abstract-block-dumper 0.0.1

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Abstract Block Dumper

    

This package provides a simplified framework for creating block processing tasks in Django applications. Define tasks with lambda conditions using the @block_task decorator and run them asynchronously with Celery.

Implementation Details

General Workflow:

Register functions -> detect new blocks -> evaluate conditions -> send to Celery -> execute -> track results -> handle retries.

WorkflowSteps

1. Register

• Functions are automatically discovered when the scheduler starts
• Functions must be located in installed apps in tasks.py or block_tasks.py
• Functions marked with @block_task decorators are stored in memory registry

2. Detect Blocks

• Scheduler is running by management command block_tasks
• Scheduler polls blockchain, finds new blocks, and batches them

3. Plan Tasks

• For each block, lambda conditions are evaluated against registered functions
• Tasks are created for matching conditions (with optional multiple argument sets)

4. Queue Tasks are sent to Celery with queue and timeout settings from celery_kwargs

5. Execute Celery runs the function with block info, capturing results and errors

6. Track Task attempts are stored in TaskAttempt model with retry logic and state tracking

Prerequisites

• Django
• Celery
• Redis (for Celery broker and result backend)
• PostgreSQL (recommended for production)

Installation

1. Install the package:

pip install abstract_block_dumper

2. Add to your Django INSTALLED_APPS:

INSTALLED_APPS = [
    # ... other apps
    'abstract_block_dumper',
]

3. Run migrations:

python manage.py migrate

Usage

1. Define Block Processing Tasks

Create block processing tasks in tasks.py or block_tasks.py file inside any of your installed Django apps.

2. Use Decorators to Register Tasks

• Use @block_task with lambda conditions to create custom block processing tasks

3. Start the Block Scheduler

Run the scheduler to start processing blocks:

$ python manage.py block_tasks

This command will:

• Automatically discover and register all decorated functions
• Start polling the blockchain for new blocks
• Schedule tasks based on your lambda conditions

4. Start Celery Workers

In separate terminals, start Celery workers to execute tasks:

$ celery -A your_project worker --loglevel=info

See examples below:

Use the @block_task decorator with lambda conditions to create block processing tasks:

from abstract_block_dumper.decorators import block_task


# Process every block
@block_task(condition=lambda bn: True)
def process_every_block(block_number: int):
    print(f"Processing every block: {block_number}")

# Process every 10 blocks
@block_task(condition=lambda bn: bn % 10 == 0)
def process_every_10_blocks(block_number: int):
    print(f"Processing every 10 blocks: {block_number}")

# Process with multiple netuids
@block_task(
    condition=lambda bn, netuid: bn % 100 == 0,
    args=[{"netuid": 1}, {"netuid": 3}, {"netuid": 22}],
    backfilling_lookback=300,
    celery_kwargs={"queue": "high-priority"}
)
def process_multi_netuid_task(block_number: int, netuid: int):
    print(f"Processing block {block_number} for netuid: {netuid}")

Maintenance Tasks

Cleanup Old Task Attempts

The framework provides a maintenance task to clean up old task records and maintain database performance:

from abstract_block_dumper.tasks import cleanup_old_tasks

# Delete tasks older than 7 days (default)
cleanup_old_tasks.delay()

# Delete tasks older than 30 days
cleanup_old_tasks.delay(days=30)

This task deletes all succeeded or unrecoverable failed tasks older than the specified number of days. It never deletes tasks with PENDING or RUNNING status to ensure ongoing work is preserved.

Running the Cleanup Task

Option 1: Manual Execution

# Using Django shell
python manage.py shell -c "from abstract_block_dumper.tasks import cleanup_old_tasks; cleanup_old_tasks.delay()"

Option 2: Cron Job (Recommended - once per day)

# Add to crontab (daily at 2 AM)
0 2 * * * cd /path/to/your/project && python manage.py shell -c "from abstract_block_dumper.tasks import cleanup_old_tasks; cleanup_old_tasks.delay()"

Option 3: Celery Beat (Automated Scheduling)

Add this to your Django settings.py:

from celery.schedules import crontab

CELERY_BEAT_SCHEDULE = {
    'cleanup-old-tasks': {
        'task': 'abstract_block_dumper.cleanup_old_tasks',
        'schedule': crontab(hour=2, minute=0),  # Daily at 2 AM
        'kwargs': {'days': 7},  # Customize retention period
    },
}

Then start the Celery beat scheduler:

celery -A your_project beat --loglevel=info

Configuration

Required Django Settings

Add these settings to your Django settings.py:

