aioinflux 0.3.3

aioinflux
=========
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Asynchronous Python client for InfluxDB. Built on top of
|aiohttp|_ and |asyncio|_.

InfluxDB is an open-source distributed time series database. Find more
about InfluxDB at http://influxdata.com/

.. |asyncio| replace:: ``asyncio``
.. _asyncio: https://docs.python.org/3/library/asyncio.html
.. |aiohttp| replace:: ``aiohttp``
.. _aiohttp: https://github.com/aio-libs/aiohttp


Installation
------------

To install the latest release:

.. code:: bash

$ pip install aioinflux
$ pip install aioinflux[pandas] # For DataFrame parsing support

The library is still in beta, so you may also want to install the latest version from
the development branch:

.. code:: bash

$ pip install git+https://github.com/plugaai/aioinflux@dev

Dependencies
~~~~~~~~~~~~

Aioinflux supports Python 3.6+ **ONLY**. For older Python versions
please use the `official Python client`_.
However, there is `some discussion <https://github.com/plugaai/aioinflux/issues/10>`_
regarding Pypy/Python 3.5 support.

The main third-party library dependency is |aiohttp|_, for all HTTP
request handling. and |pandas|_ for ``DataFrame`` reading/writing support.

There are currently no plans to support other HTTP libraries besides ``aiohttp``.
If ``aiohttp`` + ``asyncio`` is not your soup, see `Alternatives <#alternatives>`__.

.. |pandas| replace:: ``pandas``
.. _pandas: https://github.com/pandas-dev/pandas
.. _`official Python Client`: https://github.com/influxdata/influxdb-python

Usage
-----

TL;DR:
~~~~~~

This sums most of what you can do with ``aioinflux``:

.. code:: python

import asyncio
from aioinflux import InfluxDBClient

point = {
'time': '2009-11-10T23:00:00Z',
'measurement': 'cpu_load_short',
'tags': {'host': 'server01',
'region': 'us-west'},
'fields': {'value': 0.64}
}

async def main():
client = InfluxDBClient(db='testdb')
await client.create_database(db='testdb')
await client.write(point)
resp = await client.query('SELECT value FROM cpu_load_short')
print(resp)


asyncio.get_event_loop().run_until_complete(main())

Client modes
~~~~~~~~~~~~

Despite the library's name, :class:`~aioinflux.client.InfluxDBClient` can also run in non-async
mode (a.k.a ``blocking``) mode. It can be useful for debugging and exploratory
data analysis.

The running mode for can be switched on-the-fly by changing the ``mode`` attribute:

.. code:: python

client = InfluxDBClient(mode='blocking')
client.mode = 'async'

The ``blocking`` mode is implemented through a decorator that automatically runs coroutines on
the event loop as soon as they are generated.
Usage is almost the same as in the ``async`` mode, but without the need of using ``await`` and
being able to run from outside of a coroutine function:

.. code:: python

client = InfluxDBClient(db='testdb', mode='blocking')
client.ping()
client.write(point)
client.query('SELECT value FROM cpu_load_short')


Writing data
~~~~~~~~~~~~

Input data can be:

1. A string properly formatted in InfluxDB's line protocol
2. A mapping (e.g. dictionary) containing the following keys: ``measurement``, ``time``, ``tags``, ``fields``
3. A Pandas ``DataFrame`` with a ``DatetimeIndex``
4. An iterable of one of the above

Input data in formats 2-4 are parsed into the `line protocol`_ before being written to InfluxDB.
All parsing functionality is located in the |serialization|_ module.
Beware that serialization is not highly optimized (cythonization PRs are welcome!) and may become
a bottleneck depending on your application. It is however, `reasonably faster`_ than
InfluxDB's official Python client.

The ``write`` method returns ``True`` when successful and raises an
``InfluxDBError`` otherwise.

