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

1. string or bytes that is formatted as a InfluxDB’s line protocol
2. Data Point structure
3. Dictionary style mapping with keys: measurement, tags, fields and time or custom structure
4. NamedTuple
5. Data Classes
6. Pandas DataFrame
7. List of above items
8. 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 reactivex as rx
from reactivex 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.utcnow()
        _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 or a JSON 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()

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()

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()

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:

How to use Asyncio

Starting from version 1.27.0 for Python 3.7+ the influxdb-client package supports async/await based on asyncio, aiohttp and aiocsv. You can install aiohttp and aiocsv directly:

$ python -m pip install influxdb-client aiohttp aiocsv

or use the [async] extra:

$ python -m pip install influxdb-client[async]

Warning

The InfluxDBClientAsync should be initialised inside async coroutine otherwise there can be unexpected behaviour. For more info see: Why is creating a ClientSession outside of an event loop dangerous?.

Async APIs

All async APIs are available via InfluxDBClientAsync. The async version of the client supports following asynchronous APIs:

• WriteApiAsync
• QueryApiAsync
• DeleteApiAsync
• Management services into influxdb_client.service supports async operation

and also check to readiness of the InfluxDB via /ping endpoint:

import asyncio

from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


async def main():
    async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:
        ready = await client.ping()
        print(f"InfluxDB: {ready}")


if __name__ == "__main__":
    asyncio.run(main())

Async Write API

The WriteApiAsync supports ingesting data 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

import asyncio

from influxdb_client import Point
from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


async def main():
    async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:

        write_api = client.write_api()

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

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

        print(f" > successfully: {successfully}")


if __name__ == "__main__":
    asyncio.run(main())

Async Query API

The QueryApiAsync supports retrieve data as:

• List of FluxTable
• Stream of FluxRecord via AsyncGenerator
• Pandas DataFrame
• Stream of Pandas DataFrame via AsyncGenerator
• Raw str output

import asyncio

from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


async def main():
    async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:
        # Stream of FluxRecords
        query_api = client.query_api()
        records = await query_api.query_stream('from(bucket:"my-bucket") '
                                               '|> range(start: -10m) '
                                               '|> filter(fn: (r) => r["_measurement"] == "async_m")')
        async for record in records:
            print(record)


if __name__ == "__main__":
    asyncio.run(main())

Async Delete API

import asyncio
from datetime import datetime

from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


async def main():
    async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:
        start = datetime.utcfromtimestamp(0)
        stop = datetime.now()
        # Delete data with location = 'Prague'
        successfully = await client.delete_api().delete(start=start, stop=stop, bucket="my-bucket",
                                                        predicate="location = \"Prague\"")
        print(f" > successfully: {successfully}")


if __name__ == "__main__":
    asyncio.run(main())

Management API

import asyncio

from influxdb_client import OrganizationsService
from influxdb_client.client.influxdb_client_async import InfluxDBClientAsync


async def main():
    async with InfluxDBClientAsync(url='http://localhost:8086', token='my-token', org='my-org') as client:
        # Initialize async OrganizationsService
        organizations_service = OrganizationsService(api_client=client.api_client)

        # Find organization with name 'my-org'
        organizations = await organizations_service.get_orgs(org='my-org')
        for organization in organizations.orgs:
            print(f'name: {organization.name}, id: {organization.id}')


if __name__ == "__main__":
    asyncio.run(main())

Proxy and redirects

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.client.influxdb_client_async import InfluxDBClientAsync


async with InfluxDBClientAsync(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.

Client automatically follows HTTP redirects. The default redirect policy is to follow up to 10 consecutive requests. The redirects can be configured via:

• allow_redirects - If set to False, do not follow HTTP redirects. True by default.
• max_redirects - Maximum number of HTTP redirects to follow. 10 by default.

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

Authentication

InfluxDBClient supports three options how to authorize a connection:

• Token
• Username & Password
• HTTP Basic

Token

Use the token to authenticate to the InfluxDB API. In your API requests, an Authorization header will be send. The header value, provide the word Token followed by a space and an InfluxDB API token. The word token` is case-sensitive.

from influxdb_client import InfluxDBClient

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

Note

Note that this is a preferred way how to authenticate to InfluxDB API.

Username & Password

Authenticates via username and password credentials. If successful, creates a new session for the user.

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086", username="my-user", password="my-password") as client

Warning

The username/password auth is based on the HTTP “Basic” authentication. The authorization expires when the time-to-live (TTL) (default 60 minutes) is reached and client produces unauthorized exception.

HTTP Basic

Use this 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.

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086", auth_basic=True, token="my-proxy-secret") as client

Warning

Don’t use this when directly talking to InfluxDB 2.

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)

Logging

The client uses Python’s logging facility for logging the library activity. The following logger categories are exposed:

• influxdb_client.client.influxdb_client
• influxdb_client.client.influxdb_client_async
• influxdb_client.client.write_api
• influxdb_client.client.write_api_async
• influxdb_client.client.write.retry
• influxdb_client.client.write.dataframe_serializer
• influxdb_client.client.util.multiprocessing_helper
• influxdb_client.client.http
• influxdb_client.client.exceptions

The default logging level is warning without configured logger output. You can use the standard logger interface to change the log level and handler:

import logging
import sys

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as client:
    for _, logger in client.conf.loggers.items():
        logger.setLevel(logging.DEBUG)
        logger.addHandler(logging.StreamHandler(sys.stdout))

Debugging

For debug purpose you can enable verbose logging of HTTP requests and set the debug level to all client’s logger categories by:

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

Note

Both HTTP request headers and body will be logged to standard output.

