A senior engineer’s perspective on building highly available distributed systems tabel konten Introduksi: Mengapa Dynamo Mengubah Segalanya Pengertian Theorem Trade-off Core Architecture Components Consistent Hashing for Partitioning Replication Strategy (N, R, W) Vector Clocks for Versioning Sloppy Quorum and Hinted Handoff Conflict Resolution: The Shopping Cart Problem Membaca dan Menulis Flow Pohon Merkle untuk Anti-Entropy Keanggotaan dan deteksi kegagalan Karakteristik Performa: Angka Real Strategi Evolusi Partisi Comparing Dynamo to Modern Systems Apa yang tidak ditawarkan Dynamo Practical Implementation Example Key Lessons for System Design Kapan Tidak Menggunakan Sistem Dinamo-Style Kesimpulan Lampiran: Masalah Desain dan Pendekatan Ini adalah referensi bentuk panjang - setiap bagian berdiri sendiri, jadi bebas untuk melompat langsung ke apa pun yang paling relevan untuk Anda. Ini adalah referensi bentuk panjang - setiap bagian berdiri sendiri, jadi bebas untuk melompat langsung ke apa pun yang paling relevan untuk Anda. Introduksi: Mengapa Dynamo Mengubah Segalanya When Amazon published the Dynamo paper in 2007, it wasn’t just another academic exercise. It was a battle-tested solution to real problems at massive scale. I remember when I first read this paper—it fundamentally changed how I thought about distributed systems. Ini dirancang untuk mendukung layanan lalu lintas tinggi Amazon seperti keranjang belanja dan sistem manajemen sesi. Tidak ada indeks sekunder, tidak ada gabungan, tidak ada semantik relasional - hanya kunci dan nilai, dengan fokus ekstrim pada ketersediaan dan skalabilitas. Ini tidak memberikan jaminan linearisasi atau pemesanan global, bahkan pada pengaturan kuorum tertinggi. Dynamo is a distributed key-value storage system. Masalah inti yang dihadapi Amazon sederhana untuk menyatakan tetapi brutal untuk memecahkan: Ketika seseorang mencoba menambahkan item ke keranjang belanja mereka selama partisi jaringan atau kegagalan server, menolak menulis itu tidak dapat diterima. How do you build a storage system that never says “no” to customers? The CAP Theorem Trade-off: Mengapa Dynamo Memilih Ketersediaan Sebelum menyelam ke dalam bagaimana Dynamo bekerja, Anda perlu memahami pembatasan fundamental yang dirancang di sekitarnya. What is CAP Theorem? Teorema CAP menggambarkan kompromi fundamental dalam sistem terdistribusi: ketika partisi jaringan terjadi, Anda harus memilih antara konsistensi dan ketersediaan. : All nodes see the same data at the same time Consistency (C) Availability (A): Setiap permintaan mendapatkan jawaban (sukses atau kegagalan) Toleransi Partisi (P): Sistem terus bekerja meskipun gagal jaringan Pendekatan umum adalah “pilih 2 dari 3,” tetapi ini adalah over-simplification. dalam praktek, partisi jaringan tidak dapat dihindari dalam skala, jadi keputusan sebenarnya adalah: Itulah pilihan desain yang sebenarnya. when partitions occur (and they will), do you sacrifice consistency or availability? Partisi jaringan akan terjadi. kabel dipotong, switch gagal, pusat data kehilangan konektivitas. Anda tidak dapat menghindari mereka, jadi Anda harus memilih: Konsistensi atau Ketersediaan? The harsh reality Basis Data Tradisional Memilih Konsistensi : Traditional approach Database: "I can't guarantee all replicas are consistent, so I'll reject your write to be safe." Result: Customer sees error, cart is empty Impact: Lost revenue, poor experience Dynamo Chooses Availability : Dynamo’s approach Dynamo: "I'll accept your write with the replicas I can reach. The unreachable replica will catch up later." Result: Customer sees success, item in cart Impact: Sale continues, happy customer Perdagangan yang Visualisasi When a partition occurs: Traditional Database: Choose C over A → Sacrifice Availability - ✓ All replicas always have same data - ✓ No conflicts to resolve - ❌ Rejects writes during failures - ❌ Poor customer experience - ❌ Lost revenue Dynamo: Choose A over C → Sacrifice Strong Consistency - ✓ Accepts writes even during failures - ✓ Excellent customer experience - ✓ No lost revenue - ❌ Replicas might temporarily disagree - ❌ Application must handle conflicts Contoh Amazon yang nyata: Keranjang belanja Black Friday Bayangkan itu Black Friday. Jutaan pelanggan sedang berbelanja. Sebuah kabel jaringan dipotong antara pusat data. : With traditional database Time: 10:00 AM - Network partition occurs Result: - All shopping cart writes fail - "Service Unavailable" errors - Customers can't checkout - Twitter explodes with complaints - Estimated lost revenue: $100,000+ per minute : With Dynamo Time: 10:00 AM - Network partition occurs Result: - Shopping cart writes continue - Customers see success - Some carts might have conflicts (rare) - Application merges conflicting versions - Estimated lost revenue: $0 - A few edge cases need conflict resolution (acceptable) Mengapa pilihan ini masuk akal untuk e-commerce Amazon melakukan matematika: Biaya penolakan menulis: Penjualan yang hilang segera ($ 50-200) Biaya menerima tulisan yang bertentangan: Kadang-kadang perlu menggabungkan keranjang belanja (selalunya terjadi, mudah diperbaiki) Keputusan bisnis: Menerima tulisan, menangani konflik langka : Types of data where Availability > Consistency Keranjang belanja (merge conflicting additions) Session data (last-write-wins is fine) preferensi pengguna (konsistensi yang mungkin dapat diterima) Daftar penjual terbaik (aproximate is fine) : Types of data where Consistency > Availability Keseimbangan rekening bank (tidak dapat memiliki saldo yang bertentangan) Inventory counts (can’t oversell) Transaction logs (must be ordered) Inilah sebabnya mengapa Dynamo tidak untuk semua - tetapi untuk kasus penggunaan e-commerce Amazon, memilih ketersediaan atas konsistensi yang kuat adalah kompromi yang tepat. Nuansa penting: Sementara Dynamo sering digambarkan sebagai sistem AP, lebih tepat untuk menyebutnya sistem konsistensi yang dapat disesuaikan. tergantung pada konfigurasi kuorum R dan W Anda, itu dapat berperilaku lebih dekat dengan CP. Label AP berlaku untuk konfigurasi default / direkomendasikan yang dioptimalkan untuk beban kerja e-commerce. Sementara Dynamo sering digambarkan sebagai sistem AP, lebih tepat untuk menyebutnya Tergantung pada konfigurasi kuorum R dan W Anda, itu dapat berperilaku lebih dekat dengan CP. Label AP berlaku untuk konfigurasi default / direkomendasikan yang dioptimalkan untuk beban kerja e-commerce. Important nuance tunable consistency system Komponen Arsitektur Utama 1. Consistent Hashing for Partitioning Biarkan saya menjelaskan ini dengan contoh konkret, karena konsisten hashing adalah salah satu konsep yang tampaknya ajaib sampai Anda melihatnya dalam aksi. The Problem: Traditional Hash-Based Sharding Bayangkan Anda memiliki 3 server dan ingin mendistribusikan data di antara mereka. # Traditional approach - DON'T DO THIS def get_server(key, num_servers): hash_value = hash(key) return hash_value % num_servers # Modulo operation # With 3 servers: get_server("user_123", 3) # Returns server 0 get_server("user_456", 3) # Returns server 1 get_server("user_789", 3) # Returns server 2 Ini bekerja ... sampai Anda menambahkan atau menghapus server. mari kita lihat apa yang terjadi ketika kita pergi dari 3 hingga 4 server: # Before (3 servers): "user_123" → hash % 3 = 0 → Server 0 "user_456" → hash % 3 = 1 → Server 1 "user_789" → hash % 3 = 2 → Server 2 # After (4 servers): "user_123" → hash % 4 = 0 → Server 0 ✓ (stayed) "user_456" → hash % 4 = 1 → Server 1 ✓ (stayed) "user_789" → hash % 4 = 2 → Server 2 ✓ (stayed) # But wait - this is lucky! In reality, most keys MOVE: "product_ABC" → hash % 3 = 2 → Server 2 "product_ABC" → hash % 4 = 3 → Server 3 ✗ (MOVED!) : Ketika Anda mengubah jumlah server, hampir semua data Anda perlu didistribusikan ulang. Bayangkan bergerak terabyte data hanya untuk menambahkan satu server! The disaster Solusi: Hashing yang konsisten Hashing konsisten memecahkan hal ini dengan memperlakukan ruang hash sebagai lingkaran (0 sampai 2^32 – 1, membungkus sekitar). Step 1: Place servers on the ring Setiap server ditugaskan posisi acak di cincin (disebut “token”).Pikirkan ini seperti menempatkan penanda pada trek balap bulat. Step 2: Place data on the ring Ketika Anda ingin menyimpan data, Anda: Hash kunci untuk mendapatkan posisi di cincin Berjalan jam dari posisi itu Simpan data di server pertama yang Anda temui Contoh Gambar: Cincin Lengkap Berikut adalah cincin yang ditempatkan dalam urutan. kunci berjalan jam ke server berikutnya: : A key walks clockwise until it hits a server. That server owns the key. Simple rule : Examples at 30° → walks to 45° → user_123 Server A owns it user_456 pada 150° → berjalan ke 200° → Server C memilikinya cart_789 pada 250° → berjalan ke 280° → Server D memilikinya product_ABC pada 300° → melewati 360°, membungkus ke 0°, terus ke 45° → Server A memilikinya Who owns what range? Server A (45°): memiliki segala-galanya dari 281° hingga 45° (menggaruk di sekitar) Server B (120°): memiliki segala sesuatu dari 46° hingga 120° Server C (200°): memiliki segala sesuatu dari 121° hingga 200° : owns everything from 201° to 280° Server D (280°) The Magic: Menambahkan Server Sekarang mari kita lihat mengapa ini brilian. kita menambahkan Server E pada posisi 160°: BEFORE: Server A (45°) → owns 281°-45° Server B (120°) → owns 46°-120° Server C (200°) → owns 121°-200° ← THIS RANGE WILL SPLIT Server D (280°) → owns 201°-280° AFTER: Server A (45°) → owns 281°-45° ← NO CHANGE Server B (120°) → owns 46°-120° ← NO CHANGE Server E (160°) → owns 121°-160° ← NEW! Takes part of C's range Server C (200°) → owns 161°-200° ← SMALLER range Server D (280°) → owns 201°-280° ← NO CHANGE : Hanya kunci dalam kisaran 121°-160° yang perlu dipindahkan (dari C ke E). server A, B, dan D benar-benar tidak terpengaruh! Result Optimasi Virtual Nodes There’s a critical problem with the basic consistent hashing approach: . random distribution can be extremely uneven The Problem in Detail: Ketika Anda secara acak menetapkan satu posisi per server, Anda pada dasarnya melemparkan darts di papan bulat. terkadang darts berkumpul, terkadang mereka menyebar. Let me show you a