In the ever-evolving world of distributed ledger technology (DLT), blockchain has long held the spotlight as the foundational architecture for decentralized systems. However, as scalability and latency challenges persist, a new contender has emerged: DAG-based architecture. Directed Acyclic Graphs (DAGs) are being increasingly adopted by next-generation Layer 1 networks to overcome the limitations of traditional blockchains. Yet, confusion remains about what DAGs truly are, how they function, and whether they represent a genuine leap forward—or just a rebranded consensus mechanism.
This article unpacks the core principles of DAG-based systems, clarifies the difference between causally ordered and totally ordered ledgers, and explores real-world implementations such as Fantom, Avalanche, IOTA, and Sui. We’ll also examine how DAGs impact smart contract functionality, transaction throughput, and network scalability—offering a clear framework to understand where DAG technology excels and where it still faces hurdles.
What Is a DAG?
The term DAG stands for Directed Acyclic Graph—a mathematical structure used to model relationships between events. Let’s break that down:
- Directed: Data flows in one direction only.
- Acyclic: No path loops back to a previous node—there’s no cycle.
- Graph: A network of interconnected nodes (vertices) linked by edges.
When applied to distributed ledgers, a DAG allows transactions to be processed not in a linear chain, but in a web-like structure where each new transaction validates one or more prior ones.
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Total Ordering vs. Causal Ordering
The key distinction in DLT design lies in how transactions are ordered:
- Total ordering, used in traditional blockchains like Bitcoin and Ethereum, sequences every transaction in a strict chronological line. Everyone agrees on the exact order of all events.
- Causal ordering, on the other hand, only enforces sequence when transactions are interdependent. If two transactions don’t affect each other, their order doesn’t matter—only cause-and-effect relationships are preserved.
This partial ordering is what enables DAGs to achieve higher throughput: unrelated transactions can be processed in parallel, eliminating bottlenecks caused by global consensus.
Blockchains Using DAGs: Fantom’s Hybrid Approach
Not all "DAG-based" networks are true DAGs. Some, like Fantom, use DAGs internally for consensus but output a totally ordered blockchain.
Fantom and Lachesis: A DAG-Powered Consensus
Fantom’s consensus engine, Lachesis, employs a DAG of “event blocks” created by nodes. These event blocks form a web of communication, with each referencing prior events through cryptographic hashing. This structure ensures immutability and fast propagation across the network.
However, the final output is not a pure DAG. Instead:
- Nodes identify special Clotho blocks—event blocks with broad network confirmation.
- Clotho blocks timestamp and finalize consensus, becoming Atropos blocks.
- Atropos blocks form the Main Chain, a linear, totally ordered blockchain.
While this hybrid model improves speed over traditional proof-of-work chains, it still relies on total ordering—limiting some of the scalability benefits of true causal ordering. Additionally, Fantom uses the Ethereum Virtual Machine (EVM), which constrains its performance potential. To address this, Fantom plans to introduce the Fantom Virtual Machine (FVM) and support Cosmos SDK integrations, aiming for greater flexibility and throughput.
Despite these optimizations, Fantom’s reliance on EVM-based tooling faces growing competition from advanced Layer 2 scaling solutions on Ethereum itself.
True DAG Ledgers: Causally Ordered Networks
When most people refer to “DAG blockchains,” they mean systems with causally ordered ledgers—where only dependent transactions are sequenced. These architectures unlock massive parallelization and near-instant finality.
Avalanche X-Chain: UTXO Meets DAG
Avalanche’s X-Chain uses a UTXO (Unspent Transaction Output) model structured as a DAG. It leverages a family of consensus protocols—Slush, Snowflake, Snowball, and Avalanche—to achieve rapid agreement without requiring full network voting on every transaction.
Key features:
- Each transaction is a vertex in the DAG.
- New transactions approve multiple parent transactions, reinforcing the network.
- The system uses “chits”—confidence counters that accumulate based on approval—to determine transaction validity.
- Conflicting transactions (e.g., double spends) are resolved probabilistically through repeated sampling.
While highly efficient for asset transfers, the X-Chain does not natively support smart contracts due to its lack of total ordering. For that, Avalanche uses Snowman, a separate consensus protocol that creates linear chains (C-Chain and P-Chain), enabling EVM compatibility.
This dual-architecture approach lets Avalanche offer both high-speed payments and robust smart contract functionality—without forcing trade-offs within a single chain.
IOTA: Proof-of-Work Without Miners
IOTA’s Tangle is one of the earliest pure DAG implementations. Every new transaction must approve two previous “tips” (unconfirmed transactions), creating organic growth in validation density.
To prevent spam and sybil attacks:
- Each transaction carries an initial weight, reflecting computational effort.
- The cumulative weight includes all direct and indirect approvals.
- Higher cumulative weight = higher trust.
Unlike blockchain miners, IOTA users themselves perform validation—making it feeless and highly scalable for microtransactions.
However, like Avalanche’s X-Chain, IOTA struggles with smart contracts. Its causal ordering makes deterministic execution difficult. To bridge this gap, IOTA is launching Assembly, a Layer 2 framework supporting EVM and WASM smart contracts on a totally ordered chain.
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Sui: The Smart Contract DAG Pioneer
Among DAG-based Layer 1s, Sui stands out by integrating smart contracts directly into its causally ordered architecture.
Object-Centric Design
Instead of accounts or UTXOs, Sui organizes its ledger around objects:
- Each object has ownership (owned by address, owned by another object, shared, or immutable).
- Transactions mutate object states rather than updating account balances.
- Objects know their creation history via cryptographic hashes.
This design enables powerful optimizations:
- Independent transactions (e.g., sending assets between unrelated wallets) use Byzantine Consistent Broadcast (BCB)—bypassing global consensus for near-instant finality.
- Dependent transactions (e.g., interacting with shared liquidity pools) use the Narwhal & Bullshark protocol for total ordering.
Sui’s consensus thus adapts dynamically: causal ordering where possible, total ordering when necessary.
Move Language: Built for Parallel Execution
Sui uses the Move programming language, originally developed for Diem (Libra). Move enforces strict resource ownership rules that align perfectly with Sui’s object model—preventing common bugs like double-spending while enabling safe parallel execution.
As a result, Sui achieves both high throughput and full smart contract support—a combination long thought impossible in pure DAG systems.
Key Takeaways: Where Do DAGs Stand Today?
| Feature | Blockchain | Pure DAG | Hybrid DAG |
|---|---|---|---|
| Ordering | Total | Causal | Mixed |
| Throughput | Moderate | High | High |
| Smart Contracts | Native | Limited | Possible |
| Finality | Seconds to Minutes | Near Instant | Sub-second |
| Use Case | General Purpose | Payments / IoT | Scalable dApps |
The data shows that DAGs excel in latency-sensitive environments like IoT and micropayments (IOTA), or high-throughput DeFi (Sui). However, they face inherent challenges when deterministic sequencing is required—hence the rise of hybrid models.
Moreover, even non-DAG blockchains are adopting DAG components. For example:
- Celo integrates Narwhal, Sui’s DAG-based mempool, to improve transaction dissemination.
- This trend suggests that the future may not be “blockchain vs. DAG,” but rather blockchains enhanced by DAG infrastructure.
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Frequently Asked Questions
Q: Are DAGs better than blockchains?
A: Not universally. DAGs offer superior throughput and lower latency for certain use cases, especially when transactions are independent. But blockchains remain superior for applications requiring strict chronological ordering and strong consistency guarantees.
Q: Can DAGs support smart contracts?
A: Traditionally, no—due to causal ordering. But Sui demonstrates that with object-centric models and languages like Move, smart contracts can thrive even in partially ordered environments.
Q: Is every “DAG-based” project actually using a DAG ledger?
A: No. Many projects (like Fantom) use DAGs only in consensus layers but produce totally ordered blockchains. True DAG ledgers maintain causal ordering throughout.
Q: Do DAGs sacrifice decentralization for speed?
A: Not necessarily. Most DAG designs maintain decentralization by distributing validation among users. However, some implementations may rely on coordinators or centralized checkpoints during early stages (e.g., early IOTA).
Q: How do DAGs handle double-spending?
A: Through probabilistic consensus (like Avalanche) or cumulative weight mechanisms (like IOTA). Conflicting transactions are evaluated based on network approval patterns or computational effort invested.
Q: Will DAGs replace blockchains?
A: Unlikely. Instead, we’re seeing convergence—blockchains borrowing DAG techniques for scalability (e.g., mempools), while DAGs adopt blockchain-like structures for compatibility (e.g., Layer 2s). The future is hybrid.
Final Thoughts
DAG-based architecture is not a silver bullet—but it’s far more than hype. From Fantom’s optimized consensus to Sui’s breakthrough in parallel smart contract execution, we’re witnessing a maturation of DLT design that transcends old paradigms.
As developers continue to experiment with causal ordering, object models, and hybrid consensus mechanisms, the line between blockchain and DAG will blur further. What matters most isn’t the data structure itself—but how well it serves the application.
Whether you're building high-frequency trading protocols or decentralized identity systems, understanding the nuances of DAG-based architecture is essential for navigating the next wave of innovation in web3.
Core Keywords: DAG-based architecture, Directed Acyclic Graph, causal ordering, total ordering, Layer 1 scalability, smart contract execution, distributed ledger technology