.. _`line protocol`: https://docs.influxdata.com/influxdb/latest/write_protocols/line_protocol_reference/
.. _`reasonably faster`: https://gist.github.com/gusutabopb/42550f0f07628ba61b0ed6322f02855b
.. |serialization| replace:: ``serialization.py``
.. _serialization: aioinflux/serialization.py

Writing dictionary-like objects
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Aioinflux accepts any dictionary-like object (mapping) as input.
However, that dictionary must be properly formatted and contain the
following keys:

1) **measurement**: Optional. Must be a string-like object. If
omitted, must be specified when calling ``InfluxDBClient.write``
by passing a ``measurement`` argument.
2) **time**: Optional. The value can be ``datetime.datetime``,
date-like string (e.g., ``2017-01-01``, ``2009-11-10T23:00:00Z``) or
anything else that can be parsed by Pandas' |Timestamp|_ class initializer
(or |ciso8601|_ if Pandas is not available). See `below <#note-on-timestamps-and-timezones>`_ for details.
Use of ISO 8601 compliant strings is highly recommended.
3) **tags**: Optional. This must contain another mapping of field
names and values. Both tag keys and values should be strings.
4) **fields**: Mandatory. This must contain another mapping of field
names and values. Field keys should be strings. Field values can be
``float``, ``int``, ``str``, ``bool`` or ``None`` or any its subclasses.
Attempting to use Numpy types will cause errors as ``np.int64``, ``np.float64``, etc are not
subclasses of Python's builti-in numeric types.
Use dataframes for writing data using Numpy types.

.. |Timestamp| replace:: ``Timestamp``
.. _Timestamp: https://pandas.pydata.org/pandas-docs/stable/timeseries.html
.. |ciso8601| replace:: ``ciso8601``
.. _ciso8601: https://github.com/closeio/ciso8601/

Any fields other then the above will be ignored when writing data to
InfluxDB.

A typical dictionary-like point would look something like the following:

.. code:: python

{'time': '2009-11-10T23:00:00Z',
'measurement': 'cpu_load_short',
'tags': {'host': 'server01', 'region': 'us-west'},
'fields': {'value1': 0.64, 'value2': True, 'value3': 10}}

Note on timestamps and timezones
""""""""""""""""""""""""""""""""

Working with timezones in computing tends to be quite messy.
To avoid such problems, the `broadly agreed`_ upon idea is to store
timestamps in UTC. This is how both InfluxDB and Pandas treat timestamps internally.

Pandas and many other libraries also assume all input timestamps are in UTC unless otherwise
explicitly noted. Aioinflux does the same and assumes any timezone-unaware ``datetime`` object
or datetime-like strings is in UTC.
Aioinflux does not raise any warnings when timezone-unaware input is passed
and silently assumes it to be in UTC.

.. _`broadly agreed`: http://lucumr.pocoo.org/2011/7/15/eppur-si-muove/

Writing DataFrames
^^^^^^^^^^^^^^^^^^

Aioinflux also accepts Pandas dataframes as input. The only requirements
for the dataframe is that the index **must** be of type
``DatetimeIndex``. Also, any column whose ``dtype`` is ``object`` will
be converted to a string representation.

A typical dataframe input should look something like the following:

.. code:: text

LUY BEM AJW tag
2017-06-24 08:45:17.929097+00:00 2.545409 5.173134 5.532397 B
2017-06-24 10:15:17.929097+00:00 -0.306673 -1.132941 -2.130625 E
2017-06-24 11:45:17.929097+00:00 0.894738 -0.561979 -1.487940 B
2017-06-24 13:15:17.929097+00:00 -1.799512 -1.722805 -2.308823 D
2017-06-24 14:45:17.929097+00:00 0.390137 -0.016709 -0.667895 E

The measurement name must be specified with the ``measurement`` argument
when calling ``InfluxDBClient.write``.
Columns of dtype ``pd.Categorical`` will be automatically treated as tags.
Columns whose dtype is not ``pd.Categorical`` but should be treated as tags
must be specified by passing a sequence as the ``tag_columns`` argument.
Additional tags (not present in the actual dataframe) can also be passed using arbitrary keyword arguments.

**Example:**

.. code:: python

client = InfluxDBClient(db='testdb', mode='blocking')
client.write(df, measurement='prices', tag_columns=['tag'], asset_class='equities')

In the example above, ``df`` is the dataframe we are trying to write to
InfluxDB and ``measurement`` is the measurement we are writing to.

``tag_columns`` is in an optional iterable telling which of the
dataframe columns should be parsed as tag values. If ``tag_columns`` is
not explicitly passed, all columns in the dataframe whose dtype is not
``pd.Categorical`` will be treated as InfluxDB field values.

