Operator Tools

This directory contains tools for operators to submit STAC items to the data pipeline for processing.

Overview

The data pipeline processes STAC items from the EOPF STAC catalog. These tools allow operators to:

Submit individual test items for debugging/validation
Search for STAC items by area and time range
Batch submit multiple items for processing
[NEW] Manage STAC collections with S3 data cleanup
[NEW] Monitor S3 storage usage across collections

Setup

Environments

The data pipeline operates in two Kubernetes namespaces:

devseed-staging - Testing and validation environment
devseed - Production data pipeline

Examples below use devseed-staging. For production, replace with devseed.

AWS/S3 Credentials (Required for S3 Features)

For S3 cleanup and statistics features, configure AWS credentials:

# Option 1: Environment variables
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_ENDPOINT_URL="https://s3.de.io.cloud.ovh.net"

# Option 2: AWS CLI configuration
aws configure

Note: S3 features (--clean-s3, --s3-stats) require these credentials. Other features work without them.

Port Forwarding

Before using these tools, you need to set up port forwarding to access the webhook service:

For staging environment:

# Port forward from the webhook eventsource service (staging)
kubectl port-forward -n devseed-staging svc/eopf-explorer-webhook-eventsource-svc 12001:12000 &

This makes the webhook endpoint available at http://localhost:12001/samples.

For prod environment:

# Port forward from the webhook eventsource service (production)
kubectl port-forward -n devseed svc/eopf-explorer-webhook-eventsource-svc 12000:12000 &

This makes the webhook endpoint available at http://localhost:12000/samples.

Available Tools

1. `manage_item.py` - Single Item Management Tool 🆕

Purpose: Manage individual STAC items with full S3 operation support.

Use cases:

Debug problematic items before processing collections
View detailed S3 statistics for a specific item
Delete single items with S3 cleanup and validation
Test item operations before scaling to collections

Quick Examples:

# View item details with S3 stats
uv run operator-tools/manage_item.py info sentinel-2-l2a-staging ITEM_ID --s3-stats

# View storage tier statistics from STAC metadata
uv run operator-tools/manage_item.py info sentinel-2-l2a-staging ITEM_ID --s3-stac-info

# Combine both statistics
uv run operator-tools/manage_item.py info sentinel-2-l2a-staging ITEM_ID --s3-stats --s3-stac-info

# Debug S3 URL extraction
uv run operator-tools/manage_item.py info sentinel-2-l2a-staging ITEM_ID --s3-stats --debug

# Sync storage tier metadata for a single item (dry run)
uv run operator-tools/manage_item.py sync-storage-tiers sentinel-2-l2a-staging ITEM_ID \
    --s3-endpoint https://s3.de.io.cloud.ovh.net --dry-run

# Change storage tier for a single item (dry run - safe, no writes)
uv run operator-tools/manage_item.py change-storage-tier sentinel-2-l2a-staging ITEM_ID \
    --storage-class STANDARD_IA --s3-endpoint https://s3.de.io.cloud.ovh.net --dry-run

# Change storage tier for a single item (requires confirmation)
uv run operator-tools/manage_item.py change-storage-tier sentinel-2-l2a-staging ITEM_ID \
    --storage-class STANDARD_IA --s3-endpoint https://s3.de.io.cloud.ovh.net -y

# Delete single item with S3 cleanup (dry run)
uv run operator-tools/manage_item.py delete sentinel-2-l2a-staging ITEM_ID --clean-s3 --dry-run

# Actually delete the item
uv run operator-tools/manage_item.py delete sentinel-2-l2a-staging ITEM_ID --clean-s3 -y

Key Features:

Detailed item inspection with S3 statistics
Storage tier statistics from STAC metadata
Sync storage tiers with S3 (single item)
Change S3 storage tier for a single item and update STAC metadata
Debug mode for S3 URL extraction troubleshooting
Delete with automatic S3 validation
Dry-run mode for safe testing

When to use: Always start with manage_item.py to debug individual items before running batch operations with manage_collections.py.

2. `manage_collections.py` - Collection Management Tool

Comprehensive tool for managing STAC collections using the Transaction API, now with validated S3 data cleanup and comprehensive storage statistics.

🔄 Refactored: Now uses manage_item.py for all item-level operations, making the code more maintainable and easier to debug.

