influxdb-client-python
======================

.. marker-index-start

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This repository contains the Python client library for use with InfluxDB 2.x and Flux. InfluxDB 3.x users should instead use the lightweight `v3 client library <https://github.com/InfluxCommunity/influxdb3-python>`_. InfluxDB 1.x users should use the `v1 client library <https://github.com/influxdata/influxdb-python>`_.

For ease of migration and a consistent query and write experience, v2 users should consider using InfluxQL and the `v1 client library <https://github.com/influxdata/influxdb-python>`_.

The API of the **influxdb-client-python** is not the backwards-compatible with the old one - **influxdb-python**.

Documentation
-------------

This section contains links to the client library documentation.

* `Product documentation <https://docs.influxdata.com/influxdb/v2.0/tools/client-libraries/>`_, `Getting Started <#getting-started>`_
* `Examples <https://github.com/influxdata/influxdb-client-python/tree/master/examples>`_
* `API Reference <https://influxdb-client.readthedocs.io/en/stable/api.html>`_
* `Changelog <https://github.com/influxdata/influxdb-client-python/blob/master/CHANGELOG.md>`_

InfluxDB 2.0 client features
----------------------------

- Querying data
    - using the Flux language
    - into csv, raw data, `flux_table <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L33>`_ structure, `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
    - `How to queries <#queries>`_
- Writing data using
    - `Line Protocol <https://docs.influxdata.com/influxdb/latest/reference/syntax/line-protocol>`_
    - `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py#L47>`__
    - `RxPY <https://rxpy.readthedocs.io/en/latest/>`__ Observable
    - `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
    - `How to writes <#writes>`_
- `InfluxDB 2.0 API <https://github.com/influxdata/influxdb/blob/master/http/swagger.yml>`_ client for management
    - the client is generated from the `swagger <https://github.com/influxdata/influxdb/blob/master/http/swagger.yml>`_ by using the `openapi-generator <https://github.com/OpenAPITools/openapi-generator>`_
    - organizations & users management
    - buckets management
    - tasks management
    - authorizations
    - health check
    - ...
- `InfluxDB 1.8 API compatibility`_
- Examples
    - `Connect to InfluxDB Cloud`_
    - `How to efficiently import large dataset`_
    - `Efficiency write data from IOT sensor`_
    - `How to use Jupyter + Pandas + InfluxDB 2`_
- `Advanced Usage`_
    - `Gzip support`_
    - `Proxy configuration`_
    - `Nanosecond precision`_
    - `Delete data`_
    - `Handling Errors`_
    - `Logging`_

Installation
------------
.. marker-install-start

InfluxDB python library uses `RxPY <https://github.com/ReactiveX/RxPY>`__ - The Reactive Extensions for Python (RxPY).

**Python 3.7** or later is required.

.. note::

    It is recommended to use ``ciso8601`` with client for parsing dates. ``ciso8601`` is much faster than built-in Python datetime. Since it's written as a ``C`` module the best way is build it from sources:

    **Windows**:

    You have to install `Visual C++ Build Tools 2015 <http://go.microsoft.com/fwlink/?LinkId=691126&fixForIE=.exe>`_ to build ``ciso8601`` by ``pip``.

    **conda**:

    Install from sources: ``conda install -c conda-forge/label/cf202003 ciso8601``.

pip install
^^^^^^^^^^^

The python package is hosted on `PyPI <https://pypi.org/project/influxdb-client/>`_, you can install latest version directly:

.. code-block:: sh

   pip install 'influxdb-client[ciso]'

Then import the package:

.. code-block:: python

   import influxdb_client

If your application uses async/await in Python you can install with the ``async`` extra::

    $ pip install influxdb-client[async]

For more info see `How to use Asyncio`.

Setuptools
^^^^^^^^^^

Install via `Setuptools <http://pypi.python.org/pypi/setuptools>`_.

.. code-block:: sh

   python setup.py install --user

(or ``sudo python setup.py install`` to install the package for all users)

.. marker-install-end

Getting Started
---------------

Please follow the `Installation`_ and then run the following:

.. marker-query-start

.. code-block:: python

   from influxdb_client import InfluxDBClient, Point
   from influxdb_client.client.write_api import SYNCHRONOUS

   bucket = "my-bucket"

   client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

   write_api = client.write_api(write_options=SYNCHRONOUS)
   query_api = client.query_api()

   p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)

   write_api.write(bucket=bucket, record=p)

   ## using Table structure
   tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

   for table in tables:
       print(table)
       for row in table.records:
           print (row.values)


   ## using csv library
   csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
   val_count = 0
   for row in csv_result:
       for cell in row:
           val_count += 1


.. marker-query-end

Client configuration
--------------------

Via File
^^^^^^^^
A client can be configured via ``*.ini`` file in segment ``influx2``.

The following options are supported:

- ``url`` - the url to connect to InfluxDB
- ``org`` - default destination organization for writes and queries
- ``token`` - the token to use for the authorization
- ``timeout`` - socket timeout in ms (default value is 10000)
- ``verify_ssl`` - set this to false to skip verifying SSL certificate when calling API from https server
- ``ssl_ca_cert`` - set this to customize the certificate file to verify the peer
- ``cert_file`` - path to the certificate that will be used for mTLS authentication
- ``cert_key_file`` - path to the file contains private key for mTLS certificate
- ``cert_key_password`` - string or function which returns password for decrypting the mTLS private key
- ``connection_pool_maxsize`` - set the number of connections to save that can be reused by urllib3
- ``auth_basic`` - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
- ``profilers`` - set the list of enabled `Flux profilers <https://docs.influxdata.com/influxdb/v2.0/reference/flux/stdlib/profiler/>`_

.. code-block:: python

    self.client = InfluxDBClient.from_config_file("config.ini")

.. code-block:: ini

    [influx2]
    url=http://localhost:8086
    org=my-org
    token=my-token
    timeout=6000
    verify_ssl=False

Via Environment Properties
^^^^^^^^^^^^^^^^^^^^^^^^^^
A client can be configured via environment properties.

