Senior Engineer Wajib Tahu CQRS

Senior Engineer Wajib Tahu CQRS — Bikin Search 100× Lebih Cepat 🚀

Pencarian katalog produk yang awalnya cepat tiba-tiba jadi ~3 DETIK saat data
menyentuh 1 juta baris. Di video ini kita bedah masalahnya dari akar, lalu
pelan-pelan sampai ke pola CQRS (Command Query Responsibility Segregation) dan
mendemokannya LANGSUNG dengan benchmark nyata.

Yang penting: kita TIDAK buru-buru ganti arsitektur. Kita optimalkan dulu
PostgreSQL-nya (pg_trgm), ukur ulang, dan baru pindah ke CQRS SAAT optimasi
benar-benar mentok. Ini disiplin engineering yang sering dilewati.

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YANG AKAN KAMU PELAJARI

• Kenapa LIKE '%kata%' + facet GROUP BY tidak skala di data besar
• Optimasi PostgreSQL dengan index trigram (pg_trgm) — TANPA ubah kode aplikasi
• Batas optimasi database: kapan satu index sudah cukup, kapan tidak
• Pola CQRS: memisahkan model tulis (Command) dari model baca (Query)
• Sinkronisasi write → read pakai CDC (Debezium → Kafka) — tanpa dual-write
• Read model OpenSearch: n-gram (mirip LIKE tapi terindeks) & facet drill-down
• Trade-off eventual consistency — kapan worth it, kapan tidak
• Benchmark 3 engine berdampingan: PostgreSQL naif vs PG+trigram vs OpenSearch

================================
BENCHMARK (1 juta produk, search + facet)

• PostgreSQL naif : ~3.230 ms 🐌
• PostgreSQL+trigram: ~1.102 ms
• OpenSearch : ~21 ms ⚡ (facet nyaris gratis — satu pass agregasi)
Hasil pencarian IDENTIK (parity terverifikasi) — bukan "hasil beda makanya cepat".

================================
CHAPTERS

00:00 Pendahuluan
00:29 Studi Kasus: Pencarian Katalog Produk
02:50 Pendekatan Awal (Satu Database Saja)
03:44 Masalah Pendekatan Awal (Lambat di Skala Besar)
08:26 Kenapa Bisa Selambat Itu?
10:07 Sebelum Pindah ke CQRS
10:43 Optimalisasi Database (pg_trgm)
18:11 Batas Optimalisasi Database
23:00 Command Query Responsibility Segregation
25:17 Konsekuensi CQRS
27:22 Arsitektur CQRS
35:38 Kenapa OpenSearch?
40:08 Cara Membuat Facet
44:04 Eventual Consistency
47:20 Benchmark Demo
50:46 Kesimpulan

================================
TECH STACK

• product-backend — Java 25, Spring Boot 4, PostgreSQL (write model + CRUD)
• Debezium + Kafka — Kafka Connect (CDC), Kafka KRaft (sinkronisasi write → read)
• product-search — Spring Boot 4, Spring Kafka (projector + Search API)
• OpenSearch 3 — read model (search + facet)
• product-frontend — SvelteKit + nginx (UI + toggle 3 engine)
• product-faker — Bun (generator data demo)
Semua dijalankan via Docker / Podman Compose.

================================
COBA SENDIRI

Repo: https://github.com/ProgrammerZamanNow/cqrs-demo

podman compose up -d --build # naikkan seluruh stack
make register # daftarkan Debezium connector
# buka http://localhost:3000 # toggle PostgreSQL / PG+trigram / OpenSearch

================================
TAKEAWAY

Optimalkan dulu yang ada; pakai CQRS saat memang mentok. Database yang tepat
untuk pekerjaan yang tepat — tanpa mengorbankan sumber kebenaran.

Kalau video ini membantu, jangan lupa LIKE, SUBSCRIBE, dan share ke tim kamu 🙏
Punya pertanyaan atau request topik? Tulis di kolom komentar!

#CQRS #SystemDesign #SoftwareEngineering #OpenSearch #PostgreSQL #Kafka
#Debezium #SpringBoot #Java #BackendDevelopment #Microservices #DatabaseOptimization
#ProgrammerZamanNow Receive SMS online on sms24.me

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