concrete example: Scenario: 4 servers with single random tokens Server A: 10° } Server B: 25° } ← Only 75° apart! Tiny ranges Server C: 100° } Server D: 280° ← 180° away from C! Huge range Range sizes: - Server A owns: 281° to 10° = 89° (25% of ring) - Server B owns: 11° to 25° = 14° (4% of ring) ← Underutilized! - Server C owns: 26° to 100° = 74° (21% of ring) - Server D owns: 101° to 280° = 179° (50% of ring) ← Overloaded! Real-world consequences: : Server D handles 50% of all data while Server B handles only 4%. This means: Uneven load Server D CPU, disk, dan jaringan dibatasi Server B sebagian besar kosong (kapasitas yang terbuang) Latensi percentil 99,9 Anda didominasi oleh Server D yang berlebihan Hotspot Cascading: Ketika Server D menjadi lambat atau gagal: Semua beban 50% bergeser ke Server A (satu jam berikutnya) Server A now becomes overloaded Performa Sistem Merosot Secara Katastrof : Adding servers doesn’t help evenly because new servers might land in already small ranges Inefficient scaling Visualizing the problem: : Each physical server gets multiple virtual positions (tokens). Dynamo’s solution Alih-alih melemparkan satu dart per server, melemparkan banyak dart. Semakin banyak melemparkan, semakin banyak distribusi menjadi (hukum bilangan besar). How Virtual Nodes Fix the Problem: Mari kita ambil 4 server yang sama, tetapi sekarang setiap server mendapatkan 3 node virtual (token) alih-alih 1: Physical Server A gets 3 tokens: 10°, 95°, 270° Physical Server B gets 3 tokens: 25°, 180°, 310° Physical Server C gets 3 tokens: 55°, 150°, 320° Physical Server D gets 3 tokens: 75°, 200°, 340° Now the ring looks like: 10° A, 25° B, 55° C, 75° D, 95° A, 150° C, 180° B, 200° D, 270° A, 310° B, 320° C, 340° D Range sizes (approximately): - Server A total: 15° + 55° + 40° = 110° (31% of ring) - Server B total: 30° + 20° + 30° = 80° (22% of ring) - Server C total: 20° + 30° + 20° = 70° (19% of ring) - Server D total: 20° + 70° + 20° = 110° (31% of ring) Load ranges from 19% to 31% instead of 4% to 50%. Much better! Why this works: : With more samples (tokens), the random distribution averages out. This is the law of large numbers in action. Statistics : When a server fails, its load is distributed across many servers, not just one neighbor: Granular load distribution Server A fails: - Its token at 10° → load shifts to Server B's token at 25° - Its token at 95° → load shifts to Server C's token at 150° - Its token at 270° → load shifts to Server B's token at 310° Result: The load is spread across multiple servers! : When adding a new server with 3 tokens, it steals small amounts from many servers instead of a huge chunk from one server. Smooth scaling Real Dynamo configurations: The paper mentions different strategies evolved over time. In production: Early versions: 100-200 virtual nodes per physical server Kemudian dioptimalkan untuk: token Q/S per node (di mana Q = total partisi, S = jumlah server) Typical setup: Each physical server might have 128-256 virtual nodes The Trade-off: Balance vs Overhead More virtual nodes means better load distribution, but there’s a cost. What you gain with more virtual nodes: With 1 token per server (4 servers): Load variance: 4% to 50% (±46% difference) ❌ With 3 tokens per server (12 virtual nodes): Load variance: 19% to 31% (±12% difference) ✓ With 128 tokens per server (512 virtual nodes): Load variance: 24% to 26% (±2% difference) ✓✓ What it costs: : Each node maintains routing information Metadata size 1 token per server: Track 4 entries 128 tokens per server: Track 512 entries : Nodes exchange membership info periodically Gossip overhead More tokens = more data to sync between nodes Setiap detik, node gosip pandangan mereka tentang cincin : When nodes join/leave Rebalancing complexity More virtual nodes = more partition transfers to coordinate But each transfer is smaller (which is actually good for bootstrapping) Dynamo’s evolution: The paper describes how Amazon optimized this over time: Strategy 1 (Initial): - 100-200 random tokens per server - Problem: Huge metadata (multiple MB per node) - Problem: Slow bootstrapping (had to scan for specific key ranges) Strategy 3 (Current): - Q/S tokens per server (Q=total partitions, S=number of servers) - Equal-sized partitions - Example: 1024 partitions / 8 servers = 128 tokens per server - Benefit: Metadata reduced to KB - Benefit: Fast bootstrapping (transfer whole partition files) Real production sweet spot: Most Dynamo deployments use 128-256 virtual nodes per physical server. This achieves: Load distribution within 10-15% variance (good enough) Metadata overhead under 100KB per node (negligible) Fast failure recovery (load spreads across many nodes) Pergi dari 128 hingga 512 token hanya meningkatkan keseimbangan beban 2-3%, tetapi menggandakan ukuran metadata dan lalu lintas gosip. Why not more? : Physical servers (top) map to multiple virtual positions (bottom) on the ring. This distributes each server’s load across different parts of the hash space. Key concept : Benefits More even load distribution When a server fails, its load is distributed across many servers (not just one neighbor) Ketika server bergabung, ia mencuri sejumlah kecil dari banyak server Perbandingan dampak dunia nyata Let’s see the difference with real numbers: Traditional Hashing (3 servers → 4 servers): - Keys that need to move: ~75% (3 out of 4) - Example: 1 million keys → 750,000 keys must migrate Consistent Hashing (3 servers → 4 servers): - Keys that need to move: ~25% (1 out of 4) - Example: 1 million keys → 250,000 keys must migrate With Virtual Nodes (150 vnodes total → 200 vnodes): - Keys that need to move: ~12.5% (spread evenly) - Example: 1 million keys → 125,000 keys must migrate - Load is balanced across all servers Ini adalah momen “Aha!” The key insight is this: Consistent hashing decouples the hash space from the number of servers. Tradisional: server = hash(key) % num_servers ← num_servers adalah dalam rumus! Consistent: ← num_servers isn’t in the formula! server = ring.findNextClockwise(hash(key)) This is why adding/removing servers only affects a small portion of the data. The hash values don’t change—only which server “owns” which range changes, and only locally. Think of it like a circular running track with water stations (servers). If you add a new water station, runners only change stations if they’re between the old nearest station and the new one. Everyone else keeps going to their same station. 2. Replication Strategy (N, R, W) Masalah: ketersediaan vs konsistensi perdagangan Imagine you’re building Amazon’s shopping cart. A customer adds an item to their cart, but at that exact moment: One server is being rebooted for maintenance Another server has a network hiccup A third server is perfectly fine (Dengan konsistensi yang kuat) Traditional database approach Client: "Add this item to my cart" Database: "I need ALL replicas to confirm before I say yes" Server 1: ✗ (rebooting) Server 2: ✗ (network issue) Server 3: ✓ (healthy) Result: "Sorry, service unavailable. Try again later." : 😡 “I can’t add items to my cart during Black Friday?!” Customer experience This is unacceptable for e-commerce. Every rejected write is lost revenue. Dynamo’s Solution: Tunable Quorums Dynamo memberi Anda tiga tombol untuk menyesuaikan kompromi yang tepat yang Anda inginkan: : Number of replicas (how many copies of the data) N : Read quorum (how many replicas must respond for a successful read) R W: Tulis kuorum (berapa banyak replika yang harus diakui untuk menulis yang sukses) : When , you guarantee quorum overlap—meaning at least one node that received the write will be queried during any read. This overlap enables detection of the latest version, provided the reconciliation logic correctly identifies the highest vector clock. It does not automatically guarantee read-your-writes unless the coordinator properly resolves versions. The magic formula R + W > N Let me show you why this matters with real scenarios: Scenario 1: Shopping Cart (Prioritize Availability) N = 3 # Three replicas for durability R = 1 # Read from any single healthy node W = 1 # Write to any single healthy node # Trade-off analysis: # ✓ Writes succeed even if 2 out of 3 nodes are down # ✓ Reads succeed even if 2 out of 3 nodes are down # ✓ Maximum availability - never reject customer actions # ✗ Might read stale data # ✗ Higher chance of conflicts (but we can merge shopping carts) What happens during failure: Client: "Add item to cart" Coordinator tries N=3 nodes: - Node 1: ✗ Down - Node 2: ✓ ACK (W=1 satisfied!) - Node 3: Still waiting... Result: SUCCESS returned to client immediately Node 3 eventually gets the update (eventual consistency) Scenario 2: Session State (Balanced Approach) N = 3 R = 2 # Must read from 2 nodes W = 2 # Must write to 2 nodes # Trade-off analysis: # ✓ R + W = 4 > N = 3 → Read-your-writes guaranteed # ✓ Tolerates 1 node failure # ✓ Good balance of consistency and availability # ✗ Write fails if 2 nodes are down # ✗ Read fails if 2 nodes are down Why R + W > N enables read-your-writes: Write to W=2 nodes: [A, B] Later, read from R=2 nodes: [B, C] Because W + R = 4 > N = 3, there's guaranteed overlap! At least one node (B in this case) will have the latest data. The coordinator detects the newest version by comparing vector clocks. This guarantees seeing the latest write as long as reconciliation picks the causally most-recent version correctly. Scenario 3: Financial Data (Prioritize Consistency) N = 3 R = 3 # Must read from ALL nodes W = 3 # Must write to ALL nodes # Trade-off analysis: # ✓ Full replica quorum — reduces likelihood of divergent versions # ✓ Any read will overlap every write quorum # ✗ Write fails if ANY node is down # ✗ Read fails if ANY node is down # ✗ Poor availability during failures Systems requiring strict transactional guarantees typically choose CP systems instead. This configuration is technically supported by Dynamo but sacrifices the availability properties that motivate using it in the first place. Configuration Comparison Table Config N R W Availability Consistency Use Case High Availability 3 1 1 ⭐⭐⭐⭐⭐ ⭐⭐ Shopping cart, wish list Balanced 3 