Chapter 33: Performance Engineering

Performance engineering encompasses the systematic discipline of designing, implementing, and tuning database systems for optimal throughput and latency. Unlike ad-hoc optimization, performance engineering applies principled techniques—cost models, statistics, and algorithmic analysis—to make informed decisions throughout the query lifecycle. This chapter examines Cognica's performance engineering infrastructure, from cost-based query optimization to JIT compilation decisions.

33.1 Performance Architecture Overview

Cognica's performance architecture spans multiple layers:

The performance optimization pipeline follows a principled approach:

1. Statistics Collection: Gather distribution data for cost estimation
2. Cost Modeling: Estimate I/O, CPU, and memory costs for operations
3. Plan Selection: Choose optimal execution strategies
4. Execution Optimization: Apply runtime optimizations (JIT, vectorization)
5. Result Caching: Avoid redundant computation

33.2 Cost-Based Query Optimization

33.2.1 Multi-Dimensional Cost Model

The cost model captures multiple performance dimensions:

struct Cost {
  double cpu_cost = 0.0;     // CPU cycles estimate
  double io_cost = 0.0;      // I/O operations estimate
  double memory_cost = 0.0;  // Memory pressure estimate
  double spill_cost = 0.0;   // Disk spill overhead

  auto total() const -> double {
    return cpu_cost + io_cost * kIOCostWeight
           + memory_cost * kMemoryCostWeight
           + spill_cost * kSpillCostWeight;
  }

  static constexpr double kIOCostWeight = 10.0;
  static constexpr double kMemoryCostWeight = 0.1;
  static constexpr double kSpillCostWeight = 5.0;
};

The weighted total cost enables comparison:

$$C_{total} = C_{cpu} + 10 \cdot C_{io} + 0.1 \cdot C_{mem} + 5 \cdot C_{spill}$$

The weights reflect typical hardware characteristics:

• I/O Weight (10x): Disk access dominates in-memory computation
• Memory Weight (0.1x): Memory pressure matters but less than I/O
• Spill Weight (5x): Disk spill is expensive but preferable to OOM

33.2.2 Cost Estimation Functions

The cost estimator provides operation-specific estimates:

class CostEstimator {
public:
  // Sequential scan: linear in row count
  auto estimate_seq_scan_cost(int64_t total_rows, double row_width) const
      -> Cost;

  // Index scan: depends on selectivity and index structure
  auto estimate_index_scan_cost(const Index* index, double selectivity,
                                int64_t total_rows, bool index_only) const
      -> Cost;

  // Sort: O(n log n) with potential spill
  auto estimate_sort_cost(int64_t rows, double row_width,
                          size_t memory_budget) const -> Cost;

  // Hash join: build + probe with potential partition spill
  auto estimate_hash_join_cost(int64_t build_rows, int64_t probe_rows,
                               double row_width, size_t memory_budget) const
      -> Cost;

  // Aggregation: hash table or sorted groups
  auto estimate_aggregate_cost(int64_t input_rows, int64_t estimated_groups,
                               size_t memory_budget) const -> Cost;

private:
  static constexpr double kCPUTupleProcessing = 0.01;
  static constexpr double kCPUComparison = 0.0001;
  static constexpr double kCPUHashOp = 0.0005;
  static constexpr double kSpillIOCost = 0.01;
};

Sequential Scan Cost:

$$C_{seq} = N \cdot (c_{tuple} + c_{row\_width} \cdot W)$$

where $N$ is row count, $W$ is average row width, and $c_{tuple}$ and $c_{row\_width}$ are CPU cost constants.

Sort Cost:

$$C_{sort} = N \cdot \log N \cdot c_{cmp} + C_{spill}$$

where $C_{spill}$ accounts for external sort passes if data exceeds memory:

$$C_{spill} = \begin{cases} 0 & \text{if } N \cdot W \leq M \\ 2 \cdot \lceil\log_k P\rceil \cdot N \cdot W \cdot c_{io} & \text{otherwise} \end{cases}$$

where $M$ is memory budget, $k$ is merge width, and $P$ is the number of initial sorted runs.