Supported properties are:

- ``INFLUXDB_V2_URL`` - the url to connect to InfluxDB
- ``INFLUXDB_V2_ORG`` - default destination organization for writes and queries
- ``INFLUXDB_V2_TOKEN`` - the token to use for the authorization
- ``INFLUXDB_V2_TIMEOUT`` - socket timeout in ms (default value is 10000)
- ``INFLUXDB_V2_VERIFY_SSL`` - set this to false to skip verifying SSL certificate when calling API from https server
- ``INFLUXDB_V2_SSL_CA_CERT`` - set this to customize the certificate file to verify the peer
- ``INFLUXDB_V2_CERT_FILE`` - path to the certificate that will be used for mTLS authentication
- ``INFLUXDB_V2_CERT_KEY_FILE`` - path to the file contains private key for mTLS certificate
- ``INFLUXDB_V2_CERT_KEY_PASSWORD`` - string or function which returns password for decrypting the mTLS private key
- ``INFLUXDB_V2_CONNECTION_POOL_MAXSIZE`` - set the number of connections to save that can be reused by urllib3
- ``INFLUXDB_V2_AUTH_BASIC`` - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
- ``INFLUXDB_V2_PROFILERS`` - set the list of enabled `Flux profilers <https://docs.influxdata.com/influxdb/v2.0/reference/flux/stdlib/profiler/>`_

.. code-block:: python

    self.client = InfluxDBClient.from_env_properties()

Profile query
^^^^^^^^^^^^^

The `Flux Profiler package <https://docs.influxdata.com/influxdb/v2.0/reference/flux/stdlib/profiler/>`_ provides
performance profiling tools for Flux queries and operations.

You can enable printing profiler information of the Flux query in client library by:

- set QueryOptions.profilers in QueryApi,
- set ``INFLUXDB_V2_PROFILERS`` environment variable,
- set ``profilers`` option in configuration file.

When the profiler is enabled, the result of flux query contains additional tables "profiler/\*".
In order to have consistent behaviour with enabled/disabled profiler, ``FluxCSVParser`` excludes "profiler/\*" measurements
from result.

Example how to enable profilers using API:

.. code-block:: python

    q = '''
        from(bucket: stringParam)
          |> range(start: -5m, stop: now())
          |> filter(fn: (r) => r._measurement == "mem")
          |> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
          |> aggregateWindow(every: 1m, fn: mean)
          |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    '''
    p = {
        "stringParam": "my-bucket",
    }

    query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"]))
    csv_result = query_api.query(query=q, params=p)


Example of a profiler output:

.. code-block:: text

    ===============
    Profiler: query
    ===============

    from(bucket: stringParam)
      |> range(start: -5m, stop: now())
      |> filter(fn: (r) => r._measurement == "mem")
      |> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
      |> aggregateWindow(every: 1m, fn: mean)
      |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")

    ========================
    Profiler: profiler/query
    ========================
    result              : _profiler
    table               : 0
    _measurement        : profiler/query
    TotalDuration       : 8924700
    CompileDuration     : 350900
    QueueDuration       : 33800
    PlanDuration        : 0
    RequeueDuration     : 0
    ExecuteDuration     : 8486500
    Concurrency         : 0
    MaxAllocated        : 2072
    TotalAllocated      : 0
    flux/query-plan     :

    digraph {
      ReadWindowAggregateByTime11
      // every = 1m, aggregates = [mean], createEmpty = true, timeColumn = "_stop"
      pivot8
      generated_yield

      ReadWindowAggregateByTime11 -> pivot8
      pivot8 -> generated_yield
    }


    influxdb/scanned-bytes: 0
    influxdb/scanned-values: 0

    ===========================
    Profiler: profiler/operator
    ===========================
    result              : _profiler
    table               : 1
    _measurement        : profiler/operator
    Type                : *universe.pivotTransformation
    Label               : pivot8
    Count               : 3
    MinDuration         : 32600
    MaxDuration         : 126200
    DurationSum         : 193400
    MeanDuration        : 64466.666666666664

    ===========================
    Profiler: profiler/operator
    ===========================
    result              : _profiler
    table               : 1
    _measurement        : profiler/operator
    Type                : *influxdb.readWindowAggregateSource
    Label               : ReadWindowAggregateByTime11
    Count               : 1
    MinDuration         : 940500
    MaxDuration         : 940500
    DurationSum         : 940500
    MeanDuration        : 940500.0

You can also use callback function to get profilers output.
Return value of this callback is type of FluxRecord.

Example how to use profilers with callback:

.. code-block:: python

     class ProfilersCallback(object):
        def __init__(self):
            self.records = []

        def __call__(self, flux_record):
            self.records.append(flux_record.values)

    callback = ProfilersCallback()

    query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"], profiler_callback=callback))
    tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

    for profiler in callback.records:
        print(f'Custom processing of profiler result: {profiler}')

Example output of this callback:

.. code-block:: text

    Custom processing of profiler result: {'result': '_profiler', 'table': 0, '_measurement': 'profiler/query', 'TotalDuration': 18843792, 'CompileDuration': 1078666, 'QueueDuration': 93375, 'PlanDuration': 0, 'RequeueDuration': 0, 'ExecuteDuration': 17371000, 'Concurrency': 0, 'MaxAllocated': 448, 'TotalAllocated': 0, 'RuntimeErrors': None, 'flux/query-plan': 'digraph {\r\n  ReadRange2\r\n  generated_yield\r\n\r\n  ReadRange2 -> generated_yield\r\n}\r\n\r\n', 'influxdb/scanned-bytes': 0, 'influxdb/scanned-values': 0}
    Custom processing of profiler result: {'result': '_profiler', 'table': 1, '_measurement': 'profiler/operator', 'Type': '*influxdb.readFilterSource', 'Label': 'ReadRange2', 'Count': 1, 'MinDuration': 3274084, 'MaxDuration': 3274084, 'DurationSum': 3274084, 'MeanDuration': 3274084.0}


.. marker-index-end


How to use
----------

Writes
^^^^^^
.. marker-writes-start

The `WriteApi <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write_api.py>`_ supports synchronous, asynchronous and batching writes into InfluxDB 2.0.
The data should be passed as a `InfluxDB Line Protocol <https://docs.influxdata.com/influxdb/latest/write_protocols/line_protocol_tutorial/>`_\ , `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py>`_ or Observable stream.

