Sabot: High-Performance Python Data Processing

⚠️ EXPERIMENTAL - ALPHA SOFTWARE ⚠️

The Python alternative to PySpark and Ray for high-performance columnar data processing.

Sabot is a Python framework that brings Apache Arrow's columnar performance to data processing workflows. Unlike PySpark's JVM overhead or Ray's distributed complexity, Sabot provides zero-copy Arrow operations with Cython acceleration for massive throughput on single machines.

🎯 NEW: Unified Architecture (October 2025)

Sabot now has a unified entry point for all functionality:

from sabot import Sabot

# Create unified engine
engine = Sabot(mode='local')  # or 'distributed'

# Stream processing
stream = engine.stream.from_kafka('topic').filter(lambda b: b.column('x') > 10)

# SQL processing
result = engine.sql("SELECT * FROM table WHERE x > 10")

# Graph processing
matches = engine.graph.cypher("MATCH (a)-[:KNOWS]->(b) RETURN a, b")

# Clean shutdown
engine.shutdown()

See: Unified Architecture Guide

Classic API (Still Supported)

from sabot import cyarrow as ca
from sabot.api.stream import Stream
from sabot.cyarrow import compute as pc

# Load 10M rows from Parquet file
data = ca.read_parquet('transactions.parquet')

# Create batch processing pipeline
stream = (Stream.from_table(data, batch_size=100_000)
    # Filter high-value transactions (SIMD-accelerated)
    .filter(lambda batch: pc.greater(batch.column('amount'), 10000))

    # Compute fraud score using auto-compiled Numba UDF
    .map(lambda batch: batch.append_column('fraud_score',
        compute_fraud_score(batch)))  # 10-100x Numba speedup

    # Select output columns
    .select('transaction_id', 'amount', 'fraud_score'))

# Execute: 104M rows/sec hash joins, 10-100x UDF speedup
for batch in stream:
    print(f"Processed {batch.num_rows} rows")

# Same code works for streaming (infinite) sources!
# stream = Stream.from_kafka('transactions')  # Never terminates

Project Status

This is an experimental research project exploring the design space of:

• Zero-copy Arrow columnar processing in Python
• Cython acceleration for data-intensive operations
• High-performance batch processing alternatives
• Kafka streaming integration with columnar efficiency

Current State (v0.1.0-alpha):

• ✅ CyArrow: Production-ready zero-copy Arrow operations (104M rows/sec joins)
• ✅ Graph Processing: High-performance graph analytics (3-37M matches/sec pattern matching)
• ✅ SabotQL: SPARQL query engine for RDF triple stores (23,798 queries/sec, integrated into pipelines)
• ✅ DataLoader: High-performance CSV/Arrow IPC loading (52x faster than CSV)
• ✅ Streaming Agents: Faust-inspired Kafka processing with columnar data
• ✅ Cython Acceleration: SIMD-accelerated compute kernels
• ⚠️ Distributed features are experimental (checkpoints, state management)
• ⚠️ Test coverage is limited (~5%)
• ⚠️ Not recommended for production use

Performance: Why Choose Sabot Over PySpark/Ray?

Sabot delivers PySpark-level performance without the JVM overhead:

Core Data Processing (M3 MacBook Pro, 11.2M rows)

• Hash Joins: 104M rows/sec (11.2M row join in 107ms)
• Data Loading (Arrow IPC): 5M rows/sec (10M rows in 2 seconds, memory-mapped)
• Data Loading (CSV): 0.5-1.0M rows/sec (multi-threaded)
• Arrow IPC vs CSV: 52x faster loading (10M rows: 2s vs 103s)
• File Compression: 50-70% size reduction (5.6GB → 2.4GB)
• Zero-copy operations: ~2-3ns per element (SIMD-accelerated)

Fintech Enrichment Pipeline (Complete workflow):

• Total Pipeline: 2.3 seconds end-to-end
• Hash Join: 104.6M rows/sec throughput
• Window Operations: ~2-3ns per element (SIMD-accelerated)
• Spread Calculation: Vectorized compute kernels
• Data Loading: 2.1 seconds (10M + 1.2M rows)

vs PySpark (same workload, anecdotal):

