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The first request works. The second remembers more than it should. In a long-running PHP application, that can happen when data intended for one request remains in a worker that serves the next one. Semitexa’s Swoole-based runtime is designed to keep the application booted across executions, so the key is to distinguish reusable worker state from request-specific context and data that must survive worker restarts.
What Semitexa keeps alive between executions
Semitexa runs on Swoole workers that stay alive to handle multiple executions. A worker builds the root service container once, making its service graph and discovered metadata available for reuse. That changes the lifetime of application objects: a service that would be rebuilt for each request in a short-lived PHP model may remain available for the lifetime of this worker.
This is not the same as OPcache. OPcache caches compiled PHP bytecode; a persistent application can additionally reuse its bootstrapped service graph and metadata. Neither mechanism eliminates database queries, remote calls, or the business logic each execution still has to perform. Semitexa’s article describes the model qualitatively; it publishes no named throughput or latency benchmark. Its live handler counter demonstrates fresh execution-scoped handlers, not performance or concurrent tenant isolation. Semitexa.
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Worker services versus execution-scoped handlers
Stable services and discovered metadata can belong to the worker. Semitexa resolves execution-scoped handlers as fresh clones for an HTTP request, console command, or asynchronous execution. The important distinction is not simply whether a PHP object exists in memory, but whether its data is safe to retain when the next execution begins.
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As Taras Hanych (SyntaxWanderer) puts it, “The execution model shapes how dependencies are declared, how a handler is created, how state is carried, how a connection is returned and how HTML reaches the browser.”
Where state belongs: request, worker, or shared storage
Classify state by both its owner and its intended lifetime. Semitexa’s lifecycle documentation distinguishes four classes:
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| State class | Examples | Lifetime and use |
|---|---|---|
| Per-request or per-execution | Current request, authenticated principal, tenant context, locale | Belongs only to the current execution; reset during cleanup. |
| Per-worker | Immutable metadata caches, route discovery results, replay or idempotency sets, connection pools | May remain in process memory across executions handled by that worker. |
| Persistent or shared | Data in a database, Redis, a broker, or another backing service | Appropriate when state must survive worker replacement or be shared across workers. |
| Test-only resettable | Worker-level state that tests need to clear between cases | Production may leave it in place; tests can explicitly reset it to establish a clean baseline. |
Keep identity and tenant context out of shared mutable state
Concurrent Swoole requests need execution context that is aware of the coroutine handling each request. Semitexa documents coroutine-local request, authentication, tenant, and RBAC context. Treating such values as ordinary mutable worker-wide fields risks one execution observing another execution’s identity or tenant data.
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How cleanup works after a request or queued message
Cleanup is part of the persistent-worker model, not an optional optimization. Semitexa documents an HTTP finalization path that resets registered per-request stores, resets the request-scoped container and execution context, and then ends coroutine-local fallback state. The cleanup runs through a finally path so exceptional request handling is included.
Queued-message processing has its own per-message cleanup. It does not use the HTTP request-scoped container, which is why cleanup should match the execution type rather than assume every piece of work is an HTTP request. The lifecycle documentation reports verification against Semitexa Ultimate 2026.09.19.1020; implementation details can evolve, so check the documentation and source corresponding to the installed version. Lifecycle and worker documentation.
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What happens when a worker shuts down
Worker shutdown is bounded, not a guarantee that every in-flight operation will finish. Semitexa’s described bootstrap raises a drain signal on worker exit, clears timers, and attempts to cancel parked coroutines. Some driver calls may not stop immediately, and Swoole may force termination after the configured wait.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWork that must outlive a worker needs durable handling: persist it through an appropriate queue or backing service, retry failures, and make processing idempotent so retries do not duplicate effects. Semitexa documents queue:work, webhook:work, and mail:work as explicit processes. Its scheduler separates inspection, plan materialization, and execution, giving operators distinct points to observe and intervene in scheduled work. Commands and options may vary with implementation versions; consult the installed version’s help and source before reproducing them. Semitexa worker and scheduler documentation.
Long-lived SSE connections have their own resource costs
Semitexa documents an SSE endpoint as one long-lived text/event-stream response over plain HTTP. Its current documented defaults are authentication enabled, a per-IP connection cap of 5, a per-worker cap of 500, and a maximum connection age of 600 seconds. These are Semitexa settings, not limits imposed by the SSE protocol; confirm the values in the version you deploy. Semitexa SSE documentation.
An open stream occupies a coroutine, file descriptor, and response handle, so connection caps need to reflect the resources available to the deployment. The framework does not replay events missed during a reconnect. If delivery matters, retain events at the domain layer and implement the replay behavior the application requires.
Quick Recap
A practical ownership check for PHP developers
- Identify the execution boundary. Decide whether the code runs for an HTTP request, console command, or asynchronous execution; do not assume all paths share the same cleanup.
- Audit mutable service fields. For each value a handler or service writes, determine whether it is execution-specific, safe for the worker lifetime, or needs external shared storage.
- Keep request identity coroutine-local. Request, authentication, tenant, and authorization context must follow the current concurrent execution, not a mutable global or worker-wide slot.
- Verify cleanup on success and failure. Confirm per-execution stores are reset on normal completion and exceptional paths, and that message processing has its own cleanup.
- Plan for replacement and retries. Put required cross-worker or restart-surviving state in a durable backing service, and make retried work safe to run again.
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