.. warning::

    The ``WriteApi`` in batching mode (default mode) is suppose to run as a singleton.
    To flush all your data you should wrap the execution using ``with client.write_api(...) as write_api:`` statement
    or call ``write_api.close()`` at the end of your script.

*The default instance of WriteApi use batching.*

The data could be written as
""""""""""""""""""""""""""""

1. ``string`` or ``bytes`` that is formatted as a InfluxDB's line protocol
2. `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py#L16>`__ structure
3. Dictionary style mapping with keys: ``measurement``, ``tags``, ``fields`` and ``time`` or custom structure
4. `NamedTuple <https://docs.python.org/3/library/collections.html#collections.namedtuple>`_
5. `Data Classes <https://docs.python.org/3/library/dataclasses.html>`_
6. `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
7. List of above items
8. A ``batching`` type of write also supports an ``Observable`` that produce one of an above item

You can find write examples at GitHub: `influxdb-client-python/examples <https://github.com/influxdata/influxdb-client-python/tree/master/examples#writes>`__.

Batching
""""""""

The batching is configurable by ``write_options``\ :

.. list-table::
   :header-rows: 1

   * - Property
     - Description
     - Default Value
   * - **batch_size**
     - the number of data point to collect in a batch
     - ``1000``
   * - **flush_interval**
     - the number of milliseconds before the batch is written
     - ``1000``
   * - **jitter_interval**
     - the number of milliseconds to increase the batch flush interval by a random amount
     - ``0``
   * - **retry_interval**
     - the number of milliseconds to retry first unsuccessful write. The next retry delay is computed using exponential random backoff. The retry interval is used when the InfluxDB server does not specify "Retry-After" header.
     - ``5000``
   * - **max_retry_time**
     - maximum total retry timeout in milliseconds.
     - ``180_000``
   * - **max_retries**
     - the number of max retries when write fails
     - ``5``
   * - **max_retry_delay**
     - the maximum delay between each retry attempt in milliseconds
     - ``125_000``
   * - **max_close_wait**
     - the maximum amount of time to wait for batches to flush when `.close()` is called
     - ``300_000``
   * - **exponential_base**
     - the base for the exponential retry delay, the next delay is computed using random exponential backoff as a random value within the interval  ``retry_interval * exponential_base^(attempts-1)`` and ``retry_interval * exponential_base^(attempts)``. Example for ``retry_interval=5_000, exponential_base=2, max_retry_delay=125_000, total=5`` Retry delays are random distributed values within the ranges of ``[5_000-10_000, 10_000-20_000, 20_000-40_000, 40_000-80_000, 80_000-125_000]``
     - ``2``


.. code-block:: python

    from datetime import datetime, timedelta

    import pandas as pd
    import reactivex as rx
    from reactivex import operators as ops

    from influxdb_client import InfluxDBClient, Point, WriteOptions

    with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as _client:

        with _client.write_api(write_options=WriteOptions(batch_size=500,
                                                          flush_interval=10_000,
                                                          jitter_interval=2_000,
                                                          retry_interval=5_000,
                                                          max_retries=5,
                                                          max_retry_delay=30_000,
                                                          max_close_wait=300_000,
                                                          exponential_base=2)) as _write_client:

            """
            Write Line Protocol formatted as string
            """
            _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
            _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
                                                        "h2o_feet,location=coyote_creek water_level=3.0 3"])

            """
            Write Line Protocol formatted as byte array
            """
            _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
            _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
                                                        "h2o_feet,location=coyote_creek water_level=3.0 3".encode()])

            """
            Write Dictionary-style object
            """
            _write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                        "fields": {"water_level": 1.0}, "time": 1})
            _write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                         "fields": {"water_level": 2.0}, "time": 2},
                                                        {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                         "fields": {"water_level": 3.0}, "time": 3}])

            """
            Write Data Point
            """
            _write_client.write("my-bucket", "my-org",
                                Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
            _write_client.write("my-bucket", "my-org",
                                [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
                                 Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])

            """
            Write Observable stream
            """
            _data = rx \
                .range(7, 11) \
                .pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))

            _write_client.write("my-bucket", "my-org", _data)

            """
            Write Pandas DataFrame
            """
            _now = datetime.utcnow()
            _data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
                                       index=[_now, _now + timedelta(hours=1)],
                                       columns=["location", "water_level"])

            _write_client.write("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
                                data_frame_tag_columns=['location'])



Default Tags
""""""""""""

Sometimes is useful to store same information in every measurement e.g. ``hostname``, ``location``, ``customer``.
The client is able to use static value or env property as a tag value.

The expressions:

- ``California Miner`` - static value
- ``${env.hostname}`` - environment property

Via API
_______

.. code-block:: python

    point_settings = PointSettings()
    point_settings.add_default_tag("id", "132-987-655")
    point_settings.add_default_tag("customer", "California Miner")
    point_settings.add_default_tag("data_center", "${env.data_center}")

    self.write_client = self.client.write_api(write_options=SYNCHRONOUS, point_settings=point_settings)

.. code-block:: python

    self.write_client = self.client.write_api(write_options=SYNCHRONOUS,
                                                  point_settings=PointSettings(**{"id": "132-987-655",
                                                                                  "customer": "California Miner"}))

Via Configuration file
______________________

In a `init <https://docs.python.org/3/library/configparser.html>`_ configuration file you are able to specify default tags by ``tags`` segment.

.. code-block:: python

    self.client = InfluxDBClient.from_config_file("config.ini")

.. code-block::

    [influx2]
    url=http://localhost:8086
    org=my-org
    token=my-token
    timeout=6000

    [tags]
    id = 132-987-655
    customer = California Miner
    data_center = ${env.data_center}

You can also use a `TOML <https://toml.io/en/>`_  or a `JSON <https://www.json.org/json-en.html>`_ format for the configuration file.

Via Environment Properties
__________________________
You are able to specify default tags by environment properties with prefix ``INFLUXDB_V2_TAG_``.

Examples:

- ``INFLUXDB_V2_TAG_ID``
- ``INFLUXDB_V2_TAG_HOSTNAME``

.. code-block:: python

    self.client = InfluxDBClient.from_env_properties()

Synchronous client
""""""""""""""""""

Data are writes in a synchronous HTTP request.

