influxdb3-python 0.5.0

Community Python client for InfluxDB 3.0

InfluxDB 3.0 Python Client

Introduction

influxdb_client_3 is a Python module that provides a simple and convenient way to interact with InfluxDB 3.0. This module supports both writing data to InfluxDB and querying data using the Flight client, which allows you to execute SQL and InfluxQL queries on InfluxDB 3.0.

We offer a "Getting Started: InfluxDB 3.0 Python Client Library" video that goes over how to use the library and goes over the examples.

Dependencies

• pyarrow (automatically installed)
• pandas (optional)

Installation

You can install 'influxdb3-python' using pip:

pip install influxdb3-python

Note: This does not include Pandas support. If you would like to use key features such as to_pandas() and write_file() you will need to install pandas separately.

Note: Please make sure you are using 3.6 or above. For the best performance use 3.11+

Usage

One of the easiest ways to get started is to checkout the "Pokemon Trainer Cookbook". This scenario takes you through the basics of both the client library and Pyarrow.

Importing the Module

from influxdb_client_3 import InfluxDBClient3, Point

Initialization

If you are using InfluxDB Cloud, then you should note that:

1. You will need to supply your org id, this is not necessary for InfluxDB Dedicated.
2. Use a bucketname for the database argument.

client = InfluxDBClient3(token="your-token",
                         host="your-host",
                         org="your-org",
                         database="your-database")

Writing Data

You can write data using the Point class, or supplying line protocol.

Using Points

point = Point("measurement").tag("location", "london").field("temperature", 42)
client.write(point)

Using Line Protocol

point = "measurement fieldname=0"
client.write(point)

Write from file

Users can import data from CSV, JSON, Feather, ORC, Parquet

import influxdb_client_3 as InfluxDBClient3
import pandas as pd
import numpy as np
from influxdb_client_3 import write_client_options, WritePrecision, WriteOptions, InfluxDBError


class BatchingCallback(object):

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

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

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

callback = BatchingCallback()

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)

wco = write_client_options(success_callback=callback.success,
                          error_callback=callback.error,
                          retry_callback=callback.retry,
                          write_options=write_options
                        )

with  InfluxDBClient3.InfluxDBClient3(
    token="INSERT_TOKEN",
    host="eu-central-1-1.aws.cloud2.influxdata.com",
    org="6a841c0c08328fb1",
    database="python", write_client_options=wco) as client:


    client.write_file(
        file='./out.csv',
        timestamp_column='time', tag_columns=["provider", "machineID"])

    client.write_file(
        file='./out.json',
        timestamp_column='time', tag_columns=["provider", "machineID"], date_unit='ns' )

Pandas DF

client._write_api.write(bucket="pokemon-codex", record=pd_df, data_frame_measurement_name='caught', data_frame_tag_columns=['trainer', 'id', 'num'], data_frame_timestamp_column='timestamp')

Polars DF

client._write_api.write(bucket="pokemon-codex", record=pl_df, data_frame_measurement_name='caught', data_frame_tag_columns=['trainer', 'id', 'num'], data_frame_timestamp_column='timestamp')

Querying

Querying with SQL

query = "select * from measurement"
reader = client.query(query=query, language="sql")
table = reader.read_all()
print(table.to_pandas().to_markdown())

Querying with influxql

query = "select * from measurement"
reader = client.query(query=query, language="influxql")
table = reader.read_all()
print(table.to_pandas().to_markdown())

Windows Users

Currently, Windows users require an extra installation when querying via Flight natively. This is due to the fact gRPC cannot locate Windows root certificates. To work around this please follow these steps: Install certifi

pip install certifi

Next include certifi within the flight client options:

import influxdb_client_3 as InfluxDBClient3
import pandas as pd
import numpy as np
from influxdb_client_3 import flight_client_options
import certifi

fh = open(certifi.where(), "r")
cert = fh.read()
fh.close()


client = InfluxDBClient3.InfluxDBClient3(
    token="",
    host="b0c7cce5-8dbc-428e-98c6-7f996fb96467.a.influxdb.io",
    org="6a841c0c08328fb1",
    database="flightdemo",
    flight_client_options=flight_client_options(
        tls_root_certs=cert))


table = client.query(
    query="SELECT * FROM flight WHERE time > now() - 4h",
    language="influxql")

print(table.to_pandas())

You may also include your own root certificate via this manor aswell.