33.2.3 Spill Cost Prediction

The cost estimator predicts when operations will spill:

auto will_spill(int64_t rows, double row_width, size_t memory_budget) const
    -> bool {
  auto estimated_size = static_cast<size_t>(rows * row_width);
  return estimated_size > memory_budget;
}

auto estimate_sort_spill_cost(int64_t rows, double row_width,
                              size_t memory_budget) const -> double {
  auto data_size = rows * row_width;
  auto runs = static_cast<size_t>(std::ceil(data_size / memory_budget));
  auto passes = static_cast<size_t>(
      std::ceil(std::log(runs) / std::log(kMergeWidth)));
  return 2 * passes * data_size * kSpillIOCost * kCompressionFactor;
}

The spill cost model accounts for:

1. Number of Runs: Initial sorted partitions that fit in memory
2. Merge Passes: $\lceil\log_k(\text{runs})\rceil$ merge passes
3. I/O Cost: Read + write per pass
4. Compression: LZ4 compression reduces I/O volume

33.3 LSM-Tree Cost Model

Cognica uses RocksDB (LSM-tree storage), requiring specialized I/O cost modeling.

33.3.1 LSM-Tree Characteristics

class LSMCostModel {
public:
  static constexpr double kDefaultPointLookupCost = 1.0;
  static constexpr double kDefaultRangeScanCostPerRow = 0.1;
  static constexpr double kDefaultBloomFPR = 0.01;  // 1%
  static constexpr double kLevelSizeMultiplier = 10.0;

  static auto collect(cognica::rdb::DB* db) -> LSMCostModel;

  auto estimate_point_lookup_cost() const -> double;
  auto estimate_range_scan_cost(int64_t estimated_rows) const -> double;
  auto estimate_index_scan_cost(double selectivity, int64_t total_keys) const
      -> double;

private:
  int32_t num_levels_ = 0;
  std::vector<int64_t> files_per_level_;
  std::vector<int64_t> bytes_per_level_;
  double bloom_filter_fpr_ = kDefaultBloomFPR;
};

33.3.2 Point Lookup Cost

Point lookups may check multiple LSM levels:

$$C_{point} = \sum_{i=0}^{L-1} P(\text{check level } i) \cdot C_{level\_lookup}$$

With bloom filters, the probability of checking level $i$ is:

$$P(\text{check level } i) = \begin{cases} 1 & \text{if key is in level } i \\ \text{FPR}^i & \text{if key is in higher level} \end{cases}$$

where FPR is the bloom filter false positive rate (typically 1%).

33.3.3 Range Scan Cost

Range scans merge data across levels:

$$C_{range} = \sum_{i=0}^{L-1} \frac{N \cdot S_i}{S_{total}} \cdot C_{scan\_level}$$

where $S_i$ is the size of level $i$ and $S_{total}$ is total data size. The merge cost accounts for heap-based multi-way merge.

33.4 Cardinality Estimation

Accurate cardinality estimates drive cost-based optimization.

33.4.1 Cardinality Propagation

struct CardinalityEstimate {
  double rows = 0.0;
  double row_width = 512.0;

  auto memory_estimate() const -> size_t {
    return static_cast<size_t>(rows * row_width);
  }

  auto apply_selectivity(double selectivity) const -> CardinalityEstimate {
    return CardinalityEstimate {rows * selectivity, row_width};
  }
};

Each operator transforms cardinality:

• Filter: $C_{out} = C_{in} \cdot \sigma$ where $\sigma$ is selectivity
• Sort: $C_{out} = C_{in}$ (rows unchanged)
• Limit: $C_{out} = \min(C_{in}, L)$
• Group: $C_{out} = \text{NDV}(\text{group\_keys})$
• Join: $C_{out} = C_{left} \cdot C_{right} \cdot \sigma_{join}$

