Scaling Blockchain Throughput: Overcoming the Synchronization Bottleneck in High-TPS Systems
Abstract
Blockchain systems aim to maximize throughput while preserving decentralization and security. However, achieving transactions-per-second (TPS) at scale remains one of the most difficult engineering challenges. This paper explores the primary technical bottlenecks preventing blockchains from reaching millions of TPS, focusing on network propagation, consensus efficiency, state execution, data availability, and the trade-offs between scalability, security, and decentralization. We present a structured analysis of each bottleneck, drawing from existing research in distributed systems, peer-to-peer networking, and cryptographic consensus.
1. Introduction
Since Bitcoin introduced decentralized consensus in 2008, blockchain systems have faced the “scalability trilemma”: the difficulty of simultaneously maximizing scalability, security, and decentralization. While modern chains have experimented with larger blocks, parallelization, and modular architectures, the practical throughput ceiling is still bounded by the need for synchronization at scale, ensuring that thousands of distributed validators agree on the same global state in near real-time.
This paper investigates the six major bottlenecks in blockchain throughput and examines how they interact.
2. Network Propagation & Latency
In a peer-to-peer blockchain, every node must receive, validate, and relay transactions and blocks. At low TPS, this is trivial, but as throughput grows:
Propagation Delay: The time for a block/transaction to reach >90% of nodes increases with block size.
Geographic Distance: Nodes are distributed globally, bounded by the speed of light and Internet routing inefficiencies.
Fork Probability: If blocks propagate slowly, competing versions of the chain may arise, increasing reorg risk.
Model:
3. Consensus Bottlenecks
Consensus mechanisms define how nodes agree on the order of transactions. High TPS stresses consensus because:
Proof-of-Work (PoW): Limited by block interval and difficulty adjustment — increasing block size raises propagation risk.
Proof-of-Stake (PoS): Requires validator votes/signatures → bottleneck in aggregation and verification.
BFT-style consensus: Communication overhead grows as O(N^2) in naive implementations.
Optimization Techniques:
Signature aggregation (BLS, Schnorr) reduces communication overhead.
Randomized leader election (VRFs) lowers coordination costs.
DAG-based consensus (e.g., Avalanche, Hashgraph) removes strict linearity but introduces complexity in ordering.
Formula for validator bottleneck: If V validators each produce a signature of size SSS, bandwidth per round =
Even with aggregation, verification at scale is non-trivial.
. State Execution & Storage
Each transaction updates the global blockchain state (balances, contracts, etc.). At high TPS:
Execution bottleneck: Smart contract execution (EVM, WASM) consumes CPU/memory. Parallelization is difficult due to shared state conflicts.
State growth: With TTPS×T transactions per second over time T, global state size grows linearly, stressing storage.
Database performance: State is stored in Merkle/Verkle tries or similar authenticated data structures, which grow slower under high write volume.
Example: If each transaction adds 200 bytes to the state, and throughput = 1M TPS, then:
This results in ~17 TB/day of state growth, clearly unsustainable without pruning, sharding, or compression.
5. Data Availability & Compression
High throughput chains must ensure that data is available to all validators for validation. Challenges:
Data Withholding Attacks: Block producers may publish headers without full data, preventing validation.
Compression Trade-offs: Smaller block sizes = faster propagation, but compression introduces CPU overhead.
Sharding Complexity: Distributing data across shards improves scalability but complicates cross-shard consistency.
Mathematical framing: Data availability guarantees often rely on erasure coding and sampling. For N nodes, each sampling kkk pieces of data, probability of detecting withholding is:
6. Security vs. Speed Trade-off
Increasing TPS typically means:
Larger blocks → fewer nodes can keep up → centralization risk.
Shorter block times → higher fork rates.
Lower validator counts → faster consensus, but weaker security.
This tension is the blockchain trilemma:
Scalability (high TPS)
Security (resistance to attacks)
Decentralization (broad validator participation)
Optimizations usually strengthen two while weakening the third.
7. Cross-Shard and Cross-Rollup Communication
Sharded or modular blockchains scale horizontally, but cross-domain messaging becomes the new bottleneck:
Latency: Finality in shard A must be visible to shard B before cross-shard transactions are valid.
Consistency: Asynchronous execution risks double-spending if messages are delayed.
Overhead: Each message requires proof-of-inclusion (Merkle/Verkle), adding verification costs.
Throughput Model:
Thus, scalability is bounded by the fraction of cross-shard activity.
8. Conclusion
The primary issue blockchains face in achieving high TPS is not raw execution speed, but synchronization at scale. Propagation, consensus, state management, and data availability all interact to create a ceiling. While modular designs, sharding, and advanced consensus can push this ceiling higher, trade-offs with security and decentralization remain unavoidable.
Future work lies in:
Efficient erasure-coded data availability layers.
Parallelizable execution environments with conflict resolution.
Consensus algorithms with sublinear communication complexity.
Cross-domain message passing protocols with low latency and strong guarantees.
The fundamental challenge is to design a system that maximizes throughput without sacrificing verifiability, ensuring the blockchain remains both fast and trustless.
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