Write-Through Caching - Learning Module

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Consistency Benefits

Why Consistency Is the Ultimate Feature

In the world of distributed caching, consistency is not just a property—it's a promise to your users. When a customer updates their shipping address, they expect to see that change immediately. When a trader places an order, they expect the system to reflect the current state. When a doctor updates a patient's medication, lives may depend on that change being visible everywhere, immediately.

Write-through caching exists precisely because some data cannot tolerate staleness. While other caching patterns optimize for performance or throughput, write-through optimizes for correctness. It provides guarantees that other patterns cannot match, making it indispensable for certain classes of applications.

This page examines the specific consistency benefits of write-through caching, compares them to alternatives, and helps you understand when these guarantees are worth their performance cost.

What You Will Learn

By the end of this page, you will understand the precise consistency guarantees of write-through caching, how they compare to other caching patterns, and how to evaluate whether your application requires these stronger guarantees.

The Consistency Spectrum

Before diving into write-through's specific benefits, let's establish a framework for understanding consistency in caching. Consistency exists on a spectrum, and different patterns occupy different positions.

Caching Patterns and Their Consistency Characteristics
Caching Pattern	Staleness Window	Data Loss Risk	Read-After-Write	Complexity
Write-Through	Milliseconds (cache latency)	None (DB is source of truth)	✓ Guaranteed	Low
Write-Back (Write-Behind)	Seconds to minutes	High (data in cache only)	✓ Guaranteed	High
Cache-Aside (Lazy Loading)	Until TTL expiration	None (read-only cache)	✗ Not guaranteed	Low
Write-Around	Until TTL expiration	None (DB is source of truth)	✗ Not guaranteed	Low
Refresh-Ahead	Configurable	None	✗ Depends on timing	Medium

Understanding the Staleness Window:

The staleness window is the period during which a client might read stale data after a write:

Write-Through: ~1-5ms (just the cache write latency after DB confirms)
Write-Back: Unbounded until async flush occurs (could be seconds or minutes)
Cache-Aside: From 0 to full TTL duration (could be hours)
Write-Around: From immediate read (cache miss) to TTL (cache hit with stale data)

For many applications, a staleness window of hours (cache-aside) is acceptable. For others, even seconds (write-back) is too long. Write-through provides the tightest staleness window achievable without exotic solutions.

The Consistency-Performance Trade-off

There's no free lunch. Write-through's strong consistency comes at the cost of write latency. The question isn't which is better—it's which trade-off is right for your use case. Critical financial data? Write-through. User preference caching? Cache-aside is probably fine.

Core Consistency Guarantees

Write-through caching provides several fundamental consistency guarantees. Let's examine each in detail.

Guarantee 1: Read-After-Write Consistency

When a client writes data and then reads it, they are guaranteed to see their own write. This is the most important guarantee for interactive applications.

Client A: WRITE user.name = 'Alice' @ T0
Client A: READ user.name @ T1 (T1 > T0)
→ GUARANTEED to return 'Alice'

Why it works: The write operation doesn't return until both database and cache are updated. When the subsequent read occurs, the cache already contains the new value.

Why it matters:

Users see their changes immediately in the UI
Follow-up operations can rely on the written data
Eliminates confusing "my change didn't save" experiences

Common violation scenario (in cache-aside): User updates profile, page refreshes, cache still has old data because invalidation hasn't propagated. User thinks save failed, clicks save again. Now you have duplicate operations.

Guarantee 2: No Orphaned Cache Entries

Data in the cache always exists in the database. You cannot have cache entries without corresponding database records.

Invariant: ∀ key ∈ Cache: key ∈ Database

"Everything in cache is also in the database"

Why it works: The database write happens first. If it fails, the cache is never updated. The only way data enters the cache is through a successful database write.

Why it matters:

No phantom data that exists only in volatile cache
Cache loss (restart, eviction) never loses data permanently
Simplifies recovery—just repopulate cache from database
No complex reconciliation needed between cache and database

Guarantee 3: Bounded Staleness

The maximum staleness of any cache entry is bounded by a known, small duration (the cache write latency).

Staleness ≤ cache_write_latency + network_delay
         ≈ 1-10 milliseconds (typical)

Why it works: The only inconsistency window is between database write completion and cache write completion—a window of milliseconds.