33.4.2 Selectivity Estimation

class CardinalityEstimator {
public:
  auto estimate_filter_selectivity(const Document& predicates) const -> double;
  auto estimate_filter_selectivity(const query::ast::Expression* expr) const
      -> double;

  auto estimate_group_count(const std::vector<std::string>& group_keys) const
      -> int64_t;

  auto estimate_join_selectivity(const std::string& join_key) const -> double;

private:
  static constexpr double kDefaultEqualitySelectivity = 0.01;
  static constexpr double kDefaultRangeSelectivity = 0.30;
  static constexpr double kDefaultJoinSelectivity = 0.1;
  static constexpr bool kUseDampedAnd = true;
};

Equality Selectivity:

With statistics:

$$\sigma_{eq} = \frac{1}{\text{NDV}(field)}$$

Without statistics (heuristic):

\sigma_{eq} = 0.01 \quad \text{(1% default)}

Range Selectivity:

With histogram:

$$\sigma_{range} = \sum_{i} f_i \cdot \frac{|B_i|}{N}$$

where $f_i$ is the fraction of bucket $i$ overlapping the range.

33.4.3 Correlated Predicate Handling

For conjunctive predicates, naive independence assumption underestimates:

$$P(A \land B) = P(A) \cdot P(B) \quad \text{(often too small)}$$

Cognica uses damped AND estimation:

$$P(A \land B) = \sqrt{P(A) \cdot P(B)}$$

This accounts for common correlation patterns where predicates share underlying factors.

33.5 Query Plan Optimization

33.5.1 Optimization Passes

class PlanOptimizer final {
public:
  auto optimize(LogicalPlanPtr plan)
      -> std::expected<LogicalPlanPtr, db::Status>;

private:
  // Move filters closer to scans
  auto pushdown_predicates_(LogicalPlanPtr plan) -> LogicalPlanPtr;

  // Transform subqueries to joins
  auto unnest_subqueries_(LogicalPlanPtr plan) -> LogicalPlanPtr;

  // Extract repeated expressions
  auto eliminate_common_subexpressions_(LogicalPlanPtr plan) -> LogicalPlanPtr;

  // Optimize join order based on cost
  auto reorder_joins_(LogicalPlanPtr plan) -> LogicalPlanPtr;

  // Eliminate unused columns
  auto prune_columns_(LogicalPlanPtr plan) -> LogicalPlanPtr;

  // Remove redundant sorts when index provides order
  auto eliminate_redundant_sorts_(LogicalPlanPtr plan)
      -> std::expected<LogicalPlanPtr, db::Status>;

  // Transform OR joins to UNION
  auto expand_join_or_to_union_(LogicalPlanPtr plan) -> LogicalPlanPtr;
};

33.5.2 Predicate Pushdown

Predicate pushdown moves filters below blocking operators:

The optimizer checks pushability:

auto can_pushdown_predicate_(const ast::Expr* predicate,
                             const LogicalPlan* node) const -> bool {
  // Check if predicate references only columns from the target node
  return references_only_(predicate, node);
}

33.5.3 Join Reordering

For multi-way joins, the optimizer builds a join graph and selects optimal order:

struct JoinNode {
  std::string identifier;
  LogicalPlanPtr scan;
  int64_t estimated_rows;
};

struct JoinEdge {
  ast::ExprPtr condition;
  ast::JoinType join_type;
};

auto build_optimal_join_tree_(std::vector<JoinNode>& nodes,
                              std::vector<JoinEdge>& edges,
                              std::vector<ast::ExprPtr>& filters)
    -> LogicalPlanPtr;

The greedy algorithm selects joins that minimize intermediate result size:

$$\text{cost}(L \bowtie R) = |L| \cdot |R| \cdot \sigma_{join}$$

Smaller intermediate results reduce memory pressure and I/O.