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 reactivex as rx
from reactivex 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 datetime import timezone
     # import ciso8601
     # from influxdb_client.client.write.point import EPOCH
     #
     # time = (ciso8601.parse_datetime(row["Date"]).replace(tzinfo=timezone.utc) - 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 reactivex as rx
from reactivex 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

Result:

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

• sources - realtime-stream.ipynb

Other examples

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

API Reference

InfluxDBClient

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

InfluxDBClient is client for InfluxDB v2.

Initialize defaults.

Parameters:

• url – InfluxDB server API url (ex. http://localhost:8086).
• token – token to authenticate to the InfluxDB API
• 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, Write and Delete API)

Key bool verify_ssl:  

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 cert_file:  

Path to the certificate that will be used for mTLS authentication.

Key str cert_key_file:  

Path to the file contains private key for mTLS certificate.

Key str cert_key_password:  

String or function which returns password for decrypting the mTLS private key.

Key ssl.SSLContext ssl_context:  

Specify a custom Python SSL Context for the TLS/ mTLS handshake. Be aware that only delivered certificate/ key files or an SSL Context are possible.

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 str username:  

username to authenticate via username and password credentials to the InfluxDB 2.x

Key str password:  

password to authenticate via username and password credentials to the InfluxDB 2.x

Key list[str] profilers:  

list of enabled Flux profilers

authorizations_api() → influxdb_client.client.authorizations_api.AuthorizationsApi

Create the Authorizations API instance.

Returns:authorizations api

buckets_api() → influxdb_client.client.bucket_api.BucketsApi

Create the Bucket API instance.

Returns:buckets api

build() → str

Return the build type of the connected InfluxDB Server.

Returns:The type of InfluxDB build.

close()

Shutdown the client.

delete_api() → influxdb_client.client.delete_api.DeleteApi

Get the delete metrics API instance.

Returns:delete api

classmethod from_config_file(config_file: str = 'config.ini', debug=None, enable_gzip=False, **kwargs)

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

Parameters:

• config_file – Path to configuration file
• debug – Enable verbose logging of http requests
• enable_gzip – Enable Gzip compression for http requests. Currently, only the “Write” and “Query” endpoints supports the Gzip compression.

Key str proxy_headers:  

A dictionary containing headers that will be sent to the proxy. Could be used for proxy authentication.

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 ssl.SSLContext ssl_context:  

Specify a custom Python SSL Context for the TLS/ mTLS handshake. Be aware that only delivered certificate/ key files or an SSL Context are possible.

The supported formats:

• https://docs.python.org/3/library/configparser.html
• https://toml.io/en/
• https://www.json.org/json-en.html

Configuration options:

• url
• org
• token
• timeout,
• verify_ssl
• ssl_ca_cert
• cert_file
• cert_key_file
• cert_key_password
• 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}"

config.json example:

{
    "url": "http://localhost:8086",
    "token": "my-token",
    "org": "my-org",
    "active": true,
    "timeout": 6000,
    "connection_pool_maxsize": 55,
    "auth_basic": false,
    "profilers": "query, operator",
    "tags": {
        "id": "132-987-655",
        "customer": "California Miner",
        "data_center": "${env.data_center}"
    }
}

classmethod from_env_properties(debug=None, enable_gzip=False, **kwargs)

Configure client via environment properties.

Parameters:

• debug – Enable verbose logging of http requests
• enable_gzip – Enable Gzip compression for http requests. Currently, only the “Write” and “Query” endpoints supports the Gzip compression.

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 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 ssl.SSLContext ssl_context:  

Specify a custom Python SSL Context for the TLS/ mTLS handshake. Be aware that only delivered certificate/ key files or an SSL Context are possible.

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_CERT_FILE
• INFLUXDB_V2_CERT_KEY_FILE
• INFLUXDB_V2_CERT_KEY_PASSWORD
• INFLUXDB_V2_CONNECTION_POOL_MAXSIZE
• INFLUXDB_V2_AUTH_BASIC
• INFLUXDB_V2_PROFILERS
• INFLUXDB_V2_TAG

health() → influxdb_client.domain.health_check.HealthCheck

Get the health of an instance.

Returns:HealthCheck

invokable_scripts_api() → influxdb_client.client.invokable_scripts_api.InvokableScriptsApi

Create an InvokableScripts API instance.

Returns:InvokableScripts API instance

labels_api() → influxdb_client.client.labels_api.LabelsApi

Create the Labels API instance.

Returns:labels api

organizations_api() → influxdb_client.client.organizations_api.OrganizationsApi

Create the Organizations API instance.

Returns:organizations api

ping() → bool

Return 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

Create an Query API instance.

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

ready() → influxdb_client.domain.ready.Ready

Get The readiness of the InfluxDB 2.0.

Returns:Ready

tasks_api() → influxdb_client.client.tasks_api.TasksApi

Create the Tasks API instance.

Returns:tasks api

users_api() → influxdb_client.client.users_api.UsersApi

Create the Users API instance.

Returns:users api

version() → str

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

Create 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

Parameters:

• write_options – Write API configuration
• point_settings – settings to store default tags

Key success_callback:  

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>)

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

Initialize query client.

Parameters:influxdb_client – influxdb client

query(query: str, org=None, params: dict = None) → influxdb_client.client.flux_table.TableList

Execute synchronous Flux query and return result as a FluxTable list.

