Query Compilation - Learning Module

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Query Caching

Caching: The Great Performance Multiplier

We've established that query compilation and prepared statements eliminate redundant parsing and optimization within a session. But what happens when multiple users execute identical queries? When the database server restarts? When thousands of concurrent connections all need the same execution plan?

Query caching addresses these scenarios by storing and reusing query-related computations across a broader scope than individual sessions. Modern databases implement multiple layers of caching—from parsed ASTs to optimized plans to compiled code to actual result sets—each with distinct tradeoffs and use cases.

Query caching is not a single feature but an ecosystem of caching mechanisms that, when properly understood and configured, can transform database performance from acceptable to exceptional.

What You Will Learn

By the end of this page, you will understand the different types of query caches (plan cache, result cache, compiled code cache), how databases make cache lookup decisions, cache invalidation strategies, and how to monitor and tune cache behavior for optimal performance.

Types of Query Caches

Database systems implement multiple distinct caches, each storing different artifacts of query processing. Understanding these cache types is essential for effective performance tuning.

1. Parse Cache / AST Cache

Stores the Abstract Syntax Tree produced by parsing. Eliminates lexical analysis and syntax parsing for repeated query text. Least significant benefit since parsing is typically fast.

2. Plan Cache / Query Plan Cache

Stores optimized execution plans keyed by query text (or a normalized form). This is the most impactful cache—optimization is expensive, and plans are reusable across sessions.

3. Compiled Code Cache

For databases using JIT compilation, stores the compiled machine code or bytecode. Avoids repeated compilation overhead.

4. Result Cache / Query Result Cache

Stores actual query results. Most dramatic performance benefit when data is unchanged, but invalidation is complex.

5. Buffer Pool / Page Cache

Not strictly a query cache, but caches data pages in memory. Query execution benefits from hot data being in memory.

Query Cache Type Comparison
Cache Type	What's Stored	Key	Scope	Invalidation Complexity
Parse Cache	AST	Query text hash	Session/Global	Low (text changes)
Plan Cache	Execution plan	Normalized query + params	Global	Medium (schema/stats changes)
Compiled Code Cache	Machine code/bytecode	Plan hash	Global	Medium (plan changes)
Result Cache	Query output rows	Query + params	Global	High (data changes)
Buffer Pool	Data pages	Page ID	Global	Automatic (LRU)

The Caching Pyramid

Think of these caches as a pyramid. At the bottom is the buffer pool (caching raw data), then result cache (caching query outputs), then plan cache (caching how to execute), then code cache (caching compiled execution), and finally parse cache (caching parsing). Each layer above depends on the layers below, and invalidation propagates upward.

Plan Caching Deep Dive

Plan caching is the most critical query cache for OLTP workloads. Query optimization is computationally expensive—exploring join orders, evaluating access paths, estimating costs—and the same query shape often recurs with different parameter values.

How plan caching works:

Query arrives — Database receives SQL text
Normalization — Query is normalized (literals replaced with placeholders, whitespace standardized)
Cache lookup — Hash of normalized query checked against cache
Cache hit — Retrieve stored plan, bind parameters, execute
Cache miss — Parse, analyze, optimize; store plan in cache; execute

Query normalization example:

-- These queries normalize to the same cache key:
SELECT * FROM users WHERE id = 42;
SELECT * FROM users WHERE id = 123;
SELECT * FROM users WHERE id = 999999;

-- Normalized form:
SELECT * FROM users WHERE id = $1;

plan_cache_architecture.cpp
C++
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// Conceptual Plan Cache Implementation
 
