This article introduces time‑series databases, explains their core characteristics and typical use cases, and provides detailed overviews of InfluxDB, TimescaleDB, and Prometheus—including data models, query languages, configuration examples, and code snippets for practical integration.
PostgreSQL’s extensible architecture lets it evolve from a relational DB into a true multi‑model system supporting graphs, vectors, full‑text, time‑series, GIS and key‑value data, with detailed architecture explanations, practical code demos, cross‑model queries, extension selection, and production‑grade best‑practice tips.
TimescaleDB 2.26.2 focuses on fixing high‑impact bugs—continuous aggregates errors, compression task crashes, and lock‑related deadlocks—while improving memory stability and PostgreSQL compatibility, and the article provides detailed upgrade instructions and best‑practice recommendations.
While MySQL has long dominated relational databases with its open‑source stability and massive user base, the rise of AI and PostgreSQL’s extensible ecosystem—highlighted by extensions like pgvector, pg_bm25, TimescaleDB and PostGIS—are shifting developer preference, as shown by the 2025 Stack Overflow survey.
This article presents an in‑depth overview of time‑series data concepts and the practical adoption of TimescaleDB built on PostgreSQL at Hytera, covering requirements, core features, hypertable architecture, deployment models, compression, data retention, backup strategies, and performance benchmarks.
This article explains why querying the most recent record for a specific device in massive time‑series tables can be painfully slow in PostgreSQL, demonstrates the impact of using only a timestamp index, and presents several indexing and query‑design strategies—including composite indexes, lateral joins, SkipScan, recursive CTEs, and logging tables with triggers—to dramatically improve performance.
This tutorial walks through installing and configuring PostgreSQL 12 with TimescaleDB on a CentOS 7.5 server, covering kernel tweaks, required packages, compilation, initialization, start/stop commands, remote access setup, and user‑role management for a production‑ready time‑series database.
The article explains why relational databases, especially PostgreSQL combined with TimescaleDB, are better suited than NoSQL for handling complex, time‑series IoT data, offering robust SQL ecosystems, schema design, data integrity, and reduced data silos for multi‑user access.
This article introduces TimescaleDB—a PostgreSQL extension for time‑series data—explaining its core concepts, data‑model choices, architecture, native compression, and provides step‑by‑step installation instructions on a CentOS 7 environment.
This guide walks through installing Zabbix 4.4.0 on CentOS 7, configuring PostgreSQL, adding the TimescaleDB time‑series extension, setting up the Zabbix database, and tuning Linux, Nginx, and PHP so the monitoring platform runs smoothly with high‑performance time‑series storage.
