Phase 5 Interactive Simulator
Consistent Hashing Ring Simulator 🎯
Visualize how consistent hashing moves only K/N keys during cluster scale-up/down.
Node Key Distribution (6 Total Keys)
› Initialized ring with 3 physical nodes and 6 keys.
Databases: Scaling & Distribution
When single-node database capacity is reached, systems must scale across distributed clusters.
All Topics in Phase 5
0 of 18 completedScale read capacity and provide disaster recovery: Primary-Replica topology, WAL streaming, replication lag, replica promotion, and split-brain prevention.
Navigate the replication durability spectrum: Zero data loss (RPO=0) with Sync vs Maximum throughput with Async vs Balanced Semi-Synchronous replication.
Scale write capacity across geographic regions: Active-Active topologies, write conflicts, Last-Write-Wins (LWW), Vector Clocks, and CRDTs (Conflict-Free Replicated Data Types).
Scale database write capacity infinitely: Splitting monolithic tables into independent physical database instances (Shards), Shard Keys, and cross-shard join challenges.
Compare the 4 primary sharding partitioning schemes: Range-Based, Hash-Based (Modulo/Consistent), Directory-Based (Lookup Table), and Geolocation-Based Sharding.
Disentangle the 3 database scaling pillars: Vertical Partitioning (Column splitting), Table Partitioning (Single node), Sharding (Multi-node), and Replication (Duplication).
Scale a live sharded cluster without downtime: Online data migration, dual-writing, CDC catchup, cutover switching, and shadow validation.
Solve the N-node reshuffling problem: The 360° Hash Ring, O(K/N) key relocation on node failure/scaling, and Virtual Nodes (Vnodes) for uniform load balance.
Route queries dynamically: Application-level splitting (Spring/TypeORM), Database proxies (ProxySQL, Pgpool-II), read lag pinning, and transaction routing.
Prevent database connection exhaustion: Process-per-connection costs, PgBouncer (Session vs Transaction vs Statement pooling), and the Little's Law sizing formula.
Alter massive tables without locking: The Expand/Contract pattern, shadow tables, binlog streaming, and preventing metadata locks.
Design bulletproof recovery: Full vs Differential vs Incremental backups, Point-in-Time Recovery (PITR) via WAL archiving, RPO (Data loss) vs RTO (Downtime).
Stream database mutations in real time: Log-based CDC (Debezium/Kafka), Dual-write anti-patterns, cache invalidation, search index syncing, and Outbox Pattern.
Partition databases by domain context: Separating Users DB, Billing DB, Catalog DB, cross-database joins, and foreign data wrappers (FDW).
Ingest high-frequency metrics: Hypertables, Chunk partitioning, Downsampling, Rollup aggregates, Gorilla compression, and Retention policies.
Explore full-text search: Inverted index mechanics, Tokenization, Stemming, Stop words, BM25 relevance scoring, and fuzzy n-gram autocomplete.
Power modern AI & RAG systems: High-dimensional vector embeddings, Cosine Similarity, Approximate Nearest Neighbors (ANN), HNSW graph indexes, and hybrid search.