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/
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+https://github.com/plugaai/aioinflux@dev
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',
measurement='cpu_load_short',
tags={'host': 'server01',
'region': 'us-west'},
fields={'value': 0.64})
client = InfluxDBClient(db='testdb')
coros = [client.create_database(db='testdb'),
client.write(point),
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:
print(result)
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.ping()
client.write(point)
client.query('SELECT value FROM cpu_load_short')
See Retrieving DataFrames for dataframe
mode usage.
Input data can be:
- A string properly formatted in InfluxDB's line protocol
- A dictionary containing the following keys:
measurement
,time
,tags
,fields
- A Pandas
DataFrame
with aDatetimeIndex
- 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 serialization.py
.
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.
Aioinflux accepts any dictionary-like object (mapping) as input. However, that dictionary must be properly formatted and contain the following keys:
- measurement: Optional. Must be a string-like object. If
omitted, must be specified when calling
InfluxDBClient.write
by passing ameasurement
argument. - 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. - tags: Optional. This must contain another mapping of field names and values. Both tag keys and values should be strings.
- fields: Mandatory. This must contain another mapping of field
names and values. Field keys should be strings. Field values can be
float
,int
,str
, orbool
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}}
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.
Example:
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 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}]}
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.
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(...)
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):
print(i)
[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))):
print(i)
{'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'}
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.
Aioinflux supports basic HTTP authentication provided by aiohttp.BasicAuth
.
Simply pass username
and password
when instantiating InfluxDBClient
:
client = InfluxDBClient(username='user', password='pass)
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='my.host.io', ssl=True)
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.
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
logging.basicConfig()
logging.getLogger('aioinflux').setLevel(logging.DEBUG)
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.