Data Blobs (EIP-4844)

3 min read

Pronunciation

[ˈdā-tə bläbz (ē-ī-ˈpē-48-44)]

Analogy

Think of Data Blobs like specialized shipping containers in a port logistics system. Traditional shipping containers (regular transactions) are expensive because they're carefully inspected, cataloged, and permanently stored in the port's primary warehouse (the ). Blob shipping containers are designed specifically for goods that only need temporary storage—they undergo less intensive inspection, are held in temporary overflow facilities instead of the main warehouse, and have a scheduled deletion date. This makes them much cheaper for shipping certain types of cargo (rollup data) that only needs to be available for a limited time. Just as these specialized containers dramatically reduce shipping costs for appropriate cargo without compromising the port's operations, blobs provide with affordable data availability without permanently expanding 's , making layer-2 solutions substantially more cost-effective.

Definition

A specialized data structure introduced in 's Dencun upgrade that provides temporary, lower-cost storage specifically designed for layer-2 rollup solutions. These blobs (Binary Large Objects) allow to large amounts of data to the at significantly reduced cost, while maintaining data availability guarantees without permanently bloating the .

Key Points Intro

Data Blobs transform layer-2 economics through four key innovations:

Key Points

Segregated Storage: Separates rollup data from Ethereum's permanent state, creating a distinct temporary data layer optimized for high-volume transaction batches.

Cost Reduction: Dramatically decreases the expense of posting rollup transaction data to Ethereum, with fees typically 10-100x lower than equivalent calldata.

Scheduled Expiration: Implements automatic data pruning after a fixed period (approximately 18 days), preventing long-term blockchain bloat while maintaining sufficient availability for security purposes.

Verification Efficiency: Provides cryptographic commitments to blob data that enable efficient verification while requiring significantly less computation than processing equivalent calldata.

Example

An optimistic rollup processes 10,000 user transactions, generating 2MB of data that must be posted to for security guarantees. Before EIP-4844, posting this data would cost approximately 40 ETH using calldata (at 30 ), making each user bear a significant data cost. Using Data Blobs, the rollup can now the same information for approximately 0.5 ETH—an 80x reduction in cost. The rollup's sequencer packages the transactions into a blob, which is committed to with cryptographic guarantees of its contents. validators store this blob data temporarily but separately from the permanent . The data remains available for approximately 18 days, providing sufficient time for fraud proofs or validity challenges, after which it is pruned from storage. This cost reduction directly translates to lower user , with the data posting component of costs decreasing from dollars to cents, dramatically improving the rollup's economic competitiveness.

Technical Deep Dive

Data Blobs implement sophisticated cryptographic constructs to balance efficient verification with data availability guarantees. The technical foundation employs KZG (Kate-Zaverucha-Goldberg) polynomial commitments, which enable constant-sized commitments to large data sets with efficient proofs for specific elements. The blob data structure allows to publish up to 4096 blobs per , with each blob containing 128KB of data (a substantial increase compared to calldata efficiency). These blobs are committed to the header through a system of commitments and proofs that allow verification without requiring validators to process the entire blob content. Blob pricing follows a separate fee market from , with a target of 3 blobs per long-term and an EIP-1559-style pricing mechanism that adjusts based on utilization. This separate fee market prevents blob usage from affecting regular costs during demand spikes. For implementation, blobs are integrated as a new element in the structure. Validators are required to store blob data for approximately 18 days (4096 epochs) before pruning, ensuring data remains available for fraud proofs or validity verification without permanent growth. Verification leverages the mathematical properties of , where validators can efficiently verify proofs that specific data is included in a blob without processing the entire content. This creates substantial efficiency gains compared to traditional calldata, which must be fully processed by the . Rollup integration typically involves adapting sequencers and batch submission contracts to utilize the new blob type, with batches formatted to optimize for the 128KB blob size. Advanced implementations employ compression techniques and batch optimization algorithms to maximize data efficiency within the new blob format.

Security Warning

While Data Blobs reduce costs, they introduce new verification mechanisms with distinct security properties. Understand that blob data availability guarantees rely on KZG cryptographic assumptions rather than traditional verification. For critical applications, consider implementing additional verification checks during the blob availability period. Be aware that the temporary availability window creates potential risks for systems requiring data access beyond the pruning period—implement appropriate archiving solutions for data that might be needed beyond the ~18 day window.

Caveat

Despite their benefits, Data Blobs face important limitations in current implementations. The temporary availability window creates requirements for external data archiving solutions for applications needing historical data access. Blob space operates as a separate fee market that may experience its own congestion and price volatility during peak demand periods. The KZG cryptographic scheme relies on a trusted setup, introducing additional security assumptions compared to traditional data structures. Most importantly, while blobs significantly reduce costs for , they introduce additional technical complexity for rollup implementations that must adapt their batch submission processes to efficiently utilize the new data structure.

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