How to get the soft delete storage cost

How to get the Cloud Storage soft delete storage cost.

Code sample

Python

For more information, see the Cloud Storage Python API reference documentation.

To authenticate to Cloud Storage, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


import argparse
import json
from typing import Dict, List
import google.cloud.monitoring_v3 as monitoring_client


def get_relative_cost(storage_class: str) -> float:
    """Retrieves the relative cost for a given storage class and location.

    Args:
        storage_class: The storage class (e.g., 'standard', 'nearline').

    Returns:
        The price per GB from the https://cloud.google.com/storage/pricing,
        divided by the standard storage class.
    """
    relative_cost = {
        "STANDARD": 0.023 / 0.023,
        "NEARLINE": 0.013 / 0.023,
        "COLDLINE": 0.007 / 0.023,
        "ARCHIVE": 0.0025 / 0.023,
    }

    return relative_cost.get(storage_class, 1.0)


def get_soft_delete_cost(
    project_name: str,
    soft_delete_window: float,
    agg_days: int,
    lookback_days: int,
) -> Dict[str, List[Dict[str, float]]]:
    """Calculates soft delete costs for buckets in a Google Cloud project.

    Args:
        project_name: The name of the Google Cloud project.
        soft_delete_window: The time window in seconds for considering
          soft-deleted objects (default is 7 days).
        agg_days: Aggregate results over a time period, defaults to 30-day period
        lookback_days: Look back up to upto days, defaults to 360 days

    Returns:
        A dictionary with bucket names as keys and cost data for each bucket,
        broken down by storage class.
    """

    query_client = monitoring_client.QueryServiceClient()

    # Step 1: Get storage class ratios for each bucket.
    storage_ratios_by_bucket = get_storage_class_ratio(
        project_name, query_client, agg_days, lookback_days
    )

    # Step 2: Fetch soft-deleted bytes and calculate costs using Monitoring API.
    soft_deleted_costs = calculate_soft_delete_costs(
        project_name,
        query_client,
        soft_delete_window,
        storage_ratios_by_bucket,
        agg_days,
        lookback_days,
    )

    return soft_deleted_costs


def calculate_soft_delete_costs(
    project_name: str,
    query_client: monitoring_client.QueryServiceClient,
    soft_delete_window: float,
    storage_ratios_by_bucket: Dict[str, float],
    agg_days: int,
    lookback_days: int,
) -> Dict[str, List[Dict[str, float]]]:
    """Calculates the relative cost of enabling soft delete for each bucket in a
       project for certain time frame in secs.

    Args:
        project_name: The name of the Google Cloud project.
        query_client: A Monitoring API query client.
        soft_delete_window: The time window in seconds for considering
          soft-deleted objects (default is 7 days).
        storage_ratios_by_bucket: A dictionary of storage class ratios per bucket.
        agg_days: Aggregate results over a time period, defaults to 30-day period
        lookback_days: Look back up to upto days, defaults to 360 days

    Returns:
        A dictionary with bucket names as keys and a list of cost data
        dictionaries
        for each bucket, broken down by storage class.
    """
    soft_deleted_bytes_time = query_client.query_time_series(
        monitoring_client.QueryTimeSeriesRequest(
            name=f"projects/{project_name}",
            query=f"""
                    {{  # Fetch 1: Soft-deleted (bytes seconds)
                        fetch gcs_bucket :: storage.googleapis.com/storage/v2/deleted_bytes
                        | value val(0) * {soft_delete_window}\'s\'  # Multiply by soft delete window
                        | group_by [resource.bucket_name, metric.storage_class], window(), .sum;

                        # Fetch 2: Total byte-seconds (active objects)
                        fetch gcs_bucket :: storage.googleapis.com/storage/v2/total_byte_seconds 
                        | filter metric.type != 'soft-deleted-object'
                        | group_by [resource.bucket_name, metric.storage_class], window(1d), .mean  # Daily average
                        | group_by [resource.bucket_name, metric.storage_class], window(), .sum  # Total over window

                    }}  # End query definition
                    | every {agg_days}d  # Aggregate over larger time intervals
                    | within {lookback_days}d  # Limit data range for analysis
                    | ratio  # Calculate ratio (soft-deleted (bytes seconds)/ total (bytes seconds))
                    """,
        )
    )

    buckets: Dict[str, List[Dict[str, float]]] = {}
    missing_distribution_storage_class = []
    for data_point in soft_deleted_bytes_time.time_series_data:
        bucket_name = data_point.label_values[0].string_value
        storage_class = data_point.label_values[1].string_value
        # To include location-based cost analysis:
        # 1. Uncomment the line below:
        # location = data_point.label_values[2].string_value
        # 2. Update how you calculate 'relative_storage_class_cost' to factor in location
        soft_delete_ratio = data_point.point_data[0].values[0].double_value
        distribution_storage_class = bucket_name + " - " + storage_class
        storage_class_ratio = storage_ratios_by_bucket.get(
            distribution_storage_class
        )
        if storage_class_ratio is None:
            missing_distribution_storage_class.append(
                distribution_storage_class)
        buckets.setdefault(bucket_name, []).append({
            # Include storage class and location data for additional plotting dimensions.
            # "storage_class": storage_class,
            # 'location': location,
            "soft_delete_ratio": soft_delete_ratio,
            "storage_class_ratio": storage_class_ratio,
            "relative_storage_class_cost": get_relative_cost(storage_class),
        })

    if missing_distribution_storage_class:
        print(
            "Missing storage class for following buckets:",
            missing_distribution_storage_class,
        )
        raise ValueError("Cannot proceed with missing storage class ratios.")

    return buckets


def get_storage_class_ratio(
    project_name: str,
    query_client: monitoring_client.QueryServiceClient,
    agg_days: int,
    lookback_days: int,
) -> Dict[str, float]:
    """Calculates storage class ratios for each bucket in a project.

