How to Separate Execution and Storage Layers

Monolithic blockchains like Ethereum and Bitcoin combine transaction execution, consensus, and data storage into a single, tightly coupled layer. This design creates a fundamental bottleneck: every node must process and store every transaction, limiting scalability. Layered architecture addresses this by decoupling core functions into specialized layers. The primary split is between the execution layer, where smart contracts run and state is computed, and the data availability layer, which provides secure and verifiable data storage for the network.

The execution layer, often called a rollup or execution environment, is where user transactions are processed. It batches transactions, executes them using a virtual machine (e.g., EVM, SVM, WASM), and produces a new state root and a cryptographic proof of correct execution. This proof, which can be a ZK-SNARK or an optimistic fraud proof, is then posted to a base layer (Layer 1) for final settlement. This separation allows the execution layer to operate at high speeds, as it's not burdened by global consensus or storing historical data.

The data availability (DA) layer is a critical component for security. When a rollup posts its proof to an L1, it must also make the underlying transaction data available so anyone can reconstruct the state and verify the proof's correctness. If data is withheld, the system cannot detect fraud or validate zero-knowledge proofs. Dedicated DA layers like Celestia, EigenDA, and Avail provide this service as a scalable, cost-efficient resource, allowing execution layers to post only data commitments to a more expensive L1 like Ethereum.

This architectural shift enables significant improvements. Throughput increases because execution is no longer limited by L1 block times or gas limits. Costs decrease by minimizing the expensive, permanent storage footprint on the L1. Modularity allows developers to mix and match components—choosing an execution environment, a consensus layer, and a DA layer independently. For example, a team might build an app-chain using the OP Stack for execution, Celestia for data, and EigenLayer for decentralized sequencing.

Implementing this separation requires careful design. Execution layers must implement a bridge contract on the settlement L1 to verify proofs and manage deposits/withdrawals. They must also ensure reliable data publishing to the chosen DA layer. Developers working with frameworks like Arbitrum Nitro, zkSync Era, or Polygon CDK are already building on this paradigm. The core technical challenge shifts from scaling a monolith to ensuring secure, trust-minimized communication between these specialized, independent layers.

Modern blockchain architecture is evolving beyond the monolithic model, where a single node handles execution, consensus, and data storage. The execution layer is responsible for processing transactions and running smart contract logic, determining state changes. The data availability (DA) layer is responsible for ensuring transaction data is published and accessible so anyone can verify the chain's state. Separating these concerns allows each layer to be optimized independently, a principle central to modular blockchain designs like Ethereum's rollup-centric roadmap and Celestia.

This separation creates clear trust boundaries. You trust the execution layer (e.g., an Optimistic Rollup or a ZK-Rollup) for fast, cheap computation. You trust the DA layer (e.g., Ethereum, Celestia, or Avail) to honestly publish and store the data underpinning those computations. If the DA layer withholds data, the execution layer's state cannot be independently verified or challenged, breaking security. This is why data availability sampling and fraud proofs are critical innovations for light clients to trustlessly verify that data is available without downloading it all.

For developers, this means your application's design must account for where computation happens and where data lives. A smart contract on an L2 rollup executes on its dedicated execution layer, but its transaction data is typically posted to a separate DA layer. Tools like EigenDA, Celestia, and the Ethereum consensus layer provide these services. When building, you must consider the latency, cost, and security guarantees of your chosen DA provider, as they directly impact your application's performance and trust model.

Practically, interacting with these separated layers requires specific tooling and mental models. You might use an SDK like the Rollkit framework to deploy a rollup that uses Celestia for DA, or you might deploy a smart contract on an existing L2 like Arbitrum which uses Ethereum for DA. Your front-end will need RPC endpoints for the execution layer to send transactions and query state, and may need to index or query the DA layer for transaction proofs or historical data verification.

Before proceeding, ensure you have a foundational understanding of core concepts: blockchain trilemma, rollups (Optimistic & ZK), data availability problem, and light client protocols. Familiarity with a smart contract language like Solidity or a general-purpose language like Go or Rust for node development is also recommended, depending on whether you are building on a separated layer or building the layer itself.

