GitHub - Basekick-Labs/arc: High-performance analytical database. DuckDB SQL engine + Parquet storage + Arrow format. 18M+ records/sec ingestion. 6M+ rows/sec queries. Use for analytics, observability, AI, IoT, logs. Single Go binary. S3/Azure native. No vendor lock-in. AGPL-3.0

High-performance columnar analytical database. 18M+ records/sec ingestion, 6M+ rows/sec queries. Built on DuckDB + Parquet + Arrow. Use for product analytics, observability, AI agents, IoT, logs, or data warehousing. Single binary. No vendor lock-in. AGPL-3.0

The Problem

Modern applications generate massive amounts of data that needs fast ingestion and analytical queries:

Product Analytics: Events, clickstreams, user behavior, A/B testing
Observability: Metrics, logs, traces from distributed systems
AI Agent Memory: Conversation history, context, RAG, embeddings
Industrial IoT: Manufacturing telemetry, sensors, equipment monitoring
Security & Compliance: Audit logs, SIEM, security events
Data Warehousing: Analytics, BI, reporting on time-series or event data

Traditional solutions have problems:

Expensive: Cloud data warehouses cost thousands per month at scale
Complex: ClickHouse/Druid require cluster management expertise
Vendor lock-in: Proprietary formats trap your data
Slow ingestion: Most analytical DBs struggle with high-throughput writes
Overkill: Need simple deployment, not Kubernetes orchestration

Arc solves this: 10M+ records/sec ingestion, 6M+ rows/sec queries, portable Parquet files you own, single binary deployment.

-- Product analytics: user events
SELECT
  user_id,
  event_type,
  COUNT(*) as event_count,
  COUNT(DISTINCT session_id) as sessions
FROM analytics.events
WHERE timestamp > NOW() - INTERVAL '7 days'
  AND event_type IN ('page_view', 'click', 'purchase')
GROUP BY user_id, event_type
HAVING COUNT(*) > 100;

-- Observability: error rate by service
SELECT
  service_name,
  DATE_TRUNC('hour', timestamp) as hour,
  COUNT(*) as total_requests,
  SUM(CASE WHEN status >= 500 THEN 1 ELSE 0 END) as errors,
  (SUM(CASE WHEN status >= 500 THEN 1 ELSE 0 END)::FLOAT / COUNT(*)) * 100 as error_rate
FROM logs.http_requests
WHERE timestamp > NOW() - INTERVAL '24 hours'
GROUP BY service_name, hour
HAVING error_rate > 1.0;

-- AI agent memory: conversation search
SELECT
  agent_id,
  conversation_id,
  user_message,
  assistant_response,
  created_at
FROM ai.conversations
WHERE agent_id = 'support-bot-v2'
  AND created_at > NOW() - INTERVAL '30 days'
  AND user_message ILIKE '%refund%'
ORDER BY created_at DESC
LIMIT 100;

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

Live Demo

See Arc in action: https://basekick.net/demos

Performance

Benchmarked on Apple MacBook Pro M3 Max (14 cores, 36GB RAM, 1TB NVMe). Test config: 12 concurrent workers, 1000-record batches, columnar data.

Ingestion

Protocol	Throughput	p50 Latency	p99 Latency
MessagePack Columnar	18.6M rec/s	0.46ms	3.68ms
MessagePack + Zstd	16.8M rec/s	0.55ms	3.23ms
MessagePack + GZIP	15.4M rec/s	0.63ms	3.17ms
Line Protocol	3.7M rec/s	2.63ms	10.63ms

Compaction

Automatic background compaction merges small Parquet files into optimized larger files:

Metric	Before	After	Reduction
Files	43	1	97.7%
Size	372 MB	36 MB	90.4%

Benefits:

10x storage reduction via better compression and encoding
Faster queries - scan 1 file vs 43 files
Lower cloud costs - less storage, fewer API calls

Query (March 2026)

Arrow IPC format provides up to 3.6x throughput vs JSON for large result sets:

