review Go Sweet Learning Path

content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/_index.md

@@ -27,7 +27,15 @@ tools_software_languages:
 operatingsystems:
     - Linux
 
-
+further_reading:
+    - resource:
+        title: Effective Go
+        link: https://go.dev/doc/effective_go#performance
+        type: blog
+    - resource:
+        title: Benchmark testing in Go
+        link: https://dev.to/stefanalfbo/benchmark-testing-in-go-17dc
+        type: blog
 
 ### FIXED, DO NOT MODIFY
 # ================================================================================

content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/overview.md

@@ -31,7 +31,7 @@ In this Learning Path, you'll compare performance using two four-core GCP instan
 
 {{% notice Note %}}
 Arm-based c4a-standard-4 instances and Intel-based c4-standard-8 instances both utilize four cores. Both instances are categorized by GCP as members a series that demonstrates consistent high performance.
-The main difference between the two is that c4a has 16 GB of RAM, while c4 has 30 GB of RAM. This Learning Path uses equivalent core counts to ensure a fair performance comparison.
+The main difference between the two is that c4a has 16 GB of RAM, while c4 has 30 GB of RAM. This Learning Path uses equivalent core counts as an example of performance comparison.
 {{% /notice %}}   
 
 

content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/rexec_sweet_install.md

@@ -6,66 +6,65 @@ weight: 53
 layout: learningpathall
 ---
 
-In the last section, you learned how to run benchmarks and Benchstat manually. Now you'll automate that process and generate visual reports using a script called `rexec_sweet.py`.
+In the last section, you learned how to run benchmarks and Benchstat manually. Now you'll automate that process and generate visual reports using a tools called `rexec_sweet`.
 
-## What is rexec_sweet.py?
+## What is rexec_sweet?
 
-`rexec_sweet.py` is a script that automates the benchmarking workflow: it connects to your GCP instances, runs benchmarks, collects results, and generates HTML reports - all in one step.
+`rexec_sweet` is a Python project available on GitHub that automates the benchmarking workflow. It connects to your GCP instances, runs benchmarks, collects results, and generates HTML reports - all in one step.
 
 It provides several key benefits:
 
 - **Automation**: Runs benchmarks on multiple VMs without manual SSH connections
 - **Consistency**: Ensures benchmarks are executed with identical parameters
 - **Visualization**: Generates HTML reports with interactive charts for easier analysis
 
-Before running the script, ensure you've completed the "Install Go, Sweet, and Benchstat" step. All other dependencies are installed automatically by the setup script.
+Before running the tool, ensure you've completed the "Install Go, Sweet, and Benchstat" step. All other dependencies are installed automatically by the installer.
 
 ## Set up rexec_sweet
 
-Follow the steps below to set up rexec_sweet.py.
+Follow the steps below to set up `rexec_sweet`.
 
 ### Create a working directory
 
-On your local machine, open a terminal, then create and change into a directory to store the `rexec_sweet.py` script and related files:
+On your local machine, open a terminal, and create a new directory:
 
-   ```bash
-   mkdir rexec_sweet
-   cd rexec_sweet
-   ```
+```bash
+mkdir rexec_sweet
+cd rexec_sweet
+```
 
 ### Clone the repository
 
-Get the `rexec_sweet.py` script from the GitHub repository:
+Get `rexec_sweet` from GitHub:
 
-   ```bash
-   git clone https://github.com/geremyCohen/go_benchmarks.git
-   cd go_benchmarks
-   ```
+```bash
+git clone https://github.com/geremyCohen/go_benchmarks.git
+cd go_benchmarks
+```
 
 ### Run the installer
 
 Copy and paste this command into your terminal to run the installer:
 
-   ```bash
-   ./install.sh
-   ```
+```bash
+./install.sh
+```
 
-   If the install.sh script detects that you already have dependencies installed, it might ask you if you want to reinstall them:
+If the installer detects that you already have dependencies installed, it might ask you if you want to reinstall them:
 
-   ```output
-   pyenv: /Users/gercoh01/.pyenv/versions/3.9.22 already exists
-   continue with installation? (y/N)
-   ```
+```output
+pyenv: /Users/gercoh01/.pyenv/versions/3.9.22 already exists
+continue with installation? (y/N)
+```
 
-   If you see this prompt, enter `N` to continue with the installation without modifying the existing installed dependencies.
+If you see this prompt, enter `N` to continue with the installation without modifying the existing installed dependencies.
 
 ### Verify VM status
 
 Make sure the GCP VM instances you created in the previous section are running. If not, start them now, and wait a few minutes for them to finish booting.
 