Why it matters:

Staleness is predictable and measurable
You can make SLA commitments about data freshness
Debugging is simpler—if data is stale, you know the maximum age
No surprising multi-hour staleness from forgotten cache entries

The Subtle Guarantee

Note that write-through guarantees eventual consistency between cache and database, not between different clients. Client B may not immediately see Client A's write if there's a timing overlap. For cross-client consistency, you need additional mechanisms (versioning, locks, or sequential ordering).

Comparing to Other Caching Patterns

To fully appreciate write-through's consistency benefits, let's compare specific scenarios across different caching patterns.

Scenario: User Updates Their Email Address

Write-Through:

1. User submits new email: alice_new@email.com
2. Server writes to database (waits for confirmation)
3. Server updates cache with new email
4. Server returns success to user
5. Any subsequent request shows new email immediately

Result: Consistent everywhere, immediately

Cache-Aside:

1. User submits new email: alice_new@email.com
2. Server writes to database
3. Server invalidates cache entry (or does nothing, waiting for TTL)
4. Server returns success to user
5. User's next request may hit cache with old email (if not yet invalidated)
6. User sees old email, thinks update failed

Result: Potentially stale until cache expires or is invalidated

Write-Back:

1. User submits new email: alice_new@email.com
2. Server updates cache immediately
3. Server returns success to user
4. Background process eventually writes to database
5. If server crashes before flush, update is LOST

Result: Immediate visibility but durability risk

Write-Through Advantages

•Durability first — Data is in database before user sees success
•Immediate consistency — Cache always reflects latest state
•Simple mental model — One path for all writes
•No invalidation complexity — Cache is always updated, not invalidated
•Cache loss is safe — Database has all data

What You Give Up

•Write latency — Every write waits for database
•Write throughput — Limited by database capacity
•Database dependency — Writes fail if database is slow/down
•No write buffering — Can't absorb traffic bursts
•More cache writes — Every DB write also writes to cache

Decision Framework:

Choose Write-Through when:

Data correctness is more important than write performance
Users need to immediately see their changes
You cannot tolerate any data loss
The data has high read-to-write ratio (reads benefit from cache, writes are less frequent)

Choose Write-Back when:

Write performance is critical (high write throughput)
Some data loss is acceptable (analytics, metrics)
You can handle the complexity of async flushing

Choose Cache-Aside when:

Data can tolerate minutes/hours of staleness
The cache is purely a performance optimization
You want to minimize cache complexity

Consistency in Practice: Real-World Scenarios

Let's examine how write-through's consistency benefits apply to real-world systems.

Scenario 1: E-Commerce Inventory

The Problem: A product has 1 unit in stock. Two customers try to buy it simultaneously.

With write-through:

T0: Customer A initiates purchase
T1: System reads stock from cache: 1
T2: System decrements in database: 0
T3: System updates cache: 0
T4: Customer A sees success

T5: Customer B initiates purchase
T6: System reads stock from cache: 0
T7: System rejects: out of stock
T8: Customer B sees failure

Even in this race condition, the database serializes the inventory update, and the cache immediately reflects the new state. Customer B is correctly rejected.

Without write-through (cache-aside):

T0: Customer A initiates purchase
T1: System reads stock from cache: 1
T2: Customer B initiates purchase
T3: System reads stock from cache: 1 (still not invalidated)
T4: Both customers proceed with purchase
T5: Database: Two decrements attempted on 1 unit
T6: Either both succeed (oversold!) or complex conflict resolution needed

Cache-aside requires additional mechanisms (distributed locks, optimistic concurrency) to prevent overselling.

Scenario 2: User Session Management

The Problem: User changes their password and should be logged out of all other sessions.

With write-through:

1. User submits new password
2. System updates password in database
3. System updates session invalidation key in cache
4. All subsequent requests check invalidation key
5. Old sessions are immediately rejected

The synchronous cache update ensures that the security change is immediately effective.

With eventual consistency:

1. User submits new password
2. System updates password in database
3. Invalidation message queued for cache
4. Old sessions continue working until cache updates
5. Security vulnerability window: minutes?

In security-sensitive contexts, even brief inconsistency windows can be exploited.