# Celery Configuration
CELERY_BROKER_URL = 'redis://localhost:6379/0'
CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'

# Abstract Block Dumper specific settings
BITTENSOR_NETWORK = 'finney'  # Options: 'finney', 'local', 'testnet', 'mainnet'
BLOCK_DUMPER_START_FROM_BLOCK = 'current'  # Options: None, 'current', or int
BLOCK_DUMPER_POLL_INTERVAL = 1  # seconds between polling for new blocks
BLOCK_TASK_RETRY_BACKOFF = 2  # minutes for retry backoff base
BLOCK_DUMPER_MAX_ATTEMPTS = 3  # maximum retry attempts
BLOCK_TASK_MAX_RETRY_DELAY_MINUTES = 1440  # maximum retry delay (24 hours)

Configuration Options Reference

Core Settings

BITTENSOR_NETWORK (str, default: 'finney') Specifies which Bittensor network to connect to

BLOCK_DUMPER_START_FROM_BLOCK (str|int|None, default: None)

• Purpose: Determines the starting block for processing when the scheduler first runs
• Valid Values:
  • None: Resume from the last processed block stored in database
  • 'current': Start from the current blockchain block (skips historical blocks)
  • int: Start from a specific block number (e.g., 1000000)
• Example: BLOCK_DUMPER_START_FROM_BLOCK = 'current'
• Performance Impact: Starting from historical blocks may require significant processing time

Scheduler Settings

BLOCK_DUMPER_POLL_INTERVAL (int, default: 1)

• Purpose: Seconds to wait between checking for new blocks
• Valid Range: 1 to 3600 (1 second to 1 hour)
• Example: BLOCK_DUMPER_POLL_INTERVAL = 5
• Performance Impact:
  • Lower values (1-2s): Near real-time processing, higher CPU/network usage
  • Higher values (10-60s): Reduced load but delayed processing
  • Very low values (<1s) may cause rate limiting

Retry and Error Handling Settings

BLOCK_DUMPER_MAX_ATTEMPTS (int, default: 3)

• Purpose: Maximum number of attempts to retry a failed task before giving up
• Valid Range: 1 to 10
• Example: BLOCK_DUMPER_MAX_ATTEMPTS = 5
• Performance Impact: Higher values increase resilience but may delay failure detection

BLOCK_TASK_RETRY_BACKOFF (int, default: 1)

• Purpose: Base number of minutes for exponential backoff retry delays
• Valid Range: 1 to 60
• Example: BLOCK_TASK_RETRY_BACKOFF = 2
• Calculation: Actual delay = backoff ** attempt_count minutes
  • Attempt 1: 2¹ = 2 minutes
  • Attempt 2: 2² = 4 minutes
  • Attempt 3: 2³ = 8 minutes
• Performance Impact: Lower values retry faster but may overwhelm failing services

BLOCK_TASK_MAX_RETRY_DELAY_MINUTES (int, default: 1440)

• Purpose: Maximum delay (in minutes) between retry attempts, caps exponential backoff
• Valid Range: 1 to 10080 (1 minute to 1 week)
• Example: BLOCK_TASK_MAX_RETRY_DELAY_MINUTES = 720 # 12 hours max
• Performance Impact: Prevents extremely long delays while maintaining backoff benefits

Example Project

The repository includes a complete working example in the example_project/ directory that demonstrates:

• Django application setup with abstract-block-dumper
• Multiple task types (@every_block, @every_n_blocks with different configurations)
• Error handling with a randomly failing task
• Docker Compose setup with all required services
• Monitoring with Flower (Celery monitoring tool)

Running the Example

cd example_project
docker-compose up --build

This starts:

• Django application (http://localhost:8000) - Admin interface (user: admin, password: admin)
• Celery workers - Execute block processing tasks
• Block scheduler - Monitors blockchain and schedules tasks
• Flower monitoring (http://localhost:5555) - Monitor Celery tasks
• Redis & PostgreSQL - Required services

Development

Pre-requisites:

• uv
• nox
• docker and docker compose plugin

Ideally, you should run nox -t format lint before every commit to ensure that the code is properly formatted and linted. Before submitting a PR, make sure that tests pass as well, you can do so using:

nox -t check # equivalent to `nox -t format lint test`

If you wish to install dependencies into .venv so your IDE can pick them up, you can do so using:

uv sync --all-extras --dev

Release process

Run nox -s make_release -- X.Y.Z where X.Y.Z is the version you're releasing and follow the printed instructions.