Python Flight client
Limited availability
InfluxDB Clustered is currently only available to a limited group of InfluxData customers. If interested in being part of the limited access group, please contact the InfluxData Sales team.
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 Clustered. 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 Clustered 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-host.com: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-host.com: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 Clustered databaseDATABASE_TOKEN: a database token with sufficient permissions to the specified database
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