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    Home»Blockchain»Stellar (XLM) vs. Solana: A Comparative Analysis of State Archival Scalability
    Blockchain

    Stellar (XLM) vs. Solana: A Comparative Analysis of State Archival Scalability

    CryptoExpertBy CryptoExpertJuly 18, 2024No Comments3 Mins Read
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    Stellar (XLM) vs. Solana: A Comparative Analysis of State Archival Scalability
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    Alvin Lang
    Jul 18, 2024 07:29

    Explore the scalability of State Archival on Stellar (XLM) and Solana’s Avocado. Detailed analysis of network efficiency and congestion issues.





    The blockchain landscape is continually evolving, with various networks exploring innovative solutions to enhance scalability and efficiency. A recent analysis by Stellar (XLM) sheds light on the comparative scalability of its State Archival system versus Solana’s Avocado, highlighting significant differences in network performance and congestion management.

    Solana’s Network Congestion Challenges

    Solana has faced notable challenges with network congestion, particularly during periods of high transaction volumes. For instance, in April 2024, bot trading activity caused significant congestion, leading to high transaction drop rates. Although Solana implemented a patch to address these issues, the network’s fundamental design remains prone to congestion as the number of users, validators, and transactions per second (TPS) increases.

    Solana’s Avocado proposal aims to address these issues by introducing a mechanism where validators are paid to send more transactions. However, this approach has raised concerns about exacerbating congestion. Validators would be incentivized to send additional transactions, potentially overwhelming the network further.

    State Compression Mechanism

    Under Solana’s State Compression mechanism, when an account runs out of rent, a compression transaction uses zero-knowledge proofs to delete the account and add it to a Merkle binary trie. While validators only need to store the root of this trie for consensus, RPC nodes must store the full trie to produce proofs. Additionally, validators can earn extra by storing the complete trie and submitting compression transactions, which could lead to network congestion.

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    This design tradeoff has been criticized for potentially leading to redundant transactions. Validators might race to compress expired accounts to earn rewards, resulting in multiple transactions attempting to compress the same account, thus consuming valuable network bandwidth.

    Stellar’s Efficient State Archival

    In contrast, Stellar’s State Archival solution offers a more efficient approach. Stellar’s validators deterministically archive the same entries on a given schedule, eliminating the need for additional transactions. This method addresses the primary bottleneck in blockchain performance—networking.

    Stellar’s network structures archived state using a collection of small, immutable Merkle trees rather than a large trie. Validators can archive new entries by storing a small tree of recently archived entries, while the majority of the archived state is offloaded in History Archives. This design allows Stellar to maintain high TPS and allocate more bandwidth to user transactions.

    According to Stellar, Anatoly Yakovenko, co-founder of Solana, considered a deterministic compression approach but ultimately decided against it, citing the need for all validators to maintain the entire state as part of the snapshot. However, Stellar’s method of using smaller Merkle trees and History Archives offers a viable alternative that enhances network efficiency and scalability.

    Concluding Thoughts

    The comparative analysis between Stellar’s State Archival and Solana’s Avocado underscores the importance of efficient network design in blockchain scalability. While Solana’s approach may lead to increased congestion, Stellar’s deterministic archival method provides a scalable solution that optimizes network performance.

    For more detailed insights, the full analysis can be found on Stellar’s official blog.

    Image source: Shutterstock



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