• PySpark: ~30-60 seconds (JVM startup + serialization overhead)
• Sabot: 2.3 seconds (pure Python + Arrow + Cython)

vs Ray:

• Ray: Distributed coordination overhead even for single-machine
• Sabot: Direct columnar operations with zero serialization

Streaming Performance

Real-time Fraud Detection (Kafka + columnar processing):

• Throughput: 143K-260K transactions/sec
• Latency p50/p95/p99: 0.01ms / 0.01ms / 0.01ms
• Pattern Detection: velocity, amount anomaly, geo-impossible
• Stateful Processing: 1M+ state operations/sec

Experimental Features (In Development):

• Distributed agent runtime (Ray-like actor model)
• RocksDB state backend (persistent state)
• GPU acceleration via RAFT
• Complex event processing (CEP)

Auto-Numba UDF Compilation

Automatic 10-100x speedup for user-defined functions - transparently!

Sabot automatically compiles Python UDFs with Numba JIT for massive performance gains. No code changes required - just write normal Python and Sabot handles the rest.

Status: ✅ Phase 2 Complete - Fully integrated with CythonMapOperator, tested and benchmarked.

# User writes normal Python - Sabot auto-compiles it!
def my_transform(batch):
    # Extract numpy arrays from Arrow columns
    values = batch.column('value').to_numpy()
    results = []

    # Numba-compiled computation
    for i in range(len(values)):
        total = 0
        for j in range(100):
            total += values[i] * j
        results.append(total)

    # Return new RecordBatch
    return batch.append_column('computed', pa.array(results))

# Automatically compiled with Numba @njit (10-50x faster)
stream = Stream.from_kafka('data').map(my_transform)

How it works:

1. AST analysis detects function patterns (loops, NumPy array ops, etc.)
2. Chooses optimal compilation strategy (@njit vs @vectorize)
3. Compiles with Numba transparently for batch processing
4. Falls back to Python if compilation fails
5. Caches compiled functions for reuse

Performance:

• Scalar loops: 10-50x speedup
• NumPy operations: 50-100x speedup
• Compilation overhead: <100ms (first-time only)
• Cache hit: <1ms

Works with batch-first architecture!

Design Goals

🚀 PySpark Performance in Pure Python

• CyArrow: Zero-copy Arrow operations (104M rows/sec joins)
• Arrow IPC: Memory-mapped data loading (52x faster than CSV)
• SIMD Acceleration: Vectorized operations beating PySpark throughput
• Cython DataLoader: Multi-threaded CSV parsing, auto-format detection

⚡ Ray-Like Distributed Processing (Experimental)

• Actor-based agents for distributed computation
• Distributed checkpointing (Chandy-Lamport barriers)
• Complex event processing (CEP) with pattern matching
• Stateful stream processing with persistence backends

🔧 Pythonic API - No JVM, No Serialization

• Unified imports: import sabot as sb
• Zero-copy operations: from sabot.cyarrow import load_data, hash_join_batches
• Decorator-based agents: @app.agent() (experimental - see note below)
• Multiple data formats: Arrow IPC, CSV, Parquet, Avro

Note on Agent API: The @app.agent() decorator is experimental. Agents register successfully but stream consumption requires manual message deserialization. See examples/fraud_app.py for working pattern.

Quick Start

1. Install

Prerequisites: Python 3.9+, C++ compiler, CMake 3.16+

# Clone with vendored Arrow C++ submodule
git clone --recursive https://github.com/yourusername/sabot.git
cd sabot

# Option A: Quick install (builds Arrow C++ automatically, ~30-60 mins first time)
pip install -e .

# Option B: Build Arrow C++ manually first (recommended for development)
python build.py          # One-time Arrow build (~30-60 mins)
pip install -e .         # Fast install (<1 min)

# Verify vendored Arrow is working
python -c "from sabot import cyarrow; print(f'Vendored Arrow: {cyarrow.USING_ZERO_COPY}')"

# Note: Use sabot.cyarrow (our optimized Arrow), not pyarrow

# Dependencies Note: We vendor critical Cython dependencies (RocksDB, Arrow components)
# because they are tightly bound to native C/C++ libraries, not standard Python packages.
# This ensures consistent builds and performance across platforms.

2. Start Infrastructure

# Start Kafka, Postgres, Redis via Docker
docker compose up -d

# Check services
docker compose ps

3. Create Your First App

fraud_app.py:

import sabot as sb
import json

# Create Sabot application
app = sb.App(
    'fraud-detection',
    broker='kafka://localhost:19092',
    value_serializer='json'
)

# Define fraud detector with state
detector = FraudDetector()  # Your fraud detection logic

@app.agent('bank-transactions')
async def detect_fraud(stream):
    """Process transactions and detect fraud patterns."""
    async for message in stream:
        # Deserialize message (stream yields raw bytes/str)
        if isinstance(message, bytes):
            txn = json.loads(message.decode('utf-8'))
        else:
            txn = json.loads(message) if isinstance(message, str) else message

        # Process transaction
        alerts = await detector.detect_fraud(txn)

        # Handle fraud alerts
        for alert in alerts:
            print(f"🚨 FRAUD: {alert['type']} - {alert['details']}")

4. Run with CLI

# Start worker (Faust-style)
sabot -A fraud_app:app worker --loglevel=INFO

# Or with concurrency
sabot -A fraud_app:app worker -c 4

Building from Source

Sabot uses a unified build system that automatically builds all dependencies:

# Clone the repository
git clone https://github.com/sabot/sabot.git
cd sabot

# Initialize submodules (Arrow, Tonbo, etc.)
git submodule update --init --recursive

# Build everything (Arrow C++, vendored extensions, Cython modules)
python build.py

# The build system will:
# 1. Check dependencies (Arrow, RocksDB, Tonbo, Rust, hiredis)
# 2. Build Arrow C++ (20-60 min first time, then cached)
# 3. Build vendor extensions (CyRedis, RocksDB, Tonbo) if available
# 4. Auto-discover and build 56 Cython modules
# 5. Validate builds and report summary

Build Commands:

python build.py              # Build everything
python build.py --clean      # Remove .so/.c/.cpp artifacts
python build.py --clean-all  # Also remove Arrow/vendor builds
python build.py --skip-arrow # Don't rebuild Arrow (use existing)
python build.py --skip-vendor # Skip vendor extensions

Dependencies:

• Required: CMake, C++ compiler (clang++/g++), Python 3.11+, Cython, NumPy
• Optional: RocksDB (for persistent state), Rust toolchain (for Tonbo), hiredis (for CyRedis)

The build system automatically skips unavailable optional dependencies and reports what was built/skipped.

Architecture

Sabot combines Arrow's columnar performance with Python's ecosystem:

┌─────────────────────────────────────────────────────────┐
│                    Application Layer                    │
│   @app.agent() decorators, Faust-style API            │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│              Sabot Core (Clean API)                     │
│   import sabot as sb                                    │
│   - sb.App, sb.agent()                                  │
│   - sb.Barrier, sb.BarrierTracker (checkpoints)        │
│   - sb.MemoryBackend, sb.ValueState (state)            │
│   - sb.WatermarkTracker, sb.Timers (time)              │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│         Cython-Accelerated Modules (10-100x faster)     │
│   - Checkpoint coordination (Chandy-Lamport)            │
│   - State management (RocksDB, memory)                  │
│   - Time/watermark tracking                             │
│   - Arrow batch processing (SIMD-accelerated)          │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│                  Infrastructure Layer                   │
│   Kafka, Redpanda | PostgreSQL | Redis | RocksDB       │
└─────────────────────────────────────────────────────────┘