2 2 ⭐⭐⭐⭐ ⭐⭐⭐⭐ Session state, user preferences Full Quorum 3 3 3 ⭐⭐ ⭐⭐⭐⭐⭐ High-stakes reads (not linearizable) Read-Heavy 3 1 3 ⭐⭐⭐ (reads) ⭐⭐⭐⭐ Product catalog, CDN metadata Write-Heavy 3 3 1 ⭐⭐⭐ (writes) ⭐⭐⭐ Click tracking, metrics High Availability 3 1 1 ⭐⭐⭐⭐⭐ ⭐⭐ Shopping cart, wish list Balanced 3 2 2 ⭐⭐⭐⭐ ⭐⭐⭐⭐ Session state, user preferences Full Quorum 3 3 3 ⭐⭐ ⭐⭐⭐⭐⭐ Pembacaan taruhan tinggi (tidak linearizable) Read-Heavy 3 1 3 ⭐⭐⭐ (reads) ⭐⭐⭐⭐ Product catalog, CDN metadata Write-Heavy 3 3 1 ⭐⭐⭐ (writes) ⭐⭐⭐ Click tracking, metrics Catatan tentang sistem keuangan: Sistem yang membutuhkan jaminan transaksi yang kuat (misalnya, saldo rekening bank) biasanya tidak harus menggunakan Dynamo. : Systems requiring strong transactional guarantees (e.g., bank account balances) typically shouldn’t use Dynamo. That said, some financial systems do build on Dynamo-style storage for their persistence layer while enforcing stronger semantics at the application or business logic layer. Note on financial systems The Key Insight Most systems use because: N=3, R=2, W=2 : Can tolerate up to 2 replica failures before permanent data loss (assuming independent failures and no correlated outages). Durability Ketersediaan: Tolerasi 1 kegagalan node untuk kedua membaca dan menulis : R + W > N guarantees that read and write quorums overlap, enabling read-your-writes behavior in the absence of concurrent writes. Consistency : Don’t wait for the slowest node (only need 2 out of 3) Performance Real production numbers from the paper: Amazon’s shopping cart service during peak (holiday season): Configuration: N=3, R=2, W=2 Handled tens of millions of requests Over 3 million checkouts in a single day No downtime, even with server failures Pendekatan yang dapat disesuaikan ini adalah apa yang membuat Dynamo revolusioner. Anda tidak terjebak dengan satu-size-fits-all - Anda menyesuaikannya berdasarkan persyaratan bisnis Anda yang sebenarnya. 3. Vector Clocks for Versioning The Problem: Detecting Causality in Distributed Systems When multiple nodes can accept writes independently, you need to answer a critical question: Are these two versions of the same data related, or were they created concurrently? Why timestamps don’t work: Scenario: Two users edit the same shopping cart simultaneously User 1 at 10:00:01.500 AM: Adds item A → Writes to Node X User 2 at 10:00:01.501 AM: Adds item B → Writes to Node Y Physical timestamp says: User 2's version is "newer" Reality: These are concurrent! Both should be kept! Problem: - Clocks on different servers are NEVER perfectly synchronized - Clock skew can be seconds or even minutes - Network delays are unpredictable - Physical time doesn't capture causality What we really need to know: Version A happened before Version B? → B can overwrite A Version A and B are concurrent? → Keep both, merge later Version A came from reading Version B? → We can track this! The Solution: Vector Clocks A vector clock is a simple data structure: a list of pairs that tracks which nodes have seen which versions. (node_id, counter) The rules: When a node writes data, it increments its own counter When a node reads data, it gets the vector clock When comparing two vector clocks: If all counters in A ≤ counters in B → A is an ancestor of B (B is newer) If some counters in A > B and some B > A → A and B are concurrent (conflict!) Step-by-Step Example Mari kita melacak keranjang belanja melalui beberapa pembaruan: Breaking down the conflict: D3: [Sx:2, Sy:1] vs D4: [Sx:2, Sz:1] Comparing: - Sx: 2 == 2 ✓ (equal) - Sy: 1 vs missing in D4 → D3 has something D4 doesn't - Sz: missing in D3 vs 1 → D4 has something D3 doesn't Conclusion: CONCURRENT! Neither is an ancestor of the other. Both versions must be kept and merged. Real-World Characteristics The Dynamo paper reports the following conflict distribution measured over 24 hours of Amazon’s production shopping cart traffic. These numbers reflect Amazon’s specific workload — high read/write ratio, mostly single-user sessions — and should not be assumed to generalize to all Dynamo deployments: 99.94% - Single version (no conflict) 0.00057% - 2 versions 0.00047% - 3 versions 0.00009% - 4 versions : Konflik jarang terjadi dalam praktek! Key insight Why conflicts happen: Not usually from network failures Mostly from concurrent writers (often automated processes/bots) Human users rarely create conflicts because they’re slow compared to network speed The Size Problem Vector clocks can grow unbounded if many nodes coordinate writes. Dynamo’s solution: once the clock exceeds a size threshold. truncate the oldest entries // When vector clock exceeds threshold (e.g., 10 entries) // Remove the oldest entry based on wall-clock timestamp vectorClock = { 'Sx': {counter: 5, timestamp: 1609459200}, 'Sy': {counter: 3, timestamp: 1609459800}, 'Sz': {counter: 2, timestamp: 1609460400}, // ... 10 more entries } // If size > 10, remove entry with oldest timestamp // ⚠ Risk: Dropping an entry collapses causality information. // Two versions that were causally related may now appear // concurrent, forcing the application to resolve a conflict // that didn't actually exist. In practice, Amazon reports // this has not been a significant problem — but it is a // real theoretical risk in high-churn write environments // with many distinct coordinators. Sloppy Quorum dan Hinted Handoff The Problem: Strict Quorums Kill Availability Traditional quorum systems are rigid and unforgiving. Traditional strict quorum: Your data is stored on nodes: A, B, C (preference list) Write requirement: W = 2 Scenario: Node B is down for maintenance Coordinator: "I need to write to 2 nodes from {A, B, C}" Tries: A ✓, B ✗ (down), C ✓ Result: SUCCESS (got 2 out of 3) Scenario: Nodes B AND C are down Coordinator: "I need to write to 2 nodes from {A, B, C}" Tries: A ✓, B ✗ (down), C ✗ (down) Result: FAILURE (only got 1 out of 3) Customer: "Why can't I add items to my cart?!" 😡 The problem: . If those specific nodes are down, the system becomes unavailable. Strict quorums require specific nodes Real scenario at Amazon: Black Friday, 2:00 PM - Datacenter 1: 20% of nodes being rebooted (rolling deployment) - Datacenter 2: Network hiccup (1-2% packet loss) - Traffic: 10x normal load With strict quorum: - 15% of write requests fail - Customer support phones explode - Revenue impact: Millions per hour The Solution: Sloppy Quorum Dynamo relaxes the quorum requirement: “Write to the first N healthy nodes in the preference list, walking further down the ring if needed.” Preference list for key K: A, B, C But B is down... Sloppy Quorum says: "Don't give up! Walk further down the ring: A, B, C, D, E, F, ..." Coordinator walks until N=3 healthy nodes are found: A, C, D (D is a temporary substitute for B) How Hinted Handoff Works When a node temporarily substitutes for a failed node, it stores a “hint” with the data. Detailed Hinted Handoff Process Step 1: Detect failure and substitute def write_with_hinted_handoff(key, value, N, W): preference_list = get_preference_list(key) # [A, B, C] healthy_nodes = [] for node in preference_list: if is_healthy(node): healthy_nodes.append((node, is_hint=False)) # If we don't have N healthy nodes, expand the list if len(healthy_nodes) < N: extended_list = get_extended_preference_list(key) for node in extended_list: if node not in preference_list and is_healthy(node): healthy_nodes.append((node, is_hint=True)) if len(healthy_nodes) >= N: break # Write to first N healthy nodes acks = 0 for node, is_hint in healthy_nodes[:N]: if is_hint: # Store with hint metadata intended_node = find_intended_node(preference_list, node) success = node.write_hinted(key, value, hint=intended_node) else: success = node.write(key, value) if success: acks += 1 if acks >= W: return SUCCESS return FAILURE Step 2: Background hint transfer # Runs periodically on each node (e.g., every 10 seconds) def transfer_hints(): hints_db = get_hinted_replicas() for hint in hints_db: intended_node = hint.intended_for if is_healthy(intended_node): try: intended_node.write(hint.key, hint.value) hints_db.delete(hint) log(f"Successfully transferred hint to {intended_node}") except: log(f"Will retry later for {intended_node}") Why This Is Brilliant Durability maintained: Even though B is down: - We still have N=3 copies: A, C, D - Data won't be lost even if another node fails - System maintains durability guarantee Availability maximized: Client perspective: - Write succeeds immediately - No error message - No retry needed - Customer happy Traditional quorum would have failed: - Only 2 nodes available (A, C) - Need 3 for N=3 - Write rejected - Customer sees error Eventual consistency: Timeline: T=0: Write succeeds (A, C, D with hint) T=0-5min: B is down, but system works fine T=5min: B recovers T=5min+10sec: D detects B is back, transfers hint T=5min+11sec: B has the data, D deletes hint Result: Eventually, all correct replicas have the data Configuration Example // High availability configuration const config = { N: 3, // Want 3 replicas W: 2, // Only need 2 ACKs to succeed R: 2, // Read from 2 nodes // Sloppy quorum allows expanding preference list sloppy_quorum: true, // How far to expand when looking for healthy nodes max_extended_preference_list: 10, // How often to check for hint transfers hint_transfer_interval: 10_seconds, // How long to keep trying to transfer hints hint_retention: 7_days }; dampak dunia nyata From Amazon’s production experience: During normal operation: Hinted handoff rarely triggered Most writes go to preferred nodes Database Hints sebagian besar kosong During failures: Scenario: 5% of nodes failing at any time (normal at Amazon's scale) Without hinted handoff: - Write success rate: 85% - Customer impact: 15% of cart additions fail With hinted handoff: - Write success rate: 99.9%+ - Customer impact: Nearly zero During datacenter failure: Scenario: Entire datacenter unreachable (33% of nodes) Without hinted handoff: - Many keys would lose entire preference list - Massive write failures - System effectively down With hinted handoff: - Writes redirect to other datacenters - Hints accumulate temporarily - When datacenter recovers, hints transfer - Zero customer-visible failures The Trade-off Benefits: ✓ Maximum write availability ✓ Durability maintained during failures ✓ Automatic recovery when nodes come back ✓ No manual intervention required Costs: ✗ Temporary inconsistency (data not on “correct” nodes) ✗ Extra storage for hints database ✗ Background bandwidth for hint transfers ✗ Slightly more complex code ✗ If a substitute node (like D) fails before it can transfer its hint back to B, the number of true replicas drops below N until the situation resolves. This is an important edge case to understand in failure planning. Hinted handoff provides temporary durability, not permanent replication. The availability benefits far outweigh the costs for e-commerce workloads. Amazon’s verdict: Conflict Resolution: The Shopping Cart Problem Let’s talk about the most famous example from the paper: the shopping cart. This is where rubber meets road. What Is a Conflict (and Why Does It Happen)? A occurs when two writes happen to the same key on different nodes, without either write “knowing about” the other. This is only possible because Dynamo accepts writes even when nodes can’t communicate—which is the whole point! conflict Here’s a concrete sequence of events that creates a conflict: Timeline: T=0: Customer logs in. Cart has {shoes} on all 3 nodes. T=1: Network partition: Node1 can't talk to Node2. T=2: Customer adds {jacket} on their laptop → goes to Node1. Cart on Node1: {shoes, jacket} ← Vector clock: [N1:2] T=3: Customer adds {hat} on their phone → goes to Node2. Cart on Node2: {shoes, hat} ← Vector clock: [N2:2] T=4: Network heals. Node1 and Node2 compare notes. Node1 says: "I have version [N1:2]" Node2 says: "I have version [N2:2]" Neither clock dominates the other → CONFLICT! Tidak ada versi yang “salah” – keduanya mewakili tindakan nyata yang diambil pelanggan. tugas Dynamo adalah untuk mendeteksi situasi ini (melalui jam vektor) dan permukaan to the application so the application can decide what to do. both versions What Does the Application Do With a Conflict? This is the crucial part that the paper delegates to you: . Dynamo gives you all the concurrent versions; your code decides how to merge them. the application must resolve conflicts using business logic For the shopping cart, Amazon chose a : keep all items from all concurrent versions. The rationale is simple—losing an item from a customer’s cart (missing a sale) is worse than occasionally showing a stale item they already deleted. union merge Conflict versions: Version A (from Node1): {shoes, jacket} Version B (from Node2): {shoes, hat} Merge strategy: union Merged cart: {shoes, jacket, hat} ← All items preserved Here’s the actual reconciliation code: from __future__ import annotations from dataclasses import dataclass, field class VectorClock: def __init__(self, clock: dict[str, int] | None = None): self.clock: dict[str, int] = clock.copy() if clock else {} def merge(self, other: "VectorClock") -> "VectorClock": """Merged clock = max of each node's counter across both versions.""" all_keys = set(self.clock) | set(other.clock) merged = {k: max(self.clock.get(k, 0), other.clock.get(k, 0)) for k in all_keys} return VectorClock(merged) def __repr__(self): return f"VectorClock({self.clock})" @dataclass class ShoppingCart: items: list[str] = field(default_factory=list) vector_clock: VectorClock = field(default_factory=VectorClock) @staticmethod def reconcile(carts: list["ShoppingCart"]) -> "ShoppingCart": if len(carts) == 1: return carts[0] # No conflict, nothing to do # Merge strategy: union of all items (never lose additions). # This is Amazon's choice for shopping carts. # A different application might choose last-write-wins or something else. all_items: set[str] = set() merged_clock = VectorClock() for cart in carts: all_items.update(cart.items) # Union: keep everything merged_clock = merged_clock.merge(cart.vector_clock) return ShoppingCart(items=sorted(all_items), vector_clock=merged_clock) # Example conflict scenario cart1 = ShoppingCart(items=["shoes", "jacket"], vector_clock=VectorClock({"N1": 2})) cart2 = ShoppingCart(items=["shoes", "hat"], vector_clock=VectorClock({"N2": 2})) # Dynamo detected a conflict and passes both versions to our reconcile() reconciled = ShoppingCart.reconcile([cart1, cart2]) print(reconciled.items) # ['hat', 'jacket', 'shoes'] — union! The Deletion Problem (Mengapa Ini Menjadi Tricky) The union strategy has a nasty edge case: . deleted items can come back from the dead T=0: Cart: {shoes, hat} T=1: Customer removes hat → Cart: {shoes} Clock: [N1:3] T=2: Network partition — Node2 still has old state T=3: Some concurrent write to Node2 Clock: [N2:3] T=4: Network heals → conflict detected T=5: Union merge: {shoes} ∪ {shoes, hat} = {shoes, hat} Result: Hat is BACK! Customer removed it, but it reappeared. Amazon explicitly accepts this trade-off. A “ghost” item in a cart is a minor annoyance. Losing a cart addition during a Black Friday sale is lost revenue. Catatan kedalaman rekayasa: Logika merger harus spesifik untuk domain dan dirancang dengan hati-hati. Menambahkan item adalah komutatif (order tidak penting) dan mudah untuk menggabungkan. Menghapus item tidak — penghapusan dalam satu cabang yang bersamaan dapat diam-diam diabaikan selama merger berbasis persatuan. Ini adalah kompromi yang disengaja dalam desain Dynamo, tetapi itu berarti aplikasi harus berpendapat dengan hati-hati tentang add vs. menghapus semantik. Jika data Anda tidak secara alami mendukung merger persatuan (misalnya, counter, alamat pengguna), Anda membutuhkan strategi yang berbeda — seperti CRDTs, last-write-wins dengan timestamps, atau hanya menolak tulisan bersaing untuk jenis data tersebut. : Merge logic must be domain-specific and carefully designed. Adding items is commutative (order doesn’t matter) and easy to merge. Removing items is not—a deletion in one concurrent branch may be silently ignored during a union-based merge. This is an intentional trade-off in Dynamo’s design, but it means the application must reason carefully about add vs. remove semantics. If your data doesn’t naturally support union merges (e.g., a counter, a user’s address), you need a different strategy—such as CRDTs, last-write-wins with timestamps, or simply rejecting concurrent writes for that data type. Engineering depth note Membaca dan Menulis Flow Grafik di atas menunjukkan aliran tingkat tinggi, tetapi mari kita berjalan melalui apa yang sebenarnya terjadi langkah demi langkah selama membaca dan menulis. Write Path Step-by-step narration of a PUT request: to any node (via a load balancer) or directly to the coordinator. Client sends the request Koordinator ditentukan – ini adalah node pertama dalam daftar preferensi untuk posisi hash kunci di cincin. — the coordinator increments its own counter in the vector clock, creating a new version. Vector clock is updated , then fans out the write to the other N-1 nodes in the preference list simultaneously. The coordinator writes locally It does NOT wait for all N — just the first W to respond. The remaining nodes that haven’t responded yet will get the write eventually (or via hinted handoff if they’re down). The coordinator waits for W acknowledgments. Setelah W ACKs tiba, koordinator mengembalikan 200 OK ke klien. : Klien menerima respon sukses segera setelah W nodes mengkonfirmasi. nodes lain (N – W) akan menerima menulis asynchronously. memiliki data, hanya tidak perlu pada saat yang sama. Key insight about the write path will Read Path Step-by-step narration of a GET request: to the coordinator for that key. Client sends the request in the preference list simultaneously (not just R). This is important — it contacts all N, but only needs R to respond. The coordinator sends read requests to all N nodes The coordinator returns as soon as R nodes have replied, without waiting for the slower ones. Wait for R responses. The coordinator checks all the vector clocks: Compare the versions returned. If all versions are identical → return the single version immediately. If one version’s clock dominates the others (it’s causally “newer”) → return that version. If versions are concurrent (neither clock dominates) → return to the client, which must merge them. all versions Baca perbaikan terjadi di latar belakang: jika koordinator melihat mana-mana node mengembalikan versi stable, ia mengirimkan versi terbaru ke node itu untuk memperbarui. Because Dynamo is a general-purpose storage engine. It doesn’t know whether you’re storing a shopping cart, a user profile, or a session token. Only knows how to merge two conflicting versions in a way that makes business sense. The coordinator hands you the raw concurrent versions along with the vector clock context, and you do the right thing for your use case. Why does the client receive the conflict instead of the coordinator resolving it? your application : when the client writes the merged version back, it must include the context (the merged vector clock). This tells Dynamo that the new write has “seen” all the concurrent versions, so the conflict is resolved. Without this context, Dynamo might think it’s concurrent write on top of the still-unresolved conflict. The vector clock context is the key to closing the loop another Merkle Trees for Anti-Entropy Masalah: Bagaimana Anda tahu ketika replika keluar dari sinkronisasi? Setelah node pulih dari kegagalan, node mungkin telah melewatkan beberapa tulisan. Setelah partisi jaringan sembuh, dua replika mungkin berpaling. Bagaimana Dynamo mendeteksi dan memperbaiki perbedaan ini? The brute-force approach would be: “Every hour, compare every key on Node A against Node B, and sync anything that’s different.” But at Amazon’s scale, a single node might store hundreds of millions of keys. Comparing them all one by one would be so slow and bandwidth-intensive that it would interfere with normal traffic. The core idea: instead of comparing individual keys, compare . If the hash matches, that whole group is identical—skip it. Only drill down into groups where hashes differ. Dynamo uses Merkle trees to solve this efficiently. hashes of groups of keys : Merkle tree sync is a mechanism. It’s not on the hot