Any other keyword arguments passed to ``InfluxDBClient.write`` are
treated as extra tags which will be attached to the data being written
to InfluxDB. Any string which is a valid `InfluxDB identifier`_ and
valid `Python identifier`_ can be used as an extra tag key (with the
exception of the strings ``data``, ``measurement`` and ``tag_columns``).

See ``InfluxDBClient.write`` docstring for details.

.. _`InfluxDB identifier`: https://docs.influxdata.com/influxdb/latest/query_language/spec/#identifiers
.. _`Python identifier`: https://docs.python.org/3/reference/lexical_analysis.html#identifiers

Querying data
~~~~~~~~~~~~~

Querying data is as simple as passing an InfluxDB query string to
``InfluxDBClient.query``:

.. code:: python

client.query('SELECT myfield FROM mymeasurement')

The result (in ``blocking`` and ``async`` modes) is a dictionary
containing the parsed JSON data returned by the InfluxDB `HTTP API`_:

.. _`HTTP API`: https://docs.influxdata.com/influxdb/latest/guides/querying_data/#querying-data-using-the-http-api

.. code:: python

{'results': [{'series': [{'columns': ['time', 'Price', 'Volume'],
'name': 'mymeasurement',
'values': [[1491963424224703000, 5783, 100],
[1491963424375146000, 5783, 200],
[1491963428374895000, 5783, 100],
[1491963429645478000, 5783, 1100],
[1491963429655289000, 5783, 100],
[1491963437084443000, 5783, 100],
[1491963442274656000, 5783, 900],
[1491963442274657000, 5782, 5500],
[1491963442274658000, 5781, 3200],
[1491963442314710000, 5782, 100]]}],
'statement_id': 0}]}


Output formats
^^^^^^^^^^^^^^

When querying data, ``InfluxDBClient`` can return data in one of the following formats:

1) ``raw``: Default. Returns the a dictionary containing the JSON response received from InfluxDB.
2) ``iterable``: Wraps the JSON response in a ``InfluxDBResult`` or ``InfluxDBChunkedResult``
object. This object main purpose is to facilitate iteration of data.
See `Iterating responses <#iterating-responses>`__ for details.
3) ``dataframe``: Parses the result into a Pandas dataframe or a dictionary of dataframes.
See `Retrieving DataFrames <#retrieving-dataframes>`__ for details.


The output format for can be switched on-the-fly by changing the ``output`` attribute:

.. code:: python

client = InfluxDBClient(output='dataframe')
client.mode = 'raw'


Retrieving DataFrames
^^^^^^^^^^^^^^^^^^^^^

When the client is in ``dataframe`` mode, ``InfluxDBClient.query`` will
return a Pandas ``DataFrame``:


.. code:: text

Price Volume
2017-04-12 02:17:04.224703+00:00 5783 100
2017-04-12 02:17:04.375146+00:00 5783 200
2017-04-12 02:17:08.374895+00:00 5783 100
2017-04-12 02:17:09.645478+00:00 5783 1100
2017-04-12 02:17:09.655289+00:00 5783 100
2017-04-12 02:17:17.084443+00:00 5783 100
2017-04-12 02:17:22.274656+00:00 5783 900
2017-04-12 02:17:22.274657+00:00 5782 5500
2017-04-12 02:17:22.274658+00:00 5781 3200
2017-04-12 02:17:22.314710+00:00 5782 100


When generating dataframes, InfluxDB types are mapped to the following Numpy/Pandas dtypes:

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

* - InfluxDB type
- Dataframe column ``dtype``
* - Float
- ``float64``
* - Integer
- ``int64``
* - String
- ``object``
* - String (tag values)
- ``CategoricalDtype``
* - Boolean
- ``bool``
* - Timestamp
- ``datetime64``


Chunked responses
^^^^^^^^^^^^^^^^^
Aioinflux supports InfluxDB chunked queries. Passing ``chunked=True`` when calling
``InfluxDBClient.query``, returns an ``AsyncGenerator`` object, which can asynchronously
iterated. Using chunked requests allows response processing to be partially done before
the full response is retrieved, reducing overall query time.

.. code:: python

chunks = await client.query("SELECT * FROM mymeasurement", chunked=True)
async for chunk in chunks:
# do something
await process_chunk(...)

Chunked responses are not supported when using the ``dataframe`` output format.

Iterating responses
^^^^^^^^^^^^^^^^^^^

By default, ``InfluxDBClient.query`` returns a parsed JSON response from InfluxDB.
In order to easily iterate over that JSON response point by point, Aioinflux
provides the ``iterpoints`` function, which returns a generator object:

.. code:: python

from aioinflux import iterpoints

r = client.query('SELECT * from h2o_quality LIMIT 10')
for i in iterpoints(r):
print(i)

.. code:: text

[1439856000000000000, 41, 'coyote_creek', '1']
[1439856000000000000, 99, 'santa_monica', '2']
[1439856360000000000, 11, 'coyote_creek', '3']
[1439856360000000000, 56, 'santa_monica', '2']
[1439856720000000000, 65, 'santa_monica', '3']

``iterpoints`` can also be used with chunked responses:

.. code:: python

chunks = await client.query('SELECT * from h2o_quality', chunked=True)
async for chunk in chunks:
for point in iterpoints(chunk):
# do something

By default, the generator returned by ``iterpoints`` yields a plain list of values without
doing any expensive parsing.
However, in case a specific format is needed, an optional ``parser`` argument can be passed.
``parser`` is a function that takes the raw value list for each data point and an additional
metadata dictionary containing all or a subset of the following:
``{'columns', 'name', 'tags', 'statement_id'}``.


.. code:: python

r = await client.query('SELECT * from h2o_quality LIMIT 5')
for i in iterpoints(r, lambda x, meta: dict(zip(meta['columns'], x))):
print(i)

.. code:: text

{'time': 1439856000000000000, 'index': 41, 'location': 'coyote_creek', 'randtag': '1'}
{'time': 1439856000000000000, 'index': 99, 'location': 'santa_monica', 'randtag': '2'}
{'time': 1439856360000000000, 'index': 11, 'location': 'coyote_creek', 'randtag': '3'}
{'time': 1439856360000000000, 'index': 56, 'location': 'santa_monica', 'randtag': '2'}
{'time': 1439856720000000000, 'index': 65, 'location': 'santa_monica', 'randtag': '3'}

Besides being explicitly with a raw response, ``iterpoints`` is also be used "automatically"
by ``InfluxDBResult`` and ``InfluxDBChunkedResult`` when using ``iterable`` mode:

.. code:: python

client.output = 'iterable'
# Returns InfluxDBResult object
r = client.query('SELECT * from h2o_quality LIMIT 10')
for i in r:
# do something

# Returns InfluxDBChunkedResult object
r = await client.query('SELECT * from h2o_quality', chunked=True)
async for i in r:
# do something

# Returns InfluxDBChunkedResult object
r = await client.query('SELECT * from h2o_quality', chunked=True)
async for chunk in r.iterchunks():
# do something with JSON chunk


Getting tag key/value info
^^^^^^^^^^^^^^^^^^^^^^^^^^
In order to properly parse dataframes, ``InfluxDBClient`` internally uses the ``get_tag_info``,
which basically sends a series of ``SHOW TAG KEYS`` and ``SHOW TAG VALUES`` queries and gathers
key/value information for all measurements of the active database in a dictionary.

Query patterns
^^^^^^^^^^^^^^

Aioinflux provides a wrapping mechanism around ``InfluxDBClient.query`` in
order to provide convenient access to commonly used query patterns.

Query patterns are query strings containing optional named "replacement fields"
surrounded by curly braces ``{}``, just as in |str_format|_.
Replacement field values are defined by keyword arguments when calling the method
associated with the query pattern. Differently from plain |str_format|, positional
arguments are also supported and can be mixed with keyword arguments.

Aioinflux built-in query patterns are defined here_.
Users can also dynamically define additional query patterns by using
the |set_qp|_ helper function.
User-defined query patterns have the disadvantage of not being shown for
auto-completion in IDEs such as Pycharm.
However, they do show up in dynamic environments such as Jupyter.
If you have a query pattern that you think will used by many people and should be built-in,
please submit a PR.

Built-in query pattern examples:

.. code:: python

client.create_database(db='foo') # CREATE DATABASE {db}
client.drop_measurement('bar') # DROP MEASUREMENT {measurement}'
client.show_users() # SHOW USERS

# Positional and keyword arguments can be mixed
client.show_tag_values_from('bar', key='spam') # SHOW TAG VALUES FROM {measurement} WITH key = "{key}"

Please refer to InfluxDB documentation_ for further query-related information.

.. _here: aioinflux/client.py#L330
.. _documentation: https://docs.influxdata.com/influxdb/latest/query_language/
.. |str_format| replace:: ``str_format()``
.. _str_format: https://docs.python.org/3/library/string.html#formatstrings
.. |set_qp| replace:: ``InfluxDBClient.set_query_pattern``
.. _set_qp: aioinflux/client.py#L345

Other functionality
~~~~~~~~~~~~~~~~~~~