Core Modules

Module	Description	Performance
cyarrow	Zero-copy Arrow operations (hash joins, windows)	104M rows/sec
DataLoader	Multi-threaded data loading (CSV, Arrow IPC)	5M rows/sec (Arrow)
checkpoint	Distributed snapshots (Chandy-Lamport)	<10μs initiation
state	Managed state (Memory, RocksDB, Redis, Tonbo)	1M+ ops/sec
time	Watermarks, timers, event-time	<5μs tracking
agents	Actor-based stream processors (experimental)	-
features	Feature engineering for ML pipelines (CyRedis)	Streaming features
tonbo	LSM state backend (Rust FFI, production-ready)	72K writes/sec, 241K reads/sec
shuffle	Lock-free network transport (Arrow Flight)	Zero-copy distributed shuffle
graph	Graph storage, traversal, and pattern matching	3-37M matches/sec

Feature Engineering (sabot/features/)

Real-time feature computation and storage for ML pipelines:

from sabot import Stream
from sabot.features import FeatureStore

# Initialize CyRedis-backed feature store
feature_store = FeatureStore(redis_url="localhost:6379", db=0)
await feature_store.initialize()

# Compute features using standard operators + feature extractors
stream = (Stream.from_kafka("ticker-data")
    .with_features([
        'price_rolling_mean_5m',
        'volume_std_1h',
        'spread_percentile_95'
    ])
    .to_feature_store(
        feature_store=feature_store,
        entity_key_column='symbol',
        feature_columns=['price_rolling_mean_5m', 'volume_std_1h', 'spread_percentile_95'],
        ttl=300
    ))

Features:

• ✅ Cython-accelerated extractors (RollingMean, RollingStd, Percentile, TimeBased)
• ✅ CyRedis backend with async batch writes
• ✅ TTL-based expiration for streaming features
• ✅ Composable with standard Stream API operators
• ✅ Working demo: examples/crypto_features_demo.py

Tonbo State Backend

Production-ready LSM storage engine (Rust FFI):

from sabot.stores import TonboBackend

# Create Tonbo state backend
backend = TonboBackend(path="./state/tonbo", cython_enabled=True)
await backend.start()

# High-performance operations
await backend.set("user:123", {"name": "Alice", "tier": "gold"})
value = await backend.get("user:123")
await backend.batch_set([("key1", val1), ("key2", val2)])

Performance (Oct 6, 2025):

• ✅ 72K writes/sec, 241K reads/sec, 135K batch ops/sec
• ✅ Rust FFI with zero-copy Arrow integration
• ✅ Used for: dimension tables, checkpoints, materializations
• ✅ 7 test files passing (4 unit, 3 integration)
• ✅ Status: PRODUCTION READY

Lock-Free Network Shuffle

Zero-copy distributed data transfer using Arrow Flight:

Features:

• ✅ Lock-free Arrow Flight transport (flight_transport_lockfree.pyx) - atomic connection pooling
• ✅ SPSC/MPSC ring buffers (lock_free_queue.pyx) - concurrent partition queues
• ✅ Atomic partition store (atomic_partition_store.pyx) - LMAX Disruptor-style hash table
• ✅ Zero-copy network transfer - direct Arrow RecordBatch serialization
• ✅ 8 compiled modules in sabot/_cython/shuffle/

Use Cases:

• Distributed joins and aggregations
• Repartitioning operations
• Multi-stage dataflow pipelines

Graph Processing (sabot/_cython/graph/)

High-performance graph analytics and pattern matching built on Arrow columnar format.

Features:

• ✅ Columnar Graph Storage: Property graphs with CSR/CSC adjacency
• ✅ Graph Traversal: BFS, DFS, shortest paths, PageRank, centrality, connected components
• ✅ Pattern Matching: 2-hop, 3-hop, variable-length paths with cost-based optimization
• ✅ Zero-Copy Operations: Direct Arrow buffer access for maximum throughput

Performance (M1 Pro, October 2025):

• 2-hop patterns: 3-37M matches/sec
• 3-hop patterns: 2.7-5.6M matches/sec
• Graph traversal: 10-50M nodes/sec
• Join optimizer: <0.130ms overhead