33.5.4 Common Subexpression Elimination

CSE identifies repeated expressions:

struct CSECandidate {
  ast::ExprPtr expr;
  size_t hash;
  int32_t occurrence_count;
  std::string synthetic_name;  // __cse_0, __cse_1, ...
};

class ExpressionCollector {
public:
  void set_function_registry(const functions::ScalarFunctionRegistry* registry);
  void collect_from_plan(const LogicalPlan* plan);
  auto get_common_subexpressions() -> std::vector<CSECandidate>;

private:
  auto is_cse_candidate_(const ast::Expr* expr) const -> bool {
    // Volatile functions (random, gen_random_uuid) cannot be CSE'd
    if (is_volatile_(expr)) return false;
    // Simple column references don't benefit from CSE
    if (is_simple_column_ref_(expr)) return false;
    return true;
  }
};

CSE transforms:

SELECT x * y + 1, x * y + 2 FROM t;
-- Into:
SELECT __cse_0 + 1, __cse_0 + 2 FROM (SELECT x * y AS __cse_0, ... FROM t);

33.5.5 Redundant Sort Elimination

When ORDER BY matches index order, sorting is unnecessary:

auto can_eliminate_sort_(const LogicalSort* sort,
                         const std::vector<std::string>& index_fields) const
    -> bool {
  // Check if ORDER BY columns match index field prefix
  for (size_t i = 0; i < sort->sort_keys().size(); ++i) {
    if (i >= index_fields.size()) return false;
    auto column = get_order_by_column_name_(sort->sort_keys()[i].expr.get());
    if (column != index_fields[i]) return false;
    // Direction must also match
  }
  return true;
}

33.6 Pipeline Optimization

33.6.1 Pipeline Optimizer

class PipelineOptimizer final {
public:
  void set_statistics(const Statistics* stats);
  void set_base_row_count(int64_t base_rows);

  auto optimize(ParsedPipeline& pipeline) -> void;

  auto get_cardinality_estimates(const ParsedPipeline& pipeline) const
      -> std::vector<CardinalityEstimate>;

private:
  auto apply_limit_hint_optimization_(ParsedPipeline& pipeline) -> void;
  auto apply_stage_reordering_(ParsedPipeline& pipeline) -> void;
  auto apply_stage_merging_(ParsedPipeline& pipeline) -> void;
  auto order_filter_predicates_(query::ast::Expression* filter) const -> void;
};

33.6.2 Top-K Optimization

Push LIMIT hints into SORT stages:

The sort operator uses the hint for heap-based Top-K instead of full sort:

$$C_{topk} = N \cdot \log K \quad \text{vs} \quad C_{sort} = N \cdot \log N$$

33.6.3 Predicate Ordering

Order conjunctive predicates by selectivity (most selective first):

auto order_filter_predicates_(query::ast::Expression* filter) const -> void {
  // For AND expressions, reorder operands by estimated cost/selectivity
  // Most selective (lowest selectivity) should be first for short-circuit
}

auto estimate_predicate_cost_(const query::ast::Expression* expr) const
    -> double {
  // Combine selectivity with evaluation cost
  auto selectivity = cardinality_estimator_.estimate_filter_selectivity(expr);
  auto eval_cost = estimate_eval_cost_(expr);
  return selectivity * eval_cost;
}

Evaluating the most selective predicate first maximizes short-circuit benefit.

33.7 Index Selection

33.7.1 Cost-Based Index Selection

struct IndexSelector {
  static auto select(const IndexDescriptor& index_desc, const Document& query,
                     const IndexStatisticsManager* stats_mgr = nullptr,
                     const LSMCostModel* lsm_cost = nullptr)
      -> std::shared_ptr<const Index>;
};

The selector compares:

1. Full Scan Cost: $C_{seq} = N \cdot c_{tuple}$
2. Index Scan Cost: $C_{idx} = \sigma \cdot N \cdot c_{idx} + \text{heap\_accesses}$

Index scan wins when:

$$\sigma \cdot c_{idx} + (1 - \text{coverage}) \cdot c_{heap} < c_{seq}$$

where coverage indicates index-only scan capability.

