Deploy ClickHouse on Google Cloud C4A (Arm-based Axion VMs)

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_index.md

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+---
+title: Deploy ClickHouse on Google Cloud C4A (Arm-based Axion VMs)
+
+minutes_to_complete: 30
+
+who_is_this_for: This learning path is intended for software developers deploying and optimizing ClickHouse on Linux/Arm64 environments, specifically using Google Cloud C4A virtual machines powered by Axion processors.
+
+learning_objectives:
+  - Provision an Arm-based SUSE SLES virtual machine on Google Cloud (C4A with Axion processors)
+  - Install ClickHouse on a SUSE Arm64 (C4A) instance
+  - Verify ClickHouse functionality by starting the server, connecting via client, and performing baseline data insertion and simple query tests on the Arm64 VM
+  - Measure ClickHouse query performance (read, aggregation, and concurrent workloads) to evaluate throughput and latency on Arm64 (Aarch64)
+
+prerequisites:
+  - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
+  - Basic familiarity with [ClickHouse](https://clickhouse.com/)
+author: Pareena Verma
+
+##### Tags
+skilllevels: Introductory
+subjects: Databases
+cloud_service_providers: Google Cloud
+
+armips:
+  - Neoverse
+
+tools_software_languages:
+  - ClickHouse
+  - clickhouse-benchmark
+
+operatingsystems:
+  - Linux
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+further_reading:
+  - resource:
+      title: Google Cloud documentation
+      link: https://cloud.google.com/docs
+      type: documentation
+
+  - resource:
+      title: ClickHouse documentation
+      link: https://clickhouse.com/docs/
+      type: documentation
+
+  - resource:
+      title: ClickHouse benchmark documentation
+      link: https://clickhouse.com/docs/operations/utilities/clickhouse-benchmark
+      type: documentation
+
+weight: 1
+layout: "learningpathall"
+learning_path_main_page: "yes"
+---

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/_next-steps.md

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+---
+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/background.md

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+---
+title: Getting started with ClickHouse on Google Axion C4A (Arm Neoverse-V2)
+
+weight: 2
+
+layout: "learningpathall"
+---
+
+## Google Axion C4A Arm instances in Google Cloud
+
+Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for modern cloud workloads such as CI/CD pipelines, microservices, media processing, and general-purpose applications.
+
+The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability and performance benefits of the Arm architecture in Google Cloud.
+
+To learn more about Google Axion, refer to the [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu) blog.
+
+## ClickHouse
+
+ClickHouse is an open-source, columnar OLAP database designed for **high-performance analytics** and real-time reporting. It supports **vectorized execution, columnar storage, and distributed deployments** for fast queries on large datasets. It offers **scalable, fault-tolerant architecture** with support for replication and sharding. 
+
+Ideal for analytics, monitoring, and event processing, ClickHouse runs efficiently on both x86 and Arm-based platforms, including AWS Graviton and GCP Arm VMs.  
+
+Learn more at the [ClickHouse website](https://clickhouse.com/).

