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A basic issue only repo to track and store project work for DSCO
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[TASK] - Research Geocoding/Routing components, data model, etc. #91

Open NickZachary opened 1 day ago

NickZachary commented 1 day ago

FinOps Strategy for Cost-Recovery for a Public-Facing Geocoding Service

Implementing a FinOps strategy for a public-facing geocoding service involves building a clear framework for monitoring costs, allocating resources, recovering costs, and ensuring cost transparency. Below is a structured approach, covering components and data models that could be used.

1. Logical Structure and Components

To structure the FinOps model for cost recovery effectively, the following core components should be designed:

a. Cost Monitoring and Tracking

This component focuses on continuously monitoring and tracking the resources consumed by the geocoding service. It involves:

b. User Access and Authentication

Ensure the geocoding service is accessible only to registered users, ensuring accountability and accurate usage tracking:

c. Cost Allocation Model

This component involves allocating service costs back to individual users, clients, or projects. Cost allocation should be based on:

d. Billing and Invoicing System

e. Cost Reporting and Dashboards

Provide cost visibility and transparency to users via:

f. Governance and Optimization

2. Data Model

The data model should store information regarding users, usage, resources, costs, and billing. Below is a sample relational data model for this purpose:

a. Users Table (users)

Field Type Description
user_id INT (PK) Unique identifier for each user.
username VARCHAR Username or API key of the user.
email VARCHAR User's email for billing purposes.
account_type ENUM Type of account (e.g., Free, Pro).

b. Requests Table (requests)

Field Type Description
request_id INT (PK) Unique identifier for each geocoding request.
user_id INT (FK) Reference to the user who made the request.
request_type ENUM Type of request (e.g., forward, reverse).
timestamp TIMESTAMP Timestamp of the geocoding request.
latency FLOAT Time taken to process the request (in milliseconds).
compute_usage FLOAT CPU or GPU cycles used to process the request.
bandwidth FLOAT Data transferred during the request (in GB).
cost FLOAT Cost of the request based on resource usage.

c. Billing Table (billing)

Field Type Description
billing_id INT (PK) Unique identifier for each billing cycle.
user_id INT (FK) Reference to the user being billed.
start_date DATE Start date of the billing period.
end_date DATE End date of the billing period.
total_cost FLOAT Total cost for the user during the billing period.
paid BOOLEAN Indicates if the bill has been paid.

d. Usage Metrics Table (usage_metrics)

Field Type Description
metric_id INT (PK) Unique identifier for each usage metric entry.
user_id INT (FK) Reference to the user.
request_count INT Number of geocoding requests made during the period.
total_latency FLOAT Total latency in milliseconds.
total_bandwidth FLOAT Total bandwidth consumed in GB.
compute_hours FLOAT Total compute usage in hours.
reporting_period DATE Reporting period (monthly, weekly).

3. Components in Detail

a. API Rate Limiting and Quota Management

The service should limit users based on their subscription tier:

b. Cost and Usage Optimization

To ensure profitability and sustainability:

c. Pricing Model

4. FinOps Lifecycle

  1. Inform Phase: Collect and track all resource usage across geocoding services and generate reports showing how costs are allocated per project or user.
  2. Optimize Phase: Continuously analyze usage patterns to identify inefficiencies. For example, flag users consuming excess bandwidth or compute time, and introduce cost-saving measures like caching.
  3. Operate Phase: Implement policies like rate limiting, service quotas, and user authentication to ensure sustainable operation while maintaining transparency through dashboards and alerts.
NickZachary commented 1 day ago

To implement FinOps monitoring, reporting, and billing calculations using the Esri Python API in a Google Cloud Platform (GCP) environment, you can leverage the arcgis library from Esri and combine it with GCP's cloud billing data. Here's a code example to perform the following tasks:

  1. Monitor Resource Utilization: Gather usage data from the Esri platform.
  2. Report and Calculate Billing: Calculate and allocate costs to different projects in a 1:M (one-to-many) relationship.
  3. Generate Reports: Produce reports on the billing and usage breakdown for multiple projects.