Use cases:

Clean collections (remove all items)
Clean collections with validated S3 data deletion (removes items AND all S3 objects)
View comprehensive S3 storage statistics (works with any S3 asset structure)
View storage tier statistics from STAC metadata (all items processed)
Automatic validation ensures S3 cleanup succeeds before removing STAC items
Create/update collections from templates
Batch operations on multiple collections
View collection information and statistics

Prerequisites:

STAC API access to https://api.explorer.eopf.copernicus.eu/stac
Write permissions for collection management operations
[NEW] AWS credentials for S3 features (cleanup and statistics)

Quick Start:

# See all available commands
uv run operator-tools/manage_collections.py --help

# View collection info
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging

# View collection with S3 storage statistics
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stats

# View storage tier statistics from STAC metadata
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stac-info

# Combine both statistics
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stats --s3-stac-info

# Debug S3 URL extraction
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stats --debug

# Sync storage tier metadata for entire collection (dry run)
uv run operator-tools/manage_collections.py sync-storage-tiers sentinel-2-l2a-staging \
    --s3-endpoint https://s3.de.io.cloud.ovh.net --dry-run

# Change storage tier for items in a date range (dry run - safe, no writes)
uv run operator-tools/manage_collections.py change-storage-tier sentinel-2-l2a-staging \
    --storage-class STANDARD_IA --start-date 2024-01-01 --end-date 2024-03-31 \
    --s3-endpoint https://s3.de.io.cloud.ovh.net --dry-run

# Change storage tier for all items in a collection (requires confirmation)
uv run operator-tools/manage_collections.py change-storage-tier sentinel-2-l2a-staging \
    --storage-class STANDARD_IA --s3-endpoint https://s3.de.io.cloud.ovh.net -y

# Clean a collection (dry run first!)
uv run operator-tools/manage_collections.py clean sentinel-2-l2a-staging --dry-run
uv run operator-tools/manage_collections.py clean sentinel-2-l2a-staging

# Clean collection AND delete S3 data (with validation)
uv run operator-tools/manage_collections.py clean sentinel-2-l2a-staging --clean-s3 --dry-run
uv run operator-tools/manage_collections.py clean sentinel-2-l2a-staging --clean-s3 -y

# Create/update collection from template
uv run operator-tools/manage_collections.py create stac/sentinel-2-l2a.json
uv run operator-tools/manage_collections.py create stac/sentinel-2-l2a.json --update

# Batch create collections
uv run operator-tools/manage_collections.py batch-create stac/

Key Features:

Validated S3 cleanup - Verifies all S3 objects deleted before removing STAC items
Comprehensive S3 support - Handles individual files, directories, and Zarr stores
Sync storage tiers - Keep STAC metadata in sync with S3 storage classes
Change storage tier - Move items to a different storage class, with date filtering
Debug mode - Detailed S3 URL extraction and validation info
Safety first - STAC items preserved if S3 cleanup fails

Documentation: See README_collections.md for detailed usage and examples.

3. `migrate_catalog.py` - STAC Catalogue Migration Tool

Purpose: Apply catalogue-wide fixes to STAC items with history tracking and recovery support.

Use cases:

Fix incorrect media types or malformed URLs across an entire collection
Preview changes safely with --dry-run before applying
Compose multiple migrations in a single pass
Track which migrations have been applied to which collections

Quick Examples:

# List available migrations
uv run operator-tools/migrate_catalog.py list

# Preview a migration (dry run)
uv run operator-tools/migrate_catalog.py run --migration fix_url_encoding sentinel-2-l2a-staging --dry-run

# Apply a migration
uv run operator-tools/migrate_catalog.py run --migration fix_url_encoding sentinel-2-l2a-staging

# Apply multiple migrations in one pass
uv run operator-tools/migrate_catalog.py run --migration fix_url_encoding --migration fix_zarr_media_type sentinel-2-l2a-staging

# Verify a migration is fully applied
uv run operator-tools/migrate_catalog.py verify --migration fix_url_encoding sentinel-2-l2a-staging

# Clone a collection as backup before migrating
uv run operator-tools/migrate_catalog.py clone sentinel-2-l2a-staging sentinel-2-l2a-staging-backup-20260312 --yes

Documentation: See README_MIGRATIONS.md for the full safe migration procedure, CLI reference, and instructions for writing new migrations.