Authentication
^^^^^^^^^^^^^^

Aioinflux supports basic HTTP authentication provided by |basic_auth|_.
Simply pass ``username`` and ``password`` when instantiating ``InfluxDBClient``:

.. code:: python

client = InfluxDBClient(username='user', password='pass)


.. |basic_auth| replace:: ``aiohttp.BasicAuth``
.. _basic_auth: https://docs.aiohttp.org/en/stable/client_reference.html#basicauth


Unix domain sockets
^^^^^^^^^^^^^^^^^^^

If your InfluxDB server uses UNIX domain sockets you can use ``unix_socket``
when instantiating ``InfluxDBClient``:

.. code:: python

client = InfluxDBClient(unix_socket='/path/to/socket')

See |unix_connector|_ for details.

.. |unix_connector| replace:: ``aiohttp.UnixConnector``
.. _unix_connector: https://docs.aiohttp.org/en/stable/client_reference.html#aiohttp.UnixConnector


HTTPS/SSL
^^^^^^^^^
Aioinflux/InfluxDB use HTTP by default, but HTTPS can be used by passing ``ssl=True``
when instantiating ``InfluxDBClient``:


.. code:: python

client = InfluxDBClient(host='my.host.io', ssl=True)


Database selection
^^^^^^^^^^^^^^^^^^

After the instantiation of the ``InfluxDBClient`` object, database
can be switched by changing the ``db`` attribute:

.. code:: python

client = InfluxDBClient(db='db1')
client.db = 'db2'

Beware that differently from some NoSQL databases (such as MongoDB),
InfluxDB requires that a databases is explicitly created (by using the
|CREATE_DATABASE|_ query) before doing any operations on it.

.. |CREATE_DATABASE| replace:: ``CREATE DATABASE``
.. _`CREATE_DATABASE`: https://docs.influxdata.com/influxdb/latest/query_language/database_management/#create-database


Debugging
^^^^^^^^^

If you are having problems while using Aioinflux, enabling logging might be useful.

Below is a simple way to setup logging from your application:

.. code:: python

import logging

logging.basicConfig()
logging.getLogger('aioinflux').setLevel(logging.DEBUG)

For further information about logging, please refer to the
`official documentation <https://docs.python.org/3/library/logging.html>`__.


Implementation
--------------

Since InfluxDB exposes all its functionality through an `HTTP
API <https://docs.influxdata.com/influxdb/latest/tools/api/>`__,
``InfluxDBClient`` tries to be nothing more than a thin and simple
wrapper around that API.

The InfluxDB HTTP API exposes exactly three endpoints/functions:
``ping``, ``write`` and ``query``.

``InfluxDBClient`` merely wraps these three functions and provides
some parsing functionality for generating line protocol data (when
writing) and parsing JSON responses (when querying).

Additionally,
`partials <https://en.wikipedia.org/wiki/Partial_application>`__ are
used in order to provide convenient access to commonly used query
patterns. See the `Query patterns <#query-patterns>`__
section for details.

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

| To contribute, fork the repository on GitHub, make your changes and
submit a pull request.
| Aioinflux is not a mature project yet, so just simply raising issues
is also greatly appreciated :)

Alternatives
------------

- `InfluxDB-Python <https://github.com/influxdata/influxdb-python>`__: The official
blocking-only client. Based on Requests.
- `influx-sansio <https://github.com/miracle2k/influx-sansio>`__: Fork of aioinflux
using curio/trio and asks as a backend.