read/write path. Normal reads and writes use vector clocks and quorums for versioning. Merkle trees are for the repair process that runs periodically in the background to catch any inconsistencies that slipped through. Important background anti-entropy Merkle tree sync adalah a mechanism. It’s not on the hot read/write path. Normal reads and writes use vector clocks and quorums for versioning. Merkle trees are for the repair process that runs periodically in the background to catch any inconsistencies that slipped through. Important background anti-entropy How a Merkle Tree Is Built Each node builds a Merkle tree over its data, organized by key ranges: Node lembar mengandung hash dari kisaran kecil kunci data aktual (misalnya, hash dari semua nilai untuk kunci k1, k2, k3). Node internal berisi hash dari hash anak-anak mereka. is a single hash representing the data on the node. The root all How Two Nodes Sync Using Merkle Trees When Node A and Node B want to check if they’re in sync: : Compare root hashes. If they’re the same, everything is identical. Done! (No network traffic for the data itself.) Step 1 : If roots differ, compare their left children. Same? Skip that entire half of the key space. Step 2 : Keep descending only into subtrees where hashes differ, until you reach the leaf nodes. Step 3 : Sync only the specific keys in the differing leaf nodes. Step 4 Example: Comparing two nodes Node A root: abc789 ← differs from Node B! Node B root: abc788 Compare left subtrees: Node A left: xyz123 Node B left: xyz123 ← same! Skip entire left half. Compare right subtrees: Node A right: def456 Node B right: def457 ← differs! Go deeper. Compare right-left subtree: Node A right-left: ghi111 Node B right-left: ghi111 ← same! Skip. Compare right-right subtree: Node A right-right: jkl222 Node B right-right: jkl333 ← differs! These are leaves. → Sync only the keys in the right-right leaf range (e.g., k10, k11, k12) Instead of comparing all 1 million keys, we compared 6 hashes and synced only 3 keys! : Synchronization process in code def sync_replicas(node_a, node_b, key_range): """ Efficiently sync two nodes using Merkle trees. Instead of comparing all keys, we compare hashes top-down. Only the ranges where hashes differ need actual key-level sync. """ tree_a = node_a.get_merkle_tree(key_range) tree_b = node_b.get_merkle_tree(key_range) # Step 1: Compare root hashes first. # If they match, every key in this range is identical — nothing to do! if tree_a.root_hash == tree_b.root_hash: return # Zero data transferred — full match! # Step 2: Recursively find differences by traversing top-down. # Only descend into subtrees where hashes differ. differences = [] stack = [(tree_a.root, tree_b.root)] while stack: node_a_subtree, node_b_subtree = stack.pop() if node_a_subtree.hash == node_b_subtree.hash: continue # This whole subtree matches — skip it! if node_a_subtree.is_leaf: # Found a differing leaf — these keys need syncing differences.extend(node_a_subtree.keys) else: # Not a leaf yet — recurse into children for child_a, child_b in zip(node_a_subtree.children, node_b_subtree.children): stack.append((child_a, child_b)) # Step 3: Sync only the specific keys that differed at leaf level. # This might be a handful of keys, not millions. for key in differences: sync_key(node_a, node_b, key) Why This Is Efficient The power of Merkle trees is that the number of hash comparisons you need scales with the (logarithmic in the number of keys), not the number of keys themselves. depth of the tree Node with 1,000,000 keys: Naive approach: Compare 1,000,000 keys individually Cost: 1,000,000 comparisons Merkle tree: Compare O(log N) hashes top-down Tree depth ≈ 20 levels Cost: 20 comparisons to find differences Then sync only the differing leaves (~few keys) Speedup: ~50,000x fewer comparisons! Dan secara kritis, jika dua nodus adalah (which is almost always true in a healthy cluster), the root hashes often match entirely and zero data needs to be transferred. The anti-entropy process is very cheap in the common case. mostly in sync Membership and Failure Detection Dynamo uses a gossip protocol for membership management. Each node periodically exchanges membership information with random peers. There is no master node—all coordination is fully decentralized. Gossip-Based Membership Key Design Points : Setiap node mempertahankan tampilan sendiri keanggotaan cluster. tidak ada registri sentral, sehingga tidak ada titik kegagalan tunggal untuk data keanggotaan. No single coordinator : Dynamo uses an accrual-based failure detector (similar to Phi Accrual). Rather than a binary “alive/dead” judgment, nodes maintain a yang meningkat semakin lama peer tidak merespon. ini menghindari positif palsu dari hiccups jaringan transisi. Failure suspicion vs. detection suspicion level Node A's view of Node B: - Last heartbeat: 3 seconds ago → Suspicion low → Healthy - Last heartbeat: 15 seconds ago → Suspicion rising → Likely slow/degraded - Last heartbeat: 60 seconds ago → Suspicion high → Treat as failed : New nodes contact a seed node to join, then gossip spreads their presence to the rest of the cluster. Ring membership is eventually consistent—different nodes may have slightly different views of the ring momentarily, which is acceptable. Decentralized bootstrapping Performance Characteristics: Real Numbers Laporan ini memberikan data kinerja yang menarik. biarkan saya memecahkannya: Latency Distribution Metric | Average | 99.9th Percentile --------------------|---------|------------------ Read latency | ~10ms | ~200ms Write latency | ~15ms | ~200ms Key insight: 99.9th percentile is ~20x the average! The 99.9th percentile is affected by: Why the huge gap? Garbage collection pauses Disk I/O variations Network jitter Load imbalance This is why Amazon SLAs are specified at 99.9th percentile, not average. Version Conflicts From 24 hours of Amazon’s production shopping cart traffic (per the Dynamo paper). Note these reflect Amazon’s specific workload characteristics, not a universal baseline: 99.94% - Saw exactly one version (no conflict) 0.00057% - Saw 2 versions 0.00047% - Saw 3 versions 0.00009% - Saw 4 versions : Conflicts are rare in practice! Most often caused by concurrent writers (robots), not failures. Takeaway Strategi Evolusi Partisi Dynamo evolved through three partitioning strategies. This evolution teaches us important lessons: Strategy 1: Random Tokens (Initial) Problem: Random token assignment → uneven load Problem: Adding nodes → expensive data scans Problem: Can't easily snapshot the system : Random token assignment sounds elegant but is a nightmare in practice. Each node gets a random position on the ring, which means wildly different data ownership ranges and uneven load distribution. Operational lesson Strategy 2: Equal-sized Partitions + Random Tokens Improvement: Decouples partitioning from placement Problem: Still has load balancing issues Strategy 3: Q/S Tokens Per Node — Equal-sized Partitions + Deterministic Placement (Current) What Q and S mean: = the total number of fixed partitions the ring is divided into (e.g. 1024). Think of these as equally-sized, pre-cut slices of the hash space that never change shape. Q = the number of physical servers currently in the cluster (e.g. 8). S = how many of those fixed slices each server is responsible for (e.g. 1024 / 8 = ). Q/S 128 partitions per server Pergeseran kunci dari strategi sebelumnya: cincin sekarang dibagi menjadi partisi Q tetap, ukuran yang sama , and then those partitions are assigned evenly to servers. Servers no longer get random positions — they each own exactly Q/S partitions, distributed evenly around the ring. first Example: Q=12 partitions, S=3 servers Ring divided into 12 equal slices (each covers 30° of the 360° ring): Partition 1: 0°– 30° → Server A Partition 2: 30°– 60° → Server B Partition 3: 60°– 90° → Server C Partition 4: 90°–120° → Server A Partition 5: 120°–150° → Server B Partition 6: 150°–180° → Server C ...and so on, round-robin Each server owns exactly Q/S = 12/3 = 4 partitions → perfectly balanced. When a 4th server joins (S becomes 4): New Q/S = 12/4 = 3 partitions per server. Each existing server hands off 1 partition to the new server. Only 3 out of 12 partitions move — the rest are untouched. This evolution — from random tokens to fixed, equal-sized partitions with balanced ownership — is one of the most instructive operational learnings from Dynamo. The early approach prioritized simplicity of implementation; the later approach prioritized operational simplicity and predictability. Comparing Dynamo to Modern Systems System Consistency Model Use Case Dynamo Influence Cassandra Tunable (N, R, W) Time-series, analytics Direct descendant — heavily inspired by Dynamo, uses same consistent hashing and quorum concepts Riak Tunable, vector clocks Key-value store Closest faithful Dynamo implementation Amazon DynamoDB Eventually consistent by default Managed NoSQL DynamoDB is a completely different system internally, with no vector clocks and much simpler conflict resolution. Shares the name and high-level inspiration only. ⚠️ Not the same as Dynamo! Voldemort Tunable LinkedIn's data store Open-source Dynamo implementation Google Spanner Linearizable Global SQL Opposite choice to Dynamo — prioritizes CP via TrueTime clock synchronization Redis Cluster Eventually consistent Caching, sessions Uses consistent hashing; much simpler conflict resolution Cassandra Tunable (N, R, W) Time-series, analytics Direct descendant — heavily inspired by Dynamo, uses same consistent hashing and quorum concepts Riak Tunable, vector clocks Key-value store Implementasi Dynamo yang paling setia Amazon DynamoDB Eventually consistent by default Managed NoSQL DynamoDB is a completely different system internally, with no vector clocks and much simpler conflict resolution. Shares the name and high-level inspiration only. ⚠️ Not the same as Dynamo! Voldemort Tunable LinkedIn's data store Open-source Dynamo implementation Google Spanner Linearizable Global SQL Opposite choice to Dynamo — prioritizes CP via TrueTime clock synchronization Redis Cluster Eventually consistent Caching, sessions Menggunakan