1. Minimize object reads/writes—accessing too many objects increases latency.

2. Avoid large dynamic vectors or nested data structures in critical paths.

3. Use pure functions and stateless logic where possible.

4. Benchmark gas usage and execution time with sui move test or localnet tools.

5. Split complex logic into smaller modules to simplify parallelization and caching.

Optimizing Move code for low-latency execution on Sui involves a mix of resource management, efficient data structures, minimizing computation, and reducing unnecessary state changes. Since Sui is a high-performance blockchain, it's important to design smart contracts with an eye toward efficiency to meet the demands of real-time use cases. Here are best practices to optimize Move code for low-latency:

1. Optimize for Gas Efficiency

• Minimize State Changes: Each modification to state (such as moving, creating, or deleting resources) incurs gas costs. Avoid unnecessary state changes or redundant writes to state.

  • Use in-place updates or mutate existing resources instead of creating new resources whenever possible.
  • Reuse existing resources instead of creating new ones. If you need a new object, see if you can modify the existing one.

• Efficient Data Types: Use compact and minimal data structures. For example:

  • Use u64 for numeric values instead of larger integer types when possible.
  • Avoid deeply nested structures or objects with too many fields that require excessive storage operations.

• Batch Operations: Group multiple state-modifying actions together to minimize the number of transactions. Avoid calling smart contracts multiple times within the same function when you can achieve the same result in a single operation.

2. Minimize Computational Complexity

• Avoid Complex Logic in Smart Contracts: Offload complex computations to off-chain systems if they are not critical for the execution of the transaction. Move contracts should focus on managing state and validating simple business logic.

  • Example: If performing heavy computations (e.g., sorting, large-scale analytics), consider using off-chain solutions and only storing the results in the Move contract.

• Simplify Conditional Logic: Avoid deep, nested conditionals or complex loops that could increase execution time. Use simple and efficient algorithms for any data processing.

• Use Efficient Algorithms: When processing lists or data structures, use efficient algorithms with lower time complexity. For example:

  • Sorting: If sorting is necessary, use the most efficient sorting algorithms available, such as QuickSort or MergeSort, instead of BubbleSort.
  • Searching: For large sets of data, consider using hashmaps (when possible) rather than linear scans.

3. Leverage Move's Resource Model

• Resource Efficiency: Take full advantage of Move's resource system to manage data in an efficient and low-latency manner. Resources in Move are optimized for use on the blockchain and are passed by move semantics (transferred between accounts or functions) rather than copied.

• Use Borrowing and References: Instead of cloning or copying data, borrow resources when possible. This reduces the computational cost and memory usage since the data doesn’t need to be duplicated.

  Example:

  // Instead of creating a new object, borrow the existing resource.
  let existing_resource = borrow_global_mut<Token>(sender);
  existing_resource.value = existing_resource.value + amount;
  

4. Optimize Smart Contract Deployment

• Minimize Contract Complexity: Large smart contracts with many functions or heavy dependencies may lead to higher costs and longer processing times. Keep your Move contracts simple and focused.
• Pre-deploy Required Resources: If certain resources (e.g., tokens or assets) need to be available in the contract for use, pre-deploy them as needed, and minimize runtime creation of resources.

5. Efficient Data Structures

• Use Vectors or Maps Efficiently: When using collections like vectors or hashmaps, ensure that you're accessing elements efficiently:

  • Use sorted data structures if your contract requires ordering.
  • For frequently accessed data, consider storing indices or pointers rather than repeatedly iterating over large collections.

• Optimize Gas Usage for Collections: Minimize the overhead of collections by using smaller, simpler structures. For example:

  • When iterating over a vector, ensure that the vector is not too large and use efficient indexing.
  • If using a hashmap, ensure the hash function is optimized for your use case and minimizes collisions.

