Migrate from InfluxDB

QuestDB's first-party clients leverage the InfluxDB Line Protocol (ILP).

As a result, the InfluxDB clients work with QuestDB.

This guide demonstrates how to use InfluxDB's clients with QuestDB.

If you don't use the InfluxDB clients and want to use the QuestDB clients, see the ingestion overview.

While InfluxDB clients are supported, we recommend using the QuestDB clients.

Benchmark results for QuestDB 7.3.10, InfluxDB 2.7.4

Overview

If QuestDB is not up and running, see the quick start.

Tables are automatically created during insert.

There is no need for an upfront schema!

Example with InfluxDB's Python client

Our example is adapted from the InfluxDB Python client repo.

There are many ways that the client can be utilized.

The InfluxDB clients have many examples, and you may already be deep into a usage pattern.

Therefore the example below provides trivialized write examples.

Switch client URLs

By default, the InfluxDB connection string of http://localhost:8086 is included in client examples.

With QuestDB, change url in the client method to an active QuestDB instance.

The default QuestDB endpoint is: http://localhost:9000.

If you would prefer to use 8086 or any port, alter the HTTP port via http.bind.to in server.conf:

http.bind.to=0.0.0.0:8086

Now apply the client & write as per usual:

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

# Create a new client - note the QuestDB HTTP port (9000)
# Pass the username and password for basic auth
# If Enterprise, swap clients to use below username & password string:
# client = InfluxDBClient(url="http://localhost:9000", username="admin", password="questdb")
client = InfluxDBClient(url="http://localhost:9000", token="")

# Use blocking write client for writes to desired bucket
write_api = client.write_api(write_options=SYNCHRONOUS)

# Create point using full params constructor
p = Point("stat").tag("unit", "temperature").field("avg", 24.5).field("max", 45.0).time(datetime.utcnow(), WritePrecision.NS)

# Write point immediately
write_api.write(bucket="qdb", record=p, write_precision=WritePrecision.NS)

# Create point using fluent style
p = Point("stat").tag("unit", "temperature").field("avg", 23.2).field("max", 45.0).time(datetime.utcnow(), WritePrecision.NS)

# Write point
write_api.write(bucket="qdb", record=p, write_precision=WritePrecision.NS)

# Or write directly via line protocol
line = "stat,unit=temperature avg=23.5,max=45.0"
write_api.write(bucket="qdb", record=line)

Features

Error handling

Our example table above created avg & max columns which were given inferred types of double:

p = Point("stat").tag("unit", "temperature").field("avg", 24.5).field("max", 45.0).time(datetime.utcnow(), WritePrecision.NS)

If a request were then to be made that, say, passed strings instead:

p = Point("stat").tag("unit", "temperature").field("avg", "24.5").field("max", "45.0").time(datetime.utcnow(), WritePrecision.NS)

A helpful error will be presented:

panic: invalid: failed to parse line protocol:errors encountered on line(s):
error in line 1: table: stat, column: avg; cast error from protocol type: STRING to column type: DOUBLE

Precision

Pass precision parameters to ensure correct timestamp increment:

...
p = Point("stat").tag("unit", "temperature").field("avg", 24.5).field("max", 45.0).time(datetime.utcnow(), WritePrecision.NS)
...

Precision parameters include n or ns for nanoseconds, u or us for microseconds, ms for milliseconds, s for seconds, m for minutes and h for hours. Otherwise, it will default to nanoseconds.

Ping! Health check

Is your server running? Well then you'd better... catch it!

But in all seriousness, a health check endpoint is always of high usefulness.

curl -I http://localhost:9000/ping

Returns (pong!):

HTTP/1.1 204 OK
Server: questDB/1.0
Date: Fri, 2 Feb 2024 17:09:38 GMT
Transfer-Encoding: chunked
Content-Type: text/plain; charset=utf-8
X-Influxdb-Version: v2.7.4

Determine whether an instance is active and confirm the version of InfluxDB Line Protocol with which you are interacting.