.. code-block:: python

   from influxdb_client import InfluxDBClient, Point
   from influxdb_client .client.write_api import SYNCHRONOUS

   client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
   write_api = client.write_api(write_options=SYNCHRONOUS)

   _point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
   _point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

   write_api.write(bucket="my-bucket", record=[_point1, _point2])

   client.close()

.. marker-writes-end

Queries
^^^^^^^

The result retrieved by `QueryApi <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/query_api.py>`_  could be formatted as a:

1. Flux data structure: `FluxTable <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L5>`_, `FluxColumn <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L22>`_ and `FluxRecord <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/flux_table.py#L31>`_
2. :code:`influxdb_client.client.flux_table.CSVIterator` which will iterate over CSV lines
3. Raw unprocessed results as a ``str`` iterator
4. `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_

The API also support streaming ``FluxRecord`` via `query_stream <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/query_api.py#L77>`_, see example below:

.. code-block:: python

    from influxdb_client import InfluxDBClient, Point, Dialect
    from influxdb_client.client.write_api import SYNCHRONOUS

    client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

    write_api = client.write_api(write_options=SYNCHRONOUS)
    query_api = client.query_api()

    """
    Prepare data
    """

    _point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
    _point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

    write_api.write(bucket="my-bucket", record=[_point1, _point2])

    """
    Query: using Table structure
    """
    tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

    for table in tables:
        print(table)
        for record in table.records:
            print(record.values)

    print()
    print()

    """
    Query: using Bind parameters
    """

    p = {"_start": datetime.timedelta(hours=-1),
         "_location": "Prague",
         "_desc": True,
         "_floatParam": 25.1,
         "_every": datetime.timedelta(minutes=5)
         }

    tables = query_api.query('''
        from(bucket:"my-bucket") |> range(start: _start)
            |> filter(fn: (r) => r["_measurement"] == "my_measurement")
            |> filter(fn: (r) => r["_field"] == "temperature")
            |> filter(fn: (r) => r["location"] == _location and r["_value"] > _floatParam)
            |> aggregateWindow(every: _every, fn: mean, createEmpty: true)
            |> sort(columns: ["_time"], desc: _desc)
    ''', params=p)

    for table in tables:
        print(table)
        for record in table.records:
            print(str(record["_time"]) + " - " + record["location"] + ": " + str(record["_value"]))

    print()
    print()

    """
    Query: using Stream
    """
    records = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -10m)')

    for record in records:
        print(f'Temperature in {record["location"]} is {record["_value"]}')

    """
    Interrupt a stream after retrieve a required data
    """
    large_stream = query_api.query_stream('from(bucket:"my-bucket") |> range(start: -100d)')
    for record in large_stream:
        if record["location"] == "New York":
            print(f'New York temperature: {record["_value"]}')
            break

    large_stream.close()

    print()
    print()

    """
    Query: using csv library
    """
    csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)',
                                     dialect=Dialect(header=False, delimiter=",", comment_prefix="#", annotations=[],
                                                     date_time_format="RFC3339"))
    for csv_line in csv_result:
        if not len(csv_line) == 0:
            print(f'Temperature in {csv_line[9]} is {csv_line[6]}')

    """
    Close client
    """
    client.close()

Pandas DataFrame
""""""""""""""""
.. marker-pandas-start

.. note:: For DataFrame querying you should install Pandas dependency via ``pip install 'influxdb-client[extra]'``.

.. note:: Note that if a query returns more then one table then the client generates a ``DataFrame`` for each of them.

The ``client`` is able to retrieve data in `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_ format thought ``query_data_frame``:

.. code-block:: python

    from influxdb_client import InfluxDBClient, Point, Dialect
    from influxdb_client.client.write_api import SYNCHRONOUS

    client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

    write_api = client.write_api(write_options=SYNCHRONOUS)
    query_api = client.query_api()

    """
    Prepare data
    """

    _point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
    _point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

    write_api.write(bucket="my-bucket", record=[_point1, _point2])

    """
    Query: using Pandas DataFrame
    """
    data_frame = query_api.query_data_frame('from(bucket:"my-bucket") '
                                            '|> range(start: -10m) '
                                            '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") '
                                            '|> keep(columns: ["location", "temperature"])')
    print(data_frame.to_string())

    """
    Close client
    """
    client.close()

Output:

.. code-block:: text

        result table  location  temperature
    0  _result     0  New York         24.3
    1  _result     1    Prague         25.3

.. marker-pandas-end

Examples
^^^^^^^^

.. marker-examples-start

How to efficiently import large dataset
"""""""""""""""""""""""""""""""""""""""

The following example shows how to import dataset with dozen megabytes.
If you would like to import gigabytes of data then use our multiprocessing example: `import_data_set_multiprocessing.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/import_data_set_multiprocessing.py>`_ for use a full capability of your hardware.

* sources - `import_data_set.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/import_data_set.py>`_

.. code-block:: python

   """
   Import VIX - CBOE Volatility Index - from "vix-daily.csv" file into InfluxDB 2.0

   https://datahub.io/core/finance-vix#data
   """

   from collections import OrderedDict
   from csv import DictReader

   import reactivex as rx
   from reactivex import operators as ops

   from influxdb_client import InfluxDBClient, Point, WriteOptions

   def parse_row(row: OrderedDict):
       """Parse row of CSV file into Point with structure:

           financial-analysis,type=ily close=18.47,high=19.82,low=18.28,open=19.82 1198195200000000000

       CSV format:
           Date,VIX Open,VIX High,VIX Low,VIX Close\n
           2004-01-02,17.96,18.68,17.54,18.22\n
           2004-01-05,18.45,18.49,17.44,17.49\n
           2004-01-06,17.66,17.67,16.19,16.73\n
           2004-01-07,16.72,16.75,15.5,15.5\n
           2004-01-08,15.42,15.68,15.32,15.61\n
           2004-01-09,16.15,16.88,15.57,16.75\n
           ...

