influxdb-client 0.0.1

InfluxDB 2.0 Python client library

InfluxDB 2.0 python client library. The library covers InfluxDB 2.0

InfluxDB 2.0 client features

• Querying data

  • using the Flux language

  • into csv, raw data, flux_table structure

• Writing data using

  • Line Protocol

  • Data Point

  • RxPY Observable

  • Not implemented yet - write user types using decorator - write Pandas DataFrame

• 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

Installation

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

Python 3.6 or later is required.

pip install

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

pip3 install git+https://github.com/influxdata/influxdb-client-python.git

Then import the package:

import influxdb_client

Setuptools

Install via Setuptools.

python setup.py install --user

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

Getting Started

Please follow the Installation and then run the following:

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

bucket = "my-bucket"

client = InfluxDBClient(url="http://localhost:9999", 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, org="my-org", 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

How to use

Writes

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.

The default instance of ``WriteApi`` use batching.

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 unsuccessful write. The retry interval is used when the InfluxDB server does not specify “Retry-After” header.	1000

import rx
from rx import operators as ops

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

_client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
_write_client = _client.write_api(write_options=WriteOptions(batch_size=500,
                                                             flush_interval=10_000,
                                                             jitter_interval=2_000,
                                                             retry_interval=5_000))

"""
Write Line Protocol
"""
_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 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)


"""
Close client
"""
_write_client.__del__()
_client.__del__()

Asynchronous client

Data are writes in an asynchronous HTTP request.

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

client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
write_client = client.write_api(write_options=ASYNCHRONOUS)

...

client.__del__()

Synchronous client

Data are writes in a synchronous HTTP request.

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

client = InfluxDBClient(url="http://localhost:9999", token="my-token", org="my-org")
write_client = client.write_api(write_options=SYNCHRONOUS)

...

client.__del__()

How to efficiently import large dataset

• 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
from datetime import datetime

import rx
from rx import operators as ops

from influxdb_client import InfluxDBClient, Point, WriteOptions

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

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

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

    :param row: the row of CSV file
    :return: Parsed csv row to [Point]
    """
    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(datetime.strptime(row['Date'], '%Y-%m-%d'))


"""
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:9999", token="my-token", org="my-org", debug=True)

"""
Create client that writes data in batches with 500 items.
"""
write_api = client.write_api(write_options=WriteOptions(batch_size=500, jitter_interval=1_000))

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

"""
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(org="my-org", 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.__del__()

Efficiency write data from IOT sensor

• sources - iot_sensor.py

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

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

from influxdb_client import InfluxDBClient, WriteApi, WriteOptions

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

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


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:9999", 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(org="my-org", bucket="my-bucket", record=data)


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

input()

Advanced Usage

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:9999", token="my-token", org="my-org", enable_gzip=True)