hashing yang konsisten; penyelesaian konflik yang jauh lebih sederhana : Many engineers conflate Amazon DynamoDB with the Dynamo paper. They are very different. DynamoDB is a managed service optimized for operational simplicity. It does not expose vector clocks, does not use the same partitioning scheme, and uses a proprietary consistency model. The paper is about the internal Dynamo storage engine that predates DynamoDB. The DynamoDB confusion : Many engineers conflate Amazon DynamoDB with the Dynamo paper. They are very different. DynamoDB is a managed service optimized for operational simplicity. It does not expose vector clocks, does not use the same partitioning scheme, and uses a proprietary consistency model. The paper is about the internal Dynamo storage engine that predates DynamoDB. The DynamoDB confusion Apa yang tidak ditawarkan Dynamo Setiap blog insinyur senior harus jujur tentang keterbatasan. berikut adalah apa yang secara eksplisit diperdagangkan Dynamo: : Operations are single-key only. You can’t atomically update multiple keys. No transactions Tidak ada indeks sekunder: Anda hanya dapat mencari data dengan kunci utama (setidaknya dalam desain asli). No joins: Ini adalah toko nilai kunci. Tidak ada bahasa kueri. : Events across different keys have no guaranteed ordering. No global ordering Tidak ada linearizability: Bahkan pada R = W = N, Dynamo tidak memberikan pembacaan linearizable. : The system detects conflicts and surfaces them to the application. The must resolve them. If your engineers don’t understand this, you will have subtle data bugs. No automatic conflict resolution application : The anti-entropy process (Merkle tree reconciliation) is not free. At large scale, background repair traffic can be significant. Repair costs at scale : In high-churn write environments with many coordinators, vector clocks can grow large enough to require truncation, which introduces potential causality loss. Vector clock growth Understanding these limitations is critical to successfully operating Dynamo-style systems in production. Practical Implementation Example Below is a self-contained Python implementation of the core Dynamo concepts. It’s intentionally simplified—no actual networking, no persistence—but it faithfully models how vector clocks, the consistent hash ring, quorum reads/writes, and conflict detection interact. Each component is explained before its code. Part 1: Vector Clock yang class is the foundation of version tracking. It’s just a dictionary mapping . Two key operations: VectorClock node_id → counter — bump our own counter when we write increment(node) — check if one clock is causally “after” another; if neither dominates, the writes were concurrent (conflict) dominates(other) from __future__ import annotations from dataclasses import dataclass, field from typing import Optional class VectorClock: """ Tracks causality across distributed writes. A clock like {"nodeA": 2, "nodeB": 1} means: - nodeA has coordinated 2 writes - nodeB has coordinated 1 write - Any version with these counters has "seen" those writes """ def __init__(self, clock: dict[str, int] | None = None): self.clock: dict[str, int] = clock.copy() if clock else {} def increment(self, node_id: str) -> "VectorClock": """Return a new clock with node_id's counter bumped by 1.""" new_clock = self.clock.copy() new_clock[node_id] = new_clock.get(node_id, 0) + 1 return VectorClock(new_clock) def dominates(self, other: "VectorClock") -> bool: """ Returns True if self is causally AFTER other. self dominates other when: - Every counter in self is >= the same counter in other, AND - At least one counter in self is strictly greater. Meaning: self has seen everything other has seen, plus more. """ all_keys = set(self.clock) | set(other.clock) at_least_one_greater = False for key in all_keys: self_val = self.clock.get(key, 0) other_val = other.clock.get(key, 0) if self_val < other_val: return False # self is missing something other has seen if self_val > other_val: at_least_one_greater = True return at_least_one_greater def merge(self, other: "VectorClock") -> "VectorClock": """ Merge two clocks by taking the max of each counter. Used after resolving a conflict to produce a new clock that has "seen" everything both conflicting versions saw. """ all_keys = set(self.clock) | set(other.clock) merged = {k: max(self.clock.get(k, 0), other.clock.get(k, 0)) for k in all_keys} return VectorClock(merged) def __repr__(self): return f"VectorClock({self.clock})" Part 2: Versioned Value Setiap nilai yang disimpan di Dynamo dikemas dengan jam vektornya. pairing ini adalah apa yang memungkinkan koordinator untuk membandingkan versi selama membaca dan mendeteksi konflik. @dataclass class VersionedValue: """ A value paired with its causal history (vector clock). When a client reads, they get back a VersionedValue. When they write an update, they must include the context (the vector clock they read) so Dynamo knows what version they're building on top of. """ value: object vector_clock: VectorClock def __repr__(self): return f"VersionedValue(value={self.value!r}, clock={self.vector_clock})" Part 3: Simulated Node In real Dynamo each node is a separate process. Here we simulate them as in-memory objects. The key detail: each node has its own local dict. Nodes can be marked as to simulate failures. storage down class DynamoNode: """ Simulates a single Dynamo storage node. In production this would be a separate server with disk storage. Here it's an in-memory dict so we can demo the logic without networking. """ def __init__(self, node_id: str, token: int): self.node_id = node_id self.token = token # Position on the consistent hash ring self.storage: dict[str, list[VersionedValue]] = {} self.down = False # Toggle to simulate node failures def write(self, key: str, versioned_value: VersionedValue) -> bool: """ Store a versioned value. Returns False if the node is down. We store a LIST of versions per key, because a node might hold multiple concurrent (conflicting) versions until they are resolved by the application. """ if self.down: return False # In a real node this would be written to disk (e.g. BerkeleyDB) self.storage[key] = [versioned_value] return True def read(self, key: str) -> list[VersionedValue] | None: """ Return all versions of a key. Returns None if the node is down. A healthy node with no data for the key returns an empty list. """ if self.down: return None return self.storage.get(key, []) def __repr__(self): status = "DOWN" if self.down else f"token={self.token}" return f"DynamoNode({self.node_id}, {status})" Part 4: Consistent Hash Ring Kami mengatur node berdasarkan token mereka (posisi) dan menggunakan jalur arah jam untuk menemukan koordinator dan daftar preferensi untuk setiap kunci. import hashlib class ConsistentHashRing: """ Maps any key to an ordered list of N nodes (the preference list). Nodes are placed at fixed positions (tokens) on a conceptual ring from 0 to 2^32. A key hashes to a position, then walks clockwise to find its nodes. This means adding/removing one node only rebalances ~1/N of keys, rather than reshuffling everything like modulo hashing would. """ def __init__(self, nodes: list[DynamoNode]): # Sort nodes by token so we can do clockwise lookup efficiently self.nodes = sorted(nodes, key=lambda n: n.token) def _hash(self, key: str) -> int: """Consistent hash of a key into the ring's token space.""" # Use MD5 for a simple, evenly distributed hash. # Real Dynamo uses a more sophisticated hash (e.g., SHA-1). digest = hashlib.md5(key.encode()).hexdigest() return int(digest, 16) % (2**32) def get_preference_list(self, key: str, n: int) -> list[DynamoNode]: """ Return the first N nodes clockwise from key's hash position. These are the nodes responsible for storing this key. The first node in the list is the coordinator — it receives the client request and fans out to the others. """ if not self.nodes: return [] key_hash = self._hash(key) # Find the first node whose token is >= key's hash (clockwise) start_idx = 0 for i, node in enumerate(self.nodes): if node.token >= key_hash: start_idx = i break # If key_hash is greater than all tokens, wrap around to node 0 else: start_idx = 0 # Walk clockwise, collecting N unique nodes preference_list = [] for i in range(len(self.nodes)): idx = (start_idx + i) % len(self.nodes) preference_list.append(self.nodes[idx]) if len(preference_list) == n: break return preference_list Part 5: The Dynamo Coordinator This is the heart of the system — the logic that handles client requests, fans out to replicas, waits for quorum, and detects conflicts. Study this carefully; it’s where all the earlier concepts converge. class SimplifiedDynamo: """ Coordinates reads and writes across a cluster of DynamoNodes. Any node can act as coordinator for any request — there's no dedicated master. The coordinator is simply whichever node receives the client request (or the first node in the preference list, if using partition-aware routing). Configuration: N = total replicas per key R = minimum nodes that must respond to a read (read quorum) W = minimum nodes that must acknowledge a write (write quorum) """ def __init__(self, nodes: list[DynamoNode], N: int = 3, R: int = 2, W: int = 2): self.N = N self.R = R self.W = W self.ring = ConsistentHashRing(nodes) # ------------------------------------------------------------------ # # WRITE # # ------------------------------------------------------------------ # def put(self, key: str, value: object, context: VectorClock | None = None) -> VectorClock: """ Write a key-value pair to N replicas, wait for W ACKs. The 'context' is the vector clock from a previous read. Always pass context when updating an existing key — it tells Dynamo which version you're building on top of, so it can detect whether your write is concurrent with anything else. Returns the new vector clock, which the caller should store and pass back on future writes to this key. Raises: RuntimeError if fewer than W nodes acknowledged. """ preference_list = self.ring.get_preference_list(key, self.N) if not preference_list: raise RuntimeError("No nodes available") # The coordinator is always the first node in the preference list. coordinator = preference_list[0] # Increment the coordinator's counter in the vector clock. # If no context was provided (brand new key), start a fresh clock. base_clock = context if context is not None else VectorClock() new_clock = base_clock.increment(coordinator.node_id) versioned = VersionedValue(value=value, vector_clock=new_clock) # Fan out to all N replicas. # In a real system these would be concurrent RPC calls. # Here we call them sequentially for simplicity. ack_count = 0 for node in preference_list: success = node.write(key, versioned) if success: ack_count += 1 # Only need W ACKs to declare success. # The remaining replicas are updated asynchronously (or via # hinted handoff if they were down). if ack_count < self.W: raise RuntimeError( f"Write quorum not met: got {ack_count} ACKs, needed {self.W}" ) print(f"[PUT] key={key!r} value={value!r} clock={new_clock} " f"({ack_count}/{self.N} nodes wrote)") return new_clock # ------------------------------------------------------------------ # # READ # # ------------------------------------------------------------------ # def get(self, key: str) -> list[VersionedValue]: """ Read a key from N replicas, wait for R responses, reconcile. Returns a LIST of VersionedValues: - Length 1 → clean read, no conflict - Length >1 → concurrent versions detected; application must merge After reading, the caller should: 1. If no conflict: use the single value normally. 