+-----------------------------------------------------------------------------------------+
|                  Distributed Message Broker Matrix (2026)                               |
+-----------------------------------------------------------------------------------------+

APACHE KAFKA (KRaft Era - The Industry Ecosystem Titan)
- Architecture: Monolithic (Broker + Local Storage Partitions).
- Language: Java / Scala (JVM) with KRaft Quorum Controller.
- Best for: Massive enterprise deployments requiring the deepest connector ecosystem (Flink, Debezium, dbt).

REDPANDA (The C++ Thread-Per-Core Speed King)
- Architecture: Monolithic Single Binary with Seastar Thread-Per-Core async engine.
- Language: Pure C++20 (Zero JVM! Zero Garbage Collection!).
- Best for: Ultra-low tail latency (P99 < 2ms), NVMe hardware saturation, zero DevOps complexity.

APACHE PULSAR (The Disaggregated Cloud-Native Powerhouse)
- Architecture: Two-Tier Disaggregated (Stateless Brokers + Stateful Apache BookKeeper).
- Language: Java (JVM) with Segment-Based Storage Ledgers.
- Best for: Multi-tenant SaaS platforms, independent storage vs compute scaling, native geo-replication.

+-----------------------------------------------------------------------------------------+
|                  Messaging Architecture Mechanics (2026)                                |
+-----------------------------------------------------------------------------------------+

KAFKA (Thread Pool + Page Cache):
[Client Requests] ───> [JVM Thread Pool] ───> [OS Page Cache] ───> [Disk Controller]
* Flaw: JVM context switching + GC pauses create tail latency spikes under heavy load.

REDPANDA (Thread-Per-Core on Seastar Engine):
[Core 0: Pinned Thread] <===> [Local RAM Buffer] <===> [Dedicated NVMe Queue]
[Core 1: Pinned Thread] <===> [Local RAM Buffer] <===> [Dedicated NVMe Queue]
* Zero Thread Contention: Each CPU core operates an independent, shared-nothing engine!

PULSAR (Disaggregated Compute & Storage):
[STATELESS BROKER TIER] (Handles Client TCP Connections, Pub/Sub & Routing)
         │
         ▼ (Writes segment ledgers over network)
[STATEFUL BOOKKEEPER STORAGE NODES] ───> [Offloads Cold Data to Amazon S3 / GCS]

// producer/high_throughput_producer.go
package main

import (
	"context"
	"fmt"
	"log"
	"time"

	"github.com/twmb/franz-go/pkg/kgo"
)

func main() {
	// 1. Configure High-Performance Franz-Go Client for Redpanda
	seeds := []string{"redpanda-0.internal.mojostudio.in:9092", "redpanda-1.internal.mojostudio.in:9092"}
	
	client, err := kgo.NewClient(
		kgo.SeedBrokers(seeds...),
		kgo.DefaultProduceTopic("financial.transactions.v1"),
		// Optimize Batching for NVMe Saturation:
		kgo.ProducerBatchMaxBytes(1024 * 1024), // 1MB Max Batch
		kgo.ProducerLinger(2 * time.Millisecond),
		kgo.RequiredAcks(kgo.AllISRAcks()), // Guarantees Zero Data Loss!
	)
	if err != nil {
		log.Fatalf("Failed to initialize Kafka client: %v", err)
	}
	defer client.Close()

	log.Println("🚀 Publishing 500,000 streaming events to Redpanda cluster...")

	start := time.Now()
	ctx := context.Background()

	// 2. Asynchronous High-Throughput Publishing
	for i := 0; i < 500000; i++ {
		record := &kgo.Record{
			Key:   []byte(fmt.Sprintf("user_%d", i%1000)),
			Value: []byte(fmt.Sprintf(`{"tx_id": "tx_%d", "amount": 99.50, "ts": %d}`, i, time.Now().UnixNano())),
		}

		client.Produce(ctx, record, func(r *kgo.Record, err error) {
			if err != nil {
				log.Printf("❌ Failed to deliver record: %v", err)
			}
		})
	}

	// 3. Flush In-Flight Batches to Disk
	if err := client.Flush(ctx); err != nil {
		log.Fatalf("Failed to flush records: %v", err)
	}

	duration := time.Since(start)
	throughput := float64(500000) / duration.Seconds()
	log.Printf("✅ Published 500,000 events in %v (Average Throughput: %.0f msgs/sec)!", duration, throughput)
}

+-----------------------------------------------------------------------------------------+
|                  Tiered Storage Architecture (Redpanda / Kafka KIP-405)                 |
+-----------------------------------------------------------------------------------------+

[LOCAL NVMe SSD (Hot Data - Last 24 Hours)]:
- Sub-millisecond reads for real-time consumers (Flink, Spark, Webhooks).
- High IOPS, premium cloud storage cost ($0.10/GB/month).
        │
        ▼ (Automated background segment archival to Object Storage)
[AMAZON S3 / GCS OBJECT STORAGE (Cold Data - Retained for 7 Years!)]:
- Cost: $0.015/GB/month (85% Cost Reduction!).
- Seamlessly queryable by historical replay consumers without impacting real-time traffic!

+-----------------------------------------------------------------------------------------+
|                  2026 Message Broker Selection Playbook                                 |
+-----------------------------------------------------------------------------------------+
| CHOOSE REDPANDA WHEN:                                                                   |
| - Low, deterministic tail latency (P99 < 2ms) is critical (Fintech, AdTech, Gaming).   |
| - You want zero JVM tuning, zero GC pauses, and a single lightweight binary deployment. |
| - You want 100% Kafka API compatibility with significantly lower cloud infrastructure TCO|
+-----------------------------------------------------------------------------------------+
| CHOOSE APACHE KAFKA (KRAFT) WHEN:                                                       |
| - Your enterprise is heavily invested in the mature Kafka Connect / Schema ecosystem.  |
| - You have dedicated Java platform engineers to optimize JVM and OS page cache tuning. |
| - Standardization across existing enterprise contracts is the highest priority.        |
+-----------------------------------------------------------------------------------------+
| CHOOSE APACHE PULSAR WHEN:                                                              |
| - You require true multi-tenancy with hierarchical tenant/namespace resource isolation.|
| - Scaling storage independently from compute is a non-negotiable architectural need.   |
| - Native geo-replication across multiple continents is required out-of-the-box.         |
+-----------------------------------------------------------------------------------------+

       +-------------------------------------------------------------+
       |             End-to-End P99 Latency (Milliseconds)           |
       +-------------------------------------------------------------+
 Apache Pulsar (BookKeeper Quorum Writes)| ==================================== [24.5 ms]
 Apache Kafka 4.0 (KRaft + JVM ZGC)      | ======================= [14.8 ms]
 Redpanda (C++ Thread-Per-Core Engine)   | == [1.6 ms] (9x Lower Tail Latency!)
                                         +-------------------------------------+
                                         0ms     5ms     10ms    15ms    20ms

       +-------------------------------------------------------------+
       |             Broker Infrastructure Footprint (CPU / RAM)     |
       +-------------------------------------------------------------+
 Apache Pulsar (Brokers + BookKeeper + Zk)| ==================================== [24 Cores / 64 GB]
 Apache Kafka (3 Brokers + KRaft Quorum)  | ========================= [16 Cores / 32 GB]
 Redpanda (3 Nodes Single Binary)         | ====== [6 Cores / 8 GB] (4x More Efficient!)
                                         +-------------------------------------+
                                         0       10      20      30      40