4. `submit_test_workflow_wh.py` - HTTP Webhook Submission

Submits a single test STAC item via HTTP webhook endpoint.

Use case: Testing the pipeline with a known item

Prerequisites:

Pipeline webhook service running on localhost:12000
requests Python package installed

Usage:

uv run submit_test_workflow_wh.py

Configuration:

Edit the script to change:

source_url: STAC item URL to process
collection: Target collection name
action: Processing action (e.g., convert-v1-s2-hp, or convert-v1-s2-hp)

5. `submit_stac_items_notebook.ipynb` - Interactive STAC Search & Submit

Jupyter notebook for searching and batch submitting STAC items.

Use case: Bulk processing multiple items from a specific area/time range

Prerequisites:

Jupyter notebook environment
Python packages: pystac-client, pandas, requests
Optional: python-dotenv for credential management
Pipeline webhook service running on localhost:12000

Usage:

uv run jupyter notebook submit_stac_items_notebook.ipynb

Features:

Browse available STAC collections
Define area of interest (bounding box) and time range
Search and preview matching items
Submit all or selected items to the pipeline via HTTP webhook
Track submission success/failure

Debugging Workflow: When to Use Which Tool 🔍

The refactored tools follow a "single item → collection" debugging workflow:

Step 1: Debug with `manage_item.py`

When you encounter issues with collection operations, always start by examining individual items:

# 1. Identify a problematic item from collection operation output
python manage_collections.py clean test-coll --clean-s3 -y
# Output shows: "⚠️  Item S2A_MSIL2A_... skipped due to S3 failures"

# 2. Debug that specific item
python manage_item.py info test-coll S2A_MSIL2A_... --s3-stats --debug

# This shows:
# - Exact S3 URLs extracted from the item
# - Which asset fields contain S3 URLs
# - Object counts and sizes
# - Any extraction or access issues

# 3. Test deletion on single item
python manage_item.py delete test-coll S2A_MSIL2A_... --clean-s3 --dry-run

# 4. If dry-run looks good, actually delete
python manage_item.py delete test-coll S2A_MSIL2A_... --clean-s3 -y

Step 2: Scale to Collection

Once individual items work correctly, apply to the entire collection:

# Preview collection operation
python manage_collections.py clean test-coll --clean-s3 --dry-run

# Execute if preview looks good
python manage_collections.py clean test-coll --clean-s3 -y

When to Use Each Tool

Tool	Use When	Example
`manage_item.py`	Debugging single items	`python manage_item.py info coll-id item-id --debug`
`manage_item.py`	Testing operations on one item	`python manage_item.py delete coll-id item-id --dry-run`
`manage_item.py`	Investigating S3 issues	`python manage_item.py info coll-id item-id --s3-stats`
`manage_collections.py`	Viewing collection statistics	`python manage_collections.py info coll-id --s3-stats`
`manage_collections.py`	Batch operations on all items	`python manage_collections.py clean coll-id --clean-s3 -y`
`manage_collections.py`	Collection lifecycle management	`python manage_collections.py create/delete`

Benefits of This Workflow

✅ Faster debugging - Test on single items instead of entire collections ✅ Less risk - Validate operations work before scaling ✅ Better visibility - Debug mode shows exactly what's happening ✅ Easier fixes - Fix item-level issues before batch processing

S3 Data Management

The collection management tool can now interact with S3 storage with comprehensive deletion and validation:

View S3 Storage Statistics

Check how much S3 storage a collection is using:

# View collection info with S3 statistics
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stats

# View storage tier statistics from STAC metadata
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stac-info

# Combine both statistics
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stats --s3-stac-info

# With debug output (shows detailed URL extraction)
uv run operator-tools/manage_collections.py info sentinel-2-l2a-staging --s3-stats --debug

Output includes:

Sample S3 URLs from item assets
Object count and total size for sampled items
Estimated total storage across all items
Works with any S3 asset structure (individual files, Zarr stores, directories)

Storage Tier Statistics (--s3-stac-info):

Processes all items in the collection
Shows distribution of storage tiers (STANDARD, STANDARD_IA, EXPRESS_ONEZONE, MIXED)
Detailed breakdowns for mixed storage tiers
Reads from STAC metadata (no S3 queries required)

Example:

S3 Storage Statistics:
Sampling 5 of 43 items...