 {{% notice Note %}}
-The install script prompts you to authenticate with Google Cloud Platform (GCP) using the gcloud command-line tool at the end of install. If after installing you have issues running the script and/or get GCP authentication errors, you can manually authenticate with GCP by running the following command: `gcloud auth login`
+The installer prompts you to authenticate with Google Cloud Platform (GCP) using the gcloud command-line tool at the end of install. If after installing you have issues running or you get GCP authentication errors, you can manually authenticate with GCP by running the following command: `gcloud auth login`
 {{% /notice %}}   
 
-
-Continue on to the next section to run the script and see how it simplifies the benchmarking process.
+Continue on to the next section to run the tool and see how it simplifies the benchmarking process.

content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/rexec_sweet_run.md

@@ -5,16 +5,18 @@ weight: 54
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
+
 With `rexec_sweet` installed, your benchmarking instances running, and your local machine authenticated with GCP, you're ready to run automated benchmarks across your configured environments.
 
 ## Run an automated benchmark and generate results
 
 To begin, open a terminal on your local machine and run:
 
 ```bash
-rexec_sweet
+rexec-sweet
 ```
-The script will prompt you to choose a benchmark. 
+
+The tool will prompt you to choose a benchmark. 
 
 Press **Enter** to run the default benchmark, markdown, which is a good starting point for your first run.
 
@@ -33,7 +35,7 @@ Available benchmarks:
 Enter number (1-10) [default: markdown]:
 ```
 
-The script then detects your running GCP instances and displays them. You’ll be asked whether you want to use the first two instances it finds and the default install paths.
+The tool then detects your running GCP instances and displays them. You’ll be asked whether you want to use the first two instances it finds and the default install paths.
 
 ```output
 Available instances:
@@ -70,7 +72,7 @@ Output directory: /private/tmp/a/go_benchmarks/results/c4-c4a-markdown-20250610T
 ...
 ```
 
-After selecting instances and paths, the script will:
+After selecting instances and paths, the tool will:
    - Run the selected benchmark on both VMs
    - Use `benchstat` to compare the results
    - Push the results to your local machine
@@ -89,13 +91,13 @@ Report generated in results/c4-c4a-markdown-20250610T190407
 
 Once on your local machine, `rexec_sweet` will generate an HTML report that opens automatically in your web browser.
 
-   If you close the report, you can reopen it by navigating to the `results` subdirectory and opening report.html in your browser.
+If you close the report, you can reopen it by navigating to the `results` subdirectory and opening report.html in your browser.
 
 ![alt-text#center](images/run_auto/2.png "Sample HTML report")
 
 
 {{% notice Note %}}
-If you see output messages from `rexec_sweet.py` similar to "geomeans may not be comparable" or "Dn: ratios must be >0 to compute geomean", this is expected and can be ignored. These warnings typically appear when benchmark sets differ slightly between the two VMs.
+If you see output messages similar to "geomeans may not be comparable" or "Dn: ratios must be >0 to compute geomean", this is expected and can be ignored. These warnings typically appear when benchmark sets differ slightly between the two VMs.
 {{% /notice %}}
 
-Upon completion, the script generates a report in the `results` subdirectory of the current working directory of the `rexec_sweet.py` script, which opens automatically in your web browser to view the benchmark results and comparisons.
+Upon completion, the tool generates a report in the `results` subdirectory of the current working directory, which opens automatically in your web browser to view the benchmark results and comparisons.

content/learning-paths/servers-and-cloud-computing/go-benchmarking-with-sweet/running_benchmarks.md

@@ -98,15 +98,15 @@ Large gaps between average and peak memory usage suggest opportunities for memor
 
 Here are some general tips to keep in mind as you explore benchmarking across different apps and instance types:
 
-- Unlike Intel and AMD processors that use hyper-threading, Arm processors provide single-threaded cores without hyper-threading. A four-core Arm processor has four independent cores running four threads, while a four-core Intel processor provides eight logical cores through hyper-threading. This means that each Arm vCPU represents a full physical core, while each Intel/AMD vCPU represents half a physical core. For fair comparison, this Learning Path uses a 4-vCPU Arm instance against an 8-vCPU Intel instance.  When scaling up instance sizes during benchmarking, make sure to keep a 2:1 Intel/AMD:Arm vCPU ratio if you wish to keep parity on CPU resources.
+- On Intel and AMD processors with hyper-threading, each vCPU corresponds to a logical core (hardware thread), and two vCPUs share a single physical core. On Arm processors (which do not use hyper-threading), each vCPU corresponds to a full physical core. For comparison, this Learning Path uses a 4-vCPU Arm instance against an 8-vCPU Intel instance, maintaining a 2:1 Intel:Arm vCPU ratio to keep parity on physical CPU resources.
 
-- Run each benchmark at least 10 times (-count 10) to account for outliers and produce statistically meaningful results.
+- Run each benchmark at least 10 times to account for outliers and produce statistically meaningful results.
+
+- Results can be bound by CPU, memory, or I/O performance. If you see significant differences in one metric but not others, it might indicate a bottleneck in that area; running the same benchmark with different configurations (for example, using more CPU cores or more memory) can help identify the bottleneck.
 
-- Results can be bound by CPU, memory, or I/O performance.  If you see significant differences in one metric but not others, it might indicate a bottleneck in that area; running the same benchmark with different configurations (for example, using more CPU cores or more memory) can help identify the bottleneck.
 
 
 
-