InfluxDB 2.0 python client¶

User Guide¶

Query ¶

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS

bucket = "my-bucket"

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)

write_api.write(bucket=bucket, record=p)

## using Table structure
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for table in tables:
    print(table)
    for row in table.records:
        print (row.values)


## using csv library
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
val_count = 0
for row in csv_result:
    for cell in row:
        val_count += 1

Write ¶

The WriteApi supports synchronous, asynchronous and batching writes into InfluxDB 2.0. The data should be passed as a InfluxDB Line Protocol, Data Point or Observable stream.

Warning

The WriteApi in batching mode (default mode) is suppose to run as a singleton. To flush all your data you should wrap the execution using with client.write_api(...) as write_api: statement or call write_api.close() at the end of your script.

The default instance of WriteApi use batching.

The data could be written as ¶

string or bytes that is formatted as a InfluxDB’s line protocol
Data Point structure
Dictionary style mapping with keys: measurement, tags, fields and time or custom structure
NamedTuple
Data Classes
Pandas DataFrame
List of above items
A batching type of write also supports an Observable that produce one of an above item

You can find write examples at GitHub: influxdb-client-python/examples.

Batching ¶

The batching is configurable by write_options:

Property	Description	Default Value
batch_size	the number of data pointx to collect in a batch	`1000`
flush_interval	the number of milliseconds before the batch is written	`1000`
jitter_interval	the number of milliseconds to increase the batch flush interval by a random amount	`0`
retry_interval	the number of milliseconds to retry first unsuccessful write. The next retry delay is computed using exponential random backoff. The retry interval is used when the InfluxDB server does not specify “Retry-After” header.	`5000`
max_retry_time	maximum total retry timeout in milliseconds.	`180_000`
max_retries	the number of max retries when write fails	`5`
max_retry_delay	the maximum delay between each retry attempt in milliseconds	`125_000`
exponential_base	the base for the exponential retry delay, the next delay is computed using random exponential backoff as a random value within the interval `retry_interval * exponential_base^(attempts-1)` and `retry_interval * exponential_base^(attempts)`. Example for `retry_interval=5_000, exponential_base=2, max_retry_delay=125_000, total=5` Retry delays are random distributed values within the ranges of `[5_000-10_000, 10_000-20_000, 20_000-40_000, 40_000-80_000, 80_000-125_000]`	`2`

from datetime import datetime, timedelta

import pandas as pd
import rx
from pytz import UTC
from rx import operators as ops

from influxdb_client import InfluxDBClient, Point, WriteOptions

with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as _client:

    with _client.write_api(write_options=WriteOptions(batch_size=500,
                                                      flush_interval=10_000,
                                                      jitter_interval=2_000,
                                                      retry_interval=5_000,
                                                      max_retries=5,
                                                      max_retry_delay=30_000,
                                                      exponential_base=2)) as _write_client:

        """
        Write Line Protocol formatted as string
        """
        _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
        _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
                                                    "h2o_feet,location=coyote_creek water_level=3.0 3"])

        """
        Write Line Protocol formatted as byte array
        """
        _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
        _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
                                                    "h2o_feet,location=coyote_creek water_level=3.0 3".encode()])

        """
        Write Dictionary-style object
        """
        _write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                    "fields": {"water_level": 1.0}, "time": 1})
        _write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                     "fields": {"water_level": 2.0}, "time": 2},
                                                    {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
                                                     "fields": {"water_level": 3.0}, "time": 3}])

        """
        Write Data Point
        """
        _write_client.write("my-bucket", "my-org",
                            Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
        _write_client.write("my-bucket", "my-org",
                            [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
                             Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])

        """
        Write Observable stream
        """
        _data = rx \
            .range(7, 11) \
            .pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))

        _write_client.write("my-bucket", "my-org", _data)

        """
        Write Pandas DataFrame
        """
        _now = datetime.now(UTC)
        _data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
                                   index=[_now, _now + timedelta(hours=1)],
                                   columns=["location", "water_level"])

        _write_client.write("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
                            data_frame_tag_columns=['location'])

Default Tags ¶

Sometimes is useful to store same information in every measurement e.g. hostname, location, customer. The client is able to use static value or env property as a tag value.

The expressions:

California Miner - static value
${env.hostname} - environment property

Via API ¶

point_settings = PointSettings()
point_settings.add_default_tag("id", "132-987-655")
point_settings.add_default_tag("customer", "California Miner")
point_settings.add_default_tag("data_center", "${env.data_center}")

self.write_client = self.client.write_api(write_options=SYNCHRONOUS, point_settings=point_settings)

self.write_client = self.client.write_api(write_options=SYNCHRONOUS,
                                              point_settings=PointSettings(**{"id": "132-987-655",
                                                                              "customer": "California Miner"}))

Via Configuration file ¶

In a init configuration file you are able to specify default tags by tags segment.

self.client = InfluxDBClient.from_config_file("config.ini")

You can also use a TOML format for the configuration file.

Via Environment Properties ¶

You are able to specify default tags by environment properties with prefix INFLUXDB_V2_TAG_.

Examples:

INFLUXDB_V2_TAG_ID
INFLUXDB_V2_TAG_HOSTNAME

self.client = InfluxDBClient.from_env_properties()

Asynchronous client ¶

Data are writes in an asynchronous HTTP request.

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import ASYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=ASYNCHRONOUS)

_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

async_result = write_api.write(bucket="my-bucket", record=[_point1, _point2])
async_result.get()

client.close()

Synchronous client ¶

Data are writes in a synchronous HTTP request.

from influxdb_client import InfluxDBClient, Point
from influxdb_client .client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
write_api = client.write_api(write_options=SYNCHRONOUS)

_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

write_api.write(bucket="my-bucket", record=[_point1, _point2])

client.close()

Pandas DataFrame ¶

Note

For DataFrame querying you should install Pandas dependency via pip install 'influxdb-client[extra]'.

Note

Note that if a query returns more then one table then the client generates a DataFrame for each of them.

The client is able to retrieve data in Pandas DataFrame format thought query_data_frame:

from influxdb_client import InfluxDBClient, Point, Dialect
from influxdb_client.client.write_api import SYNCHRONOUS

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

"""
Prepare data
"""

_point1 = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)
_point2 = Point("my_measurement").tag("location", "New York").field("temperature", 24.3)

write_api.write(bucket="my-bucket", record=[_point1, _point2])

"""
Query: using Pandas DataFrame
"""
data_frame = query_api.query_data_frame('from(bucket:"my-bucket") '
                                        '|> range(start: -10m) '
                                        '|> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") '
                                        '|> keep(columns: ["location", "temperature"])')
print(data_frame.to_string())

"""
Close client
"""
client.close()

Output:

Delete data ¶

The delete_api.py supports deletes points from an InfluxDB bucket.

from influxdb_client import InfluxDBClient

client = InfluxDBClient(url="http://localhost:8086", token="my-token")

delete_api = client.delete_api()

"""
Delete Data
"""
start = "1970-01-01T00:00:00Z"
stop = "2021-02-01T00:00:00Z"
delete_api.delete(start, stop, '_measurement="my_measurement"', bucket='my-bucket', org='my-org')

"""
Close client
"""
client.close()

Gzip support ¶

InfluxDBClient does not enable gzip compression for http requests by default. If you want to enable gzip to reduce transfer data’s size, you can call:

from influxdb_client import InfluxDBClient

_db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", enable_gzip=True)

Proxy configuration ¶

You can configure the client to tunnel requests through an HTTP proxy. The following proxy options are supported:

proxy - Set this to configure the http proxy to be used, ex. http://localhost:3128
proxy_headers - A dictionary containing headers that will be sent to the proxy. Could be used for proxy authentication.

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086",
                    token="my-token",
                    org="my-org",
                    proxy="http://localhost:3128") as client:

Note

If your proxy notify the client with permanent redirect (HTTP 301) to different host. The client removes Authorization header, because otherwise the contents of Authorization is sent to third parties which is a security vulnerability.

You can change this behaviour by:

from urllib3 import Retry
Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT = frozenset()
Retry.DEFAULT.remove_headers_on_redirect = Retry.DEFAULT_REMOVE_HEADERS_ON_REDIRECT

Nanosecond precision ¶

The Python’s datetime doesn’t support precision with nanoseconds so the library during writes and queries ignores everything after microseconds.