Parameters:

• query – the Flux query
• Organization org (str,) – specifies the organization for executing the query; Take the ID, Name or Organization. If not specified the default value from InfluxDBClient.org is used.
• params – bind parameters

Returns:

FluxTable list wrapped into TableList

Return type:

TableList

Serialization the query results to flattened list of values via to_values():

from influxdb_client import InfluxDBClient

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

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

    # Serialize to values
    output = tables.to_values(columns=['location', '_time', '_value'])
    print(output)

[
    ['New York', datetime.datetime(2022, 6, 7, 11, 3, 22, 917593, tzinfo=tzutc()), 24.3],
    ['Prague', datetime.datetime(2022, 6, 7, 11, 3, 22, 917593, tzinfo=tzutc()), 25.3],
    ...
]

Serialization the query results to JSON via to_json():

from influxdb_client import InfluxDBClient

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

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

    # Serialize to JSON
    output = tables.to_json(indent=5)
    print(output)

[
    {
        "_measurement": "mem",
        "_start": "2021-06-23T06:50:11.897825+00:00",
        "_stop": "2021-06-25T06:50:11.897825+00:00",
        "_time": "2020-02-27T16:20:00.897825+00:00",
        "region": "north",
         "_field": "usage",
        "_value": 15
    },
    {
        "_measurement": "mem",
        "_start": "2021-06-23T06:50:11.897825+00:00",
        "_stop": "2021-06-25T06:50:11.897825+00:00",
        "_time": "2020-02-27T16:20:01.897825+00:00",
        "region": "west",
         "_field": "usage",
        "_value": 10
    },
    ...
]

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) → influxdb_client.client.flux_table.CSVIterator

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 not specified the default value from InfluxDBClient.org is used.
• dialect – csv dialect format
• params – bind parameters

Returns:

Iterator[List[str]] wrapped into CSVIterator

Return type:

CSVIterator

Serialization the query results to flattened list of values via to_values():

from influxdb_client import InfluxDBClient

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

    # Query: using CSV iterator
    csv_iterator = client.query_api().query_csv('from(bucket:"my-bucket") |> range(start: -10m)')

    # Serialize to values
    output = csv_iterator.to_values()
    print(output)

[
    ['#datatype', 'string', 'long', 'dateTime:RFC3339', 'dateTime:RFC3339', 'dateTime:RFC3339', 'double', 'string', 'string', 'string']
    ['#group', 'false', 'false', 'true', 'true', 'false', 'false', 'true', 'true', 'true']
    ['#default', '_result', '', '', '', '', '', '', '', '']
    ['', 'result', 'table', '_start', '_stop', '_time', '_value', '_field', '_measurement', 'location']
    ['', '', '0', '2022-06-16', '2022-06-16', '2022-06-16', '24.3', 'temperature', 'my_measurement', 'New York']
    ['', '', '1', '2022-06-16', '2022-06-16', '2022-06-16', '25.3', 'temperature', 'my_measurement', 'Prague']
    ...
]

If you would like to turn off Annotated CSV header’s you can use following code:

from influxdb_client import InfluxDBClient, Dialect

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

    # Query: using CSV iterator
    csv_iterator = client.query_api().query_csv('from(bucket:"my-bucket") |> range(start: -10m)',
                                                dialect=Dialect(header=False, annotations=[]))

    for csv_line in csv_iterator:
        print(csv_line)

[
    ['', '_result', '0', '2022-06-16', '2022-06-16', '2022-06-16', '24.3', 'temperature', 'my_measurement', 'New York']
    ['', '_result', '1', '2022-06-16', '2022-06-16', '2022-06-16', '25.3', 'temperature', 'my_measurement', 'Prague']
    ...
]

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

Execute synchronous Flux query and return Pandas DataFrame.

Note

If the 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 not specified the default value from InfluxDBClient.org is used.
• data_frame_index – the list of columns that are used as DataFrame index
• params – bind parameters

Returns:

DataFrame or List[DataFrame]

Warning

For the optimal processing of the query results use the pivot() function which align results as a table.

from(bucket:"my-bucket")
    |> range(start: -5m, stop: now())
    |> filter(fn: (r) => r._measurement == "mem")
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")

For more info see:

• https://docs.influxdata.com/resources/videos/pivots-in-flux/
• https://docs.influxdata.com/flux/latest/stdlib/universe/pivot/
• https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/

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

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

Note

If the 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 not specified the default value from InfluxDBClient.org is used.
• data_frame_index – the list of columns that are used as DataFrame index
• params – bind parameters

Returns:

Generator[DataFrame]

Warning

For the optimal processing of the query results use the pivot() function which align results as a table.

from(bucket:"my-bucket")
    |> range(start: -5m, stop: now())
    |> filter(fn: (r) => r._measurement == "mem")
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")

For more info see:

• https://docs.influxdata.com/resources/videos/pivots-in-flux/
• https://docs.influxdata.com/flux/latest/stdlib/universe/pivot/
• https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/

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

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 not specified the default value from InfluxDBClient.org is used.
• 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]

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 not specified the default value from InfluxDBClient.org is used.
• params – bind parameters

Returns:

Generator[‘FluxRecord’]

class influxdb_client.client.flux_table.FluxTable

A table is set of records with a common set of columns and a group key.

The table can be serialized into JSON by:

import json
from influxdb_client.client.flux_table import FluxStructureEncoder

output = json.dumps(tables, cls=FluxStructureEncoder, indent=2)
print(output)

Initialize defaults.

get_group_key()

Group key is a list of columns.

A table’s group key denotes which subset of the entire dataset is assigned to the table.

class influxdb_client.client.flux_table.FluxRecord(table, values=None)

A record is a tuple of named values and is represented using an object type.

