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.
You have to install thrift (>=0.14.1) before using the package.
First, download the latest package: pip3 install apache-iotdb
You can get an example of using the package to read and write data at here: Example
An example of aligned timeseries: Aligned Timeseries Session Example
(you need to add import iotdb in the head of the file)
Or:
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()
session = Session(ip, port_, username_, password_, fetch_size=1024, zone_id="UTC+8")
session.open(enable_rpc_compression=False)
Notice: this RPC compression status of client must comply with that of IoTDB server
session.close()
session.set_storage_group(group_name)
session.delete_storage_group(group_name)
session.delete_storage_groups(group_name_lst)
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
)
session.create_aligned_time_series(
device_id, measurements_lst, data_type_lst, encoding_lst, compressor_lst
)
Attention: Alias of measurements are not supported currently.
session.delete_time_series(paths_list)
session.check_time_series_exists(path)
It is recommended to use insertTablet to help improve write efficiency.
We have two implementations of Tablet in Python API.
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_)
Comparing with Tablet, Numpy Tablet is using numpy.ndarray to record data. With less memory footprint and time cost of serialization, the insert performance will be better.
Notice
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)
session.insert_tablets(tablet_lst)
session.insert_record(device_id, timestamp, measurements_, data_types_, values_)
session.insert_records(
device_ids_, time_list_, measurements_list_, data_type_list_, values_list_
)
session.insert_records_of_one_device(device_id, time_list, measurements_list, data_types_list, values_list)
When the data is of String type, we can use the following interface to perform type inference based on the value of the value itself. For example, if value is "true" , it can be automatically inferred to be a boolean type. If value is "3.2" , it can be automatically inferred as a flout type. Without type information, server has to do type inference, which may cost some time.
session.insert_str_record(device_id, timestamp, measurements, string_values)
The Insert of aligned timeseries uses interfaces like insert_aligned_XXX, and others are similar to the above interfaces:
session.execute_query_statement(sql)
session.execute_non_query_statement(sql)
session.execute_statement(sql)
To easily transform a query result to a Pandas Dataframe
the SessionDataSet has a method .todf() which consumes the dataset and transforms it to a pandas dataframe.
Example:
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)
result = session.execute_query_statement("SELECT * FROM root.*")
# Transform to Pandas Dataset
df = result.todf()
session.close()
# Now you can work with the dataframe
df = ...
The Test Support is based on the lib testcontainers (https://testcontainers-python.readthedocs.io/en/latest/index.html) which you need to install in your project if you want to use the feature.
To start (and stop) an IoTDB Database in a Docker container simply do:
class MyTestCase(unittest.TestCase):
def test_something(self):
with IoTDBContainer() as c:
session = Session("localhost", c.get_exposed_port(6667), "root", "root")
session.open(False)
result = session.execute_query_statement("SHOW TIMESERIES")
print(result)
session.close()
by default it will load the image apache/iotdb:latest, if you want a specific version just pass it like e.g. IoTDBContainer("apache/iotdb:0.12.0") to get version 0.12.0 running.
IoTDB DBAPI implements the Python DB API 2.0 specification (https://peps.python.org/pep-0249/), which defines a common interface for accessing databases in Python.
The initialized parameters are consistent with the session part (except for the sqlalchemy_mode).
from iotdb.dbapi import connect
ip = "127.0.0.1"
port_ = "6667"
username_ = "root"
password_ = "root"
conn = connect(ip, port_, username_, password_,fetch_size=1024,zone_id="UTC+8",sqlalchemy_mode=False)
cursor = conn.cursor()
cursor.execute("SELECT * FROM root.*")
for row in cursor.fetchall():
print(row)
IoTDB DBAPI supports pyformat style parameters
cursor.execute("SELECT * FROM root.* WHERE time < %(time)s",{"time":"2017-11-01T00:08:00.000"})
for row in cursor.fetchall():
print(row)
seq_of_parameters = [
{"timestamp": 1, "temperature": 1},
{"timestamp": 2, "temperature": 2},
{"timestamp": 3, "temperature": 3},
{"timestamp": 4, "temperature": 4},
{"timestamp": 5, "temperature": 5},
]
sql = "insert into root.cursor(timestamp,temperature) values(%(timestamp)s,%(temperature)s)"
cursor.executemany(sql,seq_of_parameters)
cursor.close()
conn.close()
The SQLAlchemy dialect of IoTDB is written to adapt to Apache Superset. This part is still being improved. Please do not use it in the production environment!