       :param row: the row of CSV file
       :return: Parsed csv row to [Point]
       """

       """
        For better performance is sometimes useful directly create a LineProtocol to avoid unnecessary escaping overhead:
        """
        # from datetime import timezone
        # import ciso8601
        # from influxdb_client.client.write.point import EPOCH
        #
        # time = (ciso8601.parse_datetime(row["Date"]).replace(tzinfo=timezone.utc) - EPOCH).total_seconds() * 1e9
        # return f"financial-analysis,type=vix-daily" \
        #        f" close={float(row['VIX Close'])},high={float(row['VIX High'])},low={float(row['VIX Low'])},open={float(row['VIX Open'])} " \
        #        f" {int(time)}"

       return Point("financial-analysis") \
           .tag("type", "vix-daily") \
           .field("open", float(row['VIX Open'])) \
           .field("high", float(row['VIX High'])) \
           .field("low", float(row['VIX Low'])) \
           .field("close", float(row['VIX Close'])) \
           .time(row['Date'])


   """
   Converts vix-daily.csv into sequence of datad point
   """
   data = rx \
       .from_iterable(DictReader(open('vix-daily.csv', 'r'))) \
       .pipe(ops.map(lambda row: parse_row(row)))

   client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)

   """
   Create client that writes data in batches with 50_000 items.
   """
   write_api = client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000))

   """
   Write data into InfluxDB
   """
   write_api.write(bucket="my-bucket", record=data)
   write_api.close()

   """
   Querying max value of CBOE Volatility Index
   """
   query = 'from(bucket:"my-bucket")' \
           ' |> range(start: 0, stop: now())' \
           ' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
           ' |> max()'
   result = client.query_api().query(query=query)

   """
   Processing results
   """
   print()
   print("=== results ===")
   print()
   for table in result:
       for record in table.records:
           print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))

   """
   Close client
   """
   client.close()

Efficiency write data from IOT sensor
"""""""""""""""""""""""""""""""""""""

* sources - `iot_sensor.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/iot_sensor.py>`_

.. code-block:: python

   """
   Efficiency write data from IOT sensor - write changed temperature every minute
   """
   import atexit
   import platform
   from datetime import timedelta

   import psutil as psutil
   import reactivex as rx
   from reactivex import operators as ops

   from influxdb_client import InfluxDBClient, WriteApi, WriteOptions

   def on_exit(db_client: InfluxDBClient, write_api: WriteApi):
       """Close clients after terminate a script.

       :param db_client: InfluxDB client
       :param write_api: WriteApi
       :return: nothing
       """
       write_api.close()
       db_client.close()


   def sensor_temperature():
       """Read a CPU temperature. The [psutil] doesn't support MacOS so we use [sysctl].

       :return: actual CPU temperature
       """
       os_name = platform.system()
       if os_name == 'Darwin':
           from subprocess import check_output
           output = check_output(["sysctl", "machdep.xcpm.cpu_thermal_level"])
           import re
           return re.findall(r'\d+', str(output))[0]
       else:
           return psutil.sensors_temperatures()["coretemp"][0]


   def line_protocol(temperature):
       """Create a InfluxDB line protocol with structure:

           iot_sensor,hostname=mine_sensor_12,type=temperature value=68

       :param temperature: the sensor temperature
       :return: Line protocol to write into InfluxDB
       """

       import socket
       return 'iot_sensor,hostname={},type=temperature value={}'.format(socket.gethostname(), temperature)


   """
   Read temperature every minute; distinct_until_changed - produce only if temperature change
   """
   data = rx\
       .interval(period=timedelta(seconds=60))\
       .pipe(ops.map(lambda t: sensor_temperature()),
             ops.distinct_until_changed(),
             ops.map(lambda temperature: line_protocol(temperature)))

   _db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)

   """
   Create client that writes data into InfluxDB
   """
   _write_api = _db_client.write_api(write_options=WriteOptions(batch_size=1))
   _write_api.write(bucket="my-bucket", record=data)


   """
   Call after terminate a script
   """
   atexit.register(on_exit, _db_client, _write_api)

   input()

Connect to InfluxDB Cloud
"""""""""""""""""""""""""
The following example demonstrate a simplest way how to write and query date with the InfluxDB Cloud.

At first point you should create an authentication token as is described `here <https://v2.docs.influxdata.com/v2.0/security/tokens/create-token/>`_.

After that you should configure properties: ``influx_cloud_url``, ``influx_cloud_token``, ``bucket`` and ``org`` in a ``influx_cloud.py`` example.

The last step is run a python script via: ``python3 influx_cloud.py``.

* sources - `influx_cloud.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/influx_cloud.py>`_

.. code-block:: python

    """
    Connect to InfluxDB 2.0 - write data and query them
    """

    from datetime import datetime

    from influxdb_client import Point, InfluxDBClient
    from influxdb_client.client.write_api import SYNCHRONOUS

    """
    Configure credentials
    """
    influx_cloud_url = 'https://us-west-2-1.aws.cloud2.influxdata.com'
    influx_cloud_token = '...'
    bucket = '...'
    org = '...'

    client = InfluxDBClient(url=influx_cloud_url, token=influx_cloud_token)
    try:
        kind = 'temperature'
        host = 'host1'
        device = 'opt-123'

        """
        Write data by Point structure
        """
        point = Point(kind).tag('host', host).tag('device', device).field('value', 25.3).time(time=datetime.utcnow())

        print(f'Writing to InfluxDB cloud: {point.to_line_protocol()} ...')

        write_api = client.write_api(write_options=SYNCHRONOUS)
        write_api.write(bucket=bucket, org=org, record=point)

        print()
        print('success')
        print()
        print()

        """
        Query written data
        """
        query = f'from(bucket: "{bucket}") |> range(start: -1d) |> filter(fn: (r) => r._measurement == "{kind}")'
        print(f'Querying from InfluxDB cloud: "{query}" ...')
        print()

        query_api = client.query_api()
        tables = query_api.query(query=query, org=org)

        for table in tables:
            for row in table.records:
                print(f'{row.values["_time"]}: host={row.values["host"]},device={row.values["device"]} '
                      f'{row.values["_value"]} °C')

        print()
        print('success')

    except Exception as e:
        print(e)
    finally:
        client.close()

How to use Jupyter + Pandas + InfluxDB 2
""""""""""""""""""""""""""""""""""""""""
The first example shows how to use client capabilities to predict stock price via `Keras <https://keras.io>`_, `TensorFlow <https://www.tensorflow.org>`_, `sklearn <https://scikit-learn.org/stable/>`_:

The example is taken from `Kaggle <https://www.kaggle.com/chaitanyacc4/predicting-stock-prices-of-apple-inc>`_.