33.7.2 Selectivity Threshold

For low selectivity queries, full scan may be cheaper:

// Typical crossover point: ~5-10% selectivity
constexpr double kIndexScanThreshold = 0.1;

if (selectivity > kIndexScanThreshold) {
  // Full scan likely cheaper due to sequential I/O
  return nullptr;
}

33.8 JIT Compilation Cost Model

33.8.1 JIT Decision Framework

struct JITDecision {
  bool should_jit = false;

  enum class Reason {
    kNotEligible,    // Cannot be JIT compiled
    kTooFewRows,     // Not enough rows to justify compilation
    kTooSimple,      // No benefit for simple expressions
    kCostEffective,  // JIT is cost effective
    kAlwaysJIT,      // Complex enough to always JIT
    kCacheHit,       // Already compiled in cache
  };
  Reason reason;

  int64_t break_even_rows = 0;
  double expected_speedup = 1.0;
  double compilation_time_us = 0.0;
};

33.8.2 Break-Even Analysis

class CostModel {
public:
  auto decide(const AnalysisResult& analysis, int64_t estimated_rows) const
      -> JITDecision;

  // Per-row costs (nanoseconds)
  double interpret_cost_per_op = 5.0;   // Interpreter overhead
  double jit_cost_per_op = 0.5;         // Native code execution

  // Compilation costs (nanoseconds)
  double compilation_base_ns = 50000.0; // 50us base
  double compilation_per_op_ns = 1000.0; // 1us per operation
};

The break-even row count:

$$N_{break} = \frac{C_{compile}}{C_{interpret/row} - C_{jit/row}}$$

For an expression with $k$ operations:

$$N_{break} = \frac{50000 + 1000 \cdot k}{(5.0 - 0.5) \cdot k} = \frac{50000 + 1000k}{4.5k}$$

For $k = 10$ operations: $N_{break} \approx 1333$ rows.

33.8.3 JIT Configuration

struct JITConfig {
  static constexpr int32_t kMinOpsForJIT = 3;
  static constexpr int64_t kMinRowsForSimpleJIT = 1000;
  static constexpr int64_t kMinRowsForComplexJIT = 500;
  static constexpr int32_t kAlwaysJITOps = 10;

  static constexpr double kInterpretCostPerOp = 5.0;
  static constexpr double kJITCostPerOp = 0.5;
  static constexpr double kCompilationBaseNs = 50000.0;
  static constexpr double kCompilationPerOpNs = 1000.0;
};

The tiered approach:

• < 3 ops: Never JIT (overhead not justified)
• 3-9 ops: JIT if $N > 1000$ rows
• >= 10 ops: Always JIT (complexity warrants compilation)

33.9 IR Optimization Passes

33.9.1 Optimization Framework

class OptimizationPass {
public:
  virtual auto run(std::shared_ptr<ir::IRModule> module) -> OptimizationResult
      = 0;
  virtual auto name() const -> std::string = 0;
  virtual auto enabled_at_level(uint8_t level) const -> bool = 0;
};

struct OptimizerConfig {
  uint8_t optimization_level = 1;  // 0-3
  bool enable_constant_folding = true;
  bool enable_dce = true;
  bool enable_cse = true;
  bool enable_strength_reduction = true;
  size_t max_iterations = 10;
};

33.9.2 Constant Folding

Evaluate constant expressions at compile time:

class ConstantFoldingPass final : public OptimizationPass {
public:
  auto enabled_at_level(uint8_t level) const -> bool override {
    return level >= 1;  // O1 and above
  }

private:
  auto fold_binary_(const ir::IRBinaryOperation& node) -> ir::IRNodePtr {
    // 5 + 3 -> 8
    // "hello" || "world" -> "helloworld"
  }
};