  Sample S3 URLs:
    • s3://bucket/product.zarr/measurements/reflectance
    • s3://bucket/product.zarr/quality/atmosphere
    ...

  Sample statistics:
    Objects: 6,235
    Size: 11.7 GB

  Estimated total (all 43 items):
    Objects: ~53,621
    Size: ~100.5 GB

Change S3 Storage Tier

Move items to a different storage class (STANDARD, STANDARD_IA, EXPRESS_ONEZONE), then automatically update the STAC metadata to reflect the change.

Debug workflow — always test a single item first:

ITEM_ID="S2A_MSIL2A_20250831T103701_N0511_R008_T31TFL_20250831T145420"

# 1. Single item dry run (no writes)
uv run operator-tools/manage_item.py change-storage-tier sentinel-2-l2a-staging $ITEM_ID \
    --storage-class STANDARD_IA --s3-endpoint https://s3.de.io.cloud.ovh.net --dry-run

# 2. Single item live run (prompts for confirmation)
uv run operator-tools/manage_item.py change-storage-tier sentinel-2-l2a-staging $ITEM_ID \
    --storage-class STANDARD_IA --s3-endpoint https://s3.de.io.cloud.ovh.net -y

# 3. Verify: storage tier info should show the new class
uv run operator-tools/manage_item.py info sentinel-2-l2a-staging $ITEM_ID --s3-stac-info

Collection-level — with optional date filtering:

# Dry run for a date range
uv run operator-tools/manage_collections.py change-storage-tier sentinel-2-l2a-staging \
    --storage-class STANDARD_IA --start-date 2024-01-01 --end-date 2024-03-31 \
    --s3-endpoint https://s3.de.io.cloud.ovh.net --dry-run

# Live run (prompts for confirmation showing item count)
uv run operator-tools/manage_collections.py change-storage-tier sentinel-2-l2a-staging \
    --storage-class STANDARD_IA --start-date 2024-01-01 --end-date 2024-03-31 \
    --s3-endpoint https://s3.de.io.cloud.ovh.net -y

# All items (no date filter)
uv run operator-tools/manage_collections.py change-storage-tier sentinel-2-l2a-staging \
    --storage-class STANDARD --s3-endpoint https://s3.de.io.cloud.ovh.net -y

Options:

Option	Description
`--storage-class`	Target class: `STANDARD`, `STANDARD_IA`, or `EXPRESS_ONEZONE` (required)
`--start-date`	Filter items on or after this date (`YYYY-MM-DD`)
`--end-date`	Filter items on or before this date (`YYYY-MM-DD`)
`--s3-endpoint`	S3 endpoint URL (falls back to `AWS_ENDPOINT_URL`)
`--include-pattern`	fnmatch pattern for objects to include (repeatable)
`--exclude-pattern`	fnmatch pattern for objects to exclude (repeatable)
`--dry-run`	Show what would change without writing anything
`-y` / `--yes`	Skip confirmation prompt

Safety guarantees:

S3 storage class is changed first; STAC metadata is only updated if S3 succeeds
Items with S3 failures are tracked and reported in the final summary — STAC is not touched
--dry-run propagates to S3 operations; nothing is written in either system

Clean S3 Data (with Validation)

Remove both STAC items and their associated S3 data with automatic validation:

# Preview what would be deleted (RECOMMENDED FIRST STEP)
uv run operator-tools/manage_collections.py clean sentinel-2-l2a-staging --clean-s3 --dry-run

# Delete items and S3 data (with validation)
uv run operator-tools/manage_collections.py clean sentinel-2-l2a-staging --clean-s3 -y

⚠️ CRITICAL WARNING:

S3 data deletion is permanent and cannot be undone
Each item may reference thousands of S3 objects
Always use --dry-run first to verify what will be deleted
Consider checking S3 statistics before cleanup to understand impact

How it works:

Extracts S3 URLs from each item's assets (alternate.s3.href or main href)
Deletes all S3 objects (handles individual files, directories, and Zarr stores)
Validates deletion - verifies all S3 objects were removed
Only if validation succeeds - removes the STAC item from catalog
If validation fails - preserves STAC item and shows warning

