7 Database Scaling Strategies
Seven database scaling strategies across replicas, sharding, caching, and partitioning.
Databases, storage engines, and data access tradeoffs covering consistency, indexing, replication, durability, and system behaviour at scale.
Seven database scaling strategies across replicas, sharding, caching, and partitioning.
ACID transaction guarantees for atomicity, consistency, isolation, and durability.
Database performance through indexing, query shape, caching, and contention control.
Database locks compared by compatibility, granularity, and concurrency control.
Database sharding through shard keys, fan-out queries, and rebalancing.
Database types compared by data model, query pattern, and consistency needs.
Consistent hashing for stable key placement as nodes join, leave, or fail.
CAP theorem under network partitions, and why pick-two thinking is misleading.
Message queue evolution from brokered delivery to partitioned event logs.
Database sharding through partition keys, routing, rebalancing, and cross-shard tradeoffs.
Read replica routing through database middleware, lag control, and failover.
Eight database data structures for indexing, storage layout, caching, and query access.
Database selection through workload-driven choices across relational and NoSQL.
PostgreSQL adoption through transactional breadth, extensions, and consolidation.
PostgreSQL combines reliable transactions, rich features, and room to grow.
Serverless databases scale managed compute and storage with demand.
SQL execution through parsing, optimisation, storage engines, and concurrency control.
Database models for flat, hierarchical, network, relational, object, and document data.
Data sharding algorithms for key placement, hotspot control, and shard rebalancing.
Memory hierarchy from registers to storage, with speed, size, and cost tradeoffs.
Cloud database types mapped to workload shape, query model, and operations.
Database selection through workload shape, consistency needs, and access patterns.
B-tree and LSM-tree tradeoffs for reads, writes, compaction, and storage layout.
Eventual consistency patterns for asynchronous propagation, sync, and replica convergence.
Six common object storage workloads, from archives and backups to data lakes.
Block, file, and object storage compared through access model and system tradeoffs.
Kafka message loss scenarios across producers, brokers, replication, and consumers.
Change Data Capture from database logs to downstream streams and derived systems.
Delivery semantics across loss, duplication, retries, and processing guarantees.
Erasure coding for durable storage with parity fragments and repair trade-offs.
Event sourcing as append-only state, replay, projections, and audit history.
Time-series database structure for timestamped writes, tags, retention, and queries.
Four queue structures and the insertion and removal rules that define them.
Kafka stays fast through append-only logs, batching, partitions, and page cache.
Data management patterns for caches, replicas, indexes, and analytical stores.
Isolation levels explained through read anomalies, locking, and concurrency tradeoffs.
Kafka fundamentals for logs, brokers, partitions, producers, and consumers.
Apache Avro schema evolution for compatible data migration between systems.
Message queue types compared by delivery model, retention, and broker behaviour.
Large S3 uploads through multipart transfers, parallel parts, and retries.
Pessimistic and optimistic locking compared by contention, retries, and consistency.
Read replica architecture for scaling queries away from the primary database.