Initialize defaults.

get_field()

Get field name.

get_measurement()

Get measurement name.

get_start()

Get ‘_start’ value.

get_stop()

Get ‘_stop’ value.

get_time()

Get timestamp.

get_value()

Get field value.

class influxdb_client.client.flux_table.TableList

FluxTable list with additionally functional to better handle of query result.

to_json(columns: List[str] = None, **kwargs) → str

Serialize query results to a JSON formatted str.

Parameters:columns – if not None then only specified columns are presented in results Returns:str

The query results is flattened to array:

[
    {
        "_measurement": "mem",
        "_start": "2021-06-23T06:50:11.897825+00:00",
        "_stop": "2021-06-25T06:50:11.897825+00:00",
        "_time": "2020-02-27T16:20:00.897825+00:00",
        "region": "north",
         "_field": "usage",
        "_value": 15
    },
    {
        "_measurement": "mem",
        "_start": "2021-06-23T06:50:11.897825+00:00",
        "_stop": "2021-06-25T06:50:11.897825+00:00",
        "_time": "2020-02-27T16:20:01.897825+00:00",
        "region": "west",
         "_field": "usage",
        "_value": 10
    },
    ...
]

The JSON format could be configured via **kwargs arguments:

from influxdb_client import InfluxDBClient

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

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

    # Serialize to JSON
    output = tables.to_json(indent=5)
    print(output)

For all available options see - json.dump.

to_values(columns: List[str] = None) → List[List[object]]

Serialize query results to a flattened list of values.

Parameters:columns – if not None then only specified columns are presented in results Returns:list of values

Output example:

[
    ['New York', datetime.datetime(2022, 6, 7, 11, 3, 22, 917593, tzinfo=tzutc()), 24.3],
    ['Prague', datetime.datetime(2022, 6, 7, 11, 3, 22, 917593, tzinfo=tzutc()), 25.3],
    ...
]

Configure required columns:

from influxdb_client import InfluxDBClient

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

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

    # Serialize to values
    output = tables.to_values(columns=['location', '_time', '_value'])
    print(output)

class influxdb_client.client.flux_table.CSVIterator(response: http.client.HTTPResponse)

Iterator[List[str]] with additionally functional to better handle of query result.

Initialize csv.reader.

to_values() → List[List[str]]

Serialize query results to a flattened list of values.

Returns:list of values

Output example:

[
    ['New York', '2022-06-14T08:00:51.749072045Z', '24.3'],
    ['Prague', '2022-06-14T08:00:51.749072045Z', '25.3'],
    ...
]

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)

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.

Parameters:

• influxdb_client – with default settings (organization)
• write_options – write api configuration
• point_settings – settings to store default tags.

Key success_callback:  

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()

Flush data and dispose a batching buffer.

flush()

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], reactivex.observable.observable.Observable, NamedTuple, Iterable[NamedTuple], dataclass, Iterable[dataclass]] = None, write_precision: influxdb_client.domain.write_precision.WritePrecision = 'ns', **kwargs) → Any

Write time-series data into InfluxDB.

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 not specified the default value from InfluxDBClient.org is used.
• 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

Key data_frame_measurement_name:  

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 data_frame_timestamp_column:  

name of DataFrame column which contains a timestamp. The column can be defined as a str value formatted as 2018-10-26, 2018-10-26 12:00, 2018-10-26 12:00:00-05:00 or other formats and types supported by pandas.to_datetime - DataFrame

Key data_frame_timestamp_timezone:  

name of the timezone which is used for timestamp column - 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:

If the data_frame_timestamp_column is not specified the index of Pandas DataFrame is used as a timestamp for written data. The index can 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)

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

Ref: https://docs.influxdata.com/influxdb/latest/reference/key-concepts/data-elements/#point

Initialize defaults.

field(field, value)

Add field with key and value.

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

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"])

Parameters:

• dictionary – dictionary for serialize into data Point
• write_precision – sets the precision for the supplied time values

Key record_measurement_key:  

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)

Create a new Point with specified measurement name.

classmethod set_str_rep(rep_function)

Set the string representation for all Points.

tag(key, value)

Add tag with key and value.

time(time, write_precision='ns')

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(precision=None)

Create LineProtocol.

param precision:  required precision of LineProtocol. If it’s not set then use the precision from Point.

write_precision

Get precision.

class influxdb_client.domain.write_precision.WritePrecision

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()

Return the model properties as a dict.

to_str()

Return the string representation of the model.

BucketsApi

class influxdb_client.BucketsApi(influxdb_client)

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

Create a bucket.

Parameters:

• bucket (Bucket|PostBucketRequest) – 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 not specified the default value from InfluxDBClient.org is used.

Returns:

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

delete_bucket(bucket)

Delete a bucket.

Parameters:bucket – bucket id or Bucket Returns:Bucket

find_bucket_by_id(id)

Find bucket by ID.

Parameters:id – Returns:

find_bucket_by_name(bucket_name)

Find bucket by name.

Parameters:bucket_name – bucket name Returns:Bucket

find_buckets(**kwargs)

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

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)

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()

Return the model properties as a dict.

to_str()

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)

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

Initialize defaults.

clone_label(cloned_name: str, label: influxdb_client.domain.label.Label) → influxdb_client.domain.label.Label

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

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])

Delete the label.

Parameters:label – label id or Label

find_label_by_id(label_id: str)

Retrieve the label by id.

Parameters:label_id – Returns:Label

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

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]

Get all available labels.

