| # 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() |