33.9.3 Dead Code Elimination

Remove unreachable or unused code:

class DeadCodeEliminationPass final : public OptimizationPass {
public:
  auto enabled_at_level(uint8_t level) const -> bool override {
    return level >= 1;
  }

private:
  void mark_live_(const ir::IRNodePtr& node,
                  std::unordered_set<const ir::IRNode*>& live);

  auto eliminate_dead_(const ir::IRNodePtr& node,
                       const std::unordered_set<const ir::IRNode*>& live,
                       size_t& nodes_removed) -> ir::IRNodePtr;
};

33.9.4 Common Subexpression Elimination

class CSEPass final : public OptimizationPass {
public:
  auto enabled_at_level(uint8_t level) const -> bool override {
    return level >= 2;  // O2 and above
  }

private:
  auto hash_node_(const ir::IRNodePtr& node) const -> uint64_t;
  auto nodes_equal_(const ir::IRNodePtr& a, const ir::IRNodePtr& b) const
      -> bool;
};

33.9.5 Strength Reduction

Replace expensive operations with cheaper equivalents:

class StrengthReductionPass final : public OptimizationPass {
public:
  auto enabled_at_level(uint8_t level) const -> bool override {
    return level >= 2;
  }

private:
  auto reduce_node_(ir::IRNodePtr node) -> ir::IRNodePtr {
    // x * 2 -> x << 1
    // x / 4 -> x >> 2
    // x * 0 -> 0
    // x + 0 -> x
  }
};

33.9.6 Register Allocation

Map virtual registers to physical registers:

class RegisterAllocationPass final : public OptimizationPass {
public:
  struct Config {
    uint32_t num_general_registers = 16;
    uint32_t num_float_registers = 8;
    bool enable_coalescing = true;
  };

  auto enabled_at_level(uint8_t /*level*/) const -> bool override {
    return true;  // Always required for codegen
  }

private:
  void compute_liveness_(const ir::IRModule& module);
  void build_interference_graph_();
  void color_graph_();
  void coalesce_registers_();
};

The register allocator uses graph coloring with coalescing to minimize register-to-register moves.

33.10 Query Caching

33.10.1 Result Cache

class SQLQueryCache final {
public:
  SQLQueryCache(int32_t num_shards, size_t capacity_per_shard,
                uint64_t ttl_seconds, size_t max_result_size);

  auto get(const std::string& sql,
           const catalog::SchemaGenerationTracker* schema_tracker = nullptr)
      const noexcept -> std::optional<executor::ResultSet>;

  void put(const std::string& sql, const executor::ResultSet& result,
           std::unordered_set<std::string> table_dependencies,
           bool depends_on_schema = false,
           const catalog::SchemaGenerationTracker* schema_tracker = nullptr);

  auto invalidate_by_table(const std::string& table_name) -> size_t;

private:
  struct Shard {
    LRUCache<std::string, std::shared_ptr<CachedEntry>> cache;
    std::unordered_map<std::string, std::unordered_set<std::string>>
        table_to_keys;
    mutable threading::spinlock lock;
  };
  std::vector<std::unique_ptr<Shard>> shards_;
};

Key design decisions:

1. Sharding: Multiple shards reduce lock contention
2. TTL Expiration: Time-based invalidation for stale data
3. Table Dependencies: Eager invalidation on writes
4. Schema Generation: Cross-session invalidation for DDL

33.10.2 Plan Cache

struct PlanCacheEntry {
  std::shared_ptr<cvm::BytecodeModule> bytecode;
  std::vector<std::string> output_columns;
  std::unordered_map<uint8_t, db::document::Document> index_queries;
  uint64_t schema_generation = 0;
  std::vector<int32_t> parameter_oids;
  std::unordered_set<std::string> table_dependencies;
  uint64_t hit_count = 0;
};

class StatementPlanCache final {
public:
  auto get(uint64_t query_hash, const std::vector<int32_t>& parameter_oids,
           uint64_t current_schema_generation) -> PlanCacheEntry*;

  void put(PlanCacheEntry entry);

  auto invalidate_by_table(const std::string& table_name) -> size_t;
};