Validation & Safety:

Counts S3 objects before and after deletion
Only removes STAC item if all S3 objects were deleted
Preserves STAC metadata if S3 cleanup fails
Reports skipped items with detailed warnings

Typical workflow:

# 1. Check current storage
uv run operator-tools/manage_collections.py info test-collection --s3-stats

# 2. Preview deletion (see object counts)
uv run operator-tools/manage_collections.py clean test-collection --clean-s3 --dry-run

# Review output:
#   Would delete 10 items
#   S3 objects to delete: ~12,470
#   Sample URLs shown for verification

# 3. Proceed if safe
uv run operator-tools/manage_collections.py clean test-collection --clean-s3 -y

# Output:
#   Deleting items and S3 data  [####################################]  10/10
#   ✅ Deleted 10 STAC items
#   ✅ Deleted 12,470 S3 objects

# 4. Verify
uv run operator-tools/manage_collections.py info test-collection

When S3 Cleanup Fails:

If some items encounter S3 deletion failures:

⚠️  Item S2A_...: Failed to delete 3 S3 objects
⚠️  Skipping STAC item deletion for S2A_... due to S3 cleanup failures

✅ Deleted 8 STAC items (2 skipped due to S3 failures)
✅ Deleted 10,123 S3 objects (3 failed)

⚠️  WARNING: 2 items were NOT deleted from STAC catalog because
    their S3 data could not be fully removed.

Fix the S3 access issues and re-run cleanup to process skipped items.

Target Collections

Common target collections for processing:

sentinel-2-l2a-staging - Staging environment for S2 L2A
sentinel-2-l2a-dp-test - Test environment for S2 L2A

Payload Format

All tools submit payloads with these fields:

source_url: Full STAC item URL (self link) - Must be a STAC API URL, not direct zarr
collection: Target collection for processed data
action: (Optional) Processing action/trigger to use

See main README payload examples for correct/incorrect formats.

Common Actions

convert-v1-s2 - Standard Sentinel-2 conversion
convert-v1-s2-hp - High-priority Sentinel-2 conversion

Troubleshooting

HTTP Webhook Not Responding

# Verify port-forward is active
ps aux | grep "port-forward.*12000"

# If not running, start port-forward (staging)
kubectl port-forward -n devseed-staging svc/eopf-explorer-webhook-eventsource-svc 12000:12000 &

# Test webhook connectivity
curl http://localhost:12000

If issues persist, check the platform-deploy troubleshooting guide (or /devseed/ for production).

STAC Search Returns No Items

Verify the bounding box coordinates (format: [min_lon, min_lat, max_lon, max_lat])
Check the date range format (YYYY-MM-DDTHH:MM:SSZ)
Confirm the collection exists: https://stac.core.eopf.eodc.eu/

S3 Access Errors

# Verify AWS credentials are set
echo $AWS_ACCESS_KEY_ID
echo $AWS_SECRET_ACCESS_KEY
echo $AWS_ENDPOINT_URL

# Or check AWS CLI configuration
aws configure list

# Set credentials if missing
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_ENDPOINT_URL="https://s3.de.io.cloud.ovh.net"

S3 Cleanup Taking Too Long

If S3 cleanup is slow:

Each item can reference thousands of S3 objects
Large collections take time (progress bar shows status)
Consider using --dry-run first to estimate total objects
Typical deletion speed: ~100-500 objects/second
Large collections (100+ items) may take several minutes

Items Skipped Due to S3 Failures

If cleanup shows "items skipped due to S3 failures":

Why this happens: The script validates that all S3 objects are deleted before removing STAC items. If any S3 objects remain or deletion fails, the STAC item is preserved to prevent orphaned metadata.