The following Python script assumes you have the Esri Python API installed and you have access to both GCP billing and the ArcGIS platform resources:

Install Esri Python API

pip install arcgis

Python Code for FINOps using Esri Python API and GCP Billing Data

from arcgis.gis import GIS
from arcgis.gis.server import Server
import json
import google.auth
from google.cloud import bigquery
from datetime import datetime, timedelta
import pandas as pd

# Authenticate with Esri GIS and GCP
def authenticate_esri(username, password, url="https://www.arcgis.com"):
    # Log into ArcGIS Online or Portal
    gis = GIS(url, username, password)
    print(f"Logged in as: {gis.users.me.username}")
    return gis

def authenticate_gcp():
    # Get GCP credentials
    credentials, project_id = google.auth.default()
    client = bigquery.Client(credentials=credentials, project=project_id)
    return client

# Get the total resource usage from Esri Enterprise (ArcGIS Server)
def get_server_usage(gis, server_url):
    # Access the server
    server = Server(url=server_url, gis=gis)
    service_usage = []

    # Query server statistics for services
    services = server.services.list()
    for service in services:
        usage_info = service.usage_reports.query(
            start_time=(datetime.now() - timedelta(days=30)).isoformat(),
            end_time=datetime.now().isoformat(),
            metrics='Requests'
        )
        for result in usage_info['usageReports']:
            service_usage.append({
                'service_name': service.properties["serviceName"],
                'usage': result['aggregated']['sum'],
                'start_time': result['startTime'],
                'end_time': result['endTime']
            })

    return pd.DataFrame(service_usage)

# Fetch the GCP billing data for a given project and calculate costs
def get_gcp_billing(client, project_id):
    # BigQuery SQL query to extract billing data
    query = f"""
    SELECT
        service.description AS service,
        sku.description AS sku,
        usage_start_time,
        usage_end_time,
        project.id AS project_id,
        usage.amount AS usage_amount,
        usage.unit AS usage_unit,
        cost,
        currency
    FROM
        `gcp_billing_dataset.gcp_billing_table`
    WHERE
        project.id = '{project_id}'
        AND usage_start_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
    """
    query_job = client.query(query)
    billing_data = query_job.result()

    # Convert to pandas DataFrame
    billing_df = pd.DataFrame([dict(row.items()) for row in billing_data])

    return billing_df

# Calculate per-project billing for Esri services based on usage
def calculate_billing(usage_df, billing_df, cost_per_request=0.001):
    # Join Esri usage data with GCP billing data for cost calculation
    usage_df['project_id'] = usage_df['service_name'].apply(lambda x: "project-id-mapping-here")  # Custom logic for 1:M mapping
    combined_df = pd.merge(usage_df, billing_df, how="left", on="project_id")

    # Calculate costs for each project
    combined_df['calculated_cost'] = combined_df['usage'] * cost_per_request
    project_billing = combined_df.groupby('project_id')['calculated_cost'].sum().reset_index()

    return project_billing

# Generate a billing report
def generate_billing_report(billing_df, report_file="billing_report.csv"):
    # Save billing data to CSV
    billing_df.to_csv(report_file, index=False)
    print(f"Billing report saved to {report_file}")

# Main function to execute the FinOps process
def main():
    # Authenticate with Esri and GCP
    gis = authenticate_esri(username="your_username", password="your_password")
    gcp_client = authenticate_gcp()

    # Get server usage from Esri Enterprise
    server_url = "https://your-server-url/server"
    usage_df = get_server_usage(gis, server_url)

    # Get billing data from GCP for the project
    gcp_billing_df = get_gcp_billing(gcp_client, project_id="your-gcp-project-id")

    # Calculate the billing costs for each project
    project_billing_df = calculate_billing(usage_df, gcp_billing_df)

    # Generate a report of the billing data
    generate_billing_report(project_billing_df)

if __name__ == "__main__":
    main()

Key Components of the Code:

  1. Authentication:

    • Uses the arcgis library to authenticate with Esri GIS.
    • Uses Google Cloud's BigQuery API to authenticate and fetch GCP billing data.

  2. Resource Monitoring:

    • Queries ArcGIS Server usage reports for the last 30 days, summarizing the number of requests per service.
    • Assumes that the service_name can be mapped to a GCP project (for 1:M mapping of services to projects).

  3. Billing Calculation:

    • Uses GCP BigQuery to extract billing data for the relevant GCP projects.
    • Calculates costs based on resource usage (e.g., cost per request, which could be adjusted to reflect actual Esri service costs).
    • Joins Esri usage data with GCP billing to calculate per-project billing.

  4. Report Generation:

    • Outputs a CSV report (billing_report.csv) summarizing the cost per project.

Customization:

Next Steps: