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Usually, request-specific state should not survive into the next unrelated request. A user’s identity, authorization context, and temporary request values belong to that request. But when the larger operation is still in progress after an HTTP exchange ends, preserve only the state needed to resume it—using a protected continuation token or a durable task record.
What does “after the request is finished” mean?
The phrase can describe two different boundaries. An HTTP request may be over even though the user’s larger operation is not: for example, a client may need to provide more input, or a background job may continue after the server has returned a response. Alternatively, a later request may be an unrelated unit of work. State handling should differ between those cases.
- Later unrelated request: discard or reinitialize mutable request data so that one user’s context cannot leak into another’s.
- Unfinished logical operation: retain the minimum recovery or continuation data needed to proceed, and make that state’s ownership, expiry, and access rules explicit.
The useful distinction is between request state and work state. Request state is temporary context for one execution. Work state belongs to an operation that may span requests, workers, or restarts.
What happens to request state after a request ends?
That depends on the runtime. A request ending does not necessarily mean the process or execution environment is destroyed. AWS documents that Lambda can freeze and later reuse an execution environment; objects initialized outside the handler and files in /tmp may remain available to a later invocation, while environments can also be terminated. That behavior makes reuse possible, not guaranteed durability. See the AWS Lambda execution environment lifecycle.
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Use longer-lived process state for suitable reusable resources, such as connection pools or compiled application definitions. Do not rely on that memory to preserve work through a crash, environment replacement, or deployment. And do not leave mutable identity, authorization, or user-specific request data in a shared context where a later invocation might observe it.
How should request state be scoped?
Keep per-request context explicit
Create a context object for each unit of work and pass it to the code that needs it. Put request-specific values—such as the authenticated principal, authorization decisions, trace identifiers, and temporary inputs—in that context rather than mutable process globals. Initialize it at the start of the request and release or reset it when the request ends.
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Separate request data from reusable infrastructure
Long-lived clients and pools can be shared when the platform and library support safe reuse. Keep them separate from mutable user data. This design avoids treating the worker’s lifetime as the lifetime of a user’s request, while still allowing reusable infrastructure to reduce repeated initialization. The exact lifetime and concurrency behavior depend on the platform and framework; do not assume that every runtime reuses workers in the same way.
Which pattern should preserve unfinished work?
Choose based on how long the operation lasts, who needs access to its state, and what must happen if a worker fails. The patterns below have different state owners and recovery properties.
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| Pattern | State owner | Good fit | Main trade-off |
|---|---|---|---|
| Fresh request scope | Current execution context | Normal short-lived API handling and user/request data | Simple isolation; unfinished work must be restarted or handed off explicitly |
| Client-carried opaque continuation | Client transports server-issued state between independent requests | Input collection, brief continuation, or load shedding when state can be encoded safely | Can avoid server-side session storage, but requires token protection, validation, expiry, version handling, and user binding |
| Durable server-side task or workflow | Server stores a task identifier, state, and progress | Long-running, costly, background, or restart-resumable work | Stronger recovery and central control, with storage, cleanup, access-control, and orchestration complexity |
| Process-local memory or cache | A possibly reused worker or execution environment | Performance caches and reusable non-user-specific resources | Not durable; reuse varies, and mutable request data can leak between invocations |
The Model Context Protocol’s SEP-2322 proposal illustrates both kinds of continuation: an ephemeral pattern in which the server returns opaque requestState for a client to return on a later independent request, and a persistent Tasks workflow for work that continues in the background. It is a proposal, so its protocol details may evolve. Read the MCP multi-round-trip proposal.
How do I resume a request after a timeout or restart?
Resume the logical operation, not the old in-memory request context. For short continuations, issue a token that refers to the minimum state needed to continue. For work that must survive worker loss or run in the background, store a task record and return a stable task identifier. A restart should be able to load that state from its owner rather than depend on the original worker.
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For a continuation token
- Keep it opaque to the client where possible; do not expose secrets in a client-visible token unless they are appropriately protected.
- Validate every returned token. The MCP SEP-2322 proposal states: “Servers MUST always validate that state, as the client is an untrusted intermediary.”
- Give the token explicit expiry and version semantics so old or incompatible state can be rejected safely.
- When state is user- or tenant-specific, bind it to the authenticated user or tenant to limit replay and cross-user use.
- Protect the token against tampering where appropriate, and limit the data it carries to what is needed for continuation.
For a durable task
- Persist the task identifier, owner, current state, progress, and the checkpoints needed to resume.
- Define terminal states such as completed, failed, and cancelled, along with retention and cleanup rules.
- Check authorization on status, resume, and cancellation operations; possession of an identifier alone should not grant access to someone else’s work.
- Make retry and cancellation behavior explicit so clients and workers agree about what happens when an operation is interrupted.
How should retries handle side effects?
A timeout tells the client that it did not receive a response in time; it does not prove that the server or an external service failed to perform the requested action. A payment might have succeeded even if the response was lost. Retrying without a recovery strategy can charge twice or repeat another irreversible action.
For non-idempotent work, checkpoint around side effects and use an idempotency key or reconciliation process so a retry can distinguish “not done” from “done, but the response was lost.” The TS-21 HTTP API guide recommends tracking named recovery points for non-idempotent operations; see TS-21 HTTP APIs. Treat the idempotency and checkpoint design as part of the operation’s recovery contract, not as a property supplied automatically by a timeout or a worker cache.
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How do I choose what state to keep?
Before extending state beyond a request, answer these questions:
- Duration: Does the operation finish with the response, continue briefly, or run for a long time in the background?
- Ownership and reach: Which user or tenant owns the state, and which workers or services need to read it?
- Recovery: Must it survive a process crash, restart, or retry, or is it acceptable to restart the operation?
- Sensitivity: Does the state contain identity, permissions, or secrets, and how will access and tampering be controlled?
- Side effects: Could a retry repeat a charge, send, or external mutation? What checkpoint, idempotency key, or reconciliation path prevents that?
- Retention: When does the state expire, and who removes abandoned tokens or task records?
In practice, use request scope for temporary context, process-local storage for non-user-specific performance aids, a validated continuation token for a short stateless handoff, and durable server-side task state when the operation needs controlled progress or restart recovery. The right choice preserves only what the next step needs without confusing a reused runtime with durable storage.
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