Scenario 3: Financial Account Balance

The Problem: User withdraws money; balance must be accurate to prevent overdraft.

With write-through:

1. Request: Withdraw $100 from account with $150 balance
2. Read balance from cache: $150 (or DB if cache miss)
3. DB transaction: Check balance ($150) ≥ $100, deduct, new balance = $50
4. Update cache: $50
5. Return success

6. Next request reads cache: $50 (correct)

The database transaction provides atomicity and the write-through ensures the cache is immediately consistent.

This is why banks use write-through: They cannot show a balance that differs from the actual account state.

The Cache-Database Contract

In all these scenarios, the consistency guarantee only holds if ALL writes go through the caching layer. Direct database modifications (admin scripts, batch jobs) break the contract. Establish organizational discipline or use database triggers to maintain cache consistency.

Measuring Consistency

To ensure your write-through implementation delivers its promised consistency, you need to measure and monitor consistency metrics.

consistency_monitoring.ts
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class ConsistencyMonitor {
  private metrics: MetricsClient;
 
  async verifyReadAfterWrite<T>(
    key: string,
    writeFn: () => Promise<T>,
    options: { sampleRate: number } = { sampleRate: 0.01 }
  ): Promise<T> {
    // Write the data
    const written = await writeFn();
    
    // Sample-based consistency check (don't check every write)
    if (Math.random() < options.sampleRate) {
      await this.checkConsistency(key, written);
    }
    
    return written;
  }
 
  private async checkConsistency<T>(key: string, expected: T): Promise<void> {
    // Small delay to allow cache propagation
    await this.sleep(10);  // 10ms
    
    // Read from cache
    const cached = await this.cache.get<T>(key);
    
    // Read from database
    const dbValue = await this.database.get<T>(key);
    
    // Check cache matches expected
    const cacheMatch = this.deepEquals(cached, expected);
    
    // Check cache matches database
    const dbCacheMatch = this.deepEquals(cached, dbValue);
    
    // Record metrics
    this.metrics.recordGauge('consistency.cache_match', cacheMatch ? 1 : 0);
    this.metrics.recordGauge('consistency.db_cache_match', dbCacheMatch ? 1 : 0);
    
    if (!cacheMatch) {
      this.metrics.incrementCounter('consistency.read_after_write_violations');
      this.logger.error('Read-after-write violation', {
        key,
        expected,
        actual: cached,
        database: dbValue,
      });
    }
    
    if (!dbCacheMatch) {
      this.metrics.incrementCounter('consistency.cache_db_mismatch');
      this.logger.error('Cache-DB mismatch', {
        key,
        cached,
        database: dbValue,
      });
    }
  }
 
  // Periodic full consistency audit
  async runConsistencyAudit(sampleSize: number = 1000): Promise<AuditResults> {
    const keys = await this.getRandomCacheKeys(sampleSize);
    let matches = 0;
    let mismatches = 0;
    const violations: Violation[] = [];
 
    for (const key of keys) {
      const cached = await this.cache.get(key);
      const dbValue = await this.database.getByKey(key);
      
      if (this.deepEquals(cached, dbValue)) {
        matches++;
      } else {
        mismatches++;
        violations.push({
          key,
          cached,
          database: dbValue,
          detectedAt: new Date(),
        });
      }
    }
 
    const results = {
      totalChecked: sampleSize,
      matches,
      mismatches,
      consistencyRate: matches / sampleSize,
      violations,
    };
 
    this.metrics.recordGauge(
      'consistency.audit_rate', 
      results.consistencyRate
    );
 
    if (results.consistencyRate < 0.999) {
      this.alerting.trigger('ConsistencyDegradation', results);
    }
 
    return results;
  }
}

Key Metrics to Track:

Consistency Metrics for Write-Through
Metric	Description	Target	Alert Threshold
read_after_write_success_rate	% of read-after-write checks that pass	99.99%	<99.9%
cache_db_consistency_rate	% of sampled keys where cache=DB	99.99%	<99.9%
cache_write_failure_rate	% of cache writes that fail	<0.1%	1%
staleness_p99	99th percentile staleness duration	<10ms	100ms
write_through_latency_p99	End-to-end write latency	<50ms	200ms

Continuous Verification

Don't rely solely on the write-through machinery. Implement continuous consistency verification in production. Sample-based checking catches edge cases, bugs, and external modifications that could violate consistency.

Strengthening Consistency Guarantees

While write-through provides strong guarantees by default, certain applications need even stronger consistency. Here are techniques to enhance the base guarantees.

Technique 1: Synchronous Cache Verification

After writing to cache, read back and verify before returning success:

verified_write.ts
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async function verifiedWriteThrough<T>(
  key: string, 
  value: T
): Promise<T> {
  // Database write
  const dbResult = await database.write(key, value);
  
  // Cache write
  await cache.set(key, dbResult);
  
  // Verify cache write succeeded
  const cached = await cache.get<T>(key);
  if (!deepEquals(cached, dbResult)) {
    // Cache write didn't take; retry once
    await cache.set(key, dbResult);
    const retryRead = await cache.get<T>(key);
    
    if (!deepEquals(retryRead, dbResult)) {
      // Log and alert, but return success (DB has the data)
      logger.error('Cache verification failed', { key, expected: dbResult, actual: retryRead });
      metrics.increment('cache_verification_failures');
    }
  }
  
  return dbResult;
}

Technique 2: Cross-Client Consistency with Leader Election

For systems requiring that all clients see writes in the same order:

1. Elect a single writer for each partition/key range
2. All writes for a key go through the elected leader
3. Leader performs write-through
4. Followers receive updates via replication or subscription

This serializes writes, providing total ordering at the cost of write throughput.

Technique 3: Database Trigger-Based Cache Invalidation

For situations where external processes modify the database:

-- PostgreSQL example
CREATE OR REPLACE FUNCTION notify_cache_update()
RETURNS trigger AS $$
BEGIN
  PERFORM pg_notify('cache_invalidation', 
    json_build_object(
      'table', TG_TABLE_NAME,
      'operation', TG_OP,
      'key', NEW.id,
      'timestamp', now()
    )::text
  );
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER users_cache_trigger
AFTER INSERT OR UPDATE OR DELETE ON users
FOR EACH ROW EXECUTE FUNCTION notify_cache_update();

A background service listens for these notifications and updates the cache, ensuring even direct database modifications are reflected.

Complexity Warning

Each technique adds complexity and potential failure modes. Only implement stronger guarantees when business requirements genuinely demand them. The base write-through pattern is sufficient for most applications.

Summary: Consistency Benefits

We've explored the consistency benefits of write-through caching in depth. Let's consolidate the key insights:

Key Takeaways

•Read-after-write consistency — Writers always see their own writes immediately, eliminating user confusion
•No orphaned cache entries — Everything in cache exists in database; cache loss is recoverable
•Bounded staleness — Maximum inconsistency window is milliseconds, not hours
•Simplicity vs alternatives — Write-through provides better consistency than cache-aside with less complexity than write-back
•Real-world necessity — Inventory, sessions, financial data, and security changes require these guarantees
•Measurable consistency — Implement monitoring to verify guarantees are maintained in production

What's Next:

While write-through provides strong consistency benefits, these come with performance costs. The next page examines the performance trade-offs in detail, helping you understand the latency and throughput implications and how to mitigate them.

Page Complete

You now understand why consistency is write-through's defining advantage—and when that advantage is worth the performance cost. Next, we'll examine those costs in detail and explore strategies to minimize their impact.

Consistency Benefits

Why Consistency Is the Ultimate Feature

This page examines the specific consistency benefits of write-through caching, compares them to alternatives, and helps you understand when these guarantees are worth their performance cost.

What You Will Learn

The Consistency Spectrum

Caching Patterns and Their Consistency Characteristics
Caching Pattern	Staleness Window	Data Loss Risk	Read-After-Write	Complexity
Write-Through	Milliseconds (cache latency)	None (DB is source of truth)	✓ Guaranteed	Low
Write-Back (Write-Behind)	Seconds to minutes	High (data in cache only)	✓ Guaranteed	High
Cache-Aside (Lazy Loading)	Until TTL expiration	None (read-only cache)	✗ Not guaranteed	Low
Write-Around	Until TTL expiration	None (DB is source of truth)	✗ Not guaranteed	Low
Refresh-Ahead	Configurable	None	✗ Depends on timing	Medium

Understanding the Staleness Window:

The staleness window is the period during which a client might read stale data after a write:

Write-Through: ~1-5ms (just the cache write latency after DB confirms)
Write-Back: Unbounded until async flush occurs (could be seconds or minutes)
Cache-Aside: From 0 to full TTL duration (could be hours)
Write-Around: From immediate read (cache miss) to TTL (cache hit with stale data)

The Consistency-Performance Trade-off

Core Consistency Guarantees

Write-through caching provides several fundamental consistency guarantees. Let's examine each in detail.