The monolithic architecture of early blockchains like Ethereum bundles execution, consensus, and data availability into a single layer. This creates a bottleneck where transaction throughput is limited by the speed at which every node must process and store all data. Execution refers to the computation of state changes from transactions (running smart contract code), while storage (or data availability) is the persistent recording of that resulting state and transaction history. Separating these concerns allows each layer to be optimized independently, a principle central to modular blockchain designs such as rollups and Celestia.

The most prominent pattern is the rollup-centric model. Here, execution is moved off the main chain (Layer 1) to a dedicated Layer 2. Transactions are executed in batches on this separate layer, and only the compressed results—or cryptographic proofs of their validity—are posted back to the L1 for consensus and data availability. Optimistic rollups (like Arbitrum and Optimism) post transaction data and assume validity, while ZK-rollups (like zkSync and Starknet) post validity proofs. In both cases, the L1 acts primarily as a secure data availability and settlement layer, decoupling expensive computation from global consensus.

Another pattern involves dedicated data availability layers that exist solely for storage. Networks like Celestia and Avail provide a scalable blockchain whose sole purpose is to order and guarantee the availability of transaction data for other chains. Execution layers, whether rollups or sovereign chains, post their data here and handle computation independently. This creates a clear market specialization: one network optimizes for cheap, abundant data storage and consensus, while others optimize for fast, varied virtual machines (VMs) like the Ethereum Virtual Machine (EVM) or WebAssembly (WASM).

Implementing this separation requires careful design of the interface between layers. The execution layer must be able to read from and write to the storage layer. This is often done via bridges or light clients that verify data availability proofs from the storage layer. For example, a rollup's smart contract on Ethereum stores a state root (a cryptographic commitment to its state). The sequencer posts transaction data as calldata on L1, and anyone can reconstruct the state by downloading this data and re-executing the transactions, verifying it against the posted state root.

The benefits of separation are significant. It enables horizontal scalability, as multiple execution layers can share one secure data layer. It allows for sovereignty and specialization, where a gaming chain can use a VM optimized for fast computation without being burdened by storing all of Ethereum's history. Furthermore, it improves node operability; nodes can choose to run only the execution client, only the consensus/client, or a full node, reducing hardware requirements. This is the architecture enabling the modular stack, where projects mix and match components like the Celestia DA layer, an Arbitrum Nitro execution environment, and Ethereum for settlement.

When designing a system with separated layers, key considerations include the data availability guarantee (how users verify data is published), the trust assumptions between layers (e.g., fraud proofs vs. validity proofs), and the cost of cross-layer communication. The future of scalable blockchain infrastructure is built on these clear separations, moving away from the "one-chain-does-all" model to a collaborative ecosystem of specialized modules.

The first step is to define a clear data model and interface. Identify which data is state (persistent storage) and which is computation (business logic). For example, in a decentralized exchange, the order book's current state is storage, while the matching engine's logic is execution. Define a standardized interface, like a set of smart contract functions or API endpoints, that the execution layer will use to query and commit state changes to the storage layer. This separation of concerns is foundational to patterns like rollups and app-specific chains.

Next, implement the storage layer as an independent, verifiable data store. This can be a smart contract on a base layer like Ethereum (acting as a data availability layer), a decentralized storage network like Arweave or Filecoin, or a purpose-built data availability committee. The key requirement is that the storage layer provides cryptographic guarantees that the data is available and immutable. For on-chain storage, you would deploy a contract with functions to pushState(bytes32 stateRoot) and getState(uint256 blockNumber). The execution layer will post state roots here after processing batches of transactions.

The execution layer is then built as a stateless computation engine. It fetches the latest state root from the storage layer and the raw transaction data from a mempool or sequencer. Using a state witness (like a Merkle proof), it validates inputs against the known state, executes the transaction logic, and computes a new state root. This is often done in a trusted execution environment (TEE) or a zk-proof circuit for enhanced security. The output is a new state root and a proof of valid execution (a validity proof in zk-rollups or a fraud proof in optimistic rollups), which is posted back to the storage layer.

Finally, you must establish a secure synchronization and consensus mechanism between the layers. The execution layer needs a way to agree on the canonical transaction ordering (sequencing) and to finalize state updates. In rollup designs, a sequencer node often batches transactions, executes them, and posts data and proofs to L1. Implement a challenge period for optimistic schemes or instant verification for zk-proofs. Tools like the OP Stack or Polygon CDK provide modular frameworks that handle much of this bridge logic, allowing developers to focus on their specific application execution environment.

In a monolithic blockchain like Ethereum's mainnet, a single layer handles execution (processing transactions), consensus (agreeing on state), and data availability (storing transaction data). This tight coupling creates a bottleneck: to verify the chain, every node must download and process all data, limiting throughput. The data availability problem asks: how can we ensure data is published without requiring every node to store it? Solutions like Ethereum's danksharding and modular rollups address this by decoupling these core functions.

Modular architectures separate the blockchain stack into specialized layers. The execution layer is where smart contracts run and state updates are computed (e.g., Optimism, Arbitrum, zkSync). The consensus and data availability layer (often called the settlement layer) orders transactions and ensures their data is published and accessible. By offloading execution, the base layer (like Ethereum) can focus on providing robust security and DA for multiple rollups, dramatically increasing overall network capacity.

Rollups are the primary execution layer that utilizes this separation. They execute transactions off-chain and post compressed data back to the main chain. Data availability is critical here: if the rollup's data is not available, the state cannot be reconstructed and verified, breaking trust. There are two main models for ensuring DA: 1) On-chain DA, where data is posted directly to a base layer like Ethereum, and 2) Off-chain DA, using external networks like Celestia or EigenDA, which can offer higher throughput and lower costs.

To verify data availability without downloading everything, networks use data availability sampling (DAS). In DAS, light clients randomly sample small chunks of the published data. If all samples are available, they can statistically guarantee the entire data blob is available. This is the core innovation behind danksharding on Ethereum, which allows the network to securely scale its data capacity far beyond what individual nodes can store, relying on a committee of validators to attest to data availability.

Developers choosing a DA layer must evaluate trade-offs. Using Ethereum for DA provides the highest security inheritance but at a premium cost. Celestia, a modular DA network, offers lower fees and high throughput by specializing solely in DA and consensus. EigenDA, a restaking-based AVS on EigenLayer, provides economically secured DA for Ethereum rollups. The choice impacts cost, throughput, time-to-finality, and the security model of your application.

Implementing a rollup with separated layers involves configuring your node software. For example, when running an OP Stack rollup, you configure a Data Availability Provider in your node's rollup.json config. Setting dataAvailabilityProvider to "Ethereum" posts data to calldata, while setting it to "Celestia" or "EigenDA" routes data to those networks. This modular setup allows developers to swap DA layers based on their application's needs for cost and security.

The separation of execution and storage layers is not merely an optimization; it's a paradigm shift for blockchain application design. By decoupling these concerns, developers can achieve significant improvements in transaction throughput, cost efficiency, and developer experience. This architecture allows the execution layer to focus on processing logic and state transitions at high speed, while the storage layer provides persistent, verifiable data availability. Protocols like Ethereum with EIP-4844 blobs, Celestia, and Avail exemplify this trend, providing specialized data availability layers that execution environments like Arbitrum, Optimism, and zkSync can utilize.

To implement this pattern, start by auditing your application's data requirements. Identify which data needs on-chain finality (e.g., asset ownership, critical state) versus what can be stored off-chain with on-chain commitments (e.g., large media files, historical logs). For smart contract development, leverage standards like EIP-4844 for blob storage or integrate with a modular data availability network. Use libraries such as The Graph for indexing off-chain data referenced by on-chain pointers, ensuring your dApp's front-end can efficiently query the complete application state.

The next evolution involves exploring verifiable computation alongside verifiable storage. Systems like zkRollups bundle execution proofs with state diffs, while validiums and volitions let users choose between storing data on-chain or on a separate data availability layer. As a next step, experiment with deploying a simple contract (e.g., an NFT mint) that stores the metadata URI on a decentralized storage network like IPFS or Arweave, with only the content hash stored on-chain. Then, explore using a rollup-as-a-service platform that abstracts the data availability layer, allowing you to focus solely on application logic.