API Example:

import pyarrow as pa
from sabot._cython.graph import PropertyGraph, VertexTable, EdgeTable
from sabot._cython.graph.query import match_2hop
from sabot._cython.graph.traversal import pagerank

# Create social graph
vertices = pa.table({
    'id': pa.array([0, 1, 2, 3], type=pa.int64()),
    'label': pa.array(['Person', 'Person', 'Person', 'Company']).dictionary_encode(),
    'name': ['Alice', 'Bob', 'Charlie', 'Acme Corp']
})

edges = pa.table({
    'source': pa.array([0, 1, 0, 2], type=pa.int64()),
    'target': pa.array([1, 2, 3, 3], type=pa.int64()),
    'type': pa.array(['KNOWS', 'KNOWS', 'WORKS_AT', 'WORKS_AT']).dictionary_encode()
})

# Create property graph
graph = PropertyGraph(VertexTable(vertices), EdgeTable(edges))
graph.build_csr()

# Find 2-hop patterns: Person → Person → Person
result = match_2hop(edges, edges)
print(f"Found {result.num_matches()} friend-of-friend connections")

# Run PageRank
ranks = pagerank(edges, num_vertices=4, damping=0.85, max_iterations=20)
print(f"PageRank scores: {ranks.to_pylist()}")

# Get neighbors
neighbors = graph.get_neighbors(0)  # Alice's neighbors
print(f"Alice knows: {neighbors.to_pylist()}")

Use Cases:

• Social network analysis (friend-of-friend recommendations)
• Fraud detection (money laundering pattern matching)
• Knowledge graph inference
• Supply chain tracking
• Network influence analysis

Comprehensive Documentation: See GRAPH_QUERY_ENGINE.md for complete API reference, examples, and benchmarks.

Working Examples:

• examples/social_network_analysis.py - Friend recommendations
• examples/fraud_detection_optimizer.py - Money laundering patterns
• examples/pattern_sabot_integration.py - Integration with Sabot operators

Test Suite: 27 tests, 100% passing

pytest tests/unit/graph/test_pattern_matching.py -v

Example: Fintech Data Enrichment (Zero-Copy Arrow)

The fintech enrichment demo shows how Sabot processes millions of rows with Arrow columnar operations:

# One-time: Convert CSV to Arrow IPC format (52x faster loading)
cd examples/fintech_enrichment_demo
python convert_csv_to_arrow.py

# Enable Arrow IPC and run
export SABOT_USE_ARROW=1
./run_demo.sh --securities 10000000 --quotes 1200000

How Arrow Batch Processing Works:

• Arrow Joins: Convert RecordBatches to Tables, perform SIMD-accelerated hash joins
• Zero-Copy Operations: Direct memory access to Arrow buffers (no Python object creation)
• SIMD Acceleration: PyArrow compute kernels use vectorized operations
• Memory-Mapped Loading: Arrow IPC files loaded directly into memory without copying

Performance Results (M3 MacBook Pro, 11.2M rows):

• Total Pipeline: 2.3 seconds end-to-end (vs 103s with CSV)
• Hash Join: 104.6M rows/sec (11.2M rows joined in 107ms)
• Data Loading: 2.1 seconds (10M securities + 1.2M quotes)
• File Size: 50-70% smaller than CSV (5.6GB → 2.4GB)
• Speedup: 46x faster than traditional CSV processing

vs PySpark for this workload:

• PySpark: ~30-60 seconds (JVM startup, serialization, garbage collection)
• Sabot: 2.3 seconds (pure Python + Arrow + Cython acceleration)

See DATA_FORMATS.md for format guide and PERFORMANCE_SUMMARY.md for detailed benchmarks.

CLI Reference

Sabot provides a Faust-style CLI for production deployments:

# Start worker
sabot -A myapp:app worker

# With concurrency
sabot -A myapp:app worker -c 4

# Override broker
sabot -A myapp:app worker -b kafka://prod:9092

# Set log level
sabot -A myapp:app worker --loglevel=DEBUG

# Full options
sabot -A myapp:app worker \
  --concurrency 4 \
  --broker kafka://localhost:9092 \
  --loglevel INFO

API Reference

Creating an App

import sabot as sb

app = sb.App(
    'my-app',
    broker='kafka://localhost:19092',      # Kafka broker URL
    value_serializer='json',                # 'json', 'arrow', 'avro'
    enable_distributed_state=True,          # Use Redis for state
    database_url='postgresql://localhost/sabot'  # For durable execution
)

Defining Agents

@app.agent('my-topic')
async def process_events(stream):
    """Stateful event processor."""
    async for event in stream:
        # Process event
        result = transform(event)
        # Yield to output
        yield result

State Management

# Memory backend (fast, not persistent)
config = sb.BackendConfig(
    backend_type="memory",
    max_size=100000,
    ttl_seconds=300.0
)
backend = sb.MemoryBackend(config)

# RocksDB backend (persistent, larger state)
rocksdb = sb.RocksDBBackend(
    sb.BackendConfig(backend_type="rocksdb", path="./state")
)

# State types
value_state = sb.ValueState(backend, "counter")      # Single value
map_state = sb.MapState(backend, "user_profiles")    # Key-value map
list_state = sb.ListState(backend, "events")         # Ordered list

Checkpointing

# Barrier tracking for distributed checkpoints
tracker = sb.BarrierTracker(num_channels=3)

# Register barrier
aligned = tracker.register_barrier(
    channel=0,
    checkpoint_id=1,
    total_inputs=3
)

# Checkpoint coordinator
coordinator = sb.Coordinator()

Time & Watermarks

# Track watermarks across partitions
watermark_tracker = sb.WatermarkTracker(num_partitions=3)
watermark_tracker.update_watermark(partition_id=0, timestamp=12345)

# Timer service for delayed processing
timers = sb.Timers()

Installation Details

System Requirements

• Python: 3.8+
• OS: Linux, macOS (Windows via WSL)
• Memory: 4GB+ recommended
• Dependencies: See requirements.txt

Optional Dependencies

# GPU acceleration (RAFT)
pip install cudf cupy raft-dask pylibraft

# Kafka support
pip install confluent-kafka aiokafka

# Redis state backend
pip install redis hiredis

# RocksDB state backend
pip install rocksdb

# All optional features
pip install sabot[all]

Building from Source

# Install Cython and dependencies
uv pip install cython numpy  # Use sabot.cyarrow, not pyarrow

# Build Cython extensions
python setup.py build_ext --inplace

# Install in development mode
pip install -e .

Docker Compose Infrastructure

The included docker-compose.yml provides a complete streaming stack:

services:
  redpanda:      # Kafka-compatible broker (port 19092)
  console:       # Redpanda web UI (port 8080)
  postgres:      # PostgreSQL for durable execution (port 5432)
  redis:         # Redis for distributed state (port 6379)

Start all services:

docker compose up -d

Access Redpanda Console:

open http://localhost:8080

View logs:

docker compose logs -f redpanda

Stop all services:

docker compose down

Examples

Example	Description	Performance	Location
Fintech Enrichment	11.2M row joins with Arrow IPC	104M rows/sec	examples/fintech_enrichment_demo/
Arrow Data Loading	CSV to Arrow IPC conversion	52x faster	examples/fintech_enrichment_demo/convert_csv_to_arrow.py
Zero-Copy Operations	Hash joins, windows, filtering	SIMD-accelerated	examples/fintech_enrichment_demo/arrow_optimized_enrichment.py
Fraud Detection	Real-time fraud detection (experimental)	3-6K txn/s	examples/fraud_app.py
Crypto Features	Real-time feature engineering pipeline	Redis feature store	examples/crypto_features_demo.py
Graph Pattern Matching	Friend-of-friend recommendations	3-37M matches/sec	examples/social_network_analysis.py
Graph Fraud Detection	Money laundering pattern detection	2.7-5.6M matches/sec	examples/fraud_detection_optimizer.py
Graph Traversal	BFS, DFS, PageRank, shortest paths	10-50M nodes/sec	examples/property_graph_demo.py

Benchmark Results

Fintech Enrichment Demo (M3 MacBook Pro, 11.2M rows):

• Hash Join: 104.6M rows/sec (11.2M rows in 107ms)
• Arrow IPC Loading: 5M rows/sec (10M rows in 2 seconds)
• CSV Loading: 0.5M rows/sec (multi-threaded)
• Total Pipeline: 2.3 seconds (vs 103s with CSV - 46x faster)
• File Size: 50-70% reduction (5.6GB → 2.4GB)
• Memory Usage: Memory-mapped (minimal footprint)

CyArrow Zero-Copy Operations:

• Window Computation: ~2-3ns per element (SIMD)
• Filtering: 50-100x faster than Python loops
• Sorting: 10M+ rows/sec with zero-copy slicing
• Buffer Access: ~5ns per element (direct C++ pointers)

State Backend Operations (MemoryBackend):

• Get/Put latency: Sub-millisecond
• Sustained throughput: 1M+ operations/second (Cython)

Notes on Benchmarks:

• Measured on M3 MacBook Pro (8-core, 18GB RAM)
• Arrow IPC with memory-mapped I/O
• Real fintech data (10M securities, 1.2M quotes)
• All benchmarks are reproducible (see PERFORMANCE_SUMMARY.md)

Documentation

Performance & Data Formats

• PERFORMANCE_SUMMARY.md - Benchmark results and analysis
• DATA_FORMATS.md - Format comparison (Arrow IPC, CSV, Parquet)
• CYARROW.md - CyArrow API reference and zero-copy operations
• DEMO_QUICKSTART.md - Fintech demo quick start

Graph Processing

• GRAPH_QUERY_ENGINE.md - Complete graph API documentation
  • Columnar graph storage (CSR/CSC adjacency)
  • Graph traversal algorithms (BFS, DFS, PageRank, etc.)
  • Pattern matching (2-hop, 3-hop, variable-length paths)
  • Query optimization and planning
  • Performance benchmarks (3-37M matches/sec)

Architecture & Development

• PROJECT_MAP.md - Directory structure and module overview
• Architecture - Deep dive into internals (if exists)
• API Reference - API documentation (if exists)

Comparison to Other Frameworks

Feature	Sabot	PySpark	Ray	Apache Flink
Language	Python	Python (JVM)	Python	Java/Scala
Performance	✅ 104M rows/sec (Arrow + Cython)	🐌 10-50x slower (JVM serialization)	⚠️ Distributed overhead	✅ Production-scale
Columnar Processing	✅ Zero-copy Arrow (SIMD-accelerated)	⚠️ Arrow integration	❌ No	⚠️ Limited
Data Loading	✅ 52x faster (Arrow IPC)	🐌 Standard	🐌 Standard	🐌 Standard
Memory Efficiency	✅ Memory-mapped (no copies)	🐌 JVM heap	⚠️ Object serialization	✅ Native
Graph Processing	✅ 3-37M matches/sec (native)	⚠️ GraphX (JVM overhead)	❌ No native support	⚠️ Gelly (limited)
Setup Complexity	✅ Single pip install	🐌 JVM + Spark cluster	🐌 Distributed setup	🐌 Cluster management
Debugging	✅ Pure Python (pdb, breakpoints)	🐌 JVM stack traces	⚠️ Distributed complexity	🐌 JVM debugging
Streaming	⚠️ Experimental agents	✅ Structured Streaming	✅ Ray Data	✅ Production
Production Ready	✅ CyArrow (yes), ✅ Graph (yes), ⚠️ Streaming (no)	✅ Yes	✅ Yes	✅ Yes

Roadmap

Current Status (v0.1.0-alpha)

Working (Production-Quality):

• ✅ CyArrow: Zero-copy hash joins (104M rows/sec)
• ✅ DataLoader: Multi-threaded CSV, memory-mapped Arrow IPC
• ✅ Arrow IPC Format: 52x faster than CSV, 50-70% smaller files
• ✅ SIMD Operations: Window functions, filtering, sorting
• ✅ Graph Processing: Storage, traversal, pattern matching (3-37M matches/sec)
• ✅ Cython checkpoint coordinator (Chandy-Lamport barriers)
• ✅ Memory state backend with Cython acceleration
• ✅ Fintech enrichment demo (11.2M rows in 2.3s)

Working (Experimental):

• ⚠️ Basic Kafka source/sink with schema registry
• ⚠️ Watermark tracking primitives
• ⚠️ CLI scaffolding (Faust-style)
• ⚠️ Fraud detection demo (3K-6K txn/s)

In Progress:

• 🚧 Agent runtime execution layer (partially stubbed)
• 🚧 RocksDB state backend integration
• 🚧 Distributed coordination
• 🚧 Complex event processing (CEP)

Known Limitations:

• ⚠️ Test coverage ~5% for streaming features
• ⚠️ Agent runtime has mock implementations
• ⚠️ CLI uses stubs in places
• ✅ CyArrow & DataLoader are well-tested and performant

Planned (v0.2.0)

• 🎯 Complete agent runtime implementation
• 🎯 Comprehensive integration tests
• 🎯 RocksDB state backend completion
• 🎯 Improved error handling and recovery
• 🎯 Performance benchmarking suite
• 🎯 Production-ready checkpointing

Future Ideas (v0.3.0+)

• 📋 GPU acceleration via RAFT
• 📋 Advanced CEP patterns
• 📋 SQL/Table API
• 📋 Web UI for monitoring
• 📋 S3/HDFS connectors
• 📋 Query optimizer

Contributing

Sabot is an experimental research project and welcomes contributions! This is a learning-focused project exploring streaming architecture design.

High-Impact Areas:

1. Testing: Expand test coverage beyond current ~5%
2. Agent Runtime: Complete the execution layer implementation
3. RocksDB Integration: Finish state backend implementation
4. Documentation: Improve guides and examples
5. Benchmarking: Add comprehensive performance tests

Before Contributing:

• This is alpha software with many incomplete features
• Focus on learning and experimentation rather than production readiness
• Check existing issues and roadmap before starting major work
• Add tests for any new functionality

See CONTRIBUTING.md for guidelines (if available).

License

GNU Affero General Public License v3.0 (AGPL-3.0) - See LICENSE file for details.

Credits

Inspired by:

• Apache Arrow - Zero-copy columnar data processing
• PySpark - DataFrame API and distributed processing concepts
• Ray - Actor model and distributed computing patterns
• Faust - Python streaming framework, CLI design, and agent patterns

Built with:

• Cython - High-performance compiled modules for Arrow acceleration
• PyArrow - SIMD-accelerated compute kernels and memory management
• Redpanda - Kafka-compatible streaming infrastructure
• PostgreSQL - Durable execution (DBOS-inspired)
• RocksDB - Embedded key-value store for state management

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [email protected]

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When to Choose Sabot vs PySpark vs Ray

Choose Sabot when:

• You need PySpark-level performance without JVM overhead
• You're processing large columnar datasets (Arrow IPC, Parquet)
• You need high-performance graph analytics (pattern matching, traversals)
• You want pure Python debugging (pdb, breakpoints, no JVM stack traces)
• Single-machine performance is your primary concern
• You need fast iteration during development

Choose PySpark when:

• You need production-scale distributed processing
• Your team is already invested in the Spark ecosystem
• You have existing Spark clusters and infrastructure
• Java/Scala performance is acceptable for your use case

Choose Ray when:

• You need distributed actor-based processing
• You're building complex ML pipelines with distributed training
• Python-first distributed computing is your priority

This is experimental alpha software. The CyArrow columnar processing and graph analytics are production-quality, but streaming features are experimental. We welcome feedback and contributions!

Ready to try it?

• Columnar processing: Fintech Enrichment Demo - 11.2M rows in 2.3 seconds
• Graph analytics: Graph Query Engine - 3-37M matches/sec pattern matching