TableCache: Efficient Structured Data Caching

• TableCache is an advanced caching strategy for structured data that enables efficient reuse of computed results, rapid invalidation, and latency optimization.
• It employs specialized primary–foreign key guided precomputation and Trie-based matching to construct and retrieve KV cache slices for complex query patterns.
• Empirical studies demonstrate substantial throughput gains and latency reductions in Text-to-SQL and web backend workloads through techniques like batch query reranking and micro-batch pipeline execution.

TableCache is an advanced caching paradigm for structured data retrieval that enables efficient reuse of computed results, fine-grained invalidation, and latency optimization in both database- and language-model-based inference settings. It encompasses specialized architectures and algorithms for primary–foreign-key–guided key–value (KV) precomputation, granular revision-based invalidation, and batch-oriented cache management. TableCache systems have demonstrated substantial throughput and latency improvements over conventional caching or inference baselines in empirical studies, particularly for Text-to-SQL and web backend workloads (Su et al., 13 Jan 2026, Łopuszański, 2023).

1. Foundational Principles and Domain Models

TableCache arose to address latency bottlenecks and cache coherence challenges in high-throughput environments where query results (or schema representations) are repeatedly requested with overlapping or structured access patterns. Common motivating use-cases include natural language–driven SQL generation, web backends atop relational databases, and retrieval-augmented generation contexts.

Key structural definitions (as formalized in (Łopuszański, 2023)):

• Records: $r \in S = (r[1], ..., r[k])$, an element of a $k$-ary relation.
• Queries: $q = (q_1, ..., q_k)$, with wildcards ($*$) permitted.
• Subspaces: $$\operatorname{subspace}(q) = \{v \in S \mid \forall i, v[i]=q[i] \vee q[i]=*\}$$.
• Dependency Graph ($E$): $(u, v) \in E$ iff $$\operatorname{subspace}(u) \cap \operatorname{subspace}(v) \neq \emptyset$$.

For neural inference settings, TableCache formalizes input schemas as collections of tables $T$, with cacheable representations derived from table serializations, and KV cache slices constructed ahead-of-time to accelerate downstream inference (Su et al., 13 Jan 2026).

2. TableCache Algorithms: KV Precomputation and Retrieval

2.1 Primary–Foreign Key Guided Precomputation

In low-latency Text-to-SQL inference, the TableCache approach precomputes KV caches for tables in offline batch mode, crucially preserving inter-table attention from primary–foreign key (PFK) joins:

• PFK Graph Construction: Nodes $V = \{T_1, ..., T_m\}$; edges $(T_j \to T_i)$ iff $T_i$ references $T_j$ via a foreign key.
• Topological Sort: An ordering $\pi$ s.t. $j < i$ for $(T_j \to T_i) \in E$.
• Iterative Encoding: For each $T_{\pi(i)}$, concatenate its metadata with the rolling context, forward through the LLM to obtain $(K_t, V_t)$, and store the result for subsequent reuse.

By introducing topological ordering, cross-table dependencies and aggregated attention paths are naturally encoded into the cache slices (Su et al., 13 Jan 2026).

2.2 Trie-Based Table Matching

Table metadata tokens are organized in a Trie for efficient lookup:

• Node: Token (table/column name).
• Edge: Next token.
• Leaf: Pointer to KV cache slice, table ID.
• Lookup: Scans input tokens $s_1..s_n$; efficiently finds longest prefix matches. Amortized cost is $O(n)$.

This enables fast table-set identification in dynamic contexts, facilitating prompt KV cache retrieval on demand (Su et al., 13 Jan 2026).

2.3 Fine-Grained Invalidations (Web Cache Contexts)

The TableCache invalidation algorithm (as presented in (Łopuszański, 2023)) guarantees strict coherence and infinite TTL for arbitrary $k$-dimensional query patterns:

• Revision Counters: Middle-layer nodes $p \in P$ (where $p[i] \in \{\text{actual value}, *, ?\}$) each store a counter in global cache.
• Invalidate on Write: For any INSERT/DELETE, increment all $2^k$ counters for overlapping subspaces.
• Read Freshness Check: SELECT computes up to $2^k$ relevant counters, tags the cache entry, and verifies freshness on cache hit.

Pseudocode and graph-theoretic invariants ensure correctness under concurrency and strict ordering guarantees for version stamps.

3. Cache Management, Query Reranking, and Pipeline Optimization

3.1 GPU Cache Replacement Policies

When a requested table’s KV cache is not resident, TableCache applies LRU, FIFO, or LFU eviction and loads new slices as needed (Su et al., 13 Jan 2026).

3.2 Batch-Oriented Query Reranking

To minimize cache switches and maximize throughput, TableCache introduces batch reranking:

• Binary Table-Set Vectors: Each query $i$ represented as $I_i[k] = 1$ if table $k$ is matched.
• Pairwise Distance: $d_{i,j} = \sum_{k=1}^m (I_i \oplus I_j)[k]$.
• Greedy Sorting: Orders batch to minimize adjacent cache distance $d_{i,j}$; complexity $O(N^2 m / 64)$.

Reranking drastically reduces TTFT by increasing table reuse locality (Su et al., 13 Jan 2026).

3.3 Computation Loading Pipeline

Pipeline execution splits compute into micro-batches ($b_c$) with asynchronous KV cache prefetch ($b_m$), effectively hiding memory latency and scaling batched TTFT linearly.

4. Correctness, Complexity, and Empirical Performance

4.1 Consistency and Monotonicity

Under the revision–dependency graph framework:

• Monotone Counters: Strictly non-decreasing with time for any subspace.
• Version Ordering: For any query $q$, version stamps (joined revision vectors) are componentwise $\succeq$ any prior revision.
• Concurrency Theorem: SELECT results are strictly fresh within a latency window $\epsilon$ bounded by invalidate propagation.

4.2 Complexity Analysis

For $k$ equality-filtered columns:

• Per-Read: $O(2^k)$ for revision vector operations.
• Per-Write: $O(2^k)$ invalidations.
• Network & Memory: With bulk cache operations, round-trips are $O(1)$; memory overhead is only for touched counters.

4.3 Benchmark Results

Empirical results exhibit substantial gains:

• Text-to-SQL (OmniSQL-7B, Spider/BIRD): Up to $3.62\times$ speedup in TTFT; drop in execution accuracy $<1\%$ (Su et al., 13 Jan 2026).
• Web Backend Synthetic Test (PHP, $k=3$): Cache hit ratios $97\%$ (99% SELECT), $73\%$ (90% SELECT), vs. 29%, 12% for naïve flush. Median freshness latency $\sim$40ms (Łopuszański, 2023).

Test	Hit Ratio	Median ε (stale)	Selects
99% SELECT	97%	0.032s	98,956
90% SELECT	73%	0.040s	89,952
33% SELECT	7%	0.036s	33,408

These results confirm TableCache efficacy across read/write mixes and validate correct freshness under concurrency.

5. Practical Recommendations and Extension Pathways

• Dimension Trimming: Restrict $k$ to columns used in equality filters for optimal cost.
• Bulk Cache Ops: Employ memcached/Redis multi-add/get/incr for round-trip minimization.
• Revision Backing: Persistent counter storage (e.g. Redis-AOF) is viable, though unbounded key growth should be monitored.
• OR/Inequality Handling: DNF or bit-decompose constraints to maintain polynomial per-query costs.

Potential extensions include entity-relation–guided block precomputation in RAG/QA, precomputing for frequently used code libraries in generation pipelines, and topological subgraph encoding for knowledge graph retrieval (Su et al., 13 Jan 2026).

TableCache establishes a rigorous, scalable methodology for reusing structured computation, maintaining coherence, and optimizing for demanding latency or concurrency requirements. Its variants have been studied for LLM inference, relational cache management, and represent a general pattern for domain-aware KV caching in modern high-throughput applications.