Senior & Lead Engineer
Interview Mastery Hub

The Cache Stampede Problem

When a hot cache item expires, hundreds of incoming concurrent threads miss the cache at the exact same millisecond and all attempt to compute the expensive query and write back to cache simultaneously.

Solution 1: Cache::flexible() in Modern Laravel

Laravel provides stale-while-revalidate out of the box:

$stats = Cache::flexible('homepage_stats', [300, 600], function () {
    return DB::table('orders')->selectRaw('count(*), sum(amount)')->first();
});
// Serves stale cache instantly while dispatching background regeneration after 300s

Solution 2: Atomic Cache Locking (Cache::lock)

$data = Cache::get('homepage_stats');

if (! $data) {
    $lock = Cache::lock('lock:homepage_stats', 10);
    if ($lock->get()) {
        try {
            $data = ComputeExpensiveStats();
            Cache::put('homepage_stats', $data, now()->addMinutes(10));
        } finally {
            $lock->release();
        }
    } else {
        // Sleep briefly and read cache populated by the winning thread
        usleep(200000);
        $data = Cache::get('homepage_stats');
    }
}

File-Level Scope of strict_types

declare(strict_types=1); is per-file and affects function calls made FROM that specific file, not the file declaring the function.

• If File A has strict_types=1 and calls calculateTax("100.50"), PHP throws TypeError.
• If File B does NOT have strict_types=1 and calls the same calculateTax("100.50"), PHP coerces the string to float 100.50.
• Best practice: Enforce declare(strict_types=1); across all project files via code standards tools (PHP-CS-Fixer / Pint).

Covering Index (Using index)

When all columns requested in SELECT, WHERE, and ORDER BY exist within the secondary index itself, MySQL retrieves data directly from the index in RAM without touching table row data on disk.

CREATE INDEX idx_users_org_email_date ON users (org_id, email, created_at);

Query: SELECT email, created_at FROM users WHERE org_id = 10; is 100% satisfied by the index.

Analyzing BUFFERS Metrics

EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM accounts WHERE balance > 50000;

• shared hit=5000: 5,000 data blocks were read directly from RAM (Shared Buffers cache) with zero disk latency.
• shared read=8000: 8,000 blocks had to be fetched from physical disk or OS filesystem cache.

Exponential Backoff with Jitter

$retryDelay = min(3600, pow(2, $attemptCount) * 10 + rand(0, 5));

• Attempt 1: 20s
• Attempt 2: 40s
• Attempt 3: 80s
• Max Retries Exceeded: Route the failed payload to a Dead Letter Queue (DLQ) for automated alerting and manual developer replay.

Comparison Matrix

Profiling Methodology

1. Instrument with SPX / Xhprof: Generates a Call Graph tracking:
   • Wall time vs CPU time
   • Memory peak allocations per function call
   • Function invocation count
2. Common Culprits:
   • Deep nested loops hydrating arrays into domain objects.
   • Repetitive serialization / JSON parsing.
   • Autoloader filesystem IO when APCu/OPcache is misconfigured.

ACID Isolation Levels

Configuring postgres_fdw

CREATE EXTENSION postgres_fdw;

CREATE SERVER billing_server
    FOREIGN DATA WRAPPER postgres_fdw
    OPTIONS (host '10.0.0.50', dbname 'billing_db', port '5432');

CREATE USER MAPPING FOR current_user
    SERVER billing_server
    OPTIONS (user 'db_reader', password 'secret');

IMPORT FOREIGN SCHEMA public FROM SERVER billing_server INTO foreign_billing;

-- Query local and remote tables seamlessly in a single SQL statement
SELECT c.name, b.invoice_total 
FROM customers c
JOIN foreign_billing.invoices b ON c.id = b.customer_id;

Sharded Counters / Write-Back Buffering

1. Redis Atomic Increment:

   Redis::incr("video:views:{$videoId}");
   

2. Batch Flusher Daemon: Every 10 seconds, a background worker consumes the aggregated count from Redis via INCRBY and writes a single consolidated SQL update:

   UPDATE videos SET views = views + 5000 WHERE id = 5;
   

Reduces database write operations by 99.9%.