* sources - `stock-predictions.ipynb <notebooks/stock-predictions.ipynb>`_

.. image:: https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/stock-price-prediction.gif

Result:

.. image:: https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/stock-price-prediction-results.png

The second example shows how to use client capabilities to realtime visualization via `hvPlot <https://hvplot.pyviz.org>`_, `Streamz <https://streamz.readthedocs.io/en/latest/>`_, `RxPY <https://rxpy.readthedocs.io/en/latest/>`_:

* sources - `realtime-stream.ipynb <notebooks/realtime-stream.ipynb>`_

.. image:: https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/realtime-result.gif

Other examples
""""""""""""""

You can find all examples at GitHub: `influxdb-client-python/examples <https://github.com/influxdata/influxdb-client-python/tree/master/examples#examples>`__.

.. marker-examples-end

Advanced Usage
--------------

Gzip support
^^^^^^^^^^^^
.. marker-gzip-start

``InfluxDBClient`` does not enable gzip compression for http requests by default. If you want to enable gzip to reduce transfer data's size, you can call:

.. code-block:: python

   from influxdb_client import InfluxDBClient

   _db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", enable_gzip=True)

.. marker-gzip-end

Authenticate to the InfluxDB
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. marker-authenticate-start

``InfluxDBClient`` supports three options how to authorize a connection:

- `Token`
- `Username & Password`
- `HTTP Basic`

Token
"""""

Use the ``token`` to authenticate to the InfluxDB API. In your API requests, an `Authorization` header will be send.
The header value, provide the word `Token` followed by a space and an InfluxDB API token. The word `token`` is case-sensitive.

.. code-block:: python

   from influxdb_client import InfluxDBClient

   with InfluxDBClient(url="http://localhost:8086", token="my-token") as client

.. note:: Note that this is a preferred way how to authenticate to InfluxDB API.

Username & Password
"""""""""""""""""""

Authenticates via username and password credentials. If successful, creates a new session for the user.

.. code-block:: python

   from influxdb_client import InfluxDBClient

   with InfluxDBClient(url="http://localhost:8086", username="my-user", password="my-password") as client

.. warning::

    The ``username/password`` auth is based on the HTTP "Basic" authentication.
    The authorization expires when the `time-to-live (TTL) <https://docs.influxdata.com/influxdb/latest/reference/config-options/#session-length>`__
    (default 60 minutes) is reached and client produces ``unauthorized exception``.

HTTP Basic
""""""""""

Use this to enable basic authentication when talking to a InfluxDB 1.8.x that does not use auth-enabled
but is protected by a reverse proxy with basic authentication.

.. code-block:: python

   from influxdb_client import InfluxDBClient

   with InfluxDBClient(url="http://localhost:8086", auth_basic=True, token="my-proxy-secret") as client


.. warning:: Don't use this when directly talking to InfluxDB 2.

.. marker-authenticate-end

Proxy configuration
^^^^^^^^^^^^^^^^^^^
.. marker-proxy-start

You can configure the client to tunnel requests through an HTTP proxy.
The following proxy options are supported:

- ``proxy`` - Set this to configure the http proxy to be used, ex. ``http://localhost:3128``
- ``proxy_headers`` - A dictionary containing headers that will be sent to the proxy. Could be used for proxy authentication.

.. code-block:: python

   from influxdb_client import InfluxDBClient

   with InfluxDBClient(url="http://localhost:8086",
                       token="my-token",
                       org="my-org",
                       proxy="http://localhost:3128") as client:

.. note::

    If your proxy notify the client with permanent redirect (``HTTP 301``) to **different host**.
    The client removes ``Authorization`` header, because otherwise the contents of ``Authorization`` is sent to third parties
    which is a security vulnerability.

    You can change this behaviour by:

    .. code-block:: python

       from urllib3 import Retry
       Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT = frozenset()
       Retry.DEFAULT.remove_headers_on_redirect = Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT

.. marker-proxy-end

Delete data
^^^^^^^^^^^
.. marker-delete-start

The `delete_api.py <influxdb_client/client/delete_api.py>`_ supports deletes `points <https://v2.docs.influxdata.com/v2.0/reference/glossary/#point>`_ from an InfluxDB bucket.

.. code-block:: python

    from influxdb_client import InfluxDBClient

    client = InfluxDBClient(url="http://localhost:8086", token="my-token")

    delete_api = client.delete_api()

    """
    Delete Data
    """
    start = "1970-01-01T00:00:00Z"
    stop = "2021-02-01T00:00:00Z"
    delete_api.delete(start, stop, '_measurement="my_measurement"', bucket='my-bucket', org='my-org')

    """
    Close client
    """
    client.close()

.. marker-delete-end

InfluxDB 1.8 API compatibility
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

`InfluxDB 1.8.0 introduced forward compatibility APIs <https://docs.influxdata.com/influxdb/v1.8/tools/api/#influxdb-2-0-api-compatibility-endpoints>`_ for InfluxDB 2.0. This allow you to easily move from InfluxDB 1.x to InfluxDB 2.0 Cloud or open source.

The following forward compatible APIs are available:

=======================================================  ====================================================================================================  =======
 API                                                     Endpoint                                                                                              Description
=======================================================  ====================================================================================================  =======
`query_api.py <influxdb_client/client/query_api.py>`_    `/api/v2/query <https://docs.influxdata.com/influxdb/v1.8/tools/api/#apiv2query-http-endpoint>`_      Query data in InfluxDB 1.8.0+ using the InfluxDB 2.0 API and `Flux <https://docs.influxdata.com/flux/latest/>`_ (endpoint should be enabled by `flux-enabled option <https://docs.influxdata.com/influxdb/v1.8/administration/config/#flux-enabled-false>`_)
`write_api.py <influxdb_client/client/write_api.py>`_    `/api/v2/write <https://docs.influxdata.com/influxdb/v1.8/tools/api/#apiv2write-http-endpoint>`_      Write data to InfluxDB 1.8.0+ using the InfluxDB 2.0 API
`ping() <influxdb_client/client/influxdb_client.py>`_    `/ping <https://docs.influxdata.com/influxdb/v1.8/tools/api/#ping-http-endpoint>`_                    Check the status of your InfluxDB instance
=======================================================  ====================================================================================================  =======

For detail info see `InfluxDB 1.8 example <examples/influxdb_18_example.py>`_.

