TXGraph - DAG-Based Concurrent Executor for Blockchain Systems

Built to answer:How do you execute millions of smart contract transactions — concurrently — without breaking determinism?

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🔍 Overview

TXGraph is a high‑performance, DAG‑based concurrent transaction executor designed for Layer 1 blockchains, rollups, and smart contract runtimes.
It analyzes read/write conflicts, builds a Directed Acyclic Graph (DAG) of dependencies, and schedules transactions for safe parallel execution — preserving determinism while unlocking massive scalability.

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🚀 Features

• DAG‑driven Scheduling – Detects conflicts and orders execution deterministically.
• Safe Parallel Execution – Executes non‑conflicting transactions across multiple cores.
• Deterministic Results – Identical output as a valid sequential run.
• State Isolation – Versioned, concurrency‑safe state transitions.
• Multi‑Backend Support – In‑memory, RocksDB, and Merkle‑tree backends.
• Metrics & Observability – Prometheus/Grafana ready. --

🔹 Executive Summary

TXGraph is a concurrency-aware, dependency-tracking transaction executor built to address the core scalability challenge in blockchain and smart contract platforms: safe parallelism without sacrificing determinism.

Traditional systems execute transactions sequentially, leaving modern multi-core systems underutilized. TXGraph unlocks parallel execution by intelligently analyzing read/write conflicts and dynamically orchestrating non-conflicting transactions across worker threads.

It ensures correctness, observability, and scalability, making it an ideal backbone for:

• Layer 1/2 blockchain nodes (EVM, WASM, UTXO)
• Rollup sequencers
• Parallelized smart contract runtimes
• Blockchain simulation/testnet environments

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🎯 Project Goals & Design Principles

Objective	Description
Concurrency-First	Maximize parallelism while preventing unsafe race conditions
Determinism-Preserving	Guarantee identical results as a canonical sequential execution
Robust Fault Tolerance	Isolate and report individual transaction errors
Horizontal Scalability	Scale execution across CPU cores
Memory Efficiency	Use lazy evaluation and snapshots to scale to millions of state entries
Extensibility	Modular support for different virtual machines or state backends

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🔗 System Overview

+--------------+ +----------------+ +------------------+ +-------------+ +-----------------+
| Input Queue | --> | RW Set Analyzer| --> | DAG Scheduler | --> | Worker Pool | --> | Result Aggregator|
+--------------+ +----------------+ +------------------+ +-------------+ +-----------------+

🔗 Achitecture Design (System level)

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flowchart TD
A[Input Queue<br/>Tx1, Tx2, ..., TxN] --> B[Dependency Analyzer<br/>Extract RW Sets<br/>Build Conflict Graph]
B --> C[Scheduler<br/>Topological Sorting<br/>Wavefront Batching]
subgraph Worker Pool [Parallel Worker Pool]
D1[Worker 1<br/>Isolated State View]
D2[Worker 2<br/>Isolated State View]
D3[Worker N<br/>Isolated State View]
end
C --> D1
C --> D2
C --> D3
D1 --> E[Global State Manager<br/>Thread-Safe Write Buffer<br/>Versioned State]
D2 --> E
D3 --> E
E --> F[Result Collector<br/>Reorder Results<br/>Aggregate Errors<br/>Commit Final State]
F --> G[Final Output<br/>State Root, Logs, Metrics]
style Worker_Pool fill:#f9f,stroke:#333,stroke-width:1px

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🧩 Component Highlights

• Input Queue
  Receives and stores the incoming batch of transactions for execution.

• Dependency Analyzer
  Extracts read/write sets for each transaction and constructs a Directed Acyclic Graph (DAG) to represent execution dependencies based on conflicts.

• Scheduler
  Performs topological sorting of the DAG and groups non-conflicting transactions into parallelizable wavefronts.

• Worker Pool
  A fixed-size or dynamically scalable pool of workers that execute independent transactions concurrently using isolated or versioned state views.

• Global State Manager
  Maintains a thread-safe, versioned view of application state. Applies transaction state deltas while enforcing serialization of conflicting writes.

• Result Collector
  Aggregates execution results, reorders them to match input sequence, logs successes/failures, and commits the final state root.

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🧱 Core Components

Transaction Model

structTransaction{id:u64,from:Address,to:Option<Address>,reads:Vec<Address>,writes:Vec<Address>,payload:Vec<u8>,}

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Dependency Graph Builder

• Builds DAG from RW conflicts
• Uses bloom filters or hashmaps for quick overlap detection
• Conflict if: Tj.writes ∩ Ti.reads ∪ Ti.writes ≠ ∅ → Tj → Ti

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Scheduler

• Topologically sorts DAG
• Identifies wavefront of independent transactions
• Dispatches batches to worker threads
• Balances load with queues or work-stealing

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Worker Engine

• Each worker has isolated state view
• Executes transaction and returns:
  • Status
  • Logs
  • Modified state
  • Errors (if any)

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Global State Manager

• Versioned state
• Copy-on-write, shadow states
• Final commit based on dependency ordering
• Supports backends: in-memory, RocksDB, MerkleDB

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📁 Directory Layout

TXGraph/
├── cmd/
│ └── app/
│ └── main.go # Entry point: wiring everything together
│
├── internal/
| |── api/
│ | └── grpc/
│ | └── executor.proto # Optional: gRPC service definition
│ │ ├── core/
│ │ ├── dependency_graph.go # DAG builder from RW conflict sets
│ │ ├── scheduler.go # Topological scheduler
│ │ └── state_manager.go # Versioned state manager
│
│ ├── executor/
│ │ ├── worker.go # Worker logic and state isolation
│ │ ├── pool.go # Worker pool manager
│ │ └── isolation.go # View-based state access
│
│ ├── model/
│ │ ├── transaction.go # Transaction struct, payload, read/write sets
│ │ └── result.go # Execution result, gas, logs, errors
│
│ ├── utils/
│ │ ├── analyzer.go # ABI or bytecode analyzer for RW sets
│ │ ├── metrics.go # Prometheus/Grafana instrumentation
│ │ └── logging.go # Structured logger (zap/logrus)
│
│ ├── storage/
│ │ ├── memstore.go # In-memory backend for state storage
│ │ ├── rocksdb_store.go # RocksDB-based state backend
│ │ └── merkle_backend.go # Optional: Merkle tree or SMT storage
│
├── examples/
│ └── simple_chain/
│ └── main.go # Simulates running the executor with mock txs
│
├── benchmarks/
│ └── throughput_test.go # Benchmarking different tx loads
│
├── go.mod # Go module definition
├── go.sum
└── README.md # Project documentation

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🔍 Execution Flow Example

Tx1: R[A], W[B]
Tx2: R[B], W[C]
Tx3: R[D], W[E]
Tx4: R[C], W[F]
DAG:
Tx1 → Tx2 → Tx4
Tx3 (independent)
Execution Waves:
Wave 1: Tx1, Tx3
Wave 2: Tx2
Wave 3: Tx4

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📊 Performance & Scalability

Metric	Target
Throughput	100,000 tx/sec on 8-core machine
DAG Build Time	<1ms per 1000 transactions
Conflict Detection	Optimized with bloom filters or maps
Memory Overhead	Minimal with copy-on-write + caching
Latency Spread	90% transactions complete in under 5ms

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🔒 Correctness Guarantees

Property	Description
Serializability	Execution matches some valid serial schedule
Linearizability	Slot-level version control ensures atomic transitions
Determinism	Same input → same DAG → same state
Atomicity	Per-transaction isolation with rollback on failure
Progress	No starvation or deadlocks

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🚀 Future Enhancements

Feature	Description
Speculative Execution	Rollback support for optimistic concurrency
Dynamic Tracing	Runtime RW detection via VM hooks
Distributed Execution	Multi-node DAG partitioning and execution
GPU Acceleration	CUDA/OpenCL batch execution pipeline
DAG Visualization	Tooling for graph rendering and inspection

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🧪 Testing and Benchmarks

• Unit & property tests with quickcheck
• Integration tests with synthetic transaction batches
• Profiling with criterion.rs
• Metrics via Prometheus

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🛠️ Recommended Tech Stack

• Language: Rust (Tokio, Petgraph, Rayon)
• Storage: RocksDB, Redis, MerkleDB
• Graph: Petgraph DAG
• State Isolation: COW maps, version clocks
• Concurrency: RwLocks, work-stealing, crossbeam

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🔌 Integration Targets

• Layer 1 clients (EVM, MoveVM)
• Layer 2 rollups (OP, ZK)
• Smart contract platforms
• Off-chain simulation frameworks
• Parallel local development tools

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📘 License

MIT License. Open source roadmap planned under TXGraph.dev.

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💡 Why TXGraph?

TXGraph enables truly parallel blockchain computation — with safety, determinism, and performance guarantees — serving as the foundation of next-generation execution engines.

It’s not just faster — it’s correctly faster.

Memory efficiency for large state sizes