Guarantee 1: Read-After-Write Consistency

When a client writes data and then reads it, they are guaranteed to see their own write. This is the most important guarantee for interactive applications.

Client A: WRITE user.name = 'Alice' @ T0
Client A: READ user.name @ T1 (T1 > T0)
→ GUARANTEED to return 'Alice'

Why it works: The write operation doesn't return until both database and cache are updated. When the subsequent read occurs, the cache already contains the new value.

Why it matters:

Users see their changes immediately in the UI
Follow-up operations can rely on the written data
Eliminates confusing "my change didn't save" experiences

Guarantee 2: No Orphaned Cache Entries

Data in the cache always exists in the database. You cannot have cache entries without corresponding database records.

Invariant: ∀ key ∈ Cache: key ∈ Database

"Everything in cache is also in the database"

Why it works: The database write happens first. If it fails, the cache is never updated. The only way data enters the cache is through a successful database write.

Why it matters:

No phantom data that exists only in volatile cache
Cache loss (restart, eviction) never loses data permanently
Simplifies recovery—just repopulate cache from database
No complex reconciliation needed between cache and database

Guarantee 3: Bounded Staleness

The maximum staleness of any cache entry is bounded by a known, small duration (the cache write latency).

Staleness ≤ cache_write_latency + network_delay
         ≈ 1-10 milliseconds (typical)

Why it works: The only inconsistency window is between database write completion and cache write completion—a window of milliseconds.

Why it matters:

Staleness is predictable and measurable
You can make SLA commitments about data freshness
Debugging is simpler—if data is stale, you know the maximum age
No surprising multi-hour staleness from forgotten cache entries

The Subtle Guarantee

Comparing to Other Caching Patterns

To fully appreciate write-through's consistency benefits, let's compare specific scenarios across different caching patterns.

Scenario: User Updates Their Email Address

Write-Through:

1. User submits new email: alice_new@email.com
2. Server writes to database (waits for confirmation)
3. Server updates cache with new email
4. Server returns success to user
5. Any subsequent request shows new email immediately

Result: Consistent everywhere, immediately

Cache-Aside:

1. User submits new email: alice_new@email.com
2. Server writes to database
3. Server invalidates cache entry (or does nothing, waiting for TTL)
4. Server returns success to user
5. User's next request may hit cache with old email (if not yet invalidated)
6. User sees old email, thinks update failed

Result: Potentially stale until cache expires or is invalidated

Write-Back:

1. User submits new email: alice_new@email.com
2. Server updates cache immediately
3. Server returns success to user
4. Background process eventually writes to database
5. If server crashes before flush, update is LOST

Result: Immediate visibility but durability risk

Write-Through Advantages

•Durability first — Data is in database before user sees success
•Immediate consistency — Cache always reflects latest state
•Simple mental model — One path for all writes
•No invalidation complexity — Cache is always updated, not invalidated
•Cache loss is safe — Database has all data

What You Give Up

•Write latency — Every write waits for database
•Write throughput — Limited by database capacity
•Database dependency — Writes fail if database is slow/down
•No write buffering — Can't absorb traffic bursts
•More cache writes — Every DB write also writes to cache

Decision Framework:

Choose Write-Through when:

Data correctness is more important than write performance
Users need to immediately see their changes
You cannot tolerate any data loss
The data has high read-to-write ratio (reads benefit from cache, writes are less frequent)

Choose Write-Back when:

Write performance is critical (high write throughput)
Some data loss is acceptable (analytics, metrics)
You can handle the complexity of async flushing

Choose Cache-Aside when:

Data can tolerate minutes/hours of staleness
The cache is purely a performance optimization
You want to minimize cache complexity

Consistency in Practice: Real-World Scenarios

Let's examine how write-through's consistency benefits apply to real-world systems.