Redis Cluster Hash Tag Pinning

In Redis Cluster, commands operating on multiple keys (like atomic queue pops) require all keys to reside on the same cluster node. Use Redis Hash Tags {horizon}:

'prefix' => '{horizon}:',

This forces all related Horizon state keys into the same hash slot, eliminating MOVED cluster exceptions while scaling workers seamlessly across nodes.

The Persistent Connection Danger in PHP-FPM

With PDO::ATTR_PERSISTENT, each PHP-FPM worker process keeps its own persistent database connection open even after the request terminates.

• If you have 500 PHP-FPM workers across 4 web servers, that equals 2,000 persistent idle database connections.
• Solution: Use dedicated external connection poolers (like ProxySQL for MySQL or PgBouncer for PostgreSQL) rather than in-process persistent PDO connections.

Analyzing LATEST DETECTED DEADLOCK

Run:

SHOW ENGINE INNODB STATUS\G

Look for:

• (1) TRANSACTION vs (2) TRANSACTION
• LOCK WAIT statements
• RECORD LOCKS space id ... lock_mode X
• Identify which transaction was chosen as the deadlock victim and rolled back based on the lowest number of modified rows.

PITR Recovery Workflow

1. Restore the most recent physical base backup (e.g. pgBackRest / WAL-G).
2. Set recovery_target_time = '2026-08-27 14:32:05 UTC'.
3. PostgreSQL replays archived WAL segments up to the exact target second and halts replay before the destructive command.

Consistent Hashing Ring

1. Maps both Cache Servers and Keys onto a 360-degree circular hash ring (e.g. 0 to $2^{32}-1$).
2. A key is assigned to the nearest server located clockwise on the ring.
3. When a server is added or removed, only $K/N$ keys need remapping, preventing total cache thrashing.
4. Virtual Nodes: Assigns multiple virtual points per physical server to ensure even distribution across nodes.

Idempotency Key Middleware Implementation

class EnsureIdempotency
{
    public function handle(Request $request, Closure $next): Response
    {
        $idempotencyKey = $request->header('Idempotency-Key');
        if (! $idempotencyKey) {
            return response()->json(['error' => 'Idempotency-Key header is required'], 400);
        }

        $cacheKey = "idempotency:{$request->user()->id}:{$idempotencyKey}";
        $lock = Cache::lock("lock:{$cacheKey}", 15);

        if (! $lock->get()) {
            return response()->json(['error' => 'Concurrent request in progress'], 409);
        }

        if ($cachedResponse = Cache::get($cacheKey)) {
            $lock->release();
            return response()->json($cachedResponse['data'], $cachedResponse['status']);
        }

        $response = $next($request);

        if ($response->isSuccessful()) {
            Cache::put($cacheKey, [
                'data' => json_decode($response->getContent(), true),
                'status' => $response->getStatusCode(),
            ], now()->addHours(24));
        }

        $lock->release();
        return $response;
    }
}

match vs switch Differences

1. Strict Comparison: match uses === identity matching; switch uses loose == comparison.
2. Returns a Value: match is an expression that evaluates directly to a result.
3. No Fallthrough: match does not require break; statements.
4. Exhaustive Matching: Throws UnhandledMatchError if no branch matches and no default is specified.

Why InnoDB Cannot Store an Exact Row Count

Because InnoDB uses MVCC (Multi-Version Concurrency Control), different concurrent transactions see different numbers of visible rows at the exact same moment.

• MyISAM locked tables and stored a single counter, but did not support transactions.
• Solution for Large Tables: Use Redis cached counters or query information_schema.TABLES.TABLE_ROWS for approximations.

Querying pg_stat_statements

SELECT query, calls, total_exec_time / 1000 as total_seconds,
       mean_exec_time, rows
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 5;

Provides normalized query text, execution counts, and total CPU time consumed.

Protocol Comparison