IoTDB 是针对时间序列数据收集、存储与分析一体化的数据管理引擎.zip

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See the License for the specific language governing permissions and limitations under the License. --> # Apache IoTDB [](https://github.com/apache/iotdb/actions/workflows/client-python.yml) [](https://github.com/apache/iotdb/releases) [](https://www.apache.org/licenses/LICENSE-2.0.html)    [](https://iotdb.apache.org/) Apache IoTDB (Database for Internet of Things) is an IoT native database with high performance for data management and analysis, deployable on the edge and the cloud. Due to its light-weight architecture, high performance and rich feature set together with its deep integration with Apache Hadoop, Spark and Flink, Apache IoTDB can meet the requirements of massive data storage, high-speed data ingestion and complex data analysis in the IoT industrial fields. ## Python Native API ### Requirements You have to install thrift (>=0.13) before using the package. ### How to use (Example) First, download the latest package: `pip3 install apache-iotdb` *Notice: If you are installing Python API v0.13.0, DO NOT install by `pip install apache-iotdb==0.13.0`, use `pip install apache-iotdb==0.13.0.post1` instead!* You can get an example of using the package to read and write data at here: [Example](https://github.com/apache/iotdb/blob/master/client-py/SessionExample.py) An example of aligned timeseries: [Aligned Timeseries Session Example](https://github.com/apache/iotdb/blob/master/client-py/SessionAlignedTimeseriesExample.py) (you need to add `import iotdb` in the head of the file) Or: ```python from iotdb.Session import Session ip = "127.0.0.1" port_ = "6667" username_ = "root" password_ = "root" session = Session(ip, port_, username_, password_) session.open(False) zone = session.get_time_zone() session.close() ``` ### Initialization * Initialize a Session ```python session = Session(ip, port_, username_, password_, fetch_size=1024, zone_id="UTC+8") ``` * Open a session, with a parameter to specify whether to enable RPC compression ```python session.open(enable_rpc_compression=False) ``` Notice: this RPC compression status of client must comply with that of IoTDB server * Close a Session ```python session.close() ``` ### Data Definition Interface (DDL Interface) #### DATABASE Management * CREATE DATABASE ```python session.set_storage_group(group_name) ``` * Delete one or several databases ```python session.delete_storage_group(group_name) session.delete_storage_groups(group_name_lst) ``` #### Timeseries Management * Create one or multiple timeseries ```python session.create_time_series(ts_path, data_type, encoding, compressor, props=None, tags=None, attributes=None, alias=None) session.create_multi_time_series( ts_path_lst, data_type_lst, encoding_lst, compressor_lst, props_lst=None, tags_lst=None, attributes_lst=None, alias_lst=None ) ``` * Create aligned timeseries ```python session.create_aligned_time_series( device_id, measurements_lst, data_type_lst, encoding_lst, compressor_lst ) ``` Attention: Alias of measurements are **not supported** currently. * Delete one or several timeseries ```python session.delete_time_series(paths_list) ``` * Check whether the specific timeseries exists ```python session.check_time_series_exists(path) ``` ### Data Manipulation Interface (DML Interface) #### Insert It is recommended to use insertTablet to help improve write efficiency. * Insert a Tablet，which is multiple rows of a device, each row has the same measurements * **Better Write Performance** * **Support null values**: fill the null value with any value, and then mark the null value via BitMap (from v0.13) We have two implementations of Tablet in Python API. * Normal Tablet ```python values_ = [ [False, 10, 11, 1.1, 10011.1, "test01"], [True, 100, 11111, 1.25, 101.0, "test02"], [False, 100, 1, 188.1, 688.25, "test03"], [True, 0, 0, 0, 6.25, "test04"], ] timestamps_ = [1, 2, 3, 4] tablet_ = Tablet( device_id, measurements_, data_types_, values_, timestamps_ ) session.insert_tablet(tablet_) values_ = [ [None, 10, 11, 1.1, 10011.1, "test01"], [True, None, 11111, 1.25, 101.0, "test02"], [False, 100, None, 188.1, 688.25, "test03"], [True, 0, 0, 0, None, None], ] timestamps_ = [16, 17, 18, 19] tablet_ = Tablet( device_id, measurements_, data_types_, values_, timestamps_ ) session.insert_tablet(tablet_) ``` * Numpy Tablet Comparing with Tablet, Numpy Tablet is using [numpy.ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) to record data. With less memory footprint and time cost of serialization, the insert performance will be better. **Notice** 1. time and value columns in Tablet are ndarray. 2. recommended to use the specific dtypes to each ndarray, see the example below (if not, the default dtypes are also ok). ```python import numpy as np data_types_ = [ TSDataType.BOOLEAN, TSDataType.INT32, TSDataType.INT64, TSDataType.FLOAT, TSDataType.DOUBLE, TSDataType.TEXT, ] np_values_ = [ np.array([False, True, False, True], TSDataType.BOOLEAN.np_dtype()), np.array([10, 100, 100, 0], TSDataType.INT32.np_dtype()), np.array([11, 11111, 1, 0], TSDataType.INT64.np_dtype()), np.array([1.1, 1.25, 188.1, 0], TSDataType.FLOAT.np_dtype()), np.array([10011.1, 101.0, 688.25, 6.25], TSDataType.DOUBLE.np_dtype()), np.array(["test01", "test02", "test03", "test04"], TSDataType.TEXT.np_dtype()), ] np_timestamps_ = np.array([1, 2, 3, 4], TSDataType.INT64.np_dtype()) np_tablet_ = NumpyTablet( device_id, measurements_, data_types_, np_values_, np_timestamps_ ) session.insert_tablet(np_tablet_) # insert one numpy tablet with none into the database. np_values_ = [ np.array([False, True, False, True], TSDataType.BOOLEAN.np_dtype()), np.array([10, 100, 100, 0], TSDataType.INT32.np_dtype()), np.array([11, 11111, 1, 0], TSDataType.INT64.np_dtype()), np.array([1.1, 1.25, 188.1, 0], TSDataType.FLOAT.np_dtype()), np.array([10011.1, 101.0, 688.25, 6.25], TSDataType.DOUBLE.np_dtype()), np.array(["test01", "test02", "test03", "test04"], TSDataType.TEXT.np_dtype()), ] np_timestamps_ = np.array([98, 99, 100, 101], TSDataType.INT64.np_dtype()) np_bitmaps_ = [] for i in range(len(measurements_)): np_bitmaps_.append(BitMap(len(np_timestamps_))) np_bitmaps_[0].mark(0) np_bitmaps_[1].mark(1) np_bitmaps_[2].mark(2) np_bitmaps_[4].mark(3) np_bitmaps_[5].mark(3) np_tablet_with_none = NumpyTablet( device_id, measurements_, data_types_, np_values_, np_timestamps_, np_bitmaps_ ) session.insert_tablet(np_tablet_with_none) ``` * Insert multiple Tablets ```python session.insert_tablets(tablet_lst) ``` *