Plan cache key combines:

1. Query Fingerprint: Structural hash ignoring literals
2. Parameter OIDs: Type-specific compilation

inline auto combine_hash_with_params(uint64_t query_fingerprint,
                                     const std::vector<int32_t>& parameter_oids)
    -> uint64_t {
  constexpr uint64_t kFNV64Prime = 1099511628211ULL;
  auto combined = query_fingerprint;
  for (auto oid : parameter_oids) {
    combined ^= static_cast<uint64_t>(static_cast<uint32_t>(oid));
    combined *= kFNV64Prime;
  }
  return combined;
}

33.11 CVM Execution Configuration

33.11.1 Execution Modes

enum class CVMExecutionMode : uint8_t {
  kDisabled = 0,       // Traditional Volcano iterator
  kExpressionOnly = 1, // CVM for expressions only
  kFullQuery = 2,      // CVM for entire query
  kAuto = 3,           // Heuristic selection
};

33.11.2 Configuration Options

struct CVMExecutionOptions {
  CVMExecutionMode mode = CVMExecutionMode::kExpressionOnly;

  bool expression_enabled = true;
  bool full_query_enabled = false;

  size_t cache_max_entries = 4096;
  size_t cache_max_bytes = 16 * 1024 * 1024;  // 16MB

  int32_t min_ops_threshold = 1;
  int64_t min_rows_for_cvm = 100;

  size_t sort_memory_limit = 256 * 1024 * 1024;   // 256MB
  size_t hash_memory_limit = 256 * 1024 * 1024;
  size_t agg_memory_limit = 256 * 1024 * 1024;

  static constexpr size_t kDefaultBatchSize = 1024;
  static constexpr int64_t kMinRowsForParallelScan = 10000;
};

33.11.3 Execution Decision

auto should_use_cvm_for_expression() const -> bool {
  return expression_enabled
         && (mode == CVMExecutionMode::kExpressionOnly
             || mode == CVMExecutionMode::kFullQuery
             || mode == CVMExecutionMode::kAuto);
}

auto should_use_cvm_for_query() const -> bool {
  return full_query_enabled
         && (mode == CVMExecutionMode::kFullQuery
             || mode == CVMExecutionMode::kAuto);
}

33.12 Summary

Cognica's performance engineering infrastructure provides systematic optimization across the query lifecycle:

1. Multi-Dimensional Cost Model: Balances CPU, I/O, memory, and spill costs with empirically-tuned weights

2. LSM-Tree Aware I/O Estimation: Models read amplification characteristics of RocksDB storage for accurate index selection

3. Cardinality Estimation: Propagates row count estimates through operators using statistics-based selectivity

4. Query Plan Optimization: Applies predicate pushdown, join reordering, CSE, and redundant sort elimination

5. Pipeline Optimization: Top-K hints, predicate ordering, and stage merging reduce execution cost

6. JIT Compilation Cost Model: Break-even analysis determines when compilation overhead is justified

7. IR Optimization Passes: Constant folding, dead code elimination, CSE, and strength reduction at multiple optimization levels

8. Query Caching: Sharded result cache and plan cache with dependency-based invalidation

9. CVM Configuration: Tunable execution modes, memory limits, and parallelism settings

The performance engineering discipline emphasizes measurement-driven optimization. Cost models require calibration against real workloads; heuristics need validation against production queries. The infrastructure provides the foundation—accurate statistics, principled cost modeling, and systematic optimization—but effective performance tuning requires continuous measurement and refinement.

The interplay between compile-time optimization (query planning, IR passes) and runtime optimization (JIT compilation, adaptive execution) enables Cognica to handle diverse workloads efficiently. Simple queries execute with minimal overhead; complex analytical queries benefit from sophisticated optimization. This adaptive approach reflects the fundamental challenge of database performance engineering: no single strategy is optimal for all workloads.