Query	Arrow (ms)	JSON (ms)	Speedup
COUNT(*) - 1.88B rows	1.9	1.8	0.95x
SELECT LIMIT 10K	70	75	1.07x
SELECT LIMIT 100K	88	106	1.20x
SELECT LIMIT 500K	127	253	1.99x
SELECT LIMIT 1M	159	438	2.75x
Time Range (7d) LIMIT 10K	45	51	1.13x
Time Bucket (1h, 7d)	986	1089	1.10x
Date Trunc (day, 30d)	2013	2190	1.09x

Best throughput:

Arrow: 6.29M rows/sec (1M row SELECT)
JSON: 2.28M rows/sec (1M row SELECT)
COUNT(*): ~1.1T rows/sec (1.88B rows, 1.8ms)

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/v26.03.1/arc_26.03.1_amd64.deb
sudo dpkg -i arc_26.03.1_amd64.deb
sudo systemctl enable arc && sudo systemctl start arc

RHEL/Fedora

wget https://github.com/basekick-labs/arc/releases/download/v26.03.1/arc-26.03.1-1.x86_64.rpm
sudo rpm -i arc-26.03.1-1.x86_64.rpm
sudo systemctl enable arc && sudo systemctl start arc

Kubernetes (Helm)

helm install arc https://github.com/basekick-labs/arc/releases/download/v26.03.1/arc-26.03.1.tgz

Build from Source

# Prerequisites: Go 1.26+

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

# Or build directly with Go (the duckdb_arrow tag is required)
go build -tags=duckdb_arrow ./cmd/arc

# Run
./arc

Ecosystem & Integrations

Tool	Description	Link
VS Code Extension	Browse databases, run queries, visualize results	Marketplace
Grafana Data Source	Native Grafana plugin for dashboards and alerting	GitHub
Telegraf Output Plugin	Ship data from 300+ Telegraf inputs directly to Arc	Docs
Python SDK	Query and ingest from Python applications	PyPI
Superset Dialect (JSON)	Apache Superset connector using JSON transport	GitHub
Superset Dialect (Arrow)	Apache Superset connector using Arrow transport	GitHub

Features

Core Capabilities

Columnar storage: Parquet format with DuckDB query engine
Multi-use-case: Product analytics, observability, AI, IoT, logs, data warehousing
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) — query, write, import, TLE, admin
│   ├── audit/            # Audit logging for API operations
│   ├── auth/             # Token authentication and RBAC
│   ├── backup/           # Backup and restore (data, metadata, config)
│   ├── circuitbreaker/   # Resilience patterns (retry, backoff)
│   ├── cluster/          # Raft consensus, node roles, WAL replication
│   ├── compaction/       # Tiered hourly/daily Parquet file merging
│   ├── config/           # TOML configuration with env var overrides
│   ├── database/         # DuckDB connection pool
│   ├── governance/       # Per-token query quotas and rate limiting
│   ├── ingest/           # MessagePack, Line Protocol, TLE, Arrow writer
│   ├── license/          # License validation and feature gating
│   ├── logger/           # Structured logging (zerolog)
│   ├── metrics/          # Prometheus metrics
│   ├── mqtt/             # MQTT subscriber — topic-to-measurement ingestion
│   ├── pruning/          # Query-time partition pruning
│   ├── query/            # Parallel partition executor
│   ├── queryregistry/    # Active/completed query tracking
│   ├── scheduler/        # Continuous queries and retention policies
│   ├── shutdown/         # Graceful shutdown coordinator
│   ├── sql/              # SQL parsing utilities
│   ├── storage/          # Local, S3, Azure backends
│   ├── telemetry/        # Usage telemetry
│   ├── tiering/          # Hot/cold storage lifecycle management
│   └── wal/              # Write-ahead log
├── pkg/models/           # Shared data structures (Record, ColumnarRecord)
├── benchmarks/           # Performance benchmarking suites
├── deploy/               # Docker Compose and Kubernetes configs
├── helm/                 # Helm charts
├── scripts/              # Utility scripts (analysis, backfill, debugging)
├── 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
@khalid244 - S3 partition pruning improvements, multi-line SQL query support