Skip to main content

Asynchronous Python client for InfluxDB

Project description

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


To install the latest release:

$ pip install aioinflux

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

$ pip install git+


Aioinflux supports Python 3.6+ ONLY. For older Python versions please use the official Python client

Third-party library dependencies are: aiohttp for all HTTP request handling and pandas for DataFrame reading/writing support.



This sums most of what you can do with aioinflux:

import asyncio
from aioinflux import InfluxDBClient

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

client = InfluxDBClient(db='testdb')

coros = [client.create_database(db='testdb'),
         client.query('SELECT value FROM cpu_load_short')]

loop = asyncio.get_event_loop()
results = loop.run_until_complete(asyncio.gather(*coros))
for result in results:

Client modes

Despite the library’s name, InfluxDBClient can also run in non-async modes. Available modes are: async (default), blocking and dataframe.

Example using blocking mode:

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

See Retrieving DataFrames for dataframe mode usage.

Writing data

Input data can be:

  1. A string properly formatted in InfluxDB’s line protocol
  2. A 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 at Beware that serialization is not highly optimized (cythonization PRs are welcome!) and may become a bottleneck depending on your application.

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

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.
  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, or bool or any equivalent type (e.g. Numpy types).

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:

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

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:

                                       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. Additional tags can also be passed using arbitrary keyword arguments.


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 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 they strings data, measurement and tag_columns).

See InfluxDBClient.write docstring for details.

Querying data

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

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:

{'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}]}

Retrieving DataFrames

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

                                  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

Mode can be chosen not only during object instantiation but also by simply changing the mode attribute.

Chunked responses

Aioinfux support 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.

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

Iterating responses

In async and blocking modes, InfluxDBClient.query returns a parsed JSON response from InfluxDB. In order to easily iterate over that JSON response point by point, Aioinflux provides the iter_resp generator:

from aioinflux import iter_resp

r = client.query('SELECT * from h2o_quality LIMIT 10')
for i in iter_resp(r):
[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']

iter_resp can also be used with chunked responses:

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

By default, iter_resp 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'}.

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

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 aioinflux.set_query_pattern 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:

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.

Other functionality


Aioinflux supports basic HTTP authentication provided by aiohttp.BasicAuth. Simply pass username and password when instantiating InfluxDBClient:

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

Unix domain sockets

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

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

See aiohttp.UnixConnector for details.


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

client = InfluxDBClient(host='', ssl=True)

Database selection

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

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.

Switching modes

After the instantiation of the InfluxDBClient object, database can be switched on-the-fly by changing the mode attribute:

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


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

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

import logging


For further information about logging, please refer to the official documentation.


Since InfluxDB exposes all its functionality through an HTTP 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 are used in order to provide convenient access to commonly used query patterns. See the Query patterns section for details.


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 :)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for aioinflux, version 0.2.0
Filename, size File type Python version Upload date Hashes
Filename, size aioinflux-0.2.0-py3-none-any.whl (21.9 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size aioinflux-0.2.0.tar.gz (18.9 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page