6. Leverage Parallel Execution (if applicable)

• Parallel Transactions: Sui allows for parallel execution of transactions, meaning multiple transactions can be processed simultaneously. Organize your contract logic to leverage this feature by structuring operations in a way that can run in parallel.
• Stateless Operations: Ensure operations that don't require state changes or only involve lightweight calculations are stateless and can run independently from others in parallel.

7. Minimize Cross-Contract Calls

• Cross-contract calls introduce additional latency as they require communication between different contracts or modules. Reduce the need for calls between multiple contracts within a single transaction whenever possible.
• If calling external contracts is necessary, ensure the calls are minimized and occur only when critical.

8. Monitor Latency and Gas Usage

• Performance Testing: Continuously test the performance of your Move contracts using test networks or gas profiling tools. Measure the execution time and gas usage for various functions and optimize the slowest ones.
• Use Profiling Tools: Use tools like Sui Explorer or local testnets to monitor the gas usage and performance of your contract under different load conditions.

9. Use Off-Chain Resources When Appropriate

• Some tasks might be better suited for off-chain execution (e.g., heavy computations or data storage). For example, using oracles or off-chain systems for real-time data processing and only using the blockchain to store results or trigger events can reduce latency.

Example of Optimized Move Code:

Here’s an optimized example of a token transfer that minimizes unnecessary operations:

module OptimizedTokenModule {
    struct Token has store {
        owner: address,
        balance: u64,
    }

    public fun transfer(sender: &mut signer, receiver: address, amount: u64) {
        // Borrow the sender's token resource to update it
        let sender_token = borrow_global_mut<Token>(sender);
        
        // Check if sender has sufficient balance
        assert!(sender_token.balance >= amount, 100);  // No unnecessary checks
        sender_token.balance = sender_token.balance - amount;

        // Borrow or create the receiver's token resource
        let receiver_token = borrow_global_mut<Token>(receiver);
        receiver_token.balance = receiver_token.balance + amount;

        // Emit transfer event if necessary (optional)
        emit TransferEvent { sender, receiver, amount };
    }
}

Conclusion:

To optimize Move code for low-latency execution on Sui, you should focus on:

1. Efficient state management: Minimize state changes and use borrowing for resources.
2. Optimizing computation: Simplify business logic and offload complex tasks off-chain.
3. Efficient data handling: Use compact data types and optimize collections and data structures.
4. Performance testing and monitoring: Regularly profile and test gas usage and execution time.

By adhering to these principles, you can reduce the latency of your Move contracts, ensuring they meet real-time use case requirements while maintaining low gas costs.

Testnet data wipes are expected. To protect CI/CD pipelines, treat Testnet as ephemeral. Automate full redeployment of packages and test data using scripts with the Sui CLI or SDK. Reinitialize state from scratch on each run, store package IDs in environment variables, and use deterministic addresses when possible. For more stable testing, prefer Sui localnet over Testnet.

Minimize storage reads/writes, avoid deep loops, and use efficient data structures (e.g., Table over nested vectors). Precompute where possible, limit event emissions, and batch operations. Keep logic simple and parallelizable—Sui excels at concurrent execution. Profile with sui move test and optimize hot paths.

1. Storage Optimization

// Bad: Large object with nested data
struct UserData {
    history: vector<Transaction>  // High read/write costs
}

// Good: Sharded design
struct UserSummary {
    id: UID,
    current_balance: u64  // Hot data
}
struct UserHistory {
    id: UID,
    logs: vector<u64>     // Cold data (separate object)
}

2. Compute Efficiency

// Bad: O(n) loops
fun sum(items: vector<u64>): u64 {
    let total = 0;
    while (!vector::is_empty(items)) {
        total = total + vector::pop_back(&mut items);
    }
    total
}

// Good: Fixed-size batches
const BATCH_SIZE: u64 = 50;
fun batch_process(items: &vector<u64>) {
    let i = 0;
    while (i < BATCH_SIZE && i < vector::length(items)) {
        // Process item[i]
        i = i + 1;
    }
}

3. Gas-Critical Patterns

4. Parallelizable Logic

// Split independent operations
module my_app::parallel {
    // Process A (no shared objects)
    entry fun task_a() { ... }
    
    // Process B (no shared objects)
    entry fun task_b() { ... }
}

5. Real-World Optimizations

DeFi (AMM)

struct Pool {
    // Keep these in same struct (frequent access)
    reserves_x: u64,
    reserves_y: u64,
    fee: u64
    // Move LP tokens to separate object
}

NFT Mint

// Batch minting
public entry fun mint_batch(
    ctx: &mut TxContext,
    count: u64
) {
    let i = 0;
    while (i < count) {
        let nft = NFT { id: object::new(ctx) };
        i = i + 1;
    }
}

Key Tools

1. Gas Profiler:

   sui move test --gas-stats
   

2. Execution Tracker:

   sui client call --gas-profile --dry-run
   

Optimization Checklist

• Used phantom types where possible
• Minimized shared object access
• Batched independent operations
• Avoided nested loops in transactions

For latency-sensitive apps:

1. Offload compute to client-side
2. Cache frequent reads
3. Queue writes via PTBs

To optimize your Move contract for low-latency execution on the Sui network, you need to focus on minimizing computational complexity, reducing dynamic storage access, and leveraging Sui’s object-centric design to take advantage of parallel transaction execution. Sui’s architecture is designed for speed, but inefficient Move code or poor contract structure can still introduce delays, especially for real-time applications like games, auctions, or trading bots.

Start by reviewing your function call structure. Avoid deep recursion, nested loops, or excessive branching logic inside entry fun. Use simple and shallow logic paths whenever possible. Next, minimize the number of objects your transaction touches. Sui executes transactions in parallel if they don't interact with shared objects or overlapping owned objects. If your contract reads or mutates multiple shared objects, that forces the transaction into serialized execution, increasing latency.

Use primitive types (u64, bool) in entry function arguments instead of structs or dynamic types like vector<T> unless absolutely necessary. This reduces BCS parsing overhead. Also, keep dynamic fields and nested structs lightweight, as deeper object hierarchies increase execution time. For mutable logic, consider passing fewer &mut references to reduce conflict risk in parallel validators.

You should also eliminate unnecessary emit_event calls or large object state diffs unless they're required for business logic. These operations cost more gas and increase transaction weight. For performance-critical flows, avoid repeatedly destroying and recreating objects; instead, mutate in-place using &mut.

Use the dry-run CLI or dev-inspect command from the SDK to benchmark your functions in localnet and profile gas usage:

sui client dry-run --function your_function ...

Finally, modularize your logic—offload slow or infrequent tasks to scheduled jobs or off-chain, and keep on-chain logic tight and deterministic.

For reference and tooling: https://docs.sui.io/build/performance

To optimize Move code for low-latency execution on Sui, try these best practices:

1. Minimize resource-heavy operations: Avoid complex loops or unnecessary data processing. Focus on keeping your logic as simple as possible.

2. Use efficient data structures: Leverage Sui's native types like Vector and Struct to store and process data efficiently.

3. Batch transactions: Instead of executing multiple transactions individually, group them together to reduce overhead and transaction fees.

4. Optimize gas usage: Keep an eye on gas costs. More efficient code reduces gas, indirectly improving latency by minimizing transaction queuing.

5. Offload computation off-chain: Where possible, offload intensive calculations to off-chain services and keep the on-chain logic minimal.

6. Avoid expensive storage reads/writes: Minimize storage access; excessive reads and writes can slow down your contract.

7. Parallelize tasks: Move tasks that can run concurrently into separate functions to improve performance.

8. Profile and test regularly: Use tools to profile and test your contract's performance. Regular testing helps catch inefficiencies early.

Implementing these optimizations can make your Move contract snappier and more suitable for real-time applications!