If you would like to use datetime with nanosecond precision you should use pandas.Timestamp that is replacement for python datetime.datetime object and also you should set a proper DateTimeHelper to the client.

sources - nanosecond_precision.py

from influxdb_client import Point, InfluxDBClient
from influxdb_client.client.util.date_utils_pandas import PandasDateTimeHelper
from influxdb_client.client.write_api import SYNCHRONOUS

"""
Set PandasDate helper which supports nanoseconds.
"""
import influxdb_client.client.util.date_utils as date_utils

date_utils.date_helper = PandasDateTimeHelper()

"""
Prepare client.
"""
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

"""
Prepare data
"""

point = Point("h2o_feet") \
    .field("water_level", 10) \
    .tag("location", "pacific") \
    .time('1996-02-25T21:20:00.001001231Z')

print(f'Time serialized with nanosecond precision: {point.to_line_protocol()}')
print()

write_api.write(bucket="my-bucket", record=point)

"""
Query: using Stream
"""
query = '''
from(bucket:"my-bucket")
        |> range(start: 0, stop: now())
        |> filter(fn: (r) => r._measurement == "h2o_feet")
'''
records = query_api.query_stream(query)

for record in records:
    print(f'Temperature in {record["location"]} is {record["_value"]} at time: {record["_time"]}')

"""
Close client
"""
client.close()

Handling Errors ¶

Errors happen and it’s important that your code is prepared for them. All client related exceptions are delivered from InfluxDBError. If the exception cannot be recovered in the client it is returned to the application. These exceptions are left for the developer to handle.

Almost all APIs directly return unrecoverable exceptions to be handled this way:

from influxdb_client import InfluxDBClient
from influxdb_client.client.exceptions import InfluxDBError
from influxdb_client.client.write_api import SYNCHRONOUS

with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    try:
        client.write_api(write_options=SYNCHRONOUS).write("my-bucket", record="mem,tag=a value=86")
    except InfluxDBError as e:
        if e.response.status == 401:
            raise Exception(f"Insufficient write permissions to 'my-bucket'.") from e
        raise

The only exception is batching WriteAPI (for more info see Batching). where you need to register custom callbacks to handle batch events. This is because this API runs in the background in a separate thread and isn’t possible to directly return underlying exceptions.

from influxdb_client import InfluxDBClient
from influxdb_client.client.exceptions import InfluxDBError


class BatchingCallback(object):

    def success(self, conf: (str, str, str), data: str):
        print(f"Written batch: {conf}, data: {data}")

    def error(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Cannot write batch: {conf}, data: {data} due: {exception}")

    def retry(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Retryable error occurs for batch: {conf}, data: {data} retry: {exception}")


with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    callback = BatchingCallback()
    with client.write_api(success_callback=callback.success,
                          error_callback=callback.error,
                          retry_callback=callback.retry) as write_api:
        pass

HTTP Retry Strategy ¶

By default the client uses a retry strategy only for batching writes (for more info see Batching). For other HTTP requests there is no one retry strategy, but it could be configured by retries parameter of InfluxDBClient.

For more info about how configure HTTP retry see details in urllib3 documentation.

from urllib3 import Retry

from influxdb_client import InfluxDBClient

retries = Retry(connect=5, read=2, redirect=5)
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", retries=retries)

Debugging ¶

For debug purpose you can enable verbose logging of http requests. Both request header and body will be logged to standard output.

_client = InfluxDBClient(url="http://localhost:8086", token="my-token", debug=True, org="my-org")

Examples ¶

How to efficiently import large dataset ¶

The following example shows how to import dataset with dozen megabytes. If you would like to import gigabytes of data then use our multiprocessing example: import_data_set_multiprocessing.py for use a full capability of your hardware.

sources - import_data_set.py

"""
Import VIX - CBOE Volatility Index - from "vix-daily.csv" file into InfluxDB 2.0

https://datahub.io/core/finance-vix#data
"""

from collections import OrderedDict
from csv import DictReader

import rx
from rx import operators as ops

from influxdb_client import InfluxDBClient, Point, WriteOptions

def parse_row(row: OrderedDict):
    """Parse row of CSV file into Point with structure:

        financial-analysis,type=ily close=18.47,high=19.82,low=18.28,open=19.82 1198195200000000000

    CSV format:
        Date,VIX Open,VIX High,VIX Low,VIX Close\n
        2004-01-02,17.96,18.68,17.54,18.22\n
        2004-01-05,18.45,18.49,17.44,17.49\n
        2004-01-06,17.66,17.67,16.19,16.73\n
        2004-01-07,16.72,16.75,15.5,15.5\n
        2004-01-08,15.42,15.68,15.32,15.61\n
        2004-01-09,16.15,16.88,15.57,16.75\n
        ...

    :param row: the row of CSV file
    :return: Parsed csv row to [Point]
    """

    """
     For better performance is sometimes useful directly create a LineProtocol to avoid unnecessary escaping overhead:
     """
     # from pytz import UTC
     # import ciso8601
     # from influxdb_client.client.write.point import EPOCH
     #
     # time = (UTC.localize(ciso8601.parse_datetime(row["Date"])) - EPOCH).total_seconds() * 1e9
     # return f"financial-analysis,type=vix-daily" \
     #        f" close={float(row['VIX Close'])},high={float(row['VIX High'])},low={float(row['VIX Low'])},open={float(row['VIX Open'])} " \
     #        f" {int(time)}"

    return Point("financial-analysis") \
        .tag("type", "vix-daily") \
        .field("open", float(row['VIX Open'])) \
        .field("high", float(row['VIX High'])) \
        .field("low", float(row['VIX Low'])) \
        .field("close", float(row['VIX Close'])) \
        .time(row['Date'])


"""
Converts vix-daily.csv into sequence of datad point
"""
data = rx \
    .from_iterable(DictReader(open('vix-daily.csv', 'r'))) \
    .pipe(ops.map(lambda row: parse_row(row)))

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)

"""
Create client that writes data in batches with 50_000 items.
"""
write_api = client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000))

"""
Write data into InfluxDB
"""
write_api.write(bucket="my-bucket", record=data)
write_api.close()

"""
Querying max value of CBOE Volatility Index
"""
query = 'from(bucket:"my-bucket")' \
        ' |> range(start: 0, stop: now())' \
        ' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
        ' |> max()'
result = client.query_api().query(query=query)

"""
Processing results
"""
print()
print("=== results ===")
print()
for table in result:
    for record in table.records:
        print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))

"""
Close client
"""
client.close()

Efficiency write data from IOT sensor ¶

sources - iot_sensor.py

"""
Efficiency write data from IOT sensor - write changed temperature every minute
"""
import atexit
import platform
from datetime import timedelta

import psutil as psutil
import rx
from rx import operators as ops

from influxdb_client import InfluxDBClient, WriteApi, WriteOptions

def on_exit(db_client: InfluxDBClient, write_api: WriteApi):
    """Close clients after terminate a script.

    :param db_client: InfluxDB client
    :param write_api: WriteApi
    :return: nothing
    """
    write_api.close()
    db_client.close()


def sensor_temperature():
    """Read a CPU temperature. The [psutil] doesn't support MacOS so we use [sysctl].

    :return: actual CPU temperature
    """
    os_name = platform.system()
    if os_name == 'Darwin':
        from subprocess import check_output
        output = check_output(["sysctl", "machdep.xcpm.cpu_thermal_level"])
        import re
        return re.findall(r'\d+', str(output))[0]
    else:
        return psutil.sensors_temperatures()["coretemp"][0]


def line_protocol(temperature):
    """Create a InfluxDB line protocol with structure:

        iot_sensor,hostname=mine_sensor_12,type=temperature value=68

    :param temperature: the sensor temperature
    :return: Line protocol to write into InfluxDB
    """

    import socket
    return 'iot_sensor,hostname={},type=temperature value={}'.format(socket.gethostname(), temperature)


"""
Read temperature every minute; distinct_until_changed - produce only if temperature change
"""
data = rx\
    .interval(period=timedelta(seconds=60))\
    .pipe(ops.map(lambda t: sensor_temperature()),
          ops.distinct_until_changed(),
          ops.map(lambda temperature: line_protocol(temperature)))

_db_client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)

"""
Create client that writes data into InfluxDB
"""
_write_api = _db_client.write_api(write_options=WriteOptions(batch_size=1))
_write_api.write(bucket="my-bucket", record=data)


"""
Call after terminate a script
"""
atexit.register(on_exit, _db_client, _write_api)

input()

Connect to InfluxDB Cloud ¶

The following example demonstrate a simplest way how to write and query date with the InfluxDB Cloud.

At first point you should create an authentication token as is described here.

After that you should configure properties: influx_cloud_url, influx_cloud_token, bucket and org in a influx_cloud.py example.