Key str org_id:The organization ID. Returns:labels

update_label(label: influxdb_client.domain.label.Label)

Update an existing label name and properties.

Parameters:label – label Returns:the updated label

OrganizationsApi

class influxdb_client.OrganizationsApi(influxdb_client)

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

Create an organization.

delete_organization(org_id: str)

Delete an organization.

find_organization(org_id)

Retrieve an organization.

find_organizations(**kwargs)

List all organizations.

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

Return the current authenticated user.

update_organization(organization: influxdb_client.domain.organization.Organization) → influxdb_client.domain.organization.Organization

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')

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()

Return the model properties as a dict.

to_str()

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)

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

Initialize defaults.

create_user(name: str) → influxdb_client.domain.user.User

Create a user.

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

Delete a user.

Parameters:user – user id or User Returns:None

find_users(**kwargs) → influxdb_client.domain.users.Users

List all users.

Key int offset:The offset for pagination. The number of records to skip. Key int limit:Limits the number of records returned. Default is 20. 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

Return the current authenticated user.

update_password(user: Union[str, influxdb_client.domain.user.User, influxdb_client.domain.user_response.UserResponse], password: str) → None

Update a password.

Parameters:

• user – User to update password (required)
• password – New password (required)

Returns:

None

update_user(user: influxdb_client.domain.user.User) → influxdb_client.domain.user_response.UserResponse

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')

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()

Return the model properties as a dict.

to_str()

Return the string representation of the model.

TasksApi

class influxdb_client.TasksApi(influxdb_client)

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

Initialize defaults.

add_label(label_id: str, task_id: str) → influxdb_client.domain.label_response.LabelResponse

Add a label to a task.

add_member(member_id, task_id)

Add a member to a task.

add_owner(owner_id, task_id)

Add an owner to a task.

cancel_run(task_id: str, run_id: str)

Cancel a currently running run.

Parameters:

• task_id –
• run_id –

clone_task(task: influxdb_client.domain.task.Task) → influxdb_client.domain.task.Task

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

Create a new task.

create_task_cron(name: str, flux: str, cron: str, org_id: str) → influxdb_client.domain.task.Task

Create a new task with cron repetition schedule.

create_task_every(name, flux, every, organization) → influxdb_client.domain.task.Task

Create a new task with every repetition schedule.

delete_label(label_id: str, task_id: str)

Delete a label from a task.

delete_member(member_id, task_id)

Remove a member from a task.

delete_owner(owner_id, task_id)

Remove an owner from a task.

delete_task(task_id: str)

Delete a task.

find_task_by_id(task_id) → influxdb_client.domain.task.Task

Retrieve a task.

find_tasks(**kwargs)

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)

List all tasks by user.

get_labels(task_id)

List all labels for a task.

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

Retrieve all logs for a task.

Parameters:task_id – task id

get_members(task_id: str)

List all task members.

get_owners(task_id)

List all owners of a task.

get_run(task_id: str, run_id: str) → influxdb_client.domain.run.Run

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]

Retrieve all logs for a run.

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

Retrieve list of run records for a task.

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

retry_run(task_id: str, run_id: str)

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/3.7.9/lib/python3.7/datetime.py'> = None)

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

Update a task.

update_task_request(task_id, task_update_request: influxdb_client.domain.task_update_request.TaskUpdateRequest) → influxdb_client.domain.task.Task

Update a task.

class influxdb_client.domain.Task(id=None, type=None, org_id=None, org=None, name=None, owner_id=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)

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 the 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.

[Cron expression](https://en.wikipedia.org/wiki/Cron#Overview) that defines the schedule on which the task runs. InfluxDB bases cron runs on the system time.

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

description

Get the description of this Task.

The description of the task.

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

every

Get the every of this Task.

An interval ([duration literal](https://docs.influxdata.com/flux/v0.x/spec/lexical-elements/#duration-literals))) at which the task runs. every also determines when the task first runs, depending on the specified time.

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.

A timestamp ([RFC3339 date/time format](https://docs.influxdata.com/flux/v0.x/data-types/basic/time/#time-syntax)) of the latest scheduled and completed run.

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.

A [duration](https://docs.influxdata.com/flux/v0.x/spec/lexical-elements/#duration-literals) to delay execution of the task after the scheduled time has elapsed. 0 removes the offset.

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

org

Get the org of this Task.

The name of the organization that owns the 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 the task.

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

owner_id

Get the owner_id of this Task.

The ID of the user who owns this Task.

Returns:The owner_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()

Return the model properties as a dict.

to_str()

Return the string representation of the model.

type

Get the type of this Task.

The type of the task, useful for filtering a task list.

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

InvokableScriptsApi

class influxdb_client.InvokableScriptsApi(influxdb_client)

Use API invokable scripts to create custom InfluxDB API endpoints that query, process, and shape data.

Initialize defaults.

create_script(create_request: influxdb_client.domain.script_create_request.ScriptCreateRequest) → influxdb_client.domain.script.Script

Create a script.

Parameters:create_request (ScriptCreateRequest) – The script to create. (required) Returns:The created script.

delete_script(script_id: str) → None

Delete a script.

Parameters:script_id (str) – The ID of the script to delete. (required) Returns:None

find_scripts(**kwargs)

List scripts.

Key int limit:The number of scripts to return. Key int offset:The offset for pagination. Returns:List of scripts. Return type:list[Script]

invoke_script(script_id: str, params: dict = None) → influxdb_client.client.flux_table.TableList

Invoke synchronously a script and return result as a TableList.

The bind parameters referenced in the script are substitutes with params key-values sent in the request body.