2. If conflict: merge the values using application logic, then call put() with the merged value and the merged vector clock as context. This "closes" the conflict. Read repair happens in the background: any replica that returned a stale version is silently updated with the latest version. """ preference_list = self.ring.get_preference_list(key, self.N) # Collect responses from all N nodes all_versions: list[VersionedValue] = [] responding_nodes: list[tuple[DynamoNode, list[VersionedValue]]] = [] for node in preference_list: result = node.read(key) if result is not None: # None means the node is down all_versions.extend(result) responding_nodes.append((node, result)) if len(responding_nodes) < self.R: raise RuntimeError( f"Read quorum not met: got {len(responding_nodes)} responses, needed {self.R}" ) # Reconcile: discard any version that is strictly dominated # (i.e., is a causal ancestor of) another version. # What remains is the set of concurrent versions. reconciled = self._reconcile(all_versions) # Background read repair: if any node returned something older # than the reconciled result, send it the latest version. # (Simplified: only meaningful when there's a single winner.) if len(reconciled) == 1: latest = reconciled[0] for node, versions in responding_nodes: if not versions or versions[0].vector_clock != latest.vector_clock: node.write(key, latest) # Repair silently in background status = "clean" if len(reconciled) == 1 else f"CONFLICT ({len(reconciled)} versions)" print(f"[GET] key={key!r} status={status} " f"({len(responding_nodes)}/{self.N} nodes responded)") return reconciled # ------------------------------------------------------------------ # # INTERNAL: VERSION RECONCILIATION # # ------------------------------------------------------------------ # def _reconcile(self, versions: list[VersionedValue]) -> list[VersionedValue]: """ Remove any version that is a causal ancestor of another version. If version A's clock is dominated by version B's clock, then B is strictly newer — A adds no new information and can be dropped. Whatever remains after pruning are CONCURRENT versions: writes that happened without either "knowing about" the other. The application must merge these using domain-specific logic. Example: versions = [clock={A:1}, clock={A:2}, clock={B:1}] {A:2} dominates {A:1} → drop {A:1} {A:2} and {B:1} are concurrent → both survive result = [{A:2}, {B:1}] ← conflict! application must merge """ dominated = set() for i, v1 in enumerate(versions): for j, v2 in enumerate(versions): if i != j and v2.vector_clock.dominates(v1.vector_clock): dominated.add(i) # v1 is an ancestor of v2, discard v1 survivors = [v for i, v in enumerate(versions) if i not in dominated] # De-duplicate: identical clocks from different replicas are the same version seen_clocks: list[VectorClock] = [] unique: list[VersionedValue] = [] for v in survivors: if not any(v.vector_clock.clock == s.clock for s in seen_clocks): unique.append(v) seen_clocks.append(v.vector_clock) return unique if unique else versions Part 6: Putting It All Together — A Demo Let’s run through a complete scenario: normal write/read, then a simulated conflict where two nodes diverge and the application must merge them. def demo(): # ── Setup ────────────────────────────────────────────────────────── # # Five nodes placed at evenly spaced positions on the hash ring. # In a real cluster these would span multiple datacenters. nodes = [ DynamoNode("node-A", token=100), DynamoNode("node-B", token=300), DynamoNode("node-C", token=500), DynamoNode("node-D", token=700), DynamoNode("node-E", token=900), ] dynamo = SimplifiedDynamo(nodes, N=3, R=2, W=2) print("=" * 55) print("SCENARIO 1: Normal write and read (no conflict)") print("=" * 55) # Write the initial shopping cart ctx = dynamo.put("cart:user-42", {"items": ["shoes"]}) # Read it back — should be a clean single version versions = dynamo.get("cart:user-42") print(f"Read result: {versions[0].value}\n") # Update the cart, passing the context from our earlier read. # The context tells Dynamo "this write builds on top of clock ctx". ctx = dynamo.put("cart:user-42", {"items": ["shoes", "jacket"]}, context=ctx) versions = dynamo.get("cart:user-42") print(f"After update: {versions[0].value}\n") print("=" * 55) print("SCENARIO 2: Simulated conflict — two concurrent writes") print("=" * 55) # Write the base version base_ctx = dynamo.put("cart:user-99", {"items": ["hat"]}) # Now simulate a network partition: # node-A and node-B can't talk to each other. # We model this by writing directly to individual nodes. pref_list = dynamo.ring.get_preference_list("cart:user-99", 3) node_1, node_2, node_3 = pref_list[0], pref_list[1], pref_list[2] # Write 1: customer adds "scarf" via node_1 (e.g., their laptop) clock_1 = base_ctx.increment(node_1.node_id) node_1.write("cart:user-99", VersionedValue({"items": ["hat", "scarf"]}, clock_1)) # Write 2: customer adds "gloves" via node_2 (e.g., their phone) # This write also descends from base_ctx, not from clock_1. # Neither write knows about the other → they are concurrent. clock_2 = base_ctx.increment(node_2.node_id) node_2.write("cart:user-99", VersionedValue({"items": ["hat", "gloves"]}, clock_2)) # Read — coordinator sees two concurrent versions and surfaces the conflict versions = dynamo.get("cart:user-99") if len(versions) > 1: print(f"\nConflict detected! {len(versions)} concurrent versions:") for i, v in enumerate(versions): print(f" Version {i+1}: {v.value} clock={v.vector_clock}") # Application-level resolution: union merge (Amazon's shopping cart strategy) # Merge items: take the union so no addition is lost all_items = set() merged_clock = versions[0].vector_clock for v in versions: all_items.update(v.value["items"]) merged_clock = merged_clock.merge(v.vector_clock) merged_value = {"items": sorted(all_items)} print(f"\nMerged result: {merged_value}") # Write the resolved version back with the merged clock as context. # This "closes" the conflict — future reads will see a single version. final_ctx = dynamo.put("cart:user-99", merged_value, context=merged_clock) versions = dynamo.get("cart:user-99") print(f"\nAfter resolution: {versions[0].value}") assert len(versions) == 1, "Should be a single version after merge" if __name__ == "__main__": demo() Expected output: ======================================================= SCENARIO 1: Normal write and read (no conflict) ======================================================= [PUT] key='cart:user-42' value={'items': ['shoes']} clock=VectorClock({'node-A': 1}) (3/3 nodes wrote) [GET] key='cart:user-42' status=clean (3/3 nodes responded) Read result: {'items': ['shoes']} [PUT] key='cart:user-42' value={'items': ['shoes', 'jacket']} clock=VectorClock({'node-A': 2}) (3/3 nodes wrote) [GET] key='cart:user-42' status=clean (3/3 nodes responded) After update: {'items': ['shoes', 'jacket']} ======================================================= SCENARIO 2: Simulated conflict — two concurrent writes ======================================================= [PUT] key='cart:user-99' value={'items': ['hat']} clock=VectorClock({'node-A': 1}) (3/3 nodes wrote) [GET] key='cart:user-99' status=CONFLICT (2 versions) (3/3 nodes responded) Conflict detected! 2 concurrent versions: Version 1: {'items': ['hat', 'scarf']} clock=VectorClock({'node-A': 2}) Version 2: {'items': ['hat', 'gloves']} clock=VectorClock({'node-A': 1, 'node-B': 1}) Merged result: {'items': ['gloves', 'hat', 'scarf']} [PUT] key='cart:user-99' value={'items': ['gloves', 'hat', 'scarf']} ... (3/3 nodes wrote) [GET] key='cart:user-99' status=clean (3/3 nodes responded) After resolution: {'items': ['gloves', 'hat', 'scarf']} In Scenario 2, the coordinator correctly identifies that dan are neither equal nor in a dominance relationship — neither is an ancestor of the other — so both are surfaced as concurrent. The application then takes responsibility for merging them and writing back a resolved version with the merged clock. What to notice: {'node-A': 2} {'node-A': 1, 'node-B': 1} Pelajaran Penting Untuk Desain Sistem After working with Dynamo-inspired systems for years, here are my key takeaways: 1. Always-On Beats Strongly-Consistent For user-facing applications, availability almost always wins. Users will tolerate seeing slightly stale data. They won’t tolerate “Service Unavailable.” 2. Application-Level Reconciliation is Powerful Don’t be afraid to push conflict resolution to the application. The application understands the business logic and can make smarter decisions than the database ever could. 3. Tunable Consistency is Essential Tambahan keranjang belanja membutuhkan ketersediaan tinggi (W = 1). transaksi keuangan membutuhkan jaminan yang lebih kuat (W = N). kemampuan untuk menyesuaikan per transaksi ini sangat berharga. 