Backup and Restore Best Practices

Backup and restore procedures for zkSync Era are not generic DevOps tasks; they are protocol-specific operational requirements driven by the node's unique state architecture. Unlike a simple stateless API, a zkSync Era node maintains a complex, high-velocity state database that includes L2 state trie data, Merkle tree proofs, transaction history, and critical cryptographic key material. A failure without a valid, tested backup can result in catastrophic data loss, requiring a full re-sync from genesis that may take days and cause extended service disruption for exchanges, RPC providers, and ZK Stack chain operators.

The operational surface area for backups must cover three distinct asset classes: the PostgreSQL database containing the canonical chain state, the node configuration files that define L1 interaction parameters and network identity, and the keystores securing the operator's block-producing or proof-submitting keys. Each asset class has a different change frequency and recovery time objective (RTO). The database, undergoing continuous state transitions, requires frequent, consistent snapshots using pg_dump or filesystem-level tools coordinated with a checkpoint. Configuration files change infrequently but a loss can cause misconfiguration on restart, while keystore loss is an irrecoverable security event that may permanently disable a validator or prover.

The most common operational failure mode is not the absence of backups, but the failure to test restoration procedures in a staging environment. A backup file that cannot be restored, or a snapshot that is silently corrupted, provides a false sense of security. Operators must validate the full restoration pipeline—snapshot import, configuration application, keystore decryption, and node startup to a healthy, syncing state—on a regular schedule. Chainscore Labs can audit backup strategies against the specific state growth patterns of zkSync Era, verify restoration integrity in isolated environments, and design automated recovery pipelines that minimize downtime for production infrastructure.

For zkSync Era node operators, a backup strategy is only as good as its completeness. A successful restore requires capturing three distinct categories of state: the PostgreSQL database containing the chain's derived state and Merkle tree, the node's configuration files that define its operational parameters and L1 connection, and the cryptographic keystores that authorize block production or proof submission. Missing any one of these components renders a backup useless for recovery, turning a routine restore into a full re-sync from genesis that can take days.

The PostgreSQL database is the single largest and most critical asset. It stores the full L2 state, including account states, contract storage, and the Merkle tree data required for generating state proofs. Operators must perform pg_dump or file-system-level snapshots of the database volume, ensuring consistency by executing the backup while the node is paused or by using pg_basebackup for continuous archiving. The frequency of these backups should be aligned with the operator's Recovery Point Objective (RPO), noting that state diffs are published to L1, but reconstructing the full state from L1 calldata is computationally prohibitive and not a substitute for database backups.

Configuration files, often overlooked, define the node's identity and its connection to the L1 network. This includes the YAML or TOML configuration specifying the L1 RPC endpoint, contract addresses for the zkSync diamond proxy and bridge, and database connection strings. The keystores, typically JSON files or paths to Hardware Security Modules (HSMs), contain the private keys for the node's Ethereum address used for L1 transactions. Loss of these keys can permanently sever the node's ability to submit blocks or proofs, leading to operational failure. Chainscore Labs can audit an operator's backup scope and restore procedures, verifying that staging environment drills correctly reconstruct all three state categories without relying on implicit production assumptions.