Scenario 1: E-Commerce Inventory

The Problem: A product has 1 unit in stock. Two customers try to buy it simultaneously.

With write-through:

T0: Customer A initiates purchase
T1: System reads stock from cache: 1
T2: System decrements in database: 0
T3: System updates cache: 0
T4: Customer A sees success

T5: Customer B initiates purchase
T6: System reads stock from cache: 0
T7: System rejects: out of stock
T8: Customer B sees failure

Even in this race condition, the database serializes the inventory update, and the cache immediately reflects the new state. Customer B is correctly rejected.

Without write-through (cache-aside):

T0: Customer A initiates purchase
T1: System reads stock from cache: 1
T2: Customer B initiates purchase
T3: System reads stock from cache: 1 (still not invalidated)
T4: Both customers proceed with purchase
T5: Database: Two decrements attempted on 1 unit
T6: Either both succeed (oversold!) or complex conflict resolution needed

Cache-aside requires additional mechanisms (distributed locks, optimistic concurrency) to prevent overselling.

Scenario 2: User Session Management

The Problem: User changes their password and should be logged out of all other sessions.

With write-through:

1. User submits new password
2. System updates password in database
3. System updates session invalidation key in cache
4. All subsequent requests check invalidation key
5. Old sessions are immediately rejected

The synchronous cache update ensures that the security change is immediately effective.

With eventual consistency:

1. User submits new password
2. System updates password in database
3. Invalidation message queued for cache
4. Old sessions continue working until cache updates
5. Security vulnerability window: minutes?

In security-sensitive contexts, even brief inconsistency windows can be exploited.

Scenario 3: Financial Account Balance

The Problem: User withdraws money; balance must be accurate to prevent overdraft.

With write-through:

1. Request: Withdraw $100 from account with $150 balance
2. Read balance from cache: $150 (or DB if cache miss)
3. DB transaction: Check balance ($150) ≥ $100, deduct, new balance = $50
4. Update cache: $50
5. Return success

6. Next request reads cache: $50 (correct)

The database transaction provides atomicity and the write-through ensures the cache is immediately consistent.

This is why banks use write-through: They cannot show a balance that differs from the actual account state.

The Cache-Database Contract

Measuring Consistency

To ensure your write-through implementation delivers its promised consistency, you need to measure and monitor consistency metrics.

consistency_monitoring.ts
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class ConsistencyMonitor {
  private metrics: MetricsClient;
 
  async verifyReadAfterWrite<T>(
    key: string,
    writeFn: () => Promise<T>,
    options: { sampleRate: number } = { sampleRate: 0.01 }
  ): Promise<T> {
    // Write the data
    const written = await writeFn();
    
    // Sample-based consistency check (don't check every write)
    if (Math.random() < options.sampleRate) {
      await this.checkConsistency(key, written);
    }
    
    return written;
  }
 
  private async checkConsistency<T>(key: string, expected: T): Promise<void> {
    // Small delay to allow cache propagation
    await this.sleep(10);  // 10ms
    
    // Read from cache
    const cached = await this.cache.get<T>(key);
    
    // Read from database
    const dbValue = await this.database.get<T>(key);
    
    // Check cache matches expected
    const cacheMatch = this.deepEquals(cached, expected);
    
    // Check cache matches database
    const dbCacheMatch = this.deepEquals(cached, dbValue);
    
    // Record metrics
    this.metrics.recordGauge('consistency.cache_match', cacheMatch ? 1 : 0);
    this.metrics.recordGauge('consistency.db_cache_match', dbCacheMatch ? 1 : 0);
    
    if (!cacheMatch) {
      this.metrics.incrementCounter('consistency.read_after_write_violations');
      this.logger.error('Read-after-write violation', {
        key,
        expected,
        actual: cached,
        database: dbValue,
      });
    }
    
    if (!dbCacheMatch) {
      this.metrics.incrementCounter('consistency.cache_db_mismatch');
      this.logger.error('Cache-DB mismatch', {
        key,
        cached,
        database: dbValue,
      });
    }
  }
 
