User Guide


This sums most of what you can do with aioinflux:

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


Client modes

Despite the library’s name, 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:

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:

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


The need for the blocking mode has been somewhat supplanted by the new async REPL available with the release of IPython 7.0. See this blog post for details.

If you are having issues running blocking mode with recent Python/IPython versions, see this issue for other possible workarounds.

Writing data

To write data to InfluxDB, use InfluxDBClient’s write() method. Successful writes will return True. In case some error occurs InfluxDBWriteError exception will be raised.

Input data to write() can be:

  1. A mapping (e.g. dict) containing the keys: measurement, time, tags, fields

  2. A pandas.DataFrame with a DatetimeIndex

  3. A user defined class decorated w/ lineprotocol() (recommended, see below)

  4. A string (str or bytes) properly formatted in InfluxDB’s line protocol

  5. An iterable of one of the above

Input data in formats 1-3 are serialized into the line protocol before being written to InfluxDB. str or bytes are assumed to already be in line protocol format and are inserted into InfluxDB as they are. All functionality regarding JSON parsing (InfluxDB’s only output format) and serialization to line protocol (InfluxDB’s only input format) is located in the serialization subpackage.

Beware that serialization is not highly optimized (C extensions / cythonization PRs are welcome!) and may become a bottleneck depending on your application’s performance requirements. It is, however, reasonably (3-10x) faster than InfluxDB’s official Python client.

Writing dictionary-like objects


This is the same format as the one used by InfluxDB’s official Python client and is implemented in Aioinflux for compatibility purposes only. Using dictionaries to write data to InfluxDB is slower and more error-prone than the other methods provided by Aioinflux and therefore discouraged.

Aioinflux accepts any dictionary-like object (mapping) as input. The dictionary must contain the following keys:

  1. measurement: Optional. Must be a string-like object. If omitted, must be specified when calling 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. See Pandas documentation for details. If Pandas is not available, ciso8601 is used instead for date-like string parsing.

  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 built-in numeric types. Use dataframes for writing data using Numpy types.

Any keys 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}}


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.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.

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 write(). Columns that 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.


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 DatetimeIndex will be treated as InfluxDB field values.

Any other keyword arguments passed to 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 API reference for details.

Writing user-defined class objects

Changed in version 0.5.0.

Aioinflux can add write any arbitrary user-defined class to InfluxDB through the use of the lineprotocol() decorator. This decorator monkey-patches an existing class and adds a to_lineprotocol method, which is used internally by Aioinflux to serialize the class data into a InfluxDB-compatible format. In order to generate to_lineprotocol, a typed schema must be defined using type hints in the form of type annotations or a schema dictionary.

This is the fastest and least error-prone method of writing data into InfluxDB provided by Aioinflux.

We recommend using lineprotocol() with NamedTuple:

from aioinflux import *
from typing import NamedTuple

class Trade(NamedTuple):
    timestamp: TIMEINT
    instrument: TAGENUM
    source: TAG
    side: TAG
    price: FLOAT
    size: INT
    trade_id: STR

Alternatively, the functional form of namedtuple() can also be used:

from collections import namedtuple

schema = dict(

# Create class
Trade = namedtuple('Trade', schema.keys())

# Monkey-patch existing class and add ``to_lineprotocol``
Trade = lineprotocol(Trade, schema=schema)

Dataclasses (or any other user-defined class) can be used as well:

from dataclasses import dataclass

class Trade:
    timestamp: TIMEINT
    instrument: TAGENUM
    source: TAG
    side: TAG
    price: FLOAT
    size: INT
    trade_id: STR

If you want to preserve type annotations for another use, you can pass your serialization schema as a dictionary as well:

@lineprotocol(schema=dict(timestamp=TIMEINT, value=FLOAT))
class MyTypedClass:
    timestamp: int
    value: float

 # {'timestamp': <class 'int'>, 'value': <class 'float'>}

 MyTypedClass(1547710904202826000, 2.1).to_lineprotocol()
 # b'MyTypedClass value=2.1 1547710904202826000'

The modified class will have a dynamically generated to_lineprotocol method which generates a line protocol representation of the data contained by the object:

trade = Trade(

# b'Trade,instrument=AAPL,source=NASDAQ,side=BUY price=219.23,size=100i,trade_id="34a1e085-3122-429c-9662-7ce82039d287" 1540184368785116000'

Calling to_lineprotocol by the end-user is not necessary but may be useful for debugging.

to_lineprotocol is automatically used by write() when present.

client = InfluxDBClient()
await client.write(trade)  # True

User-defined class schema/type annotations

In Aioinflux, InfluxDB types (and derived types) are represented by TypeVar defined in aioinflux.serialization.usertype module. All schema types (type annotations) must be one of those types. The types available are based on the native types of InfluxDB (see the InfluxDB docs for details), with some extra types to help the serialization to line protocol and/or allow more flexible usage (such as the use of Enum objects).




Optional. If missing, the measurement becomes the class name


Timestamp is a nanosecond UNIX timestamp


Timestamp is a datetime string (somewhat compliant to ISO 8601)


Timestamp is a datetime.datetime (or subclasses such as pandas.Timestamp)


Treats field as an InfluxDB tag


Same as TAG but allows the use of Enum


Boolean field


Integer field


Float field


String field


Same as STR but allows the use of Enum

TAG* types are optional. One and only one TIME* type must present. At least ONE field type be present.

@lineprotocol options

The lineprotocol() function/decorator provides some options to customize how object serialization is performed. See the API reference for details.


Serialization using lineprotocol() is about 3x faster than dictionary-like objects (or about 10x faster than the official Python client). See this notebook for a simple benchmark.

Beware that setting rm_none=True can have substantial performance impact especially when the number of fields/tags is very large (20+).

Querying data

Querying data is as simple as passing an InfluxDB query string to query():

await client.query('SELECT myfield FROM mymeasurement')

By default, this returns JSON data:

{'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}]}

See InfluxDB official docs for more on the InfluxDB’s HTTP API specifics.

Output formats

When using, query() data can return data in one of the following formats:

  1. json: Default. Returns a dictionary representation of the JSON response received from InfluxDB.

  2. dataframe: Parses the result into a Pandas dataframe(s). See Retrieving DataFrames for details.

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

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

Beware that when passing chunked=True, the result type will be an async generator. See Chunked responses for details.

Retrieving DataFrames

When the client is in dataframe mode, query() will usually 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


On multi-statement queries and/or statements that return multiple InfluxDB series (such as a GROUP by "tag" query), a list of dictionaries of dataframes will be returned. Aioinflux generates a dataframe for each series contained in the JSON returned by InfluxDB. See this Github issue for further discussion.

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

InfluxDB type

Dataframe column dtype











Chunked responses

Aioinflux supports InfluxDB chunked queries. Passing chunked=True when calling 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 (at least in theory - your mileage may vary).

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

When using chunked responses with dataframe output, the following construct may be useful:

cursor = await client.query("SELECT * FROM mymeasurement", chunked=True)
df = pd.concat([i async for i in cursor])

If you need to keep track of when the chunks are being returned, consider setting up a logging handler at DEBUG level (see Debugging for details).

See the InfluxDB official docs for more on chunked responses.

Iterating responses

By default, 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:

from aioinflux import iterpoints

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

iterpoints() 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 iterpoints(chunk):
        # do something

Using custom parsers

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/callable that takes data point values and, optionally, a meta parameter containing which takes a dictionary containing all or a subset of the following: {'columns', 'name', 'tags', 'statement_id'}.

  • Example using a regular function and meta

r = await client.query('SELECT * from h2o_quality LIMIT 5')
for i in iterpoints(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'}
from collections import namedtuple
nt = namedtuple('MyPoint', ['time', 'index', 'location', 'randtag'])

r = await client.query('SELECT * from h2o_quality LIMIT 5')
for i in iterpoints(r, parser=nt):
MyPoint(time=1439856000000000000, index=41, location='coyote_creek', randtag='1')
MyPoint(time=1439856000000000000, index=99, location='santa_monica', randtag='2')
MyPoint(time=1439856360000000000, index=11, location='coyote_creek', randtag='3')
MyPoint(time=1439856360000000000, index=56, location='santa_monica', randtag='2')
MyPoint(time=1439856720000000000, index=65, location='santa_monica', randtag='3')

Caching query results

Changed in version v0.10.0.

Caching can is useful in highly iterative/repetitive workloads (i.e.: machine learning / quantitative finance model tuning) that constantly query InfluxDB for the same historical data repeatedly. By saving query results locally, load on your InfluxDB instance can be greatly reduced.

Aioinflux used to provide a built-in caching local functionality using Redis. However, due to low perceived usage, vendor lock-in (Redis) and extra complexity added to Aioinflux, it was removed.

Here we explain how to add a simple caching layer using pickle. The example below caches dataframes as compressed pickle files on disk. It can be easily modified to use your preferred caching strategy, such as using different serialization, compression, cache key generation, etc. See function docstrings, code comments below for more details.

  • Uncached code:

from aioinflux import InfluxDBClient

c = InfluxDBClient(output='dataframe')
q = """
    SELECT * FROM executions
    WHERE product_code='BTC_JPY'
    AND time >= '2020-05-22'
    AND time < '2020-05-23'
# If this query is repeated, it will keep hitting InfluxDB,
# increasing the load on instance and using extra bandwidth
df = await c.query(q)
  • Caching code:

import re
import hashlib
import pathlib
import pandas as pd

def _hash_query(q: str) -> str:
    """Normalizes and hashes the query to generate a caching key"""
    q = re.sub("\s+", " ", q).strip().lower().encode()
    return hashlib.sha1(q).hexdigest()

async def fetch(influxdb: InfluxDBClient, q: str) -> Tuple[pd.DataFrame, bool]:
    """Tries to see if query is cached, else fetches data from the database.

    Returns a tuple containing the query results and a boolean indicating whether or not
    the data came from local cache or directly from InfluxDB
    p = pathlib.Path(_hash_query(q))
    if p.exists():
        return pd.read_pickle(p, compression="xz"), True
    df = await influxdb.query(q)
    df.to_pickle(str(p), compression="xz")
    return df, False
  • Caching code usage:

df, cached = await fetch(c, q)
print(cached)  # False - cache miss

df, cached = await fetch(c, q)
print(cached)  # True - cache hit

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.

Custom timeouts



Other aiohttp functionality


Explain how to customize aiohttp.ClientSession creation


Aioinflux/InfluxDB uses HTTP by default, but HTTPS can be used by passing ssl=True when instantiating InfluxDBClient. If you are acessing your your InfluxDB instance over the public internet, setting up HTTPS is strongly recommended.

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.


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.