Sequencer Compression

Sequencer compression is a data optimization technique employed by rollup sequencers to minimize the size and cost of the transaction data batches, or calldata, they publish to a base layer blockchain. By applying sophisticated algorithms, the sequencer removes redundant information, compresses repeated data patterns, and encodes transactions more efficiently before submitting them to the L1. This process directly reduces the gas fees associated with data availability, which is a primary cost driver for rollup operations, thereby lowering transaction costs for end-users.

The mechanics involve the sequencer processing a batch of executed L2 transactions and then applying compression algorithms—such as Brotli, zlib, or custom bytecode-specific compression—to the batch data. Common strategies include deduplication of signature data, efficient numeric encoding, and removal of predictable fields. The compressed data is then posted in the calldata of a transaction to a data availability layer, like Ethereum. A critical requirement is that the compression algorithm must be deterministic so that any verifier or full node can independently decompress the data and reconstruct the exact batch to verify state transitions.

This technique is a cornerstone of the modular blockchain stack, separating execution from data availability. Its effectiveness is measured by the compression ratio, which compares the size of the original execution data to the compressed calldata. High ratios are essential for scaling, as they allow more user transactions to be settled per unit of costly L1 block space. It is distinct from, but complementary to, other scaling solutions like validity proofs or data availability sampling, which secure the system's integrity.

For example, a sequencer might compress thousands of simple token transfers by representing repeated contract addresses with short identifiers and using run-length encoding for similar transaction types. The practical impact is significant: without compression, posting data for a high-throughput rollup could become prohibitively expensive, negating its low-fee promise. Therefore, ongoing research focuses on advancing compression schemes, including the use of zero-knowledge proofs to compress state differentials or specialized encodings for specific application domains like DeFi or gaming.

Sequencer compression is the process by which a rollup's central transaction ordering node, the sequencer, batches and compresses user transactions before submitting them to a base layer like Ethereum. This is a core mechanism for achieving data availability at a lower cost. Instead of posting each transaction's full data, the sequencer applies algorithms to create a compact cryptographic proof or summary of the batch's state changes, drastically reducing the amount of calldata published on-chain. This compression is fundamental to the rollup scaling thesis, as it minimizes the most expensive component of L2 operation: permanent data storage on the L1.

The compression process typically involves several techniques. First, the sequencer removes redundant or predictable data, such as common function selectors and zero-byte padding. Signatures, which are large, are often omitted entirely from the batch data posted to L1, as rollups can use fraud proofs or validity proofs to guarantee their correctness. The sequencer then employs standard compression algorithms (like brotli or zlib) on the remaining data. The output is a single, compressed data blob that represents hundreds or thousands of individual transactions. This blob, along with a state root or proof, is what gets anchored to the base chain in a periodic batch submission.

The efficiency of this compression directly translates to user savings. Because Ethereum transaction fees are largely driven by data storage costs (gas for calldata), a high compression ratio means the cost of securing the transaction data is split among many users. For example, a simple token transfer that might cost $10 in gas on Ethereum L1 could cost a few cents on a compressed rollup. The sequencer's role is to optimize this ratio, balancing compression time (latency) with size reduction to maintain low fees and high throughput without creating bottlenecks in transaction processing.

Different rollup architectures approach compression with varying priorities. Optimistic rollups, like Arbitrum and Optimism, focus on maximizing data reduction to lower costs, often achieving 10-100x compression. ZK-rollups, such as zkSync and StarkNet, integrate compression within their validity proof generation; the cryptographic proof itself serves as an extreme form of compression, verifying the correctness of batch execution without revealing all transaction details. The choice of technique affects the trust model, finality time, and overall system design, but the goal of minimizing on-chain data footprint is universal across all scalable rollups.