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
$ 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:
$ 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. However, there is some discussion 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.
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():
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())
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.ping()
client.write(point)
client.query('SELECT value FROM cpu_load_short')
Input data can be:
- A string properly formatted in InfluxDB's line protocol
- A mapping (e.g. 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 in the serialization.py
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.
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 (orciso8601
if Pandas is not available). Use of ISO 8601 compliant strings is highly recommended. - 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
,bool
orNone
or any its subclasses. Attempting to use Numpy types will cause errors asnp.int64
,np.float64
, etc are not subclasses of Python's builti-in numeric types. Use dataframes for writing data using 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
.
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:
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.
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 querying data, InfluxDBClient
can return data in one of the following formats:
raw
: Default. Returns the a dictionary containing the JSON response received from InfluxDB.iterable
: Wraps the JSON response in aInfluxDBResult
orInfluxDBChunkedResult
object. This object main purpose is to facilitate iteration of data. See Iterating responses for details.dataframe
: Parses the result into a Pandas dataframe or a dictionary of dataframes. 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 = 'raw'
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
When generating dataframes, InfluxDB types are mapped to the following Numpy/Pandas dtypes:
InfluxDB type | Dataframe column dtype |
---|---|
Float | float64 |
Integer | int64 |
String | object |
String (tag values) | CategoricalDtype |
Boolean | bool |
Timestamp | datetime64 |
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.
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.
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:
from aioinflux import iterpoints
r = client.query('SELECT * from h2o_quality LIMIT 10')
for i in iterpoints(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']
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
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'}
.
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)
{'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:
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
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.
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 InfluxDBClient.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.
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.
- InfluxDB-Python: The official blocking-only client. Based on Requests.
- influx-sansio: Fork of aioinflux using curio/trio and asks as a backend.