Parameters:

• script_id (str) – The ID of the script to invoke. (required)
• params – bind parameters

Returns:

FluxTable list wrapped into TableList

Return type:

TableList

Serialization the query results to flattened list of values via to_values():

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="https://us-west-2-1.aws.cloud2.influxdata.com", token="my-token", org="my-org") as client:

    # Query: using Table structure
    tables = client.invokable_scripts_api().invoke_script(script_id="script-id")

    # Serialize to values
    output = tables.to_values(columns=['location', '_time', '_value'])
    print(output)

[
    ['New York', datetime.datetime(2022, 6, 7, 11, 3, 22, 917593, tzinfo=tzutc()), 24.3],
    ['Prague', datetime.datetime(2022, 6, 7, 11, 3, 22, 917593, tzinfo=tzutc()), 25.3],
    ...
]

Serialization the query results to JSON via to_json():

from influxdb_client import InfluxDBClient

with InfluxDBClient(url="https://us-west-2-1.aws.cloud2.influxdata.com", token="my-token", org="my-org") as client:

    # Query: using Table structure
    tables = client.invokable_scripts_api().invoke_script(script_id="script-id")

    # Serialize to JSON
    output = tables.to_json(indent=5)
    print(output)

[
    {
        "_measurement": "mem",
        "_start": "2021-06-23T06:50:11.897825+00:00",
        "_stop": "2021-06-25T06:50:11.897825+00:00",
        "_time": "2020-02-27T16:20:00.897825+00:00",
        "region": "north",
         "_field": "usage",
        "_value": 15
    },
    {
        "_measurement": "mem",
        "_start": "2021-06-23T06:50:11.897825+00:00",
        "_stop": "2021-06-25T06:50:11.897825+00:00",
        "_time": "2020-02-27T16:20:01.897825+00:00",
        "region": "west",
         "_field": "usage",
        "_value": 10
    },
    ...
]

invoke_script_csv(script_id: str, params: dict = None) → influxdb_client.client.flux_table.CSVIterator

Invoke synchronously a script and return result as a CSV iterator. Each iteration returns a row of the CSV file.

The bind parameters referenced in the script are substitutes with params key-values sent in the request body.

Parameters:

• script_id (str) – The ID of the script to invoke. (required)
• params – bind parameters

Returns:

Iterator[List[str]] wrapped into CSVIterator

Return type:

CSVIterator

Serialization the query results to flattened list of values via to_values():

from influxdb_client import InfluxDBClient

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

    # Query: using CSV iterator
    csv_iterator = client.invokable_scripts_api().invoke_script_csv(script_id="script-id")

    # Serialize to values
    output = csv_iterator.to_values()
    print(output)

[
    ['', 'result', 'table', '_start', '_stop', '_time', '_value', '_field', '_measurement', 'location']
    ['', '', '0', '2022-06-16', '2022-06-16', '2022-06-16', '24.3', 'temperature', 'my_measurement', 'New York']
    ['', '', '1', '2022-06-16', '2022-06-16', '2022-06-16', '25.3', 'temperature', 'my_measurement', 'Prague']
    ...
]

invoke_script_data_frame(script_id: str, params: dict = None, data_frame_index: List[str] = None)

Invoke synchronously a script and return Pandas DataFrame.

The bind parameters referenced in the script are substitutes with params key-values sent in the request body.

Note

If the script returns tables with differing schemas than the client generates a DataFrame for each of them.

Parameters:

• script_id (str) – The ID of the script to invoke. (required)
• data_frame_index (List[str]) – The list of columns that are used as DataFrame index.
• params – bind parameters

Returns:

DataFrame or List[DataFrame]

Warning

For the optimal processing of the query results use the pivot() function which align results as a table.

from(bucket:"my-bucket")
    |> range(start: -5m, stop: now())
    |> filter(fn: (r) => r._measurement == "mem")
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")

For more info see:

• https://docs.influxdata.com/resources/videos/pivots-in-flux/
• https://docs.influxdata.com/flux/latest/stdlib/universe/pivot/
• https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/

invoke_script_data_frame_stream(script_id: str, params: dict = None, data_frame_index: List[str] = None)

Invoke synchronously a script and return stream of Pandas DataFrame as a Generator[‘pd.DataFrame’].

The bind parameters referenced in the script are substitutes with params key-values sent in the request body.

Note

If the script returns tables with differing schemas than the client generates a DataFrame for each of them.

Parameters:

• script_id (str) – The ID of the script to invoke. (required)
• data_frame_index (List[str]) – The list of columns that are used as DataFrame index.
• params – bind parameters

Returns:

Generator[DataFrame]

Warning

For the optimal processing of the query results use the pivot() function which align results as a table.

from(bucket:"my-bucket")
    |> range(start: -5m, stop: now())
    |> filter(fn: (r) => r._measurement == "mem")
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")

For more info see:

• https://docs.influxdata.com/resources/videos/pivots-in-flux/
• https://docs.influxdata.com/flux/latest/stdlib/universe/pivot/
• https://docs.influxdata.com/flux/latest/stdlib/influxdata/influxdb/schema/fieldsascols/

invoke_script_raw(script_id: str, params: dict = None) → Iterator[List[str]]

Invoke synchronously a script and return result as raw unprocessed result as a str.

The bind parameters referenced in the script are substitutes with params key-values sent in the request body.

Parameters:

• script_id (str) – The ID of the script to invoke. (required)
• params – bind parameters

Returns:

Result as a str.

invoke_script_stream(script_id: str, params: dict = None) → Generator[influxdb_client.client.flux_table.FluxRecord, Any, None]

Invoke synchronously a script and return result as a Generator[‘FluxRecord’].

