Module query Future

Navigation: Home > Modules

Hinweis: Vage Eintraege ohne messbares Ziel, Interface-Spezifikation oder Teststrategie mit `` markieren.

Query Module - Future Enhancements

Scope

• Reliability and performance hardening of parser, optimizer, execution, and federation paths.
• Safety and governance improvements for multi-model query processing.
• Operational hardening for long-running and distributed query workloads.

Design Constraints

• Query execution must remain fail-safe under malformed input and partial dependency failures (Target: ongoing)
• Resource limits must be enforced deterministically without data corruption (Target: Q4 2026)
• Optimization and JIT paths must preserve semantic equivalence to interpreter execution (Target: ongoing)
• Federation must maintain bounded memory and timeout behavior under degraded peers (Target: Q4 2026)
• Public query APIs and compatibility layers remain additive-only in active major versions (Target: ongoing)

Required Interfaces

Interface	Consumer	Notes
AQLParser	query frontends	parsing safety and bounded depth behavior
QueryOptimizer	planner	statistics-aware plan generation with stable fallbacks
QueryEngine execute entry points	server/runtime	access checks and resource limit enforcement
QueryFederation / CrossClusterFederation	distributed execution	bounded fan-out and partial-failure handling
VectorizedExecutionEngine	analytical execution paths	predictable memory and throughput envelopes
QueryCompiler	hot-query optimization	correctness-preserving JIT fallback behavior

Implementation Notes

Safety and Validation Hardening

Priority: High Target: Q3-Q4 2026

• Continue parser/translator edge-case hardening and maintain bounded-depth guarantees.
• Keep execute entry-point access checks and validation behavior consistent.
• Strengthen error-path observability without exposing sensitive internals.

Optimizer and Runtime Hardening

Priority: High Target: Q4 2026

• Improve resilience of plan selection under partial/stale stats.
• Expand deterministic regression coverage for rewrite, adaptive, and runtime re-optimization paths.
• Tighten JIT/interpreter equivalence and fallback telemetry.

Federation Hardening

Priority: Medium Target: Q4 2026

• Expand degraded-peer handling (timeouts, partial results, retries) with bounded resource usage.
• Validate shard routing and pruning behavior under failure injection.
• Harden protocol and payload guards for cross-cluster calls.

Advanced Query Feature Hardening

Priority: Medium Target: Q1 2027

• Mature approximate query processing for broader production scenarios.
• Strengthen ML-assisted optimization with strict fallback contracts.
• Expand continuous-query backpressure and persistence safety guarantees.

Test Strategy

• Focused security/reliability regressions for parser, translator, and execute paths.
• Federation and cross-cluster fault-injection matrix with bounded-memory assertions.
• Performance regressions for vectorized execution, optimizer latency, and JIT hot paths.
• Equivalence tests comparing optimized/JIT outputs with interpreter baseline.

Performance Targets

• Maintain stable planner and execution latency envelopes under representative workloads.
• Keep vectorized/federated regressions inside release budget thresholds.
• Keep optimization overhead bounded under high query concurrency.

Security / Reliability

• Fail closed on invalid critical query state and unsafe execution preconditions.
• Preserve deterministic cancellation/timeout behavior.
• Prevent unbounded growth in long-running and distributed execution paths.

Risk Backlog

Risk 1: Optimizer drift under stale statistics

Severity: High Signal: Plan quality degrades under changing data distributions. Mitigation: stronger fallback rules, regression packs, and telemetry gates.

Risk 2: Federation degradation under unstable peers

Severity: Medium Signal: Partial failures cause latency spikes or memory pressure. Mitigation: tighter timeouts, bounded accumulation, and retry policy hardening.

Risk 3: Long-running query resource pressure

Severity: Medium Signal: sustained streaming/continuous workloads push queue and memory limits. Mitigation: backpressure controls, bounded queues, and persistence safeguards.

Adoption Scenarios

Scenario A: Safety-first lane

• Prioritize parser/execute-path safety and access-control invariants.
• Promote only with full regression pass for critical safety controls.

Scenario B: Performance-first lane

• Prioritize optimizer/vectorized/JIT throughput and latency hardening.
• Promote only with benchmark and equivalence gate pass.

Scenario C: Federation-first lane

• Prioritize distributed query resilience and bounded-failure behavior.
• Promote only with fault-injection gate pass.

RAG-Readiness Audit Backlog (2026-09-23)

• Hybrid Retrieval Planner Contract fuer AQL/FTS/Vector zusammenfuehren (Target: Q4 2026)

  • Rationale: RAG-Workloads brauchen konsistente Planentscheidungen zwischen lexical, vector und graph/query Filtern.
  • Umsetzungsschritte: (1) planner-cost Features fuer lexical+vector+rerank definieren, (2) explainable plan annotations erweitern, (3) fallback-contract bei fehlenden ANN Features.
  • Abhaengigkeiten: src/index, src/rag, src/llm, src/aql.
  • Messbares DoD: Explain-Ausgabe enthaelt fuer 100% Hybrid-Queries den gewaehlten Retrieval-Plan inkl. Fallback-Grund; keine ungekennzeichneten Planner-Downgrades.

• Budget-aware Re-Ranking Trigger in Query-Ausfuehrung integrieren (Target: Q1 2027)

  • Rationale: Re-Ranking soll qualitaetsorientiert sein, aber Query-SLOs nicht brechen.
  • Umsetzungsschritte: (1) Triggerkriterien als Policy im optimizer, (2) harte latency/cost guardrails, (3) telemetry fuer trigger hit-rate und abort reasons.
  • Abhaengigkeiten: src/observability, src/llm, src/security.
  • Messbares DoD: <=2% SLO-Verletzungen auf Hybrid-Benchmarksuite bei aktivem Re-Ranking Trigger.

---

ThemisDB 1.9.0-beta · Home · Module-Index · GitHub · Issues