arc-superset-arrow 1.3.3

SQLAlchemy dialect for Arc time-series database with Apache Arrow support for Apache Superset

Arc + Apache Superset Integration (Arrow Edition)

This is the Apache Arrow-powered SQLAlchemy dialect for integrating Apache Superset with the Arc time-series data warehouse. This version uses Arrow IPC for dramatically faster query performance compared to JSON-based queries.

Features

• ✅ Custom SQLAlchemy dialect for Arc API with Apache Arrow IPC
• ✅ 28-75% faster queries compared to JSON format (depending on dataset size)
• ✅ Lower memory overhead with zero-copy Arrow buffers
• ✅ Authentication via API keys (set and forget)
• ✅ Full SQL support for dashboard creation
• ✅ Auto-discovery of Arc tables and measurements
• ✅ Docker Compose integration

Why Arrow?

This dialect uses Apache Arrow IPC (Inter-Process Communication) instead of JSON for query results, providing:

• Performance: 28-75% faster query execution
• Efficiency: Zero-copy data transfer with columnar format
• Type Safety: Preserve native data types (timestamps, integers, floats)
• Scalability: Better performance for large result sets

Quick Start

Option 1: Install in Existing Superset

# Install the Arc Arrow dialect package
pip install arc-superset-arrow

# Restart Superset
superset run -h 0.0.0.0 -p 8088

Option 2: Docker Image (Recommended for Production)

Use the included Dockerfile to build a Superset image with Arc Arrow dialect pre-installed:

# Build the image
docker build -f Dockerfile.superset -t superset-arc-arrow:latest .

# Run Superset with Arc Arrow support
docker run -d \
  -p 8088:8088 \
  -v superset_home:/app/superset_home \
  --name superset-arc \
  superset-arc-arrow:latest

# Check logs
docker logs -f superset-arc

The Dockerfile includes:

• Arc Arrow dialect pre-installed in Superset's venv
• Pip properly configured in the virtual environment
• Uses Superset's default initialization

Connect to Arc

1. Access Superset: http://localhost:8088 (admin/admin)

2. Create Arc Connection:

   • Go to Settings → Database Connections
   • Click + Database
   • Select Other as database type
   • Use this connection string:

     arc+arrow://YOUR_API_KEY@arc-api:8000/default
     

3. Replace YOUR_API_KEY with your Arc token (see below)

Getting Your API Key

Arc creates an initial admin token on first startup. Check the logs:

# Docker
docker logs arc-api | grep "Initial admin token"

# Native/systemd
journalctl -u arc | grep "Initial admin token"

Or create a new token using the Arc CLI or Python:

# Using Python directly
DB_PATH="./data/arc.db" python3 -c "
from api.auth import AuthManager
auth = AuthManager(db_path='./data/arc.db')
token = auth.create_token('superset-integration', description='Superset dashboard access')
print(f'Token: {token}')
"

Save the returned token - it's only shown once!

Multi-Database Support

Arc supports multiple databases (namespaces) within a single instance. In Superset, databases are exposed as schemas:

Database Structure

Schema: default
  ├── cpu (CPU metrics)
  ├── mem (Memory metrics)
  └── disk (Disk metrics)

Schema: production
  ├── cpu
  ├── mem
  └── disk

Schema: staging
  ├── cpu
  ├── mem
  └── disk

Using Databases in Superset

1. View All Databases (Schemas):

   • When creating a dataset, select the schema (database) from the dropdown
   • Each database appears as a separate schema in Superset

2. Query Specific Database:

   -- Query default database
   SELECT * FROM cpu WHERE timestamp > NOW() - INTERVAL 1 HOUR
   
   -- Query specific database
   SELECT * FROM production.cpu WHERE timestamp > NOW() - INTERVAL 1 HOUR
   
   -- Cross-database joins
   SELECT
       p.timestamp,
       p.usage_idle as prod_cpu,
       s.usage_idle as staging_cpu
   FROM production.cpu p
   JOIN staging.cpu s ON p.timestamp = s.timestamp
   WHERE p.timestamp > NOW() - INTERVAL 1 HOUR
   

Available Commands

-- List all databases
SHOW DATABASES;

-- List all tables in current database
SHOW TABLES;

Available Tables

The dialect auto-discovers all tables using SHOW TABLES. Common examples:

• cpu - CPU metrics
• mem - Memory metrics
• disk - Disk metrics
• Any custom measurements you've ingested

Example Queries

Basic Queries

-- Recent CPU metrics (use 'timestamp' column)
SELECT timestamp, host, usage_idle, usage_user
FROM cpu
WHERE timestamp > NOW() - INTERVAL 1 HOUR
ORDER BY timestamp DESC
LIMIT 100;

-- Average CPU usage by host
SELECT host, AVG(usage_idle) as avg_idle
FROM cpu
WHERE timestamp > NOW() - INTERVAL 24 HOUR
GROUP BY host
ORDER BY avg_idle DESC;

Time-Series Aggregation

Arc supports DuckDB's powerful time functions:

-- Time bucket aggregation (5-minute intervals)
SELECT
    time_bucket(INTERVAL '5 minutes', timestamp) as bucket,
    host,
    AVG(usage_idle) as avg_idle,
    MAX(usage_user) as max_user
FROM cpu
WHERE timestamp > NOW() - INTERVAL 6 HOUR
GROUP BY bucket, host
ORDER BY bucket DESC;