Handling Errors
^^^^^^^^^^^^^^^
.. marker-handling-errors-start

Errors happen and it's important that your code is prepared for them. All client related  exceptions are delivered from
``InfluxDBError``. If the exception cannot be recovered in the client it is returned to the application.
These exceptions are left for the developer to handle.

Almost all APIs directly return unrecoverable exceptions to be handled this way:

.. code-block:: python

    from influxdb_client import InfluxDBClient
    from influxdb_client.client.exceptions import InfluxDBError
    from influxdb_client.client.write_api import SYNCHRONOUS

    with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
        try:
            client.write_api(write_options=SYNCHRONOUS).write("my-bucket", record="mem,tag=a value=86")
        except InfluxDBError as e:
            if e.response.status == 401:
                raise Exception(f"Insufficient write permissions to 'my-bucket'.") from e
            raise


The only exception is **batching** ``WriteAPI`` (for more info see `Batching`_). where you need to register custom callbacks to handle batch events.
This is because this API runs in the ``background`` in a ``separate`` thread and isn't possible to directly
return underlying exceptions.

.. code-block:: python

    from influxdb_client import InfluxDBClient
    from influxdb_client.client.exceptions import InfluxDBError


    class BatchingCallback(object):

        def success(self, conf: (str, str, str), data: str):
            print(f"Written batch: {conf}, data: {data}")

        def error(self, conf: (str, str, str), data: str, exception: InfluxDBError):
            print(f"Cannot write batch: {conf}, data: {data} due: {exception}")

        def retry(self, conf: (str, str, str), data: str, exception: InfluxDBError):
            print(f"Retryable error occurs for batch: {conf}, data: {data} retry: {exception}")


    with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
        callback = BatchingCallback()
        with client.write_api(success_callback=callback.success,
                              error_callback=callback.error,
                              retry_callback=callback.retry) as write_api:
            pass

HTTP Retry Strategy
"""""""""""""""""""
By default the client uses a retry strategy only for batching writes (for more info see `Batching`_).
For other HTTP requests there is no one retry strategy, but it could be configured by ``retries``
parameter of ``InfluxDBClient``.

For more info about how configure HTTP retry see details in `urllib3 documentation <https://urllib3.readthedocs.io/en/latest/reference/index.html?highlight=retry#urllib3.Retry>`_.

.. code-block:: python

    from urllib3 import Retry

    from influxdb_client import InfluxDBClient

    retries = Retry(connect=5, read=2, redirect=5)
    client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", retries=retries)

.. marker-handling-errors-end

Nanosecond precision
^^^^^^^^^^^^^^^^^^^^
.. marker-nanosecond-start

The Python's `datetime <https://docs.python.org/3/library/datetime.html>`_ doesn't support precision with nanoseconds
so the library during writes and queries ignores everything after microseconds.

If you would like to use ``datetime`` with nanosecond precision you should use
`pandas.Timestamp <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp>`_
that is replacement for python ``datetime.datetime`` object and also you should set a proper ``DateTimeHelper`` to the client.

* sources - `nanosecond_precision.py <https://github.com/influxdata/influxdb-client-python/blob/master/examples/nanosecond_precision.py>`_

.. code-block:: python

    from influxdb_client import Point, InfluxDBClient
    from influxdb_client.client.util.date_utils_pandas import PandasDateTimeHelper
    from influxdb_client.client.write_api import SYNCHRONOUS

    """
    Set PandasDate helper which supports nanoseconds.
    """
    import influxdb_client.client.util.date_utils as date_utils

    date_utils.date_helper = PandasDateTimeHelper()

    """
    Prepare client.
    """
    client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

    write_api = client.write_api(write_options=SYNCHRONOUS)
    query_api = client.query_api()

    """
    Prepare data
    """

    point = Point("h2o_feet") \
        .field("water_level", 10) \
        .tag("location", "pacific") \
        .time('1996-02-25T21:20:00.001001231Z')

    print(f'Time serialized with nanosecond precision: {point.to_line_protocol()}')
    print()

    write_api.write(bucket="my-bucket", record=point)

    """
    Query: using Stream
    """
    query = '''
    from(bucket:"my-bucket")
            |> range(start: 0, stop: now())
            |> filter(fn: (r) => r._measurement == "h2o_feet")
    '''
    records = query_api.query_stream(query)

    for record in records:
        print(f'Temperature in {record["location"]} is {record["_value"]} at time: {record["_time"]}')

    """
    Close client
    """
    client.close()

.. marker-nanosecond-end

How to use Asyncio
^^^^^^^^^^^^^^^^^^
.. marker-asyncio-start

Starting from version 1.27.0 for Python 3.7+ the ``influxdb-client`` package supports ``async/await`` based on
`asyncio <https://docs.python.org/3/library/asyncio.html>`_, `aiohttp <https://docs.aiohttp.org>`_ and `aiocsv <https://pypi.org/project/aiocsv/>`_.
You can install ``aiohttp`` and ``aiocsv`` directly:

 .. code-block:: bash

    $ python -m pip install influxdb-client aiohttp aiocsv

or use the ``[async]`` extra:

 .. code-block:: bash

    $ python -m pip install influxdb-client[async]

.. warning::

    The ``InfluxDBClientAsync`` should be initialised inside ``async coroutine``
    otherwise there can be unexpected behaviour.
    For more info see: `Why is creating a ClientSession outside of an event loop dangerous? <https://docs.aiohttp.org/en/stable/faq.html#why-is-creating-a-clientsession-outside-of-an-event-loop-dangerous>`__.