The data model used by SQLAlchemy is a relational data model, which describes the relationships between different entities through tables. While the data model of IoTDB is a hierarchical data model, which organizes the data through a tree structure. In order to adapt IoTDB to the dialect of SQLAlchemy, the original data model in IoTDB needs to be reorganized. Converting the data model of IoTDB into the data model of SQLAlchemy.
The metadata in the IoTDB are：
The metadata in the SQLAlchemy are：
The mapping relationship between them is：
| The metadata in the SQLAlchemy | The metadata in the IoTDB |
|---|---|
| Schema | Database |
| Table | Path ( from database to entity ) + Entity |
| Column | Measurement |
The following figure shows the relationship between the two more intuitively:
| data type in IoTDB | data type in SQLAlchemy |
|---|---|
| BOOLEAN | Boolean |
| INT32 | Integer |
| INT64 | BigInteger |
| FLOAT | Float |
| DOUBLE | Float |
| TEXT | Text |
| LONG | BigInteger |
from sqlalchemy import create_engine
engine = create_engine("iotdb://root:root@127.0.0.1:6667")
connect = engine.connect()
result = connect.execute("SELECT ** FROM root")
for row in result.fetchall():
print(row)
from sqlalchemy import create_engine, Column, Float, BigInteger, MetaData
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
metadata = MetaData(
schema='root.factory'
)
Base = declarative_base(metadata=metadata)
class Device(Base):
__tablename__ = "room2.device1"
Time = Column(BigInteger, primary_key=True)
temperature = Column(Float)
status = Column(Float)
engine = create_engine("iotdb://root:root@127.0.0.1:6667")
DbSession = sessionmaker(bind=engine)
session = DbSession()
res = session.query(Device.status).filter(Device.temperature > 1)
for row in res:
print(row)
This is an example of how to connect to IoTDB with python, using the thrift rpc interfaces. Things are almost the same on Windows or Linux, but pay attention to the difference like path separator.
Python3.6 or later is preferred.
You have to install Thrift (0.14.1 or later) to compile our thrift file into python code. Below is the official tutorial of installation, eventually, you should have a thrift executable.
http://thrift.apache.org/docs/install/
Before starting you need to install requirements_dev.txt in your python environment, e.g. by calling
pip install -r requirements_dev.txt
In the root of IoTDB's source code folder, run mvn clean generate-sources -pl client-py -am.
This will automatically delete and repopulate the folder iotdb/thrift with the generated thrift files.
This folder is ignored from git and should never be pushed to git!
Notice Do not upload iotdb/thrift to the git repo.
We packed up the Thrift interface in client-py/src/iotdb/Session.py (similar with its Java counterpart), also provided an example file client-py/src/SessionExample.py of how to use the session module. please read it carefully.
Or, another simple example:
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()
Please add your custom tests in tests folder.
To run all defined tests just type pytest . in the root folder.
Notice Some tests need docker to be started on your system as a test instance is started in a docker container using testcontainers.
black and flake8 are installed for autoformatting and linting.
Both can be run by black . or flake8 . respectively.
To do a release just ensure that you have the right set of generated thrift files.
Then run linting and auto-formatting.
Then, ensure that all tests work (via pytest .).
Then you are good to go to do a release!
First, install all necessary dev dependencies via pip install -r requirements_dev.txt.
There is a convenient script release.sh to do all steps for a release.
Namely, these are
FAQs
Apache IoTDB client API
We found that apache-iotdb demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 6 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.
Security News
The Ultralytics' PyPI Package was compromised four times in one weekend through GitHub Actions cache poisoning and failure to rotate previously compromised API tokens.
Security News
Research
Socket researchers found a malicious Maven package impersonating the legitimate ‘XZ for Java’ library, introducing a backdoor for remote code execution.