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Database Scaling for High-Traffic Systems
Created by Shaunak Ghosh
Build a ByteByteGo-style scaling playbook: measure bottlenecks, optimize queries and indexes, then add pooling, Redis caching, read replicas, and sharding without breaking correctness. You’ll also learn how to validate real limits with P99 benchmarks and design for recovery using replayable change streams.
Requirements
- Relational database basics (tables, indexes, joins)
- Transactions and concurrency fundamentals
- Latency percentiles (p95/p99) and capacity intuition
- High-level replication and caching concepts
What you'll learn
- Diagnose scaling bottlenecks using workload signals like p95/p99 latency and working set versus RAM, and choose the next scaling move intentionally.
- Optimize read performance with indexing, materialized views, and selective denormalization, while understanding the operational trade-offs.
- Increase throughput by reducing round trips, tuning connection pooling, and selecting transaction isolation levels that meet correctness needs.
Learning path
8 modules • Each builds on the previous one
1
Scaling bottlenecks and measurement baseline
Build a scaling plan by mapping workload (QPS, read/write mix, latency SLOs) to observable bottlenecks (CPU, I/O, locks, buffer cache, network, connections). Learn a minimal measurement toolkit: slow query logs, database metrics, and load-test patterns that approximate production.
1 video4 min
2
Indexing optimization for high throughput
Optimize indexes for real access patterns: selectivity, composite index order, covering indexes, partial indexes, and avoiding redundant indexes. Include the write and maintenance cost of indexes and how they affect vacuuming/compaction and lock behavior.
1 video4 min
3
Query performance tuning with execution plans
Use EXPLAIN/ANALYZE (or equivalent) to tune joins, filters, aggregations, and pagination patterns. Learn to spot common plan pathologies (full scans, bad join order, misestimates) and tie fixes back to schema, indexes, and query shape.
1 video4 min
4
Write optimization and connection pooling
Improve write throughput by reducing lock contention and transaction overhead (batching, idempotent upserts, shorter transactions, isolation choices) while controlling database connections via pooling. Cover pool sizing, timeouts, and backpressure to prevent connection storms.
2 videos7 min
5
Caching layers with Redis patterns
Apply Redis as a caching layer using cache-aside/read-through/write-through patterns, TTL strategy, and invalidation approaches. Address production pitfalls: cache stampede, hot keys, consistency boundaries, and safe fallback behavior during cache outages.
1 video5 min
6
Read replicas and consistency tradeoffs
Scale reads with replicas while managing replication lag, read-after-write consistency, and routing. Learn patterns for read/write splitting, session consistency, and safe fallback during replica degradation or failover.
1 video4 min
7
Sharding and partitioning patterns
Scale beyond a single node via partitioning (range/list/hash) and sharding (routing layer, consistent hashing). Cover shard key selection, hotspot avoidance, resharding, and limitations around cross-shard joins and transactions.
2 videos9 min
8
Handling database failures in production
Design for failure: backups and PITR, failover strategies, retry/circuit-breaker behavior, and minimizing blast radius. Include operational readiness practices: runbooks, chaos testing, and validating recovery time (RTO) and data loss (RPO).
2 videos10 min
Start Learning
Begin your learning journey
Modules8
Duration43 min
Science-backed learning
In-video quizzes and scaffolded content to maximize retention.
Key concepts
Bottleneck-Driven Scaling And Realistic Benchmarking (P99)Indexing, Materialized Views, And Denormalization Trade-OffsQuery Efficiency Via Fewer Round Trips (De-N+1, Batching)