Reseña del libro "TimescaleDB and PostgreSQL 18 for Time-Series Data (en Inglés)"
Your sensor table just passed a billion rows, your dashboards are crawling, and storage costs are climbing every month. Time-series data does that to even well-designed PostgreSQL databases, and this book shows you how to fix it without leaving the SQL and tools you already know. TimescaleDB and PostgreSQL for Time-Series Data teaches you to design, query, optimize, and scale high-performance time-series systems for IoT, observability, finance, and analytics. You will learn to create hypertables, ingest millions of rows with COPY and batched pipelines, write fast time_bucket() queries, build continuous aggregates, compress data with the Hypercore columnstore, and automate retention so your database stays fast and affordable as it grows. Every technique is measured with EXPLAIN (ANALYZE, BUFFERS) and PostgreSQL statistics, so you make decisions from evidence instead of guesswork. TimescaleDB is an open-source extension that turns PostgreSQL into a purpose-built time-series database. You keep full SQL, joins, indexes, roles, and the entire PostgreSQL ecosystem while gaining automatic time-based partitioning, columnar compression, and real-time rollups. This book targets PostgreSQL 18 and current TimescaleDB syntax, so what you learn matches what you will deploy. Across five parts and twelve chapters, you build a working platform around three realistic workloads: IoT sensor readings, infrastructure metrics, and financial market data. You begin with a solid PostgreSQL foundation, then move into hypertables, chunk intervals, and high-volume ingestion. From there you tackle analytics, indexing, query tuning, compression, and advanced techniques such as gapfilling, interpolation, percentiles, and anomaly detection. The second half focuses on running time-series databases in production. You will plan hot, warm, and cold data lifecycles, migrate existing PostgreSQL tables safely, scale ingestion, and troubleshoot slow queries with real diagnostic workflows. The book closes with complete applications, including an IoT monitoring system, an observability backend, a market data store, and real-time dashboards. What’s inside: Step-by-step SQL examples you can run and extend, with Docker setup included Ingestion strategies that handle duplicate, late, and out-of-order data Faster queries through composite, BRIN, partial, and covering indexes Dramatic storage savings with columnstore, segmentby, and orderby tuning Automated continuous aggregates and retention policies Production checklists for monitoring, vacuum, WAL, memory, and I/O Ready-to-use appendices with an SQL quick reference, migration guide, and benchmark scripts This book is written for backend developers, database administrators, data engineers, and technical professionals who build data-intensive applications. If you know basic SQL and want to go beyond storing timestamps toward systems that stay efficient and maintainable at scale, you are ready to start. PostgreSQL experience helps, but you do not need to be a TimescaleDB expert. Stop fighting slow queries and bloated tables. Buy your copy today and start building time-series systems that scale.
