<!--

    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.

-->

## 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/rel/1.1/client-py/SessionExample.py)

An example of aligned timeseries: [Aligned Timeseries Session Example](https://github.com/apache/iotdb/blob/rel/1.1/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()
```

### Managing Session through SessionPool

Utilizing SessionPool to manage sessions eliminates the need to worry about session reuse. When the number of session connections reaches the maximum capacity of the pool, requests for acquiring a session will be blocked, and you can set the blocking wait time through parameters. After using a session, it should be returned to the SessionPool using the `putBack` method for proper management.

##### Create SessionPool

```python
pool_config = PoolConfig(host=ip,port=port, user_name=username,
                         password=password, fetch_size=1024,
                         time_zone="UTC+8", max_retry=3)
max_pool_size = 5
wait_timeout_in_ms = 3000

# # Create the connection pool
session_pool = SessionPool(pool_config, max_pool_size, wait_timeout_in_ms)
```

##### Acquiring a session through SessionPool and manually calling PutBack after use

```python
session = session_pool.get_session()
session.set_storage_group(STORAGE_GROUP_NAME)
session.create_time_series(
  TIMESERIES_PATH, TSDataType.BOOLEAN, TSEncoding.PLAIN, Compressor.SNAPPY
)
# After usage, return the session using putBack
session_pool.put_back(session)
# When closing the sessionPool, all managed sessions will be closed as well
session_pool.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 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
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)
```

* 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)
```

* Execute statement

```python
session.execute_statement(sql)
```

### Schema Template
#### Create Schema Template
The step for creating a metadata template is as follows
1. Create the template class
2. Adding child Node，InternalNode and MeasurementNode can be chose
3. Execute create schema template function

```python
template = Template(name=template_name, share_time=True)

i_node_gps = InternalNode(name="GPS", share_time=False)
i_node_v = InternalNode(name="vehicle", share_time=True)
m_node_x = MeasurementNode("x", TSDataType.FLOAT, TSEncoding.RLE, Compressor.SNAPPY)

i_node_gps.add_child(m_node_x)
i_node_v.add_child(m_node_x)

template.add_template(i_node_gps)
template.add_template(i_node_v)
template.add_template(m_node_x)

session.create_schema_template(template)
```
#### Modify Schema Template measurements
Modify measurements in a template, the template must be already created. These are functions that add or delete some measurement nodes.
* add node in template
```python
session.add_measurements_in_template(template_name, measurements_path, data_types, encodings, compressors, is_aligned)
```

* delete node in template
```python
session.delete_node_in_template(template_name, path)
```

#### Set Schema Template
```python
session.set_schema_template(template_name, prefix_path)
```

#### Uset Schema Template
```python
session.unset_schema_template(template_name, prefix_path)
```

#### Show Schema Template
* Show all schema templates
```python
session.show_all_templates()
```
* Count all measurements in templates
```python
session.count_measurements_in_template(template_name)
```

* Judge whether the path is measurement or not in templates, This measurement must be in the template
```python
session.count_measurements_in_template(template_name, path)
```

* Judge whether the path is exist or not in templates, This path may not belong to the template
```python
session.is_path_exist_in_template(template_name, path)
```

* Show nodes under in schema template
```python
session.show_measurements_in_template(template_name)
```

* Show the path prefix where a schema template is set
```python
session.show_paths_template_set_on(template_name)
```

* Show the path prefix where a schema template is used (i.e. the time series has been created)
```python
session.show_paths_template_using_on(template_name)
```

#### Drop Schema Template
Delete an existing metadata template，dropping an already set template is not supported
```python
session.drop_schema_template("template_python")
```


### 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.

### IoTDB DBAPI

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.

#### Examples
+ Initialization

The initialized parameters are consistent with the session part (except for the sqlalchemy_mode).
```python
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()
```
+ simple SQL statement execution
```python
cursor.execute("SELECT ** FROM root")
for row in cursor.fetchall():
    print(row)
```

+ execute SQL with parameter

IoTDB DBAPI supports pyformat style parameters
```python
cursor.execute("SELECT ** FROM root WHERE time < %(time)s",{"time":"2017-11-01T00:08:00.000"})
for row in cursor.fetchall():
    print(row)
```

+ execute SQL with parameter sequences
```python
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)
```

+ close the connection and cursor
```python
cursor.close()
conn.close()
```

### IoTDB SQLAlchemy Dialect (Experimental)
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!
#### Mapping of the metadata
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：

1. Database
2. Path
3. Entity
4. Measurement

The metadata in the SQLAlchemy are：
1. Schema
2. Table
3. Column

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:

![sqlalchemy-to-iotdb](https://alioss.timecho.com/docs/img/UserGuide/API/IoTDB-SQLAlchemy/sqlalchemy-to-iotdb.png?raw=true)

#### Data type mapping
| data type in IoTDB | data type in SQLAlchemy |
|--------------------|-------------------------|
| BOOLEAN            | Boolean                 |
| INT32              | Integer                 |
| INT64              | BigInteger              |
| FLOAT              | Float                   |
| DOUBLE             | Float                   |
| TEXT               | Text                    |
| LONG               | BigInteger              |

#### Example

+ execute statement

```python
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)
```

+ ORM (now only simple queries are supported)

```python
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)
```


## 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