Async APIs
""""""""""
All async APIs are available via :code:`influxdb_client.client.influxdb_client_async.InfluxDBClientAsync`.
The ``async`` version of the client supports following asynchronous APIs:

* :code:`influxdb_client.client.write_api_async.WriteApiAsync`
* :code:`influxdb_client.client.query_api_async.QueryApiAsync`
* :code:`influxdb_client.client.delete_api_async.DeleteApiAsync`
* Management services into ``influxdb_client.service`` supports async operation

and also check to readiness of the InfluxDB via ``/ping`` endpoint:

 .. code-block:: python

        import asyncio

        from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


        async def main():
            async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:
                ready = await client.ping()
                print(f"InfluxDB: {ready}")


        if __name__ == "__main__":
            asyncio.run(main())

Async Write API
"""""""""""""""

The :code:`influxdb_client.client.write_api_async.WriteApiAsync` supports ingesting data as:

* ``string`` or ``bytes`` that is formatted as a InfluxDB's line protocol
* `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py#L16>`__ structure
* Dictionary style mapping with keys: ``measurement``, ``tags``, ``fields`` and ``time`` or custom structure
* `NamedTuple <https://docs.python.org/3/library/collections.html#collections.namedtuple>`_
* `Data Classes <https://docs.python.org/3/library/dataclasses.html>`_
* `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
* List of above items

 .. code-block:: python

    import asyncio

    from influxdb_client import Point
    from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


    async def main():
        async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:

            write_api = client.write_api()

            _point1 = Point("async_m").tag("location", "Prague").field("temperature", 25.3)
            _point2 = Point("async_m").tag("location", "New York").field("temperature", 24.3)

            successfully = await write_api.write(bucket="my-bucket", record=[_point1, _point2])

            print(f" > successfully: {successfully}")


    if __name__ == "__main__":
        asyncio.run(main())


Async Query API
"""""""""""""""

The :code:`influxdb_client.client.query_api_async.QueryApiAsync` supports retrieve data as:

* List of :code:`influxdb_client.client.flux_table.FluxTable`
* Stream of :code:`influxdb_client.client.flux_table.FluxRecord` via :code:`typing.AsyncGenerator`
* `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_
* Stream of `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_ via :code:`typing.AsyncGenerator`
* Raw :code:`str` output

 .. code-block:: python

    import asyncio

    from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


    async def main():
        async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:
            # Stream of FluxRecords
            query_api = client.query_api()
            records = await query_api.query_stream('from(bucket:"my-bucket") '
                                                   '|> range(start: -10m) '
                                                   '|> filter(fn: (r) => r["_measurement"] == "async_m")')
            async for record in records:
                print(record)


    if __name__ == "__main__":
        asyncio.run(main())


Async Delete API
""""""""""""""""

 .. code-block:: python

    import asyncio
    from datetime import datetime

    from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


    async def main():
        async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:
            start = datetime.utcfromtimestamp(0)
            stop = datetime.now()
            # Delete data with location = 'Prague'
            successfully = await client.delete_api().delete(start=start, stop=stop, bucket="my-bucket",
                                                            predicate="location = \"Prague\"")
            print(f" > successfully: {successfully}")


    if __name__ == "__main__":
        asyncio.run(main())


Management API
""""""""""""""

 .. code-block:: python

    import asyncio

    from influxdb_client import OrganizationsService
    from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


    async def main():
        async with InfluxDBClientAsync(url='http://localhost:8086', token='my-token', org='my-org') as client:
            # Initialize async OrganizationsService
            organizations_service = OrganizationsService(api_client=client.api_client)

            # Find organization with name 'my-org'
            organizations = await organizations_service.get_orgs(org='my-org')
            for organization in organizations.orgs:
                print(f'name: {organization.name}, id: {organization.id}')


    if __name__ == "__main__":
        asyncio.run(main())


Proxy and redirects
"""""""""""""""""""

You can configure the client to tunnel requests through an HTTP proxy.
The following proxy options are supported:

- ``proxy`` - Set this to configure the http proxy to be used, ex. ``http://localhost:3128``
- ``proxy_headers`` - A dictionary containing headers that will be sent to the proxy. Could be used for proxy authentication.

.. code-block:: python

   from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


   async with InfluxDBClientAsync(url="http://localhost:8086",
                                  token="my-token",
                                  org="my-org",
                                  proxy="http://localhost:3128") as client:

.. note::

    If your proxy notify the client with permanent redirect (``HTTP 301``) to **different host**.
    The client removes ``Authorization`` header, because otherwise the contents of ``Authorization`` is sent to third parties
    which is a security vulnerability.

Client automatically follows HTTP redirects. The default redirect policy is to follow up to ``10`` consecutive requests. The redirects can be configured via:

- ``allow_redirects`` - If set to ``False``, do not follow HTTP redirects. ``True`` by default.
- ``max_redirects`` - Maximum number of HTTP redirects to follow. ``10`` by default.


.. marker-asyncio-end

Logging
^^^^^^^
.. marker-logging-start

The client uses Python's `logging <https://docs.python.org/3/library/logging.html>`__ facility for logging the library activity. The following logger categories are exposed:

- ``influxdb_client.client.influxdb_client``
- ``influxdb_client.client.influxdb_client_async``
- ``influxdb_client.client.write_api``
- ``influxdb_client.client.write_api_async``
- ``influxdb_client.client.write.retry``
- ``influxdb_client.client.write.dataframe_serializer``
- ``influxdb_client.client.util.multiprocessing_helper``
- ``influxdb_client.client.http``
- ``influxdb_client.client.exceptions``

The default logging level is `warning` without configured logger output. You can use the standard logger interface to change the log level and handler:

.. code-block:: python

    import logging
    import sys

    from influxdb_client import InfluxDBClient

    with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
        for _, logger in client.conf.loggers.items():
            logger.setLevel(logging.DEBUG)
            logger.addHandler(logging.StreamHandler(sys.stdout))

Debugging
"""""""""

For debug purpose you can enable verbose logging of HTTP requests and set the ``debug`` level to all client's logger categories by:

.. code-block:: python

    client = InfluxDBClient(url="http://localhost:8086", token="my-token", debug=True)

.. note::

    Both HTTP request headers and body will be logged to standard output.

.. marker-logging-end

Local tests
-----------

.. code-block:: console

    # start/restart InfluxDB2 on local machine using docker
    ./scripts/influxdb-restart.sh

    # install requirements
    pip install -e . --user
    pip install -e .\[extra\] --user
    pip install -e .\[test\] --user

    # run unit & integration tests
    pytest tests


Contributing
------------

Bug reports and pull requests are welcome on GitHub at `https://github.com/influxdata/influxdb-client-python <https://github.com/influxdata/influxdb-client-python>`_.

License
-------

The gem is available as open source under the terms of the `MIT License <https://opensource.org/licenses/MIT>`_.