#include <unordered_map>
#include <shared_mutex>
#include <memory>
#include <string>
 
struct ExecutionPlan {
    std::string normalized_query;
    std::vector<OperatorNode*> operators;
    double estimated_cost;
    int64_t creation_time;
    std::atomic<int64_t> execution_count{0};
    std::atomic<int64_t> total_execution_time{0};
};
 
class PlanCache {
private:
    std::unordered_map<size_t, std::shared_ptr<ExecutionPlan>> cache_;
    mutable std::shared_mutex mutex_;  // Read-write lock
    size_t max_entries_;
    std::atomic<int64_t> hits_{0};
    std::atomic<int64_t> misses_{0};
    
public:
    PlanCache(size_t max_entries) : max_entries_(max_entries) {}
    
    // Lookup by query hash - read lock only
    std::shared_ptr<ExecutionPlan> lookup(const std::string& query) {
        size_t hash = computeNormalizedHash(query);
        
        std::shared_lock lock(mutex_);  // Multiple readers OK
        
        auto it = cache_.find(hash);
        if (it != cache_.end()) {
            hits_++;
            return it->second;
        }
        
        misses_++;
        return nullptr;
    }
    
    // Insert new plan - write lock required
    void insert(const std::string& query, 
                std::shared_ptr<ExecutionPlan> plan) {
        size_t hash = computeNormalizedHash(query);
        
        std::unique_lock lock(mutex_);  // Exclusive access
        
        // Evict if at capacity
        if (cache_.size() >= max_entries_) {
            evictLeastValuable();
        }
        
        cache_[hash] = std::move(plan);
    }
    
    // Invalidate plans for a table (schema change)
    void invalidateTable(const std::string& table_name) {
        std::unique_lock lock(mutex_);
        
        for (auto it = cache_.begin(); it != cache_.end(); ) {
            if (planReferencesTable(it->second, table_name)) {
                it = cache_.erase(it);
            } else {
                ++it;
            }
        }
    }
    
    double hitRate() const {
        int64_t total = hits_ + misses_;
        return total > 0 ? (double)hits_ / total : 0.0;
    }
    
private:
    size_t computeNormalizedHash(const std::string& query) {
        // 1. Replace literals with placeholders
        // 2. Normalize whitespace
        // 3. Standardize case
        // 4. Compute hash
        std::string normalized = normalizeQuery(query);
        return std::hash<std::string>{}(normalized);
    }
    
    void evictLeastValuable() {
        // Eviction policy: LRU, execution frequency, or combined
        // This example uses lowest execution count
        auto victim = std::min_element(
            cache_.begin(), cache_.end(),
            [](const auto& a, const auto& b) {
                return a.second->execution_count < 
                       b.second->execution_count;
            }
        );
        if (victim != cache_.end()) {
            cache_.erase(victim);
        }
    }
};

Plan Cache Challenges

•Parameter sensitivity — A plan optimal for one parameter value may be terrible for another. Should we cache one generic plan or multiple specialized plans?
•Statistics staleness — Cached plans are based on statistics at optimization time. As data changes, plans may become suboptimal.
•Memory pressure — Plans consume memory. Too many cached plans can crowd out buffer pool space for actual data.
•Hash collisions — Different queries producing the same hash would share (wrong) plans. Quality hash functions are essential.
•Concurrent invalidation — Multiple sessions using a plan during schema changes require careful synchronization.

Result Caching

Result caching (also called query result cache or query cache) stores the actual output rows of queries. When an identical query is executed, the cached results are returned directly without accessing tables or executing plans.

The appeal: If the same query with the same parameters is executed repeatedly and the underlying data hasn't changed, why recompute?

The challenge: Knowing when cached results are stale. Any modification to tables referenced by the cached query invalidates the result.

Historical note: MySQL famously included a query cache (removed in MySQL 8.0) that became a scalability bottleneck. Every DML operation acquired a global lock to invalidate potentially-affected cached results. For write-heavy workloads, the cache caused more harm than good.

Modern result caching approaches are more sophisticated, using fine-grained invalidation or time-based expiration.

