Performance Reporting

Automating Performance Report generation using Terraform

Infrastructure-as-a-Service (IaaS) on Google Cloud Platform (GCP) provides a comprehensive suite of features for managing and reporting on your cloud infrastructure. In this blog, we’ll explore some of the key reporting features available on GCP’s IaaS platform.

Performance Reports Google Cloud provides detailed performance reports for all of the resources running on your infrastructure. These reports can be accessed through the Google Cloud Console or via the Google Cloud API. Performance reports can be used to identify bottlenecks in your infrastructure, diagnose issues with applications, and optimize resource utilization. You can generate performance reports for CPU usage, network throughput, disk usage, and much more.

To generate a monthly performance report on Google Cloud Platform (GCP) using Terraform, you can follow these steps:

1. Define the resources to monitor: First, you’ll need to define the resources you want to monitor. This could include virtual machines, storage volumes, and network configurations. You can use the Google Cloud Provider for Terraform to define these resources.

2. Collect performance data: Next, you’ll need to collect performance data on these resources. You can use the Google Cloud Monitoring API to collect metrics such as CPU usage, network throughput, and disk usage.

3. Store performance data: Once you’ve collected the performance data, you’ll need to store it in a data store that can be accessed by Terraform. You can use a variety of data stores, such as Google Cloud Storage or BigQuery.

4. Generate the report: With the performance data stored in a data store, you can use Terraform to generate a monthly performance report. This report can include metrics such as CPU utilization, network throughput, and disk usage.

5. Schedule the report generation: To automate the generation of the monthly performance report, you can use a scheduling mechanism such as cron or a third-party scheduling service. The scheduling mechanism should run the Terraform script to generate the report.

Here is an example Terraform code to automate the generation of a monthly performance report on GCP:

This block configures the Google Cloud Provider for Terraform. It sets the credentials file path, project ID, and region for the provider.

# Configure the Google Cloud Provider
provider "google" {
  credentials = file("path/to/credentials.json")
  project     = "my-project-id"
  region      = "us-central1"
}

This block defines the resource to monitor, which is a Google Compute Engine instance in this case. It sets the name, machine type, and zone for the instance.

# Define the resources
resource "google_compute_instance" "vm_instance" {
  name         = "my-vm"
  machine_type = "n1-standard-1"
  zone         = "us-central1-a"
  # other configuration options...
}

This block defines the metric to collect data on, which is CPU utilization in this case. It sets the filter to only collect data on CPU utilization and sets the project ID for the metric. It also sets the start and end times to collect data for the last hour.

# Define the metric
data "google_monitoring_metric" "cpu_usage" {
  filter    = "metric.type=\"compute.googleapis.com/instance/cpu/utilization\""
  project   = "my-project-id"
  start_time = timestamp() - 3600 # collect data for last hour
  end_time   = timestamp()
}

This block defines the data store to store the performance data, which is a BigQuery table in this case. It sets the dataset ID and table ID for the BigQuery table and defines the schema for the table. It also defines a BigQuery table data insert to insert the performance data into the table.

# Define the data store
resource "google_bigquery_dataset" "my_dataset" {
  dataset_id = "my_dataset"
}

resource "google_bigquery_table" "my_table" {
  table_id = "my_table"
  schema   = <<EOF
[
  {
    "name": "timestamp",
    "type": "TIMESTAMP",
    "mode": "REQUIRED"
  },
  {
    "name": "cpu_utilization",
    "type": "FLOAT64",
    "mode": "REQUIRED"
  }
]
EOF
  dataset_id = google_bigquery_dataset.my_dataset.dataset_id
}

resource "google_bigquery_table_data_insert_all" "insert" {
  dataset_id = google_bigquery_dataset.my_dataset.dataset_id
  table_id   = google_bigquery_table.my_table.table_id

  rows = [
    {
      "json": {
        "timestamp": data.google_monitoring_metric.cpu_usage.points[0].end_time.rfc3339,
        "cpu_utilization": data.google_monitoring_metric.cpu_usage.points[0].value
      }
    }
  ]
}

This block defines the report generation process. It defines a null resource that triggers a script to generate the report. It sets the triggers to generate the report at the end of each month. It also defines a local-exec provisioner that runs a script to generate the report.

# Define the report generation
resource "null_resource" "monthly_performance_report" {
  triggers = {
    # generate the report at the end of each month
    now = timestamp()
    monthly = timestamp() + 2678400 # 31 days in seconds
  }

  provisioner "local-exec" {
    command = "generate_performance_report.sh"
  }
}

This block defines the report generation shedule .

# Schedule the report generation
resource "google_cloud_scheduler_job" "monthly_performance_report_job" {
  name = "monthly-performance-report"
  schedule = "0 0 1

In conclusion, generating monthly performance reports on Google Cloud Platform can be a daunting task, but using Terraform can greatly simplify the process. By following the steps outlined in this guide, you can set up a workflow to automatically monitor and store performance data for a Google Compute Engine instance, and generate a monthly report using a BigQuery table and a script.

Using Terraform to automate the process of generating monthly performance reports on Google Cloud Platform has several benefits. Firstly, it reduces the risk of human error, as the process is fully automated. Secondly, it saves time, as the report is generated automatically, without the need for manual intervention. Thirdly, it allows for easy replication, as the Terraform code can be used to generate similar reports for other resources.

Overall, Terraform provides an efficient and scalable solution for generating performance reports on Google Cloud Platform. By automating the process using Terraform, you can ensure that your performance data is accurate, consistent, and easily accessible, enabling you to make informed decisions about your infrastructure.