The bind parameters referenced in the script are substitutes with params key-values sent in the request body.

Parameters:

• script_id (str) – The ID of the script to invoke. (required)
• params – bind parameters

Returns:

Stream of FluxRecord.

Return type:

Generator[‘FluxRecord’]

update_script(script_id: str, update_request: influxdb_client.domain.script_update_request.ScriptUpdateRequest) → influxdb_client.domain.script.Script

Update a script.

Parameters:

• script_id (str) – The ID of the script to update. (required)
• update_request (ScriptUpdateRequest) – Script updates to apply (required)

Returns:

The updated.

class influxdb_client.domain.Script(id=None, name=None, description=None, org_id=None, script=None, language=None, url=None, created_at=None, updated_at=None)

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

Script - a model defined in OpenAPI.

created_at

Get the created_at of this Script.

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

description

Get the description of this Script.

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

id

Get the id of this Script.

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

language

Get the language of this Script.

Returns:The language of this Script. Return type:ScriptLanguage

name

Get the name of this Script.

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

org_id

Get the org_id of this Script.

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

script

Get the script of this Script.

script to be executed

Returns:The script of this Script. Return type:str

to_dict()

Return the model properties as a dict.

to_str()

Return the string representation of the model.

updated_at

Get the updated_at of this Script.

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

url

Get the url of this Script.

invocation endpoint address

Returns:The url of this Script. Return type:str

class influxdb_client.domain.ScriptCreateRequest(name=None, description=None, script=None, language=None)

NOTE: This class is auto generated by OpenAPI Generator.

Ref: https://openapi-generator.tech

Do not edit the class manually.

ScriptCreateRequest - a model defined in OpenAPI.

description

Get the description of this ScriptCreateRequest.

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

language

Get the language of this ScriptCreateRequest.

Returns:The language of this ScriptCreateRequest. Return type:ScriptLanguage

name

Get the name of this ScriptCreateRequest.

The name of the script. The name must be unique within the organization.

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

script

Get the script of this ScriptCreateRequest.

The script to execute.

Returns:The script of this ScriptCreateRequest. Return type:str

to_dict()

Return the model properties as a dict.

to_str()

Return the string representation of the model.

DeleteApi

class influxdb_client.DeleteApi(influxdb_client)

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

Initialize defaults.

delete(start: Union[str, datetime.datetime], stop: Union[str, datetime.datetime], predicate: str, bucket: str, org: Union[str, influxdb_client.domain.organization.Organization, None] = None) → None

Delete Time series data from InfluxDB.

Parameters:

• datetime.datetime start (str,) – start time
• datetime.datetime stop (str,) – stop time
• predicate (str) – predicate
• bucket (str) – bucket id or name from which data will be deleted
• Organization org (str,) – specifies the organization to delete data from. Take the ID, Name or Organization. If not specified the default value from InfluxDBClient.org is used.

Returns:

class influxdb_client.domain.DeletePredicateRequest(start=None, stop=None, predicate=None)

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.

An expression in [delete predicate syntax](https://docs.influxdata.com/influxdb/v2.2/reference/syntax/delete-predicate/).

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

start

Get the start of this DeletePredicateRequest.

A timestamp ([RFC3339 date/time format](https://docs.influxdata.com/flux/v0.x/data-types/basic/time/#time-syntax)).

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

stop

Get the stop of this DeletePredicateRequest.

A timestamp ([RFC3339 date/time format](https://docs.influxdata.com/flux/v0.x/data-types/basic/time/#time-syntax)).

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

to_dict()

Return the model properties as a dict.

to_str()

Return the string representation of the model.

Helpers

class influxdb_client.client.util.date_utils.DateHelper(timezone: datetime.tzinfo = datetime.timezone.utc)

DateHelper to groups different implementations of date operations.

If you would like to serialize the query results to custom timezone, you can use following code:

from influxdb_client.client.util import date_utils
from influxdb_client.client.util.date_utils import DateHelper
import dateutil.parser
from dateutil import tz

def parse_date(date_string: str):
    return dateutil.parser.parse(date_string).astimezone(tz.gettz('ETC/GMT+2'))

date_utils.date_helper = DateHelper()
date_utils.date_helper.parse_date = parse_date

Initialize defaults.

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

parse_date(date_string: str)

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)

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/3.7.9/lib/python3.7/datetime.py'>)

Convert datetime to UTC timezone.

Parameters:value – datetime Returns:datetime in UTC

class influxdb_client.client.util.multiprocessing_helper.MultiprocessingWriter(**kwargs)

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()

Initialize InfluxDBClient and waits for data to writes into InfluxDB.

start() → None

Start independent process for writing data into InfluxDB.

terminate() → None

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

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

Async API Reference

InfluxDBClientAsync

QueryApiAsync

WriteApiAsync

class influxdb_client.client.write_api_async.WriteApiAsync(influxdb_client, point_settings: influxdb_client.client.write_api.PointSettings = <influxdb_client.client.write_api.PointSettings object>)

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

Example:

from influxdb_client_async import InfluxDBClientAsync


# Initialize async/await instance of Write API
async with InfluxDBClientAsync(url="http://localhost:8086", token="my-token", org="my-org") as client:
    write_api = client.write_api()

Initialize defaults.

Parameters:

• influxdb_client – with default settings (organization)
• point_settings – settings to store default tags.

