Efficient Forkless Blockchain Databases

• Efficient forkless blockchain databases are systems that enforce deterministic consensus for immediate finality, enabling streamlined state management by pruning unnecessary historical data.
• They employ advanced techniques like intrinsic pruning, lazy hashing, and modular sharding to achieve up to 10×–100× throughput and storage improvements over legacy designs.
• These designs integrate authenticated data structures and facilitate systems decoupling, making them ideal for secure, scalable deployments and further research into optimal database performance.

Efficient forkless blockchain databases are database systems underlying modern blockchains in which the finality of state transitions is enforced by deterministic, forkless consensus protocols, and storage structures are designed to remove redundant data, minimize recomputation, and maximize throughput, availability, and cost-efficiency. These designs break from legacy, fork-tolerant architectures (such as those built on Merkle Patricia Tries in Ethereum), exploiting finality guarantees to streamline storage layers, prune historical state, and decouple live execution from archival persistence. Recent research spans modular layering and system decoupling, pruning techniques, authenticated state storage, consensus-aware logging, hybrid/relational integrations, and DHT-based scaling, with experimental evidence showing 10×–100× improvements in throughput and storage over previous generations of blockchain systems.

1. Forkless Consensus: The Foundation for Efficient Blockchain Databases

Forkless blockchain databases presuppose an underlying consensus mechanism providing immediate and strong finality of blocks. In protocols such as PBFT and its derivatives—as implemented in Hyperledger Fabric and permissioned systems (Dinh et al., 2017)—committed blocks are final; reorganizations (forks) are effectively precluded. Deterministic ordering allows the database layer to optimize for a single, linear state history.

Key implications of forkless consensus for database architecture include:

• Only one branch of history is ever live or valid, permitting intrinsic pruning and removal of superfluous historic versions.
• Transaction commit order is globally agreed and can be directly reflected in the data structure without concern for future rewrites.
• Synchronization logic between nodes is simplified as the global state is always consistent (barring faults), allowing the use of non-versioned, mutable, or log-structured data stores.

Empirical studies (see ETH, Parity, Hyperledger v0.6 in (Dinh et al., 2017)) show that PBFT-style forkless consensus enables higher throughput and lower per-transaction overhead than proof-of-work (PoW)–driven, probabilistic-finality blockchains—so long as node count remains moderate (≤16 nodes, due to O(N²) scaling of PBFT).

2. Forkless State Management: Intrinsic Pruning and Storage Layouts

The architectural advantage conferred by forkless operation enables radical changes to the structure and lifecycle of blockchain databases:

• LiveDB (Latest-State-Only Store): For validator, observer, and non-archival nodes, the state database (e.g., “LiveDB” (Jordan et al., 28 Aug 2025)) becomes fully mutable. Each commit overwrites the previous state, with obsolete data pruned at the moment of update, obviating the overhead and complexity of multi-version data structures such as Merkle Patricia Tries (MPTs).
• Fixed-Length, Normalized Storage: By mapping each account address or storage key to an integer record number and packing each attribute (balance, nonce, code, etc.) into dense, fixed-length binary files, random-access lookup becomes a simple linear offset computation. As shown in (Jordan et al., 28 Aug 2025), this permits constant-time access: offset = record_number × record_size.
• Lazy Hashing: Since only one state is valid at any moment, state root hashes may be computed lazily upon request rather than eagerly at every update. Dirty flags mark modified rows/pages, and hash recomputation can be batched via reverse breadth-first traversal, further parallelized using prefix sum–like algorithms. For a tree of page size p, leaf count and hash node size shrink by n/p, reducing total hash computations:

$$n_{\text{leaves}} = \frac{n}{p},\quad \text{hash tree size} \approx \left(\frac{n}{p}\right) - 1$$

• ArchiveDB (Deltas for History): For archival nodes, history is preserved as a linear sorted delta log. Only the difference between consecutive states is stored, relying on forkless property to log just the minimal necessary state transitions (Jordan et al., 28 Aug 2025).

Compared to legacy Geth-based clients (using MPTs), this approach yields 10× improvement in throughput (from 500 tx/s to 5,000 tx/s) and 100× reduction in storage (from terabytes to tens of gigabytes) (Jordan et al., 28 Aug 2025).

3. Modular, Decoupled, and Sharded Architectures

Contemporary research advocates a modular, layered architecture, separating consensus, storage, execution, and application layers, much in the style of database system engineering (Dinh et al., 2017). Performance bottlenecks can be isolated and system components independently optimized. Prominent principles include:

• Decoupling Storage from Consensus: By allowing the consensus protocol to “order” blocks/transactions, the storage layer may focus entirely on efficiently representing and mutating state. This enables plugging efficient key–value stores such as RocksDB or BadgerDB in permissioned and forkless settings (Laishevskiy et al., 2023).
• Leveraging Hardware Parallelism: Multi-threading and deep pipelining (as in ResilientDB (Gupta et al., 2019)) distribute work streams at the granularity of input, batching, consensus, and execution. Batching strategies (determining batch size to optimize consensus load vs. latency) are particularly effective in reducing per-transaction network and computational overhead.
• Sharding and Cross-Shard Mechanisms: Modularizing via sharding (as in GriDB (Hong et al., 4 Jul 2024) and StakeCube (Durand et al., 2019)) partitions the ledger or state by deterministic hashing or DHT assignment. Off-chain cross-shard mechanisms (Hong et al., 4 Jul 2024) use randomly assigned delegates and proof-carrying protocols (with verifiable accumulators/ADSs) to run expensive operations (e.g., JOINs, migrations) off-chain and re-integrate results into the main chain via lightweight, succinct proofs.

