Clickhouse_reviewed

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

@@ -1,23 +1,21 @@
 ---
-title: Deploy ClickHouse on Google Cloud C4A (Arm-based Axion VMs)
-
-draft: true
-cascade:
-    draft: true
+title: Deploy ClickHouse on Google Cloud C4A Arm virtual machines
 
 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.
+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 behaviour on Arm-based infrastructure.
 
 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)
+  - Provision an Arm-based SUSE SLES virtual machine on Google Cloud using C4A instances powered by Axion processors
+  - Install and start a ClickHouse server on a SUSE Arm64 (C4A) virtual machine
+  - Verify ClickHouse functionality by connecting to the server and running basic insert and query operations
+  - Run baseline ClickHouse performance tests to produce throughput and query latency results for evaluating Arm-based deployments on Google Cloud
 
 prerequisites:
   - A [Google Cloud Platform (GCP)](https://cloud.google.com/free) account with billing enabled
   - Basic familiarity with [ClickHouse](https://clickhouse.com/)
+  - An install guide on [how to get started with Google Cloud Platform](/install-guides/gcp/)
+
 author: Pareena Verma
 
 ##### Tags

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

@@ -1,23 +1,27 @@
 ---
-title: Getting started with ClickHouse on Google Axion C4A (Arm Neoverse-V2)
+title: Get started with ClickHouse on Google Cloud C4A Arm virtual machines
 
 weight: 2
 
 layout: "learningpathall"
 ---
 
-## Google Axion C4A Arm instances in Google Cloud
+## Overview
 
-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.
+This section introduces the Google Axion C4A Arm platform and ClickHouse, providing the context you need before provisioning and deploying ClickHouse on Arm-based Google Cloud virtual machines.
 
-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.
+## Explore Google Axion C4A Arm instances
 
-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.
+Google Axion C4A is a family of Arm-based virtual machines built on Google's custom Axion CPU, based on Arm Neoverse-V2 cores. These virtual machines are designed for running performance-sensitive workloads on Google Cloud using the Arm architecture.
 
-## ClickHouse
+For database and analytics use cases, the C4A series provides a cost-effective alternative to x86 virtual machines while maintaining compatibility with modern Linux software stacks.
 
-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. 
+To learn more about Google Axion, see [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu).
 
-Ideal for analytics, monitoring, and event processing, ClickHouse runs efficiently on both x86 and Arm-based platforms, including AWS Graviton and GCP Arm VMs.  
+## Explore ClickHouse
 
-Learn more at the [ClickHouse website](https://clickhouse.com/).
+ClickHouse is an open-source, columnar OLAP database designed for high-performance analytics and real-time reporting, using vectorized execution and columnar storage to process large datasets efficiently.
+
+ClickHouse runs efficiently on Arm-based platforms, including AWS Graviton and Google Cloud Arm virtual machines, making it a suitable candidate for evaluation on Google Axion C4A instances.
+
+Learn more at the [ClickHouse website](https://clickhouse.com/).

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

@@ -1,50 +1,55 @@
 ---
-title: ClickHouse Baseline Testing on Google Axion C4A Arm Virtual Machine
+title: Establish a ClickHouse baseline on Arm
 weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
+## Establish a ClickHouse baseline
 
-## 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.
-
+This section shows you how to validate that ClickHouse is functioning correctly and establish a basic performance baseline on your SUSE Linux Arm64 virtual machine.
 
 ### Verify ClickHouse is running
 
+Verify that the ClickHouse server is running:
+
 ```console
 sudo systemctl status clickhouse-server
 ```
 
-This confirms that the ClickHouse server is running correctly under systemd and ready to accept connections.
+The output is similar to:
 
 ```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
+   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
+    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.
+
+Connect to the ClickHouse server using the client:
 
 ```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.
+
+Create a test database and table to establish a controlled environment for baseline evaluation:
 
 ```sql
 CREATE DATABASE baseline_test;
 USE baseline_test;
 ```
 
-You should see an output similar to:
+The output is similar to:
+
 ```output
 CREATE DATABASE baseline_test
 Query id: bc615167-ecd5-4470-adb0-918d8ce07caf
@@ -57,7 +62,8 @@ Query id: cd49553a-c0ff-4656-a3e5-f0e9fccd9eba
 Ok.
 0 rows in set. Elapsed: 0.001 sec.
 ```
-Create a simple table optimized for analytics:
+
+Create a table optimized for analytics:
 
 ```sql
 CREATE TABLE events
@@ -70,7 +76,8 @@ ENGINE = MergeTree
 ORDER BY (event_time, user_id);
 ```
 
-You should see an output similar to:
+The output is similar to:
+
 ```output
 Query id: 62ce9b9c-9a7b-45c8-9a58-fa6302b13a88
 
@@ -80,8 +87,8 @@ Ok.
 ```
 
 ### 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):
+
+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
@@ -92,7 +99,8 @@ SELECT
 FROM numbers(10000);
 ```
 
-You should see an output similar to:
+The output is similar to:
+
 ```output
 Query id: af860501-d903-4226-9e10-0e34467f7675
 
@@ -102,15 +110,14 @@ Ok.
 Peak memory usage: 3.96 MiB.
 ```
 
-**Verify row count:**
-
-Row count validation verifies that the inserted data is stored correctly and consistently.
+Verify the row count:
 
 ```sql
 SELECT count(*) FROM events;
 ```
 
-You should see an output similar to:
+The output is similar to:
+
 ```output
 Query id: 644f6556-e69b-4f98-98ec-483ee6869d6e
 
@@ -122,15 +129,19 @@ Query id: 644f6556-e69b-4f98-98ec-483ee6869d6e
 ```
 
 ### 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:
+{{% notice Note %}}
+The results below are intended to establish a baseline for this environment and configuration, not to serve as a comprehensive benchmark or comparison.
+{{% /notice %}}
+
+Run a simple filtered count query:
 
 ```sql
 SELECT count(*) FROM events WHERE event_type = 'click';
 ```
 
-You should see an output similar to:
+The output is similar to:
+
 ```output
 Query id: bd609de4-c08e-4f9f-804a-ee0528c94e4d
 
@@ -142,7 +153,7 @@ Query id: bd609de4-c08e-4f9f-804a-ee0528c94e4d
 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.
+Run a query that groups events by date and counts occurrences:
 
 ```sql
 SELECT
@@ -153,7 +164,8 @@ GROUP BY date
 ORDER BY date;
 ```
 
-You should see an output similar to:
+The output is similar to:
+
 ```output
 Query id: b3db69f8-c885-419f-9900-53d258f0b996
 
@@ -168,7 +180,9 @@ Peak memory usage: 785.05 KiB.
 Exit the client:
 
 ```console
-exit;
+exit
 ```
 
-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.
+## What you've accomplished and what's next
+
+You’ve validated that ClickHouse is stable and functional on the Arm64 virtual machine and established a basic performance baseline you can build on.