
Transactions Per Second (TPS) is a critical metric for measuring blockchain network capacity, representing the number of transactions a network can verify and record in one second. As a direct reflection of blockchain performance, TPS has become an important standard for evaluating the scalability and practicality of blockchain projects. With the proliferation of decentralized applications, high TPS has emerged as a core competitive advantage for blockchain technology to overcome the processing speed limitations of traditional financial systems and achieve large-scale commercial applications.
The concept of transactions per second originated in traditional payment systems like Visa and Mastercard networks to measure their transaction processing speeds. Following the birth of Bitcoin in 2009, this concept was introduced to the blockchain industry. Bitcoin's early network managed approximately 7 TPS, which quickly became a focus of industry attention as it directly revealed the scalability challenges faced by first-generation blockchain technology.
As blockchain technology evolved, TPS gradually developed into a core metric for evaluating different consensus mechanisms, network architectures, and blockchain solutions. Later projects such as Ethereum, Solana, and Ripple all prioritized increasing TPS as a key technological innovation goal, driving the emergence of various scaling solutions, including sharding technology, sidechains, state channels, and Layer 2 scaling solutions.
Implementing transactions per second involves multiple core components and operational mechanisms of blockchain networks:
Block Size and Block Time: The number of transactions a block can contain and the time interval for generating new blocks directly affect TPS. Larger blocks and shorter block generation times typically result in higher TPS.
Consensus Mechanism: Different consensus algorithms significantly impact TPS. Proof of Work (PoW) typically yields lower TPS, while Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and similar mechanisms can achieve higher TPS.
Network Bandwidth and Node Performance: Data transmission speed between nodes and the computational processing capacity of individual nodes constitute the physical limitations of TPS.
Transaction Verification Process: Simplified verification processes can increase processing speed but may introduce security risks; complex verification mechanisms offer greater security but reduce TPS.
Network Topology: More centralized network architectures typically achieve higher TPS but sacrifice the core value of decentralization.
The pursuit of high TPS faces multiple technical and ecosystem challenges:
Security and Decentralization Trade-offs: Increasing TPS often requires sacrificing security or degree of decentralization. Excessive focus on speed can make networks more vulnerable to attacks or centralized control.
Data Bloat Issues: The large volume of transaction data generated by high TPS leads to rapid blockchain growth, increasing the difficulty of running full nodes and potentially affecting network decentralization.
Network Congestion and Transaction Fee Spikes: When actual transaction demand exceeds network TPS limits, congestion and soaring transaction fees result, as experienced by Ethereum during congestion events in 2017 and 2021.
Discrepancies Between Test Environments and Actual Performance: Many projects claim TPS figures from ideal test environments, which may differ significantly from performance under real network loads.
Regulatory Compliance Challenges: High-TPS networks face more complex regulatory requirements, particularly regarding anti-money laundering and transaction monitoring.
Transactions per second is a key indicator of blockchain technology's transition to large-scale commercial applications, but it should not be viewed in isolation. A successful blockchain project needs to find a balance between TPS, degree of decentralization, security, and usability. As sharding, cross-chain technologies, and Layer 2 solutions continue to mature, the blockchain industry is gradually breaking through scalability bottlenecks, moving toward supporting large-scale applications on a global scale.


