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How to Use InfluxDB with Its Python Client on Kubernetes – InApps 2025

March 20, 2022 by Phu Nguyen

How to Use InfluxDB with Its Python Client on Kubernetes – InApps 2025

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How to Use InfluxDB with Its Python Client on Kubernetes – InApps is an article under the topic Software Development Many of you are most interested in today !! Today, let’s InApps.net learn How to Use InfluxDB with Its Python Client on Kubernetes – InApps in today’s post !

Key Summary

The article from InApps.net, sponsored by InfluxData and authored by Saiyam Pathak, provides a tutorial on deploying InfluxDB on a Kubernetes cluster and using its Python client to send data. Key points include:

What You’ll Learn:
- Deploying InfluxDB 2.0.1 on a Kubernetes cluster.
- Using the InfluxDB Python client to write data.
Prerequisites:
- A Kubernetes cluster (e.g., Civo’s K3s with $70 monthly credit or any other cluster).
- kubectl installed and configured with the cluster’s kubeconfig file.
- Verify cluster connection: kubectl get nodes to list nodes.
Deploying InfluxDB:
- Clone the repository: https://github.com/saiyam1814/pyconf.git, which includes scripts for Namespace, StatefulSet, and Service for InfluxDB 2.0.1.
- Configure ingress:
  - Edit ing.yaml to include the cluster’s DNS name (obtained from the cluster dashboard) in the host section (e.g., influx.{DNS_NAME}).
  - Access the InfluxDB UI via the host address.
  - Copy the generated token from the UI’s Advanced section for Python client authentication.
Using the InfluxDB Python Client:
- Key Components:
  - Import influxdb-client library.
  - Specify Org (organization for data) and Bucket (data storage location).
  - Create a Client with the host URL and token.
  - Use client.write_api to write data points (e.g., free_mem measurement with host tag and free_mem_Gb field).
  - Example: Write system’s free memory in GB every 5 seconds using write_api.write(bucket, org, point).
- Code Structure:
  - Define data points with Point for measurements.
  - Automate data collection (e.g., free memory) and write to InfluxDB periodically.
Use Cases:
- Capture and process data from scripts (e.g., system metrics) and send it to InfluxDB via the Python client.
- Supports multiple client libraries for various data ingestion needs.
Summary:
- The tutorial demonstrates deploying InfluxDB on a K3s Kubernetes cluster, writing data using the Python client, and visualizing it in the InfluxDB UI.
- Simplifies time-series data management for applications like system monitoring.

Prerequisites

A Kubernetes cluster you control. We’ll take advantage of Civo’s super-fast managed K3s service to experiment with this quickly. If you don’t yet have an account, sign up for the beta now to take advantage of quick deployment times and $70 free credit per month. Alternatively, you could also use any other Kubernetes cluster.
Install and set up kubectl, and have the kubeconfig file for your cluster downloaded.

Make sure you can connect to your Kubernetes cluster by running:

kubectl get nodes NAME STATUS ROLES AGE VERSION kube-master-18e1 Ready master 8h v1.18.6+k3s1 kube-node-4e70 Ready <none> 8h v1.18.6+k3s1 kube-node-d58a Ready <none> 8h v1.18.6+k3s1

kubectl get nodes

NAME STATUS ROLES AGE VERSION

kube–master–18e1 Ready master 8h v1.18.6+k3s1

kube–node–4e70 Ready <none> 8h v1.18.6+k3s1

kube–node–d58a Ready <none> 8h v1.18.6+k3s1

You should see the names of the nodes in your cluster displayed.

Getting Up and Running with InfluxDB

Clone the repository: https://github.com/saiyam1814/pyconf.git

git clone https://github.com/saiyam1814/pyconf.git cd pyconf/deploy # Apply the influx.yaml file kubectl create -f influx.yaml namespace/influxdb created statefulset.apps/influxdb created service/influxdb created

git clone https://github.com/saiyam1814/pyconf.git

cd pyconf/deploy

# Apply the influx.yaml file

kubectl create –f influx.yaml

namespace/influxdb created

statefulset.apps/influxdb created

service/influxdb created

The above script creates the Namespace, StatefulSet and service for InfluxDB version 2.0.1.

Now we will create the ingress, and for that, we will modify the ing.yaml file and input the DNS name of the created cluster. You can get the DNS name from the dashboard.

In the host section of ing.yaml, point to influx.{DNS NAME}.

apiVersion: extensions/v1beta1 kind: Ingress metadata: name: influxdb namespace: influxdb spec: rules: – host: influxdb.360dfac7-2adc-4e60-bdcb-0b7a283ac3c8.k8s.civo.com http: paths: – backend: serviceName: influxdb servicePort: 8086

apiVersion: extensions/v1beta1

kind: Ingress

metadata:

name: influxdb

namespace: influxdb

spec:

rules:

– host: influxdb.360dfac7–2adc–4e60–bdcb–0b7a283ac3c8.k8s.civo.com

http:

paths:

– backend:

serviceName: influxdb

servicePort: 8086

kubectl create -f ing.yml ingress.extensions/influxdb created kubectl get all -n influxdb NAME READY STATUS RESTARTS AGE pod/influxdb-0 1/1 Running 0 6m45s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/influxdb ClusterIP 192.168.145.255 <none> 8086/TCP 6m45s NAME READY AGE statefulset.apps/influxdb 1/1 6m46s kubectl get ing -n influxdb ‘NAME CLASS HOSTS ADDRESS PORTS AGE influxdb <none> influxdb.360dfac7-2adc-4e60-bdcb-0b7a283ac3c8.k8s.civo.com 91.211.153.65 80 55s

kubectl create –f ing.yml

ingress.extensions/influxdb created

kubectl get all –n influxdb

NAME READY STATUS RESTARTS AGE

pod/influxdb–0 1/1 Running 0 6m45s

NAME TYPE CLUSTER–IP EXTERNAL–IP PORT(S) AGE

service/influxdb ClusterIP 192.168.145.255 <none> 8086/TCP 6m45s

NAME READY AGE

statefulset.apps/influxdb 1/1 6m46s

kubectl get ing –n influxdb

‘NAME CLASS HOSTS ADDRESS PORTS AGE

influxdb <none> influxdb.360dfac7–2adc–4e60–bdcb–0b7a283ac3c8.k8s.civo.com 91.211.153.65 80 55s

Navigate to the HOST ADDRESS:

Now, in the Advanced section from the Token, select and copy the generated token — as that would be required for connecting to InfluxDB via the Python client.

