docs/UserGuide/API/Programming-Python-Native-API.md

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## Python Native API

### Requirements

You have to install thrift (>=0.13) before using the package.



### How to use (Example)

First, download the package: `pip3 install apache-iotdb`

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)

#### Storage Group Management

* Set storage group

```python
session.set_storage_group(group_name)
```

* Delete one or several storage groups

```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_)
```
* 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 numerical value columns in Tablet is ndarray
2. recommended to use the specific dtypes to each ndarray, see the example below
   (if not, the default dtypes are also ok).

```python
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(
  "root.sg_test_01.d_02", measurements_, data_types_, np_values_, np_timestamps_
)
session.insert_tablet(np_tablet_)
```

* Insert multiple Tablets

```python
session.insert_tablets(tablet_lst)
```

* Insert a Record

```python
session.insert_record(device_id, timestamp, measurements_, data_types_, values_)
```

* Insert multiple Records

```python
session.insert_records(
    device_ids_, time_list_, measurements_list_, data_type_list_, values_list_
)
```

* Insert multiple Records that belong to the same device.
  With type info the server has no need to do type inference, which leads a better performance


```python
session.insert_records_of_one_device(device_id, time_list, measurements_list, data_types_list, values_list)
```

#### Insert with type inference

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.

* Insert a Record, which contains multiple measurement value of a device at a timestamp

```python
session.insert_str_record(device_id, timestamp, measurements, string_values)
```

#### Insert of Aligned Timeseries

The Insert of aligned timeseries uses interfaces like insert_aligned_XXX, and others are similar to the above interfaces:

* insert_aligned_record
* insert_aligned_records
* insert_aligned_records_of_one_device
* insert_aligned_tablet
* insert_aligned_tablets


### IoTDB-SQL Interface

* Execute query statement

```python
session.execute_query_statement(sql)
```

* Execute non query statement

```python
session.execute_non_query_statement(sql)
```


### Pandas Support

To easily transform a query result to a [Pandas Dataframe](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)
the SessionDataSet has a method `.todf()` which consumes the dataset and transforms it to a pandas dataframe.

Example:

```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)
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 = ...
```


### IoTDB Testcontainer

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:
```python
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.


## Developers

### Introduction

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.



### Prerequisites

Python3.7 or later is preferred.

You have to install Thrift (0.11.0 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
```shell
pip install -r requirements_dev.txt
```



### Compile the thrift library and Debug

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.




### Session Client & Example

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:

```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()
```



### Tests

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](https://testcontainers-python.readthedocs.io/en/latest/index.html).



### Futher Tools

[black](https://pypi.org/project/black/) and [flake8](https://pypi.org/project/flake8/) are installed for autoformatting and linting.
Both can be run by `black .` or `flake8 .` respectively.



## Releasing

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!



### Preparing your environment

First, install all necessary dev dependencies via `pip install -r requirements_dev.txt`.



### Doing the Release

There is a convenient script `release.sh` to do all steps for a release.
Namely, these are

* Remove all transient directories from last release (if exists)
* (Re-)generate all generated sources via mvn
* Run Linting (flake8)
* Run Tests via pytest
* Build
* Release to pypi