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 4–17 and 20 are closed and live there.

Status: 5 open of 20 — items 1, 2, 3, 18, 19. The other 15 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

18. Sequential N+1 schema and component lookups

backend/src/Squidex.Domain.Apps.Entities/AppProviderExtensions.cs:30 backend/src/Squidex/Areas/Api/Controllers/Contents/Generator/SchemasOpenApiGenerator.cs:39,48

ResolveSchemasAsync awaits appProvider.GetSchemaAsync once per id in a loop; the OpenAPI generator awaits GetComponentsAsync(schema, ...) once per schema in a loop. For an app with 100 schemas the OpenAPI docs endpoint serialises 100 round trips that have no dependency on each other.

Fix: await Task.WhenAll(...) over the lookups, or add a batch accessor. Both are warm-cache paths, which is why this sits at S3 rather than S2.

19. Header parsing re-splits and re-allocates on every read

backend/src/Squidex.Domain.Apps.Entities/Contents/ContentHeaders.cs:140,150 backend/src/Squidex.Domain.Apps.Entities/ContextHeaders.cs:133

public static HashSet<string>? Fields(this Context context)
    => context.AsStrings(KeyFields).ToHashSet();

public static HashSet<Language> Languages(this Context context)
    => context.AsStrings(KeyLanguages).Select(Language.GetLanguage).ToHashSet();

AsStrings does value.Split(...).Select(Trim).Distinct(). Each call allocates the split array, two LINQ iterators and a HashSet. ConvertData.GenerateConverter calls Languages() and ResolveUrls().ToList() per schema group, and Fields() is read from several steps. The headers never change for the lifetime of a Context.

Fix: memoize the parsed values on Context, invalidating in the clone builder.

Suggested order of attack

Engine pooling (items 1–3) — by a wide margin the largest remaining cost, and the only one left that can dominate a request. One change in JintScriptEngine addresses it, and items 2 and 3 mostly disappear with it.
Items 18 and 19 — steady-state allocation and a warm-cache N+1; both are small and neither is likely to show up next to item 1.

Everything correctness-shaped is closed, as is everything in the stability category. What remains is pure throughput work — exactly the category that should be profiled before it is written. Item 1 in particular is worth measuring first: the estimate that it dominates a scripted content list comes from reading the loops, not 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 repositories) 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. Item counts like "200 × 4 engine constructions" are derived from reading the loops, not observed. Confirm items 1–3 with a profiler against a representative workload before investing in the larger refactors.

Line numbers were re-verified against the working tree after the first round of fixes.

6.0 KiB Raw Blame History