Next Steps

Create Kubernetes Secret from the Token

kubectl create secret generic influx –from-literal=token=”qWpu90WO31r02miedaP7-BXG9hmrtfBCPgncqu3PsU-PzZZ3jrg7eC9RE2yZzLurhNlk8tr_maKYE3zpk1GJ2A==” secret/influx created

kubectl create secret generic influx —from–literal=token=“qWpu90WO31r02miedaP7-BXG9hmrtfBCPgncqu3PsU-PzZZ3jrg7eC9RE2yZzLurhNlk8tr_maKYE3zpk1GJ2A==”

secret/influx created

Create Config Map for the Ingress HOST Address

kubectl create configmap host –from-literal=host=”influxdb.360dfac7-2adc-4e60-bdcb-0b7a283ac3c8.k8s.civo.com” configmap/host created

kubectl create configmap host —from–literal=host=“influxdb.360dfac7-2adc-4e60-bdcb-0b7a283ac3c8.k8s.civo.com”

configmap/host created

Create the Daemonset from the Deploy Folder

kubectl create -f ds.yaml daemonset.apps/pyconf-demo created kubectl get pods NAME READY STATUS RESTARTS AGE pyconf-demo-dq5c9 1/1 Running 0 40m pyconf-demo-lbxww 1/1 Running 0 40m pyconf-demo-q65ps 1/1 Running 0 86s

kubectl create –f ds.yaml

daemonset.apps/pyconf–demo created

kubectl get pods

NAME READY STATUS RESTARTS AGE

pyconf–demo–dq5c9 1/1 Running 0 40m

pyconf–demo–lbxww 1/1 Running 0 40m

pyconf–demo–q65ps 1/1 Running 0 86s

Viewing from InfluxDB UI

Understanding the Connection and Writing Points to InfluxDB via Its Python Client

from influxdb_client import InfluxDBClient, Point, WritePrecision from datetime import datetime from influxdb_client.client.write_api import SYNCHRONOUS import schedule import time import socket import os def influx(): print(“starting”) org = “demo” bucket = “demo” client = InfluxDBClient(url=”{}”.format(os.environ.get(‘host’)), token=”{}”.format(os.environ.get(‘token’))) meminfo = dict((i.split()[0].rstrip(‘:’),int(i.split()[1])) for i in open(‘/proc/meminfo’).readlines()) freemem = meminfo[‘MemFree’] / 1024 /1024 write_api = client.write_api(write_options=SYNCHRONOUS) point = Point(“free_mem”).tag(“host”, socket.gethostname()).field(“free_memory_Gb”, freemem ).time(datetime.utcnow(), WritePrecision.NS) write_api.write(bucket, org, point) schedule.every(5).seconds.do(influx) while 1: schedule.run_pending() time.sleep(1)

from influxdb_client import InfluxDBClient, Point, WritePrecision

from datetime import datetime

from influxdb_client.client.write_api import SYNCHRONOUS

import schedule

import time

import socket

import os

def influx():

print(“starting”)

org = “demo”

bucket = “demo”

client = InfluxDBClient(url=“{}”.format(os.environ.get(‘host’)), token=“{}”.format(os.environ.get(‘token’)))

meminfo = dict((i.split()[0].rstrip(‘:’),int(i.split()[1])) for i in open(‘/proc/meminfo’).readlines())

freemem = meminfo[‘MemFree’] / 1024 /1024

write_api = client.write_api(write_options=SYNCHRONOUS)

point = Point(“free_mem”).tag(“host”, socket.gethostname()).field(“free_memory_Gb”, freemem ).time(datetime.utcnow(), WritePrecision.NS)

write_api.write(bucket, org, point)

schedule.every(5).seconds.do(influx)

while 1:

schedule.run_pending()

time.sleep(1)

Import influxdb-client.
Org: Corresponds to the org where data has to be pushed.
Bucket: Corresponds to the bucket where data has to be pushed.
Client: The connection created by proving the host and the token.
Freemem: To get free memory of the host in Gb.
client.write_api for writing to InfluxDB.
Point: The actual point that will be creating measurement free_mem, tag host and field free_mem_Gb.
write_api.write(bucket,org,point)for the insertion to the Db.
Last section makes it run every 5 seconds.

Use Cases

You might have certain scripts that would capture some data, or do some level of processing and then create the data, which can then be sent via the Python client to InfluxDB (for this particular use case). There are a lot of client libraries that are supported and can be used to send the data.

Summary

We have seen how to deploy InfluxDB on an existing K3s Kubernetes cluster. Use the Python client to write data to InfluxDB, and then view the data from InfluxDB UI.

Feature image via Pixabay.

Source: InApps.net

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Phu Nguyen

As a Senior Tech Enthusiast, I bring a decade of experience to the realm of tech writing, blending deep industry knowledge with a passion for storytelling. With expertise in software development to emerging tech trends like AI and IoT—my articles not only inform but also inspire. My journey in tech writing has been marked by a commitment to accuracy, clarity, and engaging storytelling, making me a trusted voice in the tech community.