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/baseline.md

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+---
+title: ClickHouse Baseline Testing on Google Axion C4A Arm Virtual Machine
+weight: 5
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## ClickHouse Baseline Testing on GCP SUSE VMs
+This section validates that ClickHouse is functioning correctly and provides a **basic performance baseline** on a SUSE Linux Arm64 VM.
+
+
+### Verify ClickHouse is running
+
+```console
+sudo systemctl status clickhouse-server
+```
+
+This confirms that the ClickHouse server is running correctly under systemd and ready to accept connections.
+
+```output
+● clickhouse-server.service - ClickHouse Server
+     Loaded: loaded (/etc/systemd/system/clickhouse-server.service; enabled; vendor preset: disabled)
+     Active: active (running) since Thu 2025-11-27 05:07:42 UTC; 18s ago
+   Main PID: 4229 (ClickHouseWatch)
+      Tasks: 814
+        CPU: 2.629s
+     CGroup: /system.slice/clickhouse-server.service
+             ├─ 4229 clickhouse-watchdog server --config=/etc/clickhouse-server/config.xml
+             └─ 4237 /usr/bin/clickhouse server --config=/etc/clickhouse-server/config.xml
+```
+
+### Connect to ClickHouse
+Client connection ensures that the ClickHouse CLI can successfully communicate with the running server.
+
+```console
+clickhouse client
+```
+### Create a test database and table
+Database and table creation sets up a dedicated test environment and an analytics-optimized MergeTree table for baseline evaluation.
+
+```sql
+CREATE DATABASE baseline_test;
+USE baseline_test;
+```
+
+You should see an output similar to:
+```output
+CREATE DATABASE baseline_test
+Query id: bc615167-ecd5-4470-adb0-918d8ce07caf
+Ok.
+0 rows in set. Elapsed: 0.012 sec.
+
+
+USE baseline_test
+Query id: cd49553a-c0ff-4656-a3e5-f0e9fccd9eba
+Ok.
+0 rows in set. Elapsed: 0.001 sec.
+```
+Create a simple table optimized for analytics:
+
+```sql
+CREATE TABLE events
+(
+    event_time DateTime,
+    user_id UInt64,
+    event_type String
+)
+ENGINE = MergeTree
+ORDER BY (event_time, user_id);
+```
+
+You should see an output similar to:
+```output
+Query id: 62ce9b9c-9a7b-45c8-9a58-fa6302b13a88
+
+Ok.
+
+0 rows in set. Elapsed: 0.011 sec.
+```
+
+### Insert baseline test data
+Data insertion loads a small, controlled dataset to simulate real event data and validate write functionality.
+Insert sample data (10,000 rows):
+
+```sql
+INSERT INTO events
+SELECT
+    now() - number,
+    number,
+    'click'
+FROM numbers(10000);
+```
+
+You should see an output similar to:
+```output
+Query id: af860501-d903-4226-9e10-0e34467f7675
+
+Ok.
+
+10000 rows in set. Elapsed: 0.003 sec. Processed 10.00 thousand rows, 80.00 KB (3.36 million rows/s., 26.86 MB/s.)
+Peak memory usage: 3.96 MiB.
+```
+
+**Verify row count:**
+
+Row count validation verifies that the inserted data is stored correctly and consistently.
+
+```sql
+SELECT count(*) FROM events;
+```
+
+You should see an output similar to:
+```output
+Query id: 644f6556-e69b-4f98-98ec-483ee6869d6e
+
+   ┌─count()─┐
+1. │   10000 │
+   └─────────┘
+
+1 row in set. Elapsed: 0.002 sec.
+```
+
+### Baseline read performance test
+Baseline read queries measure basic query performance for filtering, aggregation, and grouping, establishing an initial performance reference on the Arm64 VM.
+
+- Run simple analytical queries:
+
+```sql
+SELECT count(*) FROM events WHERE event_type = 'click';
+```
+
+You should see an output similar to:
+```output
+Query id: bd609de4-c08e-4f9f-804a-ee0528c94e4d
+
+   ┌─count()─┐
+1. │   10000 │
+   └─────────┘
+
+1 row in set. Elapsed: 0.003 sec. Processed 10.00 thousand rows, 130.00 KB (2.98 million rows/s., 38.71 MB/s.)
+Peak memory usage: 392.54 KiB.
+```
+
+- This query groups events by date and counts how many events occurred on each day, returning a daily summary of total events in chronological order.
+
+```sql
+SELECT
+    toDate(event_time) AS date,
+    count(*) AS total_events
+FROM events
+GROUP BY date
+ORDER BY date;
+```
+
+You should see an output similar to:
+```output
+Query id: b3db69f8-c885-419f-9900-53d258f0b996
+
+   ┌───────date─┬─total_events─┐
+1. │ 2025-11-27 │        10000 │
+   └────────────┴──────────────┘
+
+1 row in set. Elapsed: 0.002 sec. Processed 10.00 thousand rows, 40.00 KB (4.08 million rows/s., 16.33 MB/s.)
+Peak memory usage: 785.05 KiB.
+```
+
+The baseline tests confirm that ClickHouse is stable, functional, and performing efficiently on the Arm64 VM. With core operations validated, the setup is now ready for detailed performance benchmarking.