When Result Cache Works Well

•Read-heavy, write-rare workloads
•Queries with expensive computation (aggregations)
•Small result sets (fit in memory)
•Predictable query patterns
•Static or slowly changing data

When Result Cache Hurts

•Write-heavy workloads (constant invalidation)
•High query variety (low hit rates)
•Large result sets (memory pressure)
•Dynamic parameters (unique queries)
•Real-time data requirements

oracle_result_cache.sql
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-- Oracle Database Result Cache Examples
 
-- Enable result cache for a specific query using hint
SELECT /*+ RESULT_CACHE */ 
    department_id,
    COUNT(*) as employee_count,
    AVG(salary) as avg_salary
FROM employees
WHERE hire_date > DATE '2020-01-01'
GROUP BY department_id;
 
-- The first execution computes results; subsequent executions return cached
 
-- Create a table with result cache enabled by default
CREATE TABLE product_catalog (
    product_id    NUMBER PRIMARY KEY,
    name          VARCHAR2(100),
    price         NUMBER,
    category_id   NUMBER
) RESULT_CACHE (MODE FORCE);
 
-- Queries on this table are automatically cached
SELECT * FROM product_catalog WHERE category_id = 5;
 
-- Monitor result cache usage
SELECT 
    name,
    value
FROM v$result_cache_statistics;
 
-- View cached results
SELECT 
    id,
    type,
    status,
    name,
    scan_count,
    row_count,
    invalidations
FROM v$result_cache_objects
WHERE type = 'Result';
 
-- Manually invalidate all cached results
EXEC DBMS_RESULT_CACHE.FLUSH;
 
-- Invalidate results for specific table
EXEC DBMS_RESULT_CACHE.INVALIDATE('SCHEMA', 'PRODUCT_CATALOG');

Cache Key Design

The effectiveness of any cache depends on its key design. For query caches, the key must uniquely identify "the same query" while maximizing hit rates.

Components of a cache key:

Normalized query text — Query with literals replaced by placeholders
Schema version/hash — Ensures cache invalidation on DDL
Parameter types — Different types may produce different plans
Session settings — search_path, date format, transaction isolation
Execution context — Role/user for permission-dependent optimization

Normalization trade-offs:

Aggressive normalization (replacing all literals) maximizes cache hits but may produce suboptimal plans for skewed data distributions. Conservative normalization (keeping some literals) reduces hit rates but allows parameter-specific optimization.

query_normalization.py
Python
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# Query Normalization for Cache Key Generation
 
import re
import hashlib
from typing import Tuple
 
def normalize_query(sql: str) -> Tuple[str, list]:
    """
    Normalize a SQL query for cache key generation.
    Returns (normalized_query, extracted_literals).
    
    Same logical query with different literal values should 
    produce identical normalized forms.
    """
    
    # Step 1: Normalize whitespace
    normalized = ' '.join(sql.split())
    
    # Step 2: Convert to uppercase (SQL is case-insensitive)
    normalized = normalized.upper()
    
    # Step 3: Extract and replace string literals
    #         'hello' -> $1, 'world' -> $2
    literals = []
    string_pattern = r"'[^']*'"
    
    def replace_string_literal(match):
        literals.append(match.group(0))
        return f"${len(literals)}"
    
    normalized = re.sub(string_pattern, replace_string_literal, normalized)
    
    # Step 4: Extract and replace numeric literals
    #         42 -> $N, 3.14 -> $M
    number_pattern = r'd+.?d*'
    
    def replace_number_literal(match):
        # Skip if part of identifier (e.g., table1)
        literals.append(match.group(0))
        return f"${len(literals)}"
    
    normalized = re.sub(number_pattern, replace_number_literal, normalized)
    
    # Step 5: Normalize IN lists (order doesn't matter for caching)
    #         IN (3, 1, 2) -> IN ($PARAMS)
    in_pattern = r'INs*(s*($d+(?:s*,s*$d+)*)s*)'
    normalized = re.sub(in_pattern, 'IN ($PARAMS)', normalized)
    
    return normalized, literals
 
 
def compute_cache_key(
    normalized_sql: str,
    schema_version: int,
    search_path: str,
    user_id: int
) -> str:
    """
    Compute deterministic cache key from query and context.
    """
    key_components = [
        normalized_sql,
        str(schema_version),
        search_path,
        str(user_id)
    ]
    
    key_string = '|'.join(key_components)
    return hashlib.sha256(key_string.encode()).hexdigest()
 
 
# Example usage:
queries = [
    "SELECT * FROM users WHERE id = 42",
    "SELECT * FROM users WHERE id = 999",
    "select * from USERS where ID = 1",  # Different case
    "SELECT * FROM users  WHERE  id = 42",  # Extra whitespace
]
 
for q in queries:
    normalized, literals = normalize_query(q)
    print(f"Original:   {q}")
    print(f"Normalized: {normalized}")
    print(f"Literals:   {literals}")
    print()
 
