types = [ | |
| ColumnType.TAG, | |
| ColumnType.TAG, | |
| ColumnType.TAG, | |
| ColumnType.ATTRIBUTE, | |
| ColumnType.ATTRIBUTE, | |
| ColumnType.ATTRIBUTE, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ColumnType.FIELD, | |
| ] | |
| timestamps = [] | |
| values = [] | |
| for row_b in range(10): | |
| timestamps.append(row_b) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| False, | |
| 0, | |
| 0, | |
| 0.0, | |
| 0.0, | |
| "1234567890", | |
| 0, | |
| date(1970, 1, 1), | |
| "1234567890".encode("utf-8"), | |
| "1234567890", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| -2147483648, | |
| -9223372036854775808, | |
| -0.12345678, | |
| -0.12345678901234567, | |
| "abcdefghijklmnopqrstuvwsyz", | |
| -9223372036854775808, | |
| date(1000, 1, 1), | |
| "abcdefghijklmnopqrstuvwsyz".encode("utf-8"), | |
| "abcdefghijklmnopqrstuvwsyz", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| 2147483647, | |
| 9223372036854775807, | |
| 0.123456789, | |
| 0.12345678901234567, | |
| "!@#$%^&*()_+}{|:'`~-=[];,./<>?~", | |
| 9223372036854775807, | |
| date(9999, 12, 31), | |
| "!@#$%^&*()_+}{|:`~-=[];,./<>?~".encode("utf-8"), | |
| "!@#$%^&*()_+}{|:`~-=[];,./<>?~", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| 1, | |
| 1, | |
| 1.0, | |
| 1.0, | |
| "没问题", | |
| 1, | |
| date(1970, 1, 1), | |
| "没问题".encode("utf-8"), | |
| "没问题", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| -1, | |
| -1, | |
| 1.1234567, | |
| 1.1234567890123456, | |
| "!@#¥%……&*()——|:“《》?·【】、;‘,。/", | |
| 11, | |
| date(1970, 1, 1), | |
| "!@#¥%……&*()——|:“《》?·【】、;‘,。/".encode("utf-8"), | |
| "!@#¥%……&*()——|:“《》?·【】、;‘,。/", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| 10, | |
| 11, | |
| 4.123456, | |
| 4.123456789012345, | |
| "1234567890abcdefghijklmnopqrstuvwsyz!@#$%^&*()_+}{|:'`~-=[];,./<>?~!@#¥%……&*()——|:“《》?·【】、;‘,。/没问题", | |
| 11, | |
| date(1970, 1, 1), | |
| "1234567890abcdefghijklmnopqrstuvwsyz!@#$%^&*()_+}{|:`~-=[];,./<>?~!@#¥%……&*()——|:“《》?·【】、;‘,。/没问题".encode( | |
| "utf-8" | |
| ), | |
| "1234567890abcdefghijklmnopqrstuvwsyz!@#$%^&*()_+}{|:`~-=[];,./<>?~!@#¥%……&*()——|:“《》?·【】、;‘,。/没问题", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| -10, | |
| -11, | |
| 12.12345, | |
| 12.12345678901234, | |
| "test01", | |
| 11, | |
| date(1970, 1, 1), | |
| "Hello, World!".encode("utf-8"), | |
| "string01", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| None, | |
| None, | |
| None, | |
| None, | |
| None, | |
| "", | |
| None, | |
| date(1970, 1, 1), | |
| "".encode("utf-8"), | |
| "", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| -0, | |
| -0, | |
| -0.0, | |
| -0.0, | |
| " ", | |
| 11, | |
| date(1970, 1, 1), | |
| " ".encode("utf-8"), | |
| " ", | |
| ] | |
| ) | |
| values.append( | |
| [ | |
| "tag1:" + str(row_b), | |
| "tag2:" + str(row_b), | |
| "tag3:" + str(row_b), | |
| "attr1:" + str(row_b), | |
| "attr2:" + str(row_b), | |
| "attr3:" + str(row_b), | |
| True, | |
| 10, | |
| 11, | |
| 1.1, | |
| 10011.1, | |
| "test01", | |
| 11, | |
| date(1970, 1, 1), | |
| "Hello, World!".encode("utf-8"), | |
| "string01", | |
| ] | |
| ) | |
| tablet = Tablet( | |
| table_name, column_names, data_types, values, timestamps, column_types | |
| ) | |
| session.insert(tablet) | |
| row = 0 | |
| with session.execute_query_statement( | |
| "select * from table_b" | |
| ) as session_data_set: | |
| print(session_data_set.get_column_names()) | |
| while session_data_set.has_next(): | |
| print(session_data_set.next()) | |
| row += 1 | |
| # Determine whether it meets expectations | |
| assert expect == row | |
| with session.execute_query_statement( | |
| "select * from table_b" | |
| ) as session_data_set: | |
| df = session_data_set.todf() | |
| rows, columns = df.shape | |
| assert rows == expect | |
| assert columns == len(column_names) + 1 | |
| row = 0 | |
| with session.execute_query_statement( | |
| "select " + ",".join(column_names) + " from table_b" | |
| ) as session_data_set: | |
| assert session_data_set.get_column_names() == column_names | |