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], NamedTuple, Iterable[NamedTuple], dataclass, Iterable[dataclass]] = None, write_precision: influxdb_client.domain.write_precision.WritePrecision = 'ns', **kwargs) → bool

Write time-series data into InfluxDB.

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 not specified the default value from InfluxDBClientAsync.org is used.
• 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

Key data_frame_measurement_name:  

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 data_frame_timestamp_column:  

name of DataFrame column which contains a timestamp. The column can be defined as a str value formatted as 2018-10-26, 2018-10-26 12:00, 2018-10-26 12:00:00-05:00 or other formats and types supported by pandas.to_datetime - DataFrame

Key data_frame_timestamp_timezone:  

name of the timezone which is used for timestamp column - 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

Returns:

True for successfully accepted data, otherwise raise an exception

Example:

# Record as Line Protocol
await 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
}
await 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)
await write_api.write("my-bucket", "my-org", point)

DataFrame:

If the data_frame_timestamp_column is not specified the index of Pandas DataFrame is used as a timestamp for written data. The index can 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')

DeleteApiAsync

class influxdb_client.client.delete_api_async.DeleteApiAsync(influxdb_client)

Async implementation for ‘/api/v2/delete’ endpoint.

Initialize defaults.

delete(start: Union[str, datetime.datetime], stop: Union[str, datetime.datetime], predicate: str, bucket: str, org: Union[str, influxdb_client.domain.organization.Organization, None] = None) → bool

Delete Time series data from InfluxDB.

Parameters:

• datetime.datetime start (str,) – start time
• datetime.datetime stop (str,) – stop time
• predicate (str) – predicate
• bucket (str) – bucket id or name from which data will be deleted
• Organization org (str,) – specifies the organization to delete data from. Take the ID, Name or Organization. If not specified the default value from InfluxDBClientAsync.org is used.

Returns:

True for successfully deleted data, otherwise raise an exception

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:

• User Guide, README.rst
• Examples
• API Reference
• CHANGELOG.md

Content

• Initializing Client
• Creating Database/Bucket
• Dropping Database/Bucket

• Writes

  • LineProtocol
  • Dictionary-style object
  • Structured data
  • Pandas DataFrame

• 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.

Development

The following document covers how to develop the InfluxDB client library locally. Including how to run tests and build the docs.

tl;dr

# from your forked repo, create and activate a virtualenv
python -m virtualenv venv
. venv/bin/activate
# install the library as editable with all dependencies
make install
# make edits
# run lint and tests
make lint test

Getting Started

1. Install Python

   Most distributions include Python by default, so before going too far, try running python --version to see if it already exists. You may also have to specify python3 --version, for example, on Ubuntu.

2. Fork and clone the repo

   The rest of this assumes you have cloned your fork of the upstream client library and are in the same directory of the forked repo.

3. Set up a virtual environment.

   Python virtual environments let you install specific versioned dependencies in a contained manner. This way, you do not pollute or have conflicts on your system with different versions.

   python -m virtualenv venv
   . venv/bin/activate
   

   Having a shell prompt change via starship or something similar is nice as it will let you know when and which virtual environment in you are in.

   To exit the virtual environment, run deactivate.

4. Install the client library

   To install the local version of the client library run:

   make install
   

   This will install the library as editable with all dependencies. This includes all dependencies that are used for all possible features as well as testing requirements.

5. Make changes and test

   At this point, a user can make the required changes necessary and run any tests or scripts they have.

   Before putting up a PR, the user should attempt to run the lint and tests locally. Lint will ensure the formatting of the code, while tests will run integration tests against an InfluxDB instance. For details on that set up see the next section.

   make lint test
   

Linting

The library uses flake8 to do linting and can be run with:

make lint

Testing

The built-in tests assume that there is a running instance of InfluxDB 2.x up and running. This can be accomplished by running the scripts/influxdb-restart.sh script. It will launch an InfluxDB 2.x instance with Docker and make it available locally on port 8086.

Once InfluxDB is available, run all the tests with:

make test

Code Coverage

After running the tests, an HTML report of the tests is available in the htmlcov directory. Users can open html/index.html file in a browser and see a full report for code coverage across the whole project. Clicking on a specific file will show a line-by-line report of what lines were or were not covered.

Documentation

The docs are built using Sphinx. To build all the docs run:

make docs

This will build and produce a sample version of the web docs at docs/_build/html/index.html. From there the user can view the entire site and ensure changes are rendered correctly.

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.

• Product documentation, Getting Started
• Examples
• API Reference
• Changelog

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

  • `Connect to InfluxDB Cloud`_
  • `How to efficiently import large dataset`_
  • `Efficiency write data from IOT sensor`_
  • `How to use Jupyter + Pandas + InfluxDB 2`_

• `Advanced Usage`_

  • `Gzip support`_
  • `Proxy configuration`_
  • `Nanosecond precision`_
  • `Delete data`_
  • `Handling Errors`_
  • `Logging`_

Installation

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

Python 3.7 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

If your application uses async/await in Python you can install with the async extra:

$ pip install influxdb-client[async]

For more info se `How to use Asyncio`_.

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
• cert_file - path to the certificate that will be used for mTLS authentication
• cert_key_file - path to the file contains private key for mTLS certificate
• cert_key_password - string or function which returns password for decrypting the mTLS private key
• 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_CERT_FILE - path to the certificate that will be used for mTLS authentication
• INFLUXDB_V2_CERT_KEY_FILE - path to the file contains private key for mTLS certificate
• INFLUXDB_V2_CERT_KEY_PASSWORD - string or function which returns password for decrypting the mTLS private key
• 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:

Indices and tables

• Index
• Module Index
• Search Page