cherry-indexer 0.1.0

A flexible blockchain indexing and data processing pipeline

Cherry Event Indexer

A flexible blockchain event indexing and data processing pipeline.

Overview

Cherry Event Indexer is a modular system for:

• Ingesting blockchain events and logs
• Processing and transforming blockchain data
• Writing data to various storage backends

Features

• Modular Pipeline Architecture

  • Configurable data providers
  • Customizable processing steps
  • Pluggable storage backends

• Built-in Steps

  • EVM block validation
  • Event decoding
  • Custom processing steps

• Storage Options

  • Local Parquet files
  • AWS S3
  • More coming soon...

Project Structure:

cherry/
├── src/
│ ├── config/ # Configuration parsing
│ ├── utils/ # Pipeline and utilities
│ └── writers/ # Storage backends
├── examples/ # Example implementations
├── tests/ # Test suite
└── config.yaml # Pipeline configuration

Prerequisites:

• Python 3.10 or higher
• Docker and Docker Compose
• MinIO (for local S3-compatible storage)

Installation Steps

Clone the repository and go to the project root:

git clone https://github.com/steelcake/cherry.git
cd cherry

Create and activate a virtual environment:

# Create virtual environment (all platforms)
python -m venv .venv

# Activate virtual environment

# For Windows with git bash:
source .venv/Scripts/activate

# For macOS/Linux:
source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

Create a .env file in the project root Add your Hypersync API token:

Quick Start

1. Create a config file (config.yaml):

2. Run the script:

python main.py

Custom Processing Steps

To add a custom processing step, you need to:

1. Define the step function
2. Add the step to the context
3. Add the step to the config

example: get_block_number_stats.py

def get_block_number_stats(data: Dict[str, pa.RecordBatch], step_config: Dict[str, Any]) -> Dict[str, pa.RecordBatch]:
    """Custom processing step for transfer events"""
    pass

config.yaml

steps:
  - name: my_get_block_number_stats
    kind: get_block_number_stats
    config:
      input_table: logs
      output_table: block_number_stats

Running the Project

Start MinIO server (for local S3 storage):

# Navigate to docker-compose directory
cd docker-compose

# Start MinIO using docker-compose
docker-compose up -d

# Return to project root
cd ..

Default credentials:

Access Key: minioadmin
Secret Key: minioadmin
Console URL: http://localhost:9001

Note: The MinIO service will be automatically configured with the correct ports and volumes as defined in the docker-compose.yml file.

Configure pipelines:

• Open config.yaml
• Adjust query, event filters, and batch sizes as needed for your pipeline
• Configure writer settings (S3/local parquet etc.)

Run the indexer:

python main.py