Build a real-time analytics pipeline with ClickHouse on Google Cloud Axion
Introduction
Get started with ClickHouse on Google Cloud C4A Arm virtual machines
Create a Firewall Rule on GCP
Create a Google Axion C4A Arm virtual machine on GCP
Set up GCP Pub/Sub and IAM for ClickHouse real-time analytics on Axion
Install ClickHouse
Establish a ClickHouse baseline on Arm
Build a Dataflow streaming ETL pipeline to ClickHouse
Benchmark ClickHouse on Google Axion processors
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  50 min
Last updated:  01 Jul 2026
Skill level:
  Introductory
Reading time:
  50 min
Last updated:
  01 Jul 2026
Author:Pareena Verma, Arm
Arm IP:Neoverse
Tags: Databases Google Cloud Linux ClickHouse Apache Beam Google Dataflow Google Cloud Pub/Sub Python 3.11
Author:
  • Pareena Verma, Arm
Arm IP:
Neoverse
Tags:
Databases Google Cloud Linux ClickHouse Apache Beam Google Dataflow Google Cloud Pub/Sub Python 3.11

Who is this for?

This is an introductory topic for developers deploying and optimizing ClickHouse on Arm-based Linux environments using Google Cloud C4A virtual machines powered by Axion processors, to evaluate ClickHouse performance and behavior on Arm-based infrastructure.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Provision an Arm-based SUSE SLES virtual machine on Google Cloud using C4A (Axion processors)
  • Configure Google Cloud Pub/Sub for real-time log ingestion
  • Deploy and validate ClickHouse on a SUSE Linux Arm64 (Axion) VM
  • Build a streaming ETL pipeline using Apache Beam and Google Dataflow
  • Ingest real-time Pub/Sub data into ClickHouse using Dataflow
  • Validate end-to-end data flow from Pub/Sub to ClickHouse
  • Perform baseline and analytical query benchmarking on ClickHouse running on Arm64
  • Measure and report query latency (including p95) on Axion processors

Prerequisites

Before starting, you will need the following:

Summary

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This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll deploy ClickHouse on an Arm-based Google Cloud C4A virtual machine and assemble a streaming analytics pipeline. After exploring C4A (Neoverse-V2) instances, you’ll provision a c4a-standard-4 SUSE Linux VM, create a firewall rule to open TCP port 8123, and install ClickHouse alongside the Google Cloud CLI. Then, you’ll set up Pub/Sub and required IAM so Apache Beam on Dataflow can stream events into ClickHouse. By the end, you’ll validate end-to-end ingestion from Pub/Sub to ClickHouse and run baseline and analytical queries, capturing latency metrics (including p95) to evaluate ClickHouse behavior on Axion processors.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which machine type should I choose for the VM in this path?
Select the c4a-standard-4 machine type (4 vCPUs, 16 GB) in the Google Cloud console when creating the instance. This matches the configuration used in the steps.
Which firewall port needs to be opened for ClickHouse access?
Open inbound TCP port 8123 by creating a firewall rule in your VPC. This exposes the ClickHouse endpoint required by the path.
How do I verify Pub/Sub is ready before launching the pipeline?
Confirm that the logs-topic exists under Pub/Sub → Topics and keep encryption and retention at their defaults. Complete the IAM setup so Dataflow and the VM can access Pub/Sub.
Where should I install and use the Google Cloud CLI (`gcloud`) during this workflow?
Install the Google Cloud CLI on the VM. Use it to authenticate to GCP, publish Pub/Sub messages, and submit Dataflow jobs.
What results should I record when validating and benchmarking ClickHouse?
Verify that events published to Pub/Sub arrive in ClickHouse and then run baseline and analytical queries. Capture query latency metrics, including p95.
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