Hercules

Write SQL. Get Prometheus Metrics.

⚡ Quickstart

Launching Hercules in a Codespace is the easiest way to get started.

A make run from inside your codespace will get things spinning.

Sources

Hercules generates Prometheus metrics by querying:

• Local files (parquet, json, csv, xlsx, etc)
• Object storage (GCS, S3, Azure Blob)
• HTTP endpoints
• Databases (PostgreSQL, MySQL, SQLite)
• Data lakes (Iceberg, Delta)
• Data warehouses (BigQuery)
• Arrow IPC buffers

Sources can be cached and periodically refreshed, or act as views to the underlying data.

Metrics from multiple sources can be materialized using a single exporter.

Metrics

Definition

Metric definitions are yml and use sql in a number of supported dialects to aggregate, enrich, and materialize metric values.

Hercules supports Prometheus gauges, counters, summaries, and histograms.

Enrichment

Sources and metrics can be externally enriched, leading to more thorough, accurate (or is it precise?), properly-labeled metrics.

Integrate, calculate, enrich, and label on the edge.

Macros

Metric definitions can be kept DRY using SQL macros.

Macros are useful for:

• Parsing log lines
• Reading useragent strings
• Schematizing unstructured data
• Enriching results with third-party tooling
• Tokenizing attributes

Labels

Hercules propagates global labels to all configured metrics. So you don't have to guess where a metric came from.

Labels are propagated from configuration or sourced from environment variables.

Packages

Hercules extensions, sources, metrics, and macros can be logically grouped and distributed by the use of packages.

Examples can be found in the hercules-packages directory.

Getting Started Locally

This guide will help you get Hercules up and running on your local machine, including instructions for configuration, testing, and loading sample data.

Prerequisites

• Go 1.22+ installed on your system
• Git to clone the repository
• Basic understanding of SQL and Prometheus metrics

Installation

1. Clone the repository:

   git clone https://github.com/jakthom/hercules.git
   cd hercules
   

2. Build the project:

   make build
   

Running Hercules

Basic Execution

Start Hercules with default settings:

make run

This command starts Hercules with the following defaults:

• Configuration file: hercules.yml in the project root
• Port: 9999 (customizable in config)
• SQLite database: h.db in the project root

Debug Mode

To run Hercules with additional debug logging:

make debug

Custom Configuration

Hercules uses a YAML configuration file. By default, it looks for hercules.yml in the project root. You can specify a custom configuration path using the environment variable:

HERCULES_CONFIG_PATH=/path/to/config.yml make run

Configuration File Structure

The hercules.yml configuration file defines your server settings and packages:

version: 1

name: your-instance-name
debug: false
port: 9100

globalLabels:
  - env: $ENV  # Inject prometheus labels from environment variables
  - region: us-west-1

packages:
  - package: hercules-packages/sample-package/1.0.yml

Testing

Run the test suite to ensure everything is working correctly:

make test

For more detailed test coverage information:

make test-cover-pkg

Working with Sample Data

Hercules includes several example packages in the /hercules-packages/ directory:

1. NYC Taxi Data:

   packages:
     - package: hercules-packages/nyc-taxi/1.0.yml
   

2. Snowflake Metrics:

   packages:
     - package: hercules-packages/snowflake/1.0.yml
   

3. TPCH Benchmarks:

   packages:
     - package: hercules-packages/tpch/1.0.yml
   

4. Bluesky Analytics:

   packages:
     - package: hercules-packages/bluesky/1.0.yml
   

To use any of these packages, add them to your hercules.yml file.

Accessing Metrics

Once Hercules is running, metrics are available at:

• http://localhost:9100/metrics (if using default port 9100)

You can connect Prometheus or any compatible metrics collector to this endpoint.

Environment Variables

Hercules supports several environment variables:

• HERCULES_CONFIG_PATH: Path to configuration file
• DEBUG: Set to enable debug logging
• TRACE: Set to enable trace logging
• ENV: Used for labeling metrics (defaults to current username)

Creating Custom Packages

To create your own package:

1. Create a new YAML file in a directory of your choice:

   name: my-package
   version: 1.0
   
   extensions:
     # SQLite extensions, if needed
   
   macros:
     # SQL macros
   
   sources:
     # Data sources
   
   metrics:
     # Metric definitions
   

2. Reference your package in the main configuration:

   packages:
     - package: path/to/your-package.yml
   

Bonus

• Calculate prometheus-compatible metrics from geospatial data
• Coerce unwieldy files to useful statistics using full-text search
• Use modern pipe sql syntax or prql for defining and transforming your metrics
• You don't need to start queries with select.

Further Resources

More to come.