Transform data in QuestDB

InfluxDB exports only one metric for each line. If you are storing more than one metric for the same series, one row will create multiple ILP lines with one valid metric value. The other metrics then show as NULL. Therefore, once your data arrives we recommend transforming it in QuestDB.

For example, if you query a table with several metrics:

SELECT * FROM diagnostics WHERE timestamp = '2016-01-01T00:00:00.000000Z' AND driver='Andy' AND name='truck_150')

Your result may be something like this:

device_version	driver	fleet	model	current_load	timestamp	fuel_capacity	fuel_state	load_capacity	nominal_fuel_consumption	status	name
v1.0	Andy	West	H-2	null	2016-01-01T00:00:00.000000Z	null	null	150	null	null	truck_150
v1.0	Andy	West	H-2	null	2016-01-01T00:00:00.000000Z	null	null	150	12	null	truck_150
v1.0	Andy	West	H-2	null	2016-01-01T00:00:00.000000Z	null	null	150	null	null	truck_150
v1.0	Andy	West	H-2	0	2016-01-01T00:00:00.000000Z	null	null	150	null	null	truck_150
v1.0	Andy	West	H-2	null	2016-01-01T00:00:00.000000Z	150	null	150	null	null	truck_150
v1.0	Andy	West	H-2	null	2016-01-01T00:00:00.000000Z	null	1	150	null	null	truck_150

To solve this, execute a SQL query grouping data by all dimensions and then select the maximum values for all the metrics:

SELECT
  timestamp,
  device_version,
  driver,
  fleet,
  model,
  name,
  max(current_load) AS current_load,
  max(fuel_capacity) AS fuel_capacity,
  max(fuel_state) AS fuel_state,
  max(load_capacity) AS load_capacity,
  max(nominal_fuel_consumption) AS nominal_fuel_consumption,
  max(status) AS status
FROM
  diagnostics;

This produces aggregated rows containing all the metrics for each dimension group:

timestamp	device_version	driver	fleet	model	name	current_load	fuel_capacity	fuel_state	load_capacity	nominal_fuel_consumption	status
2016-01-01T00:00:00.000000Z	v1.0	Derek	East	G-2000	truck_3248	0	300	1	null	null	null
2016-01-01T00:00:00.000000Z	v1.0	Derek	East	G-2000	truck_2222	0	300	1	5000	19	0
2016-01-01T00:00:00.000000Z	v1.0	Derek	East	G-2000	truck_1886	0	300	1	5000	19	0
2016-01-01T00:00:00.000000Z	v1.0	Derek	East	G-2000	truck_1540	0	300	1	5000	19	0
... (rows continue) ...	...	...	...	...	...	...	...	...	...	...	...
2016-01-01T00:00:00.000000Z	v1.0	Andy	West	H-2	truck_583	0	150	1	1500	12	0
2016-01-01T00:00:00.000000Z	v1.0	Andy	West	H-2	truck_3546	0	150	1	1500	12	0
2016-01-01T00:00:00.000000Z	v1.0	Andy	West	H-2	truck_3247	0	150	1	1500	12	0

You can use the INSERT keyword to output the processed result into a new table.

Also, if you leverage Telegraf, you can do this with a small configuration change:

# -- AGGREGATOR PLUGINS ------------------------------------------------- #
# Merge metrics into multifield metrics by series key
[[aggregators.merge]]
  ## If true, the original metric will be dropped by the
  ## aggregator and will not get sent to the output plugins.
  drop_original = true

Dig deeper

Now that you know the basics of InfluxDB Line Protocol, you may want to dig deeper into customization options for ILP or HTTP, learn more about the protocol, or compare QuestDB with Influx:

• ILP server.conf configuration
• ILP API Overview
• Comparing TimescaleDB and QuestDB performance and architecture blog
• Deep dive of QuestDB vs InfluxDB internals