4. The 99.9th Percentile Matters More Than Average Focus your optimization efforts on tail latencies. That’s what users actually experience during peak times. 5. Gossip Protocols Scale Beautifully Decentralized coordination via gossip eliminates single points of failure and scales to thousands of nodes. When NOT to Use Dynamo-Style Systems Jadilah jujur tentang kompromi.Jangan menggunakan pendekatan ini ketika: Konsistensi yang kuat diperlukan (transaksi keuangan, manajemen persediaan) Pertanyaan yang kompleks diperlukan (reporting, analytics, joins) Transaksi mencakup beberapa item (Dynamo hanya operasi satu kunci) (if developers don’t understand vector clocks and conflict resolution, you’ll have problems) Your team can’t handle eventual consistency Conclusion Dynamo mewakili perubahan mendasar dalam cara kita berpikir tentang sistem terdistribusi.Dengan merangkul konsistensi yang mungkin dan menyediakan kompromi yang dapat disesuaikan, itu memungkinkan untuk membangun sistem yang meluas ke ukuran besar sambil mempertahankan ketersediaan yang tinggi. The paper’s lessons have influenced an entire generation of distributed databases. Whether you’re using Cassandra, Riak, or DynamoDB, you’re benefiting from the insights first published in this paper. Sebagai insinyur, tugas kami adalah memahami kompromi ini secara mendalam dan menerapkannya dengan benar. Dynamo memberi kami alat yang kuat, tetapi seperti alat apa pun, itu hanya sama baiknya dengan pemahaman kita tentang kapan dan bagaimana menggunakannya. Further Reading Original Dynamo Paper: SOSP 2007 Werner Vogels’ Blog: All Things Distributed Cassandra Documentation: Understanding how these concepts are implemented “Designing Data-Intensive Applications” by Martin Kleppmann – Chapter 5 on Replication Appendix: Design Problems and Approaches Three open-ended problems that come up in system design interviews and real engineering work. Think through each before reading the discussion. Problem 1: Conflict Resolution for a Collaborative Document Editor : You’re building something like Google Docs backed by a Dynamo-style store. Two users edit the same paragraph simultaneously. How do you handle the conflict? The problem : The shopping cart strategy (union of all items) is only safe because adding items is commutative — . Text editing is not commutative. If User A deletes a sentence and User B edits the middle of it, the union of their changes is meaningless or contradictory. Why shopping cart union doesn’t work here {A} ∪ {B} = {B} ∪ {A} The right approach: Operational Transformation (OT) or CRDTs Solusi industri adalah untuk mewakili dokumen bukan sebagai blob teks, tetapi sebagai urutan operasi, dan untuk mengubah operasi bersamaan sehingga keduanya dapat diterapkan tanpa konflik: User A's operation: delete(position=50, length=20) User B's operation: insert(position=60, text="new sentence") Without OT: B's insert position (60) is now wrong because A deleted 20 chars. With OT: Transform B's operation against A's: B's insert position shifts to 40 (60 - 20). Both operations now apply cleanly. Strategi resolusi konflik untuk lapisan Dynamo akan: Simpan operasi (bukan snapshots dokumen lengkap) sebagai nilai untuk setiap kunci. Pada konflik, kumpulkan semua daftar operasi bersamaan dari setiap versi. Apply OT to merge them into a single consistent operation log. Write the merged log back with the merged vector clock as context. : The operation log per document segment, not the rendered text. This makes merges deterministic and lossless. What to store in Dynamo Layer penyimpanan mereka menggunakan OT atau varian CRDT (Conflict-free Replicated Data Types), yang merupakan struktur data yang secara matematis dijamin untuk bergabung tanpa konflik terlepas dari urutan operasi. Real-world reference Masalah 2: Memilih N, R, W untuk kasus penggunaan yang berbeda Konfigurasi apa yang akan Anda pilih untuk (a) toko sesi, (b) katalog produk, (c) profil pengguna? The problem The right way to think about this: identify the failure mode that costs more — a missed write (data loss) or a rejected write (unavailability). Then pick quorum values accordingly. Session store — prioritize availability Sessions are temporary and user-specific. If a user’s session is briefly stale or lost, they get logged out and log back in. That’s annoying but not catastrophic. You never want to reject a session write. N=3, R=1, W=1 Rationale: - W=1: Accept session writes even during heavy failures. A user can't log in if their session write is rejected. - R=1: Read from any single node. Stale session data is harmless. - N=3: Still replicate to 3 nodes for basic durability. Trade-off accepted: Stale session reads are possible but inconsequential. Product catalog — prioritize read performance and consistency Product data is written rarely (by ops teams) but read millions of times per day. Stale prices or descriptions are problematic. You want fast, consistent reads. N=3, R=2, W=3 Rationale: - W=3: All replicas must confirm a catalog update before it's live. A price change half-published is worse than a brief write delay. - R=2: Read quorum overlap with W=3 guarantees fresh data. Acceptable: catalog writes are rare, so write latency doesn't matter. - N=3: Standard replication for durability. Trade-off accepted: Writes are slow and fail if any node is down. Acceptable because catalog updates are infrequent. User profiles — balanced Profile data (name, email, preferences) is moderately important. A stale profile is annoying but not dangerous. A rejected update (e.g., user can’t update their email) is a real problem. N=3, R=2, W=2 Rationale: - The classic balanced configuration. - R + W = 4 > N = 3, so quorums overlap: reads will see the latest write. - Tolerates 1 node failure for both reads and writes. - Appropriate for data that matters but doesn't require strict consistency. Trade-off accepted: A second simultaneous node failure will cause errors. Acceptable for non-critical user data. Decision framework summary: Priority R W When to use Max availability 1 1 Sessions, ephemeral state, click tracking Balanced 2 2 User profiles, preferences, soft state Consistent reads 2 3 Catalogs, config, rarely-written reference data Highest consistency 3 3 Anywhere you need R+W > N with zero tolerance for stale reads (still not linearizable) Max availability 1 1 Sessions, ephemeral state, click tracking Keseimbangan 2 2 Profil pengguna, preferensi, status lunak Consistent reads 2 3 Catalogs, config, rarely-written reference data Konsistensi tertinggi 3 3 Di mana pun Anda membutuhkan R+W > N dengan toleransi nol untuk pembacaan stable (juga tidak linearizable) Masalah 3: Menguji sistem gaya Dynamo di bawah skenario partisi : How do you verify that your system actually behaves correctly when nodes fail and partitions occur? The problem Ini adalah salah satu masalah yang paling sulit dalam pengujian sistem terdistribusi karena bug hanya muncul dalam interleavings spesifik dari peristiwa bersamaan yang sulit untuk direproduksi secara deterministis. Layer 1: Unit tests for the logic in isolation Sebelum menguji perilaku didistribusikan, periksa blok bangunan secara independen. logika perbandingan jam vektor, deteksi konflik, dan fungsi rekonsiliasi semua dapat diuji dengan tes unit murni - tidak diperlukan jaringan. def test_concurrent_clocks_detected_as_conflict(): clock_a = VectorClock({"node-A": 2}) clock_b = VectorClock({"node-B": 2}) assert not clock_a.dominates(clock_b) assert not clock_b.dominates(clock_a) # Both survive reconciliation → conflict correctly detected def test_ancestor_clock_is_discarded(): old_clock = VectorClock({"node-A": 1}) new_clock = VectorClock({"node-A": 3}) assert new_clock.dominates(old_clock) # old_clock should be pruned during reconciliation Layer 2: Deterministic fault injection Alih-alih berharap kegagalan terjadi dalam urutan yang tepat selama pengujian beban, suntikkan dengan sengaja dan berulang kali. adalah versi sederhana dari ini. dalam sistem produksi, perpustakaan or Hal ini dilakukan pada tingkat infrastruktur. node.down = True Jepsen Chaos Monkey Key scenarios to test: Scenario A: Write succeeds with W=2, third replica is down. → Verify: the data is readable after the down node recovers. → Verify: no data loss occurred. Scenario B: Two nodes accept concurrent writes to the same key. → Verify: the next read surfaces exactly 2 conflicting versions. → Verify: after the application writes a merged version, the next read is clean. Scenario C: Node goes down mid-write (wrote to W-1 nodes). → Verify: the write is correctly rejected (RuntimeError). → Verify: no partial writes are visible to readers. Scenario D: All N nodes recover after a full partition. → Verify: no data was lost across the cluster. → Verify: vector clocks are still meaningful (no spurious conflicts). Layer 3: Property-based testing Alih-alih menuliskan kasus-kasus tes individu, definisi that must always hold and generate thousands of random operation sequences to try to violate them: invariants # Invariant: after any sequence of writes and merges, a final get() # should always return exactly one version (no unresolved conflicts). # Invariant: a value written with a context derived from a previous read # should never produce a conflict with that read's version # (it should dominate it). # Invariant: if R + W > N, a value written successfully should always # be visible in the next read (read-your-writes, absent concurrent writes). Alat seperti (Python) memungkinkan Anda mengekspresikan invariant ini dan secara otomatis menemukan counterexamples. Hypothesis Layer 4: Linearizability checkers Untuk keyakinan tertinggi, catat waktu awal, waktu akhir setiap operasi, dan hasil selama tes suntikan cacat, lalu masukan riwayat ke checker linearizability seperti Ini akan memberi tahu Anda apakah sejarah yang diamati konsisten dengan eksekusi berurutan yang benar - bahkan untuk sistem yang akhirnya konsisten yang beroperasi dalam jaminan yang dinyatakan. Knossos Ditulis dari trench dari sistem yang didistribusikan. wawasan yang diuji pertempuran, gelombang tangan nol. Pendaftaran Link Pendaftaran Link