| while session_data_set.has_next(): | |
| row_record = session_data_set.next() | |
| assert row_record.get_timestamp() == 0 | |
| for i in range(len(column_names)): | |
| if values[row][i] is not None and ( | |
| data_types[i] == TSDataType.FLOAT | |
| or data_types[i] == TSDataType.DOUBLE | |
| ): | |
| assert math.isclose( | |
| row_record.get_fields()[i].get_object_value(data_types[i]), | |
| values[row][i], | |
| rel_tol=1e-6, | |
| ) | |
| elif data_types[i] == TSDataType.TIMESTAMP: | |
| actual = row_record.get_fields()[i].get_timestamp_value() | |
| expected = convert_to_timestamp( | |
| values[row][i], "ms", get_localzone_name() | |
| ) | |
| if pd.isnull(actual): | |
| assert pd.isnull(expected) | |
| else: | |
| assert actual == expected | |
| else: | |
| assert ( | |
| row_record.get_fields()[i].get_object_value(data_types[i]) | |
| == values[row][i] | |
| ) | |
| row += 1 | |
| # Determine whether it meets expectations | |
| assert expect == row | |
| with session.execute_query_statement( | |
| "select " + ",".join(column_names) + " from table_b" | |
| ) as session_data_set: | |
| df = session_data_set.todf() | |
| assert list(df.columns) == column_names | |
| rows, columns = df.shape | |
| assert rows == expect | |
| assert columns == len(column_names) | |
| for i in range(rows): | |
| for j in range(columns): | |
| if pd.isna(df.iloc[i, j]): | |
| continue | |
| elif isinstance(values[i][j], float): | |
| assert math.isclose( | |
| df.iloc[i, j], | |
| values[i][j], | |
| rel_tol=1e-6, | |
| ) | |
| elif isinstance(df.iloc[i, j], pd.Timestamp): | |
| actual = df.iloc[i, j] | |
| expected = pd.Series( | |
| convert_to_timestamp( | |
| values[i][j], "ms", get_localzone_name() | |
| ) | |
| )[0] | |
| assert actual == expected | |
| else: | |
| assert df.iloc[i, j] == values[i][j] | |
| row = 0 | |
| with session.execute_query_statement( | |
| "select tag1, tag1 from table_b" | |
| ) as session_data_set: | |
| assert session_data_set.get_column_names() == ["tag1", "tag1"] | |
| while session_data_set.has_next(): | |
| row_record = session_data_set.next() | |
| assert row_record.get_timestamp() == 0 | |
| for i in range(len(["tag1", "tag1"])): | |
| assert ( | |
| row_record.get_fields()[i].get_object_value( | |
| row_record.get_fields()[i].get_data_type() | |
| ) | |
| == values[row][0] | |
| ) | |
| row += 1 | |
| # Determine whether it meets expectations | |
| assert expect == row | |
| with session.execute_query_statement( | |
| "select tag1, tag1 from table_b" | |
| ) as session_data_set: | |
| df = session_data_set.todf() | |
| assert list(df.columns) == ["tag1", "tag1"] | |
| rows, columns = df.shape | |
| assert rows == expect | |
| assert columns == 2 | |
| for i in range(rows): | |
| for j in range(columns): | |
| if pd.isna(df.iloc[i, j]): | |
| assert values[i][j] is None | |
| else: | |
| assert df.iloc[i, j] == values[i][0] | |
| row = 0 | |
| with session.execute_query_statement( | |
| "select tag1, attr1 as tag1 from table_b" | |
| ) as session_data_set: | |
| assert session_data_set.get_column_names() == ["tag1", "tag1"] | |
| while session_data_set.has_next(): | |
| row_record = session_data_set.next() | |
| print(row_record) | |
| assert row_record.get_timestamp() == 0 | |
| assert ( | |
| row_record.get_fields()[0].get_object_value( | |
| row_record.get_fields()[0].get_data_type() | |
| ) | |
| == values[row][0] | |
| ) | |
| assert ( | |
| row_record.get_fields()[1].get_object_value( | |
| row_record.get_fields()[1].get_data_type() | |
| ) | |
| == values[row][3] | |
| ) | |
| row += 1 | |
| # Determine whether it meets expectations | |
| assert expect == row | |
| with session.execute_query_statement( | |
| "select tag1, attr1 as tag1 from table_b" | |
| ) as session_data_set: | |
| df = session_data_set.todf() | |
| assert list(df.columns) == ["tag1", "tag1"] | |
| rows, columns = df.shape | |
| assert rows == expect | |
| assert columns == 2 | |
| for i in range(rows): | |
| assert df.iloc[i, 0] == values[i][0] | |
| assert df.iloc[i, 1] == values[i][3] | |
| session.close() |