Databases

Introductory chapter: Database types, data models, and storage tradeoffs.

A concise guide to the evolution of state storage approaches: from files and OLTP to NoSQL, NewSQL, and HTAP, and how these choices shape API contracts.

A practical framework for choosing a DBMS for a task: OLTP vs OLAP, read/write profile, consistency, replication, sharding and operational risks.

Tutorial from PostgreSQL: relational model, SQL, DBMS architecture, distributed systems and NoSQL.

In-memory key-value system: event loop execution, data structures, durability modes (RDB/AOF), replication, Sentinel, and Redis Cluster.

History of PostgreSQL, key features (MVCC, WAL, extensibility), architecture and comparison with MySQL.

History of MySQL, participation in the LAMP stack, evolution of storage engines and approaches to scaling (Cluster, Vitess).

Evolution of MongoDB from NoSQL to transactions: read/write concerns, default changes and current guarantees.

History of Apache Cassandra, masterless architecture, tunable consistency and LSM-like storage.

A consolidated NewSQL chapter: SQL + ACID on distributed architecture, practical comparison of TiDB, CockroachDB, and YDB, and selection guidance by workload.

Distributed SQL DBMS: auto-sharding tablets, ACID/serializable transactions, row/column tables, and shared-nothing horizontal scaling.

Distributed SQL DBMS: SQL + ACID on top of distributed KV, ranges/leaseholders, Raft replication, multi-region locality, and automatic rebalancing.

Column-oriented OLAP DBMS: MergeTree, partitioning, replication, materialized views and high-throughput analytics scenarios.

In-process analytical DBMS: vectorized execution, columnar storage, ACID transactions, Parquet/CSV interoperability, and embedded ELT workflows.

Distributed search and analytics engine built on Apache Lucene: indexes, sharding, replicas, relevance and near real-time search.

Practical TSDB map: native engines, SQL extensions, distributed-storage-backed designs, and columnar databases used for time-series workloads.

Prometheus from a system design perspective: timeline, layered architecture, write/read flow, and a practical DDL-like/DML-like model for monitoring workloads.

VictoriaMetrics from a system design perspective: timeline, layered architecture, write/read flow, and a practical DDL-like/DML-like model for monitoring at scale.

Graph DBMS with property graph model: Cypher, constraints/indexes, cluster read/write paths, and relationship-centric system design use cases.

Vector database for semantic and hybrid retrieval: collections/points, payload filters, HNSW indexing, distributed mode, and consistency controls.

Analysis of the book by Alex Petrov: B-Trees, LSM-Trees, transactions, replication, consensus and the internal structure of the DBMS.

Analysis of Egor Rogov's book: MVCC, buffer cache, WAL, locks, query planner and PostgreSQL index types.

Wide Column Store: Bigtable + Dynamo architecture, tunable consistency, LSM-Tree, Gossip Protocol. AP (CAP) and PA/EL (PACELC) classification.