GitHub - Basekick-Labs/arc: High-performance time-series database for Industrial IoT and Analytics. 9.47M records/sec. Racing telemetry, smart cities, mining sensors, medical devices. DuckDB SQL + Parquet + Arrow. AGPL-3.0

Arc

High-performance time-series database built on DuckDB. Go implementation.

The Problem

Industrial IoT generates massive data at scale:

Racing & Motorsport: 100M+ sensor readings per race
Smart Cities: Billions of infrastructure events daily
Mining & Manufacturing: Equipment telemetry at unprecedented scale
Energy & Utilities: Grid monitoring, smart meters, renewable output
Oil & Gas: Pipeline sensors, drilling telemetry, refinery monitoring
Logistics & Fleet: Vehicle tracking, route optimization, delivery metrics
Medical & Healthcare: Patient monitoring, clinical sleep studies, device telemetry
Observability: Metrics, logs, traces from distributed systems

Traditional time-series databases can't keep up. They're slow, expensive, and lock your data in proprietary formats.

Arc solves this: 12M records/sec ingestion, sub-second queries on billions of rows, portable Parquet files you own.

-- Analyze equipment anomalies across facilities
SELECT
  device_id,
  facility_name,
  AVG(temperature) OVER (
    PARTITION BY device_id
    ORDER BY timestamp
    ROWS BETWEEN 10 PRECEDING AND CURRENT ROW
  ) as temp_moving_avg,
  MAX(pressure) as peak_pressure,
  STDDEV(vibration) as vibration_variance
FROM data.iot_sensors
WHERE timestamp > NOW() - INTERVAL '24 hours'
  AND facility_id IN ('mining_site_42', 'plant_7')
GROUP BY device_id, facility_name, timestamp
HAVING MAX(pressure) > 850 OR STDDEV(vibration) > 2.5;

Standard DuckDB SQL. Window functions, CTEs, joins. No proprietary query language.

Performance

Benchmarked on Apple MacBook Pro M3 Max (14 cores, 36GB RAM, 1TB NVMe).

Ingestion

Protocol	Throughput	p50 Latency	p99 Latency
MessagePack Columnar	11.8M rec/s	1.06ms	9.94ms
MessagePack + Zstd	11.76M rec/s	1.41ms	22.36ms
MessagePack + GZIP	10.34M rec/s	1.85ms	22.70ms
Line Protocol	2.55M rec/s	6.77ms	20.99ms

Query (January 2026)

Format	Throughput	Response Size (500K rows)
Arrow IPC	2.5M rows/s	21.5 MB
JSON	1.2M rows/s	47.0 MB

Scan Type	Throughput	Time (321M rows)
COUNT(*)	~17 billion rows/s	19ms
SUM(column)	~2.8 billion rows/s	116ms

Why Go

Stable memory: Go's GC returns memory to OS. No leaks.
Single binary: Deploy one executable. No dependencies.
Native concurrency: Goroutines handle thousands of connections efficiently.
Production GC: Sub-millisecond pause times at scale.

Quick Start

# Build
make build

# Run
./arc

# Verify
curl http://localhost:8000/health

Installation

Docker

docker run -d \
  -p 8000:8000 \
  -v arc-data:/app/data \
  ghcr.io/basekick-labs/arc:latest

Debian/Ubuntu

wget https://github.com/basekick-labs/arc/releases/download/v26.01.2/arc_26.01.2_amd64.deb
sudo dpkg -i arc_26.01.2_amd64.deb
sudo systemctl enable arc && sudo systemctl start arc

RHEL/Fedora

wget https://github.com/basekick-labs/arc/releases/download/v26.01.2/arc-26.01.2-1.x86_64.rpm
sudo rpm -i arc-26.01.2-1.x86_64.rpm
sudo systemctl enable arc && sudo systemctl start arc

Kubernetes (Helm)

helm install arc https://github.com/basekick-labs/arc/releases/download/v26.01.2/arc-26.01.2.tgz

Build from Source

# Prerequisites: Go 1.25+

# Clone and build
git clone https://github.com/basekick-labs/arc.git
cd arc
make build

# Run
./arc

Features

Ingestion: MessagePack columnar (fastest), InfluxDB Line Protocol
Query: DuckDB SQL engine, JSON and Apache Arrow IPC responses
Storage: Local filesystem, S3, MinIO
Auth: Token-based authentication with in-memory caching
Durability: Optional write-ahead log (WAL)
Compaction: Tiered (hourly/daily) automatic file merging
Data Management: Retention policies, continuous queries, GDPR-compliant delete
Observability: Prometheus metrics, structured logging, graceful shutdown
Reliability: Circuit breakers, retry with exponential backoff

Configuration

Arc uses TOML configuration with environment variable overrides.

[server]
host = "0.0.0.0"
port = 8000

[storage]
backend = "local"        # local, s3, minio
local_path = "./data/arc"

[ingest]
flush_interval = "5s"
max_buffer_size = 50000

[auth]
enabled = true

Environment variables use ARC_ prefix:

export ARC_SERVER_PORT=8000
export ARC_STORAGE_BACKEND=s3
export ARC_AUTH_ENABLED=true

See arc.toml for complete configuration reference.

Project Structure

arc/
├── cmd/arc/           # Application entry point
├── internal/
│   ├── api/           # HTTP handlers (Fiber)
│   ├── auth/          # Token authentication
│   ├── compaction/    # Tiered file compaction
│   ├── config/        # Configuration management
│   ├── database/      # DuckDB connection pool
│   ├── ingest/        # MessagePack, Line Protocol, Arrow writer
│   ├── logger/        # Structured logging (zerolog)
│   ├── metrics/       # Prometheus metrics
│   ├── pruning/       # Query partition pruning
│   ├── shutdown/      # Graceful shutdown coordinator
│   ├── storage/       # Local, S3, MinIO backends
│   ├── telemetry/     # Usage telemetry
│   ├── circuitbreaker/# Resilience patterns
│   └── wal/           # Write-ahead log
├── test/integration/  # Integration tests
├── arc.toml           # Configuration file
├── Makefile           # Build commands
└── go.mod

Development

make deps           # Install dependencies
make build          # Build binary
make run            # Run without building
make test           # Run tests
make test-coverage  # Run tests with coverage
make bench          # Run benchmarks
make lint           # Run linter
make fmt            # Format code
make clean          # Clean build artifacts

License

Arc is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).

Free to use, modify, and distribute
If you modify Arc and run it as a service, you must share your changes under AGPL-3.0

For commercial licensing, contact: enterprise@basekick.net

Contributors

Thanks to everyone who has contributed to Arc:

@schotime (Adam Schroder) - Data-time partitioning, compaction API triggers, UTC fixes