    This information helps determine the relative cost contribution of each
    storage class to the overall soft-delete cost.

    Args:
        project_name: The Google Cloud project name.
        query_client: Google Cloud's Monitoring Client's QueryServiceClient.
        agg_days: Aggregate results over a time period, defaults to 30-day period
        lookback_days: Look back up to upto days, defaults to 360 days

    Returns:
        Ratio of Storage classes within a bucket.
    """
    request = monitoring_client.QueryTimeSeriesRequest(
        name=f"projects/{project_name}",
        query=f"""
            {{
            # Fetch total byte-seconds for each bucket and storage class
            fetch gcs_bucket :: storage.googleapis.com/storage/v2/total_byte_seconds
            | group_by [resource.bucket_name, metric.storage_class], window(), .sum;
            # Fetch total byte-seconds for each bucket (regardless of class)
            fetch gcs_bucket :: storage.googleapis.com/storage/v2/total_byte_seconds
            | group_by [resource.bucket_name], window(), .sum
            }}
            | ratio  # Calculate ratios of storage class size to total size
            | every {agg_days}d
            | within {lookback_days}d
            """,
    )

    storage_class_ratio = query_client.query_time_series(request)

    storage_ratios_by_bucket = {}
    for time_series in storage_class_ratio.time_series_data:
        bucket_name = time_series.label_values[0].string_value
        storage_class = time_series.label_values[1].string_value
        ratio = time_series.point_data[0].values[0].double_value

        # Create a descriptive key for the dictionary
        key = f"{bucket_name} - {storage_class}"
        storage_ratios_by_bucket[key] = ratio

    return storage_ratios_by_bucket


def soft_delete_relative_cost_analyzer(
    project_name: str,
    cost_threshold: float = 0.0,
    soft_delete_window: float = 604800,
    agg_days: int = 30,
    lookback_days: int = 360,
    list_buckets: bool = False,
) -> str | Dict[str, float]:  # Note potential string output
    """Identifies buckets exceeding the relative cost threshold for enabling soft delete.

    Args:
        project_name: The Google Cloud project name.
        cost_threshold: Threshold above which to consider removing soft delete.
        soft_delete_window: Time window for calculating soft-delete costs (in
          seconds).
        agg_days: Aggregate results over this time period (in days).
        lookback_days: Look back up to this many days.
        list_buckets: Return a list of bucket names (True) or JSON (False,
          default).

    Returns:
        JSON formatted results of buckets exceeding the threshold and costs
        *or* a space-separated string of bucket names.
    """

    buckets: Dict[str, float] = {}
    for bucket_name, storage_sources in get_soft_delete_cost(
        project_name, soft_delete_window, agg_days, lookback_days
    ).items():
        bucket_cost = 0.0
        for storage_source in storage_sources:
            bucket_cost += (
                storage_source["soft_delete_ratio"]
                * storage_source["storage_class_ratio"]
                * storage_source["relative_storage_class_cost"]
            )
        if bucket_cost > cost_threshold:
            buckets[bucket_name] = round(bucket_cost, 4)

    if list_buckets:
        return " ".join(buckets.keys())  # Space-separated bucket names
    else:
        return json.dumps(buckets, indent=2)  # JSON output


def soft_delete_relative_cost_analyzer_main() -> None:
    # Sample run: python storage_soft_delete_relative_cost_analyzer.py <Project Name>
    parser = argparse.ArgumentParser(
        description="Analyze and manage Google Cloud Storage soft-delete costs."
    )
    parser.add_argument(
        "project_name", help="The name of the Google Cloud project to analyze."
    )
    parser.add_argument(
        "--cost_threshold",
        type=float,
        default=0.0,
        help="Relative Cost threshold.",
    )
    parser.add_argument(
        "--soft_delete_window",
        type=float,
        default=604800.0,
        help="Time window (in seconds) for considering soft-deleted objects.",
    )
    parser.add_argument(
        "--agg_days",
        type=int,
        default=30,
        help=(
            "Time window (in days) for aggregating results over a time period,"
            " defaults to 30-day period"
        ),
    )
    parser.add_argument(
        "--lookback_days",
        type=int,
        default=360,
        help=(
            "Time window (in days) for considering the how old the bucket to be."
        ),
    )
    parser.add_argument(
        "--list",
        type=bool,
        default=False,
        help="Return the list of bucketnames seperated by space.",
    )

    args = parser.parse_args()

    response = soft_delete_relative_cost_analyzer(
        args.project_name,
        args.cost_threshold,
        args.soft_delete_window,
        args.agg_days,
        args.lookback_days,
        args.list,
    )
    if not args.list:
        print(
            "To remove soft-delete policy from the listed buckets run:\n"
            # Capture output
            "python storage_soft_delete_relative_cost_analyzer.py"
            " [your-project-name] --[OTHER_OPTIONS] --list > list_of_buckets.txt \n"
            "cat list_of_buckets.txt | gcloud storage buckets update -I "
            "--clear-soft-delete",
            response,
        )
        return
    print(response)


if __name__ == "__main__":
    soft_delete_relative_cost_analyzer_main()

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser.