Design: Use libcartesi fast load/store to keep `cartesi-jsonrpc-machine` offline during normal operation

[vfusco]

Context / Problem

With the upcoming libcartesi version 0.20.0, machines can be loaded/stored very quickly. This unlocks a potentially simpler and cheaper operational model: keep the cartesi-jsonrpc-machine offline for all running applications and instead load/store machine state as part of the input processing loop.

Today, the node architecture assumes an always-on remote machine service for running applications. The current input feed / execution loop and snapshotting strategy were not designed with fast load/store in mind, so adopting this capability will require design work, experimentation, and likely some refactoring.

Primary goal: reduce cloud footprint and cost by removing the need for a continuously running cartesi-jsonrpc-machine while preserving correctness, determinism, and the same externally observable results.

Goals

• Allow running applications with cartesi-jsonrpc-machine offline, using load/store of machine state on demand.
• Maintain deterministic execution and consistent outputs/hashes.
• Keep a safe path for incremental rollout and fallback to the current mode if needed.

Non-goals

• Full rewrite of the execution engine.
• Introducing new emulator features beyond what is necessary for load/store integration.
• Changing external APIs unless required (prefer internal implementation changes).

Proposed work (design + prototype)

1) Understand and validate the new primitives

• Identify the exact libcartesi APIs/semantics for fast load/store (file format, performance profile, atomicity guarantees, compatibility).
• Define error handling and recovery expectations (partial writes, corrupted snapshots, incompatible versions).

2) Define the target runtime model

Design how the node processes inputs without a remote machine service:

• Where machine state lives (local disk, volume, object storage, etc.).
• When machine state is loaded (per input, per epoch, etc.).
• When machine state is stored (after each input, after each epoch, etc.).
• Concurrency rules (single-writer per app; read-only access patterns; parallelism across apps).

3) Revisit snapshot strategy

Decide whether to:

• Keep the current snapshot approach unchanged and only alter how machines are executed, OR
• Replace the current snapshots approach with a new design.

Consider special cases:

• Initial syncing / catch-up: do we avoid storing and only persist at the end? Or do periodic stores to support restarts?
• Shutdown / restart semantics: what must be persisted to resume safely? Just the machine hash?
• Disk usage vs recovery time tradeoffs.
• Usage on disputes
• Garbage collect old images from accepted epochs?

4) Decide how to phase out cartesi-jsonrpc-machine always online mode

• Optional flag/config to enable “offline machine mode” per environment.
• Explicit fallback strategy: if load/store fails, can we fallback to remote server (or fail fast)?
• Observability requirements: metrics/logs to compare performance and detect regressions.

5) Prototype

• Implement a minimal prototype behind a feature flag that can:
  • Load machine for an app,
  • Process a small input batch,
  • Store machine,
  • Restart and resume from stored state,
  • Produce identical outputs/hashes compared to the current mode.

Deliverables

• Design doc (in-repo) covering:
  • runtime model (load/store cadence, storage layout, concurrency)
  • snapshot strategy decision
  • initial sync behavior
  • failure modes & recovery strategy
  • rollout plan + flags/config knobs
• Prototype implementation behind a feature flag:
  • Offline mode that does not keep cartesi-jsonrpc-machine running
  • Minimal integration into the input feed loop
• Validation plan + results:
  • Functional parity (outputs/hashes) vs current mode on at least one representative dapp workload
  • Basic performance measurements (ex:. startup time, per-input processing time, disk IO)
• Follow-up issues created for:
  • production hardening / machine storage
  • metrics/alerts
  • CI coverage additions

Acceptance criteria

• A written design is available and reviewed by relevant stakeholders.
• Prototype can run an application end-to-end with cartesi-jsonrpc-machine being shutdown after each input and still:
  • produce the same outputs and computation/machine hashes as the current architecture (within the same libcartesi version)
  • survive a restart and resume from persisted machine state
• The design includes a clear decision on snapshot handling (keep vs change) and justifies it with tradeoffs.

Notes / Open questions to resolve in the design

• How do we ensure atomic stores and prevent corruption on crash/power loss?
• How do we bound disk growth across many apps and long-running nodes? Retention Policy?

[mpolitzer]

Something to consider is that our current cloud does not support COW

[mpolitzer]

We also need cartesi/dave repository to be updated