Introduction

Solana development requires more than just good code; it demands thoughtful system design for performance. As a Solana developer, you’re an architect who must consider:

• What should your code do?

• How can you achieve it?

• What are the pros and cons of each approach, especially on Solana?

Blockchain development, particularly on Solana, has unique limitations (storage costs, data limits, compute units) and high stakes (managing assets). You will explores key architectural concepts for building fast, affordable, safe, and functional Solana programs.

Strategies for Efficient Data Handling

In typical programming, data size often feels limitless. Want a long string or a convenient integer type? Go for it! However, Solana operates under stricter rules. We pay for every byte stored on-chain (rent), and face limitations on stack, heap, and account sizes. This necessitates a more strategic approach to managing data, especially when dealing with what might be considered “large” accounts.

Two primary concerns arise when working with larger data on Solana:

1. Minimizing Footprint (Sizes): Since we pay per byte, keeping our data structures as small as possible is crucial for cost-effectiveness.

2. Overcoming Stack and Heap Limits: Operating on large data can exceed Solana’s stack and heap constraints. We’ll explore Box and Zero-Copy as solutions.

Sizes

On Solana, the account owner pays rent for the data stored within their account. While the term “rent” is a bit misleading (it’s more of a minimum balance for rent exemption), the core principle remains: on-chain data storage has a cost. This is why assets like NFT images are typically stored off-chain.

Your goal is to strike a balance between program functionality and the cost users incur for storing necessary data on-chain. The first step in optimizing for space is understanding the size of your data structures. Here’s a reference:

With this knowledge, you can start making small but significant optimizations. For instance, if a field will never exceed 100, using a u8 (max 255) instead of a u64 (massive range) saves a whopping 7 bytes per instance! Similarly, choose unsigned types (u*) if negative values are never needed.

Caution: While optimizing for size, be mindful of potential overflows with smaller number types. A u8 incrementing beyond 255 will wrap back to 0, potentially leading to unexpected behavior

Box

Now, let’s tackle the challenge of handling genuinely large data accounts. Consider this struct:

#[account]
pub struct BigData {
    pub big_data: [u8; 5000],
}

#[derive(Accounts)]
pub struct CreateBigData<'info> 
    pub big_data: Account<'info, BigData>,
}

On Solana, the stack size per function call is limited to around 4KB. If you pass a large struct like BigData (e.g., with a 5KB array), it can overflow the stack, triggering compiler warnings or even causing your program to hang. This happens because large values are stack-allocated by default.

The solution? Enter Box<T>.

#[account]
pub struct BigData {
    pub big_data: [u8; 5000],
}

#[derive(Accounts)]
pub struct CreateBigData<'info> 
    pub big_data: Box<Account<'info, BigData>>,
}

In Anchor, wrapping an Account<'info, T> within a Box<> directs Anchor to allocate the account data on the heap, a larger memory region (around 32KB on Solana). The beauty of Box is its seamless integration. You don't need to alter how you interact with the data within your function. Just wrap your large account types in Box<...> in your Accounts struct.

However, Box isn't a silver bullet for truly massive accounts. You can still hit heap limits with sufficiently large data. For that, we turn to Zero-Copy.

Zero Copy

What if you need to work with accounts approaching the maximum Solana account size (10MB)? Even with Box, you'll encounter limitations. Consider this:

#[account]
pub struct ReallyBigData {
    pub really_big_data: [u128; 1024], // 16,384 bytes
}

This account, even boxed, will likely cause issues. This is where zero-copy deserialization comes to the rescue, utilizing zero_copy and AccountLoader.

#[account(zero_copy)]
pub struct ReallyBigData {
    pub really_big_data: [u128; 1024], // 16,384 bytes
}

pub struct ConceptZeroCopy<'info> {
    #[account(zero)]
    pub really_big_data: AccountLoader<'info, ReallyBigData>,
}

Here’s the magic: with zero-copy, your program doesn’t load account data into the stack or heap. Instead, it gets direct pointer access to the account’s memory. The AccountLoader safely wraps this raw data so you can work with it like a normal struct.