Off-chain proof-based cross-shard design, as in GriDB, allows nearly linear scalability with the number of shards and unblocks on-chain workloads by minimizing consensus-bound data transfers.

4. Authenticated and Historical State: Data Verification and Advanced Features

Efficiency in forkless blockchain databases must not compromise verifiability or auditability:

• Authenticated Data Structures: Integration of authenticated data structures (e.g., accumulator-based ADSs (Xu et al., 2018, Hong et al., 4 Jul 2024), Merkle trees) ensures that state queries and cross-shard results are tamper-evident. Succinct proofs (e.g., $acc(X) = g^{\prod_{x \in X} (x + s)}$) and batch-optimized indices (both intra- and inter-block) allow light clients to verify responses without storing the full database or relaying entire query result sets.
• Historical Proofs and State Reconstruction: Recent forkless database designs (QMDB (Zhang et al., 9 Jan 2025), AlDBaran (Kauer et al., 14 Aug 2025)) feature explicit support for “historical proofs”: each entry encodes old and current pointers (OldId/OldNextKeyId), enabling clients to reconstruct the Merkle root or inclusion/exclusion proofs for past blocks, providing robust support for regulatory, auditing, or application-level requirements such as TWAP oracles and state rollbacks.
• Efficient Search and Verification: Index structures such as BMF+/BPI (Zhao et al., 30 Aug 2025) employ immutable, temporally ordered forests and stop-location tokens to accelerate keyword/incremental search in append-only blockchains. Incremental articulated search leverages the append-only property; validation models with dynamic-length CRC digests ensure completeness and resistance to malicious result modification by off-chain SPs.

These approaches facilitate both efficient operation and rigorous integrity checks, even in hybrid or outsourced-storage settings.

5. Integration with External Systems: Relational and Heterogeneous Environments

Recent work demonstrates efficient, forkless blockchain databases can be integrated with relational or legacy systems:

• ChainifyDB (Schuhknecht et al., 2019): Introduces the Whatever-LedgerConsensus (WLC) model, deferring consensus from transaction order to execution effect. Rather than requiring deterministic execution across all nodes, nodes locally execute their transactions/blocks, then compare resulting effects (state digests); consensus is reached on the effect. Rebel nodes are forced into a recovery phase, barring the formation of divergent forks.
• Tendermint + Relational DBMS (Schuhknecht et al., 2022): Uses the ABCI interface of Tendermint to sequence deterministic SQL transaction batches into a backend RDBMS (e.g., PostgreSQL, MySQL). Deterministic sequential application of SQL ensures all nodes converge to the same state, enforced by the PBFT/Polka consensus protocol—yielding an inherently forkless, fully replicated relational blockchain.
• Hybrid Storage and Search (Zhao et al., 30 Aug 2025): Articulates an architecture (BPI) for keyword/incremental search across blockchain–off-chain hybrid stores, using lightweight index forests and robust CRC-based validation to guarantee both efficiency and the completeness of query results.

These approaches facilitate vertical integration, allowing forking-resilient data pipelines in enterprise, finance, supply chain, and healthcare, particularly when strong consistency and auditability across multiple heterogeneous organizations is essential.

6. Performance, Scalability, and Resource Implications

Experimental and analytical results across recent work demonstrate substantial gains:

System/Paper	Throughput Improvement	Storage Reduction	Key Mechanism(s)
(Jordan et al., 28 Aug 2025)	10× (tx/s)	100×	LiveDB, ArchiveDB, Pruning
(Laishevskiy et al., 2023)	Up to 1.5× (TPS)	N/A	BadgerDB StateDB
(Zhang et al., 9 Jan 2025) (QMDB)	6× over RocksDB	Massive	Unified KV/Merkle, Twigs
(Kauer et al., 14 Aug 2025) (AlDBaran)	10× over QMDB	N/A	No-disk, Lock-Free, Prefetch
(Zhao et al., 30 Aug 2025) (BPI)	2.5–20× (search)	99%+	BMF+/PCM, CRC Validation

Ultra-efficient designs such as QMDB and AlDBaran achieve up to millions of state updates per second, enabling 1 million TPS for token-transfer workloads and reducing DRAM requirements per entry to 2–3 bytes (Zhang et al., 9 Jan 2025). Prefetching, lock-free sharded trees, and append-only, in-memory Merkleization sidestep traditional I/O bottlenecks. Forkless operation allows archival storage to be decoupled from live processing, and mitigates IOPS and bandwidth bottlenecks of prior approaches.

7. Limitations, Open Issues, and Research Directions

Despite the advancements, recent research recognizes several limitations and avenues for improvement:

• Determinism Dependencies: Some designs assume global ordering in assignment/appends (e.g., LiveDB (Jordan et al., 28 Aug 2025)); any divergence in block application order could result in inconsistent hashes—robust enforcement or mitigation remains a subject for future work.
• Concurrency and Extreme Loads: Behavior of these systems under extreme network churn, Byzantine writes, or high shard-migration rates remains an important area of evaluation.
• Generalization: There is ongoing investigation into extending forkless techniques—e.g., pruning, dense storage, lazy hashing—to generic, possibly forking, or multi-versioned data stores or to more complex database workloads (e.g., range queries, SQL joins).
• Integration Overhead: In relational integrations (Tendermint+DBMS, ChainifyDB), synchronous commit/consensus overhead can be significant for short transactions, necessitating careful batch/async tuning (Schuhknecht et al., 2022).

Nevertheless, the forkless paradigm, once coupled with efficient, modular data structures and pluggable consensus, positions blockchain databases to match or exceed classical distributed databases in throughput, scalability, and operational efficiency.