-- Daily aggregation with DATE_TRUNC
SELECT
    DATE_TRUNC('day', timestamp) as day,
    host,
    AVG(usage_idle) as avg_cpu_idle,
    COUNT(*) as samples
FROM cpu
WHERE timestamp > NOW() - INTERVAL 7 DAY
GROUP BY day, host
ORDER BY day DESC;

Join Queries

Join multiple measurements for correlated analysis:

-- Correlate CPU and Memory usage
SELECT
    c.timestamp,
    c.host,
    c.usage_idle as cpu_idle,
    m.used_percent as mem_used
FROM cpu c
JOIN mem m ON c.timestamp = m.timestamp AND c.host = m.host
WHERE c.timestamp > NOW() - INTERVAL 10 MINUTE
ORDER BY c.timestamp DESC
LIMIT 1000;

Window Functions

-- Moving average over last 6 data points
SELECT
    timestamp,
    host,
    usage_idle,
    AVG(usage_idle) OVER (
        PARTITION BY host
        ORDER BY timestamp
        ROWS BETWEEN 5 PRECEDING AND CURRENT ROW
    ) as moving_avg
FROM cpu
ORDER BY timestamp DESC
LIMIT 100;

Architecture

Superset → Arrow Dialect → Arrow IPC → Arc API → DuckDB → Parquet Files → MinIO/S3
    ↓           ↓            ↓           ↓         ↓           ↓              ↓
Dashboard    SQL Query   Zero-Copy   API Key    Query    Columnar    Compacted    Object
                         Buffers      Auth      Engine    Storage      Files      Storage

Arrow IPC Flow:

1. Superset sends SQL query via dialect
2. Arc executes query with DuckDB
3. Results serialized to Arrow IPC format (columnar, zero-copy)
4. Dialect deserializes Arrow buffers directly to Python objects
5. 28-75% faster than JSON serialization/deserialization

Connection String Format

arc+arrow://API_KEY@HOST:PORT/DATABASE

Examples:

# Local development
arc+arrow://your-api-key@localhost:8000/default

# Docker Compose
arc+arrow://your-api-key@arc-api:8000/default

# Remote server
arc+arrow://[e-mail]:8000/default

Note: The +arrow suffix indicates this dialect uses Apache Arrow IPC format for data transfer.

Troubleshooting

Connection Issues

• Verify your API key is correct: Test with curl -H "Authorization: Bearer YOUR_KEY" http://host:8000/health
• Check that Arc API is running: docker logs arc-api or check systemd logs
• Ensure network connectivity: Superset and Arc must be on the same network or accessible via hostname

Query Issues

• Check Arc API logs: docker logs arc-api to see query execution
• Verify table names exist: Use SHOW TABLES in Arc to list available measurements
• Use LIMIT clauses: Always limit large queries for performance
• Add time filters: Time-series queries should filter by time range

Performance Tips

• Always use LIMIT for exploratory queries to avoid loading millions of rows
• Add time range filters for time-series data: WHERE timestamp > NOW() - INTERVAL 1 HOUR
• Use column names correctly: Arc stores timestamps in timestamp column (not time)
• Leverage query cache: Arc caches query results for 60 seconds by default
• Use compacted partitions: Arc automatically merges small files for 10-50x faster queries
• Optimize aggregations: Use time_bucket() for time-series bucketing instead of DATE_TRUNC when possible
• Partition filtering: Include host or other tag filters to reduce data scanned

Package Contents

• arc_dialect.py - Custom SQLAlchemy dialect for Arc API
• setup.py - PyPI package configuration
• MANIFEST.in - Package file manifest
• README.md - This documentation
• PUBLISHING.md - Publishing guide for PyPI

Security Notes

• API keys are stored encrypted in Superset's database
• All queries go through Arc's authentication system
• Change the default Superset admin password in production
• Set a strong SUPERSET_SECRET_KEY in production
• Use HTTPS for production deployments

Advanced Features

Schema Support

Arc supports multiple schemas/databases:

-- Query specific database
SELECT * FROM my_database.cpu LIMIT 10;

-- List all databases
SHOW DATABASES;

Advanced DuckDB Features

Arc leverages DuckDB's full SQL capabilities:

-- List all measurements (tables)
SHOW TABLES;

-- Get table schema
DESCRIBE cpu;

-- Count records by measurement
SELECT COUNT(*) as total_records FROM cpu;

-- Complex aggregations with FILTER
SELECT
    host,
    COUNT(*) FILTER (WHERE usage_idle > 90) as idle_count,
    COUNT(*) FILTER (WHERE usage_idle < 50) as busy_count,
    AVG(usage_idle) as avg_idle
FROM cpu
WHERE timestamp > NOW() - INTERVAL 1 HOUR
GROUP BY host;

Development

To modify or extend the dialect:

1. Clone the repository:

   git clone https://github.com/basekick-labs/arc-superset-arrow.git
   cd arc-superset-arrow
   

2. Edit arc_dialect.py

3. Install locally for testing:

   pip install -e .
   

4. Test with Superset:

   superset run -h 0.0.0.0 -p 8088
   

5. Submit a PR if you add improvements!

License

Same license as Arc Core (AGPL-3.0)