Key Benefit: Zero-copy avoids stack/heap limits by skipping deserialization. Unlike Borsh, which copies all data into memory, zero-copy lets you handle large account types efficiently.

Caveats of Zero Copy:

1. No init Constraint: You cannot use the init constraint when defining a zero-copy account in your Accounts struct. This is due to CPI limitations on accounts larger than 10KB. You'll need to create and fund these large accounts in a separate instruction from your client-side code. Instead, your client would perform a createAccount system instruction before interacting with the zero-copy account:

2. Loading Account Data: Inside your Rust instruction handler, you need to explicitly load the zero-copy account data using one of the following methods on the AccountLoader:

• load_init(): For initializing a new zero-copy account (ignores the missing account discriminator on initial creation).

• load(): For loading an immutable zero-copy account.

• load_mut(): For loading a mutable zero-copy account.

Designing Effective Solana Accounts

On Solana, everything lives in an account — user wallets, token mints, program data, even the programs themselves. That’s why smart account design is crucial for building efficient, maintainable, and scalable Solana apps. Here are some key considerations to keep in mind.

Data Order

This might seem like a minor detail, but the order of fields within your account structs can significantly impact how easily you can query and filter accounts on-chain. The general rule of thumb is simple: place all variable-length fields at the end of your account structure.

Consider this poorly structured data:

#[account]
pub struct BadState {
    pub flags: Vec<u8>,  // Variable length
    pub id: u32,        // Fixed length
}

The flags field in BadState is a Vec<u8>, meaning its size can vary. This flexibility comes at a cost: it affects how you query accounts.

Solana supports memcmp filters, which let you search accounts by matching byte sequences at specific offsets in the account data. The catch? Offsets must be fixed.

In BadState, the id field comes after flags, so its position in memory shifts as flags grows. This makes reliable filtering using memcmp impossible.

Let’s visualize what’s happening in the raw data:

Scenario 1: flags has 4 elements

0000:   [8 bytes discriminator]
0008:   [4 bytes Vec length] [4 bytes flags data]
0010:   [4 bytes id]

Scenario 2: flags has 8 elements

0000:   [8 bytes discriminator]
0008:   [4 bytes Vec length] [8 bytes flags data]
0014:   [4 bytes id]

As you can see, the id field's offset changes. This makes it impossible to reliably query accounts based on a fixed offset for the id.

The solution is straightforward: place fixed-size fields before variable-length fields:

#[account]
pub struct GoodState {
    pub id: u32,         // Fixed length
    pub flags: Vec<u8>, // Variable length
}

Now, the id field will always be at a consistent offset (after the initial 8-byte discriminator), allowing for efficient querying.

Account Flexibility and Future-Proofing

Software evolves, and so will your Solana programs. Designing account structures with upgrades and backward compatibility in mind is key to avoiding painful migrations.

A solid strategy includes adding a version field to your accounts so your program can handle different data layouts safely. Use Option<T> for new fields to maintain compatibility with older versions.

Anchor makes this easier with tools like InitSpace, which automatically calculates account size, and #[max_len] for Vec fields to enforce limits.

#[account]
#[derive(InitSpace)]
pub struct GameState {  // V1
    pub version: u8,
    pub health: u64,
    pub mana: u64,
    pub experience: Option<u64>,
    #[max_len(50)]
    pub event_log: Vec<String>
}

When you need to upgrade this structure (e.g., increase event_log size or add a new field), you can modify the struct and use Anchor's realloc constraint in a dedicated upgrade instruction:

#[account]
#[derive(InitSpace)]
pub struct GameState { // V2
    pub version: u8,
    pub health: u64,
    pub mana: u64,
    pub experience: Option<u64>,
    #[max_len(100)] // Increased length
    pub event_log: Vec<String>,
    pub new_field: Option<u64>, // New field
}

