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 4–22 and 24–30 are closed and live there.

Status: 4 open of 30 — items 1, 2, 3 (one root cause) and 23. The other 26 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.

S2 — High

23. Full-text search inlines up to 1000 ids into a MongoDB `$in`

backend/src/Squidex.Domain.Apps.Entities/Contents/Queries/ContentQueryParser.cs:93,107

var textQuery = new TextQuery(query.FullText, 1000) { PreferredSchemaId = schema.Id };
var fullTextIds = await textIndex.SearchAsync(context.App, textQuery, context.Scope(), ct);
...
searchFilters.Add(ClrFilter.In("id", fullTextIds.Select(x => x.ToString()).ToList()));

Every full-text content query becomes: one search round trip, then a second query whose filter carries up to 1000 GUID strings — roughly 37 KB of BSON — forcing 1000 index seeks. The Select(x => x.ToString()) also allocates 1000 strings per query.

There is a correctness edge here too, which is why it outranks pure throughput items: the index returns the top 1000 by relevance, but the outer query then re-sorts by the default LastModified and pages over that. Relevance order is discarded, and anything past the 1000 cap is silently missing — invisible to the caller, who just sees fewer results than exist.

Fix: push paging into the text index so it returns only the page (plus a total), rather than a fixed 1000-id prefix that the outer query re-sorts.

Suggested order of attack

Item 23 — full-text paging. Really a correctness fix that happens to also be faster: the 1000-id cap silently truncates results and discards relevance order today.
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.

Nothing else is outstanding. Everything cheap, every correctness-shaped finding and everything in the stability category is closed.

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.

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