# All produce: "SELECT * FROM USERS WHERE ID = $1"
# -> Same cache key, same cached plan

Parameter Sniffing Problems

When a plan is cached based on the first execution's parameters, subsequent executions with different parameter values may suffer. For example, a plan optimized for status='active' (99% of rows) may be terrible for status='deleted' (1% of rows). This is called 'parameter sniffing' and is a major challenge in plan caching.

Cache Invalidation Strategies

As the famous saying goes: "There are only two hard things in Computer Science: cache invalidation and naming things."

Query cache invalidation must balance correctness (never serving stale data) with efficiency (minimizing unnecessary invalidation).

Events requiring invalidation:

For Plan Caches:

Schema changes (DDL): CREATE, ALTER, DROP
Statistics updates (ANALYZE)
Index creation or removal
Configuration changes affecting optimization

For Result Caches:

All of the above, plus:
Data modifications (DML): INSERT, UPDATE, DELETE
Underlying view changes
Time-based expiration (for time-sensitive data)

Invalidation Strategy Comparison
Strategy	How It Works	Pros	Cons
Immediate/Eager	Invalidate on every DML	Always correct	High overhead; lock contention
Lazy/Deferred	Mark invalid; purge later	Lower overhead	Stale data window
Time-Based (TTL)	Expire after duration	Predictable; simple	May serve stale; may expire valid
Dependency Tracking	Track table→query deps	Precise invalidation	Complex; memory overhead
Version-Based	Schema/data version numbers	Lockless; fast	Requires version infrastructure

cache_invalidation_patterns.py
Python
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# Cache Invalidation Patterns
 
from typing import Dict, Set
from dataclasses import dataclass, field
from threading import RLock
import time
 
@dataclass
class CachedPlan:
    query_hash: str
    plan: object
    tables_referenced: Set[str]
    created_at: float
    schema_version: int
    execution_count: int = 0
 
class PlanCacheWithInvalidation:
    """
    Plan cache with dependency-based invalidation.
    """
    
    def __init__(self, ttl_seconds: float = 3600):
        self._cache: Dict[str, CachedPlan] = {}
        self._table_to_plans: Dict[str, Set[str]] = {}  # Reverse index
        self._lock = RLock()
        self._ttl = ttl_seconds
        self._current_schema_version = 1
    
    def get(self, query_hash: str) -> Optional[object]:
        with self._lock:
            entry = self._cache.get(query_hash)
            if entry is None:
                return None
            
            # Check TTL
            if time.time() - entry.created_at > self._ttl:
                self._remove_entry(query_hash)
                return None
            
            # Check schema version
            if entry.schema_version != self._current_schema_version:
                self._remove_entry(query_hash)
                return None
            
            entry.execution_count += 1
            return entry.plan
    
    def put(
        self, 
        query_hash: str, 
        plan: object, 
        tables: Set[str]
    ):
        with self._lock:
            entry = CachedPlan(
                query_hash=query_hash,
                plan=plan,
                tables_referenced=tables,
                created_at=time.time(),
                schema_version=self._current_schema_version
            )
            
            self._cache[query_hash] = entry
            
            # Maintain reverse index for invalidation
            for table in tables:
                if table not in self._table_to_plans:
                    self._table_to_plans[table] = set()
                self._table_to_plans[table].add(query_hash)
    
    def invalidate_table(self, table_name: str):
        """Invalidate all plans that reference a table."""
        with self._lock:
            affected_plans = self._table_to_plans.get(table_name, set())
            for query_hash in list(affected_plans):
                self._remove_entry(query_hash)
    
    def invalidate_schema_change(self):
        """Invalidate all plans (schema DDL occurred)."""
        with self._lock:
            self._current_schema_version += 1
            # Lazy invalidation: plans will fail version check on next access
            # Alternative: clear all immediately
            # self._cache.clear()
            # self._table_to_plans.clear()
    
    def _remove_entry(self, query_hash: str):
        entry = self._cache.pop(query_hash, None)
        if entry:
            for table in entry.tables_referenced:
                self._table_to_plans.get(table, set()).discard(query_hash)
 
 
# Usage with database events
cache = PlanCacheWithInvalidation()
 
# Query execution
plan = cache.get("hash_abc123")
if plan is None:
    plan = optimize_query(sql)
    tables = extract_table_references(sql)
    cache.put("hash_abc123", plan, tables)
 
# On UPDATE/INSERT/DELETE to 'orders' table
cache.invalidate_table("orders")
 
# On CREATE INDEX / ALTER TABLE
cache.invalidate_schema_change()

Database-Specific Cache Implementations

Each major database system implements query caching differently. Understanding these implementations helps in tuning and troubleshooting.

PostgreSQL:

Plans cached per-connection for prepared statements
Optional global cache via extensions (e.g., pg_query_cache)
Generic plans vs. custom plans (plan_cache_mode parameter)
JIT compilation cached per-session

SQL Server:

Plan Cache stored in buffer pool memory
Plans cached globally, shared across connections
Parameterization (simple vs. forced) affects cache keys
Query Store for historical plan analysis

Oracle:

Shared Pool contains Library Cache (for plans)
Cursor sharing (SIMILAR, EXACT, FORCE) controls parameterization
Result Cache for query outputs
Bind variable peeking for initial optimization

MySQL/InnoDB:

Query cache removed in 8.0 (scalability issues)
Prepared statement cache per-connection
InnoDB buffer pool for data caching
Performance Schema for monitoring

postgresql_cache_config.sql
SQL
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-- PostgreSQL Plan Cache Configuration
 
-- Check prepared statement cache for current session
SELECT name, statement, prepare_time, parameter_types
FROM pg_prepared_statements;
 
-- Generic vs. Custom Plans
-- PostgreSQL creates custom plans for first 5 executions,
-- then switches to generic plan if cost is similar
 
-- Force generic plans (ignore parameter values for planning)
SET plan_cache_mode = 'force_generic_plan';
 
-- Force custom plans (re-optimize for each execution)
SET plan_cache_mode = 'force_custom_plan';
 
-- Automatic (default) - switch based on cost comparison
SET plan_cache_mode = 'auto';
 
-- JIT compilation settings
SET jit = on;
SET jit_above_cost = 100000;  -- Only JIT expensive queries
SET jit_inline_above_cost = 500000;
SET jit_optimize_above_cost = 500000;
 
-- Memory for JIT compilation cache
-- (per-session, part of work_mem)
 
-- View pg_stat_statements for query statistics
SELECT 
    substring(query, 1, 50) as query_preview,
    calls,
    mean_exec_time as avg_ms,
    rows,
    shared_blks_hit,
    shared_blks_read,
    (shared_blks_hit::float / nullif(shared_blks_hit + shared_blks_read, 0)) * 100 
        as cache_hit_pct
FROM pg_stat_statements
ORDER BY calls DESC
LIMIT 10;

Monitoring and Tuning Cache Performance

Effective cache tuning requires continuous monitoring and adjustment based on workload characteristics. Key metrics to track:

Cache Hit Rate:

The percentage of requests satisfied from cache. High hit rates (>90%) indicate effective caching; low rates suggest poor cache key design, insufficient size, or volatile data.

Cache Size and Utilization:

Total cache memory and percentage used. Under-utilized caches waste memory; over-utilized caches cause eviction pressure.

Eviction Rate:

How frequently entries are evicted. High eviction rates with high utilization indicate cache is too small.

Invalidation Rate:

How often entries are invalidated. High rates may indicate write-heavy workloads poorly suited for result caching.

Plan Compilation Time:

Time spent optimizing queries. If significant despite caching, investigate cache misses.

cache_monitoring_dashboard.sql
PostgreSQL
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-- PostgreSQL Cache Monitoring Dashboard
 
-- 1. Buffer Cache Hit Rate (data pages)
SELECT 
    sum(heap_blks_hit) as heap_cache_hits,
    sum(heap_blks_read) as heap_disk_reads,
    sum(heap_blks_hit) / 
        nullif(sum(heap_blks_hit) + sum(heap_blks_read), 0) * 100 
        as heap_cache_hit_ratio,
    sum(idx_blks_hit) as index_cache_hits,
    sum(idx_blks_read) as index_disk_reads,
    sum(idx_blks_hit) / 
        nullif(sum(idx_blks_hit) + sum(idx_blks_read), 0) * 100 
        as index_cache_hit_ratio
FROM pg_statio_all_tables;
 
-- 2. Shared Buffers usage
SELECT 
    pg_size_pretty(
        count(*) * (SELECT setting::int FROM pg_settings WHERE name = 'block_size')
    ) as total_buffer_usage,
    count(*) as buffers_in_use
FROM pg_buffercache
WHERE relfilenode IS NOT NULL;
 
-- 3. Most frequently executed queries (candidates for optimization)
SELECT 
    substring(query, 1, 80) as query_preview,
    calls,
    mean_exec_time::numeric(10,2) as avg_ms,
    total_exec_time::numeric(10,2) as total_ms,
    rows,
    100.0 * shared_blks_hit / 
        nullif(shared_blks_hit + shared_blks_read, 0) as cache_hit_pct
FROM pg_stat_statements
ORDER BY calls DESC
LIMIT 20;
 
-- 4. Queries with poor cache utilization
SELECT 
    substring(query, 1, 80) as query_preview,
    calls,
    shared_blks_read as disk_reads,
    shared_blks_hit as cache_hits,
    100.0 * shared_blks_hit / 
        nullif(shared_blks_hit + shared_blks_read, 0) as cache_hit_pct
FROM pg_stat_statements
WHERE shared_blks_read > 1000
ORDER BY shared_blks_read DESC
LIMIT 10;
 
-- 5. JIT compilation statistics
SELECT 
    sum(jit_functions) as total_jit_functions,
    sum(jit_generation_time) as total_generation_ms,
    sum(jit_optimization_time) as total_optimization_ms,
    sum(jit_emission_time) as total_emission_ms
FROM pg_stat_statements
WHERE jit_functions > 0;

Cache Tuning Checklist

•Monitor hit rates continuously — Establish baselines and alert on significant drops.
•Size caches appropriately — Balance with working set size; too small causes eviction, too large wastes memory.
•Analyze cache miss patterns — Are misses from new queries, invalidation, or eviction?
•Tune invalidation granularity — Fine-grained invalidation preserves more cache entries but costs complexity.
•Consider workload patterns — Read-heavy benefits from result caching; write-heavy may hurt.
•Test with production-like data — Cache behavior depends on data distribution and query patterns.
•Warm caches after restart — Consider cache priming for critical queries.

Summary: Query Caching

We've explored the multi-layered world of query caching—from parse caches to plan caches to result caches. Let's consolidate the key insights:

Key Takeaways

•Multiple cache layers exist — Parse, plan, compiled code, and result caches each serve distinct purposes with different trade-offs.
•Plan caching provides the highest impact — Optimization is expensive; caching plans amortizes this cost across many executions.
•Cache keys require careful design — Query normalization, schema versions, and session context all affect cache key effectiveness.
•Invalidation is complex but critical — Over-invalidation wastes cache; under-invalidation serves stale data.
•Result caching suits specific workloads — Read-heavy, stable data benefits; write-heavy workloads suffer from constant invalidation.
•Database implementations vary — PostgreSQL, SQL Server, Oracle, and MySQL each implement caching differently; understand your platform.
•Monitoring reveals tuning opportunities — Hit rates, eviction rates, and invalidation patterns guide optimization.

What's next:

Query caching ensures we don't repeat expensive work. But cached plans must still be applicable to new parameter values. The next page explores plan reuse—how databases decide when a cached plan is suitable, when to generate fresh plans, and how to handle parameter-sensitive queries.

Page Complete

You now have comprehensive knowledge of query caching mechanisms in database systems. You understand the different cache types, their purposes, invalidation strategies, and how to monitor and tune cache performance. Next, we'll explore plan reuse strategies.