The last step is run a python script via: python3 influx_cloud.py.

sources - influx_cloud.py

"""
Connect to InfluxDB 2.0 - write data and query them
"""

from datetime import datetime

from influxdb_client import Point, InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS

"""
Configure credentials
"""
influx_cloud_url = 'https://us-west-2-1.aws.cloud2.influxdata.com'
influx_cloud_token = '...'
bucket = '...'
org = '...'

client = InfluxDBClient(url=influx_cloud_url, token=influx_cloud_token)
try:
    kind = 'temperature'
    host = 'host1'
    device = 'opt-123'

    """
    Write data by Point structure
    """
    point = Point(kind).tag('host', host).tag('device', device).field('value', 25.3).time(time=datetime.utcnow())

    print(f'Writing to InfluxDB cloud: {point.to_line_protocol()} ...')

    write_api = client.write_api(write_options=SYNCHRONOUS)
    write_api.write(bucket=bucket, org=org, record=point)

    print()
    print('success')
    print()
    print()

    """
    Query written data
    """
    query = f'from(bucket: "{bucket}") |> range(start: -1d) |> filter(fn: (r) => r._measurement == "{kind}")'
    print(f'Querying from InfluxDB cloud: "{query}" ...')
    print()

    query_api = client.query_api()
    tables = query_api.query(query=query, org=org)

    for table in tables:
        for row in table.records:
            print(f'{row.values["_time"]}: host={row.values["host"]},device={row.values["device"]} '
                  f'{row.values["_value"]} °C')

    print()
    print('success')

except Exception as e:
    print(e)
finally:
    client.close()

How to use Jupyter + Pandas + InfluxDB 2 ¶

The first example shows how to use client capabilities to predict stock price via Keras, TensorFlow, sklearn:

The example is taken from Kaggle.

sources - stock-predictions.ipynb

https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/stock-price-prediction.gif

Result:

The second example shows how to use client capabilities to realtime visualization via hvPlot, Streamz, RxPY:

sources - realtime-stream.ipynb

https://raw.githubusercontent.com/influxdata/influxdb-client-python/master/docs/images/realtime-result.gif

Other examples ¶

You can find all examples at GitHub: influxdb-client-python/examples.

InfluxDBClient ¶

class influxdb_client.InfluxDBClient(url, token, debug=None, timeout=10000, enable_gzip=False, org: str = None, default_tags: dict = None, **kwargs)[source]¶

InfluxDBClient is client for InfluxDB v2.

Initialize defaults.

Key bool verify_ssl:
Parameters:	url – InfluxDB server API url (ex. http://localhost:8086). token – auth token debug – enable verbose logging of http requests timeout – HTTP client timeout setting for a request specified in milliseconds. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. enable_gzip – Enable Gzip compression for http requests. Currently only the “Write” and “Query” endpoints supports the Gzip compression. org – organization name (used as a default in query and write API)
	Set this to false to skip verifying SSL certificate when calling API from https server.
Key str ssl_ca_cert:
	Set this to customize the certificate file to verify the peer.
Key str proxy:	Set this to configure the http proxy to be used (ex. http://localhost:3128)
Key str proxy_headers:
	A dictionary containing headers that will be sent to the proxy. Could be used for proxy authentication.
Key int connection_pool_maxsize:
	Number of connections to save that can be reused by urllib3. Defaults to “multiprocessing.cpu_count() * 5”.
Key urllib3.util.retry.Retry retries:
	Set the default retry strategy that is used for all HTTP requests except batching writes. As a default there is no one retry strategy.
Key bool auth_basic:
	Set this to true to enable basic authentication when talking to a InfluxDB 1.8.x that does not use auth-enabled but is protected by a reverse proxy with basic authentication. (defaults to false, don’t set to true when talking to InfluxDB 2)
Key list[str] profilers:
	list of enabled Flux profilers

authorizations_api() → influxdb_client.client.authorizations_api.AuthorizationsApi[source]¶

Create the Authorizations API instance.

Returns:	authorizations api

buckets_api() → influxdb_client.client.bucket_api.BucketsApi[source]¶

Create the Bucket API instance.

Returns:	buckets api

close()[source]¶: Shutdown the client.

delete_api() → influxdb_client.client.delete_api.DeleteApi[source]¶

Get the delete metrics API instance.

Returns:	delete api

classmethod from_config_file(config_file: str = 'config.ini', debug=None, enable_gzip=False)[source]¶

Configure client via configuration file. The configuration has to be under ‘influx’ section.

The supported formats:

Configuration options:

url
org
token
timeout,
verify_ssl
ssl_ca_cert
connection_pool_maxsize
auth_basic
profilers
proxy

config.ini example:

[influx2]
url=http://localhost:8086
org=my-org
token=my-token
timeout=6000
connection_pool_maxsize=25
auth_basic=false
profilers=query,operator
proxy=http:proxy.domain.org:8080

[tags]
id = 132-987-655
customer = California Miner
data_center = ${env.data_center}

config.toml example:

[influx2]
    url = "http://localhost:8086"
    token = "my-token"
    org = "my-org"
    timeout = 6000
    connection_pool_maxsize = 25
    auth_basic = false
    profilers="query, operator"
    proxy = "http://proxy.domain.org:8080"

[tags]
    id = "132-987-655"
    customer = "California Miner"
    data_center = "${env.data_center}"

classmethod from_env_properties(debug=None, enable_gzip=False)[source]¶

Configure client via environment properties.

Supported environment properties:

INFLUXDB_V2_URL
INFLUXDB_V2_ORG
INFLUXDB_V2_TOKEN
INFLUXDB_V2_TIMEOUT
INFLUXDB_V2_VERIFY_SSL
INFLUXDB_V2_SSL_CA_CERT
INFLUXDB_V2_CONNECTION_POOL_MAXSIZE
INFLUXDB_V2_AUTH_BASIC

health() → influxdb_client.domain.health_check.HealthCheck[source]¶

Get the health of an instance.

Returns:	HealthCheck

labels_api() → influxdb_client.client.labels_api.LabelsApi[source]¶

Create the Labels API instance.

Returns:	labels api

organizations_api() → influxdb_client.client.organizations_api.OrganizationsApi[source]¶

Create the Organizations API instance.

Returns:	organizations api

ping() → bool[source]¶

Return the the status of InfluxDB instance.

Returns:	The status of InfluxDB.

query_api(query_options: influxdb_client.client.query_api.QueryOptions = <influxdb_client.client.query_api.QueryOptions object>) → influxdb_client.client.query_api.QueryApi[source]¶

Create a Query API instance.

Parameters:	query_options – optional query api configuration
Returns:	Query api instance

ready() → influxdb_client.domain.ready.Ready[source]¶

Get The readiness of the InfluxDB 2.0.

Returns:	Ready

tasks_api() → influxdb_client.client.tasks_api.TasksApi[source]¶

Create the Tasks API instance.

Returns:	tasks api

users_api() → influxdb_client.client.users_api.UsersApi[source]¶

Create the Users API instance.

Returns:	users api

version() → str[source]¶

Return the version of the connected InfluxDB Server.

Returns:	The version of InfluxDB.

write_api(write_options=<influxdb_client.client.write_api.WriteOptions object>, point_settings=<influxdb_client.client.write_api.PointSettings object>, **kwargs) → influxdb_client.client.write_api.WriteApi[source]¶

Create a Write API instance.

Example:

from influxdb_client import InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS


# Initialize SYNCHRONOUS instance of WriteApi
with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    write_api = client.write_api(write_options=SYNCHRONOUS)

If you would like to use a background batching, you have to configure client like this:

from influxdb_client import InfluxDBClient

# Initialize background batching instance of WriteApi
with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    with client.write_api() as write_api:
        pass

There is also possibility to use callbacks to notify about state of background batches:

from influxdb_client import InfluxDBClient
from influxdb_client.client.exceptions import InfluxDBError


class BatchingCallback(object):

    def success(self, conf: (str, str, str), data: str):
        print(f"Written batch: {conf}, data: {data}")

    def error(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Cannot write batch: {conf}, data: {data} due: {exception}")

    def retry(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Retryable error occurs for batch: {conf}, data: {data} retry: {exception}")


with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    callback = BatchingCallback()
    with client.write_api(success_callback=callback.success,
                          error_callback=callback.error,
                          retry_callback=callback.retry) as write_api:
        pass

Key success_callback:
Parameters:	write_options – Write API configuration point_settings – settings to store default tags
	The callable `callback` to run after successfully writen a batch. The callable must accept two arguments: Tuple: `(bucket, organization, precision)` str: written data [batching mode]
Key error_callback:
	The callable `callback` to run after unsuccessfully writen a batch. The callable must accept three arguments: Tuple: `(bucket, organization, precision)` str: written data Exception: an occurred error [batching mode]
Key retry_callback:
	The callable `callback` to run after retryable error occurred. The callable must accept three arguments: Tuple: `(bucket, organization, precision)` str: written data Exception: an retryable error [batching mode]
Returns:	write api instance

QueryApi ¶

class influxdb_client.QueryApi(influxdb_client, query_options=<influxdb_client.client.query_api.QueryOptions object>)[source]¶

Implementation for ‘/api/v2/query’ endpoint.

