Backend Performance — Open

Analysis of backend/src (2131 C# files, ~187k LOC). Ordered by severity: expected production impact × how hot the code path is.

Severity key: S1 critical (can dominate request latency or take the process down), S2 high (measurable on every request in a common path), S3 moderate (steady overhead / allocation churn), S4 low (worth fixing while nearby).

Item numbers are stable and never reused. Completed items move to resolved.md keeping their number, so gaps in the sequence here are expected — items 4–33 and 35 are closed and live there.

Status: 4 open of 35 — items 1, 2, 3 (one root cause) and 34. The other 31 are in resolved.md.

S1 — Critical

1. A fresh Jint `Engine` is constructed for every script evaluation

backend/src/Squidex.Domain.Apps.Core.Operations/Scripting/JintScriptEngine.cs:144

CreateEngine calls new Engine(...) on every Execute / ExecuteAsync / TransformAsync. Building a Jint engine allocates a complete JS realm (global object, Object/Array/JSON/Math/RegExp prototypes, intrinsics) plus runs every registered IJintExtension.Extend. Script parsing is cached via CacheParser, but engine construction — the expensive half — is not.

This is the root cause of items 2 and 3, which is why it ranks first.

Fix: pool engines (ObjectPool<Engine>) keyed by the option set, resetting globals between uses; or hoist one engine per enrichment batch instead of per item.

2. Workflow enrichment runs one Jint engine per content per transition

backend/src/Squidex.Domain.Apps.Entities/Contents/DynamicContentWorkflow.cs:90,118 backend/src/Squidex.Domain.Apps.Entities/Contents/Queries/Steps/EnrichWithWorkflows.cs:22,30,31

EnrichWithWorkflows loops over every content and awaits GetNextAsync and CanUpdateAsync sequentially. GetNextAsync loops over every transition and calls IsTrue, which calls scriptEngine.Evaluate whenever the transition has an expression — a new engine each time (item 1).

A frontend content list of 200 items with a workflow having 3 conditional transitions executes 200 × (3 + 1) = 800 engine constructions in one request, serially.

GetWorkflowAsync additionally re-scans app.Workflows.Values with SchemaIds.Contains(schemaId) on every one of those calls.

Fix: cache the resolved Workflow per (appId, schemaId) for the batch; memoize condition results per (transition, contentData); reuse one engine.

3. Query scripts execute one engine per content, serially

backend/src/Squidex.Domain.Apps.Entities/Contents/Queries/Steps/ScriptContent.cs:57

foreach (var content in group) await TransformAsync(...) — every content in the page gets its own engine construction plus its own CancellationTokenSource.CreateLinkedTokenSource. Any schema with a query script pays this on every read.

Fix: same as item 1 — reuse the engine across the group; per-content state is already isolated in ContentScriptVars.

S3 — Moderate

34. Message formatting looks up properties by reflection on every call

backend/src/Squidex.Infrastructure/Translations/ResourcesLocalizer.cs:77

ResourcesLocalizer.Get substitutes {variable} placeholders by reflecting over the anonymous args object:

var property = argsType.GetProperty(variableName);

No PropertyInfo is cached, so every call re-resolves it. Type.GetProperty(string) is one of the slower reflection calls — it does a name lookup over the type's members.

Mostly this sits on error paths, where it does not matter. The one that is not is ResolveReferences.CreateFallback (ResolveReferences.cs:125):

var text = T.Get("contents.listReferences", new { count = referencedContents.Count });

That runs inside the per-content, per-partition loop of the enrichment pipeline, for every reference field that resolves to more than one item. The same method also allocates a fresh JsonObject and loops over every app language on each call.

Fix: cache the PropertyInfo per (type, name); a small static dictionary is enough since the arg types are compiler-generated and few.

Suggested order of attack

Item 34 — small and self-contained: cache the PropertyInfo per (type, name).
Items 1–3, engine pooling — the largest single cost, and the most invasive change on the list. It touches the security boundary of user-authored scripts, since a pooled engine must not carry state from one script into the next. Profile before writing it: the estimate that engine construction dominates a scripted content list is read off the loops, not taken from a trace.

Method / caveats

Findings come from static reading of the hot paths (content query + enrichment pipeline, GraphQL execution, write/validation path, event consumers, HTTP pipeline, MongoDB and EF repositories, asset serving, response/DTO construction) plus scripted scans for sync-over-async, awaits inside loops, uncached Regex, and repeated LINQ materialisation.

No profiling or benchmarking was run. The ordering is a reasoned estimate of impact, not measured data. Counts like "200 × 4 engine constructions" or "2000 link generations" are derived from reading the loops, not observed. Confirm the expensive items with a profiler against a representative workload before investing in the larger refactors.

Items 21–30 were added in a second pass over areas the first pass had not covered: the EF data layer, asset serving and transformation, response DTO and link construction, the full-text search path, and the remaining enrichment steps. Two candidates were dropped during that pass after checking them: per-content permission checks (already memoized in Resources.Can) and the lazily built static maps in Adapt (a benign race that at worst builds the same dictionary twice).

Line numbers were verified against the working tree at the time of writing.

5.9 KiB Raw Blame History