Common causes:

S3 permission issues (need s3:DeleteObject)
Network connectivity problems
Objects locked or with retention policies
Bucket access restrictions

How to fix:

Review specific error messages in the output
Check AWS credentials and permissions
Test bucket access: aws s3 ls s3://bucket-name/
Fix the underlying S3 issue
Re-run cleanup to process previously skipped items

No S3 Data Found

If --s3-stats shows "No S3 data found":

Use debug mode to diagnose:
```
uv run operator-tools/manage_collections.py info collection-id --s3-stats --debug
```
This shows:
- Exact S3 URLs extracted from each item
- Which asset fields contain S3 URLs
- Whether URLs are found in alternate.s3.href or main href

Verify assets have S3 URLs:

# Check a sample item manually
curl https://api.explorer.eopf.copernicus.eu/stac/collections/your-collection/items/item-id | jq '.assets'

Look for S3 URLs in:

assets[*].alternate.s3.href (preferred)
assets[*].href starting with s3://

Check AWS credentials:

echo $AWS_ACCESS_KEY_ID
echo $AWS_SECRET_ACCESS_KEY
aws s3 ls  # Test AWS CLI access

Best Practices

Debug with single items first - Use manage_item.py to test and debug operations on individual items before using manage_collections.py on entire collections
Always use --dry-run for S3 operations - Preview deletions with object counts before executing
Use --debug flag when troubleshooting - Shows detailed S3 URL extraction and validation steps
Test with single items first - Use submit_test_workflow_wh.py or manage_item.py before bulk submissions
Monitor processing - Check pipeline logs/dashboards after submitting
Use appropriate collections - Use test/staging collections for validation
Validate STAC URLs - Ensure source URLs are accessible before submitting
Check webhook service - Ensure the webhook service is running before submitting items
Check S3 statistics before cleanup - Understand storage impact with --s3-stats
Review validation warnings - Pay attention to items skipped due to S3 failures
Set AWS credentials - Required for S3 features (cleanup and statistics)
Trust the validation - If items are skipped, fix S3 issues before retrying

Support

For issues or questions about the data pipeline, contact the pipeline operations team.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Operator Tools

Overview

Setup

Environments

AWS/S3 Credentials (Required for S3 Features)

Port Forwarding

For staging environment:

For prod environment:

Available Tools

1. `manage_item.py` - Single Item Management Tool 🆕

2. `manage_collections.py` - Collection Management Tool

3. `migrate_catalog.py` - STAC Catalogue Migration Tool

4. `submit_test_workflow_wh.py` - HTTP Webhook Submission

5. `submit_stac_items_notebook.ipynb` - Interactive STAC Search & Submit

Debugging Workflow: When to Use Which Tool 🔍

Step 1: Debug with `manage_item.py`

Step 2: Scale to Collection

When to Use Each Tool

Benefits of This Workflow

S3 Data Management

View S3 Storage Statistics

Change S3 Storage Tier

Clean S3 Data (with Validation)

Target Collections

Payload Format

Common Actions

Troubleshooting

HTTP Webhook Not Responding

STAC Search Returns No Items

S3 Access Errors

S3 Cleanup Taking Too Long

Items Skipped Due to S3 Failures

No S3 Data Found

Best Practices

Support

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

Operator Tools

Overview

Setup

Environments

AWS/S3 Credentials (Required for S3 Features)

Port Forwarding

For staging environment:

For prod environment:

Available Tools

1. manage_item.py - Single Item Management Tool 🆕

2. manage_collections.py - Collection Management Tool

3. migrate_catalog.py - STAC Catalogue Migration Tool

4. submit_test_workflow_wh.py - HTTP Webhook Submission

5. submit_stac_items_notebook.ipynb - Interactive STAC Search & Submit

Debugging Workflow: When to Use Which Tool 🔍

Step 1: Debug with manage_item.py

Step 2: Scale to Collection

When to Use Each Tool

Benefits of This Workflow

S3 Data Management

View S3 Storage Statistics

Change S3 Storage Tier

Clean S3 Data (with Validation)

Target Collections

Payload Format

Common Actions

Troubleshooting

HTTP Webhook Not Responding

STAC Search Returns No Items

S3 Access Errors

S3 Cleanup Taking Too Long

Items Skipped Due to S3 Failures

No S3 Data Found

Best Practices

Support

1. `manage_item.py` - Single Item Management Tool 🆕

2. `manage_collections.py` - Collection Management Tool

3. `migrate_catalog.py` - STAC Catalogue Migration Tool

4. `submit_test_workflow_wh.py` - HTTP Webhook Submission

5. `submit_stac_items_notebook.ipynb` - Interactive STAC Search & Submit

Step 1: Debug with `manage_item.py`