Initialize query client.

Parameters:	influxdb_client – influxdb client

query(query: str, org=None, params: dict = None) → List[influxdb_client.client.flux_table.FluxTable][source]¶

Execute synchronous Flux query and return result as a List[‘FluxTable’].

Parameters:	query – the Flux query Organization org (str,) – specifies the organization for executing the query; take the ID, Name or Organization; if it’s not specified then is used default from client.org. params – bind parameters
Returns:

query_csv(query: str, org=None, dialect: influxdb_client.domain.dialect.Dialect = {'annotations': ['datatype', 'group', 'default'], 'comment_prefix': '#', 'date_time_format': 'RFC3339', 'delimiter': ',', 'header': True}, params: dict = None)[source]¶

Execute the Flux query and return results as a CSV iterator. Each iteration returns a row of the CSV file.

Parameters:	query – a Flux query Organization org (str,) – specifies the organization for executing the query; take the ID, Name or Organization; if it’s not specified then is used default from client.org. dialect – csv dialect format params – bind parameters
Returns:	The returned object is an iterator. Each iteration returns a row of the CSV file (which can span multiple input lines).

query_data_frame(query: str, org=None, data_frame_index: List[str] = None, params: dict = None)[source]¶

Execute synchronous Flux query and return Pandas DataFrame.

Note that if a query returns tables with differing schemas than the client generates a DataFrame for each of them.

Parameters:	query – the Flux query Organization org (str,) – specifies the organization for executing the query; take the ID, Name or Organization; if it’s not specified then is used default from client.org. data_frame_index – the list of columns that are used as DataFrame index params – bind parameters
Returns:

query_data_frame_stream(query: str, org=None, data_frame_index: List[str] = None, params: dict = None)[source]¶

Execute synchronous Flux query and return stream of Pandas DataFrame as a Generator[‘pd.DataFrame’].

Note that if a query returns tables with differing schemas than the client generates a DataFrame for each of them.

Parameters:	query – the Flux query Organization org (str,) – specifies the organization for executing the query; take the ID, Name or Organization; if it’s not specified then is used default from client.org. data_frame_index – the list of columns that are used as DataFrame index params – bind parameters
Returns:

query_raw(query: str, org=None, dialect={'annotations': ['datatype', 'group', 'default'], 'comment_prefix': '#', 'date_time_format': 'RFC3339', 'delimiter': ',', 'header': True}, params: dict = None)[source]¶

Execute synchronous Flux query and return result as raw unprocessed result as a str.

Parameters:	query – a Flux query Organization org (str,) – specifies the organization for executing the query; take the ID, Name or Organization; if it’s not specified then is used default from client.org. dialect – csv dialect format params – bind parameters
Returns:	str

query_stream(query: str, org=None, params: dict = None) → Generator[[influxdb_client.client.flux_table.FluxRecord, Any], None][source]¶

Execute synchronous Flux query and return stream of FluxRecord as a Generator[‘FluxRecord’].

Parameters:	query – the Flux query Organization org (str,) – specifies the organization for executing the query; take the ID, Name or Organization; if it’s not specified then is used default from client.org. params – bind parameters
Returns:

WriteApi ¶

class influxdb_client.WriteApi(influxdb_client, write_options: influxdb_client.client.write_api.WriteOptions = <influxdb_client.client.write_api.WriteOptions object>, point_settings: influxdb_client.client.write_api.PointSettings = <influxdb_client.client.write_api.PointSettings object>, **kwargs)[source]¶

Implementation for ‘/api/v2/write’ endpoint.

Example:

from influxdb_client import InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS


# Initialize SYNCHRONOUS instance of WriteApi
with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    write_api = client.write_api(write_options=SYNCHRONOUS)

Initialize defaults.

Key success_callback:
Parameters:	influxdb_client – with default settings (organization) write_options – write api configuration point_settings – settings to store default tags.
	The callable `callback` to run after successfully writen a batch. The callable must accept two arguments: Tuple: `(bucket, organization, precision)` str: written data [batching mode]
Key error_callback:
	The callable `callback` to run after unsuccessfully writen a batch. The callable must accept three arguments: Tuple: `(bucket, organization, precision)` str: written data Exception: an occurred error [batching mode]
Key retry_callback:
	The callable `callback` to run after retryable error occurred. The callable must accept three arguments: Tuple: `(bucket, organization, precision)` str: written data Exception: an retryable error [batching mode]

close()[source]¶: Flush data and dispose a batching buffer.

flush()[source]¶: Flush data.

write(bucket: str, org: str = None, record: Union[str, Iterable[str], influxdb_client.client.write.point.Point, Iterable[Point], dict, Iterable[dict], bytes, Iterable[bytes], rx.core.observable.observable.Observable, NamedTuple, Iterable[NamedTuple], dataclass, Iterable[dataclass]] = None, write_precision: influxdb_client.domain.write_precision.WritePrecision = 'ns', **kwargs) → Any[source]¶

Write time-series data into InfluxDB.

Key data_frame_measurement_name:
Parameters:	bucket (str) – specifies the destination bucket for writes (required) Organization org (str,) – specifies the destination organization for writes; take the ID, Name or Organization; if it’s not specified then is used default from client.org. write_precision (WritePrecision) – specifies the precision for the unix timestamps within the body line-protocol. The precision specified on a Point has precedes and is use for write. record – Point, Line Protocol, Dictionary, NamedTuple, Data Classes, Pandas DataFrame or RxPY Observable to write
	name of measurement for writing Pandas DataFrame - `DataFrame`
Key data_frame_tag_columns:
	list of DataFrame columns which are tags, rest columns will be fields - `DataFrame`
Key record_measurement_key:
	key of record with specified measurement - `dictionary`, `NamedTuple`, `dataclass`
Key record_measurement_name:
	static measurement name - `dictionary`, `NamedTuple`, `dataclass`
Key record_time_key:
	key of record with specified timestamp - `dictionary`, `NamedTuple`, `dataclass`
Key record_tag_keys:
	list of record keys to use as a tag - `dictionary`, `NamedTuple`, `dataclass`
Key record_field_keys:
	list of record keys to use as a field - `dictionary`, `NamedTuple`, `dataclass`

Example:

# Record as Line Protocol
write_api.write("my-bucket", "my-org", "h2o_feet,location=us-west level=125i 1")

# Record as Dictionary
dictionary = {
    "measurement": "h2o_feet",
    "tags": {"location": "us-west"},
    "fields": {"level": 125},
    "time": 1
}
write_api.write("my-bucket", "my-org", dictionary)

# Record as Point
from influxdb_client import Point
point = Point("h2o_feet").tag("location", "us-west").field("level", 125).time(1)
write_api.write("my-bucket", "my-org", point)

DataFrame:

The index of Pandas DataFrame is used as a timestamp for written data. The index should be PeriodIndex or its must be transformable to datetime by pandas.to_datetime.

If you would like to transform a column to PeriodIndex, you can use something like:

import pandas as pd

# DataFrame
data_frame = ...
# Set column as Index
data_frame.set_index('column_name', inplace=True)
# Transform index to PeriodIndex
data_frame.index = pd.to_datetime(data_frame.index, unit='s')

class influxdb_client.client.write.point.Point(measurement_name)[source]¶

Point defines the values that will be written to the database.

Ref: http://bit.ly/influxdata-point

Initialize defaults.

field(field, value)[source]¶: Add field with key and value.

static from_dict(dictionary: dict, write_precision: influxdb_client.domain.write_precision.WritePrecision = 'ns', **kwargs)[source]¶

Initialize point from ‘dict’ structure.

The expected dict structure is:

measurement
tags
fields
time

Example:

# Use default dictionary structure
dict_structure = {
    "measurement": "h2o_feet",
    "tags": {"location": "coyote_creek"},
    "fields": {"water_level": 1.0},
    "time": 1
}
point = Point.from_dict(dict_structure, WritePrecision.NS)

Example:

# Use custom dictionary structure
dictionary = {
    "name": "sensor_pt859",
    "location": "warehouse_125",
    "version": "2021.06.05.5874",
    "pressure": 125,
    "temperature": 10,
    "created": 1632208639,
}
point = Point.from_dict(dictionary,
                        write_precision=WritePrecision.S,
                        record_measurement_key="name",
                        record_time_key="created",
                        record_tag_keys=["location", "version"],
                        record_field_keys=["pressure", "temperature"])

Key record_measurement_key:
Parameters:	dictionary – dictionary for serialize into data Point write_precision – sets the precision for the supplied time values
	key of dictionary with specified measurement
Key record_measurement_name:
	static measurement name for data Point
Key record_time_key:
	key of dictionary with specified timestamp
Key record_tag_keys:
	list of dictionary keys to use as a tag
Key record_field_keys:
	list of dictionary keys to use as a field
Returns:	new data point

static measurement(measurement)[source]¶: Create a new Point with specified measurement name.

tag(key, value)[source]¶: Add tag with key and value.

time(time, write_precision='ns')[source]¶

Specify timestamp for DataPoint with declared precision.

If time doesn’t have specified timezone we assume that timezone is UTC.

Examples::: Point.measurement(“h2o”).field(“val”, 1).time(“2009-11-10T23:00:00.123456Z”) Point.measurement(“h2o”).field(“val”, 1).time(1257894000123456000) Point.measurement(“h2o”).field(“val”, 1).time(datetime(2009, 11, 10, 23, 0, 0, 123456)) Point.measurement(“h2o”).field(“val”, 1).time(1257894000123456000, write_precision=WritePrecision.NS)

Parameters:	time – the timestamp for your data write_precision – sets the precision for the supplied time values
Returns:	this point

to_line_protocol()[source]¶: Create LineProtocol.

write_precision¶: Get precision.

class influxdb_client.domain.write_precision.WritePrecision[source]¶

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

WritePrecision - a model defined in OpenAPI.

NS = 'ns'¶

Attributes:

openapi_types (dict): The key is attribute name: and the value is attribute type.
attribute_map (dict): The key is attribute name: and the value is json key in definition.

to_dict()[source]¶: Return the model properties as a dict.

to_str()[source]¶: Return the string representation of the model.

BucketsApi ¶

class influxdb_client.BucketsApi(influxdb_client)[source]¶

Implementation for ‘/api/v2/buckets’ endpoint.

Initialize defaults.

create_bucket(bucket=None, bucket_name=None, org_id=None, retention_rules=None, description=None, org=None) → influxdb_client.domain.bucket.Bucket[source]¶

Create a bucket.

Parameters:

bucket (Bucket) – bucket to create
bucket_name – bucket name
description – bucket description
org_id – org_id
bucket_name – bucket name
retention_rules – retention rules array or single BucketRetentionRules
Organization org (str,) – specifies the organization for create the bucket; take the ID, Name or Organization; if it’s not specified then is used default from client.org.

Returns:

Bucket If the method is called asynchronously, returns the request thread.

delete_bucket(bucket)[source]¶

Delete a bucket.

Parameters:	bucket – bucket id or Bucket
Returns:	Bucket

find_bucket_by_id(id)[source]¶

Find bucket by ID.

Parameters:	id –
Returns:

find_bucket_by_name(bucket_name)[source]¶

Find bucket by name.

Parameters:	bucket_name – bucket name
Returns:	Bucket

find_buckets(**kwargs)[source]¶

List buckets.

Key int offset:	Offset for pagination
Key int limit:	Limit for pagination
Key str after:	The last resource ID from which to seek from (but not including). This is to be used instead of offset.
Key str org:	The organization name.
Key str org_id:	The organization ID.
Key str name:	Only returns buckets with a specific name.
Returns:	Buckets

update_bucket(bucket: influxdb_client.domain.bucket.Bucket) → influxdb_client.domain.bucket.Bucket[source]¶

Update a bucket.

Parameters:	bucket – Bucket update to apply (required)
Returns:	Bucket

class influxdb_client.domain.Bucket(links=None, id=None, type='user', name=None, description=None, org_id=None, rp=None, schema_type=None, created_at=None, updated_at=None, retention_rules=None, labels=None)[source]¶

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

Bucket - a model defined in OpenAPI.

created_at¶

Get the created_at of this Bucket.

Returns:	The created_at of this Bucket.
Return type:	datetime

description¶

Get the description of this Bucket.

Returns:	The description of this Bucket.
Return type:	str

id¶

Get the id of this Bucket.

Returns:	The id of this Bucket.
Return type:	str

labels¶

Get the labels of this Bucket.

Returns:	The labels of this Bucket.
Return type:	list[Label]

links¶

Get the links of this Bucket.

Returns:	The links of this Bucket.
Return type:	BucketLinks

name¶

Get the name of this Bucket.

Returns:	The name of this Bucket.
Return type:	str

org_id¶

Get the org_id of this Bucket.

Returns:	The org_id of this Bucket.
Return type:	str

retention_rules¶

Get the retention_rules of this Bucket.

Rules to expire or retain data. No rules means data never expires.

Returns:	The retention_rules of this Bucket.
Return type:	list[BucketRetentionRules]

rp¶

Get the rp of this Bucket.

Returns:	The rp of this Bucket.
Return type:	str

schema_type¶

Get the schema_type of this Bucket.

Returns:	The schema_type of this Bucket.
Return type:	SchemaType

to_dict()[source]¶: Return the model properties as a dict.

to_str()[source]¶: Return the string representation of the model.

type¶

Get the type of this Bucket.

Returns:	The type of this Bucket.
Return type:	str

updated_at¶

Get the updated_at of this Bucket.

Returns:	The updated_at of this Bucket.
Return type:	datetime

LabelsApi ¶

class influxdb_client.LabelsApi(influxdb_client)[source]¶

Implementation for ‘/api/v2/labels’ endpoint.

Initialize defaults.

clone_label(cloned_name: str, label: influxdb_client.domain.label.Label) → influxdb_client.domain.label.Label[source]¶

Create the new instance of the label as a copy existing label.

Parameters:	cloned_name – new label name label – existing label
Returns:	clonned Label

create_label(name: str, org_id: str, properties: Dict[str, str] = None) → influxdb_client.domain.label.Label[source]¶

Create a new label.

Parameters:	name – label name org_id – organization id properties – optional label properties
Returns:	created label

delete_label(label: Union[str, influxdb_client.domain.label.Label])[source]¶

Delete the label.

Parameters:	label – label id or Label

find_label_by_id(label_id: str)[source]¶

Retrieve the label by id.

Parameters:	label_id –
Returns:	Label

find_label_by_org(org_id) → List[influxdb_client.domain.label.Label][source]¶

Get the list of all labels for given organization.

Parameters:	org_id – organization id
Returns:	list of labels

find_labels(**kwargs) → List[influxdb_client.domain.label.Label][source]¶

Get all available labels.

Key str org_id:	The organization ID.
Returns:	labels

update_label(label: influxdb_client.domain.label.Label)[source]¶

Update an existing label name and properties.

Parameters:	label – label
Returns:	the updated label

OrganizationsApi ¶

class influxdb_client.OrganizationsApi(influxdb_client)[source]¶

Implementation for ‘/api/v2/orgs’ endpoint.

Initialize defaults.

create_organization(name: str = None, organization: influxdb_client.domain.organization.Organization = None) → influxdb_client.domain.organization.Organization[source]¶: Create an organization.

delete_organization(org_id: str)[source]¶: Delete an organization.

find_organization(org_id)[source]¶: Retrieve an organization.

find_organizations(**kwargs)[source]¶

List all organizations.

Key bool descending:
Key int offset:	Offset for pagination
Key int limit:	Limit for pagination

Key str org:	Filter organizations to a specific organization name.
Key str org_id:	Filter organizations to a specific organization ID.
Key str user_id:
	Filter organizations to a specific user ID.

me()[source]¶: Return the current authenticated user.

update_organization(organization: influxdb_client.domain.organization.Organization) → influxdb_client.domain.organization.Organization[source]¶

Update an organization.

Parameters:	organization – Organization update to apply (required)
Returns:	Organization

class influxdb_client.domain.Organization(links=None, id=None, name=None, description=None, created_at=None, updated_at=None, status='active')[source]¶

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

Organization - a model defined in OpenAPI.

created_at¶

Get the created_at of this Organization.

Returns:	The created_at of this Organization.
Return type:	datetime

description¶

Get the description of this Organization.

Returns:	The description of this Organization.
Return type:	str

id¶

Get the id of this Organization.

Returns:	The id of this Organization.
Return type:	str

links¶

Get the links of this Organization.

Returns:	The links of this Organization.
Return type:	OrganizationLinks

name¶

Get the name of this Organization.

Returns:	The name of this Organization.
Return type:	str

status¶

Get the status of this Organization.

If inactive the organization is inactive.

Returns:	The status of this Organization.
Return type:	str

to_dict()[source]¶: Return the model properties as a dict.

to_str()[source]¶: Return the string representation of the model.

updated_at¶

Get the updated_at of this Organization.

Returns:	The updated_at of this Organization.
Return type:	datetime

UsersApi ¶

class influxdb_client.UsersApi(influxdb_client)[source]¶

Implementation for ‘/api/v2/users’ endpoint.

Initialize defaults.

create_user(name: str) → influxdb_client.domain.user.User[source]¶: Create a user.

delete_user(user: Union[str, influxdb_client.domain.user.User, influxdb_client.domain.user_response.UserResponse]) → None[source]¶

Delete a user.

Parameters:	user – user id or User
Returns:	User

find_users(**kwargs) → influxdb_client.domain.users.Users[source]¶

List all users.

Key int offset:	Offset for pagination
Key int limit:	Limit for pagination
Key str after:	The last resource ID from which to seek from (but not including). This is to be used instead of offset.
Key str name:	The user name.
Key str id:	The user ID.
Returns:	Buckets

me() → influxdb_client.domain.user.User[source]¶: Return the current authenticated user.

update_user(user: influxdb_client.domain.user.User) → influxdb_client.domain.user_response.UserResponse[source]¶

Update a user.

Parameters:	user – User update to apply (required)
Returns:	User

class influxdb_client.domain.User(id=None, oauth_id=None, name=None, status='active')[source]¶

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

User - a model defined in OpenAPI.

id¶

Get the id of this User.

Returns:	The id of this User.
Return type:	str

name¶

Get the name of this User.

Returns:	The name of this User.
Return type:	str

oauth_id¶

Get the oauth_id of this User.

Returns:	The oauth_id of this User.
Return type:	str

status¶

Get the status of this User.

If inactive the user is inactive.

Returns:	The status of this User.
Return type:	str

to_dict()[source]¶: Return the model properties as a dict.

to_str()[source]¶: Return the string representation of the model.

TasksApi ¶

class influxdb_client.TasksApi(influxdb_client)[source]¶

Implementation for ‘/api/v2/tasks’ endpoint.

Initialize defaults.

add_label(label_id: str, task_id: str) → influxdb_client.domain.label_response.LabelResponse[source]¶: Add a label to a task.

add_member(member_id, task_id)[source]¶: Add a member to a task.

add_owner(owner_id, task_id)[source]¶: Add an owner to a task.

cancel_run(task_id: str, run_id: str)[source]¶

Cancel a currently running run.

Parameters:	task_id – run_id –

clone_task(task: influxdb_client.domain.task.Task) → influxdb_client.domain.task.Task[source]¶: Clone a task.

create_task(task: influxdb_client.domain.task.Task = None, task_create_request: influxdb_client.domain.task_create_request.TaskCreateRequest = None) → influxdb_client.domain.task.Task[source]¶: Create a new task.

create_task_cron(name: str, flux: str, cron: str, org_id: str) → influxdb_client.domain.task.Task[source]¶: Create a new task with cron repetition schedule.

create_task_every(name, flux, every, organization) → influxdb_client.domain.task.Task[source]¶: Create a new task with every repetition schedule.

delete_label(label_id: str, task_id: str)[source]¶: Delete a label from a task.

delete_member(member_id, task_id)[source]¶: Remove a member from a task.

delete_owner(owner_id, task_id)[source]¶: Remove an owner from a task.

delete_task(task_id: str)[source]¶: Delete a task.

find_task_by_id(task_id) → influxdb_client.domain.task.Task[source]¶: Retrieve a task.

find_tasks(**kwargs)[source]¶

List all tasks.

Key str name:	only returns tasks with the specified name
Key str after:	returns tasks after specified ID
Key str user:	filter tasks to a specific user ID
Key str org:	filter tasks to a specific organization name
Key str org_id:	filter tasks to a specific organization ID
Key int limit:	the number of tasks to return
Returns:	Tasks

find_tasks_by_user(task_user_id)[source]¶: List all tasks by user.

get_labels(task_id)[source]¶: List all labels for a task.

get_logs(task_id: str) → List[influxdb_client.domain.log_event.LogEvent][source]¶

Retrieve all logs for a task.

Parameters:	task_id – task id

get_members(task_id: str)[source]¶: List all task members.

get_owners(task_id)[source]¶: List all owners of a task.

get_run(task_id: str, run_id: str) → influxdb_client.domain.run.Run[source]¶

Get run record for specific task and run id.

Parameters:	task_id – task id run_id – run id
Returns:	Run for specified task and run id

get_run_logs(task_id: str, run_id: str) → List[influxdb_client.domain.log_event.LogEvent][source]¶: Retrieve all logs for a run.

get_runs(task_id, **kwargs) → List[influxdb_client.domain.run.Run][source]¶

Retrieve list of run records for a task.

Parameters:	task_id – task id after (str) – returns runs after specified ID limit (int) – the number of runs to return after_time (datetime) – filter runs to those scheduled after this time, RFC3339 before_time (datetime) – filter runs to those scheduled before this time, RFC3339

retry_run(task_id: str, run_id: str)[source]¶

Retry a task run.

Parameters:	task_id – task id run_id – run id

run_manually(task_id: str, scheduled_for: <module 'datetime' from '/home/docs/.pyenv/versions/3.6.12/lib/python3.6/datetime.py'> = None)[source]¶

Manually start a run of the task now overriding the current schedule.

Parameters:	task_id – scheduled_for – planned execution

update_task(task: influxdb_client.domain.task.Task) → influxdb_client.domain.task.Task[source]¶: Update a task.

update_task_request(task_id, task_update_request: influxdb_client.domain.task_update_request.TaskUpdateRequest) → influxdb_client.domain.task.Task[source]¶: Update a task.

class influxdb_client.domain.Task(id=None, type=None, org_id=None, org=None, name=None, description=None, status=None, labels=None, authorization_id=None, flux=None, every=None, cron=None, offset=None, latest_completed=None, last_run_status=None, last_run_error=None, created_at=None, updated_at=None, links=None)[source]¶

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

Task - a model defined in OpenAPI.

authorization_id¶

Get the authorization_id of this Task.

The ID of the authorization used when this task communicates with the query engine.

Returns:	The authorization_id of this Task.
Return type:	str

created_at¶

Get the created_at of this Task.

Returns:	The created_at of this Task.
Return type:	datetime

cron¶

Get the cron of this Task.

A task repetition schedule in the form ‘* * * * * *’; parsed from Flux.

Returns:	The cron of this Task.
Return type:	str

description¶

Get the description of this Task.

An optional description of the task.

Returns:	The description of this Task.
Return type:	str

every¶

Get the every of this Task.

A simple task repetition schedule; parsed from Flux.

Returns:	The every of this Task.
Return type:	str

flux¶

Get the flux of this Task.

The Flux script to run for this task.

Returns:	The flux of this Task.
Return type:	str

id¶

Get the id of this Task.

Returns:	The id of this Task.
Return type:	str

labels¶

Get the labels of this Task.

Returns:	The labels of this Task.
Return type:	list[Label]

last_run_error¶

Get the last_run_error of this Task.

Returns:	The last_run_error of this Task.
Return type:	str

last_run_status¶

Get the last_run_status of this Task.

Returns:	The last_run_status of this Task.
Return type:	str

latest_completed¶

Get the latest_completed of this Task.

Timestamp of latest scheduled, completed run, RFC3339.

Returns:	The latest_completed of this Task.
Return type:	datetime

links¶

Get the links of this Task.

Returns:	The links of this Task.
Return type:	TaskLinks

name¶

Get the name of this Task.

The name of the task.

Returns:	The name of this Task.
Return type:	str

offset¶

Get the offset of this Task.

Duration to delay after the schedule, before executing the task; parsed from flux, if set to zero it will remove this option and use 0 as the default.

Returns:	The offset of this Task.
Return type:	str

org¶

Get the org of this Task.

The name of the organization that owns this Task.

Returns:	The org of this Task.
Return type:	str

org_id¶

Get the org_id of this Task.

The ID of the organization that owns this Task.

Returns:	The org_id of this Task.
Return type:	str

status¶

Get the status of this Task.

Returns:	The status of this Task.
Return type:	TaskStatusType

to_dict()[source]¶: Return the model properties as a dict.

to_str()[source]¶: Return the string representation of the model.

type¶

Get the type of this Task.

The type of task, this can be used for filtering tasks on list actions.

Returns:	The type of this Task.
Return type:	str

updated_at¶

Get the updated_at of this Task.

Returns:	The updated_at of this Task.
Return type:	datetime

DeleteApi ¶

class influxdb_client.DeleteApi(influxdb_client)[source]¶

Implementation for ‘/api/v2/delete’ endpoint.

Initialize defaults.

delete(start: datetime.datetime, stop: object, predicate: object, bucket: str, org: str) → None[source]¶

Delete Time series data from InfluxDB.

Parameters:	start – start time stop – stop time predicate – predicate bucket – bucket id or name from which data will be deleted org – organization id or name
Returns:

class influxdb_client.domain.DeletePredicateRequest(start=None, stop=None, predicate=None)[source]¶

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

DeletePredicateRequest - a model defined in OpenAPI.

predicate¶

Get the predicate of this DeletePredicateRequest.

InfluxQL-like delete statement

Returns:	The predicate of this DeletePredicateRequest.
Return type:	str

start¶

Get the start of this DeletePredicateRequest.

RFC3339Nano

Returns:	The start of this DeletePredicateRequest.
Return type:	datetime

stop¶

Get the stop of this DeletePredicateRequest.

RFC3339Nano

Returns:	The stop of this DeletePredicateRequest.
Return type:	datetime

to_dict()[source]¶: Return the model properties as a dict.

to_str()[source]¶: Return the string representation of the model.

Helpers ¶

class influxdb_client.client.util.date_utils.DateHelper(timezone: datetime.tzinfo = <UTC>)[source]¶

DateHelper to groups different implementations of date operations.

Initialize defaults.

Parameters:	timezone – Default timezone used for serialization “datetime” without “tzinfo”. Default value is “UTC”.

parse_date(date_string: str)[source]¶

Parse string into Date or Timestamp.

Returns:	Returns a `datetime.datetime` object or compliant implementation like `class 'pandas._libs.tslibs.timestamps.Timestamp`

to_nanoseconds(delta)[source]¶

Get number of nanoseconds in timedelta.

Solution comes from v1 client. Thx. https://github.com/influxdata/influxdb-python/pull/811

to_utc(value: <module 'datetime' from '/home/docs/.pyenv/versions/3.6.12/lib/python3.6/datetime.py'>)[source]¶

Convert datetime to UTC timezone.

Parameters:	value – datetime
Returns:	datetime in UTC

class influxdb_client.client.util.multiprocessing_helper.MultiprocessingWriter(**kwargs)[source]¶

The Helper class to write data into InfluxDB in independent OS process.

Example:

from influxdb_client import WriteOptions
from influxdb_client.client.util.multiprocessing_helper import MultiprocessingWriter


def main():
    writer = MultiprocessingWriter(url="http://localhost:8086", token="my-token", org="my-org",
                                   write_options=WriteOptions(batch_size=100))
    writer.start()

    for x in range(1, 1000):
        writer.write(bucket="my-bucket", record=f"mem,tag=a value={x}i {x}")

    writer.__del__()


if __name__ == '__main__':
    main()

How to use with context_manager:

from influxdb_client import WriteOptions
from influxdb_client.client.util.multiprocessing_helper import MultiprocessingWriter


def main():
    with MultiprocessingWriter(url="http://localhost:8086", token="my-token", org="my-org",
                               write_options=WriteOptions(batch_size=100)) as writer:
        for x in range(1, 1000):
            writer.write(bucket="my-bucket", record=f"mem,tag=a value={x}i {x}")


if __name__ == '__main__':
    main()

How to handle batch events:

from influxdb_client import WriteOptions
from influxdb_client.client.exceptions import InfluxDBError
from influxdb_client.client.util.multiprocessing_helper import MultiprocessingWriter


class BatchingCallback(object):

    def success(self, conf: (str, str, str), data: str):
        print(f"Written batch: {conf}, data: {data}")

    def error(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Cannot write batch: {conf}, data: {data} due: {exception}")

    def retry(self, conf: (str, str, str), data: str, exception: InfluxDBError):
        print(f"Retryable error occurs for batch: {conf}, data: {data} retry: {exception}")


def main():
    callback = BatchingCallback()
    with MultiprocessingWriter(url="http://localhost:8086", token="my-token", org="my-org",
                               success_callback=callback.success,
                               error_callback=callback.error,
                               retry_callback=callback.retry) as writer:

        for x in range(1, 1000):
            writer.write(bucket="my-bucket", record=f"mem,tag=a value={x}i {x}")


if __name__ == '__main__':
    main()

Initialize defaults.

For more information how to initialize the writer see the examples above.

Parameters:	kwargs – arguments are passed into `__init__` function of `InfluxDBClient` and `write_api`.

run()[source]¶: Initialize InfluxDBClient and waits for data to writes into InfluxDB.

start() → None[source]¶: Start independent process for writing data into InfluxDB.

terminate() → None[source]¶

Cleanup resources in independent process.

This function cannot be used to terminate the MultiprocessingWriter. If you want to finish your writes please call: __del__.

write(**kwargs) → None[source]¶

Append time-series data into underlying queue.

For more information how to pass arguments see the examples above.

Parameters:	kwargs – arguments are passed into `write` function of `WriteApi`
Returns:	None

Migration Guide¶

This guide is meant to help you migrate your Python code from influxdb-python to influxdb-client-python by providing code examples that cover common usages.

If there is something missing, please feel free to create a new request for a guide enhancement.

Before You Start¶

Please take a moment to review the following client docs:

Content¶

Initializing Client
Creating Database/Bucket
Dropping Database/Bucket
Writes
Querying

Initializing Client¶

influxdb-python

from influxdb import InfluxDBClient

client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')

influxdb-client-python

from influxdb_client import InfluxDBClient

with InfluxDBClient(url='http://localhost:8086', token='my-token', org='my-org') as client:
    pass

Creating Database/Bucket¶

influxdb-python

from influxdb import InfluxDBClient

client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')

dbname = 'example'
client.create_database(dbname)
client.create_retention_policy('awesome_policy', '60m', 3, database=dbname, default=True)

influxdb-client-python

from influxdb_client import InfluxDBClient, BucketRetentionRules

org = 'my-org'

with InfluxDBClient(url='http://localhost:8086', token='my-token', org=org) as client:
    buckets_api = client.buckets_api()

    # Create Bucket with retention policy set to 3600 seconds and name "bucket-by-python"
    retention_rules = BucketRetentionRules(type="expire", every_seconds=3600)
    created_bucket = buckets_api.create_bucket(bucket_name="bucket-by-python",
                                               retention_rules=retention_rules,
                                               org=org)

Dropping Database/Bucket¶

influxdb-python

from influxdb import InfluxDBClient

client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')

dbname = 'example'
client.drop_database(dbname)

influxdb-client-python

from influxdb_client import InfluxDBClient

with InfluxDBClient(url='http://localhost:8086', token='my-token', org='my-org') as client:
    buckets_api = client.buckets_api()

    bucket = buckets_api.find_bucket_by_name("my-bucket")
    buckets_api.delete_bucket(bucket)

Writing LineProtocol¶

influxdb-python

from influxdb import InfluxDBClient

client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')

client.write('h2o_feet,location=coyote_creek water_level=1.0 1', protocol='line')

influxdb-client-python

from influxdb_client import InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS

with InfluxDBClient(url='http://localhost:8086', token='my-token', org='my-org') as client:
    write_api = client.write_api(write_options=SYNCHRONOUS)

    write_api.write(bucket='my-bucket', record='h2o_feet,location=coyote_creek water_level=1.0 1')

Writing Dictionary-style object¶

influxdb-python

from influxdb import InfluxDBClient

record = [
        {
            "measurement": "cpu_load_short",
            "tags": {
                "host": "server01",
                "region": "us-west"
            },
            "time": "2009-11-10T23:00:00Z",
            "fields": {
                "Float_value": 0.64,
                "Int_value": 3,
                "String_value": "Text",
                "Bool_value": True
            }
        }
    ]

client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')

client.write_points(record)

influxdb-client-python

from influxdb_client import InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS

with InfluxDBClient(url='http://localhost:8086', token='my-token', org='my-org') as client:
    write_api = client.write_api(write_options=SYNCHRONOUS)

    record = [
        {
            "measurement": "cpu_load_short",
            "tags": {
                "host": "server01",
                "region": "us-west"
            },
            "time": "2009-11-10T23:00:00Z",
            "fields": {
                "Float_value": 0.64,
                "Int_value": 3,
                "String_value": "Text",
                "Bool_value": True
            }
        }
    ]

    write_api.write(bucket='my-bucket', record=record)

Writing Structured Data¶

influxdb-python

from influxdb import InfluxDBClient
from influxdb import SeriesHelper

my_client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')


class MySeriesHelper(SeriesHelper):
    class Meta:
        client = my_client
        series_name = 'events.stats.{server_name}'
        fields = ['some_stat', 'other_stat']
        tags = ['server_name']
        bulk_size = 5
        autocommit = True


MySeriesHelper(server_name='us.east-1', some_stat=159, other_stat=10)
MySeriesHelper(server_name='us.east-1', some_stat=158, other_stat=20)

MySeriesHelper.commit()

The influxdb-client-python doesn’t have an equivalent implementation for MySeriesHelper, but there is an option to use Python Data Classes way:

influxdb-client-python

from dataclasses import dataclass

from influxdb_client import InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS


@dataclass
class Car:
    """
    DataClass structure - Car
    """
    engine: str
    type: str
    speed: float


with InfluxDBClient(url='http://localhost:8086', token='my-token', org='my-org') as client:
    write_api = client.write_api(write_options=SYNCHRONOUS)

    car = Car('12V-BT', 'sport-cars', 125.25)

    write_api.write(bucket="my-bucket",
                    record=car,
                    record_measurement_name="performance",
                    record_tag_keys=["engine", "type"],
                    record_field_keys=["speed"])

Writing Pandas DataFrame¶

influxdb-python

import pandas as pd

from influxdb import InfluxDBClient

df = pd.DataFrame(data=list(range(30)),
                  index=pd.date_range(start='2014-11-16', periods=30, freq='H'),
                  columns=['0'])

client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')

client.write_points(df, 'demo', protocol='line')

influxdb-client-python

import pandas as pd

from influxdb_client import InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS

with InfluxDBClient(url='http://localhost:8086', token='my-token', org='my-org') as client:
    write_api = client.write_api(write_options=SYNCHRONOUS)

    df = pd.DataFrame(data=list(range(30)),
                      index=pd.date_range(start='2014-11-16', periods=30, freq='H'),
                      columns=['0'])

    write_api.write(bucket='my-bucket', record=df, data_frame_measurement_name='demo')

Querying¶

influxdb-python

from influxdb import InfluxDBClient

client = InfluxDBClient(host='127.0.0.1', port=8086, username='root', password='root', database='dbname')

points = client.query('SELECT * from cpu').get_points()
for point in points:
    print(point)

influxdb-client-python

from influxdb_client import InfluxDBClient

with InfluxDBClient(url='http://localhost:8086', token='my-token', org='my-org', debug=True) as client:
    query = '''from(bucket: "my-bucket")
  |> range(start: -10000d)
  |> filter(fn: (r) => r["_measurement"] == "cpu")
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
'''

    tables = client.query_api().query(query)
    for record in [record for table in tables for record in table.records]:
        print(record.values)

If you would like to omit boilerplate columns such as _result, _table, _start, … you can filter the record values by following expression:

print({k: v for k, v in record.values.items() if k not in ['result', 'table', '_start', '_stop', '_measurement']})

For more info see Flux Response Format.

This repository contains the Python client library for the InfluxDB 2.0.

Note: Use this client library with InfluxDB 2.x and InfluxDB 1.8+. For connecting to InfluxDB 1.7 or earlier instances, use the influxdb-python client library. The API of the influxdb-client-python is not the backwards-compatible with the old one - influxdb-python.

Documentation¶

This section contains links to the client library documentation.

InfluxDB 2.0 client features¶

Querying data
- using the Flux language
- into csv, raw data, flux_table structure, Pandas DataFrame
- How to queries
Writing data using
- Line Protocol
- Data Point
- RxPY Observable
- Pandas DataFrame
- How to writes
InfluxDB 2.0 API client for management
- the client is generated from the swagger by using the openapi-generator
- organizations & users management
- buckets management
- tasks management
- authorizations
- health check
- …
`InfluxDB 1.8 API compatibility`_
Examples
`Advanced Usage`_

Installation¶

InfluxDB python library uses RxPY - The Reactive Extensions for Python (RxPY).

Python 3.6 or later is required.

Note

It is recommended to use ciso8601 with client for parsing dates. ciso8601 is much faster than built-in Python datetime. Since it’s written as a C module the best way is build it from sources:

Windows:

You have to install Visual C++ Build Tools 2015 to build ciso8601 by pip.

conda:

Install from sources: conda install -c conda-forge/label/cf202003 ciso8601.

pip install¶

The python package is hosted on PyPI, you can install latest version directly:

pip install 'influxdb-client[ciso]'

Then import the package:

import influxdb_client

Setuptools¶

Install via Setuptools.

python setup.py install --user

(or sudo python setup.py install to install the package for all users)

Getting Started¶

Please follow the Installation and then run the following:

from influxdb_client import InfluxDBClient, Point
from influxdb_client.client.write_api import SYNCHRONOUS

bucket = "my-bucket"

client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")

write_api = client.write_api(write_options=SYNCHRONOUS)
query_api = client.query_api()

p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3)

write_api.write(bucket=bucket, record=p)

## using Table structure
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for table in tables:
    print(table)
    for row in table.records:
        print (row.values)


## using csv library
csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
val_count = 0
for row in csv_result:
    for cell in row:
        val_count += 1

Client configuration¶

Via File¶

A client can be configured via *.ini file in segment influx2.

The following options are supported:

url - the url to connect to InfluxDB
org - default destination organization for writes and queries
token - the token to use for the authorization
timeout - socket timeout in ms (default value is 10000)
verify_ssl - set this to false to skip verifying SSL certificate when calling API from https server
ssl_ca_cert - set this to customize the certificate file to verify the peer
connection_pool_maxsize - set the number of connections to save that can be reused by urllib3
auth_basic - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
profilers - set the list of enabled Flux profilers

self.client = InfluxDBClient.from_config_file("config.ini")

Via Environment Properties¶

A client can be configured via environment properties.

Supported properties are:

INFLUXDB_V2_URL - the url to connect to InfluxDB
INFLUXDB_V2_ORG - default destination organization for writes and queries
INFLUXDB_V2_TOKEN - the token to use for the authorization
INFLUXDB_V2_TIMEOUT - socket timeout in ms (default value is 10000)
INFLUXDB_V2_VERIFY_SSL - set this to false to skip verifying SSL certificate when calling API from https server
INFLUXDB_V2_SSL_CA_CERT - set this to customize the certificate file to verify the peer
INFLUXDB_V2_CONNECTION_POOL_MAXSIZE - set the number of connections to save that can be reused by urllib3
INFLUXDB_V2_AUTH_BASIC - enable http basic authentication when talking to a InfluxDB 1.8.x without authentication but is accessed via reverse proxy with basic authentication (defaults to false)
INFLUXDB_V2_PROFILERS - set the list of enabled Flux profilers

self.client = InfluxDBClient.from_env_properties()

Profile query¶

The Flux Profiler package provides performance profiling tools for Flux queries and operations.

You can enable printing profiler information of the Flux query in client library by:

set QueryOptions.profilers in QueryApi,
set INFLUXDB_V2_PROFILERS environment variable,
set profilers option in configuration file.

When the profiler is enabled, the result of flux query contains additional tables “profiler/*”. In order to have consistent behaviour with enabled/disabled profiler, FluxCSVParser excludes “profiler/*” measurements from result.

Example how to enable profilers using API:

q = '''
    from(bucket: stringParam)
      |> range(start: -5m, stop: now())
      |> filter(fn: (r) => r._measurement == "mem")
      |> filter(fn: (r) => r._field == "available" or r._field == "free" or r._field == "used")
      |> aggregateWindow(every: 1m, fn: mean)
      |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
'''
p = {
    "stringParam": "my-bucket",
}

query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"]))
csv_result = query_api.query(query=q, params=p)

Example of a profiler output:

You can also use callback function to get profilers output. Return value of this callback is type of FluxRecord.

Example how to use profilers with callback:

 class ProfilersCallback(object):
    def __init__(self):
        self.records = []

    def __call__(self, flux_record):
        self.records.append(flux_record.values)

callback = ProfilersCallback()

query_api = client.query_api(query_options=QueryOptions(profilers=["query", "operator"], profiler_callback=callback))
tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')

for profiler in callback.records:
    print(f'Custom processing of profiler result: {profiler}')

Example output of this callback:

InfluxDB 2.0 python client¶

User Guide¶

API Reference¶

Migration Guide¶

Before You Start¶

Content¶

Initializing Client¶

Creating Database/Bucket¶

Dropping Database/Bucket¶

Writing LineProtocol¶

Writing Dictionary-style object¶

Writing Structured Data¶

Writing Pandas DataFrame¶

Querying¶

Documentation¶

InfluxDB 2.0 client features¶

Installation¶

pip install¶

Setuptools¶

Getting Started¶

Client configuration¶

Via File¶

Via Environment Properties¶

Profile query¶

Indices and tables¶