  // Periodic full consistency audit
  async runConsistencyAudit(sampleSize: number = 1000): Promise<AuditResults> {
    const keys = await this.getRandomCacheKeys(sampleSize);
    let matches = 0;
    let mismatches = 0;
    const violations: Violation[] = [];
 
    for (const key of keys) {
      const cached = await this.cache.get(key);
      const dbValue = await this.database.getByKey(key);
      
      if (this.deepEquals(cached, dbValue)) {
        matches++;
      } else {
        mismatches++;
        violations.push({
          key,
          cached,
          database: dbValue,
          detectedAt: new Date(),
        });
      }
    }
 
    const results = {
      totalChecked: sampleSize,
      matches,
      mismatches,
      consistencyRate: matches / sampleSize,
      violations,
    };
 
    this.metrics.recordGauge(
      'consistency.audit_rate', 
      results.consistencyRate
    );
 
    if (results.consistencyRate < 0.999) {
      this.alerting.trigger('ConsistencyDegradation', results);
    }
 
    return results;
  }
}

Key Metrics to Track:

Consistency Metrics for Write-Through
Metric	Description	Target	Alert Threshold
read_after_write_success_rate	% of read-after-write checks that pass	99.99%	<99.9%
cache_db_consistency_rate	% of sampled keys where cache=DB	99.99%	<99.9%
cache_write_failure_rate	% of cache writes that fail	<0.1%	1%
staleness_p99	99th percentile staleness duration	<10ms	100ms
write_through_latency_p99	End-to-end write latency	<50ms	200ms

Continuous Verification

Strengthening Consistency Guarantees

While write-through provides strong guarantees by default, certain applications need even stronger consistency. Here are techniques to enhance the base guarantees.

Technique 1: Synchronous Cache Verification

After writing to cache, read back and verify before returning success:

verified_write.ts
TypeScript
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async function verifiedWriteThrough<T>(
  key: string, 
  value: T
): Promise<T> {
  // Database write
  const dbResult = await database.write(key, value);
  
  // Cache write
  await cache.set(key, dbResult);
  
  // Verify cache write succeeded
  const cached = await cache.get<T>(key);
  if (!deepEquals(cached, dbResult)) {
    // Cache write didn't take; retry once
    await cache.set(key, dbResult);
    const retryRead = await cache.get<T>(key);
    
    if (!deepEquals(retryRead, dbResult)) {
      // Log and alert, but return success (DB has the data)
      logger.error('Cache verification failed', { key, expected: dbResult, actual: retryRead });
      metrics.increment('cache_verification_failures');
    }
  }
  
  return dbResult;
}

Technique 2: Cross-Client Consistency with Leader Election

For systems requiring that all clients see writes in the same order:

1. Elect a single writer for each partition/key range
2. All writes for a key go through the elected leader
3. Leader performs write-through
4. Followers receive updates via replication or subscription

This serializes writes, providing total ordering at the cost of write throughput.

Technique 3: Database Trigger-Based Cache Invalidation

For situations where external processes modify the database:

-- PostgreSQL example
CREATE OR REPLACE FUNCTION notify_cache_update()
RETURNS trigger AS $$
BEGIN
  PERFORM pg_notify('cache_invalidation', 
    json_build_object(
      'table', TG_TABLE_NAME,
      'operation', TG_OP,
      'key', NEW.id,
      'timestamp', now()
    )::text
  );
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER users_cache_trigger
AFTER INSERT OR UPDATE OR DELETE ON users
FOR EACH ROW EXECUTE FUNCTION notify_cache_update();

A background service listens for these notifications and updates the cache, ensuring even direct database modifications are reflected.

Complexity Warning

Summary: Consistency Benefits

We've explored the consistency benefits of write-through caching in depth. Let's consolidate the key insights:

Key Takeaways

•Read-after-write consistency — Writers always see their own writes immediately, eliminating user confusion
•No orphaned cache entries — Everything in cache exists in database; cache loss is recoverable
•Bounded staleness — Maximum inconsistency window is milliseconds, not hours
•Simplicity vs alternatives — Write-through provides better consistency than cache-aside with less complexity than write-back
•Real-world necessity — Inventory, sessions, financial data, and security changes require these guarantees
•Measurable consistency — Implement monitoring to verify guarantees are maintained in production

What's Next:

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