Documentation

Python Flight client

Apache Arrow Python bindings integrate with Python scripts and applications to query data stored in InfluxDB.

Use InfluxDB v3 client libraries

We recommend using the influxdb3-python Python client library for integrating InfluxDB v3 with your Python application code.

InfluxDB v3 client libraries wrap Apache Arrow Flight clients and provide convenient methods for writing, querying, and processing data stored in InfluxDB Cloud Dedicated. Client libraries can query using SQL or InfluxQL.

The following examples show how to use the pyarrow.flight and pandas Python modules to query and format data stored in an InfluxDB Cloud Dedicated database:

# Using pyarrow>=12.0.0 FlightClient
from pyarrow.flight import FlightClient, Ticket, FlightCallOptions 
import json
import pandas
import tabulate

# Downsampling query groups data into 2-hour bins
sql="""
  SELECT DATE_BIN(INTERVAL '2 hours', time) AS time,
    room,
    selector_max(temp, time)['value'] AS 'max temp',
    selector_min(temp, time)['value'] AS 'min temp',
    avg(temp) AS 'average temp'
  FROM home
  GROUP BY
    1,
    room
  ORDER BY room, 1"""
  
flight_ticket = Ticket(json.dumps({
  "namespace_name": "
DATABASE_NAME
"
,
"sql_query": sql, "query_type": "sql" })) token = (b"authorization", bytes(f"Bearer
DATABASE_TOKEN
"
.encode('utf-8')))
options = FlightCallOptions(headers=[token]) client = FlightClient(f"grpc+tls://cluster-id.influxdb.io:443") reader = client.do_get(flight_ticket, options) arrow_table = reader.read_all() # Use pyarrow and pandas to view and analyze data data_frame = arrow_table.to_pandas() print(data_frame.to_markdown())
# Using pyarrow>=12.0.0 FlightClient
from pyarrow.flight import FlightClient, Ticket, FlightCallOptions 
import json
import pandas
import tabulate

# Downsampling query groups data into 2-hour bins
influxql="""
  SELECT FIRST(temp)
  FROM home 
  WHERE room = 'kitchen'
    AND time >= now() - 100d
    AND time <= now() - 10d
  GROUP BY time(2h)"""
  
flight_ticket = Ticket(json.dumps({
  "namespace_name": "
DATABASE_NAME
"
,
"sql_query": influxql, "query_type": "influxql" })) token = (b"authorization", bytes(f"Bearer
DATABASE_TOKEN
"
.encode('utf-8')))
options = FlightCallOptions(headers=[token]) client = FlightClient(f"grpc+tls://cluster-id.influxdb.io:443") reader = client.do_get(flight_ticket, options) arrow_table = reader.read_all() # Use pyarrow and pandas to view and analyze data data_frame = arrow_table.to_pandas() print(data_frame.to_markdown())

Replace the following:

  • DATABASE_NAME: your InfluxDB Cloud Dedicated database
  • DATABASE_TOKEN: a database token with sufficient permissions to the specified database

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The future of Flux

Flux is going into maintenance mode. You can continue using it as you currently are without any changes to your code.

Flux is going into maintenance mode and will not be supported in InfluxDB 3.0. This was a decision based on the broad demand for SQL and the continued growth and adoption of InfluxQL. We are continuing to support Flux for users in 1.x and 2.x so you can continue using it with no changes to your code. If you are interested in transitioning to InfluxDB 3.0 and want to future-proof your code, we suggest using InfluxQL.

For information about the future of Flux, see the following: