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Dev Release 14

April 22, 2024

Greetings BlockDAG community!

Diving Deeper into DAG BFT: A Technical Exploration
Continuing our implementation of blockchain consensus mechanisms, we delve into Directed Acyclic Graph (DAG) Byzantine Fault Tolerance (BFT). This innovative approach departs from traditional linear chains (like Proof of Work or Proof of Stake) by leveraging DAGs to represent transaction history. This post unpacks the core principles of DAG BFT, delves into practical implementation techniques, and lays the groundwork for future discussions about linear ordering within  BlockDAG.

Core Principles & technical aspects

  • DAG-based Transaction History: Transactions are arranged as a DAG, where vertices represent transactions and directed edges signify causal dependencies (transaction B depends on transaction A). This structure enables efficient parallelization of transaction processing.
  • BFT for Byzantine Fault Tolerance: BFT protocols guarantee agreement on the final transaction order amongst honest nodes, even in the presence of byzantine faults (nodes exhibiting arbitrary behavior, including malicious intent). This is achieved through cryptographic mechanisms and message exchange protocols.

Technical Specifications:

  • Message Structure:
  • Each message encapsulates a transaction payload along with cryptographic signatures and metadata.
  • Causal relationships are explicitly encoded within the message, referencing previously delivered transactions.
  • Broadcast Function (broadcast(payload))
  1. create_message(payload): Constructs a message object containing the transaction data, necessary signatures, and a unique identifier.
  2. add_causal_references(message): Incorporates references to previously delivered transactions upon which the current transaction depends.
  3. send_message(message, all_nodes): Transmits the message asynchronously to all participating nodes in the network.
  • Deliver Function (deliver(message m))
  1. verify_message(m): Authenticates the message using cryptographic signatures and ensures message integrity.

function deliver(message m):
        if verify_message(m):
            for tx in m.transactions:
                 if not has_transaction(tx):

  1. retrieve missing transactions (for tx in m.transactions): Checks for locally missing dependent transactions referenced within the message.
  • If a dependency is missing (not has_transaction(tx)), it is retrieved using a reliable download mechanism (download_transaction(tx)).
  1. acknowledge_receipt(m): Once all dependencies are satisfied and verification is successful, the node acknowledges receipt of the message, signaling its inclusion in the local DAG.

Reliable and Causally-Ordered Broadcast (RBC) Techniques (for n = 3f + 1 nodes):

  • Echoing: Enhances message reliability and prevents forgeries by having nodes re-broadcast digests of received messages. Common approaches include:
  • All-to-all Bracha Broadcast: A cryptographic protocol guaranteeing all nodes receive every message and its origin is verifiable.
  • Rampart's Converge-cast with Signatures: A message dissemination protocol ensuring messages are delivered to all nodes in the same order, incorporating digital signatures for authenticity.
  • Layering: Manages message transmission and network congestion by:
  • Limiting each sender to a single message per layer.
  • Restricting message references to those from the preceding layer, enforcing a causal ordering.

Next steps:

Having established the foundation of DAG BFT, the next step involves extracting a linear order from the DAG structure. This crucial aspect, essential for final transaction settlement, will be explored in a subsequent discussion to solidify the understanding of BlockDAGs.
Additional Considerations:

  • Scalability: The parallel transaction processing facilitated by DAGs contributes significantly to the potential scalability of DAG BFT systems.
  • Security: Ongoing research investigates optimizing security properties within DAG BFT implementations to ensure robustness against various attack vectors.
  • Performance Optimization: Techniques like efficient causal ordering verification and garbage collection within the DAG structure are critical for maintaining optimal performance within DAG BFT systems.

This technical breakdown provides a deeper understanding of DAG BFT, its core functionalities, and implementation details. It lays the groundwork for further exploration of linear ordering within BlockDAGs and paves the way for discussions on security considerations and performance optimization techniques. stay tuned!

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