#[derive(Accounts)]
pub struct UpgradeGameState<'info> {
    #[account(
        mut,
        realloc = GameState::INIT_SPACE,
        realloc::payer = payer,
        realloc::zero = false,
    )]
    pub game_state: Account<'info, GameState>,
    #[account(mut)]
    pub payer: Signer<'info>,
    pub system_program: Program<'info, System>,
}

pub fn upgrade_game_state(ctx: Context<UpgradeGameState>) -> Result<()> {
    let game_state = &mut ctx.accounts.game_state;
    match game_state.version {
        1 => {
            game_state.version = 2;
            game_state.experience = Some(0); // Initialize new field
            msg!("Upgraded to version 2");
        },
        _ => return Err(ErrorCode::AlreadyUpgraded.into()),
    }
    Ok(())
}

The realloc constraint resizes the account to GameState::INIT_SPACE, with the payer covering any additional rent. Setting realloc::zero = false preserves existing data during resizing.

Data Optimization

We’ve already touched on choosing the right data types to minimize byte usage. However, you can often go even further by being mindful of wasted bits within those bytes.

For example, if you have a field representing the month of the year, using a u64 is overkill. A u8 (0-255) is more than sufficient (0-11 for months, perhaps with 0 representing an uninitialized state). Even better, consider using a u8 enum for better readability and type safety.

Consider a scenario with multiple boolean flags:

#[account]
pub struct BadGameFlags { // 8 bytes
    pub is_frozen: bool,
    pub is_poisoned: bool,
    pub is_burning: bool,
    pub is_blessed: bool,
    pub is_cursed: bool,
    pub is_stunned: bool,
    pub is_slowed: bool,
    pub is_bleeding: bool,
}

While a bool conceptually only needs a single bit, Borsh (Solana's serialization library) will typically allocate a full byte for each boolean. This means these eight flags consume eight bytes.

A more space-efficient approach is to use a single u8 and leverage bitwise operations:

const IS_FROZEN_FLAG: u8 = 1 << 0; // 0b00000001
const IS_POISONED_FLAG: u8 = 1 << 1; // 0b00000010
// ... and so on

#[account]
pub struct GoodGameFlags { // 1 byte
    pub status_flags: u8,
}

This optimization saves a significant 7 bytes in this example. The trade-off is the added complexity of bitwise operations in your code, but for frequently accessed or large collections of flags, the space savings can be well worth it.

Indexing

The final account design concept is indexing, and this is where Program Derived Addresses (PDAs) shine. Instead of storing account addresses directly, you can derive them deterministically using a set of seeds. This makes account addresses predictable, discoverable, and eliminates the need for manual lookups.

A prime example is Associated Token Accounts (ATAs). Their addresses are derived from:

• the wallet address

• the token program ID

• the token mint

This pattern allows programs (and users) to compute ATA addresses on-the-fly without storing them.

You can apply similar patterns in your own programs:

• One-Per-Program (Global Account): Using a fixed seed like b"GLOBAL_SETTINGS" ensures that only one account with that purpose can ever exist for your program. This is useful for storing global configuration.

• One-Per-Owner: Seeding an account with a user’s public key (e.g., seeds = [b"PLAYER_DATA", owner.key().as_ref()]) guarantees a unique account per user for a specific purpose.

• Multiple-Per-Owner: By including an additional unique identifier in the seeds (e.g., seeds = [b"ORDER", owner.key().as_ref(), order_id.to_be_bytes().as_ref()]), you can create multiple uniquely identifiable accounts per user.

• One-Per-Owner-Per-Account: This is the ATA pattern, allowing you to create a unique associated account for a user and another account (like a token mint).

Designing for Parallel Execution

Solana stands out by processing transactions in parallel, unlike many blockchains that run them sequentially. As long as transactions don’t try to modify the same account at once, they can execute concurrently. Designing your programs to take advantage of this is crucial for maximizing throughput and delivering a smooth user experience.

If you’ve seen a popular NFT mint in action, you know what happens when many users target the same “candy machine” account simultaneously. This heavy contention creates a bottleneck, causing transaction failures and user frustration.

Solana can process transactions in parallel as long as they don’t modify the same account at the same time. Let’s explore what this means with some examples.

No Contention

Alice wants to send SOL to Carol, and Bob wants to send SOL to Dean:

Alice --> Carol  
Bob   --> Dean

These transactions affect different accounts and can be processed in parallel, speeding up overall execution.

Shared Account Bottleneck

What if both Alice and Bob try to pay Carol simultaneously?

Alice --> |  
          |--> Carol  
Bob   --> |

Since both modify Carol’s account, Solana will serialize these transactions. One goes through first; the other waits. This creates a bottleneck, especially at scale.

High Traffic Bottleneck

Imagine 1000 users all trying to pay Carol at once:

Alice --> |  
1000x --> |--> Carol
Bob   --> |

All these transactions queue to update Carol’s account sequentially. Early transactions succeed quickly, but many will face delays or even fail due to timeouts.

This isn’t just theoretical. Programs that funnel many operations into a single shared account — like a treasury wallet collecting fees — can become performance chokepoints.

Boosting Parallelism

The key to avoiding contention is to separate the core user interactions from shared account updates.

Example: Donation Tally Program

Suboptimal approach:

pub fn run_concept_shared_account_bottleneck(ctx: Context<ConceptSharedAccountBottleneck>, lamports_to_donate: u64) -> Result<()> {
    let donation_tally = &mut ctx.accounts.donation_tally;

    // Transfer directly to the shared community wallet (contention point)
    let cpi_context = CpiContext::new(
        ctx.accounts.system_program.to_account_info(),
        Transfer {
            from: ctx.accounts.owner.to_account_info(),
            to: ctx.accounts.community_wallet.to_account_info(),
        }
    );
    transfer(cpi_context, lamports_to_donate)?;

    // Update tally on the shared account (also contention)
    donation_tally.lamports_donated = donation_tally.lamports_donated.checked_add(lamports_to_donate).unwrap();
    donation_tally.lamports_to_redeem = 0;

    Ok(())
}

Every donation transaction writes directly to the same community_wallet account, causing contention under heavy load.

Optimized approach:

pub fn run_concept_shared_account(ctx: Context<ConceptSharedAccount>, lamports_to_donate: u64) -> Result<()> {
    let donation_tally = &mut ctx.accounts.donation_tally;

    // Transfer to a unique PDA instead of the shared wallet
    let cpi_context = CpiContext::new(
        ctx.accounts.system_program.to_account_info(),
        Transfer {
            from: ctx.accounts.owner.to_account_info(),
            to: donation_tally.to_account_info(),
        }
    );
    transfer(cpi_context, lamports_to_donate)?;

    // Update tally on PDA
    donation_tally.lamports_donated = donation_tally.lamports_donated.checked_add(lamports_to_donate).unwrap();
    donation_tally.lamports_to_redeem = donation_tally.lamports_to_redeem.checked_add(lamports_to_donate).unwrap();

    Ok(())
}

pub fn run_concept_shared_account_redeem(ctx: Context<ConceptSharedAccountRedeem>) -> Result<()> {
    let transfer_amount: u64 = ctx.accounts.donation_tally.lamports_donated;

    // Withdraw from PDA balance
    **ctx.accounts.donation_tally.to_account_info().try_borrow_mut_lamports()? -= transfer_amount;

    // Deposit into the shared community wallet (less frequent)
    **ctx.accounts.community_wallet.to_account_info().try_borrow_mut_lamports()? += transfer_amount;

    // Reset redeemable tally
    ctx.accounts.donation_tally.lamports_to_redeem = 0;

    Ok(())
}

Here, each donation goes to a dedicated PDA, eliminating contention on the community wallet. A separate redeem step, called less often, consolidates funds into the shared wallet.

Conclusion

We’ve covered many important program architecture considerations: bytes, accounts, bottlenecks, and more. Whether or not you encounter these specific issues, I hope the examples and discussion have sparked new ideas. In the end, you are the designer of your system. Your role is to carefully weigh the pros and cons of different solutions. Think ahead, but stay practical. There is no single “right” way to design anything — just understand the trade-offs involved.

If you’re